Intelligent regulation and control task chain generation method and device, electronic equipment and medium

Abstract

The invention discloses an intelligent regulation and control task chain generation method and apparatus, an electronic device and a medium. The method comprises the steps of obtaining content texts of multiple types of regulation and control security regulations and regulation and control operations; inputting the content text into a large language model agent for content extraction so as to extract regulation and control operation sequences and calling contents under various conditions; intelligently generating a knowledge graph in a decision tree form in the knowledge graph through an intelligent agent according to the regulation and control operation sequence and the calling content under various conditions; acquiring a text instruction, performing intention recognition by an intelligent agent, intelligently generating a task chain for regulating and controlling operation logic in the generated knowledge graph after autonomous understanding, and autonomously generating a thinking chain for task execution; and according to the generated thinking chain, the intelligent agent intelligently calls related modules for regulation and control so as to realize intelligent regulation and control. According to the method, the quality and efficiency of obtaining the text information can be improved, the regulation and control thinking chain logic architecture is established by using the decision tree type knowledge graph, the accuracy is improved, and the use threshold of operation is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent generation of large language model agents, and in particular to a method, device, electronic device and medium for generating an intelligent control task chain. Background Art

[0002] With the construction of new power grids and the access of large-scale new energy power generation equipment, the manual dispatching method of traditional power systems is difficult to adapt to the real-time fluctuations and disposal operations after the access of massive distributed equipment (such as distributed power sources, new energy storage, electric vehicles, etc.). In addition, the rapid growth of the scale of the power grid has put great pressure on the operating efficiency of existing dispatchers. The continuous expansion of the scale of the power grid has led to an exponential growth in the number of dispatch objects, and the real-time dispatching business volume has also exploded. However, the operating efficiency of existing dispatchers can no longer meet such rapid demand growth. Although programmed automatic operation can improve the situation to a certain extent, it is still necessary to solve core problems such as how to ensure the security of dispatching decisions and how to understand the decision logic behind them. Therefore, in the context of the basic and relatively mature procedures of existing control operations and control decisions, large language models are highly generalized and have low operating thresholds, and the interface for calling related professional modules is relatively complete. However, the existing large language models rely on fixed pre-training data, are difficult to update or integrate new knowledge in real time, generate inaccurate information or "hallucinations", and may perform poorly when processing complex queries that require multi-entity associations and multi-step logical reasoning. As a "black box" model, its decision-making and reasoning processes often lack transparency and easy-to-understand explanations. Therefore, although intelligent regulation research has made certain progress based on large language models, existing technical solutions are limited to the artificial construction of knowledge graphs and the construction of fixed regulation task chains, and are insufficient in the dynamic generation and real-time adjustment of regulation task chains. Summary of the invention

[0003] In order to solve at least one of the technical problems existing in the prior art to a certain extent, the purpose of the present invention is to provide a method, device, electronic device and medium for generating an intelligent control task chain based on a large language model agent and a knowledge graph.

[0004] The first technical solution adopted by the present invention is:

[0005] A method for generating an intelligent control task chain comprises the following steps:

[0006] Obtain the content text of multiple types of regulatory safety regulations and regulatory operations;

[0007] Input the content text into a pre-trained large language model agent, which autonomously extracts the content to extract the control operation sequence and call content in various situations in the form of triple text;

[0008] The extracted control operation sequence and call content in various situations are used to intelligently generate a knowledge graph in the form of a decision tree in the preset knowledge graph through a large language model agent, providing a data basis for the generation of intelligent control task chains;

[0009] The text instructions input by the user are obtained, and the large language model agent recognizes the intention, and after autonomous understanding, it intelligently generates a task chain for regulating the operation logic in the generated knowledge graph, and autonomously generates a thinking chain for task execution;

[0010] According to the generated thought chain, the large language model agent intelligently calls relevant modules through the API interface for regulation to achieve intelligent regulation.

