A full life cycle management system and method for coal mine electromechanical equipment

Abstract

The invention discloses a management system and method for the whole life cycle of electromechanical equipment in a coal mine, and relates to the technical field of equipment control. The system comprises collecting operating status data of the electromechanical equipment in a coal mine through sensors installed in the electromechanical equipment in a coal mine, and preprocessing the operating status data to obtain fault diagnosis data; extracting features of the fault diagnosis data based on an empirical mode decomposition algorithm, and obtaining fault diagnosis results through a variant long short-term memory network model; performing decision analysis based on the fault diagnosis data using a simulated annealing algorithm and combining the fault diagnosis results to obtain electromechanical equipment scheduling results; and constructing an equipment diagnosis service knowledge base based on the fault diagnosis results and the electromechanical equipment scheduling results to manage the whole life cycle of the electromechanical equipment in a coal mine.

Description

Technical Field

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

[0002] Coal mine electromechanical equipment refers to various mechanical equipment used in the process of coal mining, transportation and processing, such as coal mining machines, scraper conveyors, belt conveyors, ventilators, drainage pumps, etc.; the performance and reliability of these equipment are directly related to the efficiency and safety of coal mine production; with the development of the coal mining industry, the complexity and automation of electromechanical equipment are constantly improving, which puts forward higher requirements for the management of the entire life cycle of equipment. Daily inspections, preventive maintenance, fault diagnosis and repair of equipment are required to ensure the efficient operation of equipment, otherwise it will directly affect the production efficiency of coal mines and the safe operation of the entire mine; and the failure of any key equipment may cause the production line to stop, increase maintenance costs, and increase the operating costs of the entire mine.

[0003] However, the management system of the entire life cycle of coal mine electromechanical equipment in the existing technology usually relies on regular inspections and post-analysis, which makes it difficult to achieve real-time monitoring and rapid response, and it is easy to miss the best processing time. At the same time, there is a lack of unified management and integration of equipment information, resulting in waste of coal mine electromechanical equipment resources; in particular, there is a lack of accurate prediction and optimal scheduling of coal mine electromechanical equipment failures, which may lead to unnecessary spare parts inventory backlogs and waste of manpower and material resources; therefore, it is impossible to achieve real-time monitoring, intelligent diagnosis and optimized scheduling to comprehensively improve the management level of coal mine electromechanical equipment to ensure the safety and efficiency of mine production and operation.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of the problems in the related technology, the present invention proposes a full life cycle management system and method for coal mine electromechanical equipment to overcome the above-mentioned technical problems existing in the existing related technology.

[0006] To this end, the specific technical solution adopted by the present invention is as follows: According to one aspect of the present invention, a full life cycle management system for coal mine electromechanical equipment is provided, including a data acquisition module, a fault diagnosis module, an analysis and scheduling module, and a diagnosis management module; The data acquisition module is used to collect the operating status data of the coal mine electromechanical equipment through sensors installed in the coal mine electromechanical equipment, and pre-process the operating status data to obtain fault diagnosis data; The fault diagnosis module is used to extract features from fault diagnosis data based on the empirical mode decomposition algorithm and obtain fault diagnosis results through the variant long short-term memory network model; The analysis and scheduling module is used to perform decision analysis based on the fault diagnosis data, using the simulated annealing algorithm and combining the fault diagnosis results to obtain the scheduling results of the electromechanical equipment; The diagnosis management module is used to build an equipment diagnosis service knowledge base based on fault diagnosis results and electromechanical equipment scheduling results, and manage the entire life cycle of coal mine electromechanical equipment.

[0007] Preferably, preprocessing the operating status data includes: noise removal, outlier processing and data standardization.

