Coal machine equipment operation and maintenance method and system based on data integration platform

Abstract

The invention discloses a coal machine equipment operation and maintenance method and system based on a data integration platform, and relates to the technical field of coal machine equipment operation and maintenance, and the method comprises the steps: collecting and fusing sensor data and external data of coal machine equipment in real time through the data integration platform; performing anomaly detection and fault prediction on the coal machine equipment based on the fault prediction model; and according to a fault prediction result, generating a personalized operation and maintenance strategy and automatically updating a strategy library. According to the coal machine equipment operation and maintenance method based on the data integration platform, by designing an intelligent filtering mechanism and a time window screening mechanism, the false alarm rate and the missing report rate are reduced, the adaptability is enhanced, the equipment fault rate and the operation and maintenance cost are reduced through an intelligent anomaly detection and fault prediction model, the perspectiveness and the effectiveness of operation and maintenance are improved, and the operation and maintenance efficiency is improved. Through personalized operation and maintenance strategy generation and feedback closed loop, the equipment fault risk is reduced, the service life of the equipment is prolonged, and better effects are achieved in the aspects of false alarm rate, adaptability and operation and maintenance cost.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mining equipment operation and maintenance, and in particular to a coal mining equipment operation and maintenance method based on a data integration platform. Background Art

[0002] With the continuous improvement of the intelligence and automation level of coal mining equipment, equipment operation and maintenance technology has gradually become an important link in ensuring the continuity and safety of coal production. The operation and maintenance of traditional coal mining equipment mainly rely on manual regular inspections and routine maintenance. This method is not only inefficient and costly, but may also fail to detect potential equipment failures in a timely manner. In recent years, coal mining equipment operation and maintenance methods based on data integration platforms have gradually been studied and applied. By integrating multi-source sensor data and applying intelligent algorithms for real-time monitoring and predictive analysis, active operation and maintenance management of equipment can be achieved. Especially with the support of technologies such as deep learning, intelligent sensing, and edge computing, the state monitoring and fault prediction capabilities of coal mining equipment have been significantly improved, making equipment maintenance gradually shift from "passive response" to "active prediction". The development of this technology provides important technical support and data guarantee for the efficient operation and maintenance of coal mining equipment.

[0003] However, the existing operation and maintenance methods based on data integration platforms still have certain shortcomings, especially in terms of data real-time, prediction accuracy and optimization of operation and maintenance strategies. There are technical bottlenecks. First, the real-time nature of data is the key to achieving effective operation and maintenance. Traditional data collection and processing modes cannot ensure the accuracy and stability of real-time data, and it is difficult to adapt to the complex operating environment of coal machinery equipment. Secondly, although deep learning models have been applied in fault prediction, due to the complex equipment status and changeable environmental conditions, existing models often lack adaptive update capabilities, resulting in insufficient prediction accuracy and difficulty in meeting actual operation and maintenance needs. Finally, personalized operation and maintenance strategies have not been fully optimized in existing systems. Existing technologies lack a closed-loop feedback mechanism and cannot dynamically adjust operation and maintenance strategies according to changes in equipment status and maintenance effects, thereby limiting the level of intelligent operation and maintenance of coal machinery equipment. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing coal machinery equipment operation and maintenance methods have insufficient data real-time performance, low fault prediction accuracy, lack of dynamic adjustment of operation and maintenance strategies, and how to optimize the efficient and intelligent operation and maintenance of coal machinery equipment.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a coal machinery equipment operation and maintenance method based on a data integration platform, comprising real-time collection and integration of sensor data and external data of coal machinery equipment through the data integration platform; performing abnormality detection and fault prediction on coal machinery equipment based on a fault prediction model; generating personalized operation and maintenance strategies based on fault prediction results and automatically updating the strategy library.

[0007] As a preferred solution of the coal mining equipment operation and maintenance method based on the data integration platform described in the present invention, the real-time collection and fusion of sensor data and external data of coal mining equipment includes introducing an intelligent filtering mechanism based on weighted average filtering, by evaluating the historical performance accuracy and real-time volatility of each sensor data, assigning different weights, and when processing multi-source data, analyzing the noise level and error rate of different sensors, and setting initial weights according to relative stability. While collecting data, the system automatically detects and records the real-time data fluctuations of each sensor, reduces the weight of sensor data with an error rate or noise exceeding a preset value, and gives priority to high-weight data for fusion. The intelligent filtering mechanism is expressed as:

[0008]

[0009] Among them, D f is the fused data value, D i is the data of the ith sensor, is the weight of the ith sensor, σ i is the standard deviation of the i-th sensor data.

