Cognitive radar data processing system and method based on Lambda architecture

Abstract

The invention provides a cognitive radar data processing system and method based on a Lambda architecture, and relates to the field of cognitive radars, the cognitive radar data processing system comprises an application layer, a data processing and storage layer, a data source layer and the Lambda architecture, and the Lambda architecture comprises a batch processing layer, an acceleration layer and a service layer; the data processing and storage layer comprises a data access gateway, a universal library, an offline processing module, a real-time calculation module, a historical data service interface Restful api and a real-time data interface Websocket; the batch processing layer is connected with a historical data service interface Restful api, and the acceleration layer is connected with a real-time data interface Websocket. According to the invention, a big data processing scheme is introduced into the data processing architecture of the cognitive radar, so that a large amount of valuable historical global data can be stored and managed on the premise of realizing the conventional radar function; the powerful data processing and storage capability has the function of data collaborative reasoning with other devices, and can support development of a comprehensive function system.

Description

Technical Field

[0001] The present application relates to the field of cognitive radar technology, and in particular to a cognitive radar data processing system and method based on Lambda architecture. Background Art

[0002] Cognitive radar is one of the development trends of future radars. It transmits electromagnetic waves to the environment, receives the returned electromagnetic wave signals and performs adaptive processing to obtain environmental information. Combining prior knowledge and reasoning, it continuously adjusts the transmitter and receiver parameters to enable the radar system to reach the optimal working state and realize adaptive detection of targets.

[0003] Cognitive radars combine processed radar observation data with prior knowledge for correlation and reasoning, requiring the processing of large amounts of data within a certain timeframe. Cognitive radars must process and display radar echo data while the radar is operating normally, and promptly adjust radar control parameters to adapt to the environment. Simultaneously, radars operating in the same environment also need to process and reason about global data based on long-term monitoring data. Consequently, cognitive radars require massive amounts of data storage and processing, making existing cognitive radars difficult to meet the demands of real-time and large-scale global data usage. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, this application provides a cognitive radar data processing system and method based on the Lambda architecture, which solves the difficulties faced by existing cognitive radars when storing and processing massive data.

[0005] To achieve the above objectives, this application is implemented through the following technical solutions:

[0006] In the first aspect, an embodiment of the present application provides a cognitive radar data processing system based on the Lambda architecture, which includes an application layer, a data processing and storage layer, a data source layer and a Lambda architecture. The Lambda architecture includes a batch processing layer, an acceleration layer and a service layer; wherein the data processing and storage layer includes a data access gateway, a general library, an offline processing module, a real-time computing module, a historical data service interface Restful API, and a real-time data interface Websocket; the batch processing layer is connected to the historical data service interface Restful API, and the acceleration layer is connected to the real-time data interface Websocket; the application layer is used to visualize the data processed by the data processing and storage layer and output parameter adjustment plans; the data source layer organizes and transmits device data including sensor detection data and radar echo data to the data processing and storage layer; the batch processing layer is used to store global data and perform offline calculation and reasoning after the data is stored, and the acceleration layer processes and displays radar real-time measurement data.

[0007] According to the first aspect of the embodiment of the present application, the device data corresponding to the data source layer includes sensor measurement data, radar echo data, interference data, environmental monitoring data and other device data; the application layer is used to perform Bayesian filtering, minimization of PC-CRLB, target measurement model comparison, cognitive control parameter adjustment, and historical data fitting learning.

[0008] According to the first aspect of the embodiment of the present application, the universal library of the data processing and storage layer includes: a radar target data modeling unit, an environmental parameter modeling unit, a knowledge-based expert system and a radar basic control model.

[0009] According to the first aspect of the embodiment of the present application, radar echo data includes radar terrain environment model data, ultra-wideband target recognition data, working mode optimization learning data, and data mining efficiency improvement data; in the offline processing module of the data processing and storage layer, the data required for calculation can be stored in Nosql, Mysql, Hbase and Redis databases respectively according to different data types, so as to be effectively called by the computing engine.

[0010] According to the first aspect of the embodiment of the present application, radar terrain environment model data is stored in Nosql, ultra-wideband target recognition data is stored in Mysql, working mode optimization learning data is stored in Hbase, and data mining efficiency improvement data is stored in Redis.

[0011] According to the first aspect of the embodiment of the present application, the real-time computing module of the data processing and storage layer is used to process the radar echo data after signal processing, and perform point track fitting and target identification on the data preliminarily processed by the acceleration layer.

[0012] According to the first aspect of the embodiment of the present application, the data access gateway of the data processing and storage layer is used to: transmit data through the optical terminal in the radar system to the 10G network router, and then through Nginx load balancing to the asynchronous Netty network application framework to the stream processor.

