Coal preparation plant equipment life cycle management method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a coal preparation plant equipment life cycle management method and device, equipment and a storage medium. The method comprises the following steps: collecting operation parameter information of target equipment and working condition information of the target equipment output by a digital twin model; a pre-trained equipment life cycle prediction model is utilized, and according to the operation parameter information and the working condition information, life key information of the target equipment under current expectation is obtained through analysis; and mapping the life cycle of the target equipment according to the life key information, and managing the life cycle of the target equipment according to a mapping result. On the basis, the working condition information and the operation parameter information of the target equipment are combined, and the life cycle of the target equipment can be analyzed more accurately, so that the management of the life cycle of the target equipment is more practical, and the safety of a coal preparation plant system is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of equipment maintenance, and in particular to a method, device, equipment and storage medium for life cycle management of equipment in a coal preparation plant. Background Art

[0002] In order to improve the safety of the coal preparation plant system, the life cycle management of the equipment in the coal preparation plant is usually carried out. When the equipment life reaches a certain limit, the equipment is repaired, maintained or replaced to ensure the overall safe operation of the coal preparation plant system.

[0003] At present, the ratio of equipment working time to planned working time is usually used as the life of the equipment. However, different working conditions of different equipment will cause different degrees of wear and tear on the equipment, which will also affect the life of the equipment. As a result, the currently used method cannot accurately reflect the life of the equipment, which has a certain negative impact on the safety of the coal preparation plant system. Summary of the invention

[0004] The embodiments of the present application provide a method, apparatus, equipment and storage medium for life cycle management of equipment in a coal preparation plant to improve the accuracy of equipment life analysis and improve the safety of the coal preparation plant system.

[0005] In a first aspect, an embodiment of the present application provides a method for life cycle management of equipment in a coal preparation plant, the method comprising:

[0006] Collect the operating parameter information of the target equipment and the operating condition information of the target equipment output by the digital twin model;

[0007] Using the pre-trained equipment life cycle prediction model, according to the operating parameter information and working condition information, analyze and obtain the key information of the target equipment's current expected life cycle;

[0008] The life cycle of the target device is mapped according to the life key information, and the life cycle of the target device is managed according to the mapping result.

[0009] In a second aspect, an embodiment of the present application provides a coal preparation plant equipment life cycle management device, the device comprising:

[0010] A collection module is used to collect the operating parameter information of the target equipment and the operating condition information of the target equipment output by the digital twin model;

[0011] An analysis module is used to use a pre-trained equipment life cycle prediction model to analyze and obtain key life information of the target equipment under the current expected life cycle according to the operating parameter information and working condition information;

[0012] The management module is used to map the life cycle of the target device according to the life key information, and manage the life cycle of the target device according to the mapping result.

[0013] In a third aspect, an embodiment of the present application further provides an electronic device, the electronic device comprising:

[0014] one or more processors;

[0015] a storage device for storing one or more programs,

[0016] When one or more programs are executed by one or more processors, the one or more processors implement the coal preparation plant equipment life cycle management method provided in any embodiment of the present application.

[0017] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, characterized in that when the program is executed by a processor, a method for life cycle management of coal preparation plant equipment as provided in any embodiment of the present application is implemented.

[0018] The technical solution of the embodiment of the present application collects the operating parameter information of the target equipment and the operating condition information of the target equipment output by the digital twin model; uses the pre-trained equipment life cycle prediction model to analyze and obtain the target equipment's current expected life critical information based on the operating parameter information and operating condition information; maps the target equipment's life cycle based on the life critical information, and manages the target equipment's life cycle based on the mapping result. Based on this, combined with the target equipment's operating condition information and operating parameter information, the target equipment's life cycle can be analyzed more accurately, making the management of its life cycle more realistic, thereby improving the safety of the coal preparation plant system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of a process flow of a method for managing the life cycle of equipment in a coal preparation plant provided in Example 1 of the present application;

[0020] Figure 2 A schematic diagram of the structure of a coal preparation plant equipment life cycle management device provided in Example 2 of the present application;

[0021] Figure 3 A schematic diagram of the structure of an electronic device provided in Example 3 of the present application. DETAILED DESCRIPTION

[0022] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only the parts related to the present application, rather than all structures, are shown in the accompanying drawings.

