Simulation and actual measurement-based benchmark test excitation data generation method
By combining simulation and experimental data to generate test incentives, the problem of the lack of a benchmark testing system in the data link system is solved, the normal interconnection and interoperability of the data link system is realized, and the authenticity and security of test data are ensured, providing standardized test incentive data management.
Patent Information
- Application Number
- CN202511049146.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-29
- Publication Date
- 2025-11-14
AI Technical Summary
The lack of a benchmark testing system for data link architecture in existing technologies means that new systems, devices, and software may not be able to interconnect properly or may cause interference when incorporated into the data link system, and there is a lack of standard test incentive data.
By combining simulation and field testing, test stimulus data is generated. By establishing a database of field testing and simulation stimulus data, the coverage, security, and authenticity of the data are ensured, and standardized test stimulus data is provided.
It provides testing support for data chain system certification, ensuring the authenticity, security, and reliability of test data, and provides standardized test incentive data management methods, thus guaranteeing the normal interconnection and interoperability of the data chain system and the scientific nature of testing.
Smart Images
Figure CN120949607A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data link system benchmarking technology, and in particular to a method for generating benchmarking stimulus data based on simulation and actual measurement. Background Technology
[0002] The benchmark testing system based on data links is designed to ensure that when different systems, devices, and software are incorporated into the data link system, they will not be unable to interconnect properly due to incompatibility with the system architecture. At the same time, it also ensures that newly incorporated systems, devices, and software meet the requirements and will not interfere with the data link system.
[0003] Benchmark testing systems are unique and independent for specific data link systems, such as the certification and access requirements for CDMA or 5G in mobile communications. Currently, there is no specific benchmark testing system for this particular data link system. Benchmarks in other fields include those for BeiDou navigation and 4G / 5G mobile communications, but these differ significantly from those for data link systems.
[0004] In summary, data link benchmarking systems require standard reconnaissance data for testing stimuli, but there is currently no relevant technical solution in the existing technology. Summary of the Invention
[0005] In view of this, the present invention proposes a method for generating benchmark test stimulus data based on simulation and actual measurement. This invention utilizes measured data and simulation data to generate test stimulus data, providing a data source for test stimulus, enabling mutual verification and support between simulation and measured data, achieving test support for data chain system certification, and ensuring the authenticity, security, and reliability of the test data.
[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0007] A method for generating benchmark test stimulus data based on simulation and actual measurement includes the following steps:
[0008] (1) Establish a measured stimulus database;
[0009] (2) Establish a simulation stimulus database;
[0010] (3) Based on the measured stimulus data and the simulated stimulus data, select test stimulus according to the requirements and provide corresponding test stimulus data for the benchmark test system.
[0011] Furthermore, the specific method of step 1 is as follows:
[0012] (101) Generate radar detection measured data, photoelectric reconnaissance measured data, electronic reconnaissance measured data and underwater acoustic reconnaissance measured data, and complete the collection and storage of various types of measured data;
[0013] (102) Perform data format conversion, spatiotemporal unified processing, data information labeling and data normalization on radar detection measured data, photoelectric reconnaissance measured data, electronic reconnaissance measured data and underwater acoustic reconnaissance measured data, and complete the normalization of measured excitation data;
[0014] (103) Based on the model specifications generated by the simulation excitation database, the normalized measured excitation data is modeled and processed to generate a callable measured excitation database.
[0015] Furthermore, the radar detection measured data includes reconnaissance radar measured data, detection radar measured data, fire control radar measured data, early warning radar measured data, SAR radar image data, and sensor parameters of airborne, shipborne, vehicle-mounted, and land-based fixed radars;
[0016] The measured data from photoelectric reconnaissance includes the acquisition of measured image and video data, as well as sensor parameters from white light, infrared, multispectral, hyperspectral, and laser photoelectric devices.
[0017] The measured data of electronic reconnaissance includes measured data of radar signal reconnaissance and communication signal reconnaissance, as well as sensor parameters of radar signal reconnaissance equipment, communication signal reconnaissance equipment and electronic signal reconnaissance equipment;
[0018] The measured data of underwater acoustic reconnaissance includes measured data of sonar reconnaissance and underwater acoustic array reconnaissance, as well as sensor parameters of sonar sensors, shipborne underwater acoustic sensor arrays, and coastal underwater acoustic sensor arrays.
