A pre-management method for coal mine safety production

Abstract

This invention relates to the field of coal mine safety production management technology and discloses a pre-management method for coal mine safety production. This method acquires accident-causing factor data related to personnel, machinery, environment, and management within the coal mine safety production system, and generates three pre-management plans based on this data: training, investment, and management. The production system is categorized using analytic hierarchy process (AHP), and a phased investment plan is generated based on the resulting matrix categorization results to achieve precise allocation of safety resources. Based on the same data source, differentiated training content is generated for grassroots employees, middle-level managers, and senior leaders, constructing a hierarchical risk awareness system. During the planning period of the PDCA cycle, the aforementioned investment plan, training content, and portable work instructions are integrated to ultimately form an integrated pre-management plan for the next cycle. This method improves the targeting of safety investments and the risk collaborative prevention and control capabilities of personnel at all levels.

Description

Technical Field

[0001] This invention relates to the field of coal mine safety production management technology, specifically a pre-management method for coal mine safety production. Background Technology

[0002] In coal mine safety production management, existing risk control and resource allocation models typically employ standardized management based on historical accident statistics or general safety standards. For safety investment, the common practice is to allocate funds according to annual budgets or remedial responses to exposed problems, lacking a decision-making basis that is precisely linked to the dynamic risk structure within the production system. Fund usage often manifests as "averaging" or "post-hoc additions," making it difficult to prioritize and scientifically allocate limited resources to the highest-risk and most critical subsystems or links, resulting in low investment efficiency and failure to fundamentally contain systemic risks.

[0003] Regarding personnel training and accountability, existing technologies largely focus on the standardized dissemination of operating procedures or the general learning of accident cases. Training content is highly homogenized and fails to differentiate itself based on the responsibilities, cognitive needs, and decision-making scenarios of personnel at different levels within the organization. Training for frontline operators, middle managers, and senior decision-makers often overlaps in content and lacks focus, failing to effectively improve the depth of risk understanding and corresponding performance capabilities at each level. Furthermore, the connection between job responsibilities and on-site operational instructions is insufficient, making it difficult to seamlessly transmit management requirements to specific operational behaviors, leaving preventative measures merely at the document level. Summary of the Invention

[0004] The purpose of this invention is to provide a pre-management method for coal mine safety production to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a pre-management method for coal mine safety production, the method comprising:

[0006] Acquire accident causation factor data in the coal mine safety production system, wherein the accident causation factor data includes human factor data, machine factor data, environmental factor data, and management factor data;

[0007] Based on the accident causation factor data, generate a training pre-plan, an investment pre-plan, and a management pre-plan;

[0008] Based on the aforementioned pre-training plan, knowledge training content for grassroots employees, awareness training content for middle-level managers, and awareness training content for senior leaders are generated.

[0009] Based on the aforementioned pre-investment scheme, the coal mine production system is classified using stratified analysis, resulting in multiple matrix classification results.

[0010] Multiple batch investment plans are obtained based on the grading results of the aforementioned matrix;

[0011] Based on the aforementioned management pre-planning scheme, the job responsibility drafts for personnel at all levels are obtained, and portable work instruction information corresponding to the job responsibility drafts for personnel at all levels is generated.

[0012] Obtain the pre-set cycle parameters of the PDCA cycle safety management cycle, which include the time periods of planning, execution, inspection and improvement.

[0013] During the planning period of the PDCA cycle safety management cycle, the knowledge training content of the grassroots employees, the awareness training content of the middle-level managers, the awareness training content of the senior leaders, multiple batch investment plans, and the portable work instruction information are integrated to generate an integrated pre-plan for the next cycle.

[0014] Preferably, the step of generating pre-training plans, pre-implementation plans, and pre-management plans based on the accident causation factor data includes:

[0015] Based on the aforementioned human factor data, obtain pre-training plans targeting unsafe human behavior factors;

[0016] Based on the machine's factor data and environmental factor data, pre-implementation plans are obtained to address the unsafe conditions of the object and the adverse environmental factors.

[0017] Based on the aforementioned management factor data, pre-emptive management solutions targeting management deficiencies are developed.

[0018] The step of obtaining a pre-training plan targeting unsafe behavioral factors based on the human factor data includes:

[0019] Unsafe behavior feature items are extracted from the human factor data to obtain multiple behavioral feature factors;

[0020] Based on the classification and attribution of multiple behavioral characteristic factors, the following categories are obtained: basic behavioral factors, mid-level behavioral factors, and high-level behavioral factors.

[0021] Obtain a coal mine safety production knowledge training material library corresponding to the aforementioned grassroots behavioral factors;

[0022] Obtain a coal mine safety production awareness training material library corresponding to the aforementioned mid-level behavioral factors;

[0023] Obtain a coal mine safety production awareness training material library corresponding to the aforementioned high-level behavioral factors;

[0024] Based on the coal mine safety production knowledge training material library, the coal mine safety production awareness training material library, and the coal mine safety production cognition training material library, a pre-training plan covering three levels—grassroots, middle-level, and senior-level—is generated.

[0025] Preferably, the step of obtaining the pre-implementation plan for the unsafe condition factors and adverse environmental factors of the object based on the machine factor data and environmental factor data includes:

[0026] By integrating the machine factor data and environmental factor data, a machine-environment joint evaluation dataset is generated;

[0027] Extract equipment and facility status indicators and working environment quality indicators from the aforementioned machine-environment joint assessment dataset;

[0028] A list of obsolete equipment to be phased out is obtained based on the equipment and facility status indicators, and a list of environmental areas to be improved is obtained based on the work environment quality indicators.

[0029] Obtain coal mine geological conditions data and actual mine operating condition data, and combine them with the list of outdated equipment to be eliminated and the list of environmental areas to be improved to generate an adaptive list of advanced equipment to be purchased and a list of environmental governance projects.

[0030] An initial framework for a pre-investment plan is generated based on the list of advanced equipment to be purchased and the list of environmental remediation projects.

