Real-time automatic decision method based on event activation mechanism in simulated training range
By adopting a real-time automatic adjudication method based on event activation mechanism in the simulation training range, the problem of low ruling timeliness in the existing technology is solved, realizing immediate ruling response and efficient ruling process.
Patent Information
- Application Number
- CN202411373186.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-29
- Publication Date
- 2025-02-07
AI Technical Summary
The adjudication method in the existing simulation training range is low in timeliness and cannot make a ruling response immediately after each critical event, resulting in a lag in automatic adjudication.
A real-time automatic adjudication method based on the event activation mechanism is adopted to achieve instant adjudication response by obtaining simulation training task information, configuring a list of adjudication rules, monitoring training process data in real time, judging activation conditions and calculating the adjudication results.
It improves the efficiency and versatility of simulation training rulings, can respond immediately, reduces the lag of judgment, and improves the progress and effectiveness of simulation training.
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Figure CN119394091A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of simulation training, and in particular to a real-time automatic decision method based on an event activation mechanism in a simulation training range. Background Art
[0002] A simulation training range refers to a platform or system that provides a land, sea and air simulation environment for personnel of various arms, weapons training and tactical and combat research.
[0003] Engagement adjudication refers to the process of adjudicating the effects or results of the actions of the red and blue teams participating in the training during range training.
[0004] During the simulation training process, after the red and blue teams take action, they need to judge the results of key events that occurred during the action and the validity of the results based on the arbitration rules, and feedback the arbitration results to the simulation training range so that both parties can make immediate deployment adjustments based on the results of the action.
[0005] Since the actions of both parties in the simulation training range are carried out in real time, the timeliness of the ruling results is required to be high. Therefore, the ruling should be independent of manual work and the ruling calculation should be completed in real time and automatically. The traditional method of relying on manual judgment has low timeliness, which affects the progress and effect of simulation training. Although most of the existing judgment methods use preset judgment rules, so that the system automatically completes the judgment calculation according to the judgment rules, it cannot guarantee timeliness. The system cannot make a judgment response immediately after every key event that occurs in the exercise, and automatic judgment still has a lag. Summary of the invention
[0006] The technical problem to be solved by the present invention is how to improve the efficiency and versatility of simulation training decisions and enable instant decision responses; in view of this, the present invention provides a real-time automatic decision method based on an event activation mechanism in a simulation training range.
[0007] The technical solution adopted by the present invention is a real-time automatic decision method based on an event activation mechanism in a simulation training range, comprising:
[0008] Step S1, obtaining task information of simulation training;
[0009] Step S2, for the event type in the task information, configure and save a ruling rule list in a preset format, wherein the ruling rule list includes a rule ID, an event type, a ruling rule type, an activation condition, a data element name, and a ruling calculation model;
[0010] Step S3, real-time monitoring and collection of training process data in the current simulation training range;
[0011] Step S4, according to the event type and the decision rule type in the training process data, select the corresponding decision rule id in the decision rule list to determine whether the activation condition is met;
[0012] Step S5, if the activation condition is met, extract and process the corresponding data according to the data element name in the decision rule, and calculate the decision result according to the decision calculation model, otherwise continue to monitor.
[0013] In one embodiment, in step S1, the task information includes:
[0014] Simulation training purpose, simulation training background, simulation training process, and simulation running script.
[0015] In one embodiment, in step S3, the training process data is matched with the data content in the decision rule list.
