Entity on-demand pushing method and device for large-scale LVC simulation

Abstract

The invention discloses an entity on-demand pushing method for large-scale LVC simulation, and the method comprises the steps: dividing a region which needs to be simulated according to the position of a user, the type of a simulation task, and a region which is concerned by the user; identifying a user demand based on the user behavior pattern, and determining an entity list needing to be pushed in the area needing to be simulated according to the user demand; based on the importance and complexity of the entities and the degree of association with the users, corresponding pushing priority and pushing frequency are distributed to each entity in the entity list; compressing and encoding the entity data needing to be pushed; and pushing the compressed and coded entity data of each entity in the entity list according to the corresponding pushing priority and pushing frequency. The invention further discloses an entity on-demand pushing device for the large-scale LVC simulation. According to the method, related entity data can be intelligently and efficiently pushed according to different user requirements, so that information transmission and computing resource use in a large-scale LVC simulation system are optimized.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and more specifically, to an entity on-demand push method and device for large-scale LVC simulation. Background Art

[0002] With the continuous development of simulation technology, LVC simulation has been widely used in many fields such as military training, aerospace, transportation, smart cities, etc. LVC simulation systems are usually composed of a large number of virtual entities, on-site entities and constructed entities, with complex interactivity and real-time performance. However, in large-scale LVC simulation scenarios, due to the large number of entities and complex interactions, the existing entity management and data push mechanisms have the following problems: 1. High pressure on bandwidth and computing resources: In large-scale simulations, the system needs to handle data transmission and calculations of a large number of entities, which may lead to insufficient bandwidth and computing resources, thereby affecting the simulation quality and real-time performance; 2. Inefficient entity status update mechanism: The existing entity data update mechanism is usually based on a global broadcast method, and each simulation participant must receive information from all entities. For large-scale simulation environments, data redundancy and transmission overhead are too large; 3. Unable to accurately meet the needs of different users: Since different users pay attention to different entities and information, the unified push mechanism cannot push the most relevant entity data on demand for specific users, resulting in information overload or omission. Summary of the invention

[0003] The purpose of the present invention is to provide an entity on-demand push method and device for large-scale LVC simulation, which can intelligently and efficiently push relevant entity data according to different user needs to optimize information transmission and computing resource usage in large-scale LVC simulation systems.

[0004] In order to achieve these purposes and other advantages according to the present invention, a method for entity on-demand push for large-scale LVC simulation is provided, comprising:

[0005] Divide the area that needs simulation according to the user's location, simulation task type and user's focus area;

[0006] Identify user needs based on user behavior patterns, and determine the list of entities that need to be pushed in the simulation area according to user needs;

[0007] Based on the importance, complexity and relevance of the entity to the user, assign corresponding push priority and push frequency to each entity in the entity list;

[0008] Compress and encode the entity data to be pushed;

[0009] The compressed and encoded entity data of each entity in the entity list is pushed according to the corresponding push priority and push frequency.

[0010] Preferably, the compression and encoding of entity data adopts a dynamic data compression method.

[0011] Preferably, the dynamic data compression method includes: when it is monitored that the network bandwidth is lower than a preset threshold, increasing the compression rate of the entity data to reduce the data transmission volume.

[0012] Preferably, during the entity data push process, the push strategy is also adaptively adjusted based on the network topology and network bandwidth status.

[0013] Preferably, adaptively adjusting the push strategy based on the network topology and network bandwidth status includes: when it is monitored that the network topology is a preset network topology and / or the network bandwidth is lower than a preset threshold, reducing the push frequency of entity data, or choosing to push only part of the entity data.

[0014] Preferably, when assigning a push priority and a push frequency to each entity in the entity list, a high push frequency is assigned to a high-priority entity, and a low push frequency is assigned to a low-priority entity.

[0015] The present invention also provides an entity on-demand push device for large-scale LVC simulation, comprising:

[0016] The area division module is used to divide the area to be simulated according to the user's location, simulation task type and user's focus area;

[0017] A demand perception module is used to identify user needs based on user behavior patterns and determine a list of entities that need to be pushed in the simulation area according to the user needs;

[0018] A priority scheduling module is used to assign a corresponding push priority and push frequency to each entity in the entity list based on the importance, complexity and relevance of the entity to the user;

[0019] A data compression module is used to compress and encode the entity data that needs to be pushed;

[0020] The entity push module is used to push the compressed and encoded entity data of each entity in the entity list according to the corresponding push priority and push frequency.

