Cooperative adaptive control method for networked X-band weather radars

Abstract

The present invention discloses a collaborative adaptive control method for a networked X-band weather radar, and relates to the technical field of weather radars. In order to solve the problem that the prior art fails to fully utilize real-time status data and historical data to build an accurate radar performance model, and lacks in-depth analysis of environmental characteristics when dynamically adjusting control parameters; the present invention collects and analyzes the operating status and historical data of the radar system, establishes a radar performance model, and dynamically optimizes control parameters, which significantly improves the observation performance and resource utilization efficiency of the radar system. The collaborative control mechanism based on the communication link realizes the sharing and collaborative adjustment of status information between the radar systems, effectively enhances the adaptability and reliability of the system, and by constructing an intelligent performance evaluation module, can identify the main factors affecting system performance, dynamically adjust model parameters and optimize constraints, and ensure the long-term and efficient operation of the radar network under complex environmental conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of weather radars, and in particular to a collaborative adaptive control method for networked X-band weather radars. Background Art

[0002] At present, weather radar plays a vital role in meteorological observation, especially X-band radar, which is widely used in small and medium-scale weather monitoring due to its high resolution and sensitivity. The Chinese patent with announcement number: CN110297246B discloses a collaborative adaptive control method and system for networked X-band weather radars, the method comprising: obtaining reflectivity intensity maps of S-band weather radar and C-band weather radar on different contour surfaces respectively; network fusion of the reflectivity intensity maps of the S-band weather radar and the C-band weather radar on different contour surfaces to obtain a networked reflectivity intensity map corresponding to each layer of contour surface; identifying the target scanning area in the volume scan fusion intensity map; the volume scan fusion intensity map is a collection of networked reflectivity intensity maps corresponding to each layer of contour surface; controlling the networked X-band weather radar corresponding to the working area of ​​the target scanning area to adaptively scan the target scanning area. The above scheme can improve the efficiency of the networked X-band weather radar in quickly tracking and warning strong weather processes.

[0003] However, the above patent mainly focuses on the networking and integration of S-band, C-band and X-band radars. If applied in the application scenario of multi-radar networking, it fails to fully utilize the real-time status data and historical data to build an accurate radar performance model, and lacks in-depth analysis of environmental characteristics when dynamically adjusting control parameters. The existing collaborative control methods still have shortcomings in adaptability and resource utilization efficiency in dynamic environments. Summary of the invention

[0004] The purpose of the present invention is to provide a collaborative adaptive control method for networked X-band weather radars, which provides high-quality data support for weather observation and prediction through a performance evaluation and optimization closed loop, significantly improves the capability and accuracy of radar collaborative observation, and is suitable for intelligent collaborative control of multiple radar networks to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The collaborative adaptive control method of networked X-band weather radars includes:

[0007] Step 1: Collect and analyze system operation status and historical data: Collect the operation status data of each radar system in real time from each X-band radar system, and collect and analyze the historical observation data and historical operation status data of each radar system;

[0008] Step 2: Build radar performance model: Based on the acquired operating status data, build the radar performance model of each radar system. At the same time, use physical limitations as constraints to optimize the radar performance model and determine the optimal operating parameters of each radar system in different environments.

[0009] Step 3: Status information sharing and collaborative control data fusion: Establish communication links between the X-band radar systems, establish a collaborative control mechanism based on the communication links, and dynamically adjust and optimize the optimal control parameters of each radar system;

[0010] Step 4: Implementation of collaborative control strategy: The optimized optimal control parameters are converted into control instructions and distributed to each radar system. The radar system collects data according to the control instructions, obtains observation data, integrates the observation data of each radar system, and generates weather information.

[0011] Step 5: Performance evaluation and system optimization: Regularly evaluate the observation performance of the radar network and adjust the objective function weights and constraints in the radar performance model based on the evaluation results.

[0012] Furthermore, the step 1: collecting and analyzing system operation status and historical data, further includes:

[0013] Integrate historical observation data and historical operation status data to generate historical data of the radar system, identify the corresponding data sources, extract data from different data sources, and archive and standardize the extracted historical data;

[0014] Crawl the factors affecting radar performance in the literature database, and determine the potential factors in the integrated historical data based on the factors affecting radar performance;

[0015] The operation status data is analyzed based on the relationship between each potential factor and the radar performance, the basic characteristics of the operation status data are determined, and a radar operation status chart is drawn based on the basic characteristics of the operation status data.

[0016] Furthermore, the operation status data is analyzed to determine the basic characteristics of the operation status data, including:

[0017] Performing a redundancy check on the operating status data and historical operating status data, eliminating invalid or duplicate data, using a dimensionality reduction method to reduce the data dimension, and obtaining processed target operating status data;

[0018] According to the observation data quality and observation performance, features are extracted from the processed target operation status data to establish an initial feature set;

[0019] The features related to the environmental features and control parameters in the initial feature set are classified, and the basic features of the operating status data are determined based on the classification results.

[0020] Furthermore, the step 2: constructing a radar performance model specifically includes:

[0021] Extract key control parameters related to radar performance from the initial feature set, and establish a key feature subset based on features related to observation data quality in environmental characteristics;

[0022] Obtain the physical limit parameters of the radar system as the basic constraints for optimizing the radar performance model, and determine the degree of influence of different environmental characteristics on the radar operating parameters based on the cross-analysis of operating status data and historical data;

[0023] Determine the correlation between key control parameters and observation data quality, assign basic weight values ​​to each parameter in the key feature subset, and determine the weight allocation variation range of each key control parameter based on the degree of influence of different environmental characteristics on radar operating parameters;

[0024] Obtain the optimization target, build the radar performance model of each radar system based on the integrated key feature subset and physical constraints as input samples, and the observed data quality and resource utilization as output samples, and establish the mapping relationship between input and output;

[0025] Simulate the impact of different combinations of key control parameters on data governance based on the radar performance model of each radar system and determine the key variables for optimizing the control model;

[0026] The model output is evaluated in combination with real-time environmental information to determine the optimal operating parameters for each radar system and generate actionable control instructions.