[0011] Furthermore, the control operation sequence and call content in various situations are extracted in the form of triple text, including:

[0012] After building the large language model agent framework, the agent is prompted with examples and instructions of the project through small sample texts, so that the agent can extract knowledge triples from the text through templates; the knowledge triples are in the form of:

[0013]

[0014] In the formula, E represents the entity set, R represents the relationship set, T represents the triple set in the knowledge graph, A represents the attribute set, and X represents the attribute value set; × represents the Cartesian product; (E×R×E) represents the set of triples connected by entities through relationships, (E×A×X) represents the combination of entities, attributes, and attribute values, and T is a subset of (E×R×E) and (E×A×X), which means that each triple is composed of entities, relationships / attributes, and entity / attribute values;

[0015] Therefore, a specific triple t is represented as:

[0016] t=(e1,r,e2)∈T

[0017] e1,e2∈E,r∈R

[0018] or:

[0019] t=(e,a,x)∈T

[0020] e∈E,a∈A,x∈X

[0021] In the formula, e1 and e2 represent two consecutive tasks in the control procedure, r represents the connection between the two tasks, that is, the order in which the two tasks are executed, e represents a single task in the control procedure, a represents the attribute of the task, which is generally the final output or auxiliary decision-making task in the procedure, and x represents the attribute value of the task, that is, the output result.

[0022] This enables the large model agent to correctly analyze and understand the text content based on the above form of knowledge triples.

[0023] Furthermore, the content of the small sample text prompt project includes:

[0024] Part I: Scenario setting, task description and precise definition of triples;

[0025] Part II: Small sample examples are presented for autonomous learning of large language model agents.

[0026] Furthermore, the control operation sequence and call content in various situations extracted from the control procedures are intelligently generated into a knowledge graph in the form of a decision tree in a preset knowledge graph through a large language model agent, including:

[0027] According to the obtained knowledge triples, the large language model agent distinguishes different modules in the control program into different types of nodes and determines the relationship and sequence structure between the modules;

[0028] The intelligent agent calls the relevant modules of neo4j to generate the knowledge graph, and automatically generates a directed knowledge graph in the form of a decision tree based on the knowledge triple content and the structure between modules.

[0029] Furthermore, the text instructions input by the user are obtained, the large language model agent performs intent recognition, and after autonomous understanding, intelligently generates a task chain for regulating the operation logic in the generated knowledge graph, and autonomously generates a thought chain for task execution, including:

[0030] Obtain the text instructions input by the user, input the text instructions into the large prediction model agent, and the large prediction model agent performs semantic understanding and intent recognition based on the large language model kernel;

[0031] The knowledge graph is read according to the recognition results, and the key nodes that need to be called in the knowledge graph are determined according to the keywords, so as to read the directed decision chain to which the key nodes belong and form a task thinking chain.

[0032] The second technical solution adopted by the present invention is:

[0033] An intelligent control task chain generation device, comprising:

[0034] A text acquisition module, used to obtain the content text of multiple types of regulatory safety regulations and regulatory operations;

[0035] A knowledge extraction module is used to input the content text into a pre-trained large language model agent, and the large language model agent autonomously extracts the content to extract the control operation sequence and call content in various situations in the form of triple text;

[0036] The graph generation module is used to extract the control operation sequence and call content in various situations, and intelligently generate a knowledge graph in the form of a decision tree in the preset knowledge graph through the large language model agent, providing a data basis for the generation of intelligent control task chains;

[0037] The thought generation module is used to obtain the text instructions input by the user, and the large language model agent performs intention recognition. After autonomous understanding, it intelligently generates a task chain for regulating the operation logic in the generated knowledge graph, and autonomously generates a thought chain for task execution;

[0038] The automatic control module is used to realize intelligent control by intelligently calling relevant modules through the API interface based on the generated thought chain.

[0039] Furthermore, the control operation sequence and call content in various situations are extracted in the form of triple text, including:

[0040] After building the large language model agent framework, the agent is prompted with examples and instructions of the project through small sample texts, so that the agent can extract knowledge triples from the text through templates; the knowledge triples are in the form of:

[0041]

[0042] In the formula, E represents the entity set, R represents the relationship set, T represents the triple set in the knowledge graph, A represents the attribute set, and X represents the attribute value set; × represents the Cartesian product; (E×R×E) represents the set of triples connected by entities through relationships, (E×A×X) represents the combination of entities, attributes, and attribute values, and T is a subset of (E×R×E) and (E×A×X), which means that each triple is composed of entities, relationships / attributes, and entity / attribute values;

[0043] Therefore, a specific triple t is represented as:

[0044] t=(e1,r,e2)∈T

[0045] e1,e2∈E,r∈R

[0046] or:

[0047] t=(e,a,x)∈T

[0048] e∈E,a∈A,x∈X

[0049] In the formula, e1 and e2 represent two consecutive tasks in the control procedure, r represents the connection between the two tasks, that is, the order in which the two tasks are executed, e represents a single task in the control procedure, a represents the attribute of the task, which is generally the final output or auxiliary decision-making task in the procedure, and x represents the attribute value of the task, that is, the output result.