[0031] Preferably, the step of obtaining pre-management solutions for management deficiency factors based on the management factor data includes:

[0032] Analyze the institutional documents and job responsibility files in the management factor data to identify overlapping responsibilities and missing approval processes;

[0033] A list of job responsibility clarification requirements is generated based on the aforementioned overlapping responsibilities, and a list of process completion tasks is generated for the missing items in the approval process.

[0034] The database of standardized management specifications for coal mine safety production was retrieved, and the list of job responsibility clarification requirements was compared with the standard job responsibility template to generate a difference analysis result.

[0035] Based on the results of the difference analysis and the task list for process completion, a management responsibility optimization plan and an approval process reconstruction plan were drafted.

[0036] The management responsibility optimization plan and the approval process reconstruction plan are integrated and reviewed to form a pre-management plan targeting management deficiencies.

[0037] Preferably, the step of classifying the coal mine production system using chromatographic analysis to obtain multiple matrix classification results includes:

[0038] A coal mine production system analysis model is constructed, which includes multiple subsystems.

[0039] The subsystems are input into the tomographic analysis algorithm. The subsystems include an electromechanical transportation subsystem, a roof control subsystem, a water prevention subsystem, a fire prevention and extinguishing subsystem, a gas prevention subsystem, and a dust prevention subsystem.

[0040] Obtain the security risk level value and rectification urgency value for each of the subsystems;

[0041] A comprehensive priority score for each subsystem is calculated based on the security risk level value and rectification urgency value of each subsystem.

[0042] Based on the comprehensive priority score of all the subsystems, all the subsystems are sorted in descending order and divided into multiple priority groups, and each priority group is marked as a square matrix;

[0043] The resulting priority groups are used as multiple matrix hierarchical results.

[0044] Preferably, the step of calculating the comprehensive priority score for each subsystem based on its security risk level and rectification urgency value includes:

[0045] Obtain historical accident statistics and current hazard log data for each of the subsystems;

[0046] The safety risk level value of each subsystem is calculated based on the historical accident statistics. The safety risk level value is quantified by a weighted sum of accident frequency and accident severity.

[0047] Based on the current hidden danger ledger data, the rectification urgency value of each subsystem is calculated. The rectification urgency value is quantified by a weighted product of the number of hidden dangers, the level of hidden dangers, and the remaining rectification time limit.

[0048] Set weighting coefficients for safety risk level values ​​and rectification urgency values;

[0049] Multiply the security risk level value of each subsystem by its weighting coefficient to obtain the risk-weighted score;

[0050] Multiply the urgency value of each subsystem by its weighting coefficient to obtain the urgency weighted score.

[0051] The risk-weighted score and urgency-weighted score of each subsystem are summed to obtain the comprehensive priority score of each subsystem.

[0052] Preferably, the step of obtaining multiple batch investment plans based on the multiple matrix hierarchical results includes:

[0053] Obtain the preset capital weight coefficient corresponding to each of the squares;

[0054] The total funding requirement is calculated based on the list of outdated equipment to be phased out and the list of environmental areas to be improved.

[0055] The funding allocation for each matrix is ​​calculated based on the preset funding weight coefficient and the total funding requirement.

[0056] Obtain the preset batch rules and time period division nodes for fund investment;

[0057] The funding quota of each of the aforementioned matrices is allocated to multiple batches defined by the funding investment batch rules in order of positive correlation with the comprehensive priority score of the matrices, and associated with the time period division nodes to generate multiple batch funding investment plans.

[0058] Preferably, the step of obtaining the draft job responsibilities of personnel at all levels based on the management pre-plan and generating portable work instruction information corresponding to the draft job responsibilities of personnel at all levels includes:

[0059] Analyze the management factor data to extract items with ambiguous responsibilities and deficiencies in the management system;

[0060] Based on the aforementioned ambiguities in responsibility, a job description text covering all positions was drafted and approved, forming the job responsibility formulation draft for all levels of personnel;

[0061] Based on the aforementioned deficiencies in the management system, revise the existing safety production management system and generate the revised system text;

[0062] Key information was extracted and structured from the draft job responsibilities of personnel at all levels to generate standardized job responsibility items;

[0063] The standardized job responsibility items and the key constraint clauses in the revised system text are fused and encoded to generate the portable work instruction information, which is used to be loaded into a portable terminal device.

[0064] Preferably, after generating the integration pre-plan for the next cycle, the method further includes:

[0065] During the execution period of the current PDCA cycle security management cycle, the training tasks, funding tasks, and management tasks defined in the integration pre-plan are executed.

[0066] During the inspection period of the current PDCA cycle safety management cycle, plan execution result data is collected, and the plan execution result data is compared with the expected target in the integrated pre-plan to generate an execution deviation analysis report;

[0067] During the improvement period of the current PDCA cycle safety management cycle, the training pre-plan, the investment pre-plan, and the management pre-plan are revised based on the execution deviation analysis report;

[0068] Based on the revised training pre-plan, the revised investment pre-plan, and the revised management pre-plan, initiate the planning phase of the next PDCA cycle safety management cycle.

[0069] Preferably, the step of collecting the plan execution result data and comparing the plan execution result data with the expected targets in the integrated pre-plan to generate an execution deviation analysis report includes:

[0070] Obtain the expected targets for training coverage, equipment ledger completeness, fund execution rate, and system dissemination rate set in the integrated pre-plan.

[0071] Collect actual training records, equipment management ledger data, cash flow data, and system learning and assessment data;

[0072] The training record data is compared with the expected training coverage target to calculate the training completion deviation value;

[0073] The equipment management ledger data is compared with the expected target for the equipment ledger completeness rate, and the ledger completeness deviation value is calculated.

[0074] The cash flow data is compared with the expected cash execution rate to calculate the cash execution deviation value.

[0075] The system learning and assessment data are compared with the expected target of the system dissemination rate to calculate the system dissemination deviation value;

[0076] The deviation values ​​of training completion, ledger completeness, fund execution, and system dissemination are summarized to generate an execution deviation analysis report containing multiple deviation indicators.