[0027] Furthermore, the weight distribution variation range of each key control parameter is determined according to the influence of different environmental characteristics on the radar operating parameters, including:

[0028] Extract signal propagation speed, signal reflection coefficient and signal attenuation coefficient under different environmental characteristics;

[0029] Obtaining an environmental characteristic coefficient using the signal propagation speed, signal reflection coefficient and signal attenuation coefficient;

[0030] The environmental characteristic coefficient is obtained by the following formula:

[0031] ;

[0032] Where, EFC represents the environmental characteristic coefficient; v d , R d and A represent the signal propagation speed, signal reflection coefficient and signal attenuation coefficient under different environmental characteristics; v max and v minRespectively represent the maximum and minimum values ​​of signal propagation speed; R max and R min Respectively represent the maximum and minimum values ​​of the signal reflection coefficient; A ref Indicates the preset signal attenuation coefficient reference value;

[0033] Comparing the environmental characteristic coefficient with a preset environmental characteristic coefficient threshold;

[0034] When the environmental characteristic coefficient exceeds a preset environmental characteristic coefficient threshold, a first weight distribution strategy is used to determine a weight distribution change range;

[0035] When the environmental characteristic coefficient does not exceed the preset environmental characteristic coefficient threshold, the second weight distribution strategy is used to determine the weight distribution change range.

[0036] Furthermore, the first weight allocation strategy includes:

[0037] When the environmental characteristic coefficient exceeds a preset environmental characteristic coefficient threshold, retrieving a preset target echo intensity and a noise ratio of an echo model;

[0038] Retrieve environmental characteristic coefficients;

[0039] Obtaining a first weight adjustment coefficient by using the target echo intensity and the noise ratio of the echo model in combination with an environmental characteristic coefficient;

[0040] The first weight adjustment coefficient is obtained by the following formula:

[0041] ;

[0042] Among them, W 01 represents the first weight adjustment coefficient; S represents the target echo intensity; S c Indicates the preset echo intensity reference value; EFC indicates the environmental characteristic coefficient; EFC y Describes the preset environmental characteristic coefficient threshold; P represents the noise ratio of the echo model;

[0043] Retrieve the basic weight value corresponding to each parameter;

[0044] Obtaining a weight distribution variation range corresponding to each parameter by using the first weight adjustment coefficient in combination with a basic weight value corresponding to each parameter;

[0045] The upper and lower weight limits corresponding to the weight distribution range are obtained by the following formula:

[0046] ;

[0047] Among them, W up and Wdown Indicates the upper and lower weight limits corresponding to the weight distribution change range; W b Indicates the basic weight value corresponding to each parameter; W 01 represents the first weight adjustment coefficient; δ represents the amplitude adjustment coefficient for adjusting the weight change amplitude, and λ and μ represent the first adjustment coefficient and the second adjustment coefficient respectively, and the value ranges of the first adjustment coefficient and the second adjustment coefficient are 0.18-0.21 and 0.24-0.31; wherein the amplitude adjustment coefficient is obtained by the following formula:

[0048] ;

[0049] Wherein, δ represents the amplitude adjustment coefficient used to adjust the amplitude of weight change; W 01 represents the first weight adjustment coefficient; W min and W max Indicates the minimum and maximum values ​​of the basic weight values ​​corresponding to all key control parameters.

[0050] Furthermore, the second weight allocation strategy includes:

[0051] When the environmental characteristic coefficient exceeds a preset environmental characteristic coefficient threshold, calling up a preset target echo intensity and an electronic interference intensity in the environment where the radar system is located;

[0052] Retrieve environmental characteristic coefficients;

[0053] The second weight adjustment coefficient is obtained by using the target echo intensity and the electronic interference intensity in the environment where the radar system is located in combination with the environmental characteristic coefficient;

[0054] The second weight adjustment coefficient is obtained by the following formula:

[0055] ;

[0056] Among them, W 02 represents the second weight adjustment coefficient; S represents the target echo intensity; S c Indicates the preset echo intensity reference value; EFC indicates the environmental characteristic coefficient; EFC y Describes the preset environmental characteristic coefficient threshold; S g Indicates the intensity of electronic interference in the environment where the radar system is located;

[0057] Retrieve the basic weight value corresponding to each parameter;

[0058] Obtaining a weight distribution variation range corresponding to each parameter by using the second weight adjustment coefficient in combination with a basic weight value corresponding to each parameter;

[0059] The upper and lower weight limits corresponding to the weight distribution range are obtained by the following formula:

[0060] ;

[0061] Among them, W up and W down Indicates the upper and lower weight limits corresponding to the weight distribution change range; W b Indicates the basic weight value corresponding to each parameter; W 02 represents the second weight adjustment coefficient; λ and μ represent the first adjustment coefficient and the second adjustment coefficient respectively, and the value ranges of the first adjustment coefficient and the second adjustment coefficient are 0.18-0.21 and 0.24-0.31.

[0062] Furthermore, the step 2 further includes:

[0063] Construct a correlation model between key control parameters and observation data quality, and identify the change patterns of key control parameters at different time series in the radar operation state based on the environmental category of environmental characteristics combined with time series data;

[0064] Based on the variation rules of key control parameters, performance sub-models related to different environmental conditions are established, and the impact of the combination of key control parameters on data quality is simulated based on the performance sub-models;

[0065] Analyze the contribution of each key control parameter to the optimization target in combination with different environments, extract the priority of key sub-control parameters under target conditions, and dynamically adjust the scope of each key control parameter according to the priority;

[0066] Based on the optimization objectives and the priorities of key control parameters, the value ranges in different environments are adjusted to determine the adjustment steps of key control parameters.

[0067] Furthermore, the step 3 of establishing the communication link between the X-band radar systems further includes:

[0068] Identify the local network node for each X-band weather radar;

[0069] Establish the initial network flow table of the communication link, find and identify the standard network flow of each local network node, and form the network communication characteristics of each node;

[0070] Determine the initial data cache characteristics of the local network node according to the standard network flow, obtain the unique node characteristic parameters of the local network node, and extract the advanced cache characteristics of the local network node;

[0071] Obtain data retrieval rules from the local cache database, and generate a data transmission network protocol on the communication link based on the data retrieval rules and advanced cache features;

[0072] Retrieve data resource samples through the local cache database, run the data transmission network protocol, and obtain interactive operation results and feature data;

[0073] According to the interaction records between the local cache database and the local network nodes, the data transmission characteristic parameters of the communication link are extracted, and the correlation analysis of multiple working modes and behavior characteristics of the local network nodes is performed to obtain the performance characteristics of each mode;

[0074] Based on the analysis results, the efficient data transmission working mode of the local network nodes and their corresponding target behavior characteristics are determined.