[0016] In one embodiment, in step S5, if the activation condition is met, the corresponding data is extracted and processed according to the data element name in the decision rule, and the decision result is calculated according to the decision calculation model, specifically including:
[0017] The adjudication management function submodule receives adjudication task information and creates adjudication tasks;
[0018] The adjudication management function submodule receives the adjudication rule list and binds it to the adjudication task created this time;
[0019] The decision management function submodule monitors the data of the simulation training main process, and when the event start mark is monitored, the following steps are continued;
[0020] Search the adjudication rule list according to the event type and adjudication rule type, and send the data element name of the adjudication rule to the adjudication data processing function submodule;
[0021] The decision management function submodule continues to monitor the data of the simulation training main process. When the activation parameter data of the simulation training main process is monitored, the activation condition is determined according to the decision rule. If the activation condition is met, the following steps are continued, otherwise the data is continued to be monitored;
[0022] The decision management functional submodule requests simulation training data from the simulation training main process, and after receiving the simulation training data, sends the data to the data processing functional submodule;
[0023] The data processing functional submodule extracts data elements from the simulation training data according to the data element names in the decision rules, and performs quantization, intensification, and normalization on the data, and sends the processed data to the decision calculation functional submodule;
[0024] The decision calculation functional submodule obtains the processed data, calculates the decision result according to the decision calculation model in the decision rule, and then feeds the result back to the decision management functional submodule, which sends it to the simulation training main process and uniformly saves and manages the decision tasks, decision events and decision results.
[0025] The decision management function submodule continues to monitor the data of the simulation training main process until the main process ends, and the engagement automatic decision module automatically ends its operation.
[0026] Another aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the real-time automatic decision method based on the event activation mechanism in the simulation training range as described in any one of the above items.
[0027] Another aspect of the present invention further provides a computer storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the real-time automatic decision method based on an event activation mechanism in a simulation training range as described in any of the above items are implemented.
[0028] Compared with the prior art, the present invention has at least the following advantages:
[0029] The present invention proposes an event-based activation mechanism, and the system can instantly activate the decision calculation for an event based on the matching and judgment of the event type, decision rule type, and activation conditions in real time, select the matching decision rule from the decision rule list, extract data according to the data element name specified in the decision rule, and then calculate the input data elements according to the decision calculation model in the decision rule to obtain the decision result. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] Figure 1 A schematic flow chart of a real-time automatic decision method based on an event activation mechanism in a simulation training range according to an embodiment of the present invention;
[0031] Figure 2 A logical schematic diagram of a real-time automatic decision method based on an event activation mechanism in a simulation training range according to an embodiment of the present invention;
[0032] Figure 3 The figure is a schematic diagram of the composition of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION
[0033] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose, the present invention is described in detail below in conjunction with the accompanying drawings and preferred embodiments.
[0034] Unless otherwise defined, all terms (including technical terms and scientific terms) used in this article have the same meaning as commonly understood by ordinary technicians in the field to which this application belongs. It should also be understood that terms (such as terms defined in commonly used dictionaries) should be interpreted as having the same meaning as their meaning in the context of the relevant technology, and will not be interpreted in an idealized or overly formal sense unless explicitly defined in this article.
[0035] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.
[0036] The embodiment of the present invention provides a real-time automatic decision method based on an event activation mechanism in a simulation training range, such as Figure 1 As shown, including:
[0037] Step S1, obtaining task information of simulation training;
[0038] Step S2, for the event type in the task information, configure and save a ruling rule list in a preset format, wherein the ruling rule list includes a rule ID, an event type, a ruling rule type, an activation condition, a data element name, and a ruling calculation model;
[0039] Step S3, real-time monitoring and collection of training process data in the current simulation training range;
[0040] Step S4, according to the event type and the decision rule type in the training process data, select the corresponding decision rule id in the decision rule list to determine whether the activation condition is met;
[0041] Step S5, if the activation condition is met, extract and process the corresponding data according to the data element name in the decision rule, and calculate the decision result according to the decision calculation model, otherwise continue to monitor.
[0042] In this embodiment, the task information includes: simulation training purpose, simulation training background, simulation training process, and simulation running script.
[0043] In this embodiment, the training process data matches the data content in the decision rule list.
