Coal mine primary archive management system and method

Abstract

The invention belongs to the technical field of archive management, and particularly relates to a coal mine grassroots archive management system and method.The management system comprises an employee archive collection module, an equipment archive collection module, an association mapping module, an early warning module, a storage module and a management module; a bidirectional association mapping relation is established between employee archive data and equipment archive data through the association mapping module, man-machine interaction in a specific time period is realized, a unique indexable digital link in the specific time period is created, an information island between equipment and employees is broken, and the efficiency of the equipment is improved. An administrator can quickly perform historical tracing work, and unprecedented efficiency and support are provided for accident investigation, responsibility definition and efficiency analysis; and moreover, the equipment and the employees are bidirectionally associated, so that equipment faults, accidents and efficiency can be positioned to individuals, and the responsibility of the employees in operating the equipment is effectively enhanced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of file management, in particular to a coal mine grassroots file management system and method. BACKGROUND

[0002] In the process of coal mine production, grassroots staff and production equipment are the core elements of direct safety production; at present, most coal mine enterprises have established a preliminary file management system to record the basic information of staff and static parameters of equipment.

[0003] The existing coal mine files mainly include staff files and equipment files; the staff files and the equipment files are usually independent of each other and become "information islands" to each other; it is difficult for the management personnel to quickly trace back the historical operators of a certain device and their detailed operation records, and it is also difficult to conveniently query all the devices operated by a certain staff and their operation performance; secondly, the valuable experience generated by the staff in the operation of the equipment, such as the problems found by the operators, the operation details that avoid affecting the operation of the equipment, and the matters needing attention summarized, cannot form a knowledge base that can be inherited for other operators to learn from due to the lack of effective recording and sharing channels, and the same mistakes and problems may occur repeatedly in different staff.

[0004] Therefore, the application provides a coal mine grassroots file management system and method. SUMMARY

[0005] In order to make up for the deficiencies of the prior art and solve at least one technical problem proposed in the background.

[0006] The technical scheme adopted by the application to solve the technical problems is that the coal mine grassroots file management system comprises: A staff file acquisition module is configured to acquire basic information of staff and work information of staff, and generate staff file data; An equipment file acquisition module is configured to acquire basic information of equipment and running information of equipment, and generate equipment file data; An association mapping module is configured to establish a bidirectional association mapping relationship between the staff file data and the equipment file data; the association mapping module is further configured to record and store operation experience notes recorded by the staff when operating the equipment; the operation experience notes at least include equipment problem records, operation matters needing attention, and operation experience summaries; An early warning module is configured to determine illegal operation behaviors and danger levels according to preset logical rules based on the staff file data and the equipment file data, and generate corresponding early warning information; A storage module is configured to store the staff file data, the equipment file data, the bidirectional association mapping relationship, the operation experience notes, and the early warning information; The management module is used to manage the configuration, query, modification, and deletion of all data within the system.

[0007] Preferably, the operation behavior log in the employee file collection module includes records of setting equipment parameters; the logical rules in the early warning module are as follows: Extract the operational parameters of employees on the equipment and set a record set Pi={P1,P2,...,Pn}, where Pi represents the parameter value set in a single instance; Obtain the standard operating parameter threshold range [Pmin, Pmax] of the device; Calculate the employee's violation index K on the equipment: K=(Count(Pi∉[Pmin,Pmax]) / n)*100% Where Count(Pi∉[Pmin,Pmax]) represents the number of times the parameter setting value Pi exceeds the standard threshold range [Pmin,Pmax]. The K value is compared with a preset level threshold range: when K∈[0,Ka), it is determined as no violation; when K∈[Ka,Kb), it is determined as a minor violation; when K∈[Kb,Kc), it is determined as a moderate violation; when K≥Kc, it is determined as a severe violation; where 0 <Ka<Kb<Kc≤100%。

[0008] Preferably, the early warning module can also determine employee attendance violations, and the determination logic is as follows: Based on the daily commuting time information in the employee's work information, calculate the actual daily working hours T. actual ; T actual Compared with standard working time T standard Compare; If T actual <T standard -ΔT1, then it is recorded as early departure; if T actual >T standard If +ΔT2 is added, it is recorded as overtime; if no start or end time is recorded, it is recorded as absence; where ΔT1 and ΔT2 are preset tolerance times. The system counts the number of early departures and absences within a specified period and determines the corresponding attendance violation level based on the preset range in which the number of departures falls.

[0009] Preferably, the equipment file acquisition module further includes an equipment health status assessment unit; the equipment health status assessment unit calculates the equipment health score based on the equipment's cumulative operating time, the deviation trend of historical operating parameters and standard parameters, and the equipment problem reports recorded in the association mapping module, using a preset health score algorithm, and sends an equipment maintenance warning to the management module when the score is lower than a preset threshold.

