Monitoring and prevention of gas explosions in underground coal mines using a co-prototype design model for dynamic disaster response

Abstract

The design of gas explosion rescue equipment is crucial to ensure the safety of miners while also enhancing the emergency response capabilities of safety personnel in Chinese coal mines. This study was conducted in three phases. In the first phase, in-depth interviews with coal mine personnel were conducted in accordance with crowdsourced collaborative design theory, yielding 22 specific design requirements for gas explosion rescue equipment, based upon which prototype designs were developed in the second phase. In the third phase, a Kano model questionnaire was distributed to evaluate the effectiveness of the prototyped designs. The data collected from these questionnaires were analyzed using mixed-type analysis and the Better–Worse coefficient to prioritize the most important design requirements for the development of the Co-Prototype Design Model. The results indicate that the functionality of gas explosion rescue equipment is currently evolving toward a multi-source, heterogeneous, highly-networked system. Future rescue equipment design should place a greater emphasis on user needs and leverage crowdsourced collaborative design to drive innovation.

Introduction

Coal continues to play an indispensable role in the global energy supply, accounting for more than a quarter of the world’s primary energy consumption¹. Although many regions have sought cleaner energy sources, the overall coal demand remains substantial, driven by rapid industrialization and population growth in emerging economies². In China alone, coal consumption reached 4.4 billion tons in 2023, representing nearly half of global coal usage³. As coal mines expand deeper underground to meet this demand, complex geological conditions increasingly elevate the risk of catastrophic incidents, including roof collapses, fires, water infiltration, and—most critically—gas explosions⁴. While underground mining accidents have declined overall in some developed regions due to strict regulations and advanced monitoring technologies, gas explosions remain a global threat, notably in high-production countries like India, Russia, and South Africa⁵. China’s experience particularly highlights the scale and severity of these disasters. Statistics released by the National Coal Mine Safety Administration underscore that gas explosions consistently rank among the most destructive types of coal mine accidents, often resulting in considerable loss of life, extensive property damage, and grave social consequences^6,7. Recent data from the Ministry of Emergency Management (2023) further reveal that although China’s total coal mine accidents decreased from 2021 to 2023, gas-explosion-related fatalities have not correspondingly declined. Fatalities linked to gas explosions constituted 14.81%, 8.57%, and 16.48% of the total mining deaths for 2021, 2022, and 2023, respectively⁸. Such numbers indicate that conventional mitigation strategies—ranging from ventilation enhancements to stricter safety protocols—cannot fully safeguard underground coal operations. In Fig. 1, for instance, gas incidents accounted for 90 reported cases between 2021 and 2023, surpassed only by roof accidents (168) and safety responsibility incidents (112). More troublingly, a considerable portion of these gas-related events evolved into major or catastrophic accidents, signaling that once a gas explosion occurs, its consequences can be dire and far-reaching. These outcomes align with global observations that methane-rich environments in deep coal seams pose heightened explosion risks when even minor ignition sources are present or ventilation lapses occur⁵. Consequently, proactive monitoring and effective emergency response mechanisms for gas explosions have drawn increasing attention from policymakers, industry stakeholders, and academic researchers worldwide. Addressing these concerns necessitates an integrated safety framework that not only prevents gas buildups but also ensures rapid, well-coordinated rescue efforts if an explosion occurs. This study aligns with these objectives by examining how collaborative prototyping and user-driven design can be leveraged to develop more reliable gas explosion prevention and rescue solutions.

Fig. 1

Death toll and severity of coal mine accidents in China (2021–2023).

Recent studies of occupational hazard perception have highlighted the critical role of sociodemographic and role-specific factors in shaping risk evaluations across industries. For instance, Forcael et al.⁹ demonstrated that construction safety experts exhibit highly consistent risk assessments despite demographic variations, though discrepancies emerge during early project phases where contextual uncertainties prevail. Similarly, in the mining sector, psychometric analyses by Trillo-Cabello et al.¹⁰ revealed that workers’ perceptions of occupational hazards like landslides, noise exposure, and repetitive strain are influenced by age and experience, with older workers prioritizing physical ergonomic risks and younger workers emphasizing immediate threats. Notably, Alrawad et al.¹¹ identified “dreaded” and “unknown” as dominant dimensions of miners’ risk perception, underscoring the need for tailored risk communication strategies to address cognitive gaps. These findings collectively suggest that hazard perception is not only context-dependent but also deeply intertwined with individuals’ professional roles and experiential backgrounds. However, researchers have predominantly focused on risk identification rather than translating these insights into actionable design frameworks for safety equipment. This gap is particularly pronounced in high-risk environments like underground coal mines, where discrepancies between managerial safety protocols and frontline workers’ risk perceptions can undermine the adoption and effectiveness of rescue technologies. By integrating crowdsourced hazard perception data into collaborative design processes, this study seeks to ensure that equipment development aligns with both technical requirements and the cognitive realities of end-users.