[0077] Compared with the prior art, the beneficial effects of the present invention are:

[0078] This method uses stratified analysis to classify coal mine production systems, obtaining matrix classification results reflecting the risk levels and correlations of different regions and stages, and directly generating phased investment plans based on these results. This approach transforms abstract system risk analysis conclusions into concrete and executable resource allocation sequences, ensuring that safety investments strictly correspond to the actual risk distribution within the system. Funds are targeted and invested in phases into the high-risk matrix identified through analysis, changing the extensive model of allocating funds based on experience or fixed proportions. This risk matrix-based investment mechanism ensures the accuracy and timeliness of fund use, optimizes the efficiency of safety resource allocation, and enables each stage of investment to specifically weaken the most prominent risk structure in the system, achieving a dynamic closed loop between investment and risk mitigation effects.

[0079] Based on data on the causal factors of the same accident, separate training content is generated for frontline employees, middle-level managers, and senior leaders. Information on the same risk source is deconstructed and transformed into teaching materials adapted to the cognitive models and decision-making needs of different functional levels. Frontline content focuses on the identification and operational procedures of specific risk points, middle-level content emphasizes the management and control methods of the risk chain, and senior-level content focuses on strategic decision-making and resource assurance for the risk system. This tiered and differentiated content generation ensures that every level, from execution to decision-making, receives risk information and key points for capability improvement that match their responsibilities. It enables the entire organization to form a coherent understanding of risk from micro to macro levels, improving the accuracy of execution at the frontline, the management control of middle-level managers, and the strategic foresight of senior leaders, thus constructing a coordinated and consistent risk defense cognitive system. Attached Figure Description

[0080] Figure 1 This is a schematic diagram illustrating the working principle of the pre-management method for coal mine safety production described in this invention.

[0081] Figure 2 A flowchart for generating a pre-training plan;

[0082] Figure 3 A flowchart for generating the pre-investment plan;

[0083] Figure 4 Deviation analysis chart for the PDCA cycle execution in coal mine safety production;

[0084] Figure 5 Radar chart of safety management indicators for the PDCA cycle in coal mines. Detailed Implementation

[0085] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0086] Please see Figure 1 This invention provides a pre-management method for coal mine safety production. The method includes: acquiring accident causative factor data stored in the coal mine safety production system, including human factor data, machine factor data, environmental factor data, and management factor data; generating pre-training plans, pre-investment plans, and pre-management plans based on the accident causative factor data; generating knowledge training content for grassroots employees, awareness training content for middle-level managers, and awareness training content for senior leaders based on the pre-training plans; classifying the coal mine production system using stratified analysis based on the pre-investment plans, obtaining multiple matrix classification results, and obtaining multiple batch investment plans based on the multiple matrix classification results; obtaining draft job responsibilities for personnel at all levels based on the pre-management plans, and generating portable work instructions corresponding to the draft job responsibilities for personnel at all levels; and acquiring pre-set cycle parameters for the PDCA cycle safety management cycle, including the division of the planning period, execution period, inspection period, and improvement period. During the planning phase of the PDCA cycle safety management, integrate the knowledge training content of front-line employees, the awareness training content of middle-level managers, the awareness training content of senior leaders, multiple phased investment plans, and portable work instructions to generate an integrated pre-plan for the next cycle.

[0087] In one embodiment of the present invention, see [reference] Figure 2 Based on human factor data, pre-training plans targeting unsafe human behaviors are developed. Unsafe behavior characteristics are extracted from the human factor data, resulting in multiple behavioral characteristic factors. These factors are then categorized into grassroots, mid-level, and high-level behavioral factors. A coal mine safety production knowledge training material library corresponding to the grassroots behavioral factors is obtained. A coal mine safety production awareness training material library corresponding to the mid-level behavioral factors and a coal mine safety production cognition training material library corresponding to the high-level behavioral factors are also obtained. Based on these three libraries, pre-training plans covering all three levels are generated.

[0088] In practice, the extracted behavioral characteristic factors are classified and assigned based on their associated job responsibility level and decision-making influence. For example, the "support operation standardization factor" and "equipment parameter confirmation factor" are primarily related to the specific operations of frontline workers and are classified as grassroots behavioral factors; the "safety record integrity factor" involves the daily management and supervision responsibilities of work teams and districts and is classified as a mid-level behavioral factor; and the "risk identification ability factor" is related to systemic risk assessment and safety policy formulation in the mine and is classified as a high-level behavioral factor. The quantitative basis for classification and assignment can be understood as a scoring model that calculates the hierarchical assignment score for each behavioral characteristic factor.

[0089]

[0090] Where: symbol Represents the score of hierarchical affiliation, symbol This represents the score of the daily operational frequency of the responsibilities associated with this factor, with the symbol... The score represents the systematic impact range of the decisions involved by this factor, with the symbol... and symbols They are symbols and symbols The preset weighting coefficients. Based on the symbols... The numerical range maps it to a set of behavioral factors at the grassroots, middle, or high levels.

[0091] In some embodiments, a coal mine safety production knowledge training material library corresponding to the grassroots behavioral factors is obtained. The content of this library focuses on specific, standardized safety operating procedures, correct equipment usage methods, and emergency response skills. For example, for the "support operation standardization factor," the library provides step-by-step video tutorials for roof support operations, a comparison table of support parameters under different rock strata conditions, and illustrated case studies of common operational errors. In some embodiments, a coal mine safety production awareness training material library corresponding to the mid-level behavioral factors is obtained. This library focuses on safety management processes, key points of on-site supervision, and preliminary analysis of potential safety hazards. For the "safety record integrity factor," the library includes standardized tutorials for filling out safety checklists, interpretations of legal and regulatory requirements for various safety records, and case study materials on identifying management loopholes through record analysis.