[0010] Preferably, the algorithm formula for the health score in the equipment health status assessment unit is as follows: Where S is the health score of the device calculated by the preset health algorithm, S base W is a basic health score based on the equipment's service life. op As an operational compliance weighting factor, W issue Weighting factors for problem reports; S base The calculation method is as follows: R t T represents the cumulative operating time loss rate of the equipment. accumulated T represents the total historical cumulative operating time of the equipment. expected This refers to the expected service life of this model of equipment; W op The calculation method is as follows: K avg This is the average violation index of all recent operation records of the equipment, where α is the weighting coefficient of operation compliance (0 < α < 1), used to adjust the degree of impact of operation violations on the overall health. W issue The calculation method is as follows: N issue N represents the total number of valid problem reports recorded by the device through the association mapping module within a certain statistical period. issuemax The maximum number of problem reports is set as a threshold; β is the weighting coefficient for problem reports (0 < β < 1), used to adjust the impact of the number of problem reports on the overall health score. The equipment health status assessment unit calculates the health score S of all equipment in the system in real time or periodically, and automatically sends equipment maintenance warning information to the management module when the score S is lower than the preset maintenance threshold. The equipment maintenance warning information includes at least the equipment number, the current health score and the main deduction factors.

[0011] Preferably, the warning information generated by the warning module adopts different prompting methods according to the level of danger: minor violations are prompted by an interface message; moderate violations are prompted by an interface pop-up warning and recorded in the assessment document; and severe violations are prompted by an audible and visual alarm, a strong interface prompt, and a report generated and pushed to the management.

[0012] Preferably, the management module also includes access control, which assigns different data access and operation permissions to employees of different positions and levels.

[0013] Preferably, the storage module uses a time-series database to store continuous operating parameter records of the storage device and employee operation logs, and uses a relational database to store basic information of employees and equipment, association mapping relationships, and operation experience notes.

[0014] Preferably, the system further includes a report generation module for automatically generating reports based on data in the storage module; the reports include at least monthly, quarterly and annual performance evaluation reports for employees, equipment operation reports and violation statistics reports.

[0015] A method for managing grassroots archives in coal mines, the method being applicable to the aforementioned coal mine grassroots archives management system, and the method comprising the following steps: S1: Employee file data collection steps: Centralized registration of basic employee information, daily active and automatic registration of work information by employees; S2: Equipment file data collection steps: Centralized registration of basic equipment information, and automatic registration of daily operating information of equipment; S3: Association Mapping Steps: Establish a two-way operation association between employees and equipment, and bind and store the equipment operation experience notes submitted by employees; S4: Equipment Health Assessment and Early Warning Steps: Use a health algorithm to calculate the real-time score of the equipment and trigger a maintenance early warning when the score is below the threshold; S5: Employee violation warning steps: Automatically identify and classify violations according to preset rules, and issue warnings for violations.

[0016] The beneficial effects of this invention are as follows: 1. The coal mine grassroots file management system and method described in this invention establishes a bidirectional association mapping relationship between employee file data and equipment file data through an association mapping module. This involves recording each employee's equipment operation in real time and connecting the employee file data with the equipment file data to establish a bidirectional association mapping relationship. This realizes the "human-machine" interaction time within a specific time period, creating a unique and indexable digital link within that time period. By bidirectionally associating equipment with employees, the "information silos" between equipment and employees are broken down, facilitating rapid historical tracing by administrators and providing unprecedented efficiency and support for accident investigation, responsibility determination, and performance analysis. Furthermore, by bidirectionally associating equipment with employees, equipment failures, accidents, and performance issues can be located at the individual level, effectively enhancing employees' sense of responsibility when operating equipment.

