Coal preparation plant production evaluation method, device and equipment and storage medium

Abstract

The embodiment of the invention discloses a coal preparation plant production evaluation method and device, equipment and a storage medium. The method comprises the following steps: performing digital twinborn calculation on a coal preparation plant system by utilizing a digital twinborn calculation model and coal quality data to obtain basic data of the coal preparation plant system; according to the basic data of the coal preparation plant system, the production abnormal data of the coal preparation plant system is obtained through analysis by using a pre-built fault reason knowledge graph; and performing index evaluation according to the basic data and the production abnormal data by using a pre-established evaluation index calculation model to obtain a production evaluation result of the coal preparation plant. Based on this, index evaluation can be rapidly and comprehensively carried out on the basis of all-platform data obtained by a digital twinning calculation model and by taking the knowledge graph as a medium of anomaly analysis, so that production evaluation of the coal preparation plant is completed, and the process can be based on real-time data of digital twinning, so that the real-time performance of production evaluation is improved, and the production efficiency is improved. And the production evaluation efficiency is improved by utilizing the efficient analysis characteristic of the knowledge graph.

Description

Technical Field

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

[0002] For the coal industry, reasonable coal washing can not only improve the utilization efficiency of coal resources and reduce environmental pollution, but also promote the sustainable development of the economy and society in the process of protecting energy, and help achieve the win-win goal of energy protection and economic development.

[0003] In order to achieve refined management of coal washing technology during the production process, most domestic coal preparation plants currently conduct production evaluations on a regular basis, that is, hold morning adjustment meetings to synchronize the production situation and production anomalies of the day and the shift, and issue some production indicator requirements accordingly.

[0004] However, this method usually relies on holding meetings to draw results, and the real-time performance of production evaluation is poor and the efficiency is low. Summary of the invention

[0005] The embodiments of the present application provide a coal preparation plant production evaluation method, device, equipment and storage medium to improve the real-time performance and efficiency of production evaluation.

[0006] In a first aspect, an embodiment of the present application provides a method for evaluating production in a coal preparation plant, the method comprising:

[0007] Using the digital twin computing model and coal quality data, the digital twin computing of the coal preparation plant system is performed to obtain the basic data of the coal preparation plant system;

[0008] Based on the basic data of the coal preparation plant system, the production abnormality data of the coal preparation plant system is analyzed using the pre-built knowledge graph of fault causes;

[0009] Using the pre-built evaluation index calculation model, the index evaluation is carried out according to the basic data and production abnormality data to obtain the production evaluation results of the coal preparation plant.

[0010] In a second aspect, an embodiment of the present application provides a coal preparation plant production evaluation device, the device comprising:

[0011] The digital twin module is used to perform digital twin calculations on the coal preparation plant system using the digital twin calculation model and coal quality data to obtain the basic data of the coal preparation plant system;

[0012] The abnormality analysis module is used to analyze the production abnormality data of the coal preparation plant system based on the basic data of the coal preparation plant system and the pre-built knowledge graph of fault causes;

[0013] The index evaluation module is used to utilize the pre-built evaluation index calculation model to perform index evaluation based on basic data and production abnormality data to obtain production evaluation results for the coal preparation plant.

[0014] In a third aspect, an embodiment of the present application further provides an electronic device, the electronic device comprising:

[0015] one or more processors;

[0016] a storage device for storing one or more programs,

[0017] When one or more programs are executed by one or more processors, the one or more processors implement the coal preparation plant production evaluation method provided in any embodiment of the present application.

[0018] 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 coal preparation plant production evaluation method as provided in any embodiment of the present application is implemented.