[0075] Furthermore, in step three, establishing a collaborative control mechanism based on the communication link specifically includes:

[0076] Generate response codes based on target behavior characteristics, and combine them with the connection code of the local cache database to generate a grid chain between the local network node and the local cache database, and build a data sharing mechanism between radar systems;

[0077] Extracting features from the generated grid chain to obtain first grid features, and generating second grid features through gradient inversion processing to optimize the adaptive capability of the communication link under multiple environmental conditions;

[0078] Dynamically integrate the status data transmitted in real time by each X-band radar system and the data shared in the local cache database into a data structure for collaborative control;

[0079] Based on shared data resources, combined with the performance model and environmental characteristics of each radar system, the optimal control parameters of each radar system are optimized, and the operating status of the radar is dynamically adjusted according to the optimal control parameters updated in real time;

[0080] According to the optimal control parameters after collaborative control optimization, specific control instructions are generated and distributed to each radar system to control each radar system to synchronously execute the optimal control parameters and establish a collaborative control mechanism for each radar system.

[0081] Furthermore, the observation performance of the radar network is regularly evaluated in step 5, specifically including:

[0082] Radar observation performance evaluation: Collect and integrate the observation data and operation status data of each X-band radar system, compare and analyze the data quality and system performance before and after the collaborative control mechanism, and evaluate the improvement effect of the collaborative control mechanism;

[0083] Based on the evaluation results, the main factors affecting the performance of the radar system are identified, and the weights of key control parameters in the radar performance model are adjusted. Based on the problems found in the evaluation, the constraints are optimized, and the value range and adjustment step size of the radar's optimal control parameters are adjusted;

[0084] Generate a performance evaluation and optimization report that describes in detail the current operating status of the system, optimization effects, and improvement range, provides optimization suggestions, and adjusts the operating mode of the radar system based on environmental prediction data.

[0085] Compared with the prior art, the present invention has the following beneficial effects:

[0086] By collecting and analyzing the operating status and historical data of the radar system, establishing a radar performance model, and dynamically optimizing control parameters, the observation performance and resource utilization efficiency of the radar system have been significantly improved. The collaborative control mechanism based on the communication link realizes the sharing of status information and coordinated adjustment among the radar systems, effectively enhancing the adaptability and reliability of the system. By building an intelligent performance evaluation module, it is possible to identify the main factors affecting the system performance, dynamically adjust model parameters and optimize constraints, and ensure the long-term and efficient operation of the radar network under complex environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 The present invention is a flow chart of the collaborative adaptive control method of the networked X-band weather radar. DETAILED DESCRIPTION

[0088] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0089] In order to solve the technical problems that the existing technology fails to fully utilize the real-time status data and historical data to build an accurate radar performance model, and lacks in-depth analysis of environmental characteristics when dynamically adjusting control parameters, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0090] The collaborative adaptive control method of networked X-band weather radars includes:

[0091] Step 1: Collect and analyze system operation status and historical data: Collect the operation status data of each radar system in real time from each X-band radar system, including obtaining key operation parameters such as transmission power, scanning angle, frequency stability, etc. Collect and analyze the historical observation data and historical operation status data of each radar system, including weather phenomena: precipitation intensity, wind speed, wind direction, cloud height, etc.; data quality: signal strength, noise level, data loss rate, etc.; control parameters: scanning speed, elevation angle, beam width, transmission power, etc.;

[0092] Step 2: Build radar performance model: Based on the acquired operating status data, build a radar performance model for each radar system to describe the relationship between control parameters and data quality. At the same time, use physical limitations (such as antenna pointing angle range and system power limit) as constraints to optimize the radar performance model and determine the optimal operating parameters of each radar system in different environments.

[0093] Step 3: Status information sharing and collaborative control data fusion: Establish communication links between the X-band radar systems, establish a collaborative control mechanism based on the communication links, realize two-way transmission and update of real-time status data, and dynamically adjust and optimize the optimal control parameters of each radar system;

[0094] Step 4: Implementation of collaborative control strategy: The optimized optimal control parameters are converted into control instructions and distributed to each radar system. The radar system collects data according to the control instructions, obtains observation data, integrates the observation data of each radar system, and generates weather information.

[0095] Step 5: Performance evaluation and system optimization: Regularly evaluate the observation performance of the radar network, including performance indicators such as data quality, observation range, detection accuracy, and resource utilization during system operation, compare the effects before and after collaborative control, quantify the improvement of collaborative control, and adjust the objective function weights and constraints in the radar performance model based on the evaluation results to achieve continuous optimization of the control strategy.

[0096] In this embodiment, by optimizing control parameters and collaborative control, the quality of radar observation data can be improved, the detection results and operating parameters of multiple radars can be unified into a comprehensive situation map, and coordination rules can be formulated for possible parameter conflicts in multiple radar systems to ensure the stability and efficiency of the overall operation of the system. Collaborative adaptive control of networked X-band weather radars is realized, which can effectively improve the quality and accuracy of radar observation data, expand the observation range, improve resource utilization, and provide more reliable data support for weather forecasts and disaster warnings.

[0097] In this embodiment, the step 1: collecting and analyzing system operation status and historical data also includes:

[0098] Integrate historical observation data and historical operation status data to generate historical data of the radar system, identify the corresponding data sources, extract data from different data sources, and archive and standardize the extracted historical data;

[0099] Crawl the literature database for factors that affect radar performance (such as weather characteristics in the target area and interference intensity), and determine the potential factors in the integrated historical data based on the factors that affect radar performance;

[0100] Analyze the operation status data based on the relationship between each potential factor and radar performance, determine the basic characteristics of the operation status data, and draw radar operation status charts such as scatter plots and box plots based on the basic characteristics of the operation status data to visualize data distribution;

[0101] In this embodiment, the operation status data is analyzed to determine the basic characteristics of the operation status data, specifically including:

[0102] Performing a redundancy check on the operating status data and historical operating status data, eliminating invalid or duplicate data, using a dimensionality reduction method to reduce the data dimension, and obtaining processed target operating status data;

[0103] Extract features from the processed target operation status data and establish an initial feature set based on the observation data quality (such as signal strength, noise level, data missing rate) and observation performance (such as detection accuracy, observation range);

[0104] The features related to environmental characteristics (such as precipitation intensity and wind speed) and control parameters (such as scanning speed and transmission power) in the initial feature set are classified, and the basic features of the operating status data are determined based on the classification results.