[0010] As a preferred solution of the coal mining equipment operation and maintenance method based on the data integration platform described in the present invention, the real-time collection and fusion of sensor data and external data of the coal mining equipment also includes setting a time window in the data fusion process, allowing only data within the time window to enter the fusion model, the length of the time window is comprehensively determined by the operating speed of the equipment, the data collection frequency and the maintenance requirements, the time window filters out the old data that exceeds the time range, regularly monitors the data delay, and automatically adjusts the time window length according to the actual delay distribution. The time window model is expressed as:

[0011] D rt ={D i |t c -t i ≤ΔT}

[0012] Among them, D rt is the valid data set within the time window, t c is the current time, t i D i The acquisition time is ΔT, and the time window length is ΔT.

[0013] As a preferred solution of the coal machinery equipment operation and maintenance method based on the data integration platform described in the present invention, wherein: the abnormal detection and fault prediction of the coal machinery equipment includes a deep learning fault prediction model, which is based on a deep neural network and predicts potential fault conditions by learning the complex relationship of the equipment status from the multi-dimensional sensor data. The deep learning fault prediction model has a multi-layer neuron structure, and each layer of neurons is further analyzed according to the output result of the previous layer, and the deep-level features in the recognition data are extracted layer by layer. Historical data is introduced during training, and the data includes sensor readings and actual fault conditions under different fault modes, so that the model learns to identify various types of abnormal conditions. The model accepts multi-dimensional sensor data as input in real time during operation, and outputs the probability value of fault occurrence through the calculation and weight adjustment of layers of neurons. The deep learning fault prediction model is expressed as:

[0014] P(Y=1|X)=σ(W n ·σ(W n-1 …σ(W1·X+b1)+b2)+…+b n )

[0015] Where σ is the activation function, X is the input layer sensor data vector, P is the fault probability, and W n and b n are the weight matrix and bias of each layer respectively.

[0016] As a preferred solution of the coal mining equipment operation and maintenance method based on the data integration platform described in the present invention, the abnormal detection and fault prediction of the coal mining equipment also includes adaptive updating of abnormal data based on Kalman filtering. The Kalman filter optimizes the current equipment state estimation by fusing the new data with the previous state estimation. Whenever new data enters the system, the Kalman filter recalculates the optimal estimation of the current state, so that the model continuously adapts to the dynamically changing equipment environment. The adaptive updating of abnormal data is expressed as:

[0017] X t+1 =AX t +BU t +K(Z t -HX t )

[0018] Among them, X t is the state estimation at time t, A is the state transfer matrix, B is the control input matrix, U t is the control input, K is the Kalman gain, Z t is the measurement value, and H is the observation matrix.

[0019] As a preferred solution of the coal machine equipment operation and maintenance method based on the data integration platform described in the present invention, the generation of personalized operation and maintenance strategies and automatic updating of the strategy library include analyzing the health status, expected maintenance time and spare parts demand of the equipment, selecting the strategy with the lowest cost and the best efficiency by comparing various operation and maintenance options, and evaluating the execution cost of various operation and maintenance activities, the price of equipment spare parts and the necessity of spare parts replacement when generating the operation and maintenance strategy, and determining whether the spare parts need to be replaced or postponed in combination with the current status of the equipment and historical data. The minimized cost of the operation and maintenance strategy is expressed as:

[0020]

[0021] Among them, C is the total operation and maintenance cost, c j is the replacement cost of the jth spare part, M j is the number of replacements of the spare part, p j is the execution cost of the jth operation and maintenance project, T j is the execution time of the jth operation and maintenance project.

[0022] As a preferred solution of the coal machinery equipment operation and maintenance method based on the data integration platform described in the present invention, the generating of personalized operation and maintenance strategies and automatically updating the strategy library also includes automatically recording the maintenance effect after the equipment maintenance is completed, and updating the operation and maintenance strategy library according to the data, and adjusting the strategy library using the actual operation and maintenance data, so that the system continuously optimizes the prediction and operation and maintenance plan, and identifies the current optimal maintenance plan by analyzing the historical data and the latest feedback. The operation and maintenance optimization is expressed as:

[0023]

[0024] Among them, f(x) is the effect of the strategy in the current strategy library, x + is the current optimal strategy, is the expectation operator.