[0023] Embodiment 1

[0024] Figure 1 A flow chart of a method for managing the life cycle of equipment in a coal preparation plant provided in Example 1 of the present application is shown in FIG. Figure 1 As shown, the coal preparation plant equipment life cycle management method provided in this embodiment can be applied to a coal preparation plant equipment life cycle management platform mounted on a computer or other device with data processing capability, and can be used in conjunction with some application software to achieve a better experience. Specifically, the method may include the following steps:

[0025] Step 101: Collect operating parameter information of the target device and operating condition information of the target device output by the digital twin model.

[0026] In this step, when using the digital twin calculation model to perform digital twin calculation on the coal preparation plant system, the digital twin calculation model can be used to determine the production parameter indicators of each process operation link in the coal preparation process according to the raw coal sample screening data, floating and sinking data, and coal slime step-by-step release data, real-time raw coal washing volume, and the process flow calculation model inside the digital twin entered into the coal quality information management system; the production parameter indicators are stored as system data in the data platform.

[0027] Among them, the process flow calculation model can include but is not limited to: number and mass calculation model, water volume and medium volume calculation model. The process operation links can include: screening, crushing, desliming, two-product heavy medium separation, three-product heavy medium separation, pre-de-mediuming, de-mediuming, screening dehydration, centrifugal dehydration, diversion, magnetic separation, hydraulic classification, coarse coal slime separation, flotation, concentration, combined medium preparation, circulating water, filtration, distribution, blending, etc.

[0028] The production index parameters may include: Q (output t / h), r (yield %), V (suspension volume m3 / h), G (solid amount in suspension t / h), Gc (total amount of non-magnetic matter in suspension t / h), Gf (total amount of magnetic matter in suspension t / h), W circulation (circulating water volume m3 / h), W (reference flow rate m3 / h); the quality indicators include: Ad (reference ash content %), ρ (suspension density t / m3), rc (non-magnetic matter content in suspension %), rf (magnetic matter content in suspension %), g (solid weight per unit volume of suspension t / m3), Mt (water content %).

[0029] It should be noted that after the above-mentioned process indicator parameters are calculated, they can be allocated to specific equipment according to the equipment parameter allocation model according to the allocation rules. At the same time, the production sequence can be set according to the actual downstream distance, material speed and other relationships on site. At this point, each device will have real-time coal flow, water flow, medium volume and some equipment parameters (current, etc.) and sensor parameters (flow, liquid level, temperature, etc.) connected to the system. These data are uniformly transmitted to the data platform.

[0030] It should be noted that the specific twin computing process of the above-mentioned data twin computing model can be referred to relevant technologies and will not be repeated here.

[0031] In addition, when the coal quality changes, the digital twin can use its coal quality correction model to adjust the original calculated coal quality data so that the original coal quality data that the current calculation relies on is consistent with the current actual situation.

[0032] In this step, the operating parameter information may include but is not limited to temperature, pressure, current, voltage, torque, etc., and these operating parameter information can be obtained through relevant sensors in the target device.

[0033] In addition, the operating condition information comes from the digital twin calculation results. It should be noted that the instantaneous coal carrying capacity of the belt can also come from the belt scale. The cumulative processing capacity is obtained by integrating the statistics of the instantaneous coal carrying capacity calculation results. The instantaneous load rate of the equipment is calculated based on the ratio between the instantaneous coal carrying capacity of the equipment and the rated processing capacity of the equipment. The closer the instantaneous coal carrying capacity of the equipment is to the rated capacity, the greater its load rate.

[0034] Similarly, the historical load rate is a historical statistic of the instantaneous load rate. If the equipment is under high load (80%-100%) or overload (>100%) conditions for a long time, it will be harmful to the life of the equipment.

[0035] The operating condition information in this step may include real-time operating condition information and historical operating condition information, so as to improve the accuracy of subsequent life cycle analysis.