[0019] The technical solution of the embodiment of the present application uses a digital twin computing model and coal quality data to perform digital twin computing on the coal preparation plant system to obtain the basic data of the coal preparation plant system; based on the basic data of the coal preparation plant system, a pre-built knowledge graph of fault causes is used to analyze and obtain the production abnormality data of the coal preparation plant system; a pre-built evaluation index computing model is used to perform index evaluation based on the basic data and production abnormality data to obtain the production evaluation results of the coal preparation plant. Based on this, it is possible to quickly and comprehensively perform index evaluation based on the full-platform data obtained by the digital twin computing model and use the knowledge graph as the medium for abnormal analysis, thereby completing the production evaluation of the coal preparation plant. Since this process can be based on the real-time data of the digital twin, the real-time nature of the production evaluation is improved, and the efficient analysis characteristics of the knowledge graph are used to improve the efficiency of the production evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of a process flow of a coal preparation plant production evaluation method provided in Example 1 of the present application;

[0021] Figure 2 A schematic diagram of the structure of a coal preparation plant production evaluation device provided in Example 2 of the present application;

[0022] Figure 3 A schematic diagram of the structure of an electronic device provided in Example 3 of the present application. DETAILED DESCRIPTION

[0023] 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.

[0024] Embodiment 1

[0025] Figure 1 A schematic diagram of a process flow of a coal preparation plant production evaluation method provided in Example 1 of the present application, such as Figure 1 As shown, the coal preparation plant production evaluation method provided in this embodiment can be applied to a coal preparation plant production evaluation 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:

[0026] Step 101: Use the digital twin calculation model and coal quality data to perform digital twin calculation on the coal preparation plant system to obtain basic data of the coal preparation plant system.

[0027] 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.

[0028] 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.

[0029] 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 %).

[0030] 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.

[0031] 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.

[0032] 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, providing reliable data support for subsequent knowledge graph analysis.

[0033] It should be noted that the data platform can access the basic data and intermediate computing data of the entire platform, and supports multiple data access methods such as PRE, API, data source, and offline file access. Other application layers can independently retrieve data from the data platform as needed.

[0034] Step 102: Based on the basic data of the coal preparation plant system, using the pre-built knowledge graph of fault causes, analyze and obtain the production abnormality data of the coal preparation plant system.

[0035] In this step, fault judgment can be made based on the basic data of the coal preparation plant system, and when it is determined that a fault phenomenon exists, the pre-built fault cause knowledge graph is used to analyze at least one fault cause that causes the fault phenomenon and the corresponding treatment opinions for each fault cause; then, at least one fault cause and the corresponding treatment opinions for each fault cause are determined as the production abnormality data of the coal preparation plant system.

[0036] It should be noted that the data judgment script can be used to perform fault judgment. Specifically, when the data judgment script judges whether there is a fault phenomenon, it can judge whether the system data is in a normal range under the current raw coal supply level. If it is in the normal range, the data judgment script will continue to be called in a round-robin manner to make a judgment. If it is not in the normal range, it is determined that a fault phenomenon exists, and the fault phenomenon is that a certain system data is higher than a normal range or lower than a normal range, etc.

[0037] In a specific example, the rated processing capacity of the total clean coal belt scale of a coking coal preparation plant is 500t / h. At this time, the raw coal carrying capacity is 520t / h, and the belt scale detects that the instantaneous coal carrying capacity of the equipment is 480t / h. According to the traditional evaluation and judgment method, the equipment has not reached the upper threshold of the processing capacity, and the equipment has been fully utilized, so it will be given a high score. However, through the indicator analysis capability of the knowledge graph, it is found that the total clean coal yield of the plant should be between 40% and 60%. With a total raw coal of 520t, the clean coal yield of the plant has reached 92.3%, which is obviously abnormal.

[0038] In addition, when determining the cause of the fault and the treatment suggestions, you can first search the pre-built fault cause knowledge graph to see whether there is a target knowledge graph entity corresponding to the fault phenomenon.

[0039] If it exists, according to the relationship between the target knowledge graph entity and other entities in the fault cause knowledge graph, at least one knowledge graph cause entity and the knowledge graph processing opinion entity corresponding to each knowledge graph cause entity are matched; for any knowledge graph cause entity, the entity content of the knowledge graph cause entity is determined as the fault cause, and the entity content of the knowledge graph processing opinion entity corresponding to the knowledge graph cause entity is determined as the processing opinion corresponding to the fault cause.

