Full-life-cycle digital management method and system for coal machine equipment

Abstract

The invention discloses a coal machine equipment full life cycle digital management method and system, and relates to the technical field of coal machine equipment digital management, and the method comprises the steps: collecting multi-source data, and carrying out the integration; performing equipment health state evaluation and fault prediction; and carrying out full-life-cycle management on maintenance optimization. According to the coal machine equipment full life cycle digital management method provided by the invention, dynamic visualization and real-time monitoring of the equipment operation state are realized through the equipment health state evaluation model, the scientificity of equipment management is improved through quantitative evaluation of the health index, and the risk of sudden equipment failure is reduced. Through the LSTM-based time sequence fault prediction model, the probability of unplanned shutdown of the equipment is reduced, and the perspectiveness and predictability of equipment management are improved. According to the method, better effects are achieved in the aspects of equipment management accuracy, safety and economic benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of digital management of coal mining equipment, and in particular to a method and system for digital management of coal mining equipment throughout its life cycle. Background Art

[0002] In the field of management and maintenance of coal mining equipment, with the continuous advancement of intelligent construction of coal mines, digital management technology has gradually become an important means to improve equipment reliability and reduce maintenance costs. Traditional coal mining equipment management relies on manual inspections and regular maintenance, which is not only inefficient, but also difficult to detect potential failure risks in a timely manner. In recent years, with the rapid development of the Internet of Things, big data analysis and artificial intelligence technologies, equipment management models based on multi-source data collection have gradually emerged. This method can provide real-time monitoring and early warning for equipment by collecting various data such as the operating status, environmental parameters and maintenance records of the equipment, combined with health assessment and fault prediction models, thereby effectively extending the service life of the equipment and improving the reliability of equipment operation. Although the current digital management technology has a certain level of intelligence, it still has limitations in real-time evaluation of equipment health status, accurate fault prediction and scientific maintenance optimization, and has not yet achieved efficient digital management of the entire life cycle of coal mining equipment.

[0003] Existing coal mining equipment management technologies still face many challenges in practical applications. First, although multi-source data acquisition technologies have been applied to equipment management, the inconsistency of formats and uneven data quality between different data sources make data integration difficult, and it is often difficult to form a systematic analysis of equipment operation status. In addition, existing health assessment and fault prediction technologies are mostly based on a single data source, and the accuracy and reliability of the model are low, making it difficult to adapt to the complex and changeable coal mine environment. Therefore, the real-time nature of equipment status monitoring and the accuracy of prediction have not yet reached a high standard. Furthermore, traditional equipment maintenance strategies are often based on regular or post-maintenance, lacking scientific optimization methods, which can easily lead to excessive or insufficient maintenance, wasting resources and increasing the risk of equipment failure. Existing technologies still lack effective means to dynamically manage equipment health status, accurately predict faults, and optimize maintenance plans through data-driven optimization throughout the equipment life cycle. Therefore, existing coal mining equipment management technologies still fail to meet the needs of efficient and stable operation in terms of improving fault prediction accuracy, improving equipment maintenance efficiency, and realizing intelligent management of the entire equipment life cycle. In contrast, the present invention is based on multi-source data integration, utilizes health status assessment and fault prediction models, and combines maintenance optimization with a full life cycle management strategy, which can effectively improve the level of intelligent equipment management and significantly improve equipment reliability and operating efficiency. Summary of the invention

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

[0005] Therefore, the technical problem solved by the present invention is: the existing coal machinery equipment management method has the problems of high difficulty in data integration, low fault prediction accuracy, insufficient optimization of maintenance strategy, and how to realize intelligent management of the equipment throughout its life cycle.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for digital management of the entire life cycle of coal machinery equipment, including collecting multi-source data for integration; performing equipment health status assessment and fault prediction; and performing maintenance optimization for full life cycle management.

[0007] As a preferred solution of the digital management method for the whole life cycle of coal mining equipment described in the present invention, wherein: the collecting of multi-source data for integration includes using sensors to collect equipment operation data, including vibration, temperature, and current;

[0008] Record humidity and air quality environmental data;

[0009] And access the equipment's maintenance history data and operation logs;

[0010] The collected data is labeled, including equipment status, fault type, and maintenance records, to form a data chain.