[0105] In this embodiment, by integrating and standardizing historical observation and operation data and combining the performance influencing factors in the literature database, an in-depth analysis of the radar operation status data is achieved. Redundancy and dimensionality reduction processing ensure the validity and processing efficiency of the data, strengthen the relationship between environmental characteristics and control parameters, generate accurate radar operation status charts, intuitively display data distribution, improve data quality, optimize radar performance, ensure the accuracy of weather forecasts, enhance disaster warning capabilities, and significantly improve resource utilization and observation efficiency.

[0106] In this embodiment, the step 2: constructing a radar performance model specifically includes:

[0107] Extract key control parameters related to radar performance from the initial feature set, such as the factors affecting data quality such as scanning angle, scanning speed, and transmission power, and establish a key feature subset based on the features related to the observed data quality in the environmental characteristics (such as signal strength and noise level);

[0108] Obtain the physical limit parameters of the radar system as the basic constraints for optimizing the radar performance model, and determine the degree of influence of different environmental characteristics on the radar operating parameters based on the cross-analysis of operating status data and historical data;

[0109] Determine the correlation between key control parameters and observation data quality, assign basic weight values ​​to each parameter in the key feature subset, and determine the weight distribution range of each key control parameter according to the degree of influence of different environmental characteristics on radar operating parameters. For example, in the case of high wind speed, the weight range of scanning speed may be increased to ensure data quality. Through sensitivity analysis, determine the dynamic range of weight changes under different conditions;

[0110] Obtain the optimization target, build the radar performance model of each radar system based on the integrated key feature subset and physical constraints as input samples, and the observed data quality and resource utilization as output samples, and establish the mapping relationship between input and output;

[0111] Based on the radar performance model of each radar system, simulate the impact of different combinations of key control parameters on data governance and determine the key variables of the optimized control model, including beam width, pulse repetition frequency, scanning mode, etc.

[0112] The model output is evaluated in combination with real-time environmental information to determine the optimal operating parameters for each radar system and generate actionable control instructions.

[0113] Specifically, the weight distribution variation range of each key control parameter is determined according to the influence of different environmental characteristics on the radar operating parameters, including:

[0114] Extract signal propagation speed, signal reflection coefficient and signal attenuation coefficient under different environmental characteristics;

[0115] Obtaining an environmental characteristic coefficient using the signal propagation speed, signal reflection coefficient and signal attenuation coefficient;

[0116] The environmental characteristic coefficient is obtained by the following formula:

[0117] ;

[0118] Where, EFC represents the environmental characteristic coefficient; v d , R d and A represent the signal propagation speed, signal reflection coefficient and signal attenuation coefficient under different environmental characteristics; v max and v min Respectively represent the maximum and minimum values ​​of signal propagation speed; R max and R minRespectively represent the maximum and minimum values ​​of the signal reflection coefficient; A ref Indicates the preset signal attenuation coefficient reference value;

[0119] Comparing the environmental characteristic coefficient with a preset environmental characteristic coefficient threshold;

[0120] When the environmental characteristic coefficient exceeds a preset environmental characteristic coefficient threshold, a first weight distribution strategy is used to determine a weight distribution change range;

[0121] When the environmental characteristic coefficient does not exceed the preset environmental characteristic coefficient threshold, the second weight distribution strategy is used to determine the weight distribution change range.

[0122] The technical effect of the above technical solution is: the technical solution extracts the signal propagation speed, signal reflection coefficient and signal attenuation coefficient under different environmental characteristics, and calculates the environmental characteristic coefficient (EFC) accordingly, so that the radar system can perceive and adapt to the current environmental conditions more accurately. This environmental perception capability is crucial to improving the performance and stability of the radar system, especially in a complex and changeable environment. According to the different environmental characteristic coefficients, the technical solution adopts two weight allocation strategies (the first weight allocation strategy and the second weight allocation strategy). This design enables the radar system to flexibly adjust the weight allocation of key control parameters according to different environmental conditions, thereby optimizing the operating parameters of the radar and improving its adaptability and performance in different environments. By accurately calculating the environmental characteristic coefficient and selecting a suitable weight allocation strategy based on the coefficient, the technical solution can optimize the performance of the radar system. For example, in an environment with slow signal propagation speed, low reflection coefficient or large attenuation, the radar system can improve its detection capability and accuracy by increasing the weights of relevant parameters. The technical solution reduces the need for manual intervention and improves the intelligence and automation level of the radar system through the automated environmental feature extraction and weight allocation process. This helps to reduce the complexity of operation and improve the reliability and stability of the system. The technical solution also has certain scalability and customizability. For example, the calculation formula of the environmental characteristic coefficient, the weight distribution strategy, and related threshold parameters can be adjusted according to actual needs to adapt to different application scenarios and needs.

[0123] In summary, this technical solution significantly improves the adaptability and performance of the radar system in different environments through precise environmental feature extraction and flexible weight allocation strategy, and provides new ideas and methods for the development and application of radar technology.

[0124] Specifically, the first weight allocation strategy includes:

[0125] When the environmental characteristic coefficient exceeds a preset environmental characteristic coefficient threshold, retrieving a preset target echo intensity and a noise ratio of an echo model;

[0126] Retrieve environmental characteristic coefficients;

[0127] Obtaining a first weight adjustment coefficient by using the target echo intensity and the noise ratio of the echo model in combination with an environmental characteristic coefficient;

[0128] The first weight adjustment coefficient is obtained by the following formula:

[0129] ;

[0130] Among them, W 01 represents the first weight adjustment coefficient; S represents the target echo intensity; S c Indicates the preset echo intensity reference value; EFC indicates the environmental characteristic coefficient; EFC y Describes the preset environmental characteristic coefficient threshold; P represents the noise ratio of the echo model;

[0131] Retrieve the basic weight value corresponding to each parameter;

[0132] Obtaining a weight distribution variation range corresponding to each parameter by using the first weight adjustment coefficient in combination with a basic weight value corresponding to each parameter;

[0133] The upper and lower weight limits corresponding to the weight distribution range are obtained by the following formula:

[0134] ;

[0135] Among them, W up and W down Indicates the upper and lower weight limits corresponding to the weight distribution change range; W b Indicates the basic weight value corresponding to each parameter; W 01 represents the first weight adjustment coefficient; δ represents the amplitude adjustment coefficient for adjusting the weight change amplitude, and λ and μ represent the first adjustment coefficient and the second adjustment coefficient respectively, and the value ranges of the first adjustment coefficient and the second adjustment coefficient are 0.18-0.21 and 0.24-0.31; wherein the amplitude adjustment coefficient is obtained by the following formula:

[0136] ;

[0137] Wherein, δ represents the amplitude adjustment coefficient used to adjust the amplitude of weight change; W 01 represents the first weight adjustment coefficient; W min and W max Indicates the minimum and maximum values ​​of the basic weight values ​​corresponding to all key control parameters.