[0019] Furthermore, the simulation stimulus data in the simulation stimulus database includes radar detection simulation stimulus data, optoelectronic reconnaissance simulation stimulus data, electronic reconnaissance simulation stimulus data, and underwater acoustic reconnaissance simulation stimulus data;
[0020] Among them, the radar detection simulation excitation data includes reconnaissance radar simulation excitation data, detection radar simulation excitation data, fire control radar simulation excitation data, early warning radar simulation excitation data, SAR radar image simulation excitation data, and multi-functional radar simulation excitation data, as well as corresponding sensor parameter information and platform information data.
[0021] The photoelectric reconnaissance simulation stimulus data includes image simulation stimulus data and video simulation stimulus data, as well as corresponding sensor parameter information and platform information data.
[0022] The electronic reconnaissance simulation stimulus data includes radar signal reconnaissance simulation stimulus data and communication signal reconnaissance simulation stimulus data, as well as corresponding sensor parameter information and platform information data.
[0023] The underwater acoustic reconnaissance simulation excitation data includes sonar reconnaissance simulation excitation data, ship underwater acoustic array reconnaissance simulation excitation data, and coastal underwater acoustic sensor array reconnaissance simulation data, as well as corresponding sensor parameter information and platform information data.
[0024] Furthermore, the specific method for selecting test incentives is as follows:
[0025] Each type of incentive data includes at least one set of measured incentive data as a guarantee of reliability; when the measured incentive data cannot reach the required amount of incentive data, simulated incentive data is used to supplement it.
[0026] The advantages of this invention compared to the prior art are:
[0027] 1. This invention combines simulation data with measured data to ensure data coverage and security;
[0028] 2. This invention, supported by a database of simulation data and measured data, enables real, credible, and reliable benchmark testing.
[0029] 3. The simulation stimulus data of this invention mainly provides the standard format of reconnaissance data and the standard stimulus drive for reconnaissance data for benchmark testing, ensuring the standardization, normalization and generalization of data; the actual stimulus data provides the actual reconnaissance data for benchmark testing, ensuring the authenticity and reliability of the data.
[0030] 4. This invention performs operations such as format conversion, spatiotemporal unification processing, and database modeling on measured data to build a measured database and ensure the consistency between measured data and simulation data.
[0031] 5. Based on database management and construction methods, this invention provides standardized and normalized test incentive data management methods for data chain benchmark testing. Attached Figure Description
[0032] Figure 1 This is a schematic diagram illustrating the principle of the present invention. Detailed Implementation
[0033] The present invention will be further explained and described below with reference to the accompanying drawings and specific embodiments.
[0034] A method for generating benchmark test stimulus data based on simulation and actual measurement, such as Figure 1 As shown, it includes the following steps:
[0035] (1) Establish a measured stimulus database; the specific method is as follows:
[0036] (101) Generate radar detection measured data, photoelectric reconnaissance measured data, electronic reconnaissance measured data and underwater acoustic reconnaissance measured data, and complete the collection and storage of various types of measured data;
[0037] (102) Perform data format conversion, spatiotemporal unified processing, data information labeling and data normalization on radar detection measured data, photoelectric reconnaissance measured data, electronic reconnaissance measured data and underwater acoustic reconnaissance measured data, and complete the normalization of measured excitation data;
[0038] (103) Based on the model specifications generated by the simulation excitation database, the normalized measured excitation data is modeled and processed to generate a callable measured excitation database.
[0039] (2) Establish a simulation stimulus database; the simulation stimulus data in the simulation stimulus database includes radar detection simulation stimulus data, optoelectronic reconnaissance simulation stimulus data, electronic reconnaissance simulation stimulus data and underwater acoustic reconnaissance simulation stimulus data;
[0040] (3) Based on the measured stimulus data and the simulated stimulus data, select test stimulus according to the requirements and provide corresponding test stimulus data for the benchmark test system.
[0041] The selection of incentive data follows these principles:
[0042] Real-world data is used to provide incentive data for functional and performance testing in benchmark testing, ensuring the authenticity of the data incentives; simulation data addresses the coverage of benchmark incentive data and is generated from a simulation incentive database.
[0043] The incentive data selection scheme primarily uses measured data, and each type of incentive data must include measured data as a guarantee of reliability; the remaining incentive data is supplemented by simulation data to make up for the deficiencies of the measured data.
[0044] Taking the selection of typical test data as an example: no less than 5 sets of actual test data for each type of sensor, and no less than 20 sets of simulation data for each type of sensor.
[0045] In this method, test stimuli serve as the operational support for the benchmark testing system, ensuring the data guarantee for the coverage, authenticity, and scientific rigor of the benchmark tests. To ensure data authenticity, actual measured data is selected as the benchmark test stimulus data; to ensure data coverage, simulated data is selected as the supporting test stimulus data. The combination of the two guarantees data security, reliability, trustworthiness, and feasibility. Based on the benchmark testing scheme, test stimuli can be selected according to requirements, covering both simulated and real data, enabling benchmark test data-driven management and retrieval.