[0011] As a preferred solution of the digital management method for the whole life cycle of coal mining equipment described in the present invention, wherein: the collection of multi-source data for integration includes data cleaning, removing noise data, identifying and processing, and filling in missing data values;

[0012] Perform data formatting, standardization and time series processing to arrange various collected data in time series.

[0013] As a preferred solution of the digital management method for the whole life cycle of coal mining equipment described in the present invention, the equipment health status assessment and fault prediction includes calculating the equipment health index through the equipment health status assessment model using a multi-parameter fusion method, and evaluating the current operating status of the equipment;

[0014] The health index is proportional to the device status;

[0015] Assume that the health status is represented by the parameter X i The weight of each parameter is w i , the equipment health status assessment model is expressed as:

[0016]

[0017] Among them, X i represents the current value of the i-th parameter, X i,min and X i,max Indicates the minimum and maximum values ​​of the parameter, w iRepresents the weight of the parameter, satisfying

[0018] As a preferred solution of the digital management method for the whole life cycle of coal machinery equipment described in the present invention, the equipment health status assessment and fault prediction include that the coal machinery equipment will generate time series data during operation, and the time series model based on LSTM captures the time dependency of the equipment data. The state update of the LSTM model is expressed as:

[0019] f t =σ(W f ·[h t-1 ,x t ]+b f )

[0020] i t =σ(W i ·[h t-1 ,x t ]+b i 0

[0021]

[0022] o t =σ(W o ·[h t-1 ,x t ]+b o )

[0023] h t =o t tanh(C t 0

[0024] Among them, x t represents the input data at the current moment, f t 、i t , o t Represent the activation states of the forget gate, input gate, and output gate, respectively. t and h t Represent the current memory state and hidden state respectively;

[0025] Time series data x running through the input device t ,LSTM model predicts the future equipment health index or parameter value;

[0026] When the predicted value is close to the threshold, the system will trigger an early warning.

[0027] As a preferred solution of the digital management method for the whole life cycle of coal mining equipment described in the present invention, wherein: the maintenance optimization for the whole life cycle management includes building a maintenance optimization decision model, optimizing the maintenance plan and spare parts management by analyzing the health status of the equipment and the fault prediction results;

[0028] The maintenance interval T is found through the maintenance optimization model, and the total maintenance cost is expressed as:

[0029]

[0030] Among them, C m represents the fixed cost of each maintenance, C s represents the downtime loss cost caused by failure, P(T) represents the probability of failure occurring within interval T;

[0031] Derivative the total cost C(T) and output the optimal maintenance interval T * It is expressed as:

[0032]

[0033] Use the model to output the optimal maintenance cycle T * .

[0034] As a preferred solution of the digital management method for the whole life cycle of coal mining equipment described in the present invention, wherein: the maintenance optimization for the whole life cycle management includes the life cycle management method of the equipment forming a life cycle archive by recording and analyzing the data of the installation, operation, maintenance and retirement stages of the equipment,

[0035] Divide the equipment's full life cycle data into four stages: installation, operation, maintenance, and retirement;

[0036] Keep every maintenance and failure data and conduct historical data analysis;

[0037] Generate operation reports and performance evaluation reports based on equipment life cycle data.

[0038] Another object of the present invention is to provide a digital management system for the entire life cycle of coal mining equipment, which can solve the problem of low fault prediction accuracy in current coal mining equipment management methods by performing equipment health status assessment and fault prediction.

[0039] As a preferred solution of the digital management system for the entire life cycle of coal machinery equipment described in the present invention, it includes: maintenance optimization for entire life cycle management, including a method for managing the life cycle of equipment by recording and analyzing data from the installation, operation, maintenance and retirement stages of the equipment to form a life cycle archive, dividing the entire life cycle data of the equipment into four stages: installation, operation, maintenance and retirement; retaining each maintenance and fault data for historical data analysis; generating operation reports and performance evaluation reports based on the entire life cycle data of the equipment.

[0040] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for digital management of the entire life cycle of coal machinery equipment.

[0041] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for digital management of the entire life cycle of coal mining equipment.