[0017] 2. The coal mine grassroots file management system and method described in this invention, by quantitatively assessing the health status of equipment, can issue early warnings before equipment performance significantly declines or malfunctions occur, prompting maintenance personnel to intervene at the optimal time, thereby avoiding unplanned downtime and production interruptions, and transforming the maintenance mode from "post-event remediation" to "pre-event prevention"; and, it makes maintenance decisions no longer based on a single time period or subjective experience, but on quantitative scores derived from multi-dimensional data fusion analysis; making maintenance plans more targeted, preventing both resource waste caused by over-maintenance and safety hazards caused by under-maintenance; at the same time, the "deduction factors" provided in the early warning information can directly indicate the possible causes of health deterioration (such as natural aging, improper operation, or inherent defects), helping maintenance personnel to quickly locate problems, shorten diagnosis time, and improve maintenance efficiency. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1 This is a framework diagram of the management system in this invention; Figure 2 This is a flowchart of the management method in this invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0021] Because existing employee files and equipment files are usually independent of each other, forming "information silos," it is difficult for managers to quickly trace the historical operators of a certain piece of equipment and their detailed operation records, and it is also difficult to conveniently query all the equipment operated by a certain employee and their operation performance; therefore, the file management system proposed in this application is provided. like Figures 1 to 2 As shown in the embodiment of the present invention, a coal mine grassroots file management system includes an employee file collection module, an equipment file collection module, an association mapping module, an early warning module, a storage module, and a management module. The employee file collection module is used to collect basic employee information and employee work information, and generate employee file data. Specifically, the basic employee information includes at least name, employee number, date of employment, department, position, and level; the employee work information includes at least daily get off work hours, the name of the equipment operated, and operation behavior logs. The equipment file acquisition module is used to collect basic equipment information and equipment operation information, and generate equipment file data. Specifically, the basic equipment information includes at least the equipment name, equipment number, standard operating parameter thresholds, and operation manual; the equipment operation information includes at least the daily operating time, production capacity, operator name, and operation parameter records. The association mapping module is used to establish a two-way association mapping relationship between employee file data and equipment file data. This module also records and stores operation experience notes recorded by employees while operating the equipment. These operation experience notes include at least equipment problem records, operation precautions, and operation experience summaries. Specifically, the two-way association mapping relationship allows querying all historical data and corresponding operators through the equipment file, and also allows querying all historical data and corresponding operating equipment through the employee file. The operation experience notes recorded by the association mapping module are bound to specific equipment numbers and equipment operation actions. When other employees access the equipment's operation interface or view the equipment file through the system, the system proactively pushes or provides query entry points to display relevant historical operation experience notes based on the current operation. The early warning module uses employee and equipment data to determine the level of violation and risk based on preset logical rules, and generates corresponding early warning information. Specifically, the risk levels include at least mild, moderate, and severe. The storage module is used to store employee file data, equipment file data, bidirectional association mapping relationships, operation experience notes, and early warning information; The management module is used to perform management operations such as configuration, query, modification, and deletion of all data within the system; In specific implementation, the file management system in this application establishes a two-way association mapping relationship between employee file data and equipment file data through the association mapping module. That is, it records each time an employee operates the equipment in real time, connects the employee file data with the equipment file data to establish a two-way association mapping relationship, realizes the "human-machine" interaction time within a specific time period, and creates a unique indexable digital link within a specific time period. Employees can log in, upload records, and view employee and equipment file data in the file management system via their personal mobile phones, computers, and the operation panel displays on their equipment. By logging into the file management system via their personal mobile phones, computers, and the displays on their equipment, and selecting any recorded device in the system, employees can view all employees who have historically operated that device, and directly view detailed operation logs, parameter settings, and inspection notes recorded during each operation. Furthermore, by selecting any employee in the file management system, employees can view all the equipment that employee has operated since joining the company, as well as a summary of operations on each device, such as operation time, production capacity, number of violations, and parameter settings. This creates a two-way link between equipment and employees, breaking down the "information silos" between them. This allows administrators to quickly trace historical events, providing unprecedented efficiency and support for accident investigation, liability determination, and performance analysis. Furthermore, by linking equipment and employees in two directions, equipment malfunctions, accidents, and performance can be pinpointed to individuals, effectively enhancing employees' sense of responsibility when operating equipment.

[0022] Furthermore, because existing employee files and equipment files are usually independent of each other, they become "information silos." The valuable experience gained by employees in operating equipment, such as problems discovered by operators, operational details to avoid affecting equipment operation, and precautions summarized, cannot be formed into a transferable knowledge base for other operators to learn from due to the lack of effective recording and sharing channels. The same mistakes and problems may be repeated by different employees. In practical implementation, the document management system in this application provides a convenient "Record Experience" function entry on employees' personal mobile phones, computers, and equipment operation panel displays. When employees discover equipment malfunctions, summarize optimization techniques or precautions, they can immediately choose to record them. The system will force the notes to be structured and categorized (e.g., selecting "Problem Report," "Precautions," or "Experience Summary"), and automatically capture the current equipment number, operation action, recording time, and employee information. Through the association mapping module, all recorded experience notes are strongly bound to specific equipment and specific operation actions, so that the recorded experience notes are associated with the characteristic operation actions of specific equipment. This transforms tacit knowledge, which relies on individuals, into explicit digital assets that can be permanently stored and will not be lost, forming an important part of the company's core competitiveness. It also prevents equipment from becoming inoperable due to employee leave or resignation. By sharing operational experience notes, it ensures that the lessons learned by predecessors can serve as warnings for successors, fundamentally preventing the same problems from recurring in different people, directly reducing failure rates and security risks. Furthermore, new employees can quickly learn from the experience of many senior employees on various types of equipment through operational experience notes, which is equivalent to having countless "invisible mentors," enabling them to rapidly improve their skill levels and shorten the training cycle for independent operation.