[0021] The present invention also provides an electronic device, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the above-mentioned method.

[0022] The present invention also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the above method is implemented.

[0023] The present invention includes at least the following beneficial effects: the present invention adopts an on-demand push mechanism to avoid the transmission of redundant data, reduce the pressure of network bandwidth, and improve the efficiency of data transmission; at the same time, by dynamically adjusting the push frequency and compression rate, it reduces unnecessary data calculation and storage burden, and improves the utilization rate of computing resources; and adjusts the push content and frequency according to user needs, so that the system can accurately meet the user's simulation needs and improve the real-time response capability of the simulation system; in addition, it can flexibly adjust the push strategy according to different network environments and user needs to adapt to various changing simulation scenarios. In general, it can effectively solve the problems of insufficient bandwidth, high computing pressure, and information redundancy in traditional simulation systems, and optimize the performance and resource utilization of the simulation system. This method is suitable for various large-scale simulation scenarios and has broad application prospects.

[0024] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of the entity on-demand push method for large-scale LVC simulation according to an embodiment of the present invention;

[0026] Figure 2 It is a structural diagram of the entity on-demand push device for large-scale LVC simulation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0028] It should be noted that the experimental methods described in the following embodiments are conventional methods unless otherwise specified, and the reagents and materials are commercially available unless otherwise specified; in the description of the present invention, the terms "lateral", "longitudinal", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0029] In a large-scale Live, Virtual, Constructive simulation (LVC simulation) environment, the push of entity data is a key link to ensure the simulation effect and user experience. Due to the vast simulation area and the huge number of entities, how to efficiently and accurately push entity data according to user needs, entity characteristics and network environment has become an urgent problem to be solved. The present invention proposes an entity on-demand push method for large-scale LVC simulation, which comprehensively considers multiple factors such as user location, simulation task, user focus area, entity importance, complexity, degree of association with users and network environment, and realizes intelligent and dynamic push of entity data.

[0030] like Figure 1 As shown, the entity on-demand push method for large-scale LVC simulation includes:

[0031] S1. Divide the area to be simulated according to the user's location, simulation task type and user's focus area;

[0032] In a large-scale LVC simulation environment, it is impossible to simulate the entire simulation space comprehensively and carefully due to limited resources. Therefore, it is necessary to dynamically divide the area to be simulated according to the user's location, simulation task type, and user's focus area.

[0033] Specifically, the user's location is the basis for dividing the simulation area. The system can obtain the user's real-time location information through GPS positioning, user input, and the built-in positioning system of the simulation environment.

[0034] Different simulation tasks have different requirements for simulation areas. For example, tactical exercises may focus on battlefield layout and troop deployment in a specific area, while logistics simulation may focus on optimization of transportation routes and warehouse management. Therefore, the system needs to analyze the key areas involved in the task according to the type of simulation task.

[0035] The user's attention area can be identified through the user's historical behavior, interaction records, sight direction, operation habits, etc. For example, the system can record the areas that users frequently view, the points of interest that users actively mark, and the areas where users stay for a long time, etc., as a reference for the user's attention area.

[0036] Based on the user's location, simulation task type, and user's attention area, the system uses a space partitioning algorithm (such as quadtree, grid partitioning, KD tree, etc.) to divide the entire simulation space into several sub-areas. Among them, the areas close to the user's location, closely related to the simulation task, and with high user attention are marked as "areas that need simulation" as the focus of subsequent data processing and push.

[0037] S2. Identify user needs based on user behavior patterns, and determine a list of entities that need to be pushed in the simulation area according to the user needs;

[0038] After dividing the area that needs to be simulated, the next step is to identify user needs and determine the list of entities that need to be pushed.

[0039] Specifically, user behavior pattern recognition is the key to understanding user needs. The system can analyze the user's historical behavior data, interaction records, operating habits, etc. through machine learning algorithms (such as cluster analysis, decision trees, neural networks, etc.) or rule matching methods to identify the user's preferences, interests, concerns and other behavior patterns.

[0040] Based on the user behavior pattern recognition results, the system can infer the user's specific needs in the current simulation task. For example, if the user often checks the battlefield situation in a certain area, the system can determine that the user has a high demand for battlefield information in that area.