[0008] Preferably, the fault diagnosis module includes a data decomposition module, a feature extraction module, a model training module and a diagnosis result acquisition module; The data decomposition module is used to decompose the fault diagnosis data based on the empirical mode decomposition algorithm to obtain several intrinsic mode functions; The feature extraction module is used to decompose each intrinsic mode function by wavelet transform method to obtain resolution decomposition coefficients, and extract statistical features of the resolution decomposition coefficients to obtain fault features; A model training module is used to train a variant long short-term memory network model according to fault characteristics, and optimize the variant long short-term memory network model using an attention mechanism to obtain a diagnostic model; The diagnosis result acquisition module is used to acquire fault diagnosis data and obtain fault diagnosis results based on the diagnosis model.

[0009] Preferably, based on the empirical mode decomposition algorithm, the fault diagnosis data is decomposed to obtain several intrinsic mode functions including: The local extreme values ​​of the fault diagnosis data are found by screening, the upper and lower envelopes are constructed, the mean of the upper and lower envelopes is calculated, and the mean is subtracted from the fault diagnosis data to obtain the intrinsic mode function.

[0010] Preferably, the statistical features of the resolution decomposition coefficients are extracted to obtain the fault features including: The statistical characteristics of the resolution decomposition coefficients are extracted, and the instantaneous frequency and amplitude of the intrinsic mode function are calculated using the Hilbert transform method to construct the fault composite features. The principal component analysis method is used to reduce the dimension of the fault composite features to obtain the fault features.

[0011] Preferably, according to the fault characteristics, a variant long short-term memory network model is trained, and the variant long short-term memory network model is optimized using an attention mechanism, and the obtained diagnostic model includes: Based on the fault characteristics, the fault feature data is divided into training set, validation set and test set, and the input layer is defined according to the fault feature dimension. The variant long short-term memory network layer model is constructed, and the mini-batch gradient descent method is used to train the variant long short-term memory network model. The attention mechanism is used to optimize the variant long short-term memory network model to obtain the diagnostic model.

[0012] Preferably, the analysis and scheduling module includes an objective function determination module, an initial scheduling acquisition module and a scheduling result generation module; The objective function determination module is used to define several objective functions based on the simulated annealing algorithm, and to construct the total objective function according to the different priority matching weights of the several objective functions; The initial scheduling acquisition module is used to make decision analysis on the coal mine electromechanical equipment according to the overall objective function and combined with the fault diagnosis results to generate the initial scheduling plan; The scheduling result generation module is used to obtain the scheduling result of electromechanical equipment through optimization processing such as neighborhood search, energy calculation and temperature reduction based on the initial scheduling plan.

[0013] Preferably, the diagnosis management module includes a diagnosis category acquisition module, a device diagnosis service knowledge base construction module and a device management module; Among them, the diagnosis category acquisition module is used to classify the diagnosis service types based on the fault diagnosis results and the electromechanical equipment scheduling results to obtain the total diagnosis service category; The equipment diagnosis service knowledge base construction module is used to construct the equipment diagnosis service knowledge base according to the general category of diagnosis services and in combination with the pre-acquired diagnosis knowledge of the whole life cycle of coal mine electromechanical equipment; The equipment management module is used to obtain equipment diagnosis results based on the equipment diagnosis service knowledge base and manage the entire life cycle of coal mine electromechanical equipment.

[0014] Preferably, based on the fault diagnosis result and the electromechanical equipment scheduling result, the classification of the diagnosis service types includes: classifying the diagnosis service types according to the fault degree of the fault diagnosis result and the complexity of the electromechanical equipment scheduling result.

[0015] According to another aspect of the present invention, a method for managing the entire life cycle of electromechanical equipment in a coal mine is provided, the method comprising the following steps: S1. Collecting the operating status data of the coal mine electromechanical equipment through sensors installed in the coal mine electromechanical equipment, and preprocessing the operating status data to obtain fault diagnosis data; S2. Based on the empirical mode decomposition algorithm, feature extraction is performed on the fault diagnosis data, and the fault diagnosis results are obtained through the variant long short-term memory network model; S3. Based on the fault diagnosis data, the simulated annealing algorithm is used, and the decision analysis is performed in combination with the fault diagnosis results to obtain the mechanical and electrical equipment scheduling results; S4. Based on the fault diagnosis results and electromechanical equipment scheduling results, build an equipment diagnosis service knowledge base to manage the entire life cycle of coal mine electromechanical equipment.