[0050] This enables the large model agent to correctly analyze and understand the text content based on the above form of knowledge triples.

[0051] Furthermore, the content of the small sample text prompt project includes:

[0052] Part I: Scenario setting, task description and precise definition of triples;

[0053] Part II: Small sample examples are presented for autonomous learning of large language model agents.

[0054] The third technical solution adopted by the present invention is:

[0055] An electronic device comprises a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement an intelligent control task chain generation method as described above.

[0056] The fourth technical solution adopted by the present invention is:

[0057] A computer-readable storage medium, wherein at least one instruction, at least one program, code set or instruction set is stored in the storage medium, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement an intelligent control task chain generation method as described above.

[0058] The fifth technical solution adopted by the present invention is:

[0059] A computer program product or a computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above method.

[0060] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0061] (1) The present invention designs a knowledge graph intelligent generation mechanism based on a large language model agent, which can effectively realize the key information identification and intent recognition of the text, and can intelligently generate a knowledge graph based on the above analysis.

[0062] (2) The present invention designs a decision tree knowledge graph mechanism to strengthen the logical architecture of the regulatory thinking chain based on the knowledge graph, thereby improving the retrieval accuracy and the accuracy of the thinking chain generation.

[0063] (3) The present invention is based on a large language model intelligent agent and thinking chain, and intelligently executes control tasks through user text instructions. While ensuring the realization of specific calling requirements such as the expected operating effect and the safety of scheduling decisions, it lowers the user's usage threshold, and effectively improves the operating efficiency of scheduling personnel through the intelligent generation of scheduling thinking chains and intelligent module scheduling, thereby meeting the demand for scheduling business.

[0064] In general, the present invention can improve the quality and efficiency of obtaining text information by using a knowledge graph intelligent generation mechanism based on a large language model agent, and intelligently generate a knowledge graph. In addition, the decision tree-style knowledge graph is used to establish a logical framework for regulating the thinking chain to improve accuracy. At the same time, based on the large language model agent and the thinking chain, the threshold for user regulation operations is lowered, effectively realizing intelligent regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the embodiments of the present invention or the drawings of related technical solutions in the prior art are introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0066] Figure 1 It is a framework diagram of a method for generating an intelligent control task chain based on a large language model agent and a knowledge graph in an embodiment of the present invention.

[0067] Figure 2 Schematic diagram of the day-ahead and day-intraday control task execution mechanism in an embodiment of the present invention.

[0068] Figure 3 A schematic diagram of a scheduling knowledge graph in the form of a decision tree generated in an embodiment of the present invention.

[0069] Figure 4 The present invention is a flowchart of a method for generating an intelligent control task chain according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0071] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0072] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0073] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0074] In response to the existing technical problems, the present invention proposes an intelligent control task chain generation scheme based on a large language model agent and a knowledge graph, which can effectively realize the intelligent extraction of control information and the intelligent generation and execution of task chains, improve the quality of information feature extraction, and improve the efficiency of control task chain generation and intelligent execution of control tasks. At the same time, it also supports post-generation updates and adjustments, reducing the operational threshold.

[0075] Specifically, in response to the problem that existing large language models rely on fixed pre-training data and are difficult to update or integrate new knowledge in real time, knowledge graphs can dynamically access large language models to update and introduce new, structured knowledge in real time, improve the timeliness and relevance of large models, reduce the generation of erroneous information, increase the reliability of output, and significantly improve the transparency of models, making the reasoning process traceable and easy to understand. Therefore, intelligent generation of control task chains can be achieved based on large language model agents and knowledge graphs.