[0044] In step S5, if the activation condition is met, the corresponding data is extracted and processed according to the data element name in the decision rule, and the decision result is calculated according to the decision calculation model, which specifically includes:
[0045] The adjudication management function submodule receives adjudication task information and creates adjudication tasks;
[0046] The adjudication management function submodule receives the adjudication rule list and binds it to the adjudication task created this time;
[0047] The decision management function submodule monitors the data of the simulation training main process, and when the event start mark is monitored, the following steps are continued;
[0048] Search the adjudication rule list according to the event type and adjudication rule type, and send the data element name of the adjudication rule to the adjudication data processing function submodule;
[0049] The decision management function submodule continues to monitor the data of the simulation training main process. When the activation parameter data of the simulation training main process is monitored, the activation condition is determined according to the decision rule. If the activation condition is met, the following steps are continued, otherwise the data is continued to be monitored;
[0050] The decision management functional submodule requests simulation training data from the simulation training main process, and after receiving the simulation training data, sends the data to the data processing functional submodule;
[0051] The data processing functional submodule extracts data elements from the simulation training data according to the data element names in the decision rules, and performs quantization, intensification, and normalization on the data, and sends the processed data to the decision calculation functional submodule;
[0052] The decision calculation functional submodule obtains the processed data, calculates the decision result according to the decision calculation model in the decision rule, and then feeds the result back to the decision management functional submodule, which sends it to the simulation training main process and uniformly saves and manages the decision tasks, decision events and decision results.
[0053] The decision management function submodule continues to monitor the data of the simulation training main process until the main process ends, and the engagement automatic decision module automatically ends its operation.
[0054] The method provided by this embodiment is described in detail below. The main steps of the real-time automatic decision method are as follows:
[0055] (1) Before the simulation training begins, obtain the task information of the simulation training, such as the simulation training purpose, simulation training background, simulation training process, simulation running script, etc.
[0056] (2) Based on the key event types in the training task, edit and save the adjudication rule list in the following format.
[0057] Table 1 List of ruling rules
[0058]
[0059] (3) After the simulation training begins, the automatic decision module starts running and monitors and collects the training process data in the simulation training range in real time. The simulation training range should pre-design the data system so that the output data includes the activation parameter data, event type, and data elements of the decision input required for the decision.
[0060] (4) Select the corresponding adjudication rule from the adjudication rule list according to the event type and adjudication rule type, and determine whether the activation conditions are met. If the activation conditions are met, extract and process the corresponding data according to the "data element name" in the adjudication rule, and calculate the adjudication result according to the "adjudication calculation model"; if the activation conditions are not met, continue to wait and monitor the data.
[0061] (5) After each decision calculation is completed, the decision result is immediately returned to the simulation training range.
[0062] The structure of the automatic adjudication module and the complete workflow are described as follows.
[0063] The automatic decision module is designed as a subprocess of the main process of simulation training in the simulation training range. It can interact with the main process for data. After the main process is started, the automatic decision module is automatically created and run as its subprocess. The core functions of the automatic decision module include decision management, data processing, and decision calculation. As three functional submodules, the system workflow is as follows.
[0064] First, in the early preparation stage of simulation training, the training organizer formulates the adjudication tasks and adjudication rule list in the range system according to the training tasks. In the simulation training operation stage, the automatic adjudication module receives the task information and adjudication rule list provided by the main process and starts running:
[0065] a) The adjudication management function submodule receives adjudication task information and creates adjudication tasks;
[0066] b) The adjudication management function submodule receives the adjudication rule list and binds it to the adjudication task created this time;
[0067] c) The decision management function submodule monitors the data of the simulation training main process, and when the event start mark (including the event start / end mark, event type, and decision rule type) is monitored, the following steps are continued;
[0068] d) searching for a list of adjudication rules according to the event type and adjudication rule type, and sending the data element name of the adjudication rule to the adjudication data processing functional submodule;
[0069] e) The decision management function submodule continues to monitor the data of the simulation training main process. When the activation parameter data of the simulation training main process is monitored, the activation condition is determined according to the decision rule. If the activation condition is met, the following steps are continued, otherwise the data continues to be monitored;
[0070] f) The decision management function submodule requests the simulation training data from the simulation training main process, and after receiving the simulation training data, sends the data to the data processing function submodule;
[0071] g) The data processing functional submodule extracts data elements from the simulation training data according to the data element names in the decision rules, and performs quantization, intensification, and normalization on the data, and sends the processed data to the decision calculation functional submodule;
[0072] h) The decision calculation functional submodule obtains the processed data, calculates the decision result according to the decision calculation model in the decision rule, and then feeds the result back to the decision management functional submodule, which sends it to the simulation training main process and uniformly saves and manages the decision tasks, decision events and decision results.