Literature review

Gas explosion monitoring and prevention in underground coal mines

Gas explosions in underground coal mines are a significant safety concern primarily due to methane’s highly flammable and rapidly ignitable nature¹². Wang and Hu¹³ statistically analyzed recent coal mine incidents and identified inadequate ventilation, poor equipment maintenance, and delayed emergency response as primary factors in gas explosions. They emphasized the critical need for real-time monitoring systems capable of promptly detecting subtle methane increases. Recent advances in intelligent sensor technology and IoT communication have significantly enhanced gas explosion early-warning systems by permitting the continuous monitoring of underground environments¹⁴. However, integrating these technologies into cohesive and adaptive safety frameworks remains challenging due to dynamic underground conditions¹⁵. Ma¹⁶ proposed a visual command system integrating video surveillance and wireless communication to enhance emergency response effectiveness. Nonetheless, integrating diverse technologies such as gas detection, ventilation control, and robotic rescue systems within a single, coordinated platform remains an unresolved issue in current research. Menold, Jablokow, and Simpson¹⁷ highlighted the necessity of comprehensive prototyping frameworks, advocating for iterative and context-specific design approaches to emergency rescue equipment. Yang and Zhang¹⁸ discussed dynamic safety assessment systems that monitor equipment conditions in real time, emphasizing the combination of technical solutions with effective training and stakeholder preparedness. Similarly, He et al.¹⁹ and Chen et al.²⁰ highlighted user involvement in integrated system designs, suggesting that collaborative feedback loops yield significantly more effective solutions than do isolated technological innovations. Despite advancements in predictive modeling²¹ and VR-based training systems^22,23, scholars continue to advocate for greater methodological integration to adapt systematically to evolving hazards and real-time user feedback.

The Kano model and the need for collaborative prototyping

To address complexities inherent in emergency rescue equipment design, researchers have increasingly applied the Kano model, which classifies user requirements into distinct categories such as “must-have,” “one-dimensional,” and “attractive” to effectively guide resource allocation^24,25. Such analyses are especially critical in high-risk environments where users interact with technology under stressful and rapidly changing conditions²⁶. However, identifying user requirements alone does not guarantee effective implementation. Wen et al.²⁷ indicate that successful designs result from iterative prototyping involving continuous collaboration among designers, frontline operators, and stakeholders under realistic conditions. The integration of Kano-driven prioritization with collaborative prototyping methods has emerged as a robust approach, embedding sensor data, continuous user feedback, and real-time hazard assessment into iterative development cycles. Building upon advances in gas explosion monitoring (Section “Gas Explosion Monitoring and Prevention in Underground Coal Mines”), this study proposes a Co-Prototype Design Model (CPDM) that integrates collaborative prototyping and Kano analysis to effectively address the dynamic and unpredictable hazard environment of coal mining. Central to CPDM is the iterative feedback loop, wherein designers and end-users collaboratively refine design concepts through rapid prototyping, real-time evaluation, and continuous stakeholder input. This model systematically integrates evolving user needs, environmental conditions, and technological constraints, ensuring that critical safety functions—particularly those targeting gas explosion prevention—are embedded at every design stage. While previous studies generally employed either Kano-based methods or collaborative prototyping separately, few unified these methods into a cohesive framework. The CPDM approach allows rapid implementation and iterative refinement, resulting in highly responsive and user-oriented solutions crucial for high-risk scenarios in underground coal mines. (Fig. 2).

Fig. 2

The research mechanism of CPDM.

Materials and methods

Analysis of user needs for gas explosion rescue equipment

Methodology for user needs acquisition

To systematically identify user needs for gas explosion rescue equipment, this study conducted field investigations at two representative coal enterprises: “A Coal Industry” (Anyang, Henan Province) and “B Coal Industry” (Yan’an, Shaanxi Province). This study employed face-to-face interviews and questionnaires to engage multiple stakeholders, including miners, rescue workers, engineers, and safety managers. The key findings revealed critical shortcomings in current rescue systems: Inefficient Equipment (Most surveyed mines relied on outdated rescue devices with low operational flexibility); Procedural Complexity (Rescue protocols were overly bureaucratic, delaying emergency responses); Data Transmission Lag (Real-time monitoring data from underground operations often failed to synchronize with surface command centers); and Psychological Needs (Family photos displayed on miners’ charging racks highlighted an unmet demand for emotional support tools to enhance safety awareness; Fig. 3).

Fig. 3

Coal miners introducing the use of self-rescue equipment (in Henan Anyang A Coal Industry Co., Ltd).

Based on these insights, this study developed: the A Gas Explosion Underground Scenario Map (Fig. 4) to visualize risk points and rescue pathways; and the A Coal Mine Gas Rescue Service System (Fig. 5) prioritizing rapid response mechanisms, information accuracy, equipment usability, and adaptive rescue planning.