[0092] It is understandable that this involves acquiring a coal mine safety production awareness training material library corresponding to high-level behavioral factors. The content of this library focuses on safety production strategies, safety culture construction, systemic risk management concepts, and in-depth analysis of industry regulations and policies. For the "risk identification capability factor," the library provides guidelines for identifying and assessing major coal mine safety risks, comparative analysis reports of advanced domestic and international safety management systems, and in-depth analyses of the root causes and management responsibilities of typical major accident cases. Optionally, the coal mine safety production knowledge training material library, the coal mine safety production awareness training material library, and the coal mine safety production awareness training material library can all be matched and accessed from the company's own standardized courseware library, industry safety knowledge platforms, and course libraries authorized by professional training institutions.

[0093] In one embodiment of the present invention, see [reference] Figure 3 This process integrates machine and environmental factor data to generate a machine-environment joint assessment dataset. From this dataset, equipment and facility status indicators and operational environment quality indicators are extracted. Based on the equipment and facility status indicators, a list of outdated equipment to be phased out is obtained, along with a list of environmental areas requiring improvement based on the operational environment quality indicators. Data on coal mine geological conditions and actual mine operating conditions are also acquired. Combining these lists, an adaptive list of advanced equipment to be purchased and an environmental remediation project list are generated. Based on these lists, an initial framework for a pre-investment funding plan is developed. The process analyzes the institutional documents and job responsibility files within the management factor data to identify overlapping responsibilities and missing approval processes. A job responsibility clarification requirement list is generated based on the overlapping responsibilities, and a process completion task list is generated for missing approval processes. The coal mine safety production standardization management specification database is accessed. The job responsibility clarification requirement list is compared with standard job responsibility templates to generate a difference analysis result. Based on the difference analysis result and the process completion task list, a management responsibility optimization plan and an approval process reconstruction plan are drafted. The management responsibility optimization plan and the approval process reconstruction plan were integrated and reviewed to form a proactive management plan targeting management deficiencies.

[0094] In some embodiments, a list of outdated equipment to be phased out is obtained based on equipment and facility status indicators. This process uses thresholds for judgment, for example, including equipment with a "failure frequency" exceeding 3 times per month or a "safety protection device effectiveness" below 90% in the list of outdated equipment to be phased out. Simultaneously, a list of areas requiring environmental improvement is obtained based on operational environment quality indicators, for example, including mining areas with a "gas exceedance frequency" exceeding 5 times per month or a "dust compliance rate" below 80% for a consecutive week in the list of areas requiring environmental improvement. In some embodiments, coal mine geological condition data and actual mine operating condition data are obtained. The coal mine geological condition data includes coal seam thickness, dip angle, gas content, and hydrogeological type, while the actual mine operating condition data includes the current mining level, mining method, roadway support type, and ventilation system diagram. Optionally, by combining the list of outdated equipment to be phased out and the list of areas requiring environmental improvement, an adaptive list of advanced equipment to be procured and a list of environmental remediation projects can be generated. For example, regarding the local ventilation fans with "high failure frequency" in the list of outdated equipment to be phased out, and based on the required air volume in the actual working conditions of the mine, the advanced equipment procurement list includes "high-power, low-noise, counter-rotating local ventilation fan (model FBDNo.6.3 / 2×30)". Regarding the fully mechanized tunneling faces with "low dust compliance rate" in the list of areas to be improved, and based on the coal seam hardness information in the coal mine geological data, the environmental remediation project list includes "upgrading of the high-pressure external spray dust suppression system for fully mechanized tunneling faces" and "installation of long-pressure short-extraction ventilation and dust removal devices".

[0095] In practical implementation, an initial framework for the pre-investment funding plan is generated based on the advanced equipment procurement list and the environmental governance project list. This initial framework includes a project overview, breakdown of budget items, technical specifications, expected performance indicators, and a preliminary timeline. The initial framework for the pre-investment funding plan can be described as follows:

[0096]

[0097] Where: symbol The symbol represents the total budget amount for the initial framework of the pre-investment plan. Represents the number of equipment types in the advanced equipment procurement list, symbol Representing the The planned purchase quantity of this type of equipment, symbol Representing the The estimated market unit price of this type of equipment, symbol Represents the number of projects in the environmental governance project list, symbol Representing the Total cost estimate for the environmental remediation project.

[0098] In practical implementation, management factor data is analyzed to obtain pre-management solutions for management deficiencies. This data includes safety production responsibility system documents, work procedure approval records, accident hazard investigation and management ledgers, and management review reports. The system text records and job responsibility files within the management factor data are analyzed to identify overlapping responsibilities and missing approval processes. For example, the system text records show that the responsible department for "mechanical and electrical equipment maintenance" is defined as both the Mechanical and Electrical Department and the Transportation Team, constituting an overlapping responsibility. The job responsibility files reveal overlap in the safety responsibilities described by the Deputy Chief Engineer and the Head of the Technical Department. The work procedure approval records show that the "water exploration and drainage design" lacks the signature of the head of the Geotechnical Survey Department, constituting a missing approval process. A job responsibility clarification requirement list is generated based on the overlapping responsibilities, listing each overlapping responsibility, the relevant departments and positions, and the clarification requirements. A process completion task list is generated for missing approval processes, clearly identifying the missing steps, the necessary approval positions, and the basis for approval. Understandably, this involves retrieving the coal mine safety production standardization management specification database, which stores standard job responsibility templates and standard management processes from national and industry standards. The job responsibility clarification requirement list is then compared with the standard job responsibility templates to generate a discrepancy analysis. This analysis specifically identifies the inconsistencies between the company's existing job responsibility divisions and the standard templates.