[0040] Still taking the above specific example, based on the obvious anomaly, according to the relationship between the entities in the knowledge graph of the cause of the fault, we analyze upward and find that there are no anomalies in the calculation indicators of the above sorting operations. Then the knowledge graph determines that there is an anomaly in the clean coal belt scale.

[0041] Step 103: Using the pre-built evaluation index calculation model, perform index evaluation based on basic data and production abnormality data to obtain a production evaluation result of the coal preparation plant.

[0042] In this step, the basic indicators in the evaluation indicator calculation model can be calculated according to the basic data to obtain the evaluation results of each basic indicator; then the analysis indicators in the evaluation indicator calculation model can be calculated according to the basic data and the production abnormality data to obtain the evaluation results of each analysis indicator; finally, the production evaluation results of the coal preparation plant are determined according to the evaluation results of each basic indicator and the evaluation results of each analysis indicator.

[0043] It should be noted that the basic data may be data corresponding to each basic indicator, and the basic data may participate in the calculation regardless of whether it is normal data or not.

[0044] Among them, basic indicators may include but are not limited to economic evaluation indicators, quantitative quality evaluation indicators, and equipment performance indicators; analytical indicators include production abnormality indicators.

[0045] Economic evaluation indicators may include but are not limited to coal processing cost per ton, total staff efficiency, and production worker efficiency; quantitative quality evaluation indicators include: product quantity efficiency, product quality qualification rate, and daily production plan completion rate; equipment performance indicators include: single-machine inspection of sorting equipment and equipment load rate analysis.

[0046] Production abnormality indicators may include but are not limited to: summary analysis of production abnormality problems and unplanned shutdown rate.

[0047] Specifically, among the above indicators, the product quantity efficiency may include but is not limited to quantity efficiency, clean coal loss rate, medium coal carrying rate, and gangue carrying rate; the product quality qualification rate may include ash content qualification rate, moisture content qualification rate, calorific value qualification rate, sulfur content qualification rate, etc.

[0048] It should be noted that the method for determining the above basic indicators can refer to relevant technologies and will not be repeated here.

[0049] The production abnormality index is converted from the aforementioned production abnormality data. For example, it can be converted by summarizing to obtain the summary analysis index of production abnormality problems, and the ratio of the number of unplanned stops to the total number of stops is determined as the unplanned stop rate.

[0050] In addition, when this step finally obtains the production evaluation results of the coal preparation plant, the pre-trained large prediction model can be used to convert the evaluation results of each basic indicator and the evaluation results of each analysis indicator into a universal natural language to obtain the production evaluation results of the coal preparation plant.

[0051] It should be noted that the entire analysis result is organized into a universal natural language using a large language model and output to the user. The user can print out this report every day for discussion in morning meetings and pre-shift meetings.

[0052] In this embodiment, the digital twin calculation model and coal quality data are used to perform digital twin calculation on the coal preparation plant system to obtain the basic data of the coal preparation plant system; based on the basic data of the coal preparation plant system, the pre-built fault cause knowledge graph is used to analyze the production abnormality data of the coal preparation plant system; the pre-built evaluation index calculation model is used to perform index evaluation based on the basic data and production abnormality data to obtain the production evaluation results of the coal preparation plant. Based on this, the full platform data obtained by the digital twin calculation model can be used as the basis, and the knowledge graph can be used as the medium for abnormal analysis to quickly and comprehensively perform index evaluation, thereby completing the production evaluation of the coal preparation plant. Since this process can be based on the real-time data of the digital twin, the real-time nature of the production evaluation is improved, and the efficient analysis characteristics of the knowledge graph are used to improve the efficiency of the production evaluation.

