Data processing method based on multi-element fusion

Abstract

The application provides a data processing method based on multi-element fusion. Through storage, classified storage management, database storage, calculation and sending, analysis processing, merging and fusion analysis steps, multi-element data obtained by electronic reconnaissance equipment, including thunder detection process data, thunder detection full pulse data, thunder detection intermediate frequency data, communication reconnaissance data and the like, are subjected to fusion analysis processing, so that the analysis and arrangement of battlefield data are completed, the electronic target database is perfected, combat support is provided, and data support is provided for the performance evaluation of the electronic reconnaissance equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electronic information, in particular to a data processing method based on multi-element fusion. BACKGROUND

[0002] The modern electronic warfare battlefield situation is increasingly complex, and the radar radiation source pattern is variable. The full pulse data detected by the electronic reconnaissance equipment needs to be analyzed in detail. At the same time, it is difficult to accurately complete the situation awareness task by only detecting the radar radiation source parameter information, and it is necessary to combine multi-element sensor information and other related information for fusion confirmation if necessary. The current data processing system analyzes a single data source. If there are multiple data elements in a data analysis task, the multi-element data needs to be analyzed, and the comprehensive results are usually formed by manually comparing and merging the unit analysis results. This way has the problems of complicated operation, large analysis workload and low efficiency. SUMMARY

[0003] In view of the defects in the prior art, the purpose of the present application is to provide a data processing method based on multi-element fusion.

[0004] According to the data processing method based on multi-element fusion provided by the present application, the following steps are included:

[0005] The storage step: the data information is sent from the data import terminal to the storage node;

[0006] The classified storage management step: the storage node classifies and stores the data information, and sends the data path and the data information to the database node;

[0007] The database storage step: the database node stores the information sent by the storage node into the database, and notifies the management node that the data preparation is completed;

[0008] The calculation and sending step: the management node acquires the data information, the data path information, the pre-stored data automatic analysis algorithm and the configuration file information in the database node, and sends them to the calculation node;

[0009] The analysis and processing step: the calculation node analyzes, fuses and identifies different types of data information, forms an electronic target suggestion library, and sends the electronic target suggestion library and the associated data to the database node for storage management;

[0010] The merging step: the data analysis terminal requests the electronic target suggestion library and the associated data from the management node, manually analyzes and merges the electronic target suggestion library to form an electronic target audit library, and sends it to the database node for storage management;

[0011] The fusion analysis step: after the fusion analysis of the electronic target review library and the electronic target achievement library, a new electronic target achievement library is formed.

[0012] Preferably, the investigation data information comprises investigation data and investigation related data.

[0013] Preferably, the storage node can be dynamically expanded in physical form.

[0014] Preferably, in the analysis and processing step, the calculation node first automatically / manually analyzes and processes different types of investigation data to form different processing result signal tables, and the processing result signal tables are fused to form a fused processing result signal table.

[0015] Preferably, if the electronic target achievement library is set, the fused processing result signal table is used as an expert system to perform target identification on the electronic target achievement library in the database node to form an electronic target suggestion library.

[0016] If the electronic target achievement library is not set, the fused processing result signal table directly forms the electronic target suggestion library.

[0017] Preferably, in the analysis and processing step, different types of data of different sensors in the investigation data are fused according to the hierarchical fusion mode, the data fusion of the same type of sensor is performed first, then the data fusion of different types of sensors is performed, and finally the electronic target suggestion library is fused in combination with the electronic target achievement library.

[0018] Preferably, in each level of fusion, the fusion is performed according to the parameter attribute, and the time / space fusion mode is combined; the same type of sensor is given different weight sizes according to different detection situations when the parameter attributes are merged; and the different types of sensors are given different weight sizes according to the measurement error sizes in the fusion process, and finally the fusion result is calculated.

[0019] Preferably, in the merging step, the data analysis terminal requests the electronic target suggestion library and the associated data from the management node, performs multi-dimensional data display and battlefield comprehensive situation display on the data analysis terminal, guides the electronic target suggestion library, combines battlefield situation replay, electronic target direction line plotting, pulse data visualization artificial analysis, medium frequency data visualization artificial analysis, compares with the investigation data information, manually merges and confirms the electronic target suggestion library to form an electronic target review library, and sends the electronic target review library to the database node for storage management.

[0020] Preferably, in the merging step, the data analysis terminal can be expanded.

[0021] Preferably, in the fusion analysis step, the electronic target review library and the electronic target achievement library are fused in a direct merging mode.

[0022] Compared with the prior art, the present application has the following beneficial effects:

[0023] 1. This invention provides a full-process data integration service, including acquisition, aggregation, and storage, for multiple platforms and multiple sensors by using diverse data.

[0024] 2. This invention, through the iterative fusion of electronic databases, possesses a data standard system, enabling the full lifecycle management and utilization of multi-sensor data, and the comprehensive processing of multi-level electronic target database data.