[0138] The technical effect of the above technical solution is: the technical solution introduces the first weight allocation strategy, so that the radar system can dynamically adjust the weight allocation of key control parameters according to the current environmental characteristic coefficient (EFC). When the environmental characteristic coefficient exceeds the preset threshold, the system can automatically retrieve parameters such as the target echo intensity and the noise ratio of the echo model, and calculate the first weight adjustment coefficient in combination with the environmental characteristic coefficient. This dynamic adjustment mechanism enhances the adaptability of the radar system to different environmental conditions and improves its stability and performance. By using multiple parameters such as the target echo intensity, the noise ratio of the echo model and the environmental characteristic coefficient to calculate the first weight adjustment coefficient, the technical solution can more accurately reflect the impact of the current environmental conditions on the radar operating parameters. This helps to improve the accuracy of weight allocation, so that the radar system can more accurately optimize its operating parameters, thereby improving detection capabilities and accuracy. The technical solution introduces an amplitude adjustment coefficient and a first adjustment coefficient and a second adjustment coefficient (μ) to adjust the amplitude of weight change. The value range of these adjustment coefficients is clearly defined, so that the amplitude of weight change is controllable within a certain range. This helps to avoid the situation where the weight allocation is too large or too small, and ensures the stability and reliability of the radar system. The technical solution has certain flexibility and scalability. For example, the calculation formula of the environmental feature coefficient, the weight allocation strategy, and related threshold parameters can be adjusted according to actual needs to adapt to different application scenarios and needs. In addition, this technical solution can be combined with other technologies to further expand its application scope and performance. Through the automated environmental feature extraction, weight adjustment coefficient calculation and weight allocation process, this technical solution reduces the need for manual intervention and improves the intelligence level of the radar system. This helps reduce operational complexity and improve the system's response speed and degree of automation.

[0139] In summary, this technical solution significantly improves the adaptability and performance of the radar system in different environments by introducing the first weight allocation strategy and combining multiple parameters to calculate the weight adjustment coefficient and the weight allocation variation range. At the same time, this technical solution is also flexible and scalable, and can be customized and optimized according to different application scenarios and requirements.

[0140] Specifically, the second weight allocation strategy includes:

[0141] When the environmental characteristic coefficient exceeds a preset environmental characteristic coefficient threshold, calling up a preset target echo intensity and an electronic interference intensity in the environment where the radar system is located;

[0142] Retrieve environmental characteristic coefficients;

[0143] The second weight adjustment coefficient is obtained by using the target echo intensity and the electronic interference intensity in the environment where the radar system is located in combination with the environmental characteristic coefficient;

[0144] The second weight adjustment coefficient is obtained by the following formula:

[0145] ;

[0146] Among them, W 02 represents the second weight adjustment coefficient; S represents the target echo intensity; S c Indicates the preset echo intensity reference value; EFC indicates the environmental characteristic coefficient; EFC y Describes the preset environmental characteristic coefficient threshold; S g Indicates the intensity of electronic interference in the environment where the radar system is located;

[0147] Retrieve the basic weight value corresponding to each parameter;

[0148] Obtaining a weight distribution variation range corresponding to each parameter by using the second weight adjustment coefficient in combination with a basic weight value corresponding to each parameter;

[0149] The upper and lower weight limits corresponding to the weight distribution range are obtained by the following formula:

[0150] ;

[0151] Among them, W up and W down Indicates the upper and lower weight limits corresponding to the weight distribution change range; W b Indicates the basic weight value corresponding to each parameter; W 02 represents the second weight adjustment coefficient; λ and μ represent the first adjustment coefficient and the second adjustment coefficient respectively, and the value ranges of the first adjustment coefficient and the second adjustment coefficient are 0.18-0.21 and 0.24-0.31.

[0152] The technical effect of the above technical solution is: by introducing the second weight allocation strategy, the radar system can dynamically adjust the weight allocation of key control parameters according to multiple factors such as the current environmental characteristic coefficient (EFC), the target echo intensity (S), and the electronic interference intensity in the environment where the radar system is located. This adjustment mechanism enhances the adaptability of the radar system to different environmental conditions, especially in an environment with strong electronic interference, it can maintain high performance and stability. The calculation of the second weight adjustment coefficient comprehensively considers multiple factors such as the target echo intensity, the environmental characteristic coefficient and the electronic interference intensity, which makes the weight allocation more accurate and reasonable. By accurately calculating the second weight adjustment coefficient and combining the basic weight value corresponding to each parameter, the weight allocation variation range corresponding to each parameter can be obtained, thereby further optimizing the operating parameters of the radar system. In an environment with strong electronic interference, the detection capability of the radar system is often seriously affected. By introducing the electronic interference intensity as a factor in calculating the second weight adjustment coefficient, the technical solution can offset the impact of electronic interference on the radar system to a certain extent, thereby improving the detection capability and accuracy of the radar system. The technical solution has certain flexibility and scalability. For example, the calculation formula of the environmental characteristic coefficient, the weight allocation strategy, and related threshold parameters can be adjusted according to actual needs to adapt to different application scenarios and needs. In addition, this technical solution can be combined with other technologies to further expand its application scope and performance. Through the automated environmental feature extraction, weight adjustment coefficient calculation and weight allocation process, this technical solution reduces the need for manual intervention and improves the intelligence level of the radar system. This helps to reduce operational complexity, improve the system's response speed and degree of automation, and thus enhance the overall performance of the radar system.

[0153] In summary, this technical solution significantly improves the adaptability and performance of the radar system in different environments by introducing the second weight allocation strategy and combining multiple factors to calculate the second weight adjustment coefficient and the weight allocation variation range. At the same time, this technical solution is also flexible and scalable, and can be customized and optimized according to different application scenarios and requirements.