[0041] According to user needs, the system traverses all entities in the area to be simulated, screens out entities that are highly relevant to the user's focus, and forms a list of entities that need to be pushed. During the screening process, multiple factors such as the type, attribute, state, and location of the entity can be considered. For example, in a battlefield simulation, the user may be concerned about the location, speed, weapon configuration, and other information of the enemy tank, so the system needs to include these tank entities in the push list.

[0042] S3. Assign corresponding push priority and push frequency to each entity in the entity list based on the importance, complexity and relevance of the entity to the user;

[0043] For each entity in the entity list, the system needs to assign a push priority and push frequency to it to ensure that key information can be conveyed to users in a timely and accurate manner.

[0044] Specifically, the importance of an entity is an important basis for assigning push priorities. The system can evaluate the importance of an entity in a simulation environment based on factors such as its type, role, and influence. For example, key nodes (such as command centers and transportation hubs) and event triggers (such as enemy forces and important targets) usually have higher importance.

[0045] The complexity of an entity determines the difficulty of processing and transmitting its data. The system can evaluate the complexity of an entity based on factors such as the entity's model sophistication, computing resource consumption, and data update frequency. Entities with higher complexity may require more computing resources and network bandwidth for transmission and processing.

[0046] The degree of association with the user is an important indicator of the closeness of the relationship between the entity and the user. The system can evaluate the degree of association between the entity and the user based on factors such as the location distance between the entity and the user, interaction history, and user attention. Entities with a higher degree of association with the user are usually more concerned by the user, so a higher push frequency is required.

[0047] The system uses a multi-index comprehensive evaluation method (such as weighted average, fuzzy comprehensive evaluation, etc.) to assign a unique push priority and push frequency to each entity, taking into account the importance, complexity, and relevance of the entity to the user. Usually, high-priority entities are given a higher push frequency to ensure the timely delivery of key information; while low-priority entities are given a lower push frequency to reduce unnecessary network burden and computing resource consumption.

[0048] S4. compress and encode the entity data to be pushed;

[0049] Before pushing entity data, the system needs to compress and encode the data to reduce the amount of data transmission and improve transmission efficiency.

[0050] Specifically, the choice of data compression method needs to be weighed according to the characteristics of the data and the network environment. For data with high real-time requirements (such as battlefield situation information), lossless compression methods (such as Huffman coding, LZW coding, etc.) can be used to ensure the integrity and accuracy of the data; for data with low real-time requirements (such as background environment information), lossy compression methods (such as JPEG, MP3, etc.) can be used to further reduce the amount of data transmission. At the same time, the system can also introduce predictive coding, differential coding and other technologies to further improve compression efficiency.

[0051] While compressing data, the system also needs to encode the data. The purpose of encoding is to convert the original data into a format that is easy to transmit and process. The system can use standard encoding protocols (such as TCP / IP, UDP, etc.) or custom encoding methods, depending on actual needs. During the encoding process, the system should ensure the integrity and readability of the data to avoid data loss or decoding errors.

[0052] S5. Push the compressed and encoded entity data of each entity in the entity list according to the corresponding push priority and push frequency.

[0053] Finally, the system pushes the compressed and encoded entity data to the user end in order according to the push priority and push frequency assigned to each entity. This is the last step to achieve on-demand push of entity data.

[0054] During the push process, the system can use a variety of transmission protocols and communication technologies, such as TCP / IP, UDP, WebSocket, etc., and choose according to actual needs. The TCP / IP protocol is suitable for scenarios that require reliable transmission and large data volume transmission; the UDP protocol is suitable for scenarios that have high real-time requirements but allow certain data loss; the WebSocket protocol is suitable for scenarios that require two-way communication and real-time interaction.

[0055] The system can also introduce data caching mechanisms, retransmission mechanisms, flow control mechanisms, etc. to further improve the reliability and efficiency of data transmission. For example, when the network delay is large or the data loss rate is high, the system can cache the data locally or on intermediate nodes and wait for the network condition to improve before transmitting; or use the retransmission mechanism to resend the lost data; or use the flow control mechanism to limit the data transmission rate to avoid network congestion and data loss.

[0056] Specifically, the above method can be implemented by using MySQL database language and web page php code programming.

[0057] Furthermore, the compression and encoding of entity data adopts a dynamic data compression method.

[0058] Specifically, the dynamic data compression method includes: when it is monitored that the network bandwidth is lower than a preset threshold, increasing the compression rate of the entity data to reduce the data transmission volume.