[0053] Embodiment 2

[0054] Figure 2This is a schematic diagram of the structure of a coal preparation plant production evaluation device provided in Example 2 of the present application. The coal preparation plant production evaluation device provided in the embodiment of the present application can execute the coal preparation plant production evaluation 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 coal preparation plant production evaluation device specifically includes: a digital twin module 201, an abnormality analysis module 202, and an index evaluation module 203.

[0055] Among them, the digital twin module is used to use the digital twin calculation model and coal quality data to perform digital twin calculations on the coal preparation plant system to obtain the basic data of the coal preparation plant system;

[0056] The abnormality analysis module is used to analyze the production abnormality data of the coal preparation plant system based on the basic data of the coal preparation plant system and the pre-built knowledge graph of fault causes;

[0057] The index evaluation module is used to utilize the pre-built evaluation index calculation model to perform index evaluation based on basic data and production abnormality data to obtain production evaluation results for the coal preparation plant.

[0058] Furthermore, the abnormality analysis module is specifically used for:

[0059] Fault determination is performed based on the basic data of the coal preparation plant system. When it is determined that a fault phenomenon exists, the pre-built fault cause knowledge graph is used to analyze at least one fault cause that causes the fault phenomenon and the corresponding treatment opinions for each fault cause;

[0060] At least one fault cause and a treatment opinion corresponding to each fault cause are determined as production abnormality data of the coal preparation plant system.

[0061] Furthermore, the indicator evaluation module is specifically used for:

[0062] Calculate the basic indicators in the evaluation indicator calculation model according to the basic data to obtain the evaluation results of each basic indicator;

[0063] Calculate the analysis indicators in the evaluation indicator calculation model according to the basic data and production abnormality data to obtain the evaluation results of each analysis indicator;

[0064] The production evaluation results of the coal preparation plant are determined based on the evaluation results of each basic indicator and the evaluation results of each analytical indicator.

[0065] Furthermore, the indicator evaluation module is specifically used for:

[0066] By using the pre-trained large prediction model, the evaluation results of each basic indicator and the evaluation results of each analytical indicator are converted into universal natural language to obtain the production evaluation results of the coal preparation plant.

[0067] Furthermore, the basic indicators include economic evaluation indicators, numerical quality evaluation indicators, and equipment performance indicators;

[0068] Analytical indicators include production abnormality indicators.

[0069] Furthermore, the economic evaluation indicators include the processing cost per ton of coal, total staff efficiency, and production worker efficiency;

[0070] The quantitative quality evaluation indicators include: product quantity efficiency, product quality qualification rate, and daily production plan completion rate;

[0071] Equipment performance indicators include: single-machine inspection of sorting equipment and equipment load rate analysis.

[0072] Furthermore, the production abnormality indicators include: summary analysis of production abnormality problems and unplanned shutdown rate.

[0073] Embodiment 3

[0074] 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.

[0075] The memory 320 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the coal preparation plant production evaluation 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 production evaluation method:

[0076] Using the digital twin computing model and coal quality data, the digital twin computing of the coal preparation plant system is performed to obtain the basic data of the coal preparation plant system;

[0077] Based on the basic data of the coal preparation plant system, the production abnormality data of the coal preparation plant system is analyzed using the pre-built knowledge graph of fault causes;

[0078] Using the pre-built evaluation index calculation model, the index evaluation is carried out according to the basic data and production abnormality data to obtain the production evaluation results of the coal preparation plant.

[0079] Furthermore, based on the basic data of the coal preparation plant system, the production abnormality data of the coal preparation plant system is analyzed using the pre-built fault cause knowledge graph, including:

[0080] Fault determination is performed based on the basic data of the coal preparation plant system. When it is determined that a fault phenomenon exists, the pre-built fault cause knowledge graph is used to analyze at least one fault cause that causes the fault phenomenon and the corresponding treatment opinions for each fault cause;

[0081] At least one fault cause and a treatment opinion corresponding to each fault cause are determined as production abnormality data of the coal preparation plant system.

