Mine electromechanical equipment full life cycle management method and system

Abstract

The invention relates to the technical field of mine electromechanical equipment management and maintenance, in particular to a mine electromechanical equipment full-life-cycle management method and system, and the method comprises the steps: carrying out the equipment debugging according to the production characteristics and environmental conditions of a mine; the operation state and performance indexes of the equipment are monitored in real time, the monitoring content is analyzed through an intelligent diagnosis algorithm of the mine electromechanical equipment, and fault judgment is conducted; formulating an intelligent repair scheme and a maintenance strategy according to a fault judgment result; carrying out full life cycle management and optimization on the mine motor equipment; by combining technical means of equipment debugging, real-time monitoring, intelligent diagnosis, intelligent repair and maintenance, full-life-cycle management and the like, the management and maintenance level of the mine electromechanical equipment is improved, the comprehensive benefits of the equipment are improved, and the service life of the equipment is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of mine electromechanical equipment management and maintenance, and in particular to a full life cycle management method and system for mine electromechanical equipment. Background Art

[0002] With the development of sensor technology, network communication technology and information processing technology, condition-based maintenance (CBM) has gradually become the mainstream method for mine electromechanical equipment maintenance. CBM collects equipment operating status data in real time, such as temperature, vibration, current, etc., by installing various sensors on the equipment, and uses data analysis technology to process and analyze the collected data, so as to judge the health status of the equipment and discover potential fault hazards in time. Compared with traditional regular maintenance, CBM can more accurately reflect the actual operating status of the equipment, improving the pertinence and effectiveness of maintenance.

[0003] Since the beginning of the 21st century, with the rapid development of industrial Internet, big data and artificial intelligence technologies, intelligent maintenance management has gradually become a new direction for mine electromechanical equipment management. The intelligent maintenance management system has built an all-round, all-weather, real-time online monitoring and maintenance management platform by integrating technologies such as the Internet of Things (IoT), cloud computing and artificial intelligence (AI). The platform can monitor the operating status of the equipment in real time, analyze and process the operating data of the equipment using big data analysis and artificial intelligence algorithms, promptly discover and warn equipment failures, and provide scientific decision-making support for equipment maintenance and repair through intelligent diagnosis and prediction technologies.

[0004] In recent years, research on the life cycle management of mine electromechanical equipment has also made significant progress. Life cycle management (LCM) not only focuses on the operation and maintenance of equipment, but also covers the whole process management of equipment from design, manufacturing, operation, maintenance to scrapping and recycling. Through life cycle management, the comprehensive benefits of equipment can be systematically improved, the service life of equipment can be extended, and the operating cost and environmental impact of equipment can be reduced.

[0005] Although the management technology of mine electromechanical equipment has made great progress, the existing technology still has many shortcomings. The traditional regular maintenance mode lacks real-time monitoring and analysis of the actual operating status of the equipment, which easily leads to over-maintenance or under-maintenance, thereby increasing the operating cost and failure risk of the equipment; although the state-based maintenance has improved the pertinence and effectiveness of maintenance to a certain extent, its maintenance decision still mainly relies on experience judgment and simple rule analysis, which makes it difficult to fully utilize the deep information in the equipment operation data, resulting in insufficient accuracy and timeliness of fault diagnosis.

[0006] In addition, although the existing life cycle management methods have high comprehensive benefits in theory, in actual applications, it is difficult to achieve optimized management of the entire life cycle of equipment due to the lack of systematic technical support and management means.

[0007] In summary, a method for the full life cycle management of mine electromechanical equipment comprehensively improves the scientificity and effectiveness of equipment management by combining technical means such as equipment debugging, real-time monitoring, intelligent diagnosis and full life cycle optimization; in the equipment debugging stage, the production characteristics and environmental conditions of the mine are fully considered; in the equipment operation stage, real-time monitoring and intelligent diagnosis algorithms are used to timely discover and handle equipment failures; in the equipment maintenance stage, intelligent repair plans and maintenance strategies are formulated based on fault judgment results to reduce maintenance costs; in terms of the full life cycle management of equipment, the whole process management of equipment from design, manufacturing, operation, maintenance to scrapping is achieved through system optimization and management. Summary of the invention

[0008] In view of the above problems existing in the prior art, the present invention is proposed.

[0009] Therefore, the technical problems to be solved by the present invention are: to achieve full life cycle management of equipment by integrating with internal and external systems; to apply intelligent monitoring methods to reduce the workload of mechanical and electrical management personnel and improve the efficiency of inspection and maintenance; to build a full life cycle management model based on equipment health and improve the equipment's precise operation and maintenance capabilities.

[0010] To solve the above technical problems, the present invention provides the following technical solutions: a method for managing the entire life cycle of mine electromechanical equipment, comprising: debugging equipment according to the production characteristics and environmental conditions of the mine; real-time monitoring of the operating status and performance indicators of the equipment, and analyzing the monitoring content using an intelligent diagnosis algorithm for mine electromechanical equipment to make fault judgments; formulating intelligent repair plans and maintenance strategies based on the fault judgment results; and managing and optimizing mine motor equipment throughout its entire life cycle.

[0011] As a preferred solution of the full life cycle management method of mine electromechanical equipment described in the present invention, the equipment debugging includes pre-debugging preparation, intelligent environment perception, dynamic adaptive debugging and multi-dimensional performance verification;

[0012] The pre-commissioning preparation uses high-precision sensors to conduct preliminary testing and calibration of the equipment, and establishes a three-dimensional model of the mine environment in combination with the mine production characteristics and environmental conditions;

[0013] The intelligent environmental perception can perceive the environmental changes in the mine in real time, obtain comprehensive environmental information through multi-sensor data fusion technology, and evaluate the dynamic changes of the mine environment in real time;

[0014] The dynamic adaptive debugging combines the real-time evaluation of the dynamic changes in the mine environment to dynamically adjust and optimize the equipment debugging process;

[0015] The multi-dimensional performance verification performs multi-scenario debugging on the device according to environmental conditions to verify performance indicators.