[0023] In some embodiments, existing supervision of standardized operations by equipment operators mostly relies on on-site supervision by managers and post-event spot checks. This approach cannot achieve 24 / 7, full-coverage monitoring and is prone to creating blind spots in supervision. Furthermore, the judgment criteria are highly subjective and lack unified and quantitative basis. It is impossible to track and analyze the long-term operational behavior of employees and it is difficult to detect potential risks such as habitual violations or fluctuations in operational level. Therefore, the file management system of this application is provided. like Figure 1As shown, the operation behavior log in the employee file collection module includes records of setting equipment parameters; the logical rules in the early warning module are as follows: Extract the operational parameters of employees on the equipment and set a record set Pi={P1,P2,...,Pn}, where Pi represents the parameter value set in a single instance; Obtain the standard operating parameter threshold range [Pmin, Pmax] of the device; Calculate the employee's violation index K on the equipment: K=(Count(Pi∉[Pmin,Pmax]) / n)*100% Where Count(Pi∉[Pmin,Pmax]) represents the number of times the parameter setting value Pi exceeds the standard threshold range [Pmin,Pmax]. The K value is compared with a preset level threshold range: when K∈[0,Ka), it is determined as no violation; when K∈[Ka,Kb), it is determined as a minor violation; when K∈[Kb,Kc), it is determined as a moderate violation; when K≥Kc, it is determined as a severe violation; where 0 <Ka<Kb<Kc≤100%; In specific implementation, firstly, a series of parameter setting records Pi of a specific employee when operating a specific device are extracted from the storage module; and this set of records objectively reflects the employee's historical behavior in operating the device; at the same time, the file management system in this application obtains the safety standard operating parameter threshold range ([Pmin, Pmax]) set by the device at the factory or in the procedure; and this range is an objective benchmark for judging whether the operation is compliant. Next, the violation operation index K is calculated using a mathematical formula. The essence of this formula is to calculate the percentage of times an employee's operations have exceeded the standard range in their historical operations. This index K is an intuitive and quantitative indicator that can clearly reflect the degree of compliance of the employee in operating this equipment. Finally, the calculated K value is compared with the preset level threshold range ([0, Ka), [Ka, Kb), [Kb, Kc), [Kc, 100%]). Through this range division, the continuous violation index is discretized into different danger levels (no violation, minor, moderate, and severe), thereby triggering different levels of early warning response. Ka is the lower limit of the minor violation threshold, Kb is the lower limit of the moderate violation threshold, and Kc is the lower limit of the severe violation threshold. The thresholds Ka, Kb, and Kc can be flexibly configured by the administrator according to the mine's safety management strategy. This achieves automated and intelligent supervision, greatly expanding its coverage and timeliness, and eliminating blind spots. Quantitative analysis of operational data through mathematical algorithms completely avoids interference from subjective human factors, ensuring objective, fair, and data-supported assessments of employee operational skills, providing an indisputable basis for performance evaluation. Furthermore, it not only identifies individual violations but also uses historical data trends (i.e., changes in the K-value) to identify habitual violators or employees with continuously declining operational skills, enabling early warning of potential safety risks and preventing problems before they occur. Finally, it ensures employees are clearly aware that all their operations are recorded and quantitatively scored by the system, effectively encouraging them to consciously comply with operating procedures and reducing violations from the outset.

[0024] In some embodiments, traditional attendance is mostly done by clocking in and signing; in coal mining enterprises, the working hours of grassroots employees vary due to their job positions and work locations; managers need to spend a lot of time manually checking and summarizing attendance records, which is inefficient and prone to errors; in addition, there is a lack of unified and accurate standards for defining violations such as "early departure", "late arrival" and "absence", which can easily lead to disputes; therefore, the file management system of this application is provided. like Figure 1 As shown, the early warning module can also determine employee attendance violations, and the judgment logic is as follows: Based on the daily commuting time information in the employee's work information, calculate the actual daily working hours T. actual ; T actual Compared with standard working time T standard Compare; If T actual <T standard -ΔT1, then it is recorded as early departure; if T actual >T standard If +ΔT2 is added, it is recorded as overtime; if no start or end time is recorded, it is recorded as absence; where ΔT1 and ΔT2 are preset tolerance times. The system counts the number of early departures and absences within a specified period and determines the corresponding attendance violation level based on the preset range in which the number of departures falls. In practice, firstly, this file management system directly utilizes the daily get off work hours information recorded in the employee file collection module to automatically calculate the actual daily working hours T. actual This avoids manual data entry and calculation, ensuring the originality and accuracy of the data. Secondly, the system will calculate the actual daily working time T. actual With the preset standard working time T standardThe comparison was conducted, and the concept of tolerance time (ΔT1, ΔT2) was introduced, that is, ΔT1 is the early departure tolerance, and ΔT2 is the overtime tolerance, to judge "early departure", "late arrival" and "absence"; the setting of ΔT1 and ΔT2 mainly takes into account laws and regulations, industry practices, corporate management requirements and actual operation conditions. Finally, the system counts the number of times various attendance violations occur within a specified period (such as a week or a month); by comparing the number of violations with a preset range, it automatically determines the level of attendance violation (such as: minor, moderate, or severe). The automation of attendance management has greatly reduced the workload of grassroots managers in attendance statistics and verification, improved management efficiency, eliminated omissions and errors that may occur in manual statistics, and ensured the objectivity and fairness of data. By introducing "tolerance time" and "periodic statistics", the judgment of attendance violations is made more scientific and reasonable. The hierarchical early warning mechanism helps managers distinguish between occasional violations and habitual violations, thereby taking more targeted management measures.