[0082] Furthermore, the pre-built evaluation index calculation model is used to evaluate the indexes according to the basic data and production abnormality data, and the production evaluation results of the coal preparation plant are obtained, including:

[0083] Calculate the basic indicators in the evaluation indicator calculation model according to the basic data to obtain the evaluation results of each basic indicator;

[0084] Calculate the analysis indicators in the evaluation indicator calculation model according to the basic data and production abnormality data to obtain the evaluation results of each analysis indicator;

[0085] The production evaluation results of the coal preparation plant are determined based on the evaluation results of each basic indicator and the evaluation results of each analytical indicator.

[0086] Furthermore, the production evaluation results of the coal preparation plant are determined according to the evaluation results of each basic indicator and the evaluation results of each analytical indicator, including:

[0087] By using the pre-trained large prediction model, the evaluation results of each basic indicator and the evaluation results of each analytical indicator are converted into universal natural language to obtain the production evaluation results of the coal preparation plant.

[0088] Furthermore, the basic indicators include economic evaluation indicators, numerical quality evaluation indicators, and equipment performance indicators;

[0089] Analytical indicators include production abnormality indicators.

[0090] Furthermore, the economic evaluation indicators include the processing cost per ton of coal, total staff efficiency, and production worker efficiency;

[0091] The quantitative quality evaluation indicators include: product quantity efficiency, product quality qualification rate, and daily production plan completion rate;

[0092] Equipment performance indicators include: single-machine inspection of sorting equipment and equipment load rate analysis.

[0093] Furthermore, the production abnormality indicators include: summary analysis of production abnormality problems and unplanned shutdown rate.

[0094] 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.

[0095] Embodiment 4

[0096] Embodiment 4 of the present application further provides a storage medium containing computer executable instructions, which are used to execute a coal preparation plant production evaluation method when executed by a computer processor, the method comprising:

[0097] Using the digital twin computing model and coal quality data, the digital twin computing of the coal preparation plant system is performed to obtain the basic data of the coal preparation plant system;

[0098] Based on the basic data of the coal preparation plant system, the production abnormality data of the coal preparation plant system is analyzed using the pre-built knowledge graph of fault causes;

[0099] Using the pre-built evaluation index calculation model, the index evaluation is carried out according to the basic data and production abnormality data to obtain the production evaluation results of the coal preparation plant.

[0100] Furthermore, based on the basic data of the coal preparation plant system, the production abnormality data of the coal preparation plant system is analyzed using the pre-built fault cause knowledge graph, including:

[0101] Fault determination is performed based on the basic data of the coal preparation plant system. When it is determined that a fault phenomenon exists, the pre-built fault cause knowledge graph is used to analyze at least one fault cause that causes the fault phenomenon and the corresponding treatment opinions for each fault cause;

[0102] At least one fault cause and a treatment opinion corresponding to each fault cause are determined as production abnormality data of the coal preparation plant system.

[0103] Furthermore, the pre-built evaluation index calculation model is used to evaluate the indexes according to the basic data and production abnormality data, and the production evaluation results of the coal preparation plant are obtained, including:

[0104] Calculate the basic indicators in the evaluation indicator calculation model according to the basic data to obtain the evaluation results of each basic indicator;

[0105] Calculate the analysis indicators in the evaluation indicator calculation model according to the basic data and production abnormality data to obtain the evaluation results of each analysis indicator;

[0106] The production evaluation results of the coal preparation plant are determined based on the evaluation results of each basic indicator and the evaluation results of each analytical indicator.

[0107] Furthermore, the production evaluation results of the coal preparation plant are determined according to the evaluation results of each basic indicator and the evaluation results of each analytical indicator, including:

[0108] By using the pre-trained large prediction model, the evaluation results of each basic indicator and the evaluation results of each analytical indicator are converted into universal natural language to obtain the production evaluation results of the coal preparation plant.

[0109] Furthermore, the basic indicators include economic evaluation indicators, numerical quality evaluation indicators, and equipment performance indicators;

[0110] Analytical indicators include production abnormality indicators.