[0016] As a preferred solution of the mine electromechanical equipment full life cycle management method described in the present invention, wherein: the mine electromechanical equipment intelligent diagnosis algorithm includes data preprocessing, a multi-layer fusion model and a fault judgment model;

[0017] The data preprocessing is expressed as:

[0018] X fusion =α·f(X sensor )+β·g(X env )+γ·h(X history )

[0019] Among them, X fusion represents the fused data, X sensor Represents sensor data, X env Represents environmental data, X history represents historical data, α represents the proportion of sensor data in the integrated data after fusion, β represents the proportion of environmental data in the integrated data after fusion, γ represents the proportion of historical data in the integrated data after fusion, f(X sensor ) represents the sensor data X fusion Perform nonlinear transformation, g(X env ) represents the environmental data X env Perform nonlinear transformation, h(X history ) is represented by historical data X history Perform nonlinear transformations;

[0020] The multi-layer fusion model is expressed as:

[0021] H (l) =σ(W (l) *H (l-1) +b (l) )

[0022] H (t) =σ(W H H (t-1) +W X X (t) +b)

[0023]

[0024] Among them, H (l) represents the convolutional layer of the layer, W (l)represents the convolution kernel of the layer, H (l-1) represents the output of the l-1th layer, b (l) represents the bias variable, * represents the convolution operation, σ represents the activation function, H (t) represents the hidden state of the recurrent layer at time t, H (t-1) represents the hidden state of the recurrent layer at the previous moment, W H Represents the transition matrix from the previous hidden state to the current hidden state, W X represents the weight matrix input to the hidden state, X (t) represents the input at time t, b represents the bias vector, Y pred represents the prediction result, W o represents the weight matrix of the output layer, represents the final feature vector of the combined convolutional layer and the recurrent layer, φ represents the output layer bias vector, and represents the output layer activation function;

[0025] The fault judgment model is expressed as:

[0026]

[0027]

[0028] Among them, P(F,X fusion ) represents the joint probability distribution of fault and fusion data, P(F) represents the prior probability of fault, represents the conditional probability of the fault condition in the i-th fused data, P(F|X fusion ) represents the posterior probability of failure given the fused data.

[0029] As a preferred solution of the method for managing the entire life cycle of mine electromechanical equipment described in the present invention, wherein: the fault judgment is based on dynamic monitoring and fault classification. When an indicator of sensor data during equipment operation exceeds a preset threshold, the current data indicator is recorded as an abnormal value, and a preliminary fault alarm is triggered;

[0030] According to the prediction results Y in the intelligent diagnosis algorithm of mine electromechanical equipment pred , determine the fault type, which is divided into minor fault, medium fault and serious fault, and set the prediction result threshold as [T low ,T high ], when the prediction result Y pred Less than the set threshold T low When, and according to the fault posterior probability P(F|X fusion ) is 10%, it is judged as a minor fault. pred Between T low and T highAnd according to the fault posterior probability P(F|X fusion ) is 30%, it is judged as a medium fault. pred Exceeding the set threshold T high When, and according to the fault posterior probability P(F|X fusion ) is 70%, it is judged as a serious fault;

[0031] When multiple data indicators exceed the preset threshold at the same time, and the fault posterior probability P(F|X fusion ) is 80%, a complex fault alarm is triggered.

[0032] As a preferred solution of the method for managing the entire life cycle of mine electromechanical equipment described in the present invention, the intelligent repair solution includes that when a preliminary fault alarm is triggered, the system reduces the operating level of the corresponding indicator, performs self-checking, and automatically corrects the data indicator that triggers the alarm;

[0033] If the fault is judged as a minor fault, the system automatically adjusts the operating status, reduces the equipment operating load, and continuously monitors and generates maintenance suggestions, so that the equipment can be inspected and adjusted in the next maintenance window to avoid escalation of the fault type.

[0034] When the fault is judged as a medium fault, the system generates a temporary maintenance plan, adjusts the equipment operation mode to a low-performance mode, performs on-site maintenance, and replaces and repairs key parts;

[0035] When the fault is judged as a serious fault, the system generates a comprehensive maintenance plan, triggers the emergency shutdown procedure, analyzes the cause of the fault, determines the repair steps and the list of prepared parts, and conducts a comprehensive inspection of the equipment;

[0036] When the system triggers a complex fault alarm, the system enters fault isolation mode and generates multiple maintenance plans, including data synchronization, component replacement, and overall system adjustment, and implements repair operations in steps.

[0037] As a preferred solution of the whole life cycle management method of mine electromechanical equipment described in the present invention, wherein: the maintenance strategy includes preventive maintenance and improved maintenance;

[0038] When the system is running smoothly and no faults are detected, preventive maintenance is performed based on historical data and environmental changes. When a type of fault recurs under the same environmental conditions, equipment is adjusted and inspected in advance to perform preventive maintenance.

[0039] When the frequency of system failures increases, the system automatically adjusts the equipment operating parameters, makes technical improvements and optimizations to the equipment, and performs improved maintenance. When a type of failure recurs under different environmental conditions, the cause of the failure is analyzed, equipment performance tests are conducted, processing steps are strengthened, and preventive maintenance is performed.

[0040] As a preferred solution of the method for managing the whole life cycle of mine electromechanical equipment described in the present invention, the whole life cycle management and optimization includes the design stage, the manufacturing stage, the operation stage, the maintenance stage and the scrapping stage;

[0041] In the design phase, when designing new equipment, historical data and environmental data are processed to generate design parameters. Based on the posterior probability of predicted failures, the design priority and optimization points of the equipment in different scenarios are determined, and the optimization output of the design phase directly affects the parameters in the manufacturing phase;

[0042] In the manufacturing stage, the optimization output of the design stage is used to monitor and analyze real-time data in real time, and the data is input into the intelligent diagnosis algorithm of mine electromechanical equipment for analysis, integrating the real-time manufacturing data and the benchmark data of the design stage, analyzing the potential defects in the manufacturing process, judging the abnormal conditions in the manufacturing process in real time and making timely adjustments, and the data of the manufacturing stage is fed back to the design stage in real time as a reference for the future design stage. At the same time, the potential problems detected in the manufacturing stage will be stored and used for baseline calibration in the operation stage;