[0073] i) The decision management function submodule continues to monitor the data of the simulation training main process until the main process ends, and the engagement automatic decision module automatically ends its operation.
[0074] The present invention is described below by taking a wireless signal warfare simulation training scenario as an example.
[0075] The structure of the wireless signal warfare simulation training range is as follows: Figure 2 , including simulation training engine, data bus and automatic decision module. The automatic decision module includes three functional sub-modules: decision management, data processing and decision calculation. Decision management is the management of decision tasks, preset decision rule lists, decision results, etc.; data processing extracts and processes input data; decision calculation, as the core function, calculates input data according to the decision calculation model.
[0076] (1) Decision Management
[0077] The adjudication management submodule provides the system backend operation and maintenance with the functions of managing and recording adjudication tasks and related data. It is also responsible for data monitoring and data request functions, including adjudication task management, adjudication rule management, adjudication event management, adjudication result management, etc.
[0078] A. Adjudication Task Management
[0079] The adjudication task management function is to record and manage the corresponding adjudication tasks according to different training and exercise tasks. One adjudication task contains information such as training and exercise task information, adjudication rule list, adjudication event list, etc., and the adjudication results received will correspond to the corresponding adjudication events.
[0080] B. Adjudication Event Management
[0081] Adjudication events are generated following key engagement events in training and exercise missions. A key engagement event will generate one or more adjudication events based on the adjudication rule list. Adjudication event management mainly involves the summary management of adjudication events and their results included in adjudication missions.
[0082] C. Administration of ruling rules
[0083] Adjudication rule management is to store and manage pre-established adjudication rules, associate adjudication rules with adjudication events and adjudication tasks, and the generation of adjudication events shall be based on the adjudication rules in this adjudication task. Each adjudication rule in the adjudication rule list shall include event type, adjudication rule type, activation condition, data element name, and adjudication calculation model.
[0084] For common event types in training mission scenarios, Table 2 shows the specific content of the judgment rules by taking the signal attack result judgment and bit error rate judgment in wireless signal warfare simulation training as examples.
[0085] Table 2 Example of a list of ruling rules
[0086]
[0087] D. Management of ruling results
[0088] The decision result management is to report the decision result after receiving the decision result of the decision event, send the decision result data to the simulation training engine, and store the decision result corresponding to the decision event.
[0089] (2) Data processing
[0090] The adjudication rules specify the names of the data elements required for input of each rule. The data output by the simulation training engine should contain data fields that match the input data names, or contain atomic data that can calculate the input data. The data processing function submodule extracts or calculates data elements from them, and performs adaptation and compliance checks on the data elements and the input data names specified in the adjudication rules. If the check fails, a prompt should appear to remind you to check the accuracy of the adjudication rules, engine output data, etc. If the check passes, the data is quantified, converted, grouped, and sorted according to the requirements of the adjudication calculation, and the processed data is used as the direct input data for the adjudication calculation.
[0091] (3) Calculation of the award
[0092] All decisions are based on the list of decision rules in the decision task. After the corresponding decision event is created, the decision calculation module will receive the input data processed by the decision data processing module, and then calculate the decision result according to the decision rules. The decision calculation can adopt calculation methods such as conditional judgment, random number generation, and probabilistic lottery.
[0093] After calculating the ruling result, the ruling calculation module is also responsible for returning the ruling result to the ruling management module, which manages the ruling result and the ruling event in correspondence and further reports the ruling result.
[0094] The simulation training engine is the core of the simulation training range. It is responsible for simulating the business data in the wireless signal war and the simulation operations of various businesses, and calculating the operation results. The business data and simulation operation result data in the simulation training engine are collectively referred to as simulation training data.
