Method and device for instantiation application of general knowledge graph in different coal preparation plants

Abstract

The invention provides a method and device for instantiation application of a general knowledge graph in different coal preparation plants, and the method comprises the steps: obtaining general basic data of the coal preparation plants, and constructing a general knowledge graph system based on the general basic data; the general knowledge graph system comprises a general knowledge graph and a basic exception triggering rule, wherein the general knowledge graph is used for reasoning exception reasons; obtaining target data of the target coal preparation plant, and adjusting the general knowledge graph system based on the target data to obtain a knowledge graph system of the target coal preparation plant; and obtaining real-time production data of the target coal preparation plant, and performing reasoning based on the knowledge graph system of the target coal preparation plant and the real-time production data to obtain a reasoning result. According to the method, the problems of difficulty in adaptation, complexity in implementation and the like of the general knowledge graph in instantiation application processes of different coal preparation plants are solved.

Description

Technical Field

[0001] This invention relates to the field of coal preparation production technology, and in particular to a method and apparatus for the instantiation and application of a general knowledge graph in different coal preparation plants. Background Technology

[0002] In the coal preparation industry, with the development of digital technology, knowledge graphs, as a powerful data organization and analysis tool, are gradually being introduced into production management and decision support. However, there are currently many challenges in the instantiation and application of general-purpose knowledge graphs in different coal preparation plants.

[0003] On the one hand, due to differences in their production processes, equipment configurations, and management models, different coal preparation plants cannot be directly adapted to a general knowledge graph. If a general knowledge graph is directly applied, it cannot accurately reflect the actual production status of each coal preparation plant, making it difficult for the knowledge graph to play an effective role in practical applications.

[0004] On the other hand, the implementation of existing general-purpose knowledge graphs in different coal preparation plants is complex and time-consuming. Traditional instantiation methods often require specialized technical personnel to conduct detailed research and analysis on the specific circumstances of each coal preparation plant, and then manually adjust the structure and content of the knowledge graph. This process not only requires significant manpower and time costs, but also demands extremely high levels of professional knowledge and experience from the technical personnel. This approach is inefficient and seriously hinders the speed of the promotion and application of knowledge graphs in different coal preparation plants.

[0005] In summary, current general knowledge graphs face challenges such as adaptation difficulties and implementation complexity in the instantiation and application of knowledge graphs in different coal preparation plants, which seriously restricts their widespread application and promotion in the coal preparation industry. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a method and apparatus for instantiating and applying a general knowledge graph in different coal preparation plants, so as to improve the problems of adaptation difficulties and implementation complexity faced by the general knowledge graph in the instantiating and application process in different coal preparation plants.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for instantiating and applying a general knowledge graph in different coal preparation plants, comprising: acquiring general basic data of the coal preparation plant and constructing a general knowledge graph system based on the general basic data; wherein, the general knowledge graph system includes: a general knowledge graph for inferring the causes of anomalies and basic anomaly triggering rules; acquiring target data of the target coal preparation plant and adjusting the general knowledge graph system based on the target data to obtain a knowledge graph system of the target coal preparation plant; acquiring real-time production data of the target coal preparation plant and performing inference based on the knowledge graph system of the target coal preparation plant and the real-time production data to obtain an inference result; wherein, the inference result includes at least: the abnormal phenomena and causes of anomalies in the target coal preparation plant.

[0008] Optionally, the general knowledge graph system can be adjusted based on the target data to obtain the knowledge graph system of the target coal preparation plant. This includes: deleting general basic data in the general knowledge graph system that does not conform to the production situation of the target coal preparation plant based on the target data, and closing the abnormal reasons in the general knowledge graph system that do not conform to the production situation of the target coal preparation plant; modifying and / or adding basic abnormal triggering rules and abnormal reasons in the general knowledge graph system based on the target data.

[0009] Optionally, reasoning is performed based on the knowledge graph system of the target coal preparation plant and real-time production data to obtain reasoning results, including: comparing the real-time production data with the anomaly triggering rules in the knowledge graph system of the target coal preparation plant to determine whether the target coal preparation plant has an anomaly; if an anomaly occurs, anomaly reasoning is performed based on the knowledge graph in the knowledge graph system of the target coal preparation plant to determine the cause of the anomaly.

