Knowledge data hybrid driven man-machine interaction aided decision making system and method

Abstract

The invention relates to a knowledge data hybrid-driven man-machine interaction aid decision making system and method. The system comprises a man-machine interaction module, an intelligent decision algorithm training module, a virtual action support environment module, a knowledge module and an external data interface design. In the system, a human decision maker and an intelligent algorithm can work cooperatively and complement each other. A human decision maker uses own experience and intuition to guide and adjust learning of an intelligent algorithm, and the intelligent algorithm assists human in making a more accurate and scientific decision by processing and analyzing a large amount of data. Through the mode, the knowledge data hybrid-driven man-machine interaction auxiliary decision making system can effectively cope with a complex and changeable decision making environment, the decision making quality and efficiency are improved, and especially in the field of high-risk and high-uncertainty action decision making.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a human-computer interactive decision-making assistance system and method driven by a hybrid knowledge and data approach. Background Art

[0002] In today's decision science field, data-driven intelligent algorithms play an increasingly important role. These algorithms are mainly based on big data, and use machine learning, deep learning and other methods to extract information, discover patterns, and predict the future, thereby assisting decision makers to make more scientific and accurate decisions. Data-driven intelligent algorithms have achieved remarkable success in many fields, such as business analysis, medical diagnosis, and market forecasting. The advantages of these algorithms are that they can process and analyze large amounts of data, discover hidden patterns and trends in the data, and make predictions based on historical data.

[0003] However, despite the many advantages of data-driven intelligent algorithms, they also face obvious limitations in complex decision-making scenarios. First, such algorithms usually require a large amount of labeled data to train the model, while in many practical scenarios, especially in action command decisions, it is very difficult to obtain a large amount of accurate labeled data. Second, data-driven methods often have difficulty handling highly complex and dynamically changing environments because they mainly rely on historical data, while real-world situations are constantly changing, making it difficult for models to update and adapt to new situations in real time. In addition, these algorithms often perform poorly and fail to provide effective decision support when faced with complex scenarios with unclear problem boundaries, unclear rules, and large problem scales.

[0004] These challenges are even more prominent in command decision-making problems. Real action scenarios are extremely complex, involving not only the coordination of multiple types of action entities and the constant changes of the two parties in the game, but also the influence of multiple uncertain factors such as geographical environment and weather conditions. These factors constitute a huge problem space full of uncertainty and complexity. In such an environment, algorithms that rely solely on data-driven algorithms are difficult to work effectively because they cannot accurately capture and understand all the complex variables, and they are also difficult to adapt to rapidly changing situations. Summary of the invention

[0005] In order to at least partially solve the above technical problems, the present application proposes a knowledge data hybrid driven human-computer interactive decision-making auxiliary system, comprising: A human-computer interaction module is used to obtain action task plans through human-computer interaction; A virtual action support environment module, used to configure action task parameters using the action task plan to initialize a simulation action model that simulates the execution of the action task corresponding to the action task plan; An intelligent decision-making algorithm training module is used to obtain action rules and action knowledge; divide the action task corresponding to the action task plan into multiple subtasks using the action knowledge and the action task plan, and construct a behavior tree for each subtask using the action rules and the action knowledge; select an executable action node in the latest action state on the behavior tree of the subtask to be executed, and generate an action instruction corresponding to the action node, wherein the latest action state includes the latest action state during the simulation execution of the action task; The virtual action support environment module is further used to input the action instructions generated by the intelligent decision-making algorithm training module into the simulation action model to simulate the execution of the subtask to be executed, and feed back the latest action state after the execution of the subtask to be executed to the intelligent decision-making algorithm training module until the action task is completed; The human-computer interaction module is also used to perform a traceability analysis on the action data generated by the virtual action support environment module in the process of simulating the execution of the action task, and to parse the action strategy generated by the intelligent decision-making algorithm training module, wherein the action strategy includes the division result of the action task, the calculation behavior tree of the subtask and the action instruction, and outputs the traceability analysis result and the parsing result so that the user can adjust the action task plan and / or action rules and action knowledge according to the traceability analysis result and the parsing result.