[0016] The beneficial effects of the present invention are: 1. The present invention ensures that the coal mine electromechanical equipment full life cycle management system integrates data through the acquisition module, fault diagnosis module, analysis and scheduling module and diagnosis management module, thereby significantly improving the management and maintenance effect of the coal mine electromechanical equipment; it realizes real-time monitoring and rapid response to the coal mine electromechanical equipment to avoid missing the best processing opportunity, and at the same time facilitates the unified management and integration of equipment information to avoid waste of coal mine electromechanical equipment resources; it comprehensively improves the management level of coal mine electromechanical equipment and ensures the safety and efficiency of mine production and operation.

[0017] 2. The present invention uses the empirical mode decomposition algorithm to extract fault features through the fault diagnosis module, and performs intelligent diagnosis through the variant long short-term memory network model combined with the attention mechanism, which greatly improves the accuracy and real-time performance of fault diagnosis. Compared with traditional methods, this method can more effectively capture complex nonlinear dynamic characteristics, enhance the robustness and adaptability of the system, and can more accurately predict and optimize the scheduling of coal mine electromechanical equipment failures, avoiding spare parts inventory backlogs and waste of manpower and material resources.

[0018] 3. The present invention analyzes the scheduling module and uses the simulated annealing algorithm combined with the fault diagnosis results to perform decision analysis, thereby minimizing downtime, optimizing resource allocation, and reducing operating costs; intelligent scheduling not only improves equipment utilization, but also ensures the scientificity and rationality of the maintenance plan, further improving overall production efficiency.

[0019] 4. The present invention constructs an equipment diagnostic service knowledge base based on fault diagnosis results and scheduling results through a diagnostic management module, regularly updates the knowledge base content, combines actual operation data, continuously optimizes the diagnostic service type, improves the system's adaptability and management level, and further improves the overall management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1This is a structural block diagram of a coal mine electromechanical equipment full life cycle management system according to an embodiment of the present invention; Figure 2 The present invention is a flowchart of a method for managing the entire life cycle of electromechanical equipment in a coal mine according to an embodiment of the present invention.

[0022] In the figure: 1. Data acquisition module; 2. Fault diagnosis module; 3. Analysis and scheduling module; 4. Diagnosis management module. DETAILED DESCRIPTION

[0023] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0024] According to an embodiment of the present invention, a management system and method for the entire life cycle of electromechanical equipment in a coal mine are provided.

[0025] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a full life cycle management system for coal mine electromechanical equipment is provided, including a data acquisition module 1, a fault diagnosis module 2, an analysis and scheduling module 3 and a diagnosis management module 4; The data acquisition module 1 is used to collect the operation status data of the coal mine electromechanical equipment through sensors installed in the coal mine electromechanical equipment, and pre-process the operation status data to obtain fault diagnosis data.

[0026] It should be noted that the operating status data of coal mine electromechanical equipment is collected through sensors installed in the equipment, including: sorting out the key production equipment of each coal mine or port, equipment with a high failure rate, and equipment that is difficult to effectively cover in daily inspection and maintenance, as the equipment scope of platform access, reasonably planning the intelligent sensing transformation plan for each type of equipment, and accurately collecting equipment operating status data.

[0027] Specifically, the preprocessing of the operating status data includes: noise removal processing, outlier processing and data standardization processing.

[0028] It should be noted that the operating status data includes: vibration signals, temperature signals, pressure signals, acoustic signals and current signals.

[0029] The fault diagnosis module 2 is used to extract features of the fault diagnosis data based on the empirical mode decomposition algorithm, and obtain the fault diagnosis results through the variant long short-term memory network model.

[0030] It should be noted that the equipment failure can be effectively identified through the fault diagnosis results and intuitively pushed to relevant personnel in various ways.