[0025] In some embodiments, existing coal mining enterprises generally adopt periodic planned maintenance or post-failure maintenance (retroactive maintenance) models for equipment maintenance. Periodic maintenance lacks specificity and may lead to over-maintenance, increasing costs, or under-maintenance, leaving hidden dangers. Post-failure maintenance means that the equipment must undergo downtime due to failure, which has already caused losses to production and may even lead to secondary safety accidents. In addition, abnormal wear and performance degradation of equipment is often a gradual process, and its early signs (such as deviations in equipment parameters, abnormal situations reported by employees, etc.) are scattered in different records, making it difficult to effectively integrate and use for early warning judgment. Therefore, the file management system of this application is provided. like Figure 1 As shown, the equipment file acquisition module also includes an equipment health status assessment unit; the equipment health status assessment unit calculates the equipment health score based on the equipment's cumulative running time, the deviation trend of historical operating parameters and standard parameters, and the equipment problem reports recorded in the association mapping module, and sends an equipment maintenance warning to the management module when the score is lower than a preset threshold. The algorithm formula for the health score in the equipment health status assessment unit is as follows: Where S is the health score of the device calculated by the preset health algorithm, S base W is a basic health score based on the equipment's service life. op As an operational compliance weighting factor, W issue Weighting factors for problem reports; S base The calculation method is as follows: R t T represents the cumulative operating time loss rate of the equipment. accumulated T represents the total historical cumulative operating time of the equipment. expected This refers to the expected service life of this model of equipment; W op The calculation method is as follows: K avg This is the average violation index of all recent operation records of the device. It is calculated in the same way as the employee's violation index K on the device, as described above. α is the weighting coefficient of operation compliance (0 < α < 1). The coefficient of α is set by the administrator to adjust the degree of impact of operation violations on the overall health. W issue The calculation method is as follows: N issue N represents the total number of valid problem reports recorded by the device through the association mapping module within a certain statistical period (e.g., 30 days, 90 days, etc.); issuemax The maximum number of problem reports is set as a threshold; β is the weighting coefficient for problem reports (0 < β < 1). The β coefficient is set by the administrator to adjust the impact of the number of problem reports on the overall health score. The equipment health status assessment unit calculates the health score S of all equipment in the system in real time or periodically, and automatically sends equipment maintenance warning information to the management module when the score S is lower than the preset maintenance threshold. The equipment maintenance warning information includes at least the equipment number, the current health score and the main deduction factors. In practical implementation, the document management system of this application will include three types of key data (S base W op W issue Incorporate it into the assessment system of the equipment health status assessment unit; the basic health score of equipment operating life base Based on the ratio R of the equipment's cumulative operating time to its expected lifespan. t The basic health score reflects the performance degradation of equipment due to natural aging and serves as the baseline for health; the operational compliance weighting factor W... op The historical average violation index K of the device avg To calculate the operational compliance weighting factor; reflecting the "additional damage" or accelerated wear caused to equipment by non-standard operations (such as overloading, improper parameters), and quantifying human factors into equipment health assessment; problem report weighting factor W issue The number of valid problem reports N within the statistical period. issueTo calculate the problem report weighting factor W issue It directly captures the fault symptoms and anomalies exhibited by the equipment, providing the most intuitive feedback on the equipment's health status. The health score S uses a product model, which can keenly reflect on risks: any factor (such as frequent violations leading to W) op Reduced, frequent reports of equipment malfunctions led to W issue Any deterioration (such as a decrease in health score) will lead to a significant drop in the health score S, thus triggering an early warning in a timely manner; The weighting coefficients α and β allow administrators to flexibly adjust the weighting of "operational violations" and "problem reports" in the overall score based on the equipment type and management strategy. When the score is lower than the preset threshold, the system automatically sends an early warning message containing the equipment number, score, and main deduction factors, enabling maintenance personnel to quickly locate the root cause of the problem. Among them, α determines the impact of operational violation history on equipment health; the larger the α, the greater the negative impact of operational violations on the health score S; when a high α value is used, it is suitable for equipment that is extremely sensitive to operation. Once such equipment is operated improperly, it is very easy to cause serious damage or safety accidents, such as fully mechanized mining units, tunneling machines, main hoists, high-pressure pump stations, etc.; when a low α value is used, it is suitable for equipment with greater operational flexibility or that is very robust. Small fluctuations in the operating parameters of such equipment will not immediately have a significant impact on the equipment, such as ordinary conveyor belts, lighting systems, drainage pumps, ventilation facilities, etc. β determines the impact of the number of problems reported by employees on equipment health. The larger the β, the more problems are reported, and the more severely the health score S drops. A high β value is suitable for equipment with extremely high reliability requirements or clearly defined problem symptoms, encouraging employees to report any minor anomalies and trusting that employee feedback accurately reflects the equipment status; such as safety monitoring systems (e.g., gas sensors), hydraulic systems (where leaks are easily detected), and electrical control systems (where fault symptoms are obvious). A low β value is suitable for equipment where problems are difficult to judge intuitively or where the employee false alarm rate may be high, such as equipment with complex internal structures or newly commissioned equipment. The record management system in this application can issue early warnings before equipment performance declines significantly or malfunctions by quantitatively assessing the health status of the equipment, prompting maintenance personnel to intervene at the best time, thereby avoiding unplanned downtime and production interruption, and transforming the maintenance mode from "post-event remediation" to "pre-event prevention". Furthermore, it enables maintenance decisions to move beyond a single time period or subjective experience, and instead rely on quantitative scores derived from multi-dimensional data fusion analysis. This makes maintenance plans more targeted, preventing both resource waste from over-maintenance and safety hazards from under-maintenance. At the same time, the "deduction factors" provided in the early warning information can directly indicate the possible causes of health deterioration (such as natural aging, improper operation, or inherent defects), helping maintenance personnel quickly locate problems, shorten diagnosis time, and improve maintenance efficiency.