[0006] The present application also provides a knowledge data hybrid driven human-computer interaction decision-making assistance method, including: Step 1: Obtain the action task plan through human-computer interaction; Step 2: configuring action task parameters using the action task plan to initialize a simulation action model that simulates the execution of the action task corresponding to the action task plan; Step 3: Acquire action rules and action knowledge; Step 4: using the action knowledge and the action task plan to divide the action task corresponding to the action task plan into a plurality of subtasks; Step 5: construct a behavior tree for each subtask using the action rules and the action knowledge; Step 6: Select an executable action node in the latest action state on the behavior tree of the subtask to be executed, wherein the latest action state includes the latest action state in the process of simulating the execution of the action task; Step 7: Generate an action instruction corresponding to the action node; Step 8, inputting the action instruction into the simulation action model to simulate the execution of the subtask to be executed, and returning to step 6 until the action task is completed, and executing step 9; Step 9: Conduct a traceability analysis on the action data generated during the simulation of the action task, and parse the action strategy, wherein the action strategy includes the division result of the action task, the computational behavior tree of the subtask and the action instruction, and output the traceability analysis result and the parsing result so that the user can adjust the action task plan and / or action rules, action knowledge according to the traceability analysis result and the parsing result. If the action task plan and / or action rules, action knowledge are adjusted, return to step 3.

[0007] In the system and method provided in the embodiments of the present application, human decision makers and intelligent algorithms can work together and complement each other. Human decision makers use their own experience and intuition to guide and adjust the learning of intelligent algorithms, while intelligent algorithms assist humans in making more accurate and scientific decisions by processing and analyzing large amounts of data. In this way, the human-computer interactive decision-making support system driven by a hybrid of knowledge and data can effectively cope with complex and changing decision-making environments and improve the quality and efficiency of decision-making, especially in the field of high-risk and high-uncertainty action decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of the working principle of a knowledge data hybrid driven human-computer interactive decision-making assistance system provided in one embodiment of the present application; Figure 2 A flow chart of a method for a human-computer interactive decision-making assistance system driven by a hybrid of knowledge and data provided for one embodiment of the present application. DETAILED DESCRIPTION

[0009] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0010] Human decision makers can use their rich experience, intuition, and creative thinking to solve ambiguous and uncertain problems, which are difficult to match with algorithms that rely solely on data. Humans can quickly summarize and abstract complex situations, quickly reduce the solution space through experience and intuition, and find key and effective decision points. In addition, humans have unique abilities in understanding the diversity, variability, and irregularity in complex environments, and can flexibly adapt to new situations and solve problems creatively. Therefore, combining human knowledge and experience with data-driven algorithms can not only utilize the ability of algorithms to process and analyze large amounts of data, but also use human intuition and experience to reduce the solution space and improve the quality and efficiency of decision-making. This knowledge-data hybrid-driven human-computer interactive decision-making system can more effectively cope with complex and dynamically changing decision-making environments by integrating human intuitive judgment and machine data processing capabilities, especially in the field of high-risk and high-uncertainty action decisions.

[0011] Therefore, in response to the limitations of current data-driven intelligent decision-making algorithms in complex confrontation scenarios with unclear problem boundaries, unclear rules, and large problem scales, this application constructs a human-computer interactive decision-making assistance system architecture through a knowledge and data hybrid driven model. The core of this system is to utilize the complementary advantages of human experience and intuition and the data processing capabilities of machines to achieve more accurate, flexible and efficient decision support. By effectively integrating the two capabilities of human adaptability and machine calculation, this system not only improves the accuracy and efficiency of decision-making, but also increases the transparency and understandability of the decision-making process, so that it can be analyzed and used by various action control information systems to provide auxiliary support for related action decision-making activities.