[0013] In the second aspect, an embodiment of the present application provides a cognitive radar data processing method based on the Lambda architecture. The cognitive radar data processing method based on the Lambda architecture performs cognitive radar data processing through a cognitive radar data processing system based on the Lambda architecture. The cognitive radar data processing system based on the Lambda architecture includes an application layer, a data processing and storage layer, a data source layer and a Lambda architecture. The Lambda architecture includes a batch processing layer, an acceleration layer and a service layer; the data processing and storage layer includes a data access gateway, a general library, an offline processing module, a real-time computing module, a historical data service interface Restful API, and a real-time data interface Websocket; the batch processing layer is connected to the historical data service interface Restful API, and the acceleration layer is connected to the real-time data interface Websocket.

[0014] The aforementioned cognitive radar data processing method based on the Lambda architecture includes: visually displaying the data processed by the data processing and storage layer and outputting parameter adjustment solutions through the application layer; organizing and transmitting device data including sensor detection data and radar echo data to the data processing and storage layer through the data source layer; storing global data through the batch processing layer and performing offline calculation and reasoning after the data is stored; and processing and displaying real-time radar measurement data through the acceleration layer.

[0015] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor, a memory, and a program stored in the memory and executable on the processor. When the program is executed by the processor, the cognitive radar data processing method based on the Lambda architecture in the aforementioned second aspect is implemented.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a program or instruction. When the program or instruction is executed by a processor, it implements the cognitive radar data processing method based on the Lambda architecture in the aforementioned second aspect.

[0017] This application provides a cognitive radar data processing system and method based on the Lambda architecture. Compared with the existing technology, it has the following advantages:

[0018] This application introduces the Lambda architecture to process data for cognitive radar. The Lambda architecture includes a batch processing layer, an acceleration layer, and a service layer. The cognitive radar data processing system also includes an application layer, a data processing and storage layer, and a data source layer. The data processing and storage layer has two interfaces, namely the historical data service interface Restful api and the historical data service interface Restful api. The batch processing layer of the Lambda architecture is connected to the historical data service interface Restful api to store global data and perform offline calculations and reasoning after the data is stored. The acceleration layer is connected to the real-time data interface Websocket to process and display radar measurement data. This application introduces a big data processing solution into the data processing architecture of the cognitive radar, which can store and manage a large amount of valuable historical global data while realizing conventional radar functions. The powerful data processing and storage capabilities will have the function of collaborative data reasoning with other devices, which can support the development of comprehensive functional systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is a schematic diagram of the architecture of the data source layer, data processing and storage layer, and application layer provided in an embodiment of the present application;

[0021] Figure 2 1 is a hierarchical diagram of a Lambda architecture-based cognitive radar data processing system provided in an embodiment of the present application;

[0022] Figure 3 This is a schematic diagram of the data access gateway connection between the data source layer and the data processing and storage layer provided in an embodiment of the present application;

[0023] Figure 4 This is a schematic diagram of the structure of a cognitive radar data processing system based on Lambda architecture provided in an embodiment of the present application;

[0024] Figure 5 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0027] The embodiments of the present application provide a cognitive radar data processing system and method based on the Lambda architecture to solve the difficulties faced by existing cognitive radars in storing and processing massive data. It also solves the data processing problem that cognitive radars need to process and display radar echo data and promptly control radar control parameters to adapt to the environment when the radar is working normally, while radars in the same working environment also need to process and infer global data based on long-term monitoring data.

[0028] The technical solution in the embodiments of the present application is to solve the above technical problems, and the overall idea is as follows:

[0029] Cognitive radar, one of the future development trends of radar, transmits electromagnetic waves to the environment, receives the returned electromagnetic wave signals and performs adaptive processing to obtain environmental information. Combining prior knowledge and reasoning, it learns through continuous interaction with the environment and continuously adjusts the transmitter and receiver parameters. This allows the radar system to achieve optimal working conditions and adaptive detection of targets in an efficient, reliable and robust manner to meet specific remote sensing goals.

[0030] Cognitive radars combine processed radar observation data with prior knowledge for correlation and reasoning, requiring the processing of large amounts of data within a certain timeframe. Cognitive radars must process and display radar echo data while the radar is operating normally, and promptly adjust radar control parameters to adapt to the environment. Simultaneously, radars operating in the same environment also need to process and reason about global data based on long-term monitoring data. Consequently, cognitive radars require massive amounts of data storage and processing, making existing cognitive radars difficult to meet the demands of real-time and large-scale global data usage.