Fig. 4

Gas explosion underground scenario map.

Fig. 5

Coal mine gas rescue service system.

Data collection and sampling strategy

To address the challenges of field investigation (e.g., restricted underground access, high workforce mobility), this study employed snowball sampling through chain referrals from mine managers and core miners. The sampling framework targeted a combination of five occupational groups (rescue team members, firefighters, R&D personnel, safety managers, and frontline miners). This hybrid approach ensured a balanced sample size (N = 94) while maximizing occupational diversity, creating a “point-to-area” snowballing effect. As shown in Table 1, respondents were predominantly male (89.3%, 84), aged 41–50 (44.6%, 42), and miners (78.8%, 74). This distribution feature is highly consistent with the labor force structure in the global energy industry: According to a report of the International Energy Agency²⁸, the proportion of male practitioners in the energy field has long exceeded 80%, and the age of front-line operational positions (such as miners and drilling engineers) is mostly concentrated in the 35–55 age group, highlighting the “experience-dependent” characteristics of human capital accumulation in this industry. Although the sample shows a significant skew in gender and age distribution, this “imbalance” is not due to sampling bias but reflects the current objective situation of human resource allocation in the coal mining industry; as the IEA points out, “The male-dominated labor force composition profoundly affects the technical path and safety culture of the energy system.” It should be acknowledged that the gender singularity of the sample might have restricted the ability of the survey to capture the needs of female practitioners (although this group accounts for less than 10% of the workforce in the coal mining industry). However, this limitation is logically consistent with the research goal (focusing on the end-user needs of front-line rescue equipment): As miners bear the direct risk of gas explosions, their feedback has priority for equipment optimization. Future research can combine the background of energy transformation to explore how the promotion of clean technologies can reshape the gender structure of the industry²⁹, thereby providing a new perspective for inclusive safety design.

Table 1 Basic information of the questionnaire respondents.

Research design

This research is fundamentally motivated to address the critical shortcomings of current coal mine safety practices in effectively managing and mitigating gas explosions, particularly within increasingly complex underground geological and operational environments (Fig. 6). Despite notable global advancements in mining safety technologies, the persistent occurrence and severe impact of gas explosions underline significant gaps in contemporary monitoring methods, timely intervention mechanisms, and coordinated emergency response strategies. These deficiencies not only heighten risks associated with severe human and economic losses but also hinder broader global efforts toward more sustainable and safe mining practices. In addressing these pressing challenges, the current study proposes and develops a novel Co-Prototype Design Model (CPDM) specifically tailored to optimize the design and implementation of gas rescue equipment.

Fig. 6

Visualization of an underground gas explosion.

The full research design process is depicted in Fig. 7. Initially, user requirements were systematically collected through the KJ method by eliciting the views of various stakeholders actively involved in coal mine rescue operations, including coal miners, rescue personnel, technical maintenance staff, and engineering managers. This inclusive approach facilitated a comprehensive understanding of operational demands and identified the gaps within existing equipment. Subsequently, these initial user requirements were quantitatively evaluated through a structured questionnaire. Utilizing the fuzzy Kano model, the research precisely assessed the relative importance and weight of each requirement, clearly identifying and prioritizing crucial functionalities critical to effective gas rescue operations. Guided by the prioritized insights that the fuzzy Kano analysis yielded, the CPDM further integrated principles from crowd innovation theory, systematically decomposing and refining the equipment’s design across three interrelated analytical dimensions: functional requirements, user behaviors, and structural design components. This multidimensional decomposition ensured that the resulting design solutions precisely aligned with user expectations and comprehensively addressed the operational challenges faced in gas explosion scenarios. Finally, through iterative collaborative prototyping facilitated by the CPDM, the core design elements were rigorously evaluated and refined within a structured “function-behavior-structure” (FBS) framework. Incorporating targeted assessments involving crowd intelligence methods, the study successfully delineated a refined prototype for integrated gas rescue equipment. This approach not only deepened theoretical insights into collaborative prototyping and user-centered innovation but also significantly enhanced practical capabilities, aiming ultimately to improve safety outcomes, reduce emergency response times and costs, and promote adaptive and resilient strategies within complex underground mining environments.

Fig. 7

Research design.

Prototype design and analysis

Analysis of user requirements

This study employed a user-modelling approach to systematically elicit and collect requirements for underground gas-explosion rescue equipment through interviews and structured questionnaires. Interviews were conducted between September and November 2023, while the questionnaire survey took place from August to November 2023. A total of 110 coal mining-related personnel were surveyed, yielding 94 valid responses (85% response rate). The respondents included 55 miners (labelled M1–M55), 21 rescue personnel (R56–R76), 8 engineers (E77–E84), and 10 other coal industry workers (C85–C94). Detailed information on each individual’s specific needs is presented in Table 2. In accordance with recommended sample size ranges for Kano-based analyses—typically 50 to 200 responses³⁰—the 94 valid responses collected here are considered sufficient for reliable requirement analyses. This sample encompasses frontline miners, rescue team members, engineers, and other coal industry stakeholders, providing diverse perspectives on the design and functionality of potential rescue equipment.