[0111] Furthermore, the economic evaluation indicators include the processing cost per ton of coal, total staff efficiency, and production worker efficiency;

[0112] The quantitative quality evaluation indicators include: product quantity efficiency, product quality qualification rate, and daily production plan completion rate;

[0113] Equipment performance indicators include: single-machine inspection of sorting equipment and equipment load rate analysis.

[0114] Furthermore, the production abnormality indicators include: summary analysis of production abnormality problems and unplanned shutdown rate.

[0115] 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 production evaluation method provided in any embodiment of the present application.

[0116] 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.

[0117] 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.

[0118] 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 coal preparation plant production evaluation method, characterized in that: The method comprises: Using the digital twin computing model and coal quality data, the digital twin computing of the coal preparation plant system is performed to obtain the basic data of the coal preparation plant system; According to the basic data of the coal preparation plant system, using the pre-built fault cause knowledge map, analyzing and obtaining the production abnormality data of the coal preparation plant system; By using the pre-built evaluation index calculation model, the index evaluation is performed according to the basic data and the production abnormality data to obtain the production evaluation result of the coal preparation plant.

2. The method according to claim 1, characterized in that The method of analyzing the production abnormality data of the coal preparation plant system based on the basic data of the coal preparation plant system and using the pre-built fault cause knowledge graph includes: Fault determination is performed based on the basic data of the coal preparation plant system, and when it is determined that a fault phenomenon exists, at least one fault cause causing the fault phenomenon and a corresponding treatment opinion for each fault cause are analyzed using a pre-built fault cause knowledge graph; The at least one fault cause and the treatment opinion corresponding to each fault cause are determined as production abnormality data of the coal preparation plant system.

3. The method according to claim 1, characterized in that The pre-built evaluation index calculation model is used to perform index evaluation according to the basic data and the production abnormality data to obtain the production evaluation results of the coal preparation plant, including: Calculating the basic indicators in the evaluation indicator calculation model according to the basic data to obtain evaluation results of each basic indicator; Calculating the analysis indicators in the evaluation indicator calculation model according to the basic data and the production abnormality data to obtain evaluation results of each of the analysis indicators; The production evaluation result of the coal preparation plant is determined according to the evaluation results of each of the basic indicators and the evaluation results of each of the analytical indicators.

4. The method according to claim 3, characterized in that Determining the production evaluation result of the coal preparation plant according to the evaluation results of each of the basic indicators and the evaluation results of each of the analytical indicators includes: By using the pre-trained large prediction model, the evaluation results of each of the basic indicators and the evaluation results of each of the analytical indicators are converted into a universal natural language to obtain the production evaluation results of the coal preparation plant.

5. The method according to claim 3, characterized in that: The basic indicators include economic evaluation indicators, numerical quality evaluation indicators, and equipment performance indicators; The analysis indicators include production abnormality indicators.

6. The method according to claim 5, characterized in that The economic evaluation indicators include ton coal processing cost, total staff efficiency, and production worker efficiency; The quantitative quality evaluation indicators include: product quantity efficiency, product quality qualification rate, and daily production plan completion rate; The equipment performance indicators include: single-machine inspection of sorting equipment and equipment load rate analysis.

7. The method according to claim 5, characterized in that The production abnormality indicators include: summary analysis of production abnormality problems and unplanned shutdown rate.

8. A coal preparation plant production evaluation device, characterized in that: The device comprises: The digital twin module is used to perform digital twin calculations on the coal preparation plant system using the digital twin calculation model and coal quality data to obtain the basic data of the coal preparation plant system; An abnormality analysis module is used to analyze the production abnormality data of the coal preparation plant system based on the basic data of the coal preparation plant system and using the pre-built fault cause knowledge map; The index evaluation module is used to use the pre-built evaluation index calculation model to perform index evaluation according to the basic data and the production abnormality data to obtain the production evaluation result of the coal preparation plant.

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 production evaluation 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 coal preparation plant production evaluation method as described in any one of claims 1-7 is implemented.

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