[0031] Existing radar signal processing and data processing require high timeliness, making it difficult to simultaneously store and process massive amounts of real-time and global data. Cognitive radar requires the integration of prior knowledge and reasoning to continuously adjust transmitter and receiver parameters to optimize the radar system's performance and achieve adaptive target detection. This places certain timeliness requirements on the use and storage of both real-time and global data.

[0032] To address the need for real-time data and large-scale global data usage, this application introduces the Lambda architecture, which is designed for big data processing. Radar data is processed by corresponding processing layers according to certain time requirements, which can realize the design of a cognitive radar data processing system based on the Lambda architecture.

[0033] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0034] The following first introduces a cognitive radar data processing system based on Lambda architecture provided in an embodiment of the present application.

[0035] In some embodiments, the present application provides a cognitive radar data processing system 100 based on Lambda architecture, please refer to Figures 1-4 The Lambda architecture-based cognitive radar data processing system 100 includes: an application layer 110 , a data processing and storage layer 120 , a data source layer 130 and a Lambda architecture 140 .

[0036] The Lambda architecture 140 includes a batch processing layer, an acceleration layer, and a service layer; the data processing and storage layer 120 includes a data access gateway, a general library, an offline processing module, a real-time computing module, a historical data service interface Restful API, and a real-time data interface Websocket; the batch processing layer is connected to the historical data service interface Restful API, and the acceleration layer is connected to the real-time data interface Websocket. It can be understood that the historical data service interface Restful API and the real-time data interface Websocket respectively provide corresponding data; the general library is responsible for storing the algorithms and models required for data processing, the offline processing module is responsible for responding to the global data processed by the batch processing layer, and the real-time computing is responsible for processing the real-time data processed by the acceleration layer. The data processing and storage layer 120 is connected to the data source layer 130 through the data access gateway.

[0037] The application layer 110 is used to visualize the data processed by the data processing and storage layer and output parameter adjustment plans; the data source layer 130 organizes and transmits device data, including sensor detection data and radar echo data, to the data processing and storage layer; the batch processing layer is used to store global data and perform offline calculations and inference after the data is stored; the acceleration layer processes and displays real-time radar measurement data.

[0038] The above is a specific implementation method of a cognitive radar data processing system based on the Lambda architecture provided in an embodiment of the present application. It can be understood that the present application introduces the Lambda architecture to process data for cognitive radar. The Lambda architecture includes a batch processing layer, an acceleration layer, and a service layer. The cognitive radar data processing system also includes an application layer, a data processing and storage layer, and a data source layer; the data processing and storage layer has two interfaces, namely the historical data service interface Restful api and the historical data service interface Restful api. The batch processing layer of the Lambda architecture is connected to the historical data service interface Restful api to store global data and perform offline calculation and reasoning after the data is stored; the acceleration layer is connected to the real-time data interface Websocket to process and display the radar measurement data.

[0039] Based on this, this application introduces a big data processing solution into the data processing architecture of cognitive radar, which can store and manage a large amount of valuable historical global data while realizing conventional radar functions; the powerful data processing and storage capabilities will have the function of collaborative data reasoning with other devices, which can support the development of comprehensive functional systems.

[0040] It should be noted that the primary goal of the Lambda architecture is to apply big data processing methods to the system data input of cognitive radar. Routine data such as point tracks, which the radar needs to process and display in real time, is processed by the acceleration layer to ensure normal radar operation. The large amounts of global data required by cognitive radar are processed and stored by the batch processing layer, allowing the computing engine to perform the related complex calculations and reasoning. The big data service layer visualizes the data, provides query support, control optimization, and application development within the application layer of the system architecture.

[0041] In some embodiments, the device data corresponding to the data source layer 130 includes sensor measurement data, radar echo data, interference data, environmental monitoring data, and other device data. The application layer 110 is used to perform Bayesian filtering, minimize PC-CRLB, compare target measurement models, adjust cognitive control parameters, and learn from historical data fitting. PC-CRLB is the conditional Cramer-Rao bound for prediction.

[0042] In some embodiments, the general library of the data processing and storage layer 120 includes: a radar target data modeling unit, an environmental parameter modeling unit, a knowledge-based expert system, and a radar basic control model. It is understood that the general library is responsible for storing the algorithms and models required for data processing.

[0043] In some embodiments, radar echo data includes radar terrain environment model data, ultra-wideband target recognition data, operating mode optimization learning data, and data mining efficiency improvement data. In the offline processing module of the data processing and storage layer 120, the data required for calculation can be stored in NoSQL, MySQL, HBase, and Redis databases, depending on the data type, for efficient access by the computing engine. It will be understood that this application classifies radar echo data, and specific radar data is stored and queried using an appropriate database type.