[0036] This step can also clean the collected data to eliminate obviously abnormal data. It should be noted that a corresponding normal range can be set for each parameter. Once the value of a parameter is not within the corresponding normal range, it can be said that the data is abnormal data and needs to be eliminated.

[0037] Step 102: Utilize the pre-trained equipment life cycle prediction model to analyze and obtain the key life information of the target equipment under the current expected period according to the operating parameter information and the working condition information.

[0038] In this step, the operating parameter information and operating condition information can be extended according to the preset parameter extension strategy to obtain prediction features; then the prediction features are input into the pre-trained equipment life cycle prediction model to obtain the key life information of the target equipment under the current expectation.

[0039] Parameter extension refers to extending a single parameter to a related form of the parameter, thereby improving the statistical performance of the parameter and the accuracy of subsequent prediction analysis. For example, the average value, maximum value, fluctuation rate of current and voltage, the rate of change of temperature, and the change trend of coal processing volume.

[0040] In addition, after obtaining the preset features, the prediction features can be subjected to mutation elimination processing according to the preset mutation elimination strategy. Eliminating mutations can improve the accuracy of predictions and avoid data distortion. The mutation elimination strategy can include, but is not limited to, obtaining the interaction term between coal quantity and current, obtaining the interaction term between temperature and current, and using a sliding window to calculate the moving average of historical data.

[0041] In this step, the equipment life cycle prediction model can be a decision tree regression model. The training process of the model can refer to related technologies and will not be repeated here.

[0042] It should be noted that in order to continuously improve the accuracy of the decision tree regression model during the application process, the key life information it outputs can be compared with the equipment life of periodic manual inspections, and the parameters of the decision tree regression model can be adjusted according to the comparison results.

[0043] The life key information in this step may include average operating level, key parameters, remaining life and maximum life. The average operating level is related to the nature of the target equipment. For example, if the nature of a target equipment is to output clean coal, the average operating level of the target equipment can be the clean coal yield, or the average daily input volume of upstream products, etc.

[0044] In addition, the key parameters are the parameters that currently need to be focused on. In order to improve the efficiency of the life cycle management of this embodiment, this embodiment can construct a mapping relationship network between various parameters and various equipment in the coal preparation plant; then after obtaining the key life information of the target equipment under the current expected life, determine the key equipment corresponding to the key parameters in the key life information according to the mapping relationship network; and then generate a maintenance plan according to the life cycle and key parameters of the key equipment.

[0045] Specifically, since the present embodiment analyzes and predicts the life cycle of each device, each device has its own critical life information. The present embodiment can generate a maintenance plan for the critical device based on the critical life information.

[0046] Since the core components of each device are different, different maintenance contents can be set for each device and the importance of each maintenance content can be marked. In this embodiment, when generating a maintenance plan, time sorting and maintenance cycle setting can be performed based on the importance of the maintenance contents corresponding to the key equipment.

[0047] For example, for maintenance items with high importance, a maintenance cycle of 3 days is set. First, the maintenance items are sorted in chronological order according to their importance, and then the next maintenance time of each maintenance item is set according to the maintenance cycle, thereby generating a maintenance plan.

[0048] Step 103: Map the life cycle of the target device according to the life critical information, and manage the life cycle of the target device according to the mapping result.

[0049] In this step, when mapping is performed, the remaining life and the maximum life can be mapped to the device health according to a preset mapping formula, and the device health is determined as the mapping result.

[0050] Among them, the preset mapping formula can be .

[0051] In addition, when managing, a maintenance plan for the target equipment can be generated based on the equipment health; for any maintenance content in the maintenance plan, the working hours corresponding to the maintenance content are determined based on the pre-built mapping network between working hours and maintenance content; based on the working hours and corresponding maintenance content, a maintenance work order is generated according to the time information in the maintenance plan to achieve life cycle management of the target equipment.