[0010] Optionally, anomaly reasoning is performed based on the knowledge graph in the knowledge graph system of the target coal preparation plant to determine the cause of the anomaly. This includes: performing anomaly reasoning based on the knowledge graph in the knowledge graph system of the target coal preparation plant and a pre-trained large model to obtain at least one initial cause of the anomaly and an anomaly cause that can be ruled out.

[0011] Optionally, it also includes: verifying the reasoning results and optimizing the knowledge graph system of the target coal preparation plant based on the verification results.

[0012] Optionally, the reasoning results can be verified, including: obtaining the actual production situation of the target coal preparation plant and comparing the reasoning results with the actual production situation; if the reasoning results do not match the actual production situation, the knowledge graph system of the target coal preparation plant can be optimized.

[0013] Secondly, the present invention provides an apparatus for the instantiation and application of a general knowledge graph in different coal preparation plants, comprising: a basic knowledge graph construction module, used to acquire general basic data of the coal preparation plant and construct a general knowledge graph system based on the general basic data; wherein, the general knowledge graph system includes: a general knowledge graph for inferring the causes of anomalies and basic anomaly triggering rules; a knowledge graph adjustment module, used to acquire target data of the target coal preparation plant and adjust the general knowledge graph system based on the target data to obtain the knowledge graph system of the target coal preparation plant; and an anomaly inference module, used to acquire real-time production data of the target coal preparation plant and perform inference based on the knowledge graph system of the target coal preparation plant and the real-time production data to obtain inference results; wherein, the inference results include at least: the abnormal phenomena and causes of anomalies in the target coal preparation plant.

[0014] Optionally, it also includes: a verification module for verifying the inference results and optimizing the knowledge graph system of the target coal preparation plant based on the verification results.

[0015] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the steps of the method provided in any of the first aspects above.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, performs the steps of the method provided in any of the first aspects above.

[0017] This invention brings the following beneficial effects: The method and apparatus for applying the aforementioned general knowledge graph to different coal preparation plants, provided by this invention, firstly, acquires general basic data of the coal preparation plant and constructs a general knowledge graph system based on the general basic data; wherein, the general knowledge graph system includes: a general knowledge graph for inferring the causes of anomalies and basic anomaly triggering rules; then, acquires target data of the target coal preparation plant and adjusts the general knowledge graph system based on the target data to obtain the knowledge graph system of the target coal preparation plant; finally, acquires real-time production data of the target coal preparation plant and performs inference based on the knowledge graph system of the target coal preparation plant and the real-time production data to obtain the inference result; wherein, the inference result includes at least: the abnormal phenomena and causes of anomalies in the target coal preparation plant. In the above method, a general knowledge graph system is pre-constructed and then adjusted based on the actual data information of the target coal preparation plant to construct a knowledge graph system specific to the target coal preparation plant. This shortens the construction cycle of the knowledge graph system and makes it more consistent with the actual situation of the target coal preparation plant, improving the applicability of the knowledge graph to different coal preparation plants and avoiding application failures caused by the incompatibility of the general knowledge graph.

[0018] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a method for instantiating and applying a general knowledge graph in different coal preparation plants, provided as an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a device for the instantiation and application of a general knowledge graph in different coal preparation plants, provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Currently, there are many challenges in the practical application of general knowledge graphs in different coal preparation plants. On the one hand, the differences in production processes, equipment configurations, and management models among different coal preparation plants make it difficult to directly adapt general knowledge graphs. For example, some coal preparation plants use heavy media coal preparation processes, while others use flotation processes. The key parameters, equipment operating status indicators, and potential anomalies and handling methods involved in these different processes are vastly different. Directly applying a general knowledge graph cannot accurately reflect the actual production conditions of each coal preparation plant, making it difficult for the knowledge graph to play an effective role in practical applications.