[0042] Beneficial effects of the present invention: The digital management method for the whole life cycle of coal machinery equipment provided by the present invention realizes the comprehensive acquisition and systematic integration of the whole life cycle data of coal machinery equipment through multi-source data collection and integration steps, thereby enhancing the accuracy and scientificity of equipment management. Through data preprocessing and standardization steps, the quality and consistency of the collected data are ensured, thereby improving the reliability of data analysis, making the subsequent health assessment and fault prediction process more accurate and reliable, and improving the stability of the overall data processing. Through the equipment health status assessment model, the health index of the equipment is calculated in real time, thereby realizing dynamic monitoring and early warning of the equipment operation status, which not only realizes the dynamic visualization and real-time monitoring of the equipment operation status, but also improves the scientificity of equipment management through the quantitative evaluation of the health index, and finally achieves the effect of early warning, effectively reducing the risk of sudden equipment failure. Through the LSTM-based time series fault prediction model, the future state of the equipment is accurately predicted, the potential fault risk is identified in advance, the probability of unplanned equipment downtime is reduced, and the foresight and predictability of equipment management are improved. Through the maintenance optimization decision model, the optimal maintenance interval and plan are determined, which improves the economy and operation efficiency of equipment management, not only reduces the frequency and cost of equipment maintenance, but also avoids the waste of resources caused by excessive maintenance, realizes scientific spare parts management and maintenance cycle optimization, and improves the economic benefits of the whole life cycle management of coal machinery equipment. Through the whole life cycle management method, a complete data chain of equipment from installation to retirement is constructed, the whole life cycle traceability and performance evaluation of equipment are realized, the traceability of equipment and the scientific management are improved, and the management of the whole life cycle is more systematic and complete. The present invention achieves better results in terms of accuracy, safety and economic benefits of equipment management. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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.

[0044] Figure 1An overall flow chart of a method for digital management of the entire life cycle of coal mining equipment provided in the first embodiment of the present invention.

[0045] Figure 2 An overall flow chart of a digital management system for the entire life cycle of coal mining equipment provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0046] 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.

[0047] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a digital management method for the entire life cycle of coal mining equipment, including:

[0048] S1: Collect multi-source data for integration.

[0049] Furthermore, the data collection and integration method ensures the integrity of the data throughout the life cycle and provides a data basis for subsequent analysis. Sensors are used to collect equipment operation data, such as vibration, temperature, current, etc., and environmental data such as humidity and air quality are recorded through the environmental monitoring system; and the maintenance history data and operation logs of the equipment are accessed to ensure the diversity and comprehensiveness of the data. The collected data is labeled, such as equipment status (normal / abnormal), fault type, maintenance record, etc., to form a traceable complete data chain, laying the foundation for subsequent data mining.

[0050] It should be noted that in order to ensure the reliability and consistency of data, data preprocessing and standardization methods need to be cleaned, standardized and time-series processed. Noise data should be eliminated, outliers should be identified and processed, missing data values ​​should be filled, and data accuracy should be ensured. Data formats from different sources should be unified, and raw data should be converted into a standard format recognized by the system. Various collected data should be arranged in time series to ensure the consistency of data on the time axis and provide accurate input for the prediction model.

[0051] S2: Conduct equipment health status assessment and fault prediction.

[0052] Furthermore, the equipment health status assessment model uses a multi-parameter fusion method to calculate the equipment health index (HI) and evaluate the current operating status of the equipment. The higher the health index, the healthier the equipment status; if the health index is low, it indicates that the equipment may have a risk of failure.

[0053] Assume that the health status is represented by the parameter X i The weight of each parameter is w i , the equipment health status assessment model is expressed as:

[0054]

[0055] Among them, X i represents the current value of the i-th parameter, X i,min and X i,max Indicates the minimum and maximum values ​​of the parameter, w i Represents the weight of the parameter, satisfying

[0056] It should be noted that, through the equipment health status assessment model, the present invention realizes the real-time health index calculation based on multi-parameter fusion. The model obtains the equipment health index HIHIHI by comprehensively calculating the current status and weight of each equipment parameter. The higher the health index, the healthier the equipment status, and vice versa, it indicates an increased potential risk.