[0052] It should be noted that the generation process of the maintenance plan can refer to the aforementioned process and will not be repeated here. Among them, the sorting time interval between the first maintenance contents can be determined according to the working hours of each maintenance content. For example, maintenance content 1 requires working hour 1 to complete, and maintenance content 2 requires working hour 2 to complete. The sorting is maintenance content 1 and maintenance content 2. Then the time between the two maintenance contents must be greater than working hour 1.

[0053] In addition, this embodiment can also use a recurrent neural network model to process time series data such as the cumulative amount of coal carried to capture changes in the equipment state. After capturing the changes, the life cycle in this embodiment can be updated.

[0054] In this embodiment, the operating parameter information of the target equipment and the working condition information of the target equipment output by the digital twin model are collected; the pre-trained equipment life cycle prediction model is used to analyze the target equipment's current expected life key information based on the operating parameter information and working condition information; the target equipment's life cycle is mapped based on the life key information, and the target equipment's life cycle is managed based on the mapping result. Based on this, combined with the working condition information and operating parameter information of the target equipment, the target equipment's life cycle can be analyzed more accurately, so that the management of its life cycle is more in line with reality, thereby improving the safety of the coal preparation plant system.

[0055] Embodiment 2

[0056] Figure 2 This is a schematic diagram of the structure of a coal preparation plant equipment life cycle management device provided in the second embodiment of the present application. The coal preparation plant equipment life cycle management device provided in the embodiment of the present application can execute the coal preparation plant equipment life cycle management method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method. The device can be implemented in software and / or hardware, such as Figure 2 As shown, the equipment life cycle management device of the coal preparation plant specifically includes: a collection module 201, an analysis module 202, and a management module 203.

[0057] Among them, the collection module is used to collect the operating parameter information of the target equipment and the working condition information of the target equipment output by the digital twin model;

[0058] An analysis module is used to use a pre-trained equipment life cycle prediction model to analyze and obtain key life information of the target equipment under the current expected life cycle according to the operating parameter information and working condition information;

[0059] The management module is used to map the life cycle of the target device according to the life key information, and manage the life cycle of the target device according to the mapping result.

[0060] Furthermore, the analysis module is specifically used for:

[0061] According to the preset parameter extension strategy, the operating parameter information and operating condition information are extended to obtain the prediction characteristics;

[0062] The prediction features are input into the pre-trained equipment life cycle prediction model to obtain the key information of the target equipment's current expected life.

[0063] Furthermore, the device is also used for:

[0064] According to the pre-set mutation elimination strategy, the predicted features are subjected to mutation elimination processing.

[0065] Further, the life critical information includes key parameters;

[0066] The device is also used to:

[0067] Construct a mapping relationship network between various parameters and equipment in the coal preparation plant;

[0068] After obtaining the target equipment's currently expected life critical information, determine the critical equipment corresponding to the critical parameters in the life critical information according to the mapping relationship network;

[0069] Generate maintenance plans based on the life cycle and key parameters of key equipment.

[0070] Furthermore, the lifespan key information includes the remaining lifespan and the maximum lifespan;

[0071] The analysis module is specifically used for:

[0072] According to a preset mapping formula, the remaining life and the maximum life are mapped to the device health, and the device health is determined as the mapping result.

[0073] Furthermore, the management module is specifically used for:

[0074] Generate maintenance plan for target equipment based on equipment health;

[0075] For any maintenance content in the maintenance plan, the man-hours corresponding to the maintenance content are determined based on the pre-built mapping relationship network between man-hours and maintenance content;

[0076] According to the working hours and the corresponding maintenance content, a maintenance work order is generated according to the time information in the maintenance plan to achieve the life cycle management of the target equipment.

[0077] Furthermore, the equipment life cycle prediction model is a decision tree regression model.