[0025] 3. This invention improves the efficiency and accuracy of data analysis by using parallel processing of computing nodes, and the processing algorithm module is highly replaceable.

[0026] 4. This invention provides a scientific and rigorous basis for decision-making through comprehensive and in-depth data mining and analysis.

[0027] 5. This invention provides multi-dimensional data and situational awareness displays, supports environment reconstruction and multi-person collaborative analysis, and improves the efficiency of manual analysis by data analysts. Attached Figure Description

[0028] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0029] Fig. 1 This is a system architecture diagram for a multi-source fusion-based data processing method.

[0030] Fig. 2 This is a schematic diagram of the multi-data processing flow based on a multi-source fusion data processing method.

[0031] Fig. 3 This is a schematic diagram of a data fusion method based on multi-source fusion. Detailed Implementation

[0032] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0033] like Figs. 1-3 As shown, this invention provides a multi-source fusion-based data processing method that can perform fusion analysis on multi-source data (including radar reconnaissance process data, radar reconnaissance full-pulse data, radar reconnaissance intermediate frequency data, communication reconnaissance data, etc.) acquired by electronic reconnaissance equipment, thereby completing the analysis and organization of battlefield data, improving the electronic target database, providing combat support, and also providing data support for the effectiveness evaluation of electronic reconnaissance equipment.

[0034] Fig. 1 The system demonstrates a data processing method based on multi-source fusion, comprising at least one database node, one management node, one storage node, at least one computing node, one data import terminal, and at least one data analysis terminal. The data import terminal communicates with the storage node and the data analysis terminal communicates with the management node via Ethernet.

[0035] The data processing method based on multi-source fusion includes the following steps:

[0036] Step S1: The analyst imports a batch of reconnaissance data (which may include multi-sensor multi-data) and confirmed images, technical reconnaissance materials and other relevant information from the data import terminal and notifies the storage node to send information such as the electronic reconnaissance data batch, the platform type of the equipment, the equipment number, the mission number, the mission time, and the type of reconnaissance data to the storage node.

[0037] Step S2: The storage node categorizes and stores the imported reconnaissance data and other related information according to the platform type, equipment number, mission number, mission time, and reconnaissance data type of the electronic reconnaissance equipment. After categorizing and storing the data, the storage node sends the data path and the reconnaissance data and other related information from step S1 to the database node. The storage node supports dynamic expansion in physical form.

[0038] Step S3: The database node stores the information sent by the storage node into the database and notifies the management node that the data preparation is complete.

[0039] Step S4: After receiving the information from step S3, the management node retrieves the reconnaissance data from step S1, other relevant information data, data path information from step S2, and data automatic analysis algorithm and configuration file information stored in the database node beforehand, and sends them to the computing node. The configuration file information includes data filtering configuration parameters, the number of parallel computing nodes, etc., to improve analysis efficiency. Filtered data refers to some interference information data that is not of interest.

[0040] Step S5: After receiving the information from Step S4, the computing node begins to automatically / manually analyze and process different types of reconnaissance data to form different processing result signal tables. The processing result signal tables are then fused to form a fused processing result signal table. Using the electronic target result library (if any) in the database node as an expert system, target identification is performed to form an electronic target suggestion library. The electronic target suggestion library and related data are then sent to the database node for storage and management. In this step, the computing node can be expanded as needed, and the fusion processing algorithm module is configurable.

[0041] The fusion method integrates data from different sensors and data types using a hierarchical fusion approach. First, data from sensors of the same type is fused, then data from sensors of different types is fused, and finally, data from an electronic target database (if applicable) is fused to form an electronic target suggestion database. Each fusion stage is based on parameter attributes and incorporates spatiotemporal fusion. Same-type sensor parameter attribute fusion refers to the fusion of parameters such as carrier frequency, pulse width, amplitude, repetition period, and azimuth from the same type of radar detector. Different weights are assigned based on different detection scenarios during parameter attribute merging (e.g., the accuracy of radar detector azimuth measurement is related to the incident angle, i.e., the detection scenario), significantly improving the accuracy of the results. Common parameter attributes abstracted from different types of sensors, such as target platform, azimuth, and distance, are fused. Different weights are assigned based on the magnitude of measurement error during the fusion process between different types of sensors, ultimately calculating the fusion result. Spatiotemporal fusion refers to the fusion of targets with the same attribute appearing in different time periods or targets with different attributes appearing in the same location, based on spatiotemporal uniqueness (only one electronic target can appear at a certain time and place).