[0154] In this embodiment, the step 2 further includes:

[0155] Construct a correlation model between key control parameters and observation data quality, combine time series data with environmental categories based on environmental characteristics, and identify the change patterns of key control parameters at different time series in the radar operation state. For example, in a high precipitation intensity environment, the priority of transmit power may increase to improve signal quality.

[0156] Based on the variation rules of key control parameters, performance sub-models related to different environmental conditions are established. Based on the performance sub-models, the effects of the combination of key control parameters on data quality are simulated. For example, the effect of simulating the combination of beam width and transmit power on improving signal strength under high noise conditions.

[0157] Analyze the contribution of each key control parameter to the optimization target in combination with different environments, extract the priority of key sub-control parameters under target conditions, and dynamically adjust the scope of each key control parameter according to the priority;

[0158] Based on the optimization objectives and the priorities of key control parameters, the value ranges in different environments are adjusted and the adjustment steps of key control parameters are determined so that they can adapt to the optimization objectives more accurately in different environments.

[0159] In this embodiment, by constructing a radar performance model and an association model, intelligent adjustment of key control parameters is achieved, data quality and resource utilization are improved, the impact of environmental characteristics on radar performance is taken into account, and the optimal operating parameters of the radar system under different conditions are ensured. Dynamic weight allocation and priority extraction enhance the adaptability and predictive ability of the model, so that the radar system can respond to environmental changes more accurately, improve observation efficiency and forecast accuracy, and provide reliable technical support for meteorological monitoring and early warning.

[0160] In this embodiment, the step 3 of establishing a communication link between the X-band radar systems further includes:

[0161] Determine the local network nodes of each X-band weather radar and initialize each node based on the communication protocol to ensure smooth real-time status data transmission links between radar systems;

[0162] Establish the initial network flow table of the communication link, find and identify the standard network flow of each local network node, and form the network communication characteristics of each node;

[0163] Determine the initial data cache characteristics of the local network node according to the standard network flow, obtain the unique node characteristic parameters of the local network node, and extract the advanced cache characteristics of the local network node;

[0164] Obtain data retrieval rules from the local cache database, and generate data transmission network protocols on the communication link based on the data retrieval rules and advanced cache features to optimize the data interaction efficiency between radar systems;

[0165] Retrieve data resource samples through the local cache database, run the data transmission network protocol, and obtain interactive operation results and feature data;

[0166] According to the interaction records between the local cache database and the local network nodes, the data transmission characteristic parameters of the communication link are extracted, and the correlation analysis of multiple working modes and behavior characteristics of the local network nodes is performed to obtain the performance characteristics of each mode;

[0167] Based on the analysis results, the efficient data transmission working mode of the local network nodes and their corresponding target behavior characteristics are determined.

[0168] In this embodiment, the step 3 establishes a collaborative control mechanism based on the communication link, specifically including:

[0169] Generate response codes based on target behavior characteristics, and combine them with the connection code of the local cache database to generate a grid chain between the local network node and the local cache database, and build a data sharing mechanism between radar systems;

[0170] Extracting features from the generated grid chain to obtain first grid features, and generating second grid features through gradient inversion processing to optimize the adaptive capability of the communication link under multiple environmental conditions;

[0171] Dynamically integrate the status data transmitted in real time by each X-band radar system and the data shared in the local cache database into a data structure for collaborative control;

[0172] Based on shared data resources, combined with the performance model and environmental characteristics of each radar system, the optimal control parameters of each radar system are optimized, and the operating status of the radar is dynamically adjusted according to the optimal control parameters updated in real time;

[0173] Specific control instructions are generated based on the optimal control parameters after collaborative control optimization and distributed to each radar system. Each radar system is controlled to synchronously execute the optimal control parameters, and a collaborative control mechanism for each radar system is established to achieve collaborative optimization of detection range, data quality and resource utilization.

[0174] In this embodiment, by introducing an efficient sharing mechanism of communication links and an optimized grid chain structure, combined with data fusion and collaborative control methods, accurate analysis and dynamic optimization of the operating status of the X-band weather radar network are achieved, which can further improve the efficiency of information transmission in the radar network, ensure that the collaborative control between systems has higher real-time and stability, and can optimize radar operating parameters according to real-time environmental information, significantly improving the observation data quality and resource utilization of the radar system.

[0175] In this embodiment, the observation performance of the radar network is regularly evaluated in step 5, specifically including:

[0176] Radar observation performance evaluation: Collect and integrate the observation data and operation status data of each X-band radar system, including key indicators such as data quality (such as signal strength, noise level, data loss rate), observation range, detection accuracy, resource utilization, etc., compare and analyze the data quality and system performance before and after the collaborative control mechanism, and evaluate the improvement effect of the collaborative control mechanism, including:

[0177] Data quality assessment: Collect the observation data quality indicators of each radar system before and after the implementation of the collaborative control mechanism, including signal strength, noise level, data missing rate, etc., conduct comparative analysis on the observation data quality indicators, and evaluate the effect of the collaborative control mechanism on improving the observation data quality;

[0178] Observation range evaluation: Based on the coverage and overlapping areas of radar detection, the overall observation range of the network after the implementation of the collaborative control mechanism is evaluated, the size of the observation blind area before and after the implementation of the collaborative control is compared, and the effect of collaborative control on expanding the observation range is verified;

[0179] Detection accuracy evaluation: Based on ground-measured data or higher-precision reference data, verify the accuracy of radar detection results and conduct quantitative evaluation of detection accuracy before and after collaborative control;

[0180] Resource utilization evaluation: Compare and analyze the utilization rates of key resources (such as transmission power, bandwidth, and computing resources) before and after collaborative control of the radar system, evaluate the effect of collaborative control on optimizing resource utilization, analyze resource consumption under different control parameter combinations, and verify whether collaborative control has achieved the goal of optimal resource allocation;

[0181] System stability assessment: Collect fault data or abnormal status data during system operation, analyze whether the collaborative control mechanism effectively reduces the occurrence rate of faults caused by parameter conflicts or state mismatches during operation, and evaluate the stability of radar network operation in different environments, including data transmission delay and packet loss rate;

[0182] Based on the evaluation results, the main factors affecting the performance of the radar system are identified. For example, if some control parameter settings deviate from the optimal value or the environmental characteristics change, resulting in a decrease in system performance, the weights of key control parameters in the radar performance model are adjusted. Based on the problems found in the evaluation, the constraints are optimized, and the value range and adjustment step size of the radar's optimal control parameters are adjusted;

[0183] Generate a performance evaluation and optimization report that describes in detail the current operating status of the system, optimization effects, and improvement range, and provides optimization suggestions, such as adjusting the control parameter priority of certain radars or allocating higher weights to resource utilization, etc., to achieve performance optimization under specific goals, and adjust the operating mode of the radar system in combination with environmental prediction data, such as improving detection accuracy and observation range before extreme weather arrives.

