Simulation deduction system capable of being edited dynamically

Abstract

The invention relates to the technical field of deduction systems, in particular to a dynamically editable simulation deduction system, which comprises a multi-unit grouping state calculation and group information management module, the multi-unit grouping state calculation and group information management module can receive real-time data of multiple units, automatically group the units according to a preset rule or algorithm and calculate state information of each group, and is electrically connected with a ZL intention calculation module; and the ZL intention calculation module predicts or calculates a ZL intention based on multi-unit data and historical data by using a machine learning or data mining technology to provide an intention analysis interface. According to the simulation deduction system capable of being dynamically edited, the problems that the real-time performance of the system is influenced and large-scale data and complex scenes cannot be effectively processed due to the fact that the calculation complexity of the multi-unit grouping state and the ZL intention possibly and sharply rises along with the increase of the number of units in the actual use process of a deduction system in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of deduction systems, and in particular to a dynamically editable simulation deduction system. Background Art

[0002] Deduction has become a long-term and widely used method in strategic research and crisis decision-making by major countries in the world. Today, no matter which country, region or city, or enterprise or individual, they are in a rapidly changing environment and often face various uncertain challenges. In each type of decision-making scenario, decision makers often have reduced decision-making efficiency due to few foresight means, cognitive bias, irrational decision-making or idealistic decision-making thinking. To adapt to this uncertainty, cope with complex challenges and reduce bias, it is necessary to apply game simulation methods based on deductive reasoning, that is, deduction. Deduction provides a way to do a good job in related work, that is, to fully try and fail in a virtual environment, actively expose shortcomings, and simulate and test different solutions to deal with crises. It is always better to make mistakes in a simulated future than to make mistakes in the real world when the crisis really comes. Deduction is such a risk prevention and crisis response idea that is oriented to the future and tries and corrects errors first.

[0003] However, in the actual use of the simulation system of the existing technology, as the number of units increases, the calculation complexity of multi-unit grouping status and ZL intention may increase sharply, affecting the real-time performance of the system and being unable to effectively process large-scale data and complex scenarios. To address this problem, a dynamically editable simulation system is provided. Summary of the invention

[0004] The purpose of the present invention is to provide a dynamically editable simulation deduction system to solve the problems raised in the above background technology. To achieve the above purpose, the present invention provides the following technical solutions: a dynamically editable simulation deduction system, including a multi-unit grouping state calculation and group information management module, the multi-unit grouping state calculation and group information management module can receive real-time data of multiple units and automatically group the units according to preset rules or algorithms and calculate the state information of each group, the multi-unit grouping state calculation and group information management module is electrically connected to a ZL intention calculation module, the ZL intention calculation module uses machine learning or data mining technology based on multi-unit data and historical data to predict or calculate ZL intentions and provide an intention analysis interface, and display the calculation process and results of ZL intentions, the ZL intention calculation module is electrically connected to a front-end GIS map display module, the front-end GIS map display module displays information such as unit location, height, sensor status effect in real time, distinguishes units of different groups with different colors and shapes, and displays ZL intention heat maps to help users intuitively understand the distribution of intentions.

[0005] Preferably, the multi-unit grouping status calculation and group information management module includes a data receiving unit, which is responsible for receiving real-time data from different units, such as location, speed, type, etc. These data are the basis for subsequent grouping and group information management, including location information (latitude and longitude, altitude, etc.), speed information, unit type, etc. The data receiving unit is electrically connected to the grouping calculation unit, and the grouping calculation unit is electrically connected to the group information management unit to manage the group's status information, including the group's location, size, speed, direction, etc. At the same time, it is also responsible for storing and updating the group's metadata, such as the group name, identification, and tasks to which it belongs. A database or memory data structure is used to store group information to ensure data persistence and fast access. Data update: Dynamically update the group's status and metadata based on real-time data or user input. The group information management unit is electrically connected to a dynamic editing unit to provide a user interface that allows users to adjust grouping rules and group information in real time. This It provides users with a high degree of flexibility and customization options. It receives user input and instructions through a graphical interface or command line interface, such as adding new grouping rules, modifying group attributes, etc. It updates the storage status of grouping rules and group information in real time based on user input. The dynamic editing unit is electrically connected to the result output unit, which outputs the results of grouping and group information to users or other systems in an appropriate manner, which may include displaying the location and status of the group on a GIS map, or displaying detailed information of the group on a console. The result output unit is electrically connected to the monitoring and logging unit, which monitors the operating status of the grouping and group information management module and records important operation logs and error information, which helps users understand the operation of the module and troubleshoot when problems occur.

[0006] Preferably, the output format of the result output unit includes graphical display on a GIS map, console output, data file, etc.