[0043] During the operation phase, after the equipment is put into use, the operating status and performance indicators of the equipment are monitored in real time, fault prediction and classification are carried out through the intelligent diagnosis algorithm of mine electromechanical equipment, and the corresponding intelligent repair plan is triggered according to the fault type. According to the sensor data and operating conditions, the posterior probability of equipment failure is calculated and classified into minor faults, medium faults, serious faults, and complex fault alarms are triggered. For different types of faults, the system automatically adjusts the equipment operating parameters and generates maintenance suggestions. The monitoring data of the operation phase will be fed back to the maintenance phase for adjusting the maintenance strategy. The design defects and manufacturing errors in operation will be fed back to the manufacturing phase and the design phase to promote continuous improvement of design and manufacturing;

[0044] During the maintenance phase, the system regularly evaluates the operating status and failure frequency of the equipment, and selects preventive or improved maintenance strategies based on actual conditions. Preventive maintenance is based on historical data and environmental changes to perform maintenance in advance, while improved maintenance is based on technical optimization of the equipment when the frequency of system failures increases. The system adjusts the maintenance plan based on the accumulated failure data. When different failure alarms are triggered, the system automatically executes repair measures and notifies the maintenance team to perform maintenance operations. The data during the maintenance phase directly affects the decision-making during the scrapping phase. When the equipment cannot achieve optimal performance through maintenance, the system will decide to transfer the equipment to the scrapping phase based on the failure data and maintenance history throughout its life cycle.

[0045] During the scrapping stage, the system conducts a scrapping assessment based on the accumulated data from the operation, maintenance and design stages, including the reusable materials, recycling value and environmental impact of the equipment. Based on the data throughout the entire life cycle, the system analyzes the residual value and resource recovery rate of the equipment, formulates a scrapping and recycling plan, and optimizes the scrapping strategy and maximizes resource utilization based on the performance of the equipment in design, manufacturing, operation and maintenance. The recycling data from the scrapping stage will be fed back to the design stage to provide a reference for the design of new equipment.

[0046] Another object of the present invention is to provide a full life cycle management system for mine electromechanical equipment, including an equipment debugging module, an operation monitoring module, a fault and repair module, and a full life cycle management module. Through the coordinated work of these modules, all-round intelligent management of equipment from debugging, monitoring, fault diagnosis and repair to full life cycle management and optimization can be achieved.

[0047] In order to solve the above technical problems, the present invention provides the following technical solutions: a full life cycle management system for mine electromechanical equipment, including: equipment debugging module, operation monitoring module, fault and repair module and full life cycle management module;

[0048] The equipment debugging module performs equipment debugging according to the production characteristics and environmental conditions of the mine;

[0049] The operation monitoring module monitors the operation status and performance indicators of the equipment in real time, and uses the intelligent diagnosis algorithm of mine electromechanical equipment to analyze the monitoring content and make fault judgments;

[0050] The fault and repair module formulates intelligent repair plans and maintenance strategies based on the fault judgment results;

[0051] The full life cycle module manages and optimizes the mine motor equipment throughout its life cycle.

[0052] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the above-mentioned method for managing the entire life cycle of mine electromechanical equipment are implemented.

[0053] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a method for managing the entire life cycle of mine electromechanical equipment as described above are implemented.

[0054] The beneficial effects of the present invention are as follows: by combining technical means such as equipment debugging, real-time monitoring, intelligent diagnosis, intelligent repair and maintenance, and full life cycle management, the management and maintenance level of mine electromechanical equipment is comprehensively improved. The operational reliability and safety of the equipment are significantly improved, and the comprehensive benefits and service life of the equipment are significantly improved. At the same time, the resource recovery and reuse of the equipment at the scrapping stage are scientifically guided, reducing resource waste and environmental pollution, and improving the environmental friendliness of equipment management. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0056] Figure 1 A flowchart of a method for managing the entire life cycle of mine electromechanical equipment is provided as an embodiment of the present invention.

[0057] Figure 2 A comparison chart of operating status and failure frequency of a method for managing the entire life cycle of mine electromechanical equipment provided by an embodiment of the present invention.

[0058] Figure 3 A temperature and vibration comparison diagram of a method for managing the entire life cycle of mine electromechanical equipment provided by an embodiment of the present invention.

[0059] Figure 4 A maintenance cost comparison chart of a method for managing the entire life cycle of mine electromechanical equipment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0061] Example 1, reference Figure 1 , is an embodiment of the present invention, which provides a method for managing the entire life cycle of mine electromechanical equipment, including:

[0062] S1: Debug equipment according to the production characteristics and environmental conditions of the mine.

[0063] It should be noted that equipment commissioning includes pre-commissioning preparation, intelligent environment perception, dynamic adaptive commissioning, and multi-dimensional performance verification;

[0064] Pre-commissioning preparation: use high-precision sensors to conduct preliminary testing and calibration of equipment, and establish a three-dimensional model of the mine environment based on the mine production characteristics and environmental conditions;

[0065] Intelligent environmental perception: real-time perception of environmental changes in the mine, acquisition of comprehensive environmental information through multi-sensor data fusion technology, and real-time assessment of dynamic changes in the mine environment;

[0066] Dynamic adaptive debugging, combined with real-time evaluation of the dynamic changes in the mine environment, dynamically adjusts and optimizes the equipment debugging process;

[0067] Multi-dimensional performance verification: According to environmental conditions, the device is debugged in multiple scenarios to verify performance indicators.

[0068] Furthermore, production characteristics, including production scale, production rhythm, ore type and production process, are the unique attributes and requirements exhibited in the production process of the mine, which directly affect the equipment selection, configuration and commissioning process;

[0069] Environmental conditions include temperature, humidity, air pressure, ventilation conditions, dust concentration, vibration and noise. They are the specific conditions of the mine and surrounding environment and directly affect the operating stability and life of the equipment.

[0070] The three-dimensional model is obtained by comprehensively scanning and measuring the internal space of the mine to establish a three-dimensional image of each structure inside the mine. It is mainly obtained by capturing data through laser and photography, and performing point cloud data processing and image data processing on the data.

[0071] Performance indicators, including adaptability to the environment, response speed and efficiency, are key parameters for evaluating the operating effects of mine electromechanical equipment in multi-scenario debugging.