[0042] Step S6: The data analysis terminal requests the electronic target suggestion library and associated data for each electronic target (including lightning reconnaissance process data, lightning reconnaissance full pulse data, lightning reconnaissance intermediate frequency data, communication reconnaissance data, technical reconnaissance materials, etc.) from the management node. Multi-dimensional data display and comprehensive battlefield situation display are then performed on the data analysis terminal. Guided by the electronic target suggestion library from Step S5, and combined with battlefield situation reenactment, electronic target direction finding line plotting, manual analysis of pulse data visualization, manual analysis of intermediate frequency data visualization, and comparison with other relevant information data from Step S1, the electronic target suggestion library is manually merged and confirmed to form an electronic target review library, which is then sent to the database node for storage and management. In this step, the data analysis terminal can be expanded to enable multi-user collaborative analysis. Analysts can simultaneously request multiple data sources for correlation analysis on the same interface and directly view other metadata information on the same interface, eliminating the need for tedious manual searching.

[0043] Step S7: After merging and analyzing the electronic target review database and the electronic target results database, a new electronic target results database is formed. This database achieves data association across a large time span and wide spatial area by linking accumulated historical data. The specific fusion method is as follows: assuming that all electronic targets in the review database have the same confidence level, a direct merging approach is used for fusion. Simultaneously, the electronic target results database supports manual correction methods such as merging, splitting, and creating new databases, and supports backtracking of data from steps S1-S7.

[0044] Step S8: After the new batch of reconnaissance data arrives, repeat steps S1-S7.

[0045] Step S9: From the formed electronic target results database, the activity patterns, style patterns, and geographical distribution of electronic targets can be statistically analyzed in terms of time, space, and frequency. This allows for further inference of their operational intentions and threat levels, enabling in-depth information mining and providing operational support.

[0046] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0047] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A data processing method based on multi-source fusion, characterized in that, Includes the following steps: Storage steps: Send the reconnaissance data from the data import terminal to the storage node; Categorized storage management steps: After classifying and storing the reconnaissance data, the storage node sends the data path and reconnaissance data information to the database node; Database storage steps: The database node stores the information sent by the storage node into the database and notifies the management node that the data preparation is complete; The calculation and transmission steps are as follows: The management node obtains the reconnaissance data information, data path information, data automatic analysis algorithm and configuration file information stored in the database node from the database node and sends them to the computing node; Analysis and processing steps: The computing node analyzes, integrates, and identifies different types of reconnaissance data to form an electronic target suggestion library, and sends the electronic target suggestion library and related data to the database node for storage and management; The reconnaissance data is fused according to a hierarchical fusion method. First, data from the same type of sensor is fused, then data from different types of sensor is fused, and finally, the data is fused with the electronic target results database to form an electronic target suggestion database. The fusion process at each level is based on parameter attributes, combined with spatiotemporal fusion methods; when merging parameter attributes of similar sensors, different weights are assigned according to different detection situations. In the process of fusing different types of sensors, different weights are assigned according to the magnitude of the measurement error, and the final fusion result is calculated. Merging steps: The data analysis terminal requests the electronic target suggestion library and related data from the management node, manually analyzes and merges the electronic target suggestion library to form the electronic target review library, and sends it to the database node for storage management; Integration and analysis steps: After integrating and analyzing the electronic target review database and the electronic target results database, a new electronic target results database is formed.

2. The data processing method based on multi-source fusion according to claim 1, characterized in that, The investigation data information includes investigation data and investigation-related data.

3. The data processing method based on multi-source fusion according to claim 1, characterized in that, The storage nodes can be dynamically expanded in terms of physical form.

4. The data processing method based on multi-source fusion according to claim 1, characterized in that, In the analysis and processing steps, the computing nodes first perform automatic / manual analysis and processing on different types of reconnaissance data to form different processing result signal tables. The processing result signal tables are then fused to form a fused processing result signal table.

5. The data processing method based on multi-source fusion according to claim 4, characterized in that: If an electronic target results database is set up, the fusion processing result signal table uses the electronic target results database in the database node as the expert system to perform target identification and form an electronic target suggestion database. If no electronic target results database is set up, the fusion processing result signal table will directly form the electronic target suggestion database.

6. The data processing method based on multi-source fusion according to claim 1, characterized in that, During the merging process, the data analysis terminal requests the electronic target suggestion library and related data from the management node. The data analysis terminal then displays multi-dimensional data and a comprehensive battlefield situation. Guided by the electronic target suggestion library, and combined with battlefield situation reenactment, electronic target direction finding line plotting, manual analysis of pulse data visualization, and manual analysis of intermediate frequency data visualization, the electronic target suggestion library is manually merged and confirmed to form an electronic target audit library, which is then sent to the database node for storage and management.

7. The data processing method based on multi-source fusion according to claim 1, characterized in that, During the merging process, the data analysis terminal can be expanded.

8. The data processing method based on multi-source fusion according to claim 1, characterized in that, In the fusion analysis step, the electronic target review database and the electronic target results database are merged by direct merging.

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