[0024] On the other hand, the implementation of existing general-purpose knowledge graphs in different coal preparation plants is complex and time-consuming. Traditional instantiation methods often require specialized technicians to conduct detailed research and analysis on the specific circumstances of each coal preparation plant, and then manually adjust the structure and content of the knowledge graph. This process not only requires significant manpower and time costs, but also demands extremely high levels of professional knowledge and experience from the technicians. For example, when instantiating a knowledge graph for a specific coal preparation plant, technicians need to thoroughly understand the plant's production process, equipment performance, historical production data, and other aspects, and then customize the knowledge graph based on this information. This approach is inefficient and severely hinders the speed of knowledge graph adoption and application in different coal preparation plants.

[0025] Furthermore, verifying the accuracy of instantiated knowledge graphs is a significant challenge. Currently, there is a lack of effective and rapid verification methods and tools; typically, the accuracy and usability of knowledge graphs can only be tested through long-term actual production operation. This means that by the time problems with the knowledge graph are discovered, a considerable amount of time and resources have already been consumed, posing potential risks and losses to the coal preparation plant's production. For example, if the analysis of the cause of a certain equipment failure in the knowledge graph is inaccurate, it may lead maintenance personnel to take incorrect maintenance measures, thereby prolonging equipment downtime and affecting production efficiency.

[0026] In summary, current general knowledge graphs face challenges such as adaptation difficulties, implementation complexity, and inconvenient verification during their instantiation in different coal preparation plants, which severely restricts their widespread application and promotion in the coal preparation industry.

[0027] Based on this, the purpose of this invention is to provide a method and apparatus for instantiating and applying a general knowledge graph in different coal preparation plants, which can improve the problems of adaptation difficulties and implementation complexity faced by the general knowledge graph in the instantiating and application process in different coal preparation plants.

[0028] To facilitate understanding of this embodiment, a method for instantiating a general knowledge graph disclosed in this invention in different coal preparation plants will first be described in detail. This method can be executed by electronic devices, such as smartphones, computers, and tablets. See also Figure 1 The flowchart shown illustrates a method for instantiating and applying a general knowledge graph in different coal preparation plants, indicating that the method mainly includes the following steps S101 to S103: Step S101: Obtain general basic data of the coal preparation plant and build a general knowledge graph system based on the general basic data.

[0029] The general knowledge graph system includes a general knowledge graph for reasoning about the causes of anomalies and basic anomaly triggering rules. In one implementation phase, during the on-site implementation phase, general basic data of the coal preparation plant is first acquired. This data covers common production processes, equipment information, process parameters, and potential anomalies. Then, a general knowledge graph system is constructed using this data to store the general knowledge graph for reasoning about the causes of anomalies and the basic anomaly triggering rules. This system deploys the general basic data along with the general knowledge graph, thus providing a common framework for instantiated applications in different coal preparation plants, significantly reducing the workload of building a knowledge graph separately for each plant.

[0030] Step S102: Obtain target data for the target coal preparation plant, and adjust the general knowledge graph system based on the target data to obtain the knowledge graph system for the target coal preparation plant.

[0031] In one implementation, unique production information (i.e., target data) of the target coal preparation plant is acquired, such as specific technological processes and personalized operating parameter ranges for equipment. Based on this information, the system can make targeted adjustments to the deployed general basic data, including but not limited to: deleting general basic data in the general knowledge graph system that does not conform to the production situation of the target coal preparation plant based on the target data, and closing abnormal causes in the general knowledge graph system that do not conform to the production situation of the target coal preparation plant; modifying and / or adding basic anomaly triggering rules and anomaly causes in the general knowledge graph system based on the target data.

[0032] Specifically, the system allows for the easy deletion of general basic data that is not needed by the target coal preparation plant; and it can directly shut down any abnormal causes that do not conform to the actual situation of the target coal preparation plant. Furthermore, it allows for the modification or addition of abnormal causes and triggering rules based on the actual needs of the target coal preparation plant. For example, if the target coal preparation plant has a special product quality problem due to the characteristics of local raw coal, technical personnel can add an abnormal cause analysis and corresponding abnormal triggering rules to the system to address this problem.