[0072] Furthermore, multi-sensor data fusion technology involves comprehensive processing of data from multiple types of sensors to obtain comprehensive and accurate environmental information. In the intelligent diagnosis of mine electromechanical equipment, it is used to monitor environmental changes in the mine in real time to ensure efficient and safe operation of equipment in complex environments. The main steps include data synchronization, data calibration, data fusion algorithm, and feature extraction and fusion;

[0073] Real-time evaluation of dynamic changes in the mine environment: By monitoring the changes in environmental conditions in real time, predict the factors that affect equipment operation and evaluate the potential impact of the factors on the subsequent operation of the equipment;

[0074] Dynamic adjustment and optimization: input the real-time evaluation data of mine environment changes and analysis results into the dynamic adaptive control method as a reference for adjusting equipment operating parameters, providing real-time data support for adaptive parameter adjustment;

[0075] Multi-scenario debugging, including debugging under different temperature, humidity, vibration, air pressure, dust concentration and different workload conditions.

[0076] S2: Monitor the operating status and performance indicators of the equipment in real time, use the intelligent diagnosis algorithm of mine electromechanical equipment to analyze the monitoring content, and make fault judgments.

[0077] It should be noted that the operating status is a comprehensive performance of various working parameters and operating conditions of the equipment during operation. It is obtained through a real-time monitoring system and is used to evaluate equipment performance, fault detection and maintenance.

[0078] Furthermore, the intelligent diagnosis algorithm for mine electromechanical equipment includes data preprocessing, multi-layer fusion model and fault judgment model;

[0079] Data preprocessing is expressed as:

[0080] X fusion =α·f(X sensor )+β·g(X env )+γ·h(X history )

[0081] Among them, X fusion represents the fused data, X sensor Represents sensor data, X env Represents environmental data, X history represents historical data, α represents the proportion of sensor data in the integrated data after fusion, β represents the proportion of environmental data in the integrated data after fusion, γ represents the proportion of historical data in the integrated data after fusion, f(X sensor ) represents the sensor data X fusionPerform nonlinear transformation, g(X env ) represents the environmental data X env Perform nonlinear transformation, h(X history ) is represented by historical data X history Perform nonlinear transformations;

[0082] The multi-layer fusion model is expressed as:

[0083] H (l) =σ(W (l) *H (l-1) +b (l) )

[0084] H (t) =σ(W H H (t-1) +W X X (t) +b)

[0085]

[0086] Among them, H (l) represents the convolutional layer of the layer, W (l) represents the convolution kernel of the layer, H (l-1) represents the output of the l-1th layer, b (l) represents the bias variable, * represents the convolution operation, σ represents the activation function, H (t) represents the hidden state of the recurrent layer at time t, H (t-1) represents the hidden state of the recurrent layer at the previous moment, W H Represents the transition matrix from the previous hidden state to the current hidden state, W X represents the weight matrix input to the hidden state, X (t) represents the input at time t, b represents the bias vector, Y pred represents the prediction result, W o represents the weight matrix of the output layer, represents the final feature vector of the combined convolutional layer and the recurrent layer, φ represents the output layer bias vector, and represents the output layer activation function;

[0087] The fault judgment model is expressed as:

[0088]

[0089] Among them, P(F,X fusion ) represents the joint probability distribution of fault and fusion data, P(F) represents the prior probability of fault, represents the conditional probability of the fault condition in the i-th fused data, P(F|Z fusion ) represents the posterior probability of failure after given fusion data;

[0090] Data preprocessing fuses and preprocesses data from different sensors to provide high-quality input data for subsequent multi-layer fusion models. Data preprocessing fuses and nonlinearly transforms data from sensors, environment, and history to generate fused data X fusion The multi-layer fusion model is used to extract useful features from the preprocessed data and perform feature fusion to provide feature vectors for the final fault judgment model. The multi-layer fusion model receives the output X of the data preprocessing model. fusion , through the feature extraction of convolutional layer and loop layer, a high-dimensional feature vector is generated and These feature vectors are combined and processed by the output layer to generate the prediction result Y pred ; The fault judgment model combines the feature vector extracted by the multi-layer fusion model to perform fault prediction and diagnosis. The fault judgment model receives the prediction result Y generated by the multi-layer fusion model pred , calculate the posterior probability of failure P(F|X fusion ), which integrates the operating status of the equipment and environmental changes to perform accurate fault prediction and diagnosis.

[0091] Furthermore, fault judgment is based on dynamic monitoring and fault classification. When an indicator of sensor data during equipment operation exceeds a preset threshold, the current data indicator is recorded as an abnormal value and a preliminary fault alarm is triggered;

[0092] According to the prediction results Y in the intelligent diagnosis algorithm of mine electromechanical equipment pred , determine the fault type, which is divided into minor fault, medium fault and serious fault, and set the prediction result threshold as [T low ,T high ], when the prediction result Y pred Less than the set threshold T low When, and according to the fault posterior probability P(F|X fusion ) is 10%, it is judged as a minor fault. pred Between T low and T high And according to the fault posterior probability P(F|X fusion ) is 30%, it is judged as a medium fault. pred Exceeding the set threshold T high When, and according to the fault posterior probability P(F|X fusion ) is 70%, it is judged as a serious fault;

[0093] When multiple data indicators exceed the preset threshold at the same time, and the fault posterior probability P(F|X fusion ) is 80%, a complex fault alarm is triggered;

[0094] Minor faults refer to minor problems that occur during the operation of the equipment. These problems usually do not immediately affect the overall operation of the equipment, but may cause reduced equipment efficiency or small performance fluctuations.

[0095] Moderate failures refer to obvious problems with the equipment, which may have a significant impact on the operating efficiency and performance of the equipment, but will not cause the equipment to completely shut down in a short period of time;

[0096] Serious failure means that there are major problems with the equipment, which will pose a serious threat to the safety and overall operation of the equipment and must be shut down immediately for comprehensive maintenance.

[0097] S3: Develop intelligent repair plans and maintenance strategies based on fault diagnosis results.