[0099] In one embodiment of the present invention, a coal mine production system analysis model is constructed, comprising multiple subsystems, which are input into a tomographic analysis algorithm. These subsystems include an electromechanical transportation subsystem, a roof control subsystem, a water prevention subsystem, a fire prevention and extinguishing subsystem, a gas prevention subsystem, and a dust control subsystem. The safety risk level and rectification urgency value of each subsystem are obtained. A comprehensive priority score for each subsystem is calculated based on its safety risk level and rectification urgency value. All subsystems are then sorted in descending order and divided into multiple priority groups based on their comprehensive priority scores. Each priority group is labeled as a square matrix, and the resulting multiple priority groups are used as the classification results of multiple square matrices. Historical accident statistics and current hazard log data for each subsystem are obtained. The safety risk level value for each subsystem is calculated based on the historical accident statistics, quantified by a weighted sum of accident frequency and severity. The rectification urgency value for each subsystem is calculated based on the current hazard log data, quantified by a weighted product of the number of hazards, hazard level, and remaining rectification time. Weighting coefficients are set for the safety risk level value and the rectification urgency value. The safety risk level value of each subsystem is multiplied by its weighting coefficient to obtain a risk-weighted score. The rectification urgency value of each subsystem is multiplied by its weighting coefficient to obtain an urgency-weighted score. The risk-weighted score and the urgency-weighted score of each subsystem are summed to obtain the comprehensive priority score of each subsystem.

[0100] In some embodiments, historical accident statistics and current hazard log data for each subsystem are obtained for quantitative calculation. Historical accident statistics are derived from the enterprise's accident report database over the past five years, while current hazard log data is derived from recent safety inspection records and unresolved hazard entries in the safety information management system. The safety risk level value for each subsystem is calculated based on the historical accident statistics, and this value is quantified by a weighted sum of accident frequency and accident severity. For example, for the gas prevention subsystem, the number of various gas-related accidents occurring within five years is used as the accident frequency. A severity level score is assigned based on the number of casualties, economic losses, and downtime caused by each accident. The weighted sum of the accident frequency and the average accident severity level score is the safety risk level value for the gas prevention subsystem. In some embodiments, the urgency value for rectification of each subsystem is calculated based on the current hazard log data. This urgency value is quantified by a weighted product of the number of hazards, hazard level, and remaining rectification time. For example, for the roof control subsystem, the number of all existing roof-related hazards is counted from the ledger. Each hazard is classified into major, significant, or general hazards according to industry standards and assigned a corresponding level coefficient. At the same time, the remaining rectification days are calculated based on the rectification deadline for each hazard and converted into a time urgency coefficient. These factors are then substituted into the calculation formula to obtain the rectification urgency value of the roof control subsystem.

[0101] In practical implementation, weighting coefficients are set for the safety risk level value and the rectification urgency value. The weighting coefficients are set based on the coal mine safety management strategy. For example, if the management strategy focuses more on preventing a recurrence of past problems, the weighting coefficient for the safety risk level value is set higher; if the management strategy focuses more on solving current outstanding problems, the weighting coefficient for the rectification urgency value is set higher. The safety risk level value of each subsystem is multiplied by its weighting coefficient to obtain a risk-weighted score; the rectification urgency value of each subsystem is multiplied by its weighting coefficient to obtain an urgency-weighted score. The risk-weighted score and the urgency-weighted score of each subsystem are summed to obtain the comprehensive priority score for each subsystem. The process of calculating the comprehensive priority score can be described as follows:

[0102]

[0103] Where: symbol Representing the The overall priority score of each subsystem, symbol Representing the The security risk level value of each subsystem, symbol Representing the The urgency value for rectification of each subsystem, symbol The weighting coefficient represents the safety risk level value, with the symbol... The weighting coefficient represents the urgency of rectification.

[0104] In practical implementation, all subsystems are ranked in descending order based on their comprehensive priority scores. This ranking results in a sequence from high to low comprehensive priority scores. This sequence is then divided into multiple priority groups. For example, if the six subsystems, ranked by comprehensive priority score, are planned to be divided into three matrices, the first and second-ranked subsystems are placed in the first priority group, the third and fourth in the second priority group, and the fifth and sixth in the third priority group. Each priority group is labeled as a matrices; for example, the first priority group is called the "first matrices," the second priority group is called the "second matrices," and so on. The resulting multiple priority groups serve as the classification results for multiple matrices. It can be understood that the classification results directly reflect the order of safety investment and governance rectification for different subsystem groups. For example, calculations might show that the gas prevention subsystem and roof control subsystem have the highest comprehensive priority scores and are placed in the first matrices; the water control subsystem and electromechanical transportation subsystem are next and placed in the second matrices; and the fire prevention and dust control subsystem have relatively lower scores and are placed in the third matrices. Optionally, the division rules can be adjusted based on the actual number of subsystems and management granularity requirements, for example, dividing into two or four matrices. It is understandable that the resulting division into multiple matrices provides a clear decision-making basis for subsequent phased funding plans.

[0105] In one embodiment of the present invention, a preset funding weight coefficient corresponding to each matrix is ​​obtained, and the total funding requirement is calculated based on the list of outdated equipment to be eliminated and the list of environmental areas to be improved. The funding allocation for each matrix is ​​calculated based on the preset funding weight coefficient and the total funding requirement, and preset funding batch rules and time period division nodes are obtained. The funding allocation for each matrix is ​​allocated to multiple batches defined by the funding batch rules in an order positively correlated with the matrix's comprehensive priority score, and linked to the time period division nodes to generate multiple batch funding plans. Management factor data is analyzed to extract responsibility ambiguities and management system deficiency items. Based on the responsibility ambiguities, a responsibility description text covering all positions is drafted and approved to form a draft of job responsibilities for personnel at all levels. The existing safety production management system is revised based on the management system deficiency items to generate a revised system text. Key information is extracted and structured from the draft of job responsibilities for personnel at all levels to generate standardized job responsibility items. The standardized job responsibility items and key constraint clauses in the revised system text are fused and encoded to generate portable work instruction information, which is then loaded into a portable terminal device.

[0106] In some embodiments, the funding allocation for each matrix is ​​calculated based on a preset funding weighting coefficient and the total funding requirement. The calculation formula is expressed as follows:

[0107]

[0108] Where: symbol Representing the The funding allocation for each square, symbol Representing the The preset capital weight coefficients of each matrix, with symbols... This represents the total funding requirement. Based on the example data above, the funding allocation for the first tier is... The second tier of funding is 10,000 yuan, and the second tier of funding is... Ten thousand yuan, third-party funding quota is Ten thousand yuan. In some embodiments, a preset funding batch rule and time period division node are obtained. The preset funding batch rule is defined, for example, as "funds are invested in three batches, with the first batch accounting for 50% of the total annual safety expenses, the second batch accounting for 30%, and the third batch accounting for 20%". The time period division node is defined, for example, as aligned with the financial quarter, as "the first batch ends at the end of the first quarter, the second batch ends at the end of the second quarter, and the third batch ends at the end of the third quarter".