[0031] Specifically, the fault diagnosis module 2 includes a data decomposition module, a feature extraction module, a model training module and a diagnosis result acquisition module; The data decomposition module is used to decompose the fault diagnosis data based on the empirical mode decomposition algorithm to obtain several intrinsic mode functions; The feature extraction module is used to decompose each intrinsic mode function by wavelet transform method to obtain resolution decomposition coefficients, and extract statistical features of the resolution decomposition coefficients to obtain fault features; A model training module is used to train a variant long short-term memory network model according to fault characteristics, and optimize the variant long short-term memory network model using an attention mechanism to obtain a diagnostic model; The diagnosis result acquisition module is used to acquire fault diagnosis data and obtain fault diagnosis results based on the diagnosis model.

[0032] Specifically, based on the empirical mode decomposition algorithm, the fault diagnosis data is decomposed to obtain several intrinsic mode functions including: The local extreme values ​​of the fault diagnosis data are found by screening, the upper and lower envelopes are constructed, the mean of the upper and lower envelopes is calculated, and the mean is subtracted from the fault diagnosis data to obtain the intrinsic mode function.

[0033] Specifically, the statistical characteristics of the resolution decomposition coefficients are extracted, and the fault characteristics obtained include: The statistical characteristics of the resolution decomposition coefficients are extracted, and the instantaneous frequency and amplitude of the intrinsic mode function are calculated using the Hilbert transform method to construct the fault composite features. The principal component analysis method is used to reduce the dimension of the fault composite features to obtain the fault features.

[0034] Specifically, according to the fault characteristics, the variant long short-term memory network model is trained, and the variant long short-term memory network model is optimized using the attention mechanism. The obtained diagnostic model includes: Based on the fault characteristics, the fault feature data is divided into training set, validation set and test set, and the input layer is defined according to the fault feature dimension. The variant long short-term memory network layer model is constructed, and the mini-batch gradient descent method is used to train the variant long short-term memory network model. The attention mechanism is used to optimize the variant long short-term memory network model to obtain the diagnostic model.

[0035] It should be noted that according to the fault characteristics, the variant long short-term memory network model is trained and optimized using the attention mechanism to obtain the diagnostic model; that is, the extracted fault feature data is divided into training set, validation set and test set to ensure that the time series data is divided in chronological order to avoid future information leakage; the input layer is defined to receive the extracted fault features, that is, the input size is determined according to the feature dimension; a bidirectional variant long short-term memory network layer is constructed, and each layer contains a certain number of variant long short-term memory network units; a self-attention mechanism is added after the variant long short-term memory network layer, so that the model can automatically learn the weights between different time steps and enhance the focus on key features; a more effective feature representation is generated through weighted summation; multiple fully connected layers are added to further process the features and output the final result; the output layer is defined, and the activation function is selected to handle the classification problem; the cross entropy loss function is selected to handle classification problem; select optimizer, adjust learning rate and other hyperparameters to ensure effective convergence of the model; apply regularization technology to prevent overfitting; set early stopping method to terminate training early when performance on the validation set no longer improves; use small batch gradient descent for training to balance computational efficiency and model stability; use confusion matrix, accuracy, precision, recall and other indicators to evaluate the performance of the model on the test set; adjust hyperparameters through grid search, random search or Bayesian optimization to find the optimal configuration; deploy the model to the actual environment to achieve real-time fault diagnosis; establish a feedback mechanism to monitor model performance in real time, collect new fault cases, and continuously update and improve the model; obtain an efficient diagnostic model; this method not only improves the accuracy and timeliness of fault diagnosis, but also enhances the model's ability to analyze complex nonlinear dynamic characteristics, providing a scientific basis for the health management of electromechanical equipment.

[0036] The analysis and scheduling module 3 is used to perform decision analysis based on the fault diagnosis data, using the simulated annealing algorithm and combining the fault diagnosis results to obtain the electromechanical equipment scheduling results.