[0026] Furthermore, existing early warning methods are often quite simplistic, relying on audible alarms or simple screen prompts, which fail to differentiate between the urgency and importance of events. This often results in high-risk events receiving only basic alerts or no alerts at all, leading to delayed responses and missed opportunities for optimal intervention. like Figure 1 As shown, the warning information generated by the warning module adopts different prompting methods according to the level of danger: minor violations are prompted by an interface message; moderate violations are prompted by an interface pop-up warning and recorded in the assessment document; severe violations are prompted by an audible and visual alarm, a strong interface prompt, and a report is generated and pushed to the management. In practice, the early warning module in the document management system integrates a tiered response strategy. The core of this strategy is to link the way early warning information is presented with the management consequences, forming a progressively escalating response mechanism. For minor violations, interface messages (such as system notification bars and message lists) are used. This method is minimally disruptive and primarily serves to remind and inform employees, making them aware of operational flaws and prompting self-correction. For moderate violations, interface pop-up warnings are used. This method is interactive, interrupting the user's current operation and requiring confirmation of the warning information, ensuring effective reception. Simultaneously, it is automatically recorded in performance evaluation documents, meaning the behavior is included in the performance evaluation system and will have substantial management consequences, better constraining employee behavior. For severe violations, audible and visual alarms (such as warning lights and sirens) and strong interface prompts (such as full-screen red alerts) are used. This method has the highest level of alert, immediately attracting the attention of the entire work area and requiring immediate action. Simultaneously, it automatically generates real-time reports and pushes them to management, ensuring that responsible personnel are aware of major risks immediately and initiate emergency response procedures. Differentiated alerts ensure that the intensity of alarms matches the severity of risks; low-risk events do not cause excessive interference, while high-risk events attract the highest level of attention, effectively avoiding alarm fatigue and the neglect of key information; the record management system in this application automatically associates different levels of violations with corresponding management consequences (recording assessments, reporting to leaders), greatly reducing the workload of managers in manually screening, classifying, and reporting information, improving management efficiency, and ensuring the standardization and fairness of the processing flow; and the immediate reporting mechanism in the event of "serious violations" provides management with valuable response time, enabling management to quickly intervene, make decisions, and direct, maximizing the containment of major security risks in their infancy, and significantly improving the company's emergency response capabilities.

[0027] Furthermore, data security and information confidentiality are crucial in the construction of information systems in coal mining enterprises. The management system involves a large amount of sensitive data, including employees' personal privacy information, records of operational violations, equipment operating parameters, and internal assessment documents. If all users can access and operate all data without discrimination, it will bring serious risks: for example, ordinary employees may see others' privacy or assessment results, causing internal conflicts; key equipment parameters may be mistakenly modified by unauthorized personnel, leading to production accidents. like Figure 1 As shown, the management module also includes access control, which assigns different data access and operation permissions to employees of different positions and levels; In practice, ordinary employees can only view their own files, relevant equipment files, and experience notes; team leaders or administrators have the authority to query data on all employees and equipment within their jurisdiction, confirm warning information, and manage the publication of experience notes; the access control of the file management system in this application tightly binds user permissions to job responsibilities, eliminating the possibility of unauthorized access and operation from the source; it effectively prevents unauthorized access, tampering, and leakage of sensitive information, protects employee privacy and core corporate data assets, and meets the company's regulatory requirements for data security and compliance.

[0028] Furthermore, such as Figure 1 As shown, the storage module uses a time-series database to store the continuous operating parameter records of the storage device and the operation logs of employees, and uses a relational database to store the basic information of employees and equipment, the association mapping relationship and the operation experience notes. In practice, the document management system in this application uses time-series databases and relational databases, and selects different database models for storage based on data characteristics and access patterns; This time-series database is suitable for high-speed streaming data, such as continuous operating parameter records of equipment (speed, temperature, pressure, etc. collected every second) and employee operation logs (such as records of each parameter modification). It can easily handle high-frequency, high-speed writing of millions of data points per second, meeting the needs of continuous monitoring of downhole equipment. It has been optimized for range queries, aggregation calculations (such as calculating average, maximum, and minimum values) and downsampling display of time-series data, with performance far exceeding that of traditional relational databases, providing powerful support for real-time monitoring and historical trend analysis. Relational databases are suitable for highly correlated structured data, such as basic information about employees and equipment, correlation mapping relationships (such as who operated which equipment at what time), and structured operation experience notes; ensuring the accuracy and consistency of key basic information (such as employee positions and equipment parameter thresholds) during updates.