[0098] It should be noted that the intelligent repair solution includes that when a preliminary fault alarm is triggered, the system reduces the operating level of the corresponding indicator and performs self-checking to automatically correct the data indicator that triggered the alarm;

[0099] When the fault is judged as a minor fault, the system automatically adjusts the operating status, reduces the equipment operating load, and continuously monitors and generates maintenance suggestions, so that the equipment can be inspected and adjusted at the next moment to avoid escalation of the fault type.

[0100] When the fault is judged as a medium fault, the system generates a temporary maintenance plan, adjusts the equipment operation mode to a low-performance mode, performs on-site maintenance, and replaces and repairs key parts;

[0101] When the fault is judged as a serious fault, the system generates a comprehensive maintenance plan, triggers the emergency shutdown procedure, analyzes the cause of the fault, determines the repair steps and the list of prepared parts, and conducts a comprehensive inspection of the equipment;

[0102] When the system triggers a complex fault alarm, the system enters fault isolation mode and generates multiple maintenance plans, including data synchronization, component replacement, and overall system adjustment, and implements repair operations in steps.

[0103] Furthermore, maintenance strategies include preventive maintenance and improvement maintenance;

[0104] When the system is running smoothly and no faults are detected, preventive maintenance is performed based on historical data and environmental changes. When a type of fault recurs under the same environmental conditions, equipment is adjusted and inspected in advance to perform preventive maintenance.

[0105] When the frequency of system failures increases, the system automatically adjusts the equipment operating parameters, makes technical improvements and optimizations to the equipment, and performs improved maintenance. When a type of failure recurs under different environmental conditions, the cause of the failure is analyzed, equipment performance tests are conducted, processing steps are strengthened, and preventive maintenance is performed.

[0106] Furthermore, minor faults can be self-repaired, with the system automatically adjusting operating parameters and attempting to self-repair through software adjustments or temporary measures. It can also monitor components with minor faults, record their operating status for further processing at any time, and provide maintenance recommendations, such as regular cleaning or inspection, to ensure that minor faults do not develop into more serious problems.

[0107] Temporary maintenance plan for medium faults: the system temporarily adjusts the operation mode according to the fault type to reduce the impact of the fault and ensure that the equipment can continue to operate for a period of time. It also generates a short-term maintenance plan to notify technicians to perform troubleshooting and component replacement within the planned maintenance window, record the handling process and results of medium faults, and update the fault database to provide data support for subsequent fault prevention and handling;

[0108] The comprehensive maintenance plan for serious faults is that the system immediately triggers the emergency shutdown procedure to prevent the fault from further deteriorating or causing a chain reaction, and generates a detailed maintenance plan, arranges technicians to perform a comprehensive maintenance, analyzes the cause of the serious fault, formulates and implements improvement measures, and prevents similar faults from happening again.

[0109] S4: Manage and optimize the entire life cycle of mine motor equipment.

[0110] It should be noted that the whole life cycle management and optimization includes the design stage, manufacturing stage, operation stage, maintenance stage and scrapping stage;

[0111] During the design phase, when designing new equipment, historical data and environmental data are processed to generate design parameters. Based on the posterior probability of predicted failures, the design priority and optimization points of the equipment in different scenarios are determined. The optimization output of the design phase directly affects the parameters during the manufacturing phase, ensuring that the equipment reduces potential failure points during the manufacturing process and improves the stability and consistency of the production process. The data and models generated during the design phase will also be used in real time during the manufacturing phase to calibrate and adjust the manufacturing process.

[0112] In the manufacturing stage, the optimized output of the design stage is used to monitor and analyze real-time data in real time, and the data is input into the intelligent diagnosis algorithm of mine electromechanical equipment for analysis. The real-time manufacturing data and the benchmark data of the design stage are integrated to analyze the potential defects in the manufacturing process, and the abnormal conditions in the manufacturing process are judged in real time and adjusted in time. The data of the manufacturing stage is fed back to the design stage in real time as a reference for the future design stage. At the same time, the potential problems detected in the manufacturing stage will be stored and used for baseline calibration in the operation stage to ensure the stability of the equipment in the initial operation;

[0113] During the operation phase, after the equipment is put into use, the operating status and performance indicators of the equipment are monitored in real time. Fault prediction and classification are carried out through the intelligent diagnosis algorithm of mine electromechanical equipment, and the corresponding intelligent repair plan is triggered according to the fault type. According to the sensor data and operating conditions, the posterior probability of equipment failure is calculated and classified into minor faults, medium faults, severe faults, and complex fault alarms are triggered. For different types of faults, the system automatically adjusts the equipment operating parameters and generates maintenance suggestions. The monitoring data of the operation phase will be fed back to the maintenance phase to adjust the maintenance strategy. The design defects and manufacturing errors in operation will be fed back to the manufacturing and design phases to promote continuous improvement of design and manufacturing.

[0114] During the maintenance phase, the system regularly evaluates the operating status and failure frequency of the equipment, and selects preventive or improved maintenance strategies based on actual conditions. Preventive maintenance is based on historical data and environmental changes to perform maintenance in advance, while improved maintenance optimizes the equipment technically when the system failure frequency increases. The system adjusts the maintenance plan based on the accumulated failure data. When different failure alarms are triggered, the system automatically executes repair measures and notifies the maintenance team to perform maintenance operations. The data during the maintenance phase directly affects the decision-making during the scrapping phase. When the equipment cannot achieve optimal performance through maintenance, the system will decide whether the equipment should be transferred to the scrapping phase based on the failure data and maintenance history throughout its life cycle.

[0115] During the scrapping stage, the system conducts a scrapping assessment based on the accumulated data from the operation, maintenance and design stages, including the equipment's reusable materials, recycling value and environmental impact. Based on the data throughout the entire life cycle, the system analyzes the equipment's residual value and resource recovery rate, formulates scrapping and recycling plans, and optimizes scrapping strategies and maximizes resource utilization based on the equipment's performance in design, manufacturing, operation and maintenance. The recycling data from the scrapping stage will be fed back to the design stage to provide a reference for the design of new equipment, ensuring optimal resource utilization and minimization of environmental impact. At the same time, the design and manufacturing defects identified in scrapped equipment will promote iterative improvements of future equipment, forming a continuously optimized closed-loop system.