[0078] Embodiment 3

[0079] Figure 3 A schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present application is shown in FIG. Figure 3 As shown, the electronic device includes a processor 310, a memory 320, an input device 330 and an output device 340; the number of the processor 310 in the electronic device can be one or more. Figure 3 A processor 310 is taken as an example; the processor 310, the memory 320, the input device 330 and the output device 340 in the electronic device can be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0080] The memory 320, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the coal preparation plant equipment life cycle management method in the embodiment of the present invention. The processor 310 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the memory 320, that is, to implement the above-mentioned coal preparation plant equipment life cycle management method:

[0081] Collect the operating parameter information of the target equipment and the operating condition information of the target equipment output by the digital twin model;

[0082] Using the pre-trained equipment life cycle prediction model, according to the operating parameter information and working condition information, analyze and obtain the key information of the target equipment's current expected life cycle;

[0083] The life cycle of the target device is mapped according to the life key information, and the life cycle of the target device is managed according to the mapping result.

[0084] Furthermore, the pre-trained equipment life cycle prediction model is used to analyze the key life information of the target equipment under the current expected life according to the operating parameter information and working condition information, including:

[0085] According to the preset parameter extension strategy, the operating parameter information and operating condition information are extended to obtain the prediction characteristics;

[0086] The prediction features are input into the pre-trained equipment life cycle prediction model to obtain the key information of the target equipment's current expected life.

[0087] Furthermore, after obtaining the prediction features, the method further includes:

[0088] According to the pre-set mutation elimination strategy, the predicted features are subjected to mutation elimination processing.

[0089] Further, the life critical information includes key parameters;

[0090] The method also includes:

[0091] Construct a mapping relationship network between various parameters and equipment in the coal preparation plant;

[0092] After obtaining the target equipment's currently expected life critical information, determine the critical equipment corresponding to the critical parameters in the life critical information according to the mapping relationship network;

[0093] Generate maintenance plans based on the life cycle and key parameters of key equipment.

[0094] Furthermore, the lifespan key information includes the remaining lifespan and the maximum lifespan;

[0095] Map the life cycle of the target equipment based on key life information, including:

[0096] According to a preset mapping formula, the remaining life and the maximum life are mapped to the device health, and the device health is determined as the mapping result.

[0097] Furthermore, the life cycle of the target device is managed according to the mapping results, including:

[0098] Generate maintenance plan for target equipment based on equipment health;

[0099] For any maintenance content in the maintenance plan, the man-hours corresponding to the maintenance content are determined based on the pre-built mapping relationship network between man-hours and maintenance content;

[0100] According to the working hours and the corresponding maintenance content, a maintenance work order is generated according to the time information in the maintenance plan to achieve the life cycle management of the target equipment.

[0101] Furthermore, the equipment life cycle prediction model is a decision tree regression model.

[0102] The memory 320 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 320 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 320 may further include a memory remotely arranged relative to the processor 310, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0103] Embodiment 4

[0104] Embodiment 4 of the present application further provides a storage medium containing computer executable instructions, which are used to execute a method for life cycle management of equipment in a coal preparation plant when executed by a computer processor, the method comprising:

[0105] Collect the operating parameter information of the target equipment and the operating condition information of the target equipment output by the digital twin model;

[0106] Using the pre-trained equipment life cycle prediction model, according to the operating parameter information and working condition information, analyze and obtain the key information of the target equipment's current expected life cycle;

[0107] The life cycle of the target device is mapped according to the life key information, and the life cycle of the target device is managed according to the mapping result.

[0108] Furthermore, the pre-trained equipment life cycle prediction model is used to analyze the key life information of the target equipment under the current expected life according to the operating parameter information and working condition information, including:

[0109] According to the preset parameter extension strategy, the operating parameter information and operating condition information are extended to obtain the prediction characteristics;

[0110] The prediction features are input into the pre-trained equipment life cycle prediction model to obtain the key information of the target equipment's current expected life.

[0111] Furthermore, after obtaining the prediction features, the method further includes:

[0112] According to the pre-set mutation elimination strategy, the predicted features are subjected to mutation elimination processing.

[0113] Further, the life critical information includes key parameters;

[0114] The method also includes:

[0115] Construct a mapping relationship network between various parameters and equipment in the coal preparation plant;

[0116] After obtaining the target equipment's currently expected life critical information, determine the critical equipment corresponding to the critical parameters in the life critical information according to the mapping relationship network;

[0117] Generate maintenance plans based on the life cycle and key parameters of key equipment.