[0029] In some embodiments, such as Figure 1 As shown, the system also includes a report generation module, which is used to automatically generate reports based on the data in the storage module; the reports include at least monthly, quarterly and annual performance evaluation reports for employees, equipment operation reports and violation operation statistics reports; In practice, the document management system integrates a report generation module. As the data value output terminal of the system, the report generation module is based on the powerful data support of the underlying hybrid database to realize the automatic generation of reports. It can obtain basic information and relationships of employees and equipment from relational databases, and extract equipment operation parameter sequences and employee operation log records within a specified time range from time-series databases. The report generation module completely frees managers from time-consuming and labor-intensive manual data organization, calculation, and tabulation, allowing them to focus on more valuable data analysis and decision-making, resulting in an order-of-magnitude improvement in management efficiency. The report data comes directly from first-hand data automatically recorded by the system, eliminating copying, calculation errors, and subjective tampering that may occur during manual processing, thus ensuring the accuracy of the reports.

[0030] like Figure 2 As shown, a method for managing grassroots archives in coal mines is provided. This method is applicable to the aforementioned coal mine grassroots archives management system and includes the following steps: S1: Employee file data collection steps: Centralized registration of basic employee information, daily active and automatic registration of work information by employees; S2: Equipment file data collection steps: Centralized registration of basic equipment information, and automatic registration of daily operating information of equipment; S3: Association Mapping Steps: Establish a two-way operation association between employees and equipment, and bind and store the equipment operation experience notes submitted by employees; S4: Equipment Health Assessment and Early Warning Steps: Use a health algorithm to calculate the real-time score of the equipment and trigger a maintenance early warning when the score is below the threshold; S5: Employee violation warning steps: Automatically identify and classify violations according to preset rules, and issue warnings for violations.

[0031] Working principle: First, the employee file collection module collects the static basic information (name, employee number, position) and dynamic work information (daily get off work hours, operated equipment, and specific operation logs) of employees; the equipment file collection module collects the static basic information (name, number, standard parameters) and dynamic operating information (running time, capacity, real-time operating parameters, and operator) of equipment. Then, by using the association mapping module, a two-way, traceable mapping relationship is established between employees and equipment. This allows you to find all historical operators and their detailed operation records from a single device; you can also find all the devices operated by an employee and their performance from a single employee. In addition, this module also receives operation experience notes (problems, precautions, experiences) submitted by employees when operating the equipment, and strongly binds them to specific devices and operation contexts to form an enterprise knowledge base. Then, the collected data is analyzed using the early warning module: (1) Violation analysis: calculate the employee's violation index K=(Count(Pi∉[Pmin,Pmax]) / n)*100%), and determine the level of minor, medium and major violations according to the preset rules; (2) Attendance violation analysis: automatically compare the actual working hours with the standard working hours, and combine the tolerance settings to determine early departure, overtime or absence, and perform periodic statistics and level determination; (3) Equipment health assessment: adopt the health algorithm. , taking into account the inherent wear and tear of the equipment, operational compliance, and frequency of problem reports, the equipment health score is quantitatively calculated to achieve predictive maintenance; (4) graded response: based on the risk level of the analysis results, a differentiated early warning response mechanism is triggered (from interface prompts and pop-up warnings to sound and light alarms and reporting to the management level) to form a management closed loop; The storage module utilizes a time-series database to efficiently store and process massive amounts of timestamped device parameters and operational log data; a relational database stores highly correlated structured information (basic information, mapping relationships, experience notes); meanwhile, the management module's access control assigns precise data access and operation permissions to employees in different positions, ensuring data security and system stability. Finally, the report generation module automatically, quickly, and accurately generates various management reports (performance reports, equipment reports, and violation statistics reports) based on the data accumulated in all the aforementioned steps.

[0032] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A coal mine grassroots file management system, characterized in that, include: The employee file collection module is used to collect basic employee information and employee work information, and generate employee file data. The equipment file acquisition module is used to collect basic equipment information and equipment operation information, and generate equipment file data; The association mapping module is used to establish a bidirectional association mapping relationship between employee file data and equipment file data; the association mapping module is also used to record and associate the operation experience notes recorded by employees when operating the equipment; the operation experience notes include at least equipment problem records, operation precautions, and operation experience summaries. The early warning module, based on employee and equipment data, judges the violation and risk level according to preset logical rules and generates corresponding early warning information. The storage module is used to store employee file data, equipment file data, two-way association mapping relationships, operation experience notes, and early warning information; The management module is used to manage the configuration, query, modification, and deletion of all data within the system.