[0116] Furthermore, in the design stage, virtual simulation is performed through simulation analysis, and the equipment is optimized for multiple iterations; in the manufacturing stage, automated equipment is used to reduce interference from human factors, and a quality database is built to provide data support for the follow-up; in the operation stage, the monitoring data is synchronized with the cloud database in real time, and the equipment operation data is deeply mined and trend analyzed to predict potential failure risks, optimize equipment operating parameters, and realize adaptive adjustment of the equipment; in the maintenance stage, an intelligent maintenance management platform is established to upload and analyze the operation data in real time to improve maintenance efficiency; in the scrapping stage, the concept of circular economy is introduced, and a comprehensive resource reuse assessment is conducted on scrapped equipment. Through advanced recycling technology, useful materials and components in scrapped equipment are efficiently separated and reused to reduce resource waste and environmental pollution, and an equipment scrapping and recycling management system is established to realize the full life cycle tracking and management of scrapped equipment, providing data support for the design and manufacture of new equipment.

[0117] Furthermore, this full life cycle management and optimization model not only realizes data sharing and feedback at each stage, but also ensures that every link of the equipment from design to scrapping can be optimally managed and optimized through the application of intelligent diagnosis algorithms for mine electromechanical equipment.

[0118] The above is a schematic scheme of a method for managing the entire life cycle of electromechanical equipment in a mine in this embodiment. It should be noted that the technical scheme of the system of the method for managing the entire life cycle of electromechanical equipment in a mine is the same as the technical scheme of the method for managing the entire life cycle of electromechanical equipment in a mine in this embodiment. For details not described in detail in the technical scheme of the method for managing the entire life cycle of electromechanical equipment in a mine in this embodiment, please refer to the description of the technical scheme of the method for managing the entire life cycle of electromechanical equipment in a mine in this embodiment.

[0119] A mine electromechanical equipment full life cycle management system in this embodiment is characterized by comprising: an equipment debugging module, an operation monitoring module, a fault and repair module and a full life cycle management module;

[0120] The equipment debugging module performs equipment debugging according to the production characteristics and environmental conditions of the mine;

[0121] The operation monitoring module monitors the operation status and performance indicators of the equipment in real time, and uses the intelligent diagnosis algorithm of mine electromechanical equipment to analyze the monitoring content and make fault judgments;

[0122] The fault and repair module formulates intelligent repair plans and maintenance strategies based on the fault judgment results;

[0123] The full life cycle module manages and optimizes the mine motor equipment throughout its life cycle.

[0124] This embodiment also provides a computing device applicable to a method for managing the entire life cycle of mine electromechanical equipment, including:

[0125] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a full life cycle management method for mine electromechanical equipment as proposed in the above embodiment.

[0126] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a method for managing the entire life cycle of mine electromechanical equipment as proposed in the above embodiment is implemented.

[0127] The storage medium proposed in this embodiment and the method for managing the entire life cycle of mine electromechanical equipment proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0128] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0129] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0130] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0131] Example 2, reference Figure 2-Figure 4 , which is an embodiment of the present invention. This embodiment provides a method for managing the entire life cycle of mine electromechanical equipment. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0132] The effectiveness of the full life cycle management method for mine electromechanical equipment was verified by setting up an experimental group and a control group. The test objects included mine electromechanical equipment. The experimental group applied the method of the present invention, while the control group applied the traditional equipment management method. The test was conducted in an actual mine environment, and the test period was 6 months.

[0133] The specific implementation process is as follows:

[0134] 1) Equipment debugging:

[0135] Experimental group: Debug the equipment according to the production characteristics and environmental conditions of the mine to ensure the best operating state of the equipment in a specific environment.

[0136] Control group: The equipment was debugged according to the traditional method without considering the real-time adjustment of environmental changes.

[0137] 2) Operation monitoring:

[0138] Experimental group: monitor the operating status and performance indicators of the equipment in real time, use the intelligent diagnosis algorithm of mine and electromechanical equipment to analyze the monitoring content, and make fault judgments.

[0139] Control group: Only the operating status of the equipment is checked regularly, without real-time monitoring and intelligent analysis.

[0140] 3) Fault diagnosis and repair:

[0141] Experimental group: Based on the fault judgment results, an intelligent repair plan is formulated. When a minor fault is detected, the system automatically adjusts the operating status and performs self-repair; in the case of a moderate fault, a temporary repair plan is generated; in the case of a serious fault, a comprehensive repair plan is generated.

[0142] Control group: Manual inspection and repair were performed after a fault occurred, and the fault status could not be responded to in time.

[0143] 4) Maintenance strategy:

[0144] Experimental group: Implement preventive and improvement maintenance strategies. Preventive maintenance is performed when the system is running smoothly and without failures; preventive maintenance is performed when a type of failure occurs repeatedly under the same environmental conditions; improvement maintenance is performed when the frequency of system failures increases.

[0145] Control group: Maintenance was performed according to the fixed walking cycle without considering the frequency of failures and environmental changes.

[0146] 5) Full life cycle management and optimization:

[0147] Experimental group: Use historical fault data and environmental data to perform simulation analysis and optimize equipment design during the design phase; monitor sensor data in real time to detect manufacturing defects during the manufacturing phase; monitor equipment status and performance indicators in real time to make fault judgments during the operation phase; select maintenance strategies based on equipment status and fault frequency during the maintenance phase; conduct scrap evaluation and recycle resources during the scrap phase.

[0148] Control group: There is no systematic full life cycle management, only routine operations are performed at each stage.

[0149] The experimental group is devices A, B, and C, and the control group is devices D, E, and F, and data is collected.

[0150] The experimental data are shown in Table 1:

[0151] Table 1 Data record table

[0152]

[0153] Data Analysis:

[0154] 1) Operation status and fault frequency:

[0155] The operating status of equipment A, B and C in the experimental group were 1200, 1500 and 1350 hours respectively, and the number of failures were 2 times, 3 times and 2 times respectively; the operating status of equipment D, E and F in the control group were 1600, 1450 and 1550 hours respectively, and the number of failures were 4 times, 3 times and 3 times respectively.