[0007] Preferably, the monitoring and logging unit includes user operation records, system operation status, error information, warning information, etc.

[0008] Preferably, the ZL intention calculation module is electrically connected to a data preprocessing unit, which is responsible for receiving real-time data from the multi-unit grouping state calculation module, including unit position, speed, type, etc., as well as historical data. The unit cleans, converts and standardizes the data to provide high-quality data input for subsequent intention calculation. The data preprocessing unit is electrically connected to a feature extraction unit, which extracts features related to the ZL intention from the preprocessed data. These features may include the unit's behavior pattern, position change, speed change, interaction with other units, etc. By extracting these features, the original data can be converted into a format that is easier for the model to process. The feature extraction unit is electrically connected to an intention calculation model unit, and a model for calculating the ZL intention is constructed using algorithms such as machine learning, data mining or deep learning. The model predicts or calculates the ZL intention based on the extracted features. Common algorithms include support vector machines (SVM), random forests, neural networks, etc. The model can identify patterns related to specific intentions through training and optimization, and give corresponding intention calculation results. The intention calculation model unit is electrically connected to a result output unit, which outputs the results obtained by the intention calculation model in an appropriate manner, which may include displaying the intention distribution in the form of a heat map on a GIS map, or displaying specific intention classifications and probability values ​​in a console or user interface. Through intuitive graphical display or detailed numerical output, users can clearly understand the calculation results of the ZL intention. The result output unit is electrically connected to a model update and optimization unit, which is responsible for monitoring the performance of the intention calculation model and updating and optimizing the model based on new data or user feedback. By continuously iterating and improving the model, its prediction accuracy and robustness can be improved, so as to better adapt to the complex and changeable battlefield environment. The model update and optimization unit is electrically connected to a user interaction configuration unit, which provides a user interaction interface and allows users to configure relevant parameters and options of the intention calculation module, including selecting the algorithm to be used, adjusting the parameters of feature extraction, setting the training cycle of the model, etc. Through the user interaction and configuration unit, users can customize the behavior and output of the intention calculation module according to specific needs.

[0009] Preferably, the intention calculation model unit is calculated by algorithms such as support vector machine (SVM), random forest, neural network, etc.

[0010] Preferably, the user interaction configuration unit includes selecting the algorithm to be used, adjusting the parameters of feature extraction, setting the training cycle of the model, etc.

[0011] Preferably, the front-end GIS map display module includes a map rendering unit, the map rendering unit is electrically connected to a real-time data update unit, the real-time data update unit is electrically connected to a clustering and group display unit, the clustering and group display unit is electrically connected to an intention heat map display unit, the intention heat map display unit is electrically connected to a ZZ fog and civilian unit generation unit, and the ZZ fog and civilian unit generation unit is electrically connected to a user interaction and configuration unit.

[0012] Preferably, the intention heat map display unit supports users to zoom and pan the map to view intention heat maps of different areas.

[0013] Preferably, the user interaction and configuration unit uses technologies such as HTML, CSS, JavaScript, etc. to construct a user interface.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] 1. Through the setting of multi-unit grouping status calculation and group information management module, real-time data update unit, grouping and group display unit and front-end GIS map display module, the problem that in the actual use of the deduction system of the prior art, as the number of units increases, the calculation complexity of multi-unit grouping status and ZL intention may increase sharply, affecting the real-time performance of the system and unable to effectively handle large-scale data and complex scenarios is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A system block diagram of a dynamically editable simulation deduction system of the present invention;

[0017] Figure 2 It is a system block diagram of multi-unit grouping state calculation and group information management of the simulation and deduction system that can be dynamically edited in the present invention;

[0018] Figure 3 It is a system block diagram of the ZL intention calculation module of the simulation deduction system that can be dynamically edited in the present invention;

[0019] Figure 4 This is a system block diagram of the front-end GIS map display of the dynamically editable simulation and deduction system of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technical personnel in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figures 1 to 4 The present invention provides a technical solution: a dynamically editable simulation and deduction system, including a multi-unit grouping state calculation and group information management module, which can receive real-time data of multiple units and automatically group the units according to preset rules or algorithms and calculate the state information of each group. The multi-unit grouping state calculation and group information management module is electrically connected to a ZL intention calculation module. The ZL intention calculation module uses machine learning or data mining technology based on multi-unit data and historical data to predict or calculate ZL intentions and provide an intention analysis interface to display the calculation process and results of ZL intentions. The ZL intention calculation module is electrically connected to a front-end GIS map display module. The front-end GIS map display module displays information such as unit location, height, sensor status effect, etc. in real time, distinguishes units of different groups with different colors and shapes, and displays a ZL intention heat map to help users intuitively understand the distribution of intentions.