[0025] Another object of the present invention is to provide a coal machinery equipment operation and maintenance system based on a data integration platform, which can perform abnormal detection and fault prediction on coal machinery equipment through a fault prediction module, solving the current problem of being unable to adapt to dynamic changes in the environment and having low prediction accuracy.

[0026] As a preferred solution of the coal machinery equipment operation and maintenance system based on the data integration platform described in the present invention, it includes: an acquisition and fusion module, a fault prediction module, and an operation and maintenance strategy module; the acquisition and fusion module is used to collect and fuse the sensor data and external data of the coal machinery equipment in real time through the data integration platform; the fault prediction module is used to perform abnormality detection and fault prediction on the coal machinery equipment based on the fault prediction model; the operation and maintenance strategy module is used to generate personalized operation and maintenance strategies according to the fault prediction results and automatically update the strategy library.

[0027] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that the processor executes the computer program to implement a step of a coal machinery equipment operation and maintenance method based on a data integration platform.

[0028] 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 coal machinery equipment operation and maintenance method based on a data integration platform are implemented.

[0029] Beneficial effects of the present invention: The coal machinery equipment operation and maintenance method based on the data integration platform provided by the present invention collects and fuses multi-source data from different sensors in real time by designing an intelligent filtering mechanism and a time window screening mechanism, thereby realizing dynamic data integration of coal machinery equipment, reducing false alarm rate and missed alarm rate, and enhancing adaptability. Through intelligent anomaly detection and fault prediction models, accurate analysis and abnormal warning of equipment status are realized, equipment failure rate and operation and maintenance costs are reduced, and the foresight and effectiveness of operation and maintenance are improved. Through personalized operation and maintenance strategy generation and feedback closed loop, the risk of equipment failure is reduced and the service life of equipment is extended. The present invention achieves better results in terms of false alarm rate, adaptability and operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 creative labor. Among them:

[0031] Figure 1 An overall flow chart of a coal mining equipment operation and maintenance method based on a data integration platform provided for the first embodiment of the present invention.

[0032] Figure 2 An overall module diagram of a coal mining equipment operation and maintenance system based on a data integration platform provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0033] 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.

[0034] Example 1

[0035] Reference Figure 1 , which is an embodiment of the present invention, provides a coal machine equipment operation and maintenance method based on a data integration platform, comprising:

[0036] S1: Through the data integration platform, the sensor data and external data of coal mining equipment are collected and integrated in real time.

[0037] Furthermore, real-time collection and fusion of sensor data and external data of coal mining equipment includes the introduction of an intelligent filtering mechanism based on weighted average filtering.

[0038] It should be noted that by evaluating the historical performance accuracy and real-time volatility of each sensor data and assigning different weights, when processing multi-source data, the noise level and error rate of different sensors are analyzed, and the initial weight is set according to the relative stability. While collecting data, the system automatically detects and records the real-time data fluctuations of each sensor, reduces the weight of sensor data with error rate or noise exceeding the preset value, and gives priority to high-weight data for fusion. The intelligent filtering mechanism is expressed as:

[0039]

[0040] Among them, D f is the fused data value, D i is the data of the ith sensor, is the weight of the ith sensor, σ i is the standard deviation of the i-th sensor data.

[0041] Furthermore, real-time collection and fusion of sensor data and external data of coal mining equipment are also included in the data fusion process.

[0042] It should be noted that a time window is set, and only data within the time window is allowed to enter the fusion model. The length of the time window is determined by the operating speed of the equipment, the frequency of data collection, and the maintenance requirements. The time window filters out old data that exceeds the time range, regularly monitors data delays, and automatically adjusts the time window length according to the actual delay distribution. The time window model is expressed as:

[0043] D rt ={D i |t c -t i ≤ΔT}

[0044] Among them, D rt is the valid data set within the time window, t c is the current time, t i D i The acquisition time is ΔT, and the time window length is ΔT.

[0045] S2: Based on the fault prediction model, perform abnormality detection and fault prediction on coal mining equipment.

[0046] Furthermore, anomaly detection and fault prediction of coal mining equipment include deep learning fault prediction models.