[0109] In practice, the funding quota for each matrix is ​​allocated to multiple batches defined by the funding batching rules, in order of positive correlation with the matrix's overall priority score. The matrix's overall priority score is calculated as the average of the overall priority scores of all subsystems within that matrix. Matrixes with the highest funding quotas and the highest overall priority scores are prioritized for earlier batches. For example, the first matrix, with the highest overall priority score, has its entire 6.5 million yuan funding quota allocated to the first batch of funding plans; the second matrix's 3.9 million yuan quota is allocated to the second batch; and the third matrix's 2.6 million yuan quota is allocated to the third batch. This allocation method ensures that the most pressing security issues receive priority funding. Multiple batch funding plans are generated by linking to time period division nodes. These multiple batch funding plans are presented in tabular form, see Table 1.

[0110] Table 1: Allocation Table of Safety Investment Funds in Batches

[0111] Batch of input Related time period nodes Array of inputs Allocated Funds (Ten Thousand Yuan) Examples of main investment directions The first batch End of the first quarter First Formation 650 Upgrade of gas extraction system and installation of roof online monitoring system Second batch End of the second quarter Second Formation 390 Drainage pumping station renovation and main lifting system protection device upgrade Third batch End of the third quarter Third-party formation 260 A complete fire prevention and extinguishing grouting system was established, and dust removal fans were purchased.

[0112] In practice, the process of obtaining draft job responsibility documents for all levels of personnel based on the pre-management plan involves analyzing management factor data. This data includes current safety production responsibility documents, job descriptions, and the responsibility determination sections in accident investigation reports. Analyzing this data reveals ambiguous responsibilities and deficiencies in management systems. Examples of ambiguous responsibilities include "the responsibility for explosion-proof inspections of underground electrical equipment is unclear between the electromechanical department and the ventilation team," while examples of deficiencies include "the lack of specific safety assessment procedures for the application of new technologies and processes." Based on these ambiguous responsibilities, a job description text covering all positions is drafted and approved, forming draft job responsibility documents for all levels of personnel. The drafting work is jointly conducted by the human resources and safety management departments. Based on the national coal mine safety regulations and the basic requirements of the coal mine safety production standardization management system, overlapping, redundant, or blank responsibilities are redefined and clearly articulated, forming a complete list of job safety production responsibilities from the mine manager to frontline workers. This list is then reviewed and approved at a meeting to form draft job responsibility documents for all levels of personnel.

[0113] See Figure 4 This is a deviation analysis chart for the PDCA cycle in coal mine safety production. It's a multi-indicator comparative bar chart used in coal mine safety management to evaluate the effectiveness of pre-implementation management plans. The data shows that the actual values ​​of each indicator are slightly lower than expected, with similar deviations (approximately 5%), indicating a relatively consistent overall implementation of the plan, but with widespread small gaps. Further strengthening of implementation supervision or appropriate adjustments to the target's rationality are needed. This type of chart is used in the PDCA inspection phase of coal mine safety management. By comparing the expected-actual deviations of multiple indicators, it identifies weaknesses in plan implementation, providing a basis for adjusting solutions in subsequent improvement phases.

[0114] In one embodiment of the present invention, during the execution period of the current PDCA cycle safety management cycle, the training tasks, funding tasks, and management tasks defined in the integration pre-plan are executed, and during the inspection period of the current PDCA cycle safety management cycle, plan execution result data is collected. The plan execution result data is compared with the expected targets in the integration pre-plan to generate an execution deviation analysis report. During the improvement period of the current PDCA cycle safety management cycle, the training pre-plan, funding pre-plan, and management pre-plan are revised based on the execution deviation analysis report. Based on the revised training pre-plan, revised funding pre-plan, and revised management pre-plan, the planning phase of the next PDCA cycle safety management cycle is initiated. The expected targets for training coverage, equipment ledger completeness, funding execution rate, and system dissemination rate set in the integration pre-plan are obtained, and actual training record data, equipment management ledger data, fund flow data, and system learning and assessment data are collected. The training completion deviation is calculated by comparing training record data with the expected training coverage rate; the equipment management ledger data is calculated by comparing equipment ledger completeness rate with the expected equipment ledger completeness rate; the fund execution deviation is calculated by comparing fund flow data with the expected fund execution rate; and the system dissemination deviation is calculated by comparing system learning and assessment data with the expected system dissemination rate. The training completion deviation, ledger completeness deviation, fund execution deviation, and system dissemination deviation are then summarized to generate an execution deviation analysis report containing multiple deviation indicators.

[0115] In some embodiments, the expected targets for training coverage, equipment ledger completeness, fund execution, and policy dissemination rate set in the pre-integration plan are obtained. These expected targets are set as specific values ​​during the planning period, such as a training coverage target of 95%, an equipment ledger completeness target of 100%, a fund execution rate target of 90%, and a policy dissemination rate target of 100%. In some embodiments, actual training record data, equipment management ledger data, fund flow data, and policy learning and assessment data are collected. Training record data includes the list and total number of employees who have participated in training; equipment management ledger data records whether all equipment in the warehouse has completed information entry and label affixing; fund flow data shows the invoices and transfer records actually paid for safety projects; and policy learning and assessment data saves the score sheets of all personnel who participated in the policy examination.