[0156] like Figure 2 As shown, the Y-axis represents the operating time and the number of failures. By comparing the bar chart and the line chart, it can be concluded that the operating status of the equipment in the experimental group is slightly lower than that in the control group, but the number of failures is less; this indicates that under the debugging and monitoring of the method of the present invention, the failure rate of the equipment is reduced and the operation is more stable.

[0157] 2) Temperature and vibration:

[0158] The temperature and vibration values ​​of the experimental group were relatively stable, with the temperature between 34-36℃ and the vibration between 3.5-3.7mm / s; the temperature and vibration values ​​of the control group were higher, with the temperature between 35-37℃ and the vibration between 3.5-3.8mm / s.

[0159] like Figure 3 As shown in the figure, the Y-axis represents temperature, the upper X-axis represents vibration, and the horizontal bar graph represents device A to device F from bottom to top. It can be seen from the two groups of bar graphs that the experimental group equipment can maintain a relatively stable operating environment under the monitoring and regulation of the intelligent diagnosis algorithm, thereby reducing failures caused by abnormal temperature and vibration.

[0160] 3) Maintenance cost:

[0161] By comparing the experimental group and the control group, it was found that the maintenance costs of equipment A, B and C in the experimental group were 5,000 yuan, 5,500 yuan and 5,200 yuan respectively; the maintenance costs of equipment D, E and F in the control group were 6,000 yuan, 5,300 yuan and 5,800 yuan respectively.

[0162] like Figure 4 As shown in the figure, the Y-axis represents the maintenance cost. The maintenance cost of the equipment in the experimental group is lower than that in the control group as a whole, especially for equipment A and equipment C, where the maintenance cost is significantly reduced. This shows that through the intelligent repair plan and maintenance strategy, the maintenance efficiency of the equipment in the experimental group has been significantly improved, reducing the maintenance cost.

[0163] Through comparative analysis of the data of the experimental group and the control group, it can be seen that the full life cycle management method of mine electromechanical equipment of the present invention has significant advantages in improving equipment operation stability, reducing the number of failures, optimizing the operating environment and reducing maintenance costs; this method comprehensively improves the management and maintenance level of mine electromechanical equipment through technical means such as equipment debugging, real-time monitoring, intelligent diagnosis and full life cycle management, and has high innovation and practicality.

[0164] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for managing the entire life cycle of mine electromechanical equipment, characterized in that: include: Carry out equipment commissioning according to the production characteristics and environmental conditions of the mine; Monitor the operating status and performance indicators of the equipment in real time, and use the intelligent diagnosis algorithm of mine electromechanical equipment to analyze the monitoring content and make fault judgments; Develop intelligent repair plans and maintenance strategies based on fault diagnosis results; Carry out full life cycle management and optimization of mine motor equipment.

2. A method for managing the entire life cycle of mine electromechanical equipment according to claim 1, characterized in that: The equipment debugging includes pre-debugging preparation, intelligent environment perception, dynamic adaptive debugging and multi-dimensional performance verification; The pre-commissioning preparation uses high-precision sensors to conduct preliminary testing and calibration of the equipment, and establishes a three-dimensional model of the mine environment in combination with the mine production characteristics and environmental conditions; The intelligent environmental perception can sense the environmental changes in the mine in real time, obtain comprehensive environmental information through multi-sensor data fusion technology, and evaluate the dynamic changes of the mine environment in real time; The dynamic adaptive debugging combines the real-time evaluation of the dynamic changes in the mine environment to dynamically adjust and optimize the equipment debugging process; The multi-dimensional performance verification performs multi-scenario debugging on the device according to environmental conditions to verify performance indicators.

3. A method for managing the entire life cycle of mine electromechanical equipment according to claim 2, characterized in that: The intelligent diagnosis algorithm for mine electromechanical equipment includes data preprocessing, multi-layer fusion model and fault judgment model; The data preprocessing is expressed as: X fusion =α·f(X sensor )+β·g(X env )+γ·h(X history ) Among them, X fusion represents the fused data, X sensor Represents sensor data, X env Represents environmental data, X history represents historical data, α represents the proportion of sensor data in the integrated data after fusion, β represents the proportion of environmental data in the integrated data after fusion, γ represents the proportion of historical data in the integrated data after fusion, f(X sensor ) represents the sensor data X fusion Perform nonlinear transformation, g(X env ) represents the environmental data X env Perform nonlinear transformation, h(X history ) is represented by historical data X history Perform nonlinear transformations; The multi-layer fusion model is expressed as: H (l) =σ(W (l) *H (l-1) +b (l) ) H (t) =σ(W H H (t-1) +W X X (t) +b) Among them, H (l) represents the convolutional layer of the layer, W (l) represents the convolution kernel of the layer, H (l-1) represents the output of the l-1th layer, b (l) represents the bias variable, * represents the convolution operation, σ represents the activation function, H (t) represents the hidden state of the recurrent layer at time t, H (t-1) represents the hidden state of the recurrent layer at the previous moment, W H Represents the transition matrix from the previous hidden state to the current hidden state, W X represents the weight matrix input to the hidden state, X (t) represents the input at time t, b represents the bias vector, Y pred represents the prediction result, W o represents the weight matrix of the output layer, represents the final feature vector of the combined convolutional layer and the recurrent layer, φ represents the output layer bias vector, and represents the output layer activation function; The fault judgment model is expressed as: Among them, P(F, X fusion ) represents the joint probability distribution of fault and fusion data, P(F) represents the prior probability of fault, represents the conditional probability of the fault condition in the i-th fused data, P(F|X fusion ) represents the posterior probability of failure given the fused data.