[0184] In this embodiment, through the cyclic iteration of performance evaluation and system optimization, the radar system performance is continuously improved, the collaborative efficiency and stability of the X-band radar network are improved, the key factors affecting the system performance can be quickly identified, and the collaborative adaptive control strategy of the radar network is continuously improved, the data quality is continuously improved, the observation range is expanded, the detection accuracy is improved, and the resource utilization is optimized, so as to provide more efficient and reliable data support for weather forecasting and disaster warning.

[0185] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A collaborative adaptive control method for networked X-band weather radars, characterized in that: include: Step 1: Collect and analyze system operation status and historical data: Collect the operation status data of each radar system in real time from each X-band radar system, and collect and analyze the historical observation data and historical operation status data of each radar system; Step 2: Build radar performance model: Based on the acquired operating status data, build the radar performance model of each radar system. At the same time, use physical limitations as constraints to optimize the radar performance model and determine the optimal operating parameters of each radar system in different environments. Step 3: Status information sharing and collaborative control data fusion: Establish communication links between the X-band radar systems, establish a collaborative control mechanism based on the communication links, and dynamically adjust and optimize the optimal control parameters of each radar system; Step 4: Implementation of collaborative control strategy: The optimized optimal control parameters are converted into control instructions and distributed to each radar system. The radar system collects data according to the control instructions, obtains observation data, integrates the observation data of each radar system, and generates weather information. Step 5: Performance evaluation and system optimization: Regularly evaluate the observation performance of the radar network and adjust the objective function weights and constraints in the radar performance model based on the evaluation results; The step 3 of establishing a collaborative control mechanism based on the communication link specifically includes: Generate response codes based on target behavior characteristics, and combine them with the connection code of the local cache database to generate a grid chain between the local network node and the local cache database, and build a data sharing mechanism between radar systems; Extracting features from the generated grid chain to obtain first grid features, and generating second grid features through gradient inversion processing to optimize the adaptive capability of the communication link under multiple environmental conditions; Dynamically integrate the status data transmitted in real time by each X-band radar system and the data shared in the local cache database into a data structure for collaborative control; Based on shared data resources, combined with the performance model and environmental characteristics of each radar system, the optimal control parameters of each radar system are optimized, and the operating status of the radar is dynamically adjusted according to the optimal control parameters updated in real time; According to the optimal control parameters after collaborative control optimization, specific control instructions are generated and distributed to each radar system to control each radar system to synchronously execute the optimal control parameters and establish a collaborative control mechanism for each radar system.

2. The collaborative adaptive control method for networked X-band weather radars according to claim 1, characterized in that: The step 1: collecting and analyzing system operation status and historical data, further includes: Integrate historical observation data and historical operation status data to generate historical data of the radar system, identify the corresponding data sources, extract data from different data sources, and archive and standardize the extracted historical data; Crawl the factors affecting radar performance in the literature database, and determine the potential factors in the integrated historical data based on the factors affecting radar performance; The operation status data is analyzed based on the relationship between each potential factor and the radar performance, the basic characteristics of the operation status data are determined, and a radar operation status chart is drawn based on the basic characteristics of the operation status data.

3. The collaborative adaptive control method for networked X-band weather radars according to claim 2, characterized in that: Analyze the operation status data and determine the basic characteristics of the operation status data, including: Performing a redundancy check on the operating status data and historical operating status data, eliminating invalid or duplicate data, using a dimensionality reduction method to reduce the data dimension, and obtaining processed target operating status data; According to the observation data quality and observation performance, features are extracted from the processed target operation status data to establish an initial feature set; The features related to the environmental features and control parameters in the initial feature set are classified, and the basic features of the operating status data are determined based on the classification results.

4. The collaborative adaptive control method for networked X-band weather radars according to claim 3, characterized in that: The step 2: constructing a radar performance model specifically includes: Extract key control parameters related to radar performance from the initial feature set, and establish a key feature subset based on features related to observation data quality in environmental characteristics; Obtain the physical limit parameters of the radar system as the basic constraints for optimizing the radar performance model, and determine the degree of influence of different environmental characteristics on the radar operating parameters based on the cross-analysis of operating status data and historical data; Determine the correlation between key control parameters and observation data quality, assign basic weight values ​​to each parameter in the key feature subset, and determine the weight allocation variation range of each key control parameter based on the degree of influence of different environmental characteristics on radar operating parameters; Obtain the optimization target, build the radar performance model of each radar system based on the integrated key feature subset and physical constraints as input samples, and the observed data quality and resource utilization as output samples, and establish the mapping relationship between input and output; Simulate the impact of different combinations of key control parameters on data governance based on the radar performance model of each radar system and determine the key variables for optimizing the control model; The model output is evaluated in combination with real-time environmental information to determine the optimal operating parameters for each radar system and generate actionable control instructions.

5. The collaborative adaptive control method for networked X-band weather radars according to claim 4, characterized in that: The weight distribution range of each key control parameter is determined according to the influence of different environmental characteristics on the radar operating parameters, including: Extract signal propagation speed, signal reflection coefficient and signal attenuation coefficient under different environmental characteristics; Obtaining an environmental characteristic coefficient using the signal propagation speed, signal reflection coefficient and signal attenuation coefficient; The environmental characteristic coefficient is obtained by the following formula: ； Where, EFC represents the environmental characteristic coefficient; v d , R d and A represent the signal propagation speed, signal reflection coefficient and signal attenuation coefficient under different environmental characteristics; v max and v min Respectively represent the maximum and minimum values ​​of signal propagation speed; R max and R min Respectively represent the maximum and minimum values ​​of the signal reflection coefficient; A ref Indicates the preset signal attenuation coefficient reference value; Comparing the environmental characteristic coefficient with a preset environmental characteristic coefficient threshold; When the environmental characteristic coefficient exceeds a preset environmental characteristic coefficient threshold, a first weight distribution strategy is used to determine a weight distribution change range; When the environmental characteristic coefficient does not exceed the preset environmental characteristic coefficient threshold, the second weight distribution strategy is used to determine the weight distribution change range.