[0116] In practice, the training completion deviation is calculated by comparing training record data with the expected training coverage rate; the equipment management ledger data is calculated by comparing equipment ledger completeness rate with the expected equipment ledger completeness rate; the fund execution deviation is calculated by comparing fund flow data with the expected fund execution rate; and the system dissemination deviation is calculated by comparing system learning and assessment data with the expected system dissemination rate. The calculation of the deviation value can be expressed as follows:

[0117]

[0118] Where: symbol Represents the deviation value of a certain item, sign The symbol represents the actual data achieved for this item. This represents the expected target value for that item. For example, if the expected target for the execution rate of funds for the procurement of gas extraction pipes is 90%, but the actual payment amount only reaches 80% of the plan, then the fund execution deviation value is calculated as follows: This can be understood as summarizing deviations in training completion, record completeness, fund execution, and system dissemination, generating an execution deviation analysis report containing multiple deviation indicators. The execution deviation analysis report is presented as a structured document, clearly listing the expected target, actual completion value, calculated deviation value, and a brief description of each deviation.

[0119] In practical implementation, during the improvement period of the current PDCA cycle safety management cycle, the pre-training plan, pre-investment plan, and pre-management plan are revised based on the execution deviation analysis report. For example, the execution deviation analysis report shows a large deviation in the completion of "hydraulic support operation knowledge training," with an actual coverage rate of only 85%, lower than the expected target of 95%. The analysis indicates that some night shift personnel failed to attend centralized training. Therefore, the pre-training plan is revised by adding an online video training module and arranging supplementary training sessions. The execution deviation analysis report shows that the execution deviation of the first batch of equipment procurement funds is 0.111. The analysis indicates that the reason is supply chain delays. Therefore, the procurement time nodes and supplier selection criteria in the pre-investment plan are revised. It can be understood that, based on the revised pre-training plan, revised pre-investment plan, and revised pre-management plan, the planning phase of the next PDCA cycle safety management cycle is initiated. The planning phase of the next PDCA cycle safety management cycle will integrate and generate a new round of more targeted and operable integrated pre-plans based on the revised plans, thereby forming a continuously cyclical and dynamically optimized safety management closed loop.

[0120] See Figure 5This is a radar chart of safety management indicators for the PDCA cycle in coal mines. It is used to comprehensively assess the gap between the expected targets, actual results, and deviations of the four core indicators in safety production management. As can be seen from the chart, the actual implementation of the four core indicators has not met expectations. Targeted strengthening of implementation supervision and plan revision are needed in subsequent PDCA cycles, especially focusing on areas with high deviations. This type of radar chart is used in the full-cycle assessment phase of coal mine safety management. Through a visual comparison of multiple indicators' "expected-actual-deviation," it intuitively presents management shortcomings and provides a global reference for optimizing plans in subsequent PDCA cycles.

[0121] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0122] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A pre-management method for coal mine safety production, characterized in that, include: Acquire accident causation factor data in the coal mine safety production system, wherein the accident causation factor data includes human factor data, machine factor data, environmental factor data, and management factor data; Based on the accident causation factor data, generate a training pre-plan, an investment pre-plan, and a management pre-plan; Based on the aforementioned pre-training plan, knowledge training content for grassroots employees, awareness training content for middle-level managers, and awareness training content for senior leaders are generated. Based on the aforementioned pre-investment scheme, the coal mine production system is classified using stratified analysis, resulting in multiple matrix classification results. Multiple batch investment plans are obtained based on the grading results of the aforementioned matrix; Based on the aforementioned management pre-planning scheme, the job responsibility drafts for personnel at all levels are obtained, and portable work instruction information corresponding to the job responsibility drafts for personnel at all levels is generated. Obtain the pre-set cycle parameters of the PDCA cycle safety management cycle, which include the time periods of planning, execution, inspection and improvement. During the planning period of the PDCA cycle safety management cycle, the knowledge training content of the grassroots employees, the awareness training content of the middle-level managers, the awareness training content of the senior leaders, multiple batch investment plans, and the portable work instruction information are integrated to generate an integrated pre-plan for the next cycle.

2. The pre-management method for coal mine safety production according to claim 1, characterized in that, The process of generating pre-training plans, pre-implementation plans, and pre-management plans based on the accident causation factor data includes: Based on the aforementioned human factor data, obtain pre-training plans targeting unsafe human behavior factors; Based on the machine's factor data and environmental factor data, pre-implementation plans are obtained to address the unsafe conditions of the object and the adverse environmental factors. Based on the aforementioned management factor data, pre-emptive management solutions targeting management deficiencies are developed. The step of obtaining a pre-training plan targeting unsafe behavioral factors based on the human factor data includes: Unsafe behavior feature items are extracted from the human factor data to obtain multiple behavioral feature factors; Based on the classification and attribution of multiple behavioral characteristic factors, the following categories are obtained: basic behavioral factors, mid-level behavioral factors, and high-level behavioral factors. Obtain a coal mine safety production knowledge training material library corresponding to the aforementioned grassroots behavioral factors; Obtain a coal mine safety production awareness training material library corresponding to the aforementioned mid-level behavioral factors; Obtain a coal mine safety production awareness training material library corresponding to the aforementioned high-level behavioral factors; Based on the coal mine safety production knowledge training material library, the coal mine safety production awareness training material library, and the coal mine safety production cognition training material library, a pre-training plan covering three levels—grassroots, middle-level, and senior-level—is generated.

3. The pre-management method for coal mine safety production according to claim 2, characterized in that, The step of obtaining a pre-implementation plan for unsafe conditions and adverse environmental factors of an object based on the machine's factor data and environmental factor data includes: By integrating the machine factor data and environmental factor data, a machine-environment joint evaluation dataset is generated; Extract equipment and facility status indicators and working environment quality indicators from the aforementioned machine-environment joint assessment dataset; A list of obsolete equipment to be phased out is obtained based on the equipment and facility status indicators, and a list of environmental areas to be improved is obtained based on the work environment quality indicators. Obtain coal mine geological conditions data and actual mine operating condition data, and combine them with the list of outdated equipment to be eliminated and the list of environmental areas to be improved to generate an adaptive list of advanced equipment to be purchased and a list of environmental governance projects. An initial framework for a pre-investment plan is generated based on the list of advanced equipment to be purchased and the list of environmental remediation projects.