[0057] Existing health status monitoring usually relies on specific parameters, resulting in limitations in the evaluation results. The present invention weights multiple key parameters by weight, so that the evaluation of health status is no longer limited to a single factor. In particular, through the reasonable setting of wiw_iwi, the key parameters of different devices can be weighted in a targeted manner, thereby improving the practicality and applicability of the device health index. This method not only realizes real-time monitoring of device status, but also provides a more scientific quantitative basis for the equipment early warning mechanism.

[0058] Furthermore, the fault prediction model (time series prediction based on LSTM) can capture the time dependency of equipment data and effectively predict the equipment status or health index in the future.

[0059] The state update of the LSTM model is expressed as:

[0060] f t =σ(W f ·[h t-1 ,x t ]+b f )

[0061] i t =σ(W i ·[h t-1 ,x t ]+b i )

[0062]

[0063] o t =σ(W o ·[h t-1 ,x t ]+b o )

[0064] h t =o t tanh(C t )

[0065] Among them, x t represents the input data at the current moment, f t 、i t , o t Represent the activation states of the forget gate, input gate, and output gate, respectively. t and h t Represent the current memory state and hidden state respectively;

[0066] The equipment operation data is input into the LSTM model, and the model learns the rules of historical data to predict the future health index or key parameter value of the equipment. If the predicted value exceeds the safety threshold range, a fault warning is triggered in advance.

[0067] It should be noted that the LSTM model can capture the long-term and short-term dependencies of equipment operation data through the gating mechanism of time series, and achieve accurate prediction of future equipment status or health index. Once the prediction result approaches the danger threshold, the system will trigger an early warning, allowing managers to take maintenance measures before a failure occurs and avoid unplanned downtime.

[0068] The current equipment management system has a certain lag in fault prediction and mostly relies on simple trend extrapolation. The LSTM model can accurately grasp the time-dependent characteristics through its unique time series gating structure, avoiding the prediction distortion caused by simple extrapolation. The application of the LSTM model is particularly effective when the equipment data is highly volatile and has obvious periodic patterns. It can learn the operating characteristics of the equipment in real time and dynamically adjust the prediction results, thereby achieving accurate prediction of the future status of the equipment and providing advanced maintenance reminders for equipment management.

[0069] S3: Maintenance optimization for full life cycle management.

[0070] Furthermore, the maintenance optimization decision model analyzes the equipment health status and fault prediction results, optimizes maintenance plans and spare parts management, ensures that the equipment operates in the best condition, and reduces maintenance costs.

[0071] The goal of the maintenance optimization model is to find the best maintenance interval T to minimize the total cost. The total maintenance cost is expressed as:

[0072]

[0073] Among them, C m represents the fixed cost of each maintenance, C s represents the downtime loss cost caused by failure, P(T) represents the probability of failure occurring within interval T;

[0074] Derivative the total cost C(T) and output the optimal maintenance interval T * It is expressed as:

[0075]

[0076] Use the model to output the optimal maintenance cycle T * .

[0077] It should be noted that by establishing a maintenance optimization decision model, the present invention achieves quantification and optimization of maintenance strategies. The model finds the optimal maintenance interval that minimizes the total cost by comprehensively considering maintenance costs, downtime loss costs, and failure probability. This makes the maintenance plan more scientific and reasonable, avoiding the waste of resources caused by excessive maintenance and reducing the risk of equipment failure caused by insufficient maintenance.

[0078] In existing technologies, maintenance plans are often based on experience or fixed cycles, lacking quantitative analysis of cost-effectiveness, resulting in suboptimal maintenance strategies. Through this model, managers can dynamically adjust maintenance intervals based on the actual operating conditions and historical data of the equipment, thereby achieving a balance between cost and risk. This effectively solves the problem of excessive or insufficient maintenance and improves the operating efficiency and economic benefits of the equipment.

[0079] Furthermore, the equipment life cycle management method forms a complete life cycle archive by recording and analyzing data from the installation, operation, maintenance and decommissioning stages of the equipment, facilitating the comprehensive management and performance evaluation of the equipment.