4. A method for managing the entire life cycle of mine electromechanical equipment according to claim 3, characterized in that: The fault judgment is performed based on dynamic monitoring and fault classification. When an indicator of sensor data during equipment operation exceeds a preset threshold, the current data indicator is recorded as an abnormal value and a preliminary fault alarm is triggered; According to the prediction results Ypre in the intelligent diagnosis algorithm of mine electromechanical equipment d , determine the fault type, which is divided into minor fault, medium fault and serious fault, and set the prediction result threshold as [T low , T high ], when the prediction result Y pred Less than the set threshold T low When, and according to the fault posterior probability P(F|X fusion ) is 10%, it is judged as a minor fault. pred Between T low and T high And according to the fault posterior probability P(F|X fusion ) is 30%, it is judged as a medium fault. pred Exceeding the set threshold T high When, and according to the fault posterior probability P(F|X fusion ) is 70%, it is judged as a serious fault; When multiple data indicators exceed the preset threshold at the same time, and the fault posterior probability P(F|X fusion ) is 80%, a complex fault alarm is triggered.

5. A method for managing the entire life cycle of mine electromechanical equipment according to claim 4, characterized in that: The intelligent repair scheme includes, when a preliminary fault alarm is triggered, the system reduces the operating level of the corresponding indicator, performs self-checking, and automatically corrects the data indicator that triggered the alarm; If the fault is judged as a minor fault, the system automatically adjusts the operating status, reduces the equipment operating load, and continuously monitors and generates maintenance suggestions, so that the equipment can be inspected and adjusted in the next maintenance window to avoid escalation of the fault type. When the fault is judged as a medium fault, the system generates a temporary maintenance plan, adjusts the equipment operation mode to a low-performance mode, performs on-site maintenance, and replaces and repairs key parts; When the fault is judged as a serious fault, the system generates a comprehensive maintenance plan, triggers the emergency shutdown procedure, analyzes the cause of the fault, determines the repair steps and the list of prepared parts, and conducts a comprehensive inspection of the equipment; When the system triggers a complex fault alarm, the system enters fault isolation mode and generates multiple maintenance plans, including data synchronization, component replacement, and overall system adjustment, and implements repair operations in steps.

6. A method for managing the entire life cycle of mine electromechanical equipment according to claim 5, characterized in that: The maintenance strategy includes preventive maintenance and improvement maintenance; When the system is running smoothly and no faults are detected, preventive maintenance is performed based on historical data and environmental changes. When a type of fault recurs under the same environmental conditions, equipment is adjusted and inspected in advance to perform preventive maintenance. When the frequency of system failures increases, the system automatically adjusts the equipment operating parameters, makes technical improvements and optimizations to the equipment, and performs improved maintenance. When a type of failure recurs under different environmental conditions, the cause of the failure is analyzed, equipment performance tests are conducted, processing steps are strengthened, and preventive maintenance is performed.

7. A method for managing the entire life cycle of mine electromechanical equipment according to claim 6, characterized in that: The full life cycle management and optimization includes the design stage, manufacturing stage, operation stage, maintenance stage and scrapping stage; In the design phase, when designing new equipment, historical data and environmental data are processed to generate design parameters. Based on the posterior probability of predicted failures, the design priority and optimization points of the equipment in different scenarios are determined, and the optimization output of the design phase directly affects the parameters in the manufacturing phase; In the manufacturing stage, the optimization output of the design stage is used to monitor and analyze real-time data in real time, and the data is input into the intelligent diagnosis algorithm of mine electromechanical equipment for analysis, integrating the real-time manufacturing data and the benchmark data of the design stage, analyzing the potential defects in the manufacturing process, judging the abnormal conditions in the manufacturing process in real time and making timely adjustments, and the data of the manufacturing stage is fed back to the design stage in real time as a reference for the future design stage. At the same time, the potential problems detected in the manufacturing stage will be stored and used for baseline calibration in the operation stage; During the operation phase, after the equipment is put into use, the operating status and performance indicators of the equipment are monitored in real time, fault prediction and classification are carried out through the intelligent diagnosis algorithm of mine electromechanical equipment, and the corresponding intelligent repair plan is triggered according to the fault type. According to the sensor data and operating conditions, the posterior probability of equipment failure is calculated and classified into minor faults, medium faults, serious faults, and complex fault alarms are triggered. For different types of faults, the system automatically adjusts the equipment operating parameters and generates maintenance suggestions. The monitoring data of the operation phase will be fed back to the maintenance phase for adjusting the maintenance strategy. The design defects and manufacturing errors in operation will be fed back to the manufacturing phase and the design phase to promote continuous improvement of design and manufacturing; During the maintenance phase, the system regularly evaluates the operating status and failure frequency of the equipment, and selects preventive or improved maintenance strategies based on actual conditions. Preventive maintenance is based on historical data and environmental changes to perform maintenance in advance, while improved maintenance is based on technical optimization of the equipment when the frequency of system failures increases. The system adjusts the maintenance plan based on the accumulated failure data. When different failure alarms are triggered, the system automatically executes repair measures and notifies the maintenance team to perform maintenance operations. The data during the maintenance phase directly affects the decision-making during the scrapping phase. When the equipment cannot achieve optimal performance through maintenance, the system will decide to transfer the equipment to the scrapping phase based on the failure data and maintenance history throughout its life cycle. During the scrapping stage, the system conducts a scrapping assessment based on the accumulated data from the operation, maintenance and design stages, including the reusable materials, recycling value and environmental impact of the equipment. Based on the data throughout the entire life cycle, the system analyzes the residual value and resource recovery rate of the equipment, formulates a scrapping and recycling plan, and optimizes the scrapping strategy and maximizes resource utilization based on the performance of the equipment in design, manufacturing, operation and maintenance. The recycling data from the scrapping stage will be fed back to the design stage to provide a reference for the design of new equipment.

8. A system based on the method for managing the entire life cycle of mine electromechanical equipment according to any one of claims 1 to 7, characterized in that: include: Equipment debugging module, operation monitoring module, fault and repair module and full life cycle management module; The equipment debugging module performs equipment debugging according to the production characteristics and environmental conditions of the mine; The operation monitoring module monitors the operation status and performance indicators of the equipment in real time, and uses the intelligent diagnosis algorithm of mine electromechanical equipment to analyze the monitoring content and make fault judgments; The fault and repair module formulates intelligent repair plans and maintenance strategies based on the fault judgment results; The full life cycle module manages and optimizes the mine motor equipment throughout its life cycle.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for managing the entire life cycle of mine electromechanical equipment as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for managing the entire life cycle of mine electromechanical equipment described in any one of claims 1 to 7 are implemented.

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