[0033] Step S103: Obtain the real-time production data of the target coal preparation plant, and perform reasoning based on the knowledge graph system of the target coal preparation plant and the real-time production data to obtain the reasoning result.

[0034] The reasoning results include at least the abnormal phenomena and causes of the target coal preparation plant. In one implementation, after the knowledge graph system of the target coal preparation plant is adjusted, online reasoning can be performed based on the new data. During operation, the system acquires real-time production data of the target coal preparation plant and performs online reasoning based on the adjusted knowledge graph to obtain the abnormal phenomena and causes of the target coal preparation plant.

[0035] The method provided by this invention enables a general knowledge graph to quickly adapt to the actual conditions of different coal preparation plants, achieving rapid implementation. Furthermore, because the entire instantiation process is closely integrated with the actual situation of the coal preparation plant, and through thorough communication with plant staff and precise data adjustments, the accuracy of the knowledge graph application in that plant is greatly improved.

[0036] The method for instantiating and applying the general knowledge graph in different coal preparation plants provided in this embodiment of the invention constructs a knowledge graph system specifically for the target coal preparation plant by pre-constructing a general knowledge graph system and adjusting the general knowledge graph system in combination with the actual data information of the target coal preparation plant. This shortens the construction cycle of the knowledge graph system and makes the knowledge graph system more consistent with the actual situation of the target coal preparation plant, improving the applicability of the knowledge graph in different coal preparation plants and avoiding application failures caused by the inability of the general knowledge graph to adapt.

[0037] In one implementation, for the aforementioned step S103, i.e., when reasoning based on the knowledge graph system of the target coal preparation plant and real-time production data to obtain the reasoning result, it can be implemented in ways including but not limited to the following: First, compare the real-time production data with the anomaly triggering rules in the knowledge graph system of the target coal preparation plant to determine whether the target coal preparation plant has an anomaly; if an anomaly occurs, perform anomaly reasoning based on the knowledge graph in the knowledge graph system of the target coal preparation plant to determine the cause of the anomaly.

[0038] In practical implementation, anomaly reasoning can be performed based on the knowledge graph in the knowledge graph system of the target coal preparation plant and the pre-trained large model to obtain at least one initial cause of an anomaly and an anomaly cause that can be ruled out.

[0039] In one implementation, the knowledge graph of the target coal preparation plant can reflect the plant's equipment and their interrelationships, production process flow, normal operating parameter ranges, and common failure modes. This knowledge graph includes entities (such as production equipment, sensors, process parameters, etc.) and the relationships between them (such as causal relationships, correlational relationships, etc.). For example, a piece of equipment may be associated with specific sensor readings, which in turn may affect the final product quality.

[0040] In this embodiment of the invention, the knowledge graph system of the target coal preparation plant can collect real-time production data. Specifically, the sensor network installed on key equipment can monitor production data such as vibration, temperature, pressure, and flow rate, and transmit this production data to the central processing system. The knowledge graph system of the target coal preparation plant can then read the real-time production data from the central processing system and use the collected real-time production data to identify abnormal phenomena by comparing it with the anomaly triggering rules defined in the knowledge graph system. For example, when a parameter deviates from its normal range, an anomaly is triggered.

[0041] Furthermore, when an anomaly is detected, a pre-trained large model (such as a large model pre-trained using machine learning or deep learning methods) can be used to infer the cause of the anomaly. The large model can combine various relationships in the knowledge graph, including direct causal relationships and indirect influence paths, to infer the most likely cause of the current anomaly.

[0042] In practice, contextual information related to the anomaly is extracted from real-time production data, such as the time and location of the anomaly, the key parameters involved and their historical trends, and the status of other related equipment / parameters. Then, using this contextual information, semantic matching and association retrieval are performed within the constructed knowledge graph of the coal preparation plant. This locates the corresponding equipment nodes (e.g., heavy medium cyclones), sensor nodes (e.g., density meter A301), and process unit nodes (e.g., the main washing system) within the knowledge graph. Following the causal, dependency, and influence relationships in the knowledge graph, a local subgraph highly relevant to the current anomaly is extracted. For example, if the anomaly is high ash content in the clean coal, the local subgraph might include paths such as cyclone underflow port wear → decreased sorting accuracy → increased ash content and feed pressure fluctuations → unstable sorting.