[0080] The equipment life cycle data is divided into four stages: installation, operation, maintenance and retirement, which is convenient for tracking and management. Each maintenance and failure data is retained, and historical data analysis is performed to understand the performance change pattern of the equipment. Based on the equipment life cycle data, operation reports and performance evaluation reports are generated to support scientific equipment management decisions.

[0081] It should be noted that the full life cycle management method achieves comprehensive management and performance evaluation of the equipment life cycle by recording and analyzing the data of the equipment at each stage of installation, operation, maintenance and retirement in segments. This method helps to identify the performance change patterns of equipment at different stages and provides a scientific basis for optimizing equipment selection, maintenance strategies and retirement plans.

[0082] Traditional equipment management often lacks attention to the entire life cycle, which may lead to performance degradation of equipment in the later stage of use without timely detection. Through the retention and analysis of historical data, the present invention can identify the deterioration trend of equipment performance in advance, formulate corresponding maintenance or replacement plans, and avoid sudden failures caused by equipment aging. At the same time, the life cycle performance evaluation report provides objective data support for management and promotes the scientific nature of equipment management decisions.

[0083] Example 2, an embodiment of the present invention, provides a digital management method for the entire life cycle of coal mining equipment. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0084] First, in order to verify the effectiveness of the health status assessment and maintenance optimization method for coal mine equipment, the 7-day continuous operation data of a coal mine equipment was selected as the test object. The test collected the equipment temperature (℃), vibration (m / s 2 ), air quality (PM2.5), humidity (%), current (A) and other key parameters to reflect the operating status of the equipment. Each parameter is monitored in real time through sensors, and the data collection frequency is once a day and recorded in the data system. In addition, the health index (HI) and the predicted probability of failure are also recorded synchronously to observe the changing trend of the equipment health status and the accuracy of the prediction of failure risk.

[0085] During the implementation of the experiment, the collected raw data first went through the data preprocessing step. Data preprocessing includes the following operations: (1) cleaning the raw data to remove noise and abnormal data points that may be generated during the collection process; (2) standardizing all data to ensure that data from different sensors can be processed uniformly; (3) sorting the time series data to ensure consistency when the data is input into the model. On this basis, the health index of the data of each day was calculated through the equipment health status assessment model to quantify the equipment status; and the fault prediction model (based on LSTM) was used to predict the probability of future faults.

[0086] Table 1 Experimental data table

[0087]

[0088] The table data clearly shows the changes in key parameters of the equipment during the experiment. Over time, the temperature, vibration, humidity, air quality, and current of the equipment all showed an upward trend, indicating that the equipment load gradually increased and the environmental conditions changed. The health index (HI) gradually decreased from 95 on the first day to 75 on the seventh day, which is consistent with the state changes of the equipment under high load and environmental deterioration, indicating that the health of the equipment operation is gradually deteriorating. At the same time, the predicted failure probability increased from 2.0% to 15.0%, reflecting the increase in the potential failure risk of the equipment and verifying the reliability of the prediction model.

[0089] This data shows that the multi-source data integration and preprocessing solution of the present invention can effectively identify the changing trend of equipment status. Compared with traditional methods, the present invention not only provides real-time health status monitoring, but also helps managers optimize maintenance decisions through early warning of failure probability.

[0090] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a digital management system for the entire life cycle of coal machinery equipment, including a data acquisition and integration module, an equipment health status assessment and fault prediction module, and a maintenance optimization and entire life cycle management module.

[0091] The data collection and integration module is used to collect equipment operation data through sensors, and integrate environmental data and historical maintenance records for data annotation. The equipment health status assessment and fault prediction module is used to output the equipment health index of the health status assessment model. Based on historical data and real-time data, the LSTM time series prediction model is applied to predict the operation status in the future. The maintenance optimization and life cycle management module is used to combine health status and fault prediction, apply maintenance optimization models, record data from each stage of equipment installation, operation, maintenance and decommissioning, and automatically generate equipment operation reports and life cycle performance reports.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 digital management method for the entire life cycle of coal mining equipment, characterized in that: include: Collect multi-source data for integration; Conduct equipment health status assessment and fault prediction; Maintenance optimization for full life cycle management.