Table 2 Evaluation of the user requirements for rescue equipment used.

Demand satisfaction analysis using the Kano method

To accurately identify key user requirements and determine the core design elements for the proposed gas explosion rescue equipment, this study employed the Kano questionnaire and statistical analysis. The Kano model is renowned for its ability to categorize customer needs into distinct types—such as basic, performance, and excitement attributes—thereby facilitating a nuanced understanding of how specific product features influence overall user satisfaction. This categorization is particularly valuable in the context of safety equipment design, where distinguishing between essential and desirable features is critical. Given the subjective nature of user evaluations, incorporating fuzziness into the Kano model allows for a more flexible and precise analysis of customer preferences. The fuzzy Kano model enhances traditional methods by accommodating the inherent uncertainty and vagueness in user responses, leading to more accurate prioritization of product features. This approach has been effectively applied in various studies to translate qualitative user feedback into quantitative assessments, thereby informing design decisions that closely align with user expectations. In this study, qualitative evaluations were transformed into quantitative assessments by converting the degree of positive and negative user requirements, as well as the satisfaction ratings of each survey item, into fuzzy numerical values within the interval [0,1]. This methodological approach ensures that the design of gas explosion rescue equipment is closely aligned with the nuanced preferences and needs of its users. The evaluation design standards for the Kano questionnaire are presented in Table 3. By employing the fuzzy Kano model, this research aims to develop user-centric rescue equipment that not only meets but exceeds user expectations, thereby enhancing safety and operational efficiency in gas explosion scenarios.

Table 3 Weights of the Kano evaluation categories.

This study employed a standardized Kano questionnaire that incorporated both positive and negative questions and identified the type of respondent. The questionnaire data were statistically analyzed and categorized using the Kano quality characteristics classification table. Product design elements were classified into Attractive (A), One-Dimensional (O), Indifferent (I), Must-Be (M), and Reverse (R) elements based on varying levels of user satisfaction. The Kano quality characteristics classification is presented in Table 4.

Table 4 Kano quality characteristics classification.

The feasible function matrix X and unachieved function matrix Y were constructed based on the results of the Kano model questionnaire. An interaction matrix was then used to derive the membership degree vector T for the demand categories, thereby determining the ideal category for each user requirement. For example, the feasible function matrix for user requirement F₁₁ is X = [0.6, 0.3, 0.1, 0, 0], while its unachieved function matrix is Y = [0, 0, 0.1, 0.2, 0.7]. Thus, the resulting fuzzy satisfaction interaction matrix is:

$${\varvec{S}}_{{{\varvec{F11}}}} \user2{ = }\left[ {\begin{array}{*{20}l} {\varvec{0}} \hfill & {\varvec{0}} \hfill & {{\varvec{0}}\user2{.06}} \hfill & {{\varvec{0}}\user2{.12}} \hfill & {{\varvec{0}}\user2{.42}} \hfill \\ {\varvec{0}} \hfill & {\varvec{0}} \hfill & {{\varvec{0}}\user2{.03}} \hfill & {{\varvec{0}}\user2{.06}} \hfill & {{\varvec{0}}\user2{.21}} \hfill \\ {\varvec{0}} \hfill & {\varvec{0}} \hfill & {0.1} \hfill & 0 \hfill & 0 \hfill \\ {\varvec{0}} \hfill & {\varvec{0}} \hfill & 0 \hfill & 0 \hfill & 0 \hfill \\ {\varvec{0}} \hfill & {\varvec{0}} \hfill & 0 \hfill & 0 \hfill & 0 \hfill \\ \end{array} } \right]$$

(1)

By combining the Kano quality characteristics classification table with the fuzzy satisfaction value matrix, a demand membership degree vector is obtained:

$$T11\user2{ = }\left( {\frac{0.21}{{M^{\prime } }},\frac{0.42}{{O^{\prime } }},\frac{0.18}{{A^{\prime } }},\frac{0.19}{{I^{\prime } }},\frac{0}{{R^{\prime } }}} \right)$$

(2)

In Eq. 2, each ratio represents the proportion of survey respondents that indicated a preference for each demand attribute category (Attractive, One-Dimensional, Indifferent, Must-Be, and Reverse, respectively). A confidence level α is introduced to improve the accuracy of the evaluation results²². The value of α was defined as the maximum fuzzy satisfaction value across all demand elements, i.e., α = 0.4. When the elements in the demand membership degree vector are greater than or equal to α, their values are set to 1; otherwise, they are set to 0. Based on this definition, T_F11 = (0, 1, 0, 0, 0), indicating that users perceived F₁₁ as a One-Dimensional requirement (i.e., O-type attribute) in the fuzzy Kano questionnaire. The attribute categories of all user requirements for gas explosion rescue equipment are summarized in Table 5.