[0037] It should be noted that the fault diagnosis data is obtained by capturing the original data of coal mine or port equipment. The fault diagnosis result is the diagnosis result data. Through decision analysis, the electromechanical equipment scheduling result is obtained, and a management system that can support scheduling and decision-making analysis and management is formed on the coal mine group side.

[0038] Specifically, the analysis and scheduling module 3 includes an objective function determination module, an initial scheduling acquisition module and a scheduling result generation module; The objective function determination module is used to define several objective functions based on the simulated annealing algorithm, and to construct the total objective function according to the different priority matching weights of the several objective functions; The initial scheduling acquisition module is used to make decision analysis on the coal mine electromechanical equipment according to the overall objective function and combined with the fault diagnosis results to generate the initial scheduling plan; The scheduling result generation module is used to obtain the scheduling result of electromechanical equipment through optimization processing such as neighborhood search, energy calculation and temperature reduction based on the initial scheduling plan.

[0039] The diagnosis management module 4 is used to build an equipment diagnosis service knowledge base based on the fault diagnosis results and the electromechanical equipment scheduling results, and manage the entire life cycle of coal mine electromechanical equipment.

[0040] It should be noted that, as the management level, the coal mining group needs to empower each coal mine or port with the platform, and provide core data and diagnostic services for equipment diagnosis to the coal mines or ports through unified management of algorithms or models and diagnostic service knowledge bases.

[0041] Specifically, the diagnosis management module 4 includes a diagnosis category acquisition module, a device diagnosis service knowledge base construction module and a device management module; Among them, the diagnosis category acquisition module is used to classify the diagnosis service types based on the fault diagnosis results and the electromechanical equipment scheduling results to obtain the total diagnosis service category; The equipment diagnosis service knowledge base construction module is used to construct the equipment diagnosis service knowledge base according to the general category of diagnosis services and in combination with the pre-acquired diagnosis knowledge of the whole life cycle of coal mine electromechanical equipment; The equipment management module is used to obtain equipment diagnosis results based on the equipment diagnosis service knowledge base and manage the entire life cycle of coal mine electromechanical equipment.

[0042] Specifically, based on the fault diagnosis result and the electromechanical equipment scheduling result, the classification of the diagnosis service type includes: classifying the diagnosis service type according to the fault degree of the fault diagnosis result and the complexity of the electromechanical equipment scheduling result.

[0043] It should be noted that the degree of fault is divided into minor fault, moderate fault and major fault according to whether the equipment can continue to operate; among them, minor fault does not affect the normal operation of the equipment; moderate fault means that there is obvious abnormality, but the equipment can continue to operate for a period of time; major fault means failure of key components or system paralysis, and immediate action is required.

[0044] It should be noted that the complexity of the scheduling results is divided into simple scheduling, medium scheduling and difficult scheduling according to the scheduling scope; among them, simple scheduling is a regular maintenance plan and does not require special coordination; medium scheduling involves collaboration among multiple departments and requires the allocation of specific resources; difficult scheduling involves the linkage of multiple devices and cross-departmental coordination, requiring emergency response and large-scale resource allocation.

[0045] According to another embodiment of the present invention, Figure 2 As shown, a method for managing the entire life cycle of coal mine electromechanical equipment is provided, the method comprising the following steps: S1. Collecting the operating status data of the coal mine electromechanical equipment through sensors installed in the coal mine electromechanical equipment, and preprocessing the operating status data to obtain fault diagnosis data; S2. Based on the empirical mode decomposition algorithm, feature extraction is performed on the fault diagnosis data, and the fault diagnosis results are obtained through the variant long short-term memory network model; S3. Based on the fault diagnosis data, the simulated annealing algorithm is used, and the decision analysis is performed in combination with the fault diagnosis results to obtain the mechanical and electrical equipment scheduling results; S4. Based on the fault diagnosis results and electromechanical equipment scheduling results, build an equipment diagnosis service knowledge base to manage the entire life cycle of coal mine electromechanical equipment.