[0046] The experimental stimulus database provides experimental data for the benchmark testing system and manages data generation, database normalization, database modeling, maintenance, and invocation based on the experimental data. It also performs database modeling, maintenance, management, and invocation according to the task.
[0047] Database construction mainly includes the collection and storage of various types of measured data, including radar detection measured data, optoelectronic reconnaissance measured data, electronic reconnaissance measured data, and underwater acoustic measured data.
[0048] Radar detection data generation mainly includes the acquisition of various types of radar detection data, the acquisition of sensor parameters, and data storage. Radar detection sensors include reconnaissance radar, detection radar, fire control radar, early warning radar, SAR radar, and multi-functional radar, etc. Sensor types include airborne radar, shipborne radar, vehicle-mounted radar, and land-based fixed radar, etc. Optoelectronic reconnaissance data generation mainly includes the acquisition of measured images and video data from various optoelectronic sensors, the acquisition of sensor parameters, and data storage. Optoelectronic reconnaissance sensors include white light, infrared, multispectral, hyperspectral, and laser optoelectronic devices, etc. Sensor types include airborne optoelectronic devices, shipborne optoelectronic devices, etc. Electronic reconnaissance data generation mainly includes the acquisition of measured data from radar signal reconnaissance equipment and communication signal reconnaissance equipment, sensor parameter acquisition, and data storage. Electronic reconnaissance sensors include radar signal reconnaissance equipment, communication signal reconnaissance equipment, and electronic signal (radar signal and communication signal) reconnaissance equipment. Sensors include airborne, shipborne, vehicle-mounted, and land-based fixed electronic reconnaissance equipment. Underwater acoustic reconnaissance data generation mainly includes the acquisition of measured data from sonar reconnaissance equipment, underwater acoustic array reconnaissance equipment, and coastal underwater acoustic sensor array equipment, sensor parameter acquisition, and data storage.
[0049] Database management mainly involves normalizing and modeling the generated results of radar detection, optoelectronic reconnaissance, electronic reconnaissance, and underwater acoustic reconnaissance data to form a measured reconnaissance database.
[0050] For the measured reconnaissance data used in the database construction, normalization processing of the measured incentive data is carried out, including reconnaissance data format conversion, spatiotemporal unified processing of reconnaissance data, reconnaissance data information annotation, collaborative reconnaissance data processing, and data normalization and organization. Based on the model specifications generated by the simulation incentive database, the normalized data is modeled and generated to produce a measured incentive database with the same data structure, data format, and data annotation as the preventive incentive database. Based on the accumulation and updates of the measured data, the incentive database is added, deleted, maintained, and managed. Based on the test incentive selection results, the measured incentive database is called to output the measured data incentives for benchmark testing.
[0051] The simulation stimulus data includes: radar detection simulation stimulus data, electro-optical reconnaissance simulation stimulus data, electronic reconnaissance simulation stimulus data, and underwater acoustic reconnaissance simulation stimulus data; based on the test stimulus selection results, the simulation stimulus database is called, and the benchmark test simulation data stimulus is output; among which:
[0052] Radar detection simulation excitation data mainly refers to the reconnaissance data of radar detection sensors, including reconnaissance radar simulation excitation data, detection radar simulation excitation data, fire control radar simulation excitation data, early warning radar simulation excitation data, SAR radar image simulation excitation data, and multi-functional radar simulation excitation data, and superimposed reconnaissance sensor parameter information, superimposed platform information data, etc.
[0053] The photoelectric reconnaissance simulation stimulus data mainly refers to image simulation stimulus data and video simulation stimulus data. The simulation stimulus data includes white light simulation data, infrared simulation data, etc., and is superimposed with reconnaissance sensor parameter information, superimposed with platform information data, etc.
[0054] Electronic reconnaissance simulation stimulus data mainly refers to radar signal reconnaissance simulation stimulus data and communication signal reconnaissance simulation stimulus data, and is superimposed with reconnaissance sensor parameter information, platform information data, etc.
[0055] The underwater acoustic reconnaissance simulation excitation data mainly refers to the sonar reconnaissance simulation excitation data and the ship's underwater acoustic array reconnaissance simulation excitation data, and is superimposed with reconnaissance sensor parameter information, platform information data, etc.