Table 5 Kano classification of user requirements for gas explosion rescue equipment.

The statistical results indicate that no user requirements fell into the Reverse (R) or Questionable requirements (Q) categories (Table 4). F₂, F₃, F₅, F₉, F₁₂, F₁₃, and F₁₈ were classified as Must-Have requirements (M), while F₁, F₁₇, and F₂₁ were classified as Indifferent requirements (I); the latter were discarded during product function development. Instead, the study focused on conducting a more in-depth analysis of the Attractive (A) and One-Dimensional requirements (O). To ensure the structural validity of the questionnaire design, this study used SPSS software to conduct a statistical analysis of the data results of 94 valid questionnaires. The KMO test (KMO = 0.6) and Bartlett’s sphericity test (p < 0.05) indicate that the questionnaire has good construct validity (Table 6).

Table 6 KMO and Bartlett’s test.

Better–Worse coefficient analysis

This study used the Better–Worse coefficient to determine the importance of each user requirement in order to clarify the direction for equipment design²³. Proposed by Charles Berger, the Better–Worse coefficient is used to quantitatively evaluate the impact of the increase or decrease of a particular functional requirement on user satisfaction²⁴. The Better–Worse coefficient analysis method was employed to quantify and categorize each functional requirement indicator within the Kano model. When a product feature is provided, the Better coefficient is presented in Eq. (3):

$${\text{Better}} = \frac{{\left( {A^{\prime } + O^{\prime } } \right){ }}}{{\left( {A^{\prime } + O^{\prime } + M^{\prime } + I^{\prime } } \right)}}$$

(3)

In this formula, A′, O′, M′, and I′ represent the respective counts of user responses categorized as Attractive, One-dimensional, Must-be, and Indifferent requirements from the Kano questionnaire. The Better coefficient represents the increase in user satisfaction when a specific demand attribute is provided, where the higher its value, the greater the increase in user satisfaction. In contrast, the Worse coefficient is presented in Eq. (4).

$${\text{Worse}} = - \frac{{\left( {O^{\prime } + M^{\prime } } \right){ }}}{{\left( {A^{\prime } + O^{\prime } + M^{\prime } + I^{\prime } } \right)}},$$

(4)

where A′, O′, M′, and I′ are the same as in (3)

The demand categories are identified as follows:

• If both Better and Worse coefficients have absolute values greater than 0.5, the requirement is classified as One-Dimensional.

• If both Better and Worse coefficients have absolute values less than 0.5, the requirement is classified as Indifferent.

• If the Better coefficient is greater than 0.5 and the absolute value of the Worse coefficient is less than 0.5, the requirement is classified as Attractive.

• If the Better coefficient is less than 0.5 and the absolute value of the Worse coefficient is greater than 0.5, the requirement is classified as Must-Be.

• Reverse requirements rarely occur in practical situations and are thus omitted from this classification.

The Worse coefficient represents the decrease in user satisfaction when a specific demand attribute is absent, where a lower value indicates higher user dissatisfaction³². The Better–Worse coefficients of each demand attribute are presented in Table 7. Furthermore, the relationship between the absolute value of the dissatisfaction index (Worse value) and the satisfaction index (Better value) for each demand attribute is presented in Fig. 8 using the analysis method outlined in¹³. The mean values of the satisfaction and dissatisfaction indices are also plotted, splitting the diagram into a four-quadrant scatter plot that illustrates the importance of each demand attribute for gas explosion rescue equipment. This visualization style allows for the intuitive assessment of the demand importance of each user requirement based on its location within the four quadrants.

Table 7 Satisfaction evaluation of relevant demand attributes of gas explosion rescue equipment.

Fig. 8

The four quadrant scatter plot of demand importance.

Indifferent requirements were not considered due to their low impact on user satisfaction; instead, Must-be, One-dimensional, and Attractive requirements were chosen to guide the primary design direction for rescue equipment. Must-Be requirements are fundamental needs that must be met and do not require excessive attention once functionality has been ensured. One-Dimensional requirements are closely related to user satisfaction and are prioritized in the design process. Finally, attractive requirements represent unexpected features with a strong positive impact on user expectations; innovative design is centered around these features. For different items within the same attribute category, priority was given in the design process to the highest-ranked requirements.

• Form elements (F₂, F₃, and F₄) Among these elements, the One-Dimensional requirement F₄ should be prioritized: this involves features such as intelligent sensing, remote control, and data sharing. F₂ and F₃ are classified as Must-Be requirements and should be comprehensively inspected to ensure that the design process accounts for the structure, technology, and operational form of these features.

• Structural elements (F₆ and F₇) Both elements are classified as One-Dimensional requirements and should be designed with a focus on structure, technology, and operational form to improve equipment safety.