[0043] Furthermore, structured subgraph information (causal chains, constraints, and typical failure modes expressed in natural language or graph descriptions) and unstructured contextual information (textual descriptions of real-time anomalies) are input into a pre-trained large model. The large model can follow paths in the knowledge graph to determine which antecedent factors are most likely to cause the currently observed anomaly. Combining this with real-time production data, it eliminates anomaly causes inconsistent with current operating conditions (e.g., a fault is usually accompanied by high temperatures, but the current temperature is normal, thus reducing its probability). Finally, it outputs several possible causes of the anomaly, along with confidence levels or explanatory evidence. Finally, the inference results of the large model are fused with deterministic rules in the knowledge graph. If strong causal rules exist in the knowledge graph, the inference results from the knowledge graph are adopted. For fuzzy or competing hypotheses, the large model weighs and ranks them to generate the final inference results, including: the most likely cause of the anomaly, supporting evidence, and suggested investigation steps or remedial measures.

[0044] In one embodiment, the method further includes: verifying the inference result and optimizing the knowledge graph system of the target coal preparation plant based on the verification result. In specific implementation, the actual production situation of the target coal preparation plant is obtained, and the inference result is compared with the actual production situation; if the inference result does not match the actual production situation, the knowledge graph system of the target coal preparation plant is optimized.

[0045] Specifically, embodiments of the present invention can quickly verify the correctness of a knowledge graph by closely integrating real-time online reasoning with actual production data. During operation, the system acquires production data from the coal preparation plant in real time, performs online reasoning based on the adjusted knowledge graph, and then compares the reasoning results with the actual production situation. If the reasoning results match the actual production situation, it indicates that the instantiation and application of the knowledge graph in the plant is correct; if discrepancies occur, problems in the knowledge graph can be identified in a timely manner for further adjustment and optimization.

[0046] For example, when the system infers from the knowledge graph that a piece of equipment is about to malfunction and provides a corresponding warning, if the equipment does indeed show similar malfunction signs in actual production, then the accuracy of the knowledge graph in this regard is verified. Conversely, if the system does not detect a malfunction, the reasoning logic in the knowledge graph regarding the equipment malfunction needs to be checked and corrected. This real-time comparison and verification method enables rapid verification of the correctness of knowledge graph applications in different coal preparation plants.

[0047] The method for instantiating and applying the general knowledge graph in different coal preparation plants provided in this embodiment of the invention has the following advantages compared with the traditional method for instantiating general knowledge graphs in different coal preparation plants: (1) The embodiments of the present invention greatly shorten the implementation cycle by pre-deploying basic data and making targeted adjustments in conjunction with communication with employees in the plant. It can be completed in just a few weeks or even less time, which greatly improves the implementation efficiency and enables knowledge graphs to be applied to coal preparation plant production more quickly, saving a lot of time costs and allowing enterprises to quickly benefit from knowledge graph technology.

[0048] (2) The embodiments of the present invention allow for the convenient deletion of unnecessary data and the closure of unnecessary reasons according to actual needs. It also allows for the addition of reasons and triggering rules, which enables the knowledge graph to accurately adapt to the unique production processes, equipment and management models of each coal preparation plant. This greatly improves the applicability of the knowledge graph in different coal preparation plants and avoids the application failure problem caused by the inability of the general knowledge graph to adapt.

[0049] (3) By comparing the online reasoning results with the actual production situation in real time, the embodiments of the present invention can quickly identify problems in the knowledge graph. Once the reasoning results do not match the actual situation, feedback can be provided immediately and optimization can be carried out to ensure that the knowledge graph always accurately reflects the actual production situation. This not only reduces the risk of production accidents caused by errors in the knowledge graph, but also reduces the production losses that may be caused by long verification cycles, thus ensuring the stability and efficiency of coal preparation plant production.