[0056] This invention enables the generation of benchmark test incentive data based on simulation and actual measurement in data link system benchmark testing applications. It involves a method for generating and managing incentive data for data link system certification testing, including test incentive selection, actual measurement incentive database construction, generation of radar detection actual measurement data, generation of electro-optical reconnaissance actual measurement data, generation of electronic reconnaissance actual measurement data, generation of underwater acoustic reconnaissance actual measurement data, database management, data normalization processing, database modeling and generation, database access, and simulation incentive databases, radar detection simulation incentive data, electro-optical reconnaissance simulation incentive data, electronic reconnaissance simulation incentive data, and underwater acoustic reconnaissance simulation incentive data. It utilizes actual measurement data and simulation data to generate test incentive data, performs normalization processing on the actual measurement data, and performs database modeling and management operations to achieve the function of generating and managing the actual measurement incentive database.
[0057] This invention can provide data source incentives for test stimulation, realize mutual verification and support between simulation and actual test data, realize test support for data chain system certification, and ensure the authenticity, security and reliability of test data.
Claims
1. A method for generating benchmark test stimulus data based on simulation and actual measurement, characterized in that, Includes the following steps: (1) Establish a measured stimulus database; (2) Establish a simulation stimulus database; (3) Based on the measured stimulus data and the simulated stimulus data, select test stimulus according to the requirements and provide corresponding test stimulus data for the benchmark test system.
2. The method for generating benchmark test stimulus data based on simulation and actual measurement according to claim 1, characterized in that, The specific method for step 1 is as follows: (101) Generate radar detection measured data, photoelectric reconnaissance measured data, electronic reconnaissance measured data and underwater acoustic reconnaissance measured data, and complete the collection and storage of various types of measured data; (102) Perform data format conversion, spatiotemporal unified processing, data information labeling and data normalization on radar detection measured data, photoelectric reconnaissance measured data, electronic reconnaissance measured data and underwater acoustic reconnaissance measured data, and complete the normalization of measured excitation data; (103) Based on the model specifications generated by the simulation excitation database, the normalized measured excitation data is modeled and processed to generate a callable measured excitation database.
3. The method for generating benchmark test stimulus data based on simulation and actual measurement according to claim 2, characterized in that, The radar detection measured data includes reconnaissance radar measured data, detection radar measured data, fire control radar measured data, early warning radar measured data, SAR radar image data, and sensor parameters of airborne, shipborne, vehicle-mounted, and land-based fixed radars; The measured data from photoelectric reconnaissance includes the acquisition of measured image and video data, as well as sensor parameters from white light, infrared, multispectral, hyperspectral, and laser photoelectric devices. The measured data of electronic reconnaissance includes measured data of radar signal reconnaissance and communication signal reconnaissance, as well as sensor parameters of radar signal reconnaissance equipment, communication signal reconnaissance equipment and electronic signal reconnaissance equipment; The measured data of underwater acoustic reconnaissance includes measured data of sonar reconnaissance and underwater acoustic array reconnaissance, as well as sensor parameters of sonar sensors, shipborne underwater acoustic sensor arrays, and coastal underwater acoustic sensor arrays.
4. The method for generating benchmark test stimulus data based on simulation and actual measurement according to claim 1, characterized in that, The simulation stimulus data in the simulation stimulus database includes radar detection simulation stimulus data, optoelectronic reconnaissance simulation stimulus data, electronic reconnaissance simulation stimulus data, and underwater acoustic reconnaissance simulation stimulus data; Among them, the radar detection simulation excitation data includes reconnaissance radar simulation excitation data, detection radar simulation excitation data, fire control radar simulation excitation data, early warning radar simulation excitation data, SAR radar image simulation excitation data, and multi-functional radar simulation excitation data, as well as corresponding sensor parameter information and platform information data. The photoelectric reconnaissance simulation stimulus data includes image simulation stimulus data and video simulation stimulus data, as well as corresponding sensor parameter information and platform information data. The electronic reconnaissance simulation stimulus data includes radar signal reconnaissance simulation stimulus data and communication signal reconnaissance simulation stimulus data, as well as corresponding sensor parameter information and platform information data. The underwater acoustic reconnaissance simulation excitation data includes sonar reconnaissance simulation excitation data, ship underwater acoustic array reconnaissance simulation excitation data, and coastal underwater acoustic sensor array reconnaissance simulation data, as well as corresponding sensor parameter information and platform information data.
5. The method for generating benchmark test stimulus data based on simulation and actual measurement according to claim 1, characterized in that, The specific method for selecting test stimuli is as follows: Each type of incentive data includes at least one set of measured incentive data as a guarantee of reliability; when the measured incentive data cannot reach the required amount of incentive data, simulated incentive data is used to supplement it.