2. The coal mine grassroots file management system according to claim 1, characterized in that: The operation behavior log in the employee file collection module includes records of setting equipment parameters; the logical rules in the early warning module are as follows: Extract the operational parameters of employees on the equipment and set a record set Pi={P1,P2,...,Pn}, where Pi represents the parameter value set in a single instance; Obtain the standard operating parameter threshold range [Pmin, Pmax] of the device; Calculate the employee's violation index K on the equipment: K=(Count(Pi∉[Pmin,Pmax]) / n)*100% Where Count(Pi∉[Pmin,Pmax]) represents the number of times the parameter setting value Pi exceeds the standard threshold range [Pmin,Pmax]. The K value is compared with a preset level threshold range: when K∈[0,Ka), it is determined as no violation; when K∈[Ka,Kb), it is determined as a minor violation; when K∈[Kb,Kc), it is determined as a moderate violation; when K≥Kc, it is determined as a severe violation; where 0 <Ka<Kb<Kc≤100%。 3. The coal mine grassroots file management system according to claim 2, characterized in that: The early warning module can also determine employee attendance violations, and the judgment logic is as follows: Based on the daily commuting time information in the employee's work information, calculate the actual daily working hours T. actual ; T actual Compared with standard working time T standard Compare; If T actual <T standard -ΔT1, then it is recorded as early departure; if T actual >T standard If +ΔT2 is added, it is recorded as overtime; if no start or end time is recorded, it is recorded as absence. Where ΔT1 and ΔT2 are preset tolerance times; The system counts the number of early departures and absences within a specified period and determines the corresponding attendance violation level based on the preset range in which the number of departures falls.

4. The coal mine grassroots file management system according to claim 1, characterized in that: The equipment file acquisition module also includes an equipment health status assessment unit. The equipment health status assessment unit calculates the equipment health score based on the equipment's cumulative operating time, the deviation trend of historical operating parameters and standard parameters, and the equipment problem reports recorded in the association mapping module, using a preset health score algorithm. When the score is lower than a preset threshold, it sends an equipment maintenance warning to the management module.

5. A coal mine grassroots file management system according to claim 4, characterized in that: The algorithm formula for the health score in the equipment health status assessment unit is as follows: Where S is the health score of the device calculated by the preset health algorithm, S base W is a basic health score based on the equipment's service life. op W is the weighting factor for operational compliance. issue Assign weighting factors to the problem report; S base The calculation method is as follows: R t T represents the cumulative operating time loss rate of the equipment. accumulated T represents the total historical cumulative operating time of the equipment. expected This refers to the expected service life of this model of equipment; W op The calculation method is as follows: K avg This is the average violation index of all recent operation records of the equipment, where α is the weighting coefficient of operation compliance (0 < α < 1), used to adjust the degree of impact of operation violations on the overall health. W issue The calculation method is as follows: N issue N represents the total number of valid problem reports recorded by the device through the association mapping module within a certain statistical period. issuemax The maximum number of problem reports is set as a threshold; β is the weighting coefficient for problem reports (0 < β < 1), used to adjust the impact of the number of problem reports on the overall health score. The equipment health status assessment unit calculates the health score S of all equipment in the system in real time or periodically, and automatically sends equipment maintenance warning information to the management module when the score S is lower than the preset maintenance threshold. The equipment maintenance warning information includes at least the equipment number, the current health score and the main deduction factors.

6. The coal mine grassroots file management system according to claim 1, characterized in that: The warning information generated by the warning module takes different prompting methods according to the level of danger: minor violations are prompted by an interface message; moderate violations are prompted by an interface pop-up warning and recorded in the assessment document; and severe violations are prompted by an audible and visual alarm, a strong interface prompt, and a report is generated and pushed to management.

7. A coal mine grassroots archive management system according to claim 1, characterized in that: The management module also includes access control, which assigns different data access and operation permissions to employees of different positions and levels.

8. A coal mine grassroots file management system according to claim 7, characterized in that: The storage module uses a time-series database to store continuous operating parameter records of the storage device and employee operation logs, and uses a relational database to store basic information of employees and equipment, association mapping relationships, and operation experience notes.

9. A coal mine grassroots archive management system according to claim 8, characterized in that: The system also includes a report generation module, which is used to automatically generate reports based on the data in the storage module; the reports include at least monthly, quarterly and annual performance evaluation reports for employees, equipment operation reports and violation statistics reports.

10. A method for managing grassroots archives in coal mines, characterized in that: The management method is applicable to a coal mine grassroots file management system as described in any one of claims 1-9, and the management method includes the following steps: S1: Employee file data collection steps: Centralized registration of basic employee information, daily active and automatic registration of work information by employees; S2: Equipment file data collection steps: Centralized registration of basic equipment information, and automatic registration of daily operating information of equipment; S3: Association Mapping Steps: Establish a two-way operation association between employees and equipment, and bind and store the equipment operation experience notes submitted by employees; S4: Equipment Health Assessment and Early Warning Steps: Use a health algorithm to calculate the real-time score of the equipment and trigger a maintenance early warning when the score is below the threshold; S5: Employee violation warning steps: Automatically identify and classify violations according to preset rules, and issue warnings for violations.

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