• Color elements F₈ This One-Dimensional requirement can be divided into three main components: primary color, auxiliary color, and reflective materials. The primary color of the equipment should have high saturation, such as orange or red, while the auxiliary color should be blue or green and used to indicate operational buttons, switches, and the operational status of the safety equipment.

• Functional elements (F₉, F₁₀, F₁₁, F₁₂, F₁₃, F₁₄, F₁₅, F₁₆, F₁₈, F₁₉, and F₂₀) Among these elements, F₉, F₁₂, F₁₃, and F₁₈ are classified as Must-Be requirements (Table 8). F₁₄, F₁₅, F₁₆, F₁₉, and F₂₀ are classified as One-Dimensional requirements (Table 9).

• Interactive elements (F₂₂) A One-Dimensional requirement. Due to the unique environmental factors of coal mines, the design of gas explosion rescue equipment should focus on interactive elements that involve the transmission of information and emotional awareness. Emotional interaction systems could include interactions with the users’ families or the government to reduce the psychological burden on miners.

Table 8 Summary of the Must-Be requirements among the demand attributes identified.

Table 9 Summary of the one-dimensional requirements among the demand attributes identified.

To determine the order of priority among the demand attributes, the importance of each demand attribute was calculated by finding the distance (ω) between each demand item and the origin based on their Better–Worse coefficients, where the greater the distance, the higher the importance of that item within its category of demand. The calculation of ω is shown in Eq. (5).

User Demand Sensitivity:

$$\omega_{i} = \sqrt {\mathop P\nolimits_{{_{i} }}^{2} + \mathop D\nolimits_{{_{i} }}^{2} }$$

(5)

The closer the user demand sensitivity (ω_i) is to 1, the higher the importance of that demand attribute. The importance of the 22 demand attributes for gas explosion rescue equipment was determined based on this measure, with a summary of the results presented in Table 10.

Table 10 Requirement attributes results.

Design and evaluation of underground gas explosion equipment

Prototype design

Key user requirements and design elements were identified based on the analysis conducted in the previous section. Design concepts were proposed based on three aspects: the ontological level, the behavioral level, and the value level. User requirements were used to guide the prototype design, key elements of which are illustrated in Fig. 9. Appendix 1—Fig. 14 is the detailed parameters of the prototype design model. An escape path and rescue paths are shown in Fig. 10. In terms of Must-Be requirements, the Personal Locator will monitor environmental, positional, and personal information, allowing miners to send emergency alerts, transmit information, and broadcast their precise location at critical moments (F₉, F₁₂). The Mobile Lifesaving Cabin is a rectangular flat-top structure that is divided into a space for internal use and an external information display. The interior includes an area for supplies, a rest area, and an equipment area, while the exterior houses a top-level drone docking station, a side display screen for guidance information, and an information screen above the front door of the cabin. The oxygen regulation system of the cabin automatically adjusts oxygen levels to maintain a balance between oxygen consumption and release (F₁₈). The cabin uses ultra-bright cold light LED lamps, allowing miners to choose between the main or auxiliary light sources for illumination (F₁₃). The Drone possesses sensors to collect environmental information and cameras for positional information, flight support, and data analysis (F₂, F₅). In terms of One-Dimensional requirements, the cabin stores contain emergency self-rescue items such as compressed oxygen devices, food, water, and first aid supplies (F₁₉). There is also an emotional interaction device for communicating with family members, companies, and the government to alleviate the psychological and mental stress that miners may face (F₂₂). The real-time early warning system can monitor gas concentrations and issue warning signals to prevent accidents (F₁₁, F₁₄); it also includes a variety of systems that further ensure the miners’ safety (F₄, F₇). Reflective signs outside the cabin act as escape indicators in dark environments to guide miners to the cabin’s location (F₈, F₁₅). In terms of Attractive requirements, a focus was placed on visualization and information transmission to enhance data accuracy and improve rescue efficiency.

Fig. 9

The design of prototype gas explosion rescue equipment: (a) Monitoring Drones; (b) Unmanned Aerial Vehicle; (c) personal locator devices.

Fig. 10

Coal mine gas rescue service system.

Evaluation of the crowd intelligence design

The proposed design for gas explosion rescue equipment was evaluated by a panel of coal mine safety experts, structural engineers, coal mine managers, rescue personnel, miners, and industrial product designers (Fig. 11). The panel focused on five evaluation criteria: safety, aesthetics, functionality, interactivity, and usability. Each criterion was scored on a 10-point scale, and experts were asked to provide suggestions for modifications. A detailed summary of their feedback can be found in Fig. 12. The average scores for form, structure, color, functionality, and interactivity were 9.58, 9.52, 9.7, 9.72, and 9.65, respectively, indicating that each aspect of the gas explosion rescue equipment received high scores from a diverse range of users. This prototype design passed the assessment.

Fig. 11

From risk perception to design transformation.

Fig. 12

Evaluation of gas explosion rescue equipment.