[0118] Furthermore, the lifespan key information includes the remaining lifespan and the maximum lifespan;

[0119] Map the life cycle of the target equipment based on key life information, including:

[0120] According to a preset mapping formula, the remaining life and the maximum life are mapped to the device health, and the device health is determined as the mapping result.

[0121] Furthermore, the life cycle of the target device is managed according to the mapping results, including:

[0122] Generate maintenance plan for target equipment based on equipment health;

[0123] For any maintenance content in the maintenance plan, the man-hours corresponding to the maintenance content are determined based on the pre-built mapping relationship network between man-hours and maintenance content;

[0124] According to the working hours and the corresponding maintenance content, a maintenance work order is generated according to the time information in the maintenance plan to achieve the life cycle management of the target equipment.

[0125] Furthermore, the equipment life cycle prediction model is a decision tree regression model.

[0126] Of course, the storage medium containing computer executable instructions provided in the embodiment of the present application, whose computer executable instructions are not limited to the above method operations, can also execute related operations in the coal preparation plant equipment life cycle management method provided in any embodiment of the present application.

[0127] Through the above description of the implementation method, the technicians in the relevant field can clearly understand that the present application can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0128] It is worth noting that in the embodiment of the above-mentioned search device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application.

[0129] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for managing the life cycle of equipment in a coal preparation plant, characterized in that: The method comprises: Collecting operating parameter information of the target device and operating condition information of the target device output by the digital twin model; Using a pre-trained equipment life cycle prediction model, according to the operating parameter information and the operating condition information, analyzing and obtaining key life information of the target equipment under the current expectation; The life cycle of the target device is mapped according to the life critical information, and the life cycle of the target device is managed according to the mapping result.

2. The method according to claim 1, characterized in that The pre-trained equipment life cycle prediction model is used to analyze and obtain the key life information of the target equipment under the current expected period according to the operating parameter information and the operating condition information, including: According to a preset parameter extension strategy, the operating parameter information and the operating condition information are extended to obtain prediction features; The prediction features are input into a pre-trained equipment life cycle prediction model to obtain key information on the life of the target equipment under the current expectation.

3. The method according to claim 2, characterized in that After obtaining the prediction features, the method further includes: According to a pre-set mutation elimination strategy, mutation elimination processing is performed on the predicted features.

4. The method according to claim 2, characterized in that: The life key information includes key parameters; The method further comprises: Constructing a mapping relationship network between various parameters and various equipment in the coal preparation plant; After obtaining the critical life information of the target device under the current expectation, determining the critical device corresponding to the critical parameter in the critical life information according to the mapping relationship network; A maintenance plan is generated according to the life cycle of the key equipment and the key parameters.

5. The method according to claim 1, characterized in that The life key information includes remaining life and maximum life; The mapping of the life cycle of the target device according to the life critical information includes: According to a preset mapping formula, the remaining life and the maximum life are mapped to device health, and the device health is determined as a mapping result.

6. The method according to claim 5, characterized in that The managing the life cycle of the target device according to the mapping result includes: Generate a maintenance plan for the target device according to the device health; For any maintenance content in the maintenance plan, the man-hours corresponding to the maintenance content are determined according to the pre-built mapping relationship network between man-hours and maintenance content; According to the working hours and the corresponding maintenance contents, a maintenance work order is generated according to the time information in the maintenance plan to achieve the management of the life cycle of the target equipment.

7. The method according to claim 1, characterized in that The equipment life cycle prediction model is a decision tree regression model.

8. A coal preparation plant equipment life cycle management device, characterized in that: The device comprises: A collection module, used to collect operating parameter information of the target device and operating condition information of the target device output by the digital twin model; An analysis module, used to use a pre-trained equipment life cycle prediction model to analyze and obtain key life information of the target equipment under the current expected period according to the operating parameter information and the operating condition information; A management module is used to map the life cycle of the target device according to the life critical information, and manage the life cycle of the target device according to the mapping result.

9. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the coal preparation plant equipment life cycle management method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for life cycle management of coal preparation plant equipment as described in any one of claims 1-7 is implemented.

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