[0022] In this embodiment, the multi-unit grouping status calculation and group information management module includes a data receiving unit, which is responsible for receiving real-time data from different units, such as location, speed, type, etc. These data are the basis for subsequent grouping and group information management, including location information (latitude and longitude, altitude, etc.), speed information, unit type, etc. The data receiving unit is electrically connected to the grouping calculation unit, and the grouping calculation unit is electrically connected to the group information management unit to manage the group's status information, including the group's location, size, speed, direction, etc. At the same time, it is also responsible for storing and updating the group's metadata, such as group name, identification, belonging tasks, etc., using a database or memory data structure to store group information to ensure data persistence and fast access, data update: dynamically update the group's status and metadata based on real-time data or user input, the group information management unit is electrically connected to a dynamic editing unit, providing a user interface, allowing users to adjust grouping rules and group information in real time, this It provides users with a high degree of flexibility and customization options. It receives user input and instructions through a graphical interface or command line interface, such as adding new grouping rules, modifying group attributes, etc. It updates the storage status of grouping rules and group information in real time based on user input. The dynamic editing unit is electrically connected to the result output unit, which outputs the results of grouping and group information to users or other systems in an appropriate manner, which may include displaying the location and status of the group on a GIS map, or displaying detailed information of the group on a console. The result output unit is electrically connected to the monitoring and logging unit, which monitors the operating status of the grouping and group information management module and records important operation logs and error information, which helps users understand the operation of the module and troubleshoot when problems occur.

[0023] In this embodiment, the output format of the result output unit includes graphical display on a GIS map, console output, data file, etc.

[0024] In this embodiment, the monitoring and logging unit includes user operation records, system operation status, error information, warning information, etc.

[0025] In this embodiment, the ZL intention calculation module is electrically connected to a data preprocessing unit, which is responsible for receiving real-time data from the multi-unit grouping state calculation module, including unit position, speed, type, etc., as well as historical data. The unit cleans, converts and standardizes the data to provide high-quality data input for subsequent intention calculation. The data preprocessing unit is electrically connected to a feature extraction unit, which extracts features related to the ZL intention from the preprocessed data. These features may include the unit's behavior pattern, position change, speed change, interaction with other units, etc. By extracting these features, the original data can be converted into a format that is easier for the model to process. The feature extraction unit is electrically connected to an intention calculation model unit, and a model for calculating the ZL intention is constructed using algorithms such as machine learning, data mining or deep learning. The model predicts or calculates the ZL intention based on the extracted features. Common algorithms include support vector machines (SVM), random forests, neural networks, etc. The model can identify patterns related to specific intentions through training and optimization, and give corresponding intention calculation results. The intention calculation model unit is electrically connected to a result output unit, which outputs the result obtained by the intention calculation model in an appropriate manner, which may include displaying the intention distribution in the form of a heat map on a GIS map, or displaying specific intention classifications and probability values ​​in a console or user interface. Through intuitive graphical display or detailed numerical output, users can clearly understand the calculation results of the ZL intention. The result output unit is electrically connected to a model update and optimization unit, which is responsible for monitoring the performance of the intention calculation model and updating and optimizing the model based on new data or user feedback. By continuously iterating and improving the model, its prediction accuracy and robustness can be improved, so as to better adapt to the complex and changeable battlefield environment. The model update and optimization unit is electrically connected to a user interaction configuration unit, which provides a user interaction interface and allows users to configure relevant parameters and options of the intention calculation module, including selecting the algorithm to be used, adjusting the parameters of feature extraction, setting the training cycle of the model, etc. Through the user interaction and configuration unit, users can customize the behavior and output of the intention calculation module according to specific needs.

[0026] In this embodiment, the intention calculation model unit is calculated by algorithms such as support vector machine (SVM), random forest, neural network, etc.

[0027] In this embodiment, the user interaction configuration unit includes selecting the algorithm to be used, adjusting the parameters of feature extraction, setting the training cycle of the model, etc.