[0059] This embodiment adopts a dynamic data compression method, that is, the compression rate is dynamically adjusted according to the real-time monitored network bandwidth. When the network bandwidth is sufficient, a lower compression rate is used to maintain data quality; when the network bandwidth is tight, the compression rate is increased to reduce the amount of data transmission to ensure smooth data transmission. The dynamic data compression method can be implemented by adjusting the parameters of the compression algorithm, selecting an appropriate compression level, etc.

[0060] The specific implementation of the dynamic data compression method can be improved and optimized based on the existing compression algorithm. For example, the system can use Huffman coding, LZ77 / LZ78 algorithm, or more advanced compression algorithms such as JPEG, MPEG, etc., and select the appropriate compression method according to the type and characteristics of the entity data. At the same time, the system can also introduce predictive coding, differential coding and other technologies to further improve the compression efficiency.

[0061] Furthermore, during the entity data push process, the push strategy is adaptively adjusted based on the network topology and network bandwidth status.

[0062] Specifically, adaptively adjusting the push strategy based on the network topology and network bandwidth status includes: when it is monitored that the network topology is a preset network topology and / or the network bandwidth is lower than a preset threshold, reducing the push frequency of entity data, or choosing to push only part of the entity data.

[0063] In this embodiment, during the entity data push process, the system needs to continuously monitor the network topology and network bandwidth status, and adaptively adjust the push strategy accordingly. This is to cope with the dynamic changes in the network environment and ensure the stability and efficiency of data transmission.

[0064] Specifically, the system can monitor changes in the network topology in real time, such as the addition of nodes, interruption of links, increase in network latency, etc. When these changes are detected, the system should automatically adjust the push strategy according to preset rules or algorithms. For example, when the network topology becomes complex or unstable, the system can reduce the push frequency of entity data to reduce network burden and transmission delay; or choose to push only some key entity data to ensure the timely delivery of the most important information.

[0065] At the same time, the system also needs to monitor the network bandwidth status in real time. When the network bandwidth is lower than the preset threshold, it means that the network transmission capacity is limited. At this time, the system should increase the data compression rate to reduce the data transmission volume; or reduce the push frequency to reduce the number of data transmissions; or choose to push high-priority entity data first to ensure the transmission of key information.

[0066] The adjustment of the adaptive push strategy can be implemented based on existing network monitoring technologies and algorithms. The system can introduce network traffic monitoring tools, bandwidth measurement algorithms, etc. to monitor the network status in real time and obtain relevant data. Then, the system automatically adjusts the push strategy based on these data and analysis results to achieve the best data transmission effect.

[0067] Furthermore, when assigning a push priority and a push frequency to each entity in the entity list, a high push frequency is assigned to a high-priority entity, and a low push frequency is assigned to a low-priority entity.

[0068] In this embodiment, for each entity in the entity list, the system needs to assign a corresponding push priority and push frequency to it, which is one of the key steps to achieve on-demand push of entity data.

[0069] The allocation of push priority and push frequency should be based on the importance, complexity and relevance of the entity to the user. Specifically, the system can use a multi-index comprehensive evaluation method to calculate a comprehensive score for each entity. This score can reflect the importance of the entity in the simulation system (such as whether it is a key node, whether it participates in important events, etc.), the complexity of the entity (such as model refinement, computing resource consumption, etc.) and the degree of relevance to the user (such as distance, interaction history, user preferences, etc.).

[0070] Based on the comprehensive score, the system can classify entities into different priority categories, such as high priority, medium priority, and low priority. For high-priority entities, the system should assign a higher push frequency to ensure that users can obtain the latest status and information of these entities in a timely manner; for low-priority entities, a lower push frequency can be used to reduce unnecessary network burden and data processing costs.

[0071] It is not difficult to see from the above embodiments that the present invention adopts an on-demand push mechanism to avoid the transmission of redundant data, reduce the pressure of network bandwidth, and improve data transmission efficiency; at the same time, by dynamically adjusting the push frequency and compression rate, it reduces unnecessary data calculation and storage burdens and improves the utilization rate of computing resources; and adjusts the push content and frequency according to user needs, so that the system can accurately meet the user's simulation needs and improve the real-time response capability of the simulation system; in addition, it can flexibly adjust the push strategy according to different network environments and user needs to adapt to various changing simulation scenarios. In general, it can effectively solve the problems of insufficient bandwidth, high computing pressure, and information redundancy in traditional simulation systems, and optimize the performance and resource utilization of the simulation system. This method is suitable for various large-scale simulation scenarios and has broad application prospects.