Discussion

Co-prototype design model (CPDM)

Gas explosion rescue equipment plays a critical role in coal mining production by reducing the mortality rate associated with gas explosion accidents and improving the efficiency of rescue operations. Consequently, the development of strategies to reduce the occurrence of gas explosions and effectively conduct rescue operations are key elements in constructing a risk-monitoring and emergency response system for gas explosion incidents. This study integrates crowdsourced innovation theory and the Kano model to propose the Co-Prototype Design Model (CPDM), based on prototype design testing, to identify the key design requirements for gas explosion rescue equipment in coal mines in China. The main findings of this study are as follows.

First, safety, functionality, and interactivity are the most important design elements for gas explosion rescue equipment and play a decisive role in improving rescue efficiency. Designers should consider the potential safety risks during rescue operations and be fully aware of the physiological and psychological needs of miners to ensure that they are provided with effective safety protection.

Second, rescue equipment should focus on reliability and durability to ensure stable operation over long-term use and in harsh environments. Equipment should incorporate survival support features such as oxygen systems and emergency food supplies to provide miners with ample support while underground.

Finally, the aesthetics of rescue equipment are an important factor, as evidenced by the personal locator devices, monitoring drones, and mobile lifesaving cabins proposed in this study. Attention should be paid to the appearance and form of the products during the design process to ensure that the product meets the users’ requirements for functionality and aesthetic preferences. This includes a consideration of form, color, and materials to make the equipment visually distinctive and facilitate quick identification and use in mine environments.

Emergency rescue operations in gas explosion incidents require both rapid response and the efficient and precise use of rescue equipment to ensure miners’ safety. Current gas rescue equipment often focuses on basic rescue functions, neglecting the need for coordination and intelligence in complex emergency scenarios. Thus, there is a distinct need for co-creation design during the conceptual prototyping of gas explosion rescue equipment.

To achieve these goals, this study proposes the CPDM (Fig. 13), which integrates user needs, collaborative design, and prototype testing in an innovative approach to equipment design. Through CPDM, user requirements can be more effectively identified and translated into specific design solutions.

Fig. 13

The co-prototype design model (CPDM).

First, co-creation design leverages the actual needs and experiences of users to ensure that the product design is closely aligned with user requirements. Through the close collaboration with miners, rescue personnel, and other stakeholders, the proposed equipment design is more applicable to real-world use scenarios. User innovation theory suggests that the direct users of a product can provide valuable feedback and suggestions for improvement that constitute crucial references for equipment design³². This collaborative approach significantly enhances the practicality and relevance of the product. Furthermore, the concept of co-creating value between users and enterprises highlights the significant innovation and market adaptability that user participation can effectuate in products¹³. In other words, the co-creation design model not only enhances user engagement and satisfaction but also facilitates technological innovation.

Second, conceptual prototyping is a critical step in translating theory into practical applications. Prototype design allows for design ideas to be validated and optimized while also identifying potential problems, allowing improvements to be made. This procedure allows designers to begin iterating at an early stage in the design process, reducing subsequent modification costs. Prototypes serve both as the preliminary form of the final product and as a testing platform where repeated trials and feedback can be used to continuously refine the design, improving the practicality and reliability of the equipment. Ulrich and Eppinger³³ emphasize the crucial role that prototyping plays in the product development process. Additionally, prototyping is seen as an opportunity for innovation and the discovery of new opportunities, promoting the development of more competitive products³⁴.

Table 11 compares the core differences between CPDM and the existing models³⁵ along three dimensions: user integration depth, participation mechanism, and demand analysis, and the prototype iteration mode: 1. User integration: CPDM dynamically identifies the “Must-Have—Expected—Attractive” demand hierarchy through the Kano model at the initial design stage, avoiding the limitation of post hoc verification of traditional methods; 2. Participation mechanism: This realizes the multi-role full-process collaboration of miners, rescue personnel, and management, and compared with the fragmented collaboration of existing models (such as the participation of a single technical team), multi-dimensional feedback drives the design iteration in real time; 3. Demand analysis: This breaks through the traditional technology compliance orientation and uses the Fault Tree Analysis (FTA) system to systematically map the demands and the causes of gas explosions (ventilation failures, monitoring delays, etc.), reducing the risk of systematic deviation in the design. The comparison results show that the closed-loop feedback architecture and systematic demand integration mechanism of CPDM are expected to help the rescue equipment design achieve a certain breakthrough in scene adaptability and risk control ability.

Table 11 CPDM versus existing models.

Mapping to root causes of underground incidents

The mapping presented in Table 12 demonstrates how critical factors from historical incidents align closely with specific CPDM requirements, highlighting the model’s targeted capability in addressing known root causes. A high “consistency” value (≥ 80%) signifies that the identified design solutions effectively cover most risk scenarios derived from real accident analyses. While CPDM remains in the exploratory stage and awaits large-scale practical validation, this preliminary analysis underscores its potential for systematically translating incident data into actionable and targeted design improvements. Moving forward, further validation through simulation-based evaluations and selective pilot testing in actual mining conditions is planned in order to provide incremental yet robust evidence for CPDM’s efficacy.