[0028] In this embodiment, the front-end GIS map display module includes a map rendering unit, which is responsible for rendering and displaying the GIS map on the front-end interface, including loading map base maps, layers, annotations and other elements to ensure the accuracy and real-time performance of the map, using WebGL, Canvas or other graphics libraries to efficiently render map images and layers, and receiving map data and layer data from a map service or database. The map rendering unit is electrically connected to a real-time data update unit, which receives real-time data from a back-end or data source, such as unit location, altitude, sensor status, etc., and dynamically updates the corresponding elements on the map. WebSocket, AJAX and other technologies are used to realize real-time push and pull of data, and the received data is parsed and converted to meet the needs of map display. The real-time data update unit is electrically connected to a grouping and group display unit, and different groups are displayed on the map in different colors, shapes or icons according to the grouping and group information calculated by the back-end. SVG, Canvas and other technologies are used to draw and render group elements on the map, and users are supported to interact with group elements by clicking, dragging, etc., such as viewing group details. , adjust group settings, etc. The grouping and group display unit is electrically connected to the intention heat map display unit, which displays the calculated ZL intention in the form of a heat map on the map to help users intuitively understand the distribution and changes of intentions, use visual elements such as color and transparency to represent different levels of intention strength, and support users to zoom and pan the map to view the intention heat maps of different areas. The intention heat map display unit is electrically connected to the ZZ fog and civilian unit generation unit, which adds a ZZ fog effect to the map to simulate the uncertainty of the battlefield environment; at the same time, a large number of civilian units are randomly generated to increase the complexity and authenticity of the map, and the fog effect is simulated using graphics rendering technology; the location and attributes of civilian units are randomly generated through algorithms, and users are supported to adjust the density and range of the fog; users are allowed to view and edit the generated civilian unit information, and the ZZ fog and civilian unit generation unit are electrically connected to the user interaction and configuration unit, which provides a user interaction interface and allows users to configure the display options, layer settings, zoom levels, etc. of the map; at the same time, users are supported to search and query through the map, allowing users to customize the display style and interaction method of the map according to personal preferences and needs.

[0029] In this embodiment, the intent heat map display unit supports users to zoom and pan the map to view intent heat maps of different areas.

[0030] In this embodiment, the user interaction and configuration unit uses technologies such as HTML, CSS, and JavaScript to construct a user interface.

[0031] The above shows and describes the basic principles, main features and advantages of the present invention. Technical personnel in this industry should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A dynamically editable simulation system, characterized in that: It includes a multi-unit grouping status calculation and group information management module, which can receive real-time data of multiple units, automatically group the units according to preset rules or algorithms, and calculate the status information of each group. The multi-unit grouping status calculation and group information management module is electrically connected to a ZL intention calculation module, which uses machine learning or data mining technology based on multi-unit data and historical data to predict or calculate ZL intentions, provide an intention analysis interface, and display the calculation process and results of ZL intentions. The ZL intention calculation module is electrically connected to a front-end GIS map display module, which displays unit location, altitude, sensor status effect and other information in real time, distinguishes units of different groups with different colors and shapes, and displays a ZL intention heat map to help users intuitively understand the distribution of intentions.

2. The dynamically editable simulation deduction system according to claim 1, characterized in that: The multi-unit grouping status calculation and group information management module includes a data receiving unit, the data receiving unit is electrically connected to the grouping calculation unit, the grouping calculation unit is electrically connected to the group information management unit, the group information management unit is electrically connected to the dynamic editing unit, the dynamic editing unit is electrically connected to the result output unit, and the result output unit is electrically connected to the monitoring and logging unit.

3. The dynamically editable simulation deduction system according to claim 2, characterized in that: The output formats of the result output unit include graphical display on GIS map, console output, data file, etc.

4. The dynamically editable simulation deduction system according to claim 2, characterized in that: The monitoring and logging unit includes user operation records, system operation status, error information, warning information, etc.

5. The dynamically editable simulation system according to claim 1, characterized in that: The ZL intention calculation module is electrically connected to a data preprocessing unit, the data preprocessing unit is electrically connected to a feature extraction unit, the feature extraction unit is electrically connected to an intention calculation model unit, the intention calculation model unit is electrically connected to a result output unit, the result output unit is electrically connected to a model update and optimization unit, and the model update and optimization unit is electrically connected to a user interaction configuration unit.

6. The dynamically editable simulation deduction system according to claim 5, characterized in that: The intention calculation model unit is calculated by algorithms such as support vector machine (SVM), random forest, neural network, etc.

7. The dynamically editable simulation system according to claim 5, characterized in that: The user interaction configuration unit includes selecting the algorithm to be used, adjusting the parameters of feature extraction, setting the training cycle of the model, etc.

8. The dynamically editable simulation system according to claim 1, characterized in that: The front-end GIS map display module includes a map rendering unit, the map rendering unit is electrically connected to a real-time data update unit, the real-time data update unit is electrically connected to a clustering and group display unit, the clustering and group display unit is electrically connected to an intention heat map display unit, the intention heat map display unit is electrically connected to a ZZ fog and civilian unit generation unit, and the ZZ fog and civilian unit generation unit is electrically connected to a user interaction and configuration unit.

9. The dynamically editable simulation system according to claim 8, characterized in that: The intent heat map display unit supports users to zoom and pan the map to view intent heat maps of different areas.

10. The dynamically editable simulation deduction system according to claim 8, characterized in that: The user interaction and configuration unit uses technologies such as HTML, CSS, JavaScript, etc. to build a user interface.

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