[0012] The knowledge data hybrid driven human-computer interaction decision-making assistance system and method provided in the embodiments of the present application can be applied to, but not limited to, drone action scenarios.

[0013] Specifically, an embodiment of the present application provides a knowledge-data hybrid-driven human-computer interaction decision-making support system, including a human-computer interaction module, an intelligent decision-making algorithm training module, and a virtual action support environment module, and optionally, also includes a knowledge module and an external data interface.

[0014] The functions and implementation methods of each module are described in detail below.

[0015] A human-computer interaction module, which is used to acquire, analyze and display data through human-computer interaction. Specifically, the human-computer interaction module is used to acquire action task plans through human-computer interaction; it is also used to trace the action data generated during the simulation of the action task by the virtual action support environment module, and to parse the action strategy generated by the intelligent decision-making algorithm training module, wherein the action strategy includes the division result of the action task, the calculation behavior tree of the subtask and the action instruction, and outputs the traceability analysis result and the parsing result, so that the user can adjust the action task plan and / or action rules and action knowledge according to the traceability analysis result and the parsing result. As an example and not a limitation, the functions of the human-computer interaction module include: action task plan editing, action process display, action plan evaluation, action case recommendation and data traceability, etc.

[0016] The virtual action support environment module is used to configure the action task parameters using the action task plan to initialize the simulation action model that simulates the execution of the action task corresponding to the action task plan. The action task parameters include the map data of the environment where the action task is located, the simulation model parameters of the simulated execution of the action task, the instruction rule encoding, the time management configuration parameters, and the task management configuration parameters. Furthermore, the map data may include detailed terrain and weather condition map data. The virtual action support environment module is also used to input the action instructions generated by the intelligent decision algorithm training module into the simulation action model to simulate the execution of the subtask to be executed, and feed back the latest action status after the execution of the subtask to be executed to the intelligent decision algorithm training module until the action task is completed.

[0017] The intelligent decision-making algorithm training module is used to obtain action rules and action knowledge; use the action knowledge and the action task plan to divide the action task corresponding to the action task plan into multiple subtasks, and use the action rules and the action knowledge to build a behavior tree for each subtask; select an executable action node in the latest action state on the behavior tree of the subtask to be executed, and generate an action instruction corresponding to the action node, wherein the latest action state includes the latest action state during the simulation execution of the action task.

[0018] The knowledge module is used to store action rules, action knowledge, and action task plans.

[0019] External data interface, which mainly includes human-computer interaction module interface, intelligent decision algorithm training module interface, virtual action support environment module interface and knowledge module interface. External data interface is mainly used to complete the system integration work and ensure that each module and algorithm can work together seamlessly.

[0020] The embodiment of the present application designs an external data interface to standardize and format all data passing through the interface to ensure that data can be transmitted and interacted between various modules in the system. The external data interface of the embodiment of the present application is shown in Table 1: Table 1 Main system interfaces In a more specific embodiment, the architecture and function implementation process of the knowledge data hybrid driven human-computer interactive decision-making support system provided in the embodiment of the present application are as follows: Figure 1 shown.

[0021] The human-computer interaction module includes a solution editing submodule, a solution evaluation submodule, a data tracing submodule, an action process display submodule, and a case recommendation submodule. Furthermore, the function entrances of the above submodules can be provided through the human-computer interaction interface so that the user can select the required submodule function through the human-computer interaction interface.

[0022] After the user selects the program editing function, the human-computer interaction module calls the program editing interface, obtains the action task program input by the user through human-computer interaction, and outputs the action task program input by the user to the virtual action support environment module and the intelligent decision algorithm training module respectively. In the embodiment of the present application, the action task program includes but is not limited to the identification of the action participants, the scale of the action participants, the type of action means of the action participants, the type of action tools of the action participants, the goals of the action participants, the action restrictions of the action participants, etc. After the user selects the program evaluation function, the human-computer interaction module calls the program evaluation interface, evaluates the action task program, and visualizes the evaluation results.