[0076] The intelligent control task chain generation method based on large language model agent and knowledge graph realizes intelligent processing and task chain generation of multi-type control safety regulations and operations through large language model agent and knowledge graph technology. Its processing process generally includes feature representation of multi-type control safety regulations and operation content; establishing an association mechanism between large language model agent and knowledge graph; generating a knowledge graph in the form of a decision tree based on the extracted features, and generating an intelligent control task chain on this basis.

[0077] According to the different feature representations, the intelligent control task chain generation methods can be divided into large language model agent-based methods and knowledge graph-based methods. The large language model agent-based method uses the natural language processing capabilities of the large language model to perform semantic analysis and feature extraction on text information to generate knowledge triples for control operations. The knowledge graph-based method uses the structured data storage and association capabilities of the knowledge graph to convert knowledge triples into knowledge graphs in the form of decision trees, providing a data basis for the generation of intelligent control task chains.

[0078] The intelligent regulation task chain generation method is based on small sample learning, that is, model training is carried out through pre-annotated text data to extract semantic information, improve the accuracy of regulation task chain generation, and perform deep feature learning through a large language model agent to convert multi-type regulation content into knowledge triples and generate a knowledge graph.

[0079] Example 1

[0080] like Figure 4 As shown, this embodiment provides a method for generating an intelligent control task chain, comprising the following steps:

[0081] S1. Obtain the content text of multiple types of regulatory safety regulations and regulatory operations.

[0082] S2. Input the content text into a pre-trained large language model agent, and the large language model agent autonomously extracts the content to extract the control operation sequence and calling content in various situations in the form of triple text.

[0083] As an optional implementation, step S2 specifically includes:

[0084] After building the large language model agent framework, the examples and instructions of the small sample text prompt project enable the agent to extract knowledge triples from the text through templates, and the knowledge triples are in the form of:

[0085]

[0086] In the formula, E represents the entity set, R represents the relationship set, T represents the triple set in the knowledge graph, A represents the attribute set, X represents the attribute value set, and the "×" in the formula represents the Cartesian product, that is, all possible ordered pair combinations. Therefore, (E×R×E) represents the set of triples connected by relationships between entities, (E×A×X) represents the combination of entities, attributes, and attribute values, and T, as a subset of (E×R×E) and (E×A×X), means that each triple is composed of entities, relationships / attributes, and entity / attribute values.

[0087] Therefore, a specific triple t can be expressed as:

[0088] t=(e1,r,e2)∈T

[0089] e1,e2∈E,r∈R

[0090] or:

[0091] t=(e,a,x)∈T

[0092] e∈E,a∈A,x∈X

[0093] This enables the large model agent to correctly analyze and understand the text content based on the above form of knowledge triples.

[0094] As an optional implementation method, the specific contents of the small and medium sample text prompt project include:

[0095] Part 1, scenario setting, task description and precise definition of triples: You are a network agent that helps track and analyze all relevant knowledge triples about all relevant people, things, concepts, etc., and integrates them with the knowledge stored in your weights and stored in the knowledge graph. A knowledge triple is a clause containing a subject, a predicate and an object. The subject is the entity being described, the predicate is a description of the subject's attributes, and the object is the value of the attribute.

[0096] In the second part, small sample examples are presented for autonomous learning of large language model agents:

[0097] The following is an example:

[0098] Example 1: Nevada is a state in the United States. It is also the largest gold producing state in the United States. Output: (Nevada, located in, United States) {KG_TRIPLE_DELIMITER} (Nevada, is the largest gold producing state, United States)

[0099] End of example

[0100] Example 2: The Northern Zhou Dynasty belonged to the Five Dynasties and Ten Kingdoms period, and the dynasty after the Northern Zhou Dynasty was the Song Dynasty.

[0101] Output: (Northern Zhou, belongs to, Five Dynasties and Ten Kingdoms period) {KG_TRIPLE_DELIMITER} (Northern Zhou, the dynasty after it, Song Dynasty)

[0102] End of example

[0103] The existing regulatory safety regulations and regulatory operations are provided as input to the large prediction model agent, which understands the sequential logic in the text content and extracts it into knowledge triples.

[0104] S3. The extracted control operation sequences and call contents in various situations are used to intelligently generate a knowledge graph in the form of a decision tree in the preset knowledge graph through a large language model agent, providing a data basis for the generation of intelligent control task chains.