[0047] It should be noted that based on deep neural networks, the model predicts potential faults by learning the complex relationship of equipment status from multi-dimensional sensor data. The deep learning fault prediction model has a multi-layer neuron structure. Each layer of neurons further analyzes the output results of the previous layer, extracts deep features in the recognition data layer by layer, and introduces historical data during training. The data includes sensor readings and actual fault conditions under different fault modes, so that the model can learn to recognize various types of abnormal conditions. The model accepts multi-dimensional sensor data as input in real time during operation, and outputs the probability value of fault occurrence through the calculation and weight adjustment of layers of neurons. The deep learning fault prediction model is expressed as:

[0048] P(Y=1|X)=σ(W n ·σ(W n-1 …σ(W1·X+b1)+b2)+…+b n )

[0049] Where σ is the activation function, X is the input layer sensor data vector, P is the fault probability, and W n and b n are the weight matrix and bias of each layer respectively.

[0050] Furthermore, abnormality detection and fault prediction of coal mining equipment also includes adaptive updating of abnormal data based on Kalman filtering.

[0051] It should be noted that the Kalman filter optimizes the current device state estimate by fusing new data with previous state estimates. Whenever new data enters the system, the Kalman filter recalculates the optimal estimate of the current state, allowing the model to continuously adapt to the dynamically changing device environment. The adaptive update of abnormal data is expressed as:

[0052] X t+1 =AX t +BU t +K(Z t -HX t )

[0053] Among them, X t is the state estimation at time t, A is the state transfer matrix, B is the control input matrix, U t is the control input, K is the Kalman gain, Z t is the measurement value, and H is the observation matrix.

[0054] S3: Generate personalized operation and maintenance strategies based on fault prediction results and automatically update the strategy library.

[0055] Furthermore, generating personalized O&M strategies and automatically updating the strategy library includes analyzing the health status of the equipment, estimated maintenance time, and spare parts requirements.

[0056] It should be noted that by comparing various operation and maintenance options, the strategy with the lowest cost and the best efficiency is selected. When generating the operation and maintenance strategy, the execution cost of various operation and maintenance activities, the price of equipment spare parts and the necessity of spare parts replacement are evaluated. Combined with the current status of the equipment and historical data, it is determined whether the spare parts need to be replaced or postponed. The minimum cost of the operation and maintenance strategy is expressed as:

[0057]

[0058] Among them, C is the total operation and maintenance cost, c j is the replacement cost of the jth spare part, M j is the number of replacements of the spare part, p j is the execution cost of the jth operation and maintenance project, T j is the execution time of the jth operation and maintenance project.

[0059] Furthermore, generating personalized operation and maintenance strategies and automatically updating the strategy library also includes automatically recording the maintenance results after the equipment maintenance is completed.

[0060] It should be noted that the operation and maintenance strategy library is updated according to the data, and the strategy library is adjusted using the actual operation and maintenance data, so that the system continuously optimizes the prediction and operation and maintenance plan. By analyzing historical data and the latest feedback, the current optimal maintenance plan is identified. The operation and maintenance optimization is expressed as:

[0061]

[0062] Among them, f(x) is the effect of the strategy in the current strategy library, x + is the current optimal strategy, is the expectation operator.

[0063] Example 2

[0064] One embodiment of the present invention provides a coal mining equipment operation and maintenance method based on a data integration platform. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0065] The test subjects were six coal mining equipment, and their performance in key parameters such as multi-source data fusion accuracy, fault prediction accuracy, real-time data response time, reduced operation and maintenance costs, increased maintenance cycle and equipment normal operation time was recorded. The test preparation work included building a data integration platform and installing a variety of sensor devices to collect temperature, vibration, pressure and environmental information of each coal mining equipment in real time. The platform was equipped with intelligent filtering and time window screening mechanisms to ensure the real-time and accuracy of data fusion. In addition, deep neural network models and adaptive Kalman filters were used to realize equipment fault prediction and dynamic update. After the equipment status prediction was completed, the platform would generate a personalized maintenance plan, and through a closed-loop feedback mechanism, the results of each maintenance would be fed back to the strategy library for continuous optimization.

[0066] During the implementation process, the real-time data of the equipment is first collected and integrated through the platform. After the data of each device is filtered, the fluctuation of its multi-source data is monitored in real time to eliminate noise interference. Subsequently, the data integration platform detects and evaluates the status of the equipment based on the fault prediction model, and issues early warnings for potential faults. The generated personalized operation and maintenance strategy covers the maintenance cycle, spare parts requirements and optimal maintenance methods of each equipment. After each maintenance, the platform feeds back the latest status and maintenance results of the equipment to the strategy library, automatically adjusts subsequent maintenance strategies, and realizes continuous optimization of the equipment status. The entire process strictly follows the predetermined experimental process to ensure the scientificity and accuracy of the results.