[0023] After the user selects the case tracing function, the human-computer interaction module calls the case tracing interface to trace the action task plan and visualize the tracing results. As an example and not a limitation, evaluating the action task plan includes parsing the action strategy corresponding to the action task plan, and the evaluation result includes the parsing result.

[0024] After the user selects the action process display function, the human-computer interaction module calls the action process display interface to display the action process in a visual manner. Specifically, the received action data is displayed in a visual manner in real time so that the user can intuitively know the execution status of the action task.

[0025] After the user selects the case recommendation function, the human-computer interaction module calls the case recommendation interface, selects and displays the recommended cases according to the predetermined conditions and strategies. The present application embodiment does not limit the conditions and strategies for case recommendation. In actual applications, the conditions and strategies for case recommendation can be determined as needed.

[0026] The knowledge module includes a basic database, an action task plan library and a computing tool library. Among them, the basic database stores the basic data required for the operation of the system. This application does not specifically limit the content of the basic data. In practical applications, the basic data and the basic database can be defined as needed. The action task plan library stores historical action task plans. The computing tool library stores tools such as action rules and action knowledge. On the one hand, the knowledge module receives and saves the updated action task plan, updated action knowledge, and updated action rules output by the human-computer interaction module, and on the other hand, outputs action rules and action knowledge to the intelligent decision-making algorithm training module. Among them, the action task plan is used to guide the action strategy of the entities on both sides of the action, the action rules define the constraints on the entity behavior, and the action knowledge accumulates past action experience and action layout.

[0027] The virtual action support environment module includes a map data submodule, a time management submodule, an instruction rule encoding submodule, a task management submodule and a simulation action model. The virtual action support environment module outputs the action state to the intelligent decision algorithm training module and receives the action instructions output by the intelligent decision algorithm training module. Among them, the action state includes the action state after the corresponding action instruction is executed, such as map data, the scale of the action participants, the action means type of the action participants, the action tool type of the action participants, etc.

[0028] The intelligent decision-making algorithm training module includes behavioral components, adversarial data management models, and learning algorithm development frameworks. The intelligent decision-making algorithm training module obtains action status and map data from the virtual action support environment module, action task plans from the human-computer interaction module, action rules and action knowledge from the knowledge module, outputs action instructions to the virtual action support environment module, and outputs strategy plans and action process data to the human-computer interaction module.

[0029] The virtual action support environment module inputs the action instructions generated by the intelligent decision algorithm training module into the simulation action model to simulate the execution of the subtask to be executed, and feeds back the latest action state after the execution of the subtask to be executed to the intelligent decision algorithm training module. The virtual action support environment module and the intelligent decision algorithm training module repeat the above operations until the action task is completed.

[0030] The human-computer interaction module performs traceability analysis on the action data generated by the virtual action support environment module during the simulation of the action task, and parses the action strategy generated by the intelligent decision-making algorithm training module, where the action strategy includes the division results of the action task, the calculation behavior tree of the subtask and the action instructions, and outputs the traceability analysis results and the parsing results so that the user can adjust the action task plan and / or action rules and action knowledge according to the traceability analysis results and the parsing results.

[0031] In the disclosed embodiment, the action task parameters include map data of the environment where the action task is located, simulation model parameters of the action task simulation execution, instruction rule encoding, time management configuration parameters, and task management configuration parameters.

[0032] In the disclosed embodiment, the action knowledge and the action task plan are used to divide the action task corresponding to the action task plan into multiple subtasks, including: inputting the action task into a pre-trained task division model to obtain multiple subtasks output by the task division model, and the task division model is trained using the action knowledge.

[0033] In an embodiment of the present disclosure, selecting an executable action node in the latest action state on a behavior tree of a subtask to be executed includes: inputting the behavior tree of the subtask to be executed and the latest action state into a pre-trained action node selection model, and obtaining the executable action node in the latest action state output by the action node selection model.