[0105] As an optional implementation, step S3 specifically includes the following steps:

[0106] S31: According to the knowledge triples generated in step S2, the large language model agent distinguishes different modules therein into different types of nodes, and determines the belonging relationship and sequence structure between the modules;

[0107] S32: The large language model agent calls neo4j to generate relevant modules of the knowledge graph, and automatically generates a directed knowledge graph in the form of a decision tree based on the knowledge triple content in step S31 and the structure between each module.

[0108] S4. Obtain input text instructions, and the large language model agent recognizes the intention. After autonomous understanding, it intelligently generates a task chain for regulating the operation logic in the generated knowledge graph, and autonomously generates a thinking chain for task execution.

[0109] As an optional implementation, step S4 specifically includes the following steps:

[0110] S41: The user inputs text into the big prediction model agent, and the big language model agent performs semantic understanding and intent recognition based on the big language model kernel;

[0111] S42: Read the knowledge graph according to the recognition result, and determine the key nodes that need to be called in the knowledge graph according to the keywords, so as to read the directed decision chain to which it belongs and form a task thinking chain;

[0112] S43: Based on the task thinking chain, the large language model agent automatically calls relevant professional modules to meet user needs.

[0113] S5. Based on the generated thought chain, the large language model agent intelligently calls relevant modules through the API interface for regulation to achieve intelligent regulation.

[0114] Specifically, according to the thought chain generated in step S4, the large language model agent intelligently calls relevant modules through the API interface to perform calculation, analysis, evaluation and operation steps to achieve intelligent regulation.

[0115] The above method is explained in detail below with reference to the accompanying drawings and specific embodiments.

[0116] like Figure 1 As shown, this embodiment provides a method for generating an intelligent control task chain based on a large language model agent and a knowledge graph, which uses a large language model agent to analyze the control data content and the user control instructions in turn, and establishes a decision tree knowledge graph and a control execution thinking chain in turn, which includes the following steps:

[0117] 1) Intelligent generation of decision trees:

[0118] The content of the control data is input into the large language model agent that has been adjusted through small sample learning. The agent intelligently recognizes and understands the input content and intelligently extracts key information, and calls the knowledge graph to convert the extracted information into the form of knowledge graph triples and input them into the knowledge graph, and intelligently generates a knowledge graph in the form of a decision tree for control, such as Figure 3 shown.

[0119] 2) Intelligent execution of task chain:

[0120] like Figure 2 As shown, for the power system control content, it is mainly divided into two parts: day-ahead dispatch plan and intraday real-time control, and the association and order between the various parts are relatively fixed, and the main focus is on improving execution efficiency and reducing the operating threshold. This embodiment designs a control task thinking chain intelligent generation execution mechanism based on a decision tree knowledge graph and a large language model agent for the intelligent execution of control tasks, which includes the following steps:

[0121] 2.1) For the input user instructions and real-time control data monitored by the SCADA system, the large language model agent performs intention recognition and data analysis, and intelligently forms a thinking chain for executing the relevant control modules based on the generated control decision tree knowledge graph.

[0122] 2.2) Based on the generated control task thinking chain, the large language model agent automatically calls the relevant control modules to intelligently execute according to the thinking chain, and finally intelligently outputs the execution results.

[0123] Experimental conclusion: In view of the problem that the existing control methods are not intelligent enough and rely heavily on manual labor, resulting in low efficiency, the present invention proposes an intelligent control task chain generation method based on a large language model agent and a knowledge graph. In the instructions for use of the existing control day-ahead scheduling plan and intraday real-time scheduling, the use of the method of the present invention introduces a large language model agent, so that the control tasks in different control scenarios can intelligently generate thought chains, and can intelligently call related professional modules, effectively improving the efficiency and accuracy of control, and effectively lowering the threshold of control operations. In the following research, we will explore how the large language model agent can realize intelligent planning based on the control situation in the context of integrated control and planning, as well as the problem of how the decision tree knowledge graph participates in the formation of thought chains in planning. It has a good application prospect and is worth promoting.