[0095] The data bus can be implemented using data push-subscription related technologies, such as the common Kafaka technology. The simulation training engine pushes the simulation training data to the data bus in real time. The simulation training data is divided into different subscription topics, such as task information, activation data, simulation training data, etc. The automatic decision module can subscribe to and monitor the data in the data bus.
[0096] The following describes the system workflow. In the early preparation stage of simulation training, the training organizer needs to formulate a list of adjudication rules in the range system according to the types of key events in the training mission. After the simulation training is started, the simulation training engine sends the training mission information and the list of adjudication rules to the automatic adjudication module and starts running.
[0097] The system workflow is as follows:
[0098] a) The decision management function submodule receives training task information and creates a decision task;
[0099] b) The adjudication management function submodule receives the adjudication rule list and binds it to the adjudication task created this time;
[0100] c) All kinds of data during the operation of the simulation training engine are sent to the data bus in real time;
[0101] d) The decision management function submodule monitors the data of the simulation training engine through the data bus. When the event start flag is monitored (the data includes the event start / end flag, event type, and decision rule type), the following steps are continued;
[0102] e) searching for a list of adjudication rules according to the event type and adjudication rule type, and sending the data element name of the adjudication rule to the data processing function submodule;
[0103] f) The decision management function submodule continues to monitor the data of the simulation training engine. When the activation parameter data in the decision rule is monitored, the activation condition is determined according to the decision rule. If the activation condition is met, the following steps are continued, otherwise the data continues to be monitored;
[0104] g) the decision management functional submodule adds a decision event to the decision event list, requests simulation training data from the simulation training engine, and after receiving the simulation training data, sends the data to the data processing functional submodule;
[0105] h) The data processing functional submodule extracts data elements from the simulation training data according to the data element names in the decision rules, and performs quantization, intensification, and normalization on the data, and sends the processed data to the decision calculation functional submodule;
[0106] i) The decision calculation submodule obtains the processed data, calculates the decision result according to the decision calculation model in the decision rule, and then feeds the result back to the decision management submodule. The decision management submodule binds the decision result to the current decision event, uniformly saves and manages the decision tasks, decision events and decision results, and sends the decision result to the simulation training engine.
[0107] j) The decision management function submodule continues to monitor the data of the simulation training engine until the engine runs out. The decision module saves the data of this decision task to the database and automatically ends the run.
[0108] Compared with the prior art, this embodiment has at least the following effects:
[0109] 1) This embodiment proposes a new adjudication rule paradigm, where each adjudication rule consists of a rule ID, an event type, an adjudication rule type, an activation condition, a data element name, and an adjudication calculation model;
[0110] 2) This embodiment designs and proposes an event-based activation mechanism. The system can instantly activate the decision calculation for an event based on the matching and judgment of the event type, decision rule type, and activation conditions, select the matching decision rule from the decision rule list, extract data based on the data element name specified in the decision rule, and then calculate the input data elements based on the decision calculation model in the decision rule to obtain the decision result;
[0111] 3) This embodiment designs a complete automatic decision system structure and workflow in a simulation training range. An automatic decision module is designed in the simulation training range, which provides instant automatic decision services for the simulation training engine and interacts with the simulation training engine through the data bus. The automatic decision module includes three functions: decision management, data processing, and decision calculation. Decision management is the management of decision tasks, preset decision rule lists, decision results, etc.; data processing extracts and processes input data; decision calculation, as a core function, calculates input data according to the decision calculation model.
[0112] A second embodiment of the present invention is an electronic device, such as Figure 3 As shown, it can be understood as a physical device, including a processor and a memory storing instructions executable by the processor. When the instructions are executed by the processor, the following operations are performed:
[0113] Step S1, obtaining task information of simulation training;
[0114] Step S2, for the event type in the task information, configure and save a ruling rule list in a preset format, wherein the ruling rule list includes a rule ID, an event type, a ruling rule type, an activation condition, a data element name, and a ruling calculation model;
[0115] Step S3, real-time monitoring and collection of training process data in the current simulation training range;
[0116] Step S4, according to the event type and the decision rule type in the training process data, select the corresponding decision rule id in the decision rule list to determine whether the activation condition is met;
[0117] Step S5, if the activation condition is met, extract and process the corresponding data according to the data element name in the decision rule, and calculate the decision result according to the decision calculation model, otherwise continue to monitor.