[0067] As shown in Table 1, the multi-source data fusion accuracy of each device is higher than 95%, and the highest reaches 98.7%, indicating that the intelligent filtering and time window screening mechanism effectively improves the integration accuracy of the data. Compared with the false alarm problem caused by data noise in the traditional operation and maintenance mode, the fusion accuracy of the present invention is significantly enhanced, ensuring the reliability of the monitoring data and providing a solid data foundation for subsequent analysis. The fault prediction accuracy is between 93.0% and 95.9% in each test object, which is significantly improved compared with the error caused by the lack of adaptive adjustment of the traditional prediction model. Especially under dynamic environmental conditions, the adaptive Kalman filter realizes the real-time update of the prediction model, ensuring the high reliability of the prediction results. This prediction advantage provides early warning. It provides higher accuracy and further reduces the occurrence rate of equipment failures. The real-time data response time is between 150 and 162 milliseconds, indicating that the data integration platform can complete data fusion processing within a millisecond response time. Compared with the delayed feedback problem in traditional equipment operation and maintenance, the present invention realizes real-time management of equipment status through a time window screening mechanism, which helps to improve the response speed of the system. The operation and maintenance cost of each device is reduced by an average of about 19.6%, and the highest is 21.2%. Due to the personalized operation and maintenance strategy and closed-loop feedback mechanism of the present invention, while extending the maintenance cycle and reducing unnecessary spare parts replacement, the operation and maintenance cost is optimized. The average maintenance cycle of the equipment is more than 45 days, which significantly extends the maintenance interval and reduces the operation and maintenance frequency and labor costs.

[0068] Table 1 Experimental data table

[0069]

[0070]

[0071] Example 3

[0072] Reference Figure 2 , which is an embodiment of the present invention, provides a coal machinery equipment operation and maintenance system based on a data integration platform, including: an acquisition fusion module, a fault prediction module, and an operation and maintenance strategy module.

[0073] The acquisition and fusion module is used to collect and fuse the sensor data and external data of coal machinery equipment in real time through the data integration platform; the fault prediction module is used to perform anomaly detection and fault prediction on coal machinery equipment based on the fault prediction model; the operation and maintenance strategy module is used to generate personalized operation and maintenance strategies according to the fault prediction results and automatically update the strategy library.

[0074] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it 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, server, or network device, etc.) to perform all or part of the steps of the methods of 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. Various media that can store program codes.

[0075] 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.

[0076] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0077] 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, multiple 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. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0078] 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 coal mining equipment operation and maintenance method based on a data integration platform, characterized in that: include: Through the data integration platform, sensor data and external data of coal mining equipment are collected and integrated in real time; Based on the fault prediction model, abnormal detection and fault prediction of coal mining equipment are carried out; Based on the fault prediction results, a personalized operation and maintenance strategy is generated and the strategy library is automatically updated.

2. The coal mining equipment operation and maintenance method based on the data integration platform according to claim 1, characterized in that: The real-time collection and fusion of sensor data and external data of coal mining equipment includes the introduction of an intelligent filtering mechanism based on weighted average filtering, by evaluating the historical performance accuracy and real-time volatility of each sensor data, assigning different weights, and when processing multi-source data, analyzing the noise level and error rate of different sensors, and setting initial weights according to relative stability. While collecting data, the system automatically detects and records the real-time data fluctuations of each sensor, reduces the weight of sensor data with an error rate or noise exceeding a preset value, and gives priority to high-weight data for fusion. The intelligent filtering mechanism is expressed as: Among them, D f is the fused data value, D i is the data of the ith sensor, is the weight of the ith sensor, σ i is the standard deviation of the i-th sensor data.

3. The coal mining equipment operation and maintenance method based on the data integration platform according to claim 2, characterized in that: The real-time collection and fusion of sensor data and external data of coal mining equipment also includes setting a time window in the data fusion process, allowing only data within the time window to enter the fusion model. The length of the time window is determined by the operating speed of the equipment, the data collection frequency and the maintenance requirements. The time window filters out old data that exceeds the time range, regularly monitors data delays, and automatically adjusts the time window length according to the actual delay distribution. The time window model is expressed as: D rt ={D i |t c -t i ≤ΔT} Among them, D rt is the valid data set within the time window, t c is the current time, t i D i The acquisition time is ΔT, and the time window length is ΔT.