2. The method for digital management of the entire life cycle of coal mining equipment according to claim 1, characterized in that: The collecting of multi-source data for integration includes using sensors to collect equipment operation data, including vibration, temperature, and current; Record humidity and air quality environmental data; And access the equipment's maintenance history data and operation logs; The collected data is labeled, including equipment status, fault type, and maintenance records, to form a data chain.

3. The method for digital management of the entire life cycle of coal mining equipment according to claim 2, characterized in that: The collecting and integrating of multi-source data includes data cleaning, eliminating noise data, identifying and processing, and filling in missing data values; Perform data formatting, standardization and time series processing to arrange various collected data in time series.

4. The method for digital management of the entire life cycle of coal mining equipment according to claim 3, characterized in that: The equipment health status assessment and fault prediction includes calculating the equipment health index through the equipment health status assessment model using a multi-parameter fusion method and assessing the current operating status of the equipment; The health index is proportional to the device status; Assume that the health status is represented by the parameter X i The weight of each parameter is w i , the equipment health status assessment model is expressed as: Among them, X i represents the current value of the i-th parameter, X i,min and X i,max Indicates the minimum and maximum values ​​of the parameter, w i Represents the weight of the parameter, satisfying 5. The method for digital management of the entire life cycle of coal mining equipment according to claim 4, characterized in that: The equipment health status assessment and fault prediction include that the coal machine equipment will generate time series data during operation, and the time series model based on LSTM captures the time dependency of the equipment data. The state update of the LSTM model is expressed as: f t =σ(W f ·[h t-1 ,x t ]+b f ) i t =σ(W i ·[h t-1 ,x t ]+b i ) the t =σ(W o ·[h t-1 ,x t ]+b o ) h t =o t ·tanh(C t ) Among them, x t represents the input data at the current moment, f t 、i t , o t Represent the activation states of the forget gate, input gate, and output gate, respectively. t and h t Represent the current memory state and hidden state respectively; Time series data x running through the input device t ,LSTM model predicts the future equipment health index or parameter value; When the predicted value approaches the threshold, the system will trigger an early warning.

6. The method for digital management of the entire life cycle of coal mining equipment according to claim 5, characterized in that: The maintenance optimization for full life cycle management includes building a maintenance optimization decision model, optimizing maintenance plans and spare parts management by analyzing equipment health status and fault prediction results; The maintenance interval T is found through the maintenance optimization model, and the total maintenance cost is expressed as: Among them, C m represents the fixed cost of each maintenance, C s represents the downtime loss cost caused by failure, P(T) represents the probability of failure occurring within interval T; Derivative the total cost C(T) and output the optimal maintenance interval T * It is expressed as: Use the model to output the optimal maintenance cycle T * .

7. The method for digital management of the entire life cycle of coal mining equipment according to claim 6, characterized in that: The maintenance optimization for full life cycle management includes the equipment life cycle management method, which forms a life cycle archive by recording and analyzing the data of the equipment installation, operation, maintenance and decommissioning stages. Divide the equipment's full life cycle data into four stages: installation, operation, maintenance, and retirement; Keep every maintenance and failure data and conduct historical data analysis; Generate operation reports and performance evaluation reports based on equipment life cycle data.

8. A system using the digital management method for the entire life cycle of coal mining equipment as claimed in any one of claims 1 to 7, characterized in that: Including data collection and integration module, equipment health status assessment and fault prediction module, maintenance optimization and full life cycle management module; The data collection and integration module is used to collect equipment operation data through sensors, and integrate environmental data and historical maintenance records for data annotation; The equipment health status assessment and fault prediction module is used for the health status assessment model to output the equipment health index, and based on historical data and real-time data, the LSTM time series prediction model is applied to predict the future operation status; The maintenance optimization and full life cycle management module is used to combine health status and fault prediction, apply maintenance optimization models, record data from each stage of equipment installation, operation, maintenance and decommissioning, and automatically generate equipment operation reports and life cycle performance reports.

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 method for digital management of the entire life cycle of coal machinery equipment 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 method for digital management of the entire life cycle of coal machinery equipment described in any one of claims 1 to 7 are implemented.

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