[0044] In one example, radar terrain environment model data is stored in Nosql, ultra-wideband target recognition data is stored in MySQL, working mode optimization learning data is stored in Hbase, and data mining efficiency improvement data is stored in Redis.

[0045] In some embodiments, the real-time computing module of the data processing and storage layer 120 is used to process the radar echo data after signal processing, perform point track fitting on the data preliminarily processed by the acceleration layer, and perform target recognition.

[0046] In some embodiments, please refer to Figure 3The data access gateway of the data processing and storage layer 120 is used to transmit data to the 10G network router through the optical terminal in the radar system, and then to the asynchronous Netty network application framework to the stream processor through Nginx load balancing.

[0047] In the embodiment of the present application, it can be understood that the process of data from the data source layer 130 to the data processing and storage layer 120 is to transmit the data to the 10 Gigabit network router through the optical terminal in the radar system, and then to the Netty network application framework to the stream processor through Nginx load balancing. The load balancing design and asynchronous Netty network architecture will effectively improve the data transmission efficiency.

[0048] In some embodiments, the present application provides a flow chart of a cognitive radar data processing method based on the Lambda architecture. The cognitive radar data processing method based on the Lambda architecture performs cognitive radar data processing through a cognitive radar data processing system based on the Lambda architecture. The cognitive radar data processing system based on the Lambda architecture includes an application layer 110, a data processing and storage layer 120, a data source layer 130 and a Lambda architecture 140. The Lambda architecture 140 includes a batch processing layer, an acceleration layer and a service layer; the data processing and storage layer 120 includes a data access gateway, a general library, an offline processing module, a real-time computing module, a historical data service interface Restful API, and a real-time data interface Websocket; the batch processing layer is connected to the historical data service interface Restful API, and the acceleration layer is connected to the real-time data interface Websocket.

[0049] The cognitive radar data processing method based on the Lambda architecture may include the following steps S210-S230.

[0050] S210, visually displaying the data processed by the data processing and storage layers and outputting parameter adjustment solutions through the application layer;

[0051] S220, arranging and transmitting the device data including the sensor detection data and the radar echo data to the data processing and storage layer through the data source layer;

[0052] S230: Store global data through the batch processing layer and perform offline calculation and reasoning after the data is stored, and process and display the radar real-time measurement data through the acceleration layer.

[0053] This Lambda architecture-based cognitive radar data processing method has the functions of realizing the aforementioned Lambda architecture-based cognitive radar data processing system and can achieve its corresponding technical effects. For the sake of brevity, it will not be described here in detail.

[0054] In some embodiments, the present application provides an electronic device, the structural diagram of the electronic device is as follows Figure 5 shown.

[0055] The electronic device may include a processor 310 and a memory 320 storing computer program instructions.

[0056] Specifically, the processor 310 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0057] The memory 320 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 320 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 320 may include removable or non-removable (or fixed) media. Where appropriate, the memory 320 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 320 is a non-volatile solid-state memory.

[0058] The memory 320 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Therefore, generally, the memory 320 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it may perform the operations described in any of the Lambda architecture-based cognitive radar data processing methods in the above-mentioned embodiments.

[0059] The processor 310 implements any one of the Lambda architecture-based cognitive radar data processing methods in the above embodiments by reading and executing computer program instructions stored in the memory 320 .

[0060] In one example, the electronic device may further include a communication interface 330 and a bus 300. Figure 5 As shown, the processor 310 , the memory 320 , and the communication interface 330 are connected via a bus 300 and communicate with each other.

[0061] The communication interface 330 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0062] Bus 300 includes hardware, software or both, and the components of online data flow metering equipment are coupled to each other. For example, and not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnect (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 300 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0063] In addition, in conjunction with the Lambda architecture-based cognitive radar data processing method in the above-mentioned embodiments, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when executed by a processor, the computer program instructions implement any of the Lambda architecture-based cognitive radar data processing methods in the above-mentioned embodiments.

[0064] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0065] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. Programs or code segments can be stored in machine-readable media, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable media" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0066] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0067] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0068] In summary, compared with the prior art, this application has the following beneficial effects:

[0069] 1. This application introduces a Lambda architecture to process cognitive radar data. The Lambda architecture's batch processing layer connects to the historical data service interface Restful API to store global data and perform offline computation and reasoning after the data is stored. The acceleration layer connects to the real-time data interface Websocket to process and display radar measurement data. This application introduces a big data processing solution into the cognitive radar's data processing architecture, allowing it to store and manage large amounts of valuable historical global data while still achieving conventional radar functions. Its powerful data processing and storage capabilities enable collaborative data reasoning with other devices, supporting the development of comprehensive functional systems.