[0034] The present application also provides a knowledge data hybrid driven human-computer interaction decision-making method, and its specific implementation method is as follows: Figure 2 As shown, the specific process is: Step 1: Obtain the action task plan through human-computer interaction; Step 2: configuring action task parameters using the action task plan to initialize a simulation action model that simulates the execution of the action task corresponding to the action task plan; Step 3: Acquire action rules and action knowledge; Step 4: using the action knowledge and the action task plan to divide the action task corresponding to the action task plan into a plurality of subtasks; Step 5: construct a behavior tree for each subtask using the action rules and the action knowledge; Step 6: Select an executable action node in the latest action state on the behavior tree of the subtask to be executed, where the latest action state includes the latest action state in the process of simulating the execution of the action task; Step 7: Generate an action instruction corresponding to the action node; Step 8, inputting the action instruction into the simulation action model to simulate the execution of the subtask to be executed, and returning to step 6 until the action task is completed, and executing step 9; Step 9: Conduct a traceability analysis on the action data generated during the simulation of the action task, and parse the action strategy generated by the intelligent decision-making algorithm training module, wherein the action strategy includes the division result of the action task, the calculation behavior tree of the subtask and the action instruction, and output the traceability analysis result and the parsing result so that the user can adjust the action task plan and / or action rules, action knowledge according to the traceability analysis result and the parsing result. If the action task plan and / or action rules, action knowledge are adjusted, return to step 3.

[0035] In a more specific implementation, the knowledge data hybrid driven human-computer interaction decision-making assistance method provided in the embodiment of the present application is implemented as follows: The user uses the human-computer interaction module to edit the action plan, inputs the action plan into the virtual action support environment module through the external data interface between the human-computer interaction module and the virtual action support environment module, and inputs the action plan into the intelligent decision algorithm training module through the external data interface between the human-computer interaction module and the intelligent decision algorithm training module.

[0036] The virtual action support environment module instantiates a simulated action scenario. Specifically, it sets various action task parameters according to the received action plan, including map data, the scale and type of action participants, the expected action effect of the party, the main actions of the other party, etc.

[0037] The intelligent decision algorithm training module receives the latest action status and map data from the virtual action support environment module, receives action rules, action knowledge, action instructions, etc. from the knowledge module, and receives action plans from the human-computer interaction module; The intelligent decision-making algorithm training module divides the action tasks corresponding to the action plan into multiple subtasks based on the action plan and action knowledge. .

[0038] The intelligent decision-making algorithm training module constructs a Constructing a behavior tree. The present application embodiment does not limit the specific construction process of the behavior tree. In practical applications, the behavior tree can be constructed using existing behavior tree construction technology as needed. The behavior tree can be a two-layer behavior tree or a multi-layer behavior tree. In the embodiment of the present application, each subtask may have a serial timing relationship, a parallel timing relationship, or a combination of serial and parallel timing relationships (i.e., the timing relationship of some subtasks is serial, while the timing relationship of some subtasks is parallel). In addition, all subtasks may need to be executed, or some subtasks may be conditionally triggered and executed. If the triggering condition is not met during the action, they do not need to be executed.

[0039] The intelligent decision algorithm training module uses the latest action state as the judgment basis for the behavior tree condition node, outputs the action nodes that can be executed under the current latest action state, and generates action instructions through the action nodes. Among them, the action nodes that can be executed under the current latest action state may be the action nodes under the current subtask, or the action nodes under other subtasks.

[0040] The intelligent decision algorithm training module transmits the action instructions to the virtual action environment to drive the operation of the simulation action model. After the simulation action model executes an action instruction, it sends data to the intelligent decision algorithm training module, including the latest action status and the latest map data. The intelligent decision algorithm manages the data generated during the action using the adversarial data management model.

[0041] The intelligent decision-making algorithm training module transmits the intelligent algorithm strategy or plan, as well as the action process data to the human-computer interaction module.