Table 12 Historical coal mine accidents and CPDM requirement priorities.

Notably, although the CPDM was initially developed based on incident data and operational contexts primarily from China, the methodological framework possesses broader international applicability. Its structured integration of collaborative prototyping, fuzzy Kano modeling, and systematic mapping of historical incidents is universally relevant to mining safety challenges globally. The fundamental principles of identifying and prioritizing safety–critical functionalities, addressing complex stakeholder needs, and iteratively validating solutions through real-world scenarios are adaptable across diverse geographic and operational contexts. For instance, regions with distinct geological settings and regulatory environments can readily adapt the CPDM to their specific contexts by recalibrating user requirement categories and priority weightings based on local conditions. Future research and application efforts should thus aim to validate and adapt CPDM through case studies in other major coal-producing countries, integrating context-specific adjustments to ensure its efficacy and practical relevance internationally. This expanded approach will further strengthen the model’s versatility, reliability, and global impact on coal mine safety practices.

Conclusions

Research limitations

The design of gas explosion rescue equipment requires the integration of complex functional modules. Traditional design methods often have difficulty addressing the diverse and dynamic needs of rescue equipment, while collaborative design approaches can harness the collective intelligence of users to solve complex design challenges associated with the development of functional modules. The advantage of crowdsourced design lies in its ability to generate high-quality ideas and solutions through collaboration with diverse groups of users⁴⁰. This approach not only encourages technological innovation but also promotes knowledge-sharing and collaboration among teams. Despite these advantages, it is important to acknowledge that the Co-Prototype Design Model (CPDM) proposed in this study has not yet been fully tested in actual underground mining operations. Constraints such as budget limitations, safety regulations, and the inherent challenges of conducting large-scale trials in active mines have so far prevented extensive field deployment. Nonetheless, preliminary feedback from domain experts and frontline mining personnel has indicated that the CPDM framework offers a systematic way to capture user requirements, rapidly iterate on conceptual prototypes, and promote collaboration.

In summary, co-creation design and conceptual prototyping for gas explosion rescue equipment are essential approaches that enhance the effectiveness and user-centeredness of rescue technologies. CPDM can be used to fully explore the needs of users, ensuring that any proposed equipment is closely aligned with real-world requirements. Conceptual prototyping allows for rapid optimization, increasing the practicality and reliability of the final product, while crowdsourced design provides innovative solutions to complex challenges and fosters knowledge-sharing among stakeholders. Future research should explore cost-effective methods to simulate real-world conditions and validate the effectiveness of CPDM in controlled pilot studies. Collaborations with industry partners and rescue teams could enable iterative refinements based on smaller-scale field testing, ultimately laying the groundwork for broader implementation once sufficient resources and operational clearances are secured. Co-creative design and conceptual prototyping remain essential for advances in gas explosion rescue equipment, providing a versatile framework for continuous improvement in both product functionality and user safety.

Future research recommendations

Gas explosion incidents can be caused by both natural and human factors, making emergency rescue an essential component of coal mine safety management. The CPDM, combining collaborative prototyping and the fuzzy Kano model, effectively identifies essential user requirements, significantly contributing to the targeted design and development of rescue equipment. Nevertheless, several practical limitations identified in this study require further investigation. First, the proposed warning systems and rescue equipment have yet to be rigorously validated under the realistic, complex terrain and environmental conditions characteristic of coal mines. Therefore, future research should involve extensive field validations and scenario-based testing within actual mining contexts to confirm the model’s operational reliability and effectiveness. Second, constraints associated with data collection and the limited sample size of this study could potentially affect the generalizability of the findings. Future investigations should systematically expand sample sizes, ensuring representation from diverse mining conditions and geographical locations. Broader participation would strengthen the validity of the results and enhance their applicability across varying mining environments. Third, future research should explicitly incorporate a comprehensive cost–benefit analysis. Detailed cost evaluations should include assessments of equipment production, operational expenses, maintenance costs, and potential economic savings from reduced accident rates. An accurate cost analysis will provide essential insights into the economic feasibility and scalability of the CPDM-designed equipment, ensuring alignment with industry expectations for cost-effective safety improvements. Moreover, researchers should simulate diverse emergency rescue scenarios to validate the practical integration of advanced warning systems with active rescue mechanisms. Such realistic simulations will help refine both the design and practical applicability of the proposed solutions. Lastly, future work should actively explore the integration of advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and virtual reality (VR). Leveraging AI for predictive analytics could significantly improve early-warning accuracy; IoT-enabled devices would facilitate enhanced real-time monitoring and communication; and VR-based training could offer immersive preparation for rescue teams, substantially enhancing response readiness and safety outcomes in coal mining environments.