[0070] 2. This application classifies radar echo data and uses appropriate database types for storage and query management. Radar terrain environment model data is stored in NoSQL, ultra-wideband target recognition data is stored in MySQL, working mode optimization learning data is stored in HBase, and data mining efficiency improvement data is stored in Redis. This will effectively improve data storage efficiency and processing speed.

[0071] 3. The process of data from the data source to the data storage and processing layer in this application is to transmit the data to the 10G network router through the optical terminal in the radar system, and then to the Netty network application framework to the stream processor through Nginx load balancing. The load balancing design and asynchronous Netty network architecture will effectively improve the data transmission efficiency.

[0072] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A cognitive radar data processing system based on Lambda architecture, characterized in that: It includes an application layer, a data processing and storage layer, a data source layer, and a Lambda architecture, wherein the Lambda architecture includes a batch processing layer, an acceleration layer, and a service layer; The data processing and storage layer includes a data access gateway, a general library, an offline processing module, a real-time computing module, a historical data service interface Restful API, and a real-time data interface Websocket; the batch processing layer is connected to the historical data service interface Restful API, and the acceleration layer is connected to the real-time data interface Websocket; The application layer is used to display the data processed by the data processing and storage layer in a visual manner and output parameter adjustment solutions; The data source layer organizes and transmits device data including sensor detection data and radar echo data to the data processing and storage layer; The batch processing layer is used to store global data and perform offline calculation and reasoning after the data is stored, and the acceleration layer processes and displays radar real-time measurement data.

2. The Lambda architecture-based cognitive radar data processing system according to claim 1, wherein: The device data corresponding to the data source layer includes sensor measurement data, radar echo data, interference data, environmental monitoring data and other device data; The application layer is used to perform Bayesian filtering, minimization of PC-CRLB, target measurement model comparison, cognitive control parameter adjustment, and historical data fitting learning.

3. The Lambda architecture-based cognitive radar data processing system according to claim 1 or 2, characterized in that: The universal library of the data processing and storage layer includes: a radar target data modeling unit, an environmental parameter modeling unit, a knowledge-based expert system and a radar basic control model.

4. The Lambda architecture-based cognitive radar data processing system according to claim 1 or 2, characterized in that: The radar echo data includes radar terrain environment model data, ultra-wideband target recognition data, working mode optimization learning data, and data mining efficiency improvement data; In the offline processing module of the data processing and storage layer, the data required for calculation can be stored in Nosql, Mysql, Hbase and Redis databases according to different data types, so as to be effectively called by the computing engine.

5. The Lambda architecture-based cognitive radar data processing system according to claim 4, characterized in that: The radar terrain environment model data is stored in Nosql, the ultra-wideband target recognition data is stored in MySQL, the working mode optimization learning data is stored in Hbase, and the data mining efficiency improvement data is stored in Redis.

6. The Lambda architecture-based cognitive radar data processing system according to claim 1 or 2, characterized in that: The real-time computing module of the data processing and storage layer is used to process the radar echo data after signal processing, and perform point track fitting and target recognition on the data preliminarily processed by the acceleration layer.

7. The Lambda architecture-based cognitive radar data processing system according to claim 1 or 2, characterized in that: The data access gateway of the data processing and storage layer is used to transmit data to the 10G network router through the optical terminal in the radar system, and then to the asynchronous Netty network application framework to the stream processor through Nginx load balancing.

8. A cognitive radar data processing method based on Lambda architecture, characterized in that: Processing cognitive radar data using a Lambda architecture-based cognitive radar data processing system, comprising an application layer, a data processing and storage layer, a data source layer, and a Lambda architecture, wherein the Lambda architecture comprises a batch processing layer, an acceleration layer, and a service layer; The data processing and storage layer includes a data access gateway, a general library, an offline processing module, a real-time computing module, a historical data service interface Restful API, and a real-time data interface Websocket; the batch processing layer is connected to the historical data service interface Restful API, and the acceleration layer is connected to the real-time data interface Websocket; The cognitive radar data processing method based on the Lambda architecture includes: Performing visual display and parameter adjustment scheme output on the data processed by the data processing and storage layer through the application layer; The device data including sensor detection data and radar echo data are sorted and transmitted to the data processing and storage layer through the data source layer; The batch processing layer is used to store global data and perform offline calculation and reasoning after the data is stored. The acceleration layer is used to process and display the real-time measurement data of the radar.

9. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the method for processing cognitive radar data based on the Lambda architecture according to claim 8 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the cognitive radar data processing method based on the Lambda architecture according to claim 8 is implemented.

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