6. The collaborative adaptive control method for networked X-band weather radars according to claim 5, characterized in that: The first weight distribution strategy includes: When the environmental characteristic coefficient exceeds a preset environmental characteristic coefficient threshold, retrieving a preset target echo intensity and a noise ratio of an echo model; Retrieve environmental characteristic coefficients; Obtaining a first weight adjustment coefficient by using the target echo intensity and the noise ratio of the echo model in combination with an environmental characteristic coefficient; The first weight adjustment coefficient is obtained by the following formula: ； Among them, W 01 represents the first weight adjustment coefficient; S represents the target echo intensity; S c Indicates the preset echo intensity reference value; EFC indicates the environmental characteristic coefficient; EFC y Describes the preset environmental characteristic coefficient threshold; P represents the noise ratio of the echo model; Retrieve the basic weight value corresponding to each parameter; Obtaining a weight distribution variation range corresponding to each parameter by using the first weight adjustment coefficient in combination with a basic weight value corresponding to each parameter; The upper and lower weight limits corresponding to the weight distribution range are obtained by the following formula: ； Among them, W up and W down Indicates the upper and lower weight limits corresponding to the weight distribution change range; W b Indicates the basic weight value corresponding to each parameter; W 01 represents the first weight adjustment coefficient; δ represents the amplitude adjustment coefficient for adjusting the weight change amplitude, and λ and μ represent the first adjustment coefficient and the second adjustment coefficient respectively, and the value ranges of the first adjustment coefficient and the second adjustment coefficient are 0.18-0.21 and 0.24-0.31; wherein the amplitude adjustment coefficient is obtained by the following formula: ； Wherein, δ represents the amplitude adjustment coefficient used to adjust the amplitude of weight change; W 01 represents the first weight adjustment coefficient; W min and W max Indicates the minimum and maximum values ​​of the basic weight values ​​corresponding to all key control parameters.

7. The collaborative adaptive control method for networked X-band weather radars according to claim 5, characterized in that: The second weight distribution strategy includes: When the environmental characteristic coefficient exceeds a preset environmental characteristic coefficient threshold, calling up a preset target echo intensity and an electronic interference intensity in the environment where the radar system is located; Retrieve environmental characteristic coefficients; The second weight adjustment coefficient is obtained by using the target echo intensity and the electronic interference intensity in the environment where the radar system is located in combination with the environmental characteristic coefficient; The second weight adjustment coefficient is obtained by the following formula: ； Among them, W 02 represents the second weight adjustment coefficient; S represents the target echo intensity; S c Indicates the preset echo intensity reference value; EFC indicates the environmental characteristic coefficient; EFC y Describes the preset environmental characteristic coefficient threshold; S g Indicates the intensity of electronic interference in the environment where the radar system is located; Retrieve the basic weight value corresponding to each parameter; Obtaining a weight distribution variation range corresponding to each parameter by using the second weight adjustment coefficient in combination with a basic weight value corresponding to each parameter; The upper and lower weight limits corresponding to the weight distribution range are obtained by the following formula: ； Among them, W up and W down Indicates the upper and lower weight limits corresponding to the weight distribution change range; W b Indicates the basic weight value corresponding to each parameter; W 02 represents the second weight adjustment coefficient; λ and μ represent the first adjustment coefficient and the second adjustment coefficient respectively, and the value ranges of the first adjustment coefficient and the second adjustment coefficient are 0.18-0.21 and 0.24-0.

31.

8. The collaborative adaptive control method for networked X-band weather radars according to claim 4, characterized in that: The step 2 further includes: Construct a correlation model between key control parameters and observation data quality, and identify the change patterns of key control parameters at different time series in the radar operation state based on the environmental category of environmental characteristics combined with time series data; Based on the variation rules of key control parameters, performance sub-models related to different environmental conditions are established, and the impact of the combination of key control parameters on data quality is simulated based on the performance sub-models; Analyze the contribution of each key control parameter to the optimization target in combination with different environments, extract the priority of key sub-control parameters under target conditions, and dynamically adjust the scope of each key control parameter according to the priority; Based on the optimization objectives and the priorities of key control parameters, the value ranges in different environments are adjusted to determine the adjustment steps of key control parameters.

9. The collaborative adaptive control method for networked X-band weather radars according to claim 8, characterized in that: The step 3 of establishing a communication link between the X-band radar systems also includes: Identify the local network node for each X-band weather radar; Establish the initial network flow table of the communication link, find and identify the standard network flow of each local network node, and form the network communication characteristics of each node; Determine the initial data cache characteristics of the local network node according to the standard network flow, obtain the unique node characteristic parameters of the local network node, and extract the advanced cache characteristics of the local network node; Obtain data retrieval rules from the local cache database, and generate a data transmission network protocol on the communication link based on the data retrieval rules and advanced cache features; Retrieve data resource samples through the local cache database, run the data transmission network protocol, and obtain interactive operation results and feature data; According to the interaction records between the local cache database and the local network nodes, the data transmission characteristic parameters of the communication link are extracted, and the correlation analysis of multiple working modes and behavior characteristics of the local network nodes is performed to obtain the performance characteristics of each mode; Based on the analysis results, the efficient data transmission working mode of the local network nodes and their corresponding target behavior characteristics are determined.

10. The collaborative adaptive control method for networked X-band weather radars according to claim 9, characterized in that: The observation performance of the radar network is regularly evaluated in step 5, specifically including: Radar observation performance evaluation: Collect and integrate the observation data and operation status data of each X-band radar system, compare and analyze the data quality and system performance before and after the collaborative control mechanism, and evaluate the improvement effect of the collaborative control mechanism; Based on the evaluation results, the main factors affecting the performance of the radar system are identified, and the weights of key control parameters in the radar performance model are adjusted. Based on the problems found in the evaluation, the constraints are optimized, and the value range and adjustment step size of the radar's optimal control parameters are adjusted; Generate a performance evaluation and optimization report that describes in detail the current operating status of the system, optimization effects, and improvement range, provides optimization suggestions, and adjusts the operating mode of the radar system based on environmental prediction data.

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