4. The pre-management method for coal mine safety production according to claim 3, characterized in that, The step of obtaining pre-management solutions for management deficiencies based on the management factor data includes: Analyze the institutional documents and job responsibility files in the management factor data to identify overlapping responsibilities and missing approval processes; A list of job responsibility clarification requirements is generated based on the aforementioned overlapping responsibilities, and a list of process completion tasks is generated for the missing items in the approval process. The database of standardized management specifications for coal mine safety production was retrieved, and the list of job responsibility clarification requirements was compared with the standard job responsibility template to generate a difference analysis result. Based on the results of the difference analysis and the task list for process completion, a management responsibility optimization plan and an approval process reconstruction plan were drafted. The management responsibility optimization plan and the approval process reconstruction plan are integrated and reviewed to form a pre-management plan targeting management deficiencies.

5. The pre-management method for coal mine safety production according to claim 4, characterized in that, The steps of classifying the coal mine production system using chromatographic analysis to obtain multiple matrix classification results include: A coal mine production system analysis model is constructed, which includes multiple subsystems. The subsystems are input into the tomographic analysis algorithm. The subsystems include an electromechanical transportation subsystem, a roof control subsystem, a water prevention subsystem, a fire prevention and extinguishing subsystem, a gas prevention subsystem, and a dust prevention subsystem. Obtain the security risk level value and rectification urgency value for each of the subsystems; A comprehensive priority score for each subsystem is calculated based on the security risk level value and rectification urgency value of each subsystem. Based on the comprehensive priority score of all the subsystems, all the subsystems are sorted in descending order and divided into multiple priority groups, and each priority group is marked as a square matrix; The resulting priority groups are used as multiple matrix hierarchical results.

6. The pre-management method for coal mine safety production according to claim 5, characterized in that, The comprehensive priority score for each subsystem, calculated based on its security risk level and rectification urgency value, includes: Obtain historical accident statistics and current hazard log data for each of the subsystems; The safety risk level value of each subsystem is calculated based on the historical accident statistics. The safety risk level value is quantified by a weighted sum of accident frequency and accident severity. Based on the current hidden danger ledger data, the rectification urgency value of each subsystem is calculated. The rectification urgency value is quantified by a weighted product of the number of hidden dangers, the level of hidden dangers, and the remaining rectification time limit. Set weighting coefficients for safety risk level values ​​and rectification urgency values; Multiply the security risk level value of each subsystem by its weighting coefficient to obtain the risk-weighted score; Multiply the urgency value of each subsystem by its weighting coefficient to obtain the urgency weighted score. The risk-weighted score and urgency-weighted score of each subsystem are summed to obtain the comprehensive priority score of each subsystem.

7. The pre-management method for coal mine safety production according to claim 6, characterized in that, The step of obtaining multiple batch investment plans based on the hierarchical results of multiple matrixes includes: Obtain the preset capital weight coefficient corresponding to each of the squares; The total funding requirement is calculated based on the list of outdated equipment to be phased out and the list of environmental areas to be improved. The funding allocation for each matrix is ​​calculated based on the preset funding weight coefficient and the total funding requirement. Obtain the preset batch rules and time period division nodes for fund investment; The funding quota of each of the aforementioned matrices is allocated to multiple batches defined by the funding investment batch rules in order of positive correlation with the comprehensive priority score of the matrices, and associated with the time period division nodes to generate multiple batch funding investment plans.

8. The pre-management method for coal mine safety production according to claim 1, characterized in that, The step of obtaining draft job responsibilities for personnel at all levels based on the management pre-planning scheme and generating portable work instruction information corresponding to the draft job responsibilities for personnel at all levels includes: Analyze the management factor data to extract items with ambiguous responsibilities and deficiencies in the management system; Based on the aforementioned ambiguities in responsibility, a job description text covering all positions was drafted and approved, forming the job responsibility formulation draft for all levels of personnel; Based on the aforementioned deficiencies in the management system, revise the existing safety production management system and generate the revised system text; Key information was extracted and structured from the draft job responsibilities of personnel at all levels to generate standardized job responsibility items; The standardized job responsibility items and the key constraint clauses in the revised system text are fused and encoded to generate the portable work instruction information, which is used to be loaded into a portable terminal device.

9. The pre-management method for coal mine safety production according to claim 1, characterized in that, After generating the integrated pre-planning for the next cycle, the following is also included: During the execution period of the current PDCA cycle security management cycle, the training tasks, funding tasks, and management tasks defined in the integration pre-plan are executed. During the inspection period of the current PDCA cycle safety management cycle, plan execution result data is collected, and the plan execution result data is compared with the expected target in the integrated pre-plan to generate an execution deviation analysis report; During the improvement period of the current PDCA cycle safety management cycle, the training pre-plan, the investment pre-plan, and the management pre-plan are revised based on the execution deviation analysis report; Based on the revised training pre-plan, the revised investment pre-plan, and the revised management pre-plan, initiate the planning phase of the next PDCA cycle safety management cycle.

10. The pre-management method for coal mine safety production according to claim 9, characterized in that, The steps of collecting the execution result data of the plan, comparing the execution result data with the expected targets in the integrated pre-plan, and generating an execution deviation analysis report include: Obtain the expected targets for training coverage, equipment ledger completeness, fund execution rate, and system dissemination rate set in the integrated pre-plan. Collect actual training records, equipment management ledger data, cash flow data, and system learning and assessment data; The training record data is compared with the expected training coverage target to calculate the training completion deviation value; The equipment management ledger data is compared with the expected target for the equipment ledger completeness rate, and the ledger completeness deviation value is calculated. The cash flow data is compared with the expected cash execution rate to calculate the cash execution deviation value. The system learning and assessment data are compared with the expected target of the system dissemination rate to calculate the system dissemination deviation value; The deviation values ​​of training completion, ledger completeness, fund execution, and system dissemination are summarized to generate an execution deviation analysis report containing multiple deviation indicators.

Images