[0072] Based on the same inventive concept, the present invention also provides an entity on-demand push device for large-scale LVC simulation, which can be a personal computer, a server, or other devices that implement the aforementioned entity on-demand push method for large-scale LVC simulation.

[0073] See also Figure 2 As shown, the entity on-demand push device for large-scale LVC simulation provided by this embodiment includes:

[0074] The area division module is used to divide the area to be simulated according to the user's location, simulation task type and user's focus area;

[0075] A demand perception module is used to identify user needs based on user behavior patterns and determine a list of entities that need to be pushed in the simulation area according to the user needs;

[0076] A priority scheduling module is used to assign a corresponding push priority and push frequency to each entity in the entity list based on the importance, complexity and relevance of the entity to the user;

[0077] A data compression module is used to compress and encode the entity data that needs to be pushed;

[0078] The entity push module is used to push the compressed and encoded entity data of each entity in the entity list according to the corresponding push priority and push frequency.

[0079] All relevant contents of each step involved in the aforementioned embodiment of the entity on-demand push method for large-scale LVC simulation can be referred to the functional description of the functional module corresponding to the entity on-demand push device in the embodiment of the present application, and will not be repeated here.

[0080] The division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0081] In the drawings of the system embodiment provided by the present invention, the connection relationship between modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines.

[0082] The present invention also provides an electronic device, comprising: at least one processor, and a memory connected to the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the above-mentioned entity on-demand push method for large-scale LVC simulation. The electronic device can be any terminal device including a mobile phone, a laptop computer, a desktop computer, a tablet computer, a PDA (Personal Digital Assistant), a POS (Point of Sales), a vehicle-mounted computer, etc.

[0083] The present invention also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned entity on-demand push method for large-scale LVC simulation is implemented.

[0084] Through the description of the above implementation mode, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present invention, software program implementation is a better implementation mode in more cases. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0085] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A method for pushing entities on demand for large-scale LVC simulation, characterized in that: include: Divide the area that needs simulation according to the user's location, simulation task type and user's focus area; Identify user needs based on user behavior patterns, and determine the list of entities that need to be pushed in the simulation area according to user needs; Based on the importance, complexity and relevance of the entity to the user, assign corresponding push priority and push frequency to each entity in the entity list; Compress and encode the entity data to be pushed; The compressed and encoded entity data of each entity in the entity list is pushed according to the corresponding push priority and push frequency.

2. The entity on-demand push method for large-scale LVC simulation according to claim 1, characterized in that: The compression and encoding of entity data adopts dynamic data compression method.

3. The entity on-demand push method for large-scale LVC simulation according to claim 2, characterized in that: The dynamic data compression method includes: when it is monitored that the network bandwidth is lower than a preset threshold, increasing the compression rate of the entity data to reduce the data transmission volume.

4. The entity on-demand push method for large-scale LVC simulation according to claim 1, characterized in that: During the entity data push process, the push strategy is also adaptively adjusted based on the network topology and network bandwidth status.

5. The entity on-demand push method for large-scale LVC simulation according to claim 3, characterized in that: Adaptively adjusting the push strategy based on the network topology and the network bandwidth status includes: when it is monitored that the network topology is a preset network topology and / or the network bandwidth is lower than a preset threshold, reducing the push frequency of the entity data, or choosing to push only part of the entity data.

6. The entity on-demand push method for large-scale LVC simulation according to claim 1, characterized in that: When assigning a push priority and a push frequency to each entity in the entity list, a high push frequency is assigned to a high-priority entity, and a low push frequency is assigned to a low-priority entity.

7. A physical on-demand push device for large-scale LVC simulation, characterized in that: include: The area division module is used to divide the area to be simulated according to the user's location, simulation task type and user's focus area; A demand perception module is used to identify user needs based on user behavior patterns and determine a list of entities that need to be pushed in the simulation area according to the user needs; A priority scheduling module is used to assign a corresponding push priority and push frequency to each entity in the entity list based on the importance, complexity and relevance of the entity to the user; A data compression module is used to compress and encode the entity data that needs to be pushed; The entity push module is used to push the compressed and encoded entity data of each entity in the entity list according to the corresponding push priority and push frequency.

8. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the method according to any one of claims 1 to 6.

9. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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