[0046] In summary, with the help of the above-mentioned technical scheme of the present invention, the present invention ensures that the coal mine electromechanical equipment full life cycle management system integrates data through the acquisition module, fault diagnosis module, analysis and scheduling module and diagnosis management module, thereby significantly improving the management and maintenance effect of coal mine electromechanical equipment; it realizes real-time monitoring and rapid response to coal mine electromechanical equipment to avoid missing the best processing opportunity, and at the same time facilitates the unified management and integration of equipment information to avoid waste of coal mine electromechanical equipment resources; it comprehensively improves the management level of coal mine electromechanical equipment to ensure safe and efficient mine production and operation; the present invention uses the empirical mode decomposition algorithm to extract fault features through the fault diagnosis module, and uses the variant long short-term memory network model combined with the attention mechanism for intelligent diagnosis, which greatly improves the accuracy and real-time performance of fault diagnosis; compared with traditional methods, this method can be more The invention can effectively capture complex nonlinear dynamic characteristics, enhance the robustness and adaptability of the system; it can more accurately predict and optimize the scheduling of coal mine electromechanical equipment failures, avoid spare parts inventory backlog and waste of manpower and material resources; the present invention analyzes the scheduling module, uses the simulated annealing algorithm and combines the fault diagnosis results for decision analysis, minimizes downtime, optimizes resource allocation, and reduces operating costs; intelligent scheduling not only improves equipment utilization, but also ensures the scientificity and rationality of the maintenance plan, and further improves the overall production efficiency; the present invention uses the diagnosis management module to build an equipment diagnosis service knowledge base based on the fault diagnosis results and the scheduling results, regularly updates the knowledge base content, combines the actual operation data, continuously optimizes the diagnosis service type, improves the system's adaptability and management level, and further improves the overall management efficiency.

[0047] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A full life cycle management system for coal mine electromechanical equipment, characterized in that: It comprises a data acquisition module (1), a fault diagnosis module (2), an analysis and scheduling module (3) and a diagnosis management module (4); The data acquisition module (1) is used to collect operating status data of the coal mine electromechanical equipment through sensors installed in the coal mine electromechanical equipment, and to pre-process the operating status data to obtain fault diagnosis data; The fault diagnosis module (2) is used to extract features from the fault diagnosis data based on an empirical mode decomposition algorithm, and obtain a fault diagnosis result through a variant long short-term memory network model; The analysis and scheduling module (3) is used to perform decision analysis based on the fault diagnosis data using a simulated annealing algorithm and in combination with the fault diagnosis results to obtain a scheduling result for the electromechanical equipment; The diagnosis management module (4) is used to build an equipment diagnosis service knowledge base based on the fault diagnosis results and the electromechanical equipment scheduling results, and to manage the entire life cycle of the coal mine electromechanical equipment.

2. A coal mine electromechanical equipment full life cycle management system according to claim 1, characterized in that: The preprocessing of the operating status data includes: noise removal processing, outlier processing and data standardization processing.

3. A coal mine electromechanical equipment full life cycle management system according to claim 1, characterized in that: The fault diagnosis module (2) comprises a data decomposition module, a feature extraction module, a model training module and a diagnosis result acquisition module; The data decomposition module is used to decompose the fault diagnosis data based on the empirical mode decomposition algorithm to obtain a number of intrinsic mode functions; The feature extraction module is used to decompose each intrinsic mode function by wavelet transform method to obtain resolution decomposition coefficients, and extract statistical features of the resolution decomposition coefficients to obtain fault features; The model training module is used to train a variant long short-term memory network model according to fault characteristics, and optimize the variant long short-term memory network model using an attention mechanism to obtain a diagnostic model; The diagnosis result acquisition module is used to acquire fault diagnosis data and obtain fault diagnosis results based on the diagnosis model.

4. A coal mine electromechanical equipment full life cycle management system according to claim 3, characterized in that: Based on the empirical mode decomposition algorithm, the fault diagnosis data is decomposed to obtain several intrinsic mode functions including: The local extreme values ​​of the fault diagnosis data are found by screening, the upper and lower envelopes are constructed, the mean of the upper and lower envelopes is calculated, and the mean is subtracted from the fault diagnosis data to obtain the intrinsic mode function.