[0050] Regarding the method for instantiating and applying a general knowledge graph in different coal preparation plants provided in the foregoing embodiments, this embodiment of the invention also provides an apparatus for instantiating and applying a general knowledge graph in different coal preparation plants, see [link to apparatus]. Figure 2 The diagram shows the structure of a device for the instantiation and application of a general knowledge graph in different coal preparation plants. It illustrates that the device mainly includes the following parts: The basic knowledge graph construction module 201 is used to acquire general basic data of the coal preparation plant and construct a general knowledge graph system based on the general basic data; wherein, the general knowledge graph system includes: a general knowledge graph for reasoning the cause of anomalies and basic anomaly triggering rules.

[0051] The knowledge graph adjustment module 202 is used to acquire target data of the target coal preparation plant and adjust the general knowledge graph system based on the target data to obtain the knowledge graph system of the target coal preparation plant.

[0052] The anomaly reasoning module 203 is used to acquire real-time production data of the target coal preparation plant and to perform reasoning based on the knowledge graph system of the target coal preparation plant and the real-time production data to obtain reasoning results; wherein, the reasoning results include at least: the abnormal phenomena and causes of the target coal preparation plant.

[0053] The apparatus for instantiating the general knowledge graph in different coal preparation plants provided in this embodiment of the invention constructs a knowledge graph system specific to the target coal preparation plant by pre-constructing a general knowledge graph system and adjusting the general knowledge graph system in combination with the actual data information of the target coal preparation plant. This shortens the construction cycle of the knowledge graph system and makes the knowledge graph system more consistent with the actual situation of the target coal preparation plant, improving the applicability of the knowledge graph in different coal preparation plants and avoiding application failures caused by the inability of the general knowledge graph to adapt.

[0054] In one implementation, the knowledge graph adjustment module 202 is specifically used to: delete general basic data in the general knowledge graph system that does not conform to the production situation of the target coal preparation plant based on the target data, and close the abnormal reasons in the general knowledge graph system that do not conform to the production situation of the target coal preparation plant; and modify and / or add basic abnormal triggering rules and abnormal reasons in the general knowledge graph system based on the target data.

[0055] In one implementation, the above-mentioned anomaly reasoning module 203 is specifically used to: compare real-time production data with the anomaly triggering rules in the knowledge graph system of the target coal preparation plant to determine whether the target coal preparation plant has an anomaly; if an anomaly occurs, perform anomaly reasoning based on the knowledge graph in the knowledge graph system of the target coal preparation plant to determine the cause of the anomaly.

[0056] In one embodiment, the above-mentioned anomaly reasoning module 203 is further used to: perform anomaly reasoning based on the knowledge graph in the knowledge graph system of the target coal preparation plant and the pre-trained large model to obtain at least one initial anomaly cause and an anomaly cause that can be excluded.

[0057] In one embodiment, the above-mentioned apparatus further includes: a verification module, used to: verify the reasoning results and optimize the knowledge graph system of the target coal preparation plant based on the verification results.

[0058] In one implementation, the verification module is further configured to: obtain the actual production status of the target coal preparation plant and compare the inference result with the actual production status; if the inference result does not match the actual production status, optimize the knowledge graph system of the target coal preparation plant.

[0059] It should be noted that the device provided in the embodiments of the present invention has the same implementation principle and technical effect as the aforementioned method embodiments. For the sake of brevity, any parts not mentioned in the device embodiments can be referred to the corresponding content in the aforementioned method embodiments.

[0060] This invention also provides an electronic device, specifically, the electronic device includes a processor and a storage device; the storage device stores a computer program, and the computer program, when run by the processor, executes the method described in any of the above embodiments.

[0061] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 100 includes: a processor 30, a memory 31, a bus 32 and a communication interface 33. The processor 30, the communication interface 33 and the memory 31 are connected through the bus 32. The processor 30 is used to execute executable modules, such as computer programs, stored in the memory 31.