[0118] The third embodiment of the present invention, the process of the real-time automatic arbitration method based on the event activation mechanism in the simulation training range of this embodiment is the same as that of the first and second embodiments, the difference is that, in engineering implementation, this embodiment can be implemented by means of software plus a necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the method of the present invention can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), including a number of instructions for a device to execute the method described in the embodiment of the present invention.
[0119] Through the description of the specific implementation methods, a deeper and more specific understanding of the technical means and effects adopted by the present invention to achieve the predetermined purpose should be obtained. However, the accompanying drawings are only for reference and illustration purposes and are not intended to limit the present invention.
Claims
1. A real-time automatic decision method based on event activation mechanism in a simulation training range, characterized in that: include: Step S1, obtaining task information of simulation training; Step S2, for the event type in the task information, configure and save a ruling rule list in a preset format, wherein the ruling rule list includes a rule ID, an event type, a ruling rule type, an activation condition, a data element name, and a ruling calculation model; Step S3, real-time monitoring and collection of training process data in the current simulation training range; Step S4, according to the event type and the decision rule type in the training process data, select the corresponding decision rule id in the decision rule list to determine whether the activation condition is met; Step S5, if the activation condition is met, extract and process the corresponding data according to the data element name in the decision rule, and calculate the decision result according to the decision calculation model, otherwise continue to monitor.
2. The real-time automatic decision method based on event activation mechanism in the simulation training range according to claim 1 is characterized in that: In step S1, the task information includes: Simulation training purpose, simulation training background, simulation training process, and simulation running script.
3. The real-time automatic decision method based on event activation mechanism in the simulation training range according to claim 2 is characterized in that: In step S3, the training process data is matched with the data content in the decision rule list.
4. The real-time automatic decision method based on event activation mechanism in the simulation training range according to claim 3 is characterized in that: In step S5, if the activation condition is met, the corresponding data is extracted and processed according to the data element name in the decision rule, and the decision result is calculated according to the decision calculation model, which specifically includes: The adjudication management function submodule receives adjudication task information and creates adjudication tasks; The adjudication management function submodule receives the adjudication rule list and binds it to the adjudication task created this time; The decision management function submodule monitors the data of the simulation training main process. When the event start flag is detected, the following steps are continued: Search the adjudication rule list according to the event type and adjudication rule type, and send the data element name of the adjudication rule to the adjudication data processing function submodule; The decision management function submodule continues to monitor the data of the simulation training main process. When the activation parameter data of the simulation training main process is monitored, the activation condition is determined according to the decision rule. If the activation condition is met, the following steps are continued, otherwise the data is continued to be monitored; The decision management functional submodule requests simulation training data from the simulation training main process, and after receiving the simulation training data, sends the data to the data processing functional submodule; The data processing functional submodule extracts data elements from the simulation training data according to the data element names in the decision rules, and performs quantization, intensification, and normalization on the data, and sends the processed data to the decision calculation functional submodule; The decision calculation submodule obtains the processed data, calculates the decision result according to the decision calculation model in the decision rule, and then feeds the result back to the decision management submodule, which sends it to the simulation training main process and uniformly saves and manages the decision tasks, decision events and decision results. The decision management function submodule continues to monitor the data of the simulation training main process until the main process ends, and the engagement automatic decision module automatically ends its operation.
5. An electronic device, characterized in that: The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the real-time automatic decision method based on the event activation mechanism in the simulation training range as described in any one of claims 1 to 4 are implemented.
6. A computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the real-time automatic decision method based on an event activation mechanism in a simulation training range as described in any one of claims 1 to 5 are implemented.