[0124] Example 2

[0125] This embodiment provides an intelligent control task chain generation device, including:

[0126] A text acquisition module, used to obtain the content text of multiple types of regulatory safety regulations and regulatory operations;

[0127] A knowledge extraction module is used to input the content text into a pre-trained large language model agent, and the large language model agent autonomously extracts the content to extract the control operation sequence and call content in various situations in the form of triple text;

[0128] The graph generation module is used to extract the control operation sequence and call content in various situations, and intelligently generate a knowledge graph in the form of a decision tree in the preset knowledge graph through the large language model agent, providing a data basis for the generation of intelligent control task chains;

[0129] The thought generation module is used to obtain the text instructions input by the user, and the large language model agent performs intention recognition. After autonomous understanding, it intelligently generates a task chain for regulating the operation logic in the generated knowledge graph, and autonomously generates a thought chain for task execution;

[0130] The automatic control module is used to realize intelligent control by intelligently calling relevant modules through the API interface based on the generated thought chain.

[0131] Since the device is an intelligent control task chain generation device of an embodiment of the present invention, and the principle of solving the problem by the device is similar to that of the method, the implementation of the device can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0132] Example 3

[0133] An embodiment of the present invention further provides an electronic device, the electronic device comprising a processor and a memory, the memory storing at least one instruction, at least one program, a code set or an instruction set, the at least one instruction, the at least one program, the code set or the instruction set being loaded and executed by the processor to implement the following Figure 4 A method for generating an intelligent control task chain is shown.

[0134] It is understood that the memory may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory may be used to store instructions, programs, codes, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data created according to the use of the server, etc.

[0135] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor can integrate one or a combination of a central processing unit (CPU) and a modem. Among them, the CPU mainly processes the operating system and application programs; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but implemented separately through a chip.

[0136] Since the electronic device is an electronic device corresponding to an intelligent control task chain generation method of an embodiment of the present invention, and the principle of solving the problem by the electronic device is similar to that of the method, the implementation of the electronic device can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0137] Example 4

[0138] The embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to implement the following Figure 4 A method for generating an intelligent control task chain is shown.

[0139] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0140] Since the storage medium is a storage medium corresponding to an intelligent control task chain generation method in an embodiment of the present invention, and the principle of solving the problem by the storage medium is similar to that of the method, the implementation of the storage medium can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0141] Example 5

[0142] In some possible implementations, various aspects of the method of the embodiment of the present invention may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a computer device, the program code is used to cause the computer device to execute the steps of the method for generating an intelligent control task chain according to various exemplary embodiments of the present application described above in this specification. Among them, the executable computer program code or "code" for executing each embodiment can be written in a high-level programming language such as C, C++, C#, Smalltalk, Java, JavaScript, Visual Basic, structured query language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0143] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0144] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0145] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for generating an intelligent control task chain, characterized in that: The following steps are involved: Obtain the content text of multiple types of regulatory safety regulations and regulatory operations; Input the content text into a pre-trained large language model agent, and the large language model agent autonomously extracts the content to extract the control operation sequence and call content in various situations in the form of triple text; The extracted control operation sequence and call content in various situations are used to intelligently generate a knowledge graph in the form of a decision tree in the preset knowledge graph through a large language model agent, providing a data basis for the generation of intelligent control task chains; After receiving the input text instructions, the large language model agent recognizes the intent and autonomously generates a task chain for regulating the operation logic in the generated knowledge graph, and autonomously generates a thought chain for task execution. According to the generated thought chain, the large language model agent intelligently calls relevant modules through the API interface for regulation to achieve intelligent regulation.

2. The method for generating an intelligent control task chain according to claim 1, characterized in that: The control operation sequence and call content in various situations are extracted in the form of triple text, including: After building the large language model agent framework, the agent is prompted with examples and instructions of the project through small sample texts, so that the agent can extract knowledge triples from the text through templates; the knowledge triples are in the form of: In the formula, E represents the entity set, R represents the relationship set, T represents the triple set in the knowledge graph, A represents the attribute set, and X represents the attribute value set; × represents the Cartesian product; (E×R×E) represents the set of triples connected by entities through relationships, (E×A×X) represents the combination of entities, attributes, and attribute values, and T is a subset of (E×R×E) and (E×A×X), which means that each triple is composed of entities, relationships / attributes, and entity / attribute values; Therefore, in the construction of the library transfer task thinking chain, a specific triple t is expressed as: t=(e1,r,e2)∈T e1,e2∈E,r∈R or: t=(e,a,x)∈T e∈E,a∈A,x∈X In the formula, e1 and e2 represent two consecutive tasks in the control procedure, r represents the connection between the two tasks, that is, the order in which the two tasks are executed, e represents a single task in the control procedure, a represents the attribute of the task, and x represents the attribute value of the task, that is, the output result.