[0062] The memory 31 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 33 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0063] Bus 32 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0064] The memory 31 is used to store programs. After receiving an execution instruction, the processor 30 executes the program. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 30 or implemented by the processor 30.

[0065] Processor 30 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 30 or by instructions in software form. Processor 30 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 31. The processor 30 reads the information in memory 31 and, in conjunction with its hardware, completes the steps of the above method.

[0066] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0067] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0068] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for instantiating and applying a general knowledge graph in different coal preparation plants, characterized in that, include: Acquire general basic data of the coal preparation plant, and construct a general knowledge graph system based on the general basic data; wherein, the general knowledge graph system includes: a general knowledge graph for reasoning the causes of anomalies and basic anomaly triggering rules; Obtain target data for the target coal preparation plant, and adjust the general knowledge graph system based on the target data to obtain the knowledge graph system for the target coal preparation plant; The real-time production data of the target coal preparation plant is obtained, and reasoning is performed based on the knowledge graph system of the target coal preparation plant and the real-time production data to obtain the reasoning result; wherein, the reasoning result includes at least: the abnormal phenomena and causes of the abnormality of the target coal preparation plant.

2. The method according to claim 1, characterized in that, Based on the target data, the general knowledge graph system is adjusted to obtain the knowledge graph system of the target coal preparation plant, including: Based on the target data, delete general basic data in the general knowledge graph system that does not conform to the production situation of the target coal preparation plant, and close abnormal reasons in the general knowledge graph system that do not conform to the production situation of the target coal preparation plant; Based on the target data, the basic anomaly triggering rules and anomaly causes in the general knowledge graph system are modified and / or added.

3. The method according to claim 1, characterized in that, Based on the knowledge graph system of the target coal preparation plant and the real-time production data, inference is performed to obtain the inference results, including: The real-time production data is compared with the anomaly triggering rules in the knowledge graph system of the target coal preparation plant to determine whether the target coal preparation plant has experienced any abnormal phenomena. If an anomaly occurs, anomaly reasoning is performed based on the knowledge graph in the knowledge graph system of the target coal preparation plant to determine the cause of the anomaly.

4. The method according to claim 3, characterized in that, Anomaly reasoning is performed based on the knowledge graph in the knowledge graph system of the target coal preparation plant to determine the cause of the anomaly, including: Based on the knowledge graph in the knowledge graph system of the target coal preparation plant and the pre-trained large model, anomaly reasoning is performed to obtain at least one initial cause of the anomaly and an anomaly cause that can be ruled out.

5. The method according to claim 1, characterized in that, Also includes: The reasoning results are verified, and the knowledge graph system of the target coal preparation plant is optimized based on the verification results.

6. The method according to claim 5, characterized in that, Verification of the inference result includes: Obtain the actual production status of the target coal preparation plant and compare the inference result with the actual production status; If the reasoning result does not match the actual production situation, the knowledge graph system of the target coal preparation plant will be optimized.

7. A device for the instantiation and application of a general knowledge graph in different coal preparation plants, characterized in that, include: A basic knowledge graph construction module is used to acquire general basic data of coal preparation plants and construct a general knowledge graph system based on the general basic data; wherein, the general knowledge graph system includes: a general knowledge graph for reasoning the causes of anomalies and basic anomaly triggering rules; The knowledge graph adjustment module is used to acquire target data of the target coal preparation plant and adjust the general knowledge graph system based on the target data to obtain the knowledge graph system of the target coal preparation plant. An anomaly reasoning module is used to acquire real-time production data of the target coal preparation plant and to perform reasoning based on the knowledge graph system of the target coal preparation plant and the real-time production data to obtain reasoning results; wherein, the reasoning results include at least: the abnormal phenomena and causes of the target coal preparation plant.

8. The apparatus according to claim 7, characterized in that, Also includes: The verification module is used to verify the reasoning results and optimize the knowledge graph system of the target coal preparation plant based on the verification results.

9. An electronic device, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by the processor to perform the steps of the method described in any one of claims 1 to 6.

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