[0042] The human-computer interaction module performs traceability analysis on the action process data, parses the intelligent algorithm solution for user reference, and passes the knowledge or rules condensed from the analysis to the knowledge module.

[0043] Users provide feedback based on the presented options and modify their action plans, ensuring that the decision-making process continues to evolve and adapt to the changing environment by continuously optimizing behavior trees and reinforcement learning models.

[0044] In addition, the system is extensively tested to ensure that it can provide reliable and effective decision support in a variety of operational environments.

[0045] The knowledge-data hybrid-driven human-computer interaction decision-making assistance system and method provided in the embodiments of the present application are applied to UAV action scenarios, and can fully utilize the advantages of the decision-making system driven by both knowledge and data to make UAV action decisions.

[0046] In the application scenario based on drone action decision-making, the method provided by the embodiment of the present application is as follows: At the UAV action decision control end, the UAV action task plan is obtained through human-computer interaction; at the decision control end, the UAV action task plan is used to configure the UAV action task parameters to initialize the simulation action model of the action task corresponding to the UAV action task plan; at the decision control end, the UAV action rules and UAV action knowledge are obtained, and the action task corresponding to the action task plan is divided into multiple subtasks using the UAV action knowledge and the UAV action task plan; at the decision control end, the UAV action rules and UAV action knowledge are used to construct a behavior tree for each subtask, and the action node that can be executed under the latest action state is selected on the behavior tree of the subtask to be executed, and the latest action state includes the latest action state during the simulation execution of the action task ... obtained. The decision control end generates an action instruction corresponding to the action node; the decision control end inputs the action instruction into the UAV simulation action model to simulate the execution of the subtask to be executed; the above operation is repeated for each subtask until the action task is completed; the decision control end performs a traceability analysis on the action data generated during the simulation execution of the action task, and parses the UAV action strategy. The UAV action strategy includes the division results of the action task, the calculation behavior tree of the subtask and the action instruction, and outputs the traceability analysis results and the parsing results, so that the user can adjust the action UAV task plan and / or the UAV action rules, UAV action knowledge according to the traceability analysis results and the parsing results. When the UAV action task plan and / or the UAV action rules, UAV action knowledge are adjusted, the above processing process is repeated.

[0047] The technical effects of this application are described as follows: (1) In this technical solution, the knowledge-data hybrid-driven human-computer interaction decision-making support system uses the ideas of intelligence and modularization to establish a module for each of the functions of simulation, human-computer interaction, intelligent decision-making algorithm, and knowledge storage. The four modules are integrated into a whole using standardized interfaces to form a complete system architecture, providing intelligent decision-making support information for user decision-making.

[0048] (2) In this technical solution, the knowledge-data hybrid-driven human-computer interaction decision-making support system is a closed-loop decision-making system, in which the intelligent decision-making algorithm module can perform offline training of the model. Users input manual decision results and knowledge into the intelligent decision-making algorithm training process through the human-computer interaction module, continuously optimize the model, and achieve the purpose of improving the efficiency and accuracy of decision-making support.

[0049] (3) In this technical solution, the system has excellent adaptability and can adjust its support strategy according to different decision-making scenarios and user needs. Whether it is a change in the opponent's strategy, the input of a new action state, or a change in the environment, the system can flexibly adjust to ensure the most appropriate decision support.