5. A coal mine electromechanical equipment full life cycle management system according to claim 3, characterized in that: The extraction of the statistical characteristics of the resolution decomposition coefficient to obtain the fault characteristics includes: The statistical characteristics of the resolution decomposition coefficients are extracted, and the instantaneous frequency and amplitude of the intrinsic mode function are calculated using the Hilbert transform method to construct the fault composite features. The principal component analysis method is used to reduce the dimension of the fault composite features to obtain the fault features.

6. A coal mine electromechanical equipment full life cycle management system according to claim 3, characterized in that: The variant long short-term memory network model is trained according to the fault characteristics, and the variant long short-term memory network model is optimized by using the attention mechanism to obtain a diagnostic model including: Based on the fault characteristics, the fault feature data is divided into training set, validation set and test set, and the input layer is defined according to the fault feature dimension. The variant long short-term memory network layer model is constructed, and the mini-batch gradient descent method is used to train the variant long short-term memory network model. The attention mechanism is used to optimize the variant long short-term memory network model to obtain the diagnostic model.

7. A coal mine electromechanical equipment full life cycle management system according to claim 1, characterized in that: The analysis and scheduling module (3) includes an objective function determination module, an initial scheduling acquisition module and a scheduling result generation module; The objective function determination module is used to define a plurality of objective functions based on a simulated annealing algorithm, and to construct a total objective function according to the different priority matching weights of the plurality of objective functions; The initial scheduling acquisition module is used to perform decision analysis on the coal mine electromechanical equipment according to the overall objective function and in combination with the fault diagnosis results to generate an initial scheduling plan; The scheduling result generation module is used to obtain the electromechanical equipment scheduling result by performing optimization processing through neighborhood search, energy calculation and temperature reduction processing based on the initial scheduling plan.

8. A coal mine electromechanical equipment full life cycle management system according to claim 1, characterized in that: The diagnosis management module (4) includes a diagnosis category acquisition module, an equipment diagnosis service knowledge base construction module and an equipment management module; The diagnostic category acquisition module is used to classify diagnostic service types based on the fault diagnosis results and the electromechanical equipment scheduling results to obtain the total diagnostic service category; The equipment diagnosis service knowledge base construction module is used to construct an equipment diagnosis service knowledge base according to the general category of diagnosis services and in combination with the pre-acquired diagnosis knowledge of the whole life cycle of coal mine electromechanical equipment; The equipment management module is used to obtain equipment diagnosis results based on the equipment diagnosis service knowledge base and manage the entire life cycle of coal mine electromechanical equipment.

9. A coal mine electromechanical equipment full life cycle management system according to claim 8, characterized in that: The classifying of the diagnostic service types based on the fault diagnosis result and the electromechanical equipment scheduling result includes: classifying the diagnostic service types according to the fault degree of the fault diagnosis result and the complexity of the electromechanical equipment scheduling result.

10. A method for managing the entire life cycle of electromechanical equipment in a coal mine, which is implemented based on the entire life cycle management system for electromechanical equipment in a coal mine according to any one of claims 1 to 9, characterized in that: The method comprises the following steps: S1. Collecting the operating status data of the coal mine electromechanical equipment through sensors installed in the coal mine electromechanical equipment, and preprocessing the operating status data to obtain fault diagnosis data; S2. Based on the empirical mode decomposition algorithm, feature extraction is performed on the fault diagnosis data, and the fault diagnosis results are obtained through the variant long short-term memory network model; S3. Based on the fault diagnosis data, the simulated annealing algorithm is used, and the decision analysis is performed in combination with the fault diagnosis results to obtain the mechanical and electrical equipment scheduling results; S4. Based on the fault diagnosis results and electromechanical equipment scheduling results, build an equipment diagnosis service knowledge base to manage the entire life cycle of coal mine electromechanical equipment.

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