4. The coal mining equipment operation and maintenance method based on the data integration platform according to claim 3 is characterized in that: The abnormal detection and fault prediction of coal mining equipment includes a deep learning fault prediction model, which is based on a deep neural network and predicts potential fault conditions by learning the complex relationship of equipment status from multi-dimensional sensor data. The deep learning fault prediction model has a multi-layer neuron structure. Each layer of neurons further analyzes the output results of the previous layer, extracts deep features in the recognition data layer by layer, and introduces historical data during training. The data includes sensor readings and actual fault conditions under different fault modes, so that the model learns to recognize various types of abnormal conditions. The model accepts multi-dimensional sensor data as input in real time during operation, and outputs the probability value of fault occurrence through the calculation and weight adjustment of layers of neurons. The deep learning fault prediction model is expressed as: P(Y=1|X)=σ(W n ·σ(W n-1 …σ(W1·X+b1)+b2)+…+b n ) Where σ is the activation function, X is the input layer sensor data vector, P is the fault probability, and W n and b n are the weight matrix and bias of each layer respectively.

5. The coal mining equipment operation and maintenance method based on the data integration platform according to claim 4, characterized in that: The abnormality detection and fault prediction of coal mining equipment also includes adaptive updating of abnormal data based on Kalman filtering. Kalman filtering optimizes the current equipment state estimation by fusing new data with previous state estimation. Whenever new data enters the system, Kalman filtering recalculates the optimal estimation of the current state, so that the model continuously adapts to the dynamically changing equipment environment. The adaptive updating of abnormal data is expressed as: X t+1 =AX t +BU t +K(Z t -HX t ) Among them, X t is the state estimation at time t, A is the state transfer matrix, B is the control input matrix, U t is the control input, K is the Kalman gain, Z t is the measurement value, and H is the observation matrix.

6. The coal mining equipment operation and maintenance method based on the data integration platform according to claim 5, characterized in that: The generation of personalized operation and maintenance strategies and automatic updating of the strategy library includes analyzing the health status, expected maintenance time and spare parts demand of the equipment, selecting the strategy with the lowest cost and the best efficiency by comparing various operation and maintenance options, and evaluating the execution cost of various operation and maintenance activities, the price of equipment spare parts and the necessity of spare parts replacement when generating the operation and maintenance strategy. In combination with the current status of the equipment and historical data, it is determined whether the spare parts need to be replaced or replaced later. The minimized cost of the operation and maintenance strategy is expressed as: Among them, C is the total operation and maintenance cost, c j is the replacement cost of the jth spare part, M j is the number of replacements of the spare part, p j is the execution cost of the jth operation and maintenance project, T j is the execution time of the jth operation and maintenance project.

7. The coal mining equipment operation and maintenance method based on the data integration platform according to claim 6, characterized in that: The generation of personalized operation and maintenance strategies and automatic updating of the strategy library also includes automatically recording the maintenance effect after the equipment maintenance is completed, and updating the operation and maintenance strategy library according to the data, and adjusting the strategy library using actual operation and maintenance data, so that the system continuously optimizes the prediction and operation and maintenance plan, and identifies the current optimal maintenance plan by analyzing historical data and the latest feedback. The operation and maintenance optimization is expressed as: Among them, f(x) is the effect of the strategy in the current strategy library, x + is the current optimal strategy, is the expectation operator.

8. A system using the coal mining equipment operation and maintenance method based on the data integration platform as claimed in any one of claims 1 to 7, characterized in that: Includes collection and fusion module, fault prediction module, and operation and maintenance strategy module; The acquisition and fusion module is used to collect and fuse sensor data and external data of coal mining equipment in real time through the data integration platform; The fault prediction module is used to perform abnormality detection and fault prediction on coal mining equipment based on the fault prediction model; The operation and maintenance strategy module is used to generate personalized operation and maintenance strategies and automatically update the strategy library according to the fault prediction results.

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 the coal machinery equipment operation and maintenance method based on the data integration platform 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 the coal machinery equipment operation and maintenance method based on a data integration platform described in any one of claims 1 to 7 are implemented.

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