3. The method for generating an intelligent control task chain according to claim 2, characterized in that: The content of the small sample text prompt project includes: Part I: Scenario setting, task description and precise definition of triples; Part II: Small sample examples are presented for autonomous learning of large language model agents.

4. The method for generating an intelligent control task chain according to claim 1, characterized in that: The control operation sequence and call content in various situations extracted from the control procedures are used to intelligently generate a knowledge graph in the form of a decision tree in a preset knowledge graph through a large language model agent, including: According to the obtained knowledge triples, the large language model agent distinguishes different modules in the control program into different types of nodes and determines the relationship and sequence structure between the modules; The intelligent agent calls the relevant modules of neo4j to generate the knowledge graph, and automatically generates a directed knowledge graph in the form of a decision tree based on the knowledge triple content and the structure between modules.

5. The method for generating an intelligent control task chain according to claim 1, characterized in that: The input text instruction is obtained, the large language model agent performs intent recognition, and after autonomous understanding, intelligently generates a task chain for regulating the operation logic in the generated knowledge graph, and autonomously generates a thinking chain for task execution, including: Get the input text instructions, input the text instructions into the big prediction model agent, and the big prediction model agent performs semantic understanding and intent recognition based on the big language model kernel; The knowledge graph is read according to the recognition results, and the key nodes that need to be called in the knowledge graph are determined according to the keywords, so as to read the directed decision chain to which the key nodes belong and form a task thinking chain.

6. An intelligent control task chain generation device, characterized in that: include: A text acquisition module, used to obtain the content text of multiple types of regulatory safety regulations and regulatory operations; A knowledge extraction module is used to input the content text into a pre-trained large language model agent, and the large language model agent autonomously extracts the content to extract the control operation sequence and call content in various situations in the form of triple text; The graph generation module is used to extract the control operation sequence and call content in various situations, and intelligently generate a knowledge graph in the form of a decision tree in the preset knowledge graph through the large language model agent, providing a data basis for the generation of intelligent control task chains; The thought generation module is used to obtain input text instructions, and the large language model agent performs intent recognition. After autonomous understanding, it intelligently generates a task chain for regulating the operation logic in the generated knowledge graph, and autonomously generates a thought chain for task execution; The automatic control module is used to realize intelligent control by intelligently calling relevant modules through the API interface based on the generated thought chain.

7. The intelligent control task chain generation device according to claim 6, characterized in that: The control operation sequence and call content in various situations are extracted in the form of triple text, including: After building the large language model agent framework, the agent is prompted with examples and instructions of the project through small sample texts, so that the agent can extract knowledge triples from the text through templates; the knowledge triples are in the form of: In the formula, E represents the entity set, R represents the relationship set, T represents the triple set in the knowledge graph, A represents the attribute set, and X represents the attribute value set; × represents the Cartesian product; (E×R×E) represents the set of triples connected by entities through relationships, (E×A×X) represents the combination of entities, attributes, and attribute values, and T is a subset of (E×R×E) and (E×A×X), which means that each triple is composed of entities, relationships / attributes, and entity / attribute values; Therefore, a specific triple t is represented as: t=(e1,r,e2)∈T e1,e2∈E,r∈R or: t=(e,a,x)∈T e∈E,a∈A,x∈X In the formula, e1 and e2 represent two consecutive tasks in the control procedure, r represents the connection between the two tasks, that is, the order in which the two tasks are executed, e represents a single task in the control procedure, a represents the attribute of the task, and x represents the attribute value of the task, that is, the output result.

8. The intelligent control task chain generation device according to claim 7, characterized in that: The content of the small sample text prompt project includes: Part I: Scenario setting, task description and precise definition of triples; Part II: Small sample examples are presented for autonomous learning of large language model agents.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 5.

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