[0050] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A knowledge data hybrid driven human-computer interactive decision-making support system, characterized in that: include: A human-computer interaction module is used to obtain action task plans through human-computer interaction; A virtual action support environment module, used to configure action task parameters using the action task plan to initialize a simulation action model that simulates the execution of the action task corresponding to the action task plan; Intelligent decision-making algorithm training module, used to obtain action rules and action knowledge; Using the action knowledge and the action task plan, the action task corresponding to the action task plan is divided into a plurality of subtasks, and using the action rules and the action knowledge, a behavior tree is constructed for each subtask; Selecting an executable action node in the latest action state on the behavior tree of the subtask to be executed, and generating an action instruction corresponding to the action node, wherein the latest action state includes the latest action state in the process of simulating the execution of the action task; The virtual action support environment module is further used to input the action instructions generated by the intelligent decision-making algorithm training module into the simulation action model to simulate the execution of the subtask to be executed, and feed back the latest action state after the execution of the subtask to be executed to the intelligent decision-making algorithm training module until the action task is completed; The human-computer interaction module is also used to perform a traceability analysis on the action data generated by the virtual action support environment module in the process of simulating the execution of the action task, and to parse the action strategy generated by the intelligent decision-making algorithm training module, wherein the action strategy includes the division result of the action task, the calculation behavior tree of the subtask and the action instruction, and outputs the traceability analysis result and the parsing result so that the user can adjust the action task plan and / or action rules and action knowledge according to the traceability analysis result and the parsing result.

2. The system according to claim 1, characterized in that The action task parameters include map data of the environment where the action task is located, simulation model parameters of the action task simulation execution, instruction rule coding, time management configuration parameters, and task management configuration parameters.

3. The system according to claim 1, characterized in that The utilizing the action knowledge and the action task plan to divide the action task corresponding to the action task plan into a plurality of subtasks includes: The action task is input into a pre-trained task division model to obtain a plurality of subtasks output by the task division model, wherein the task division model is trained using the action knowledge.

4. The system according to claim 1, characterized in that The step of selecting an executable action node in the latest action state on the behavior tree of the subtask to be executed includes: The behavior tree of the subtask to be executed and the latest action state are input into a pre-trained action node selection model to obtain an executable action node under the latest action state output by the action node selection model.

5. The system according to claim 1, characterized in that The system also includes a knowledge module for storing action rules, action knowledge and action task plans.

6. The system according to claim 1, characterized in that The system further comprises an external data interface, and the external data interface is used for data communication between various modules.

7. A knowledge data hybrid driven human-computer interaction decision-making method, characterized in that: include: Step 1: Obtain the action task plan through human-computer interaction; Step 2: configuring action task parameters using the action task plan to initialize a simulation action model that simulates the execution of the action task corresponding to the action task plan; Step 3: Acquire action rules and action knowledge; Step 4: using the action knowledge and the action task plan to divide the action task corresponding to the action task plan into a plurality of subtasks; Step 5: construct a behavior tree for each subtask using the action rules and the action knowledge; Step 6: Select an executable action node in the latest action state on the behavior tree of the subtask to be executed, wherein the latest action state includes the latest action state in the process of simulating the execution of the action task; Step 7: Generate an action instruction corresponding to the action node; Step 8, inputting the action instruction into the simulation action model to simulate the execution of the subtask to be executed, and returning to step 6 until the action task is completed, and executing step 9; Step 9: Conduct a traceability analysis on the action data generated during the simulation of the action task, and parse the action strategy, wherein the action strategy includes the division result of the action task, the computational behavior tree of the subtask and the action instruction, and output the traceability analysis result and the parsing result so that the user can adjust the action task plan and / or action rules, action knowledge according to the traceability analysis result and the parsing result. If the action task plan and / or action rules, action knowledge are adjusted, return to step 3.

8. The method according to claim 7, characterized in that The action task parameters include map data of the environment where the action task is located, simulation model parameters of the action task simulation execution, instruction rule coding, time management configuration parameters, and task management configuration parameters.

9. The method according to claim 7, characterized in that: The utilizing the action knowledge and the action task plan to divide the action task corresponding to the action task plan into a plurality of subtasks includes: The action task is input into a pre-trained task division model to obtain a plurality of subtasks output by the task division model, wherein the task division model is trained using the action knowledge.

10. The method according to claim 7, characterized in that The step of selecting an executable action node in the latest action state on the behavior tree of the subtask to be executed includes: The behavior tree of the subtask to be executed and the latest action state are input into a pre-trained action node selection model to obtain an executable action node under the latest action state output by the action node selection model.

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