Design method and system of beam electronic scanning radar for target deformation detection

Abstract

The present invention relates to the field of electronic scanning technology, specifically to a beam electronic scanning radar design method and system for target deformation detection, comprising the following steps: based on a set transmission frequency, adjusting the input parameters of a slow-wave structure antenna, changing the offset direction of the radar antenna beam, recording the pointing position of the radar antenna beam at each frequency, and generating radar antenna beam offset data. In the present invention, by adjusting the parameters of the slow-wave structure antenna, adapting to the set transmission frequency and changing the offset direction of the radar antenna beam, the pointing position of the radar antenna beam at each frequency can be accurately recorded and analyzed. At the same time, a data monitoring model is constructed to analyze the change in the pointing position monitoring data corresponding to the radar antenna beam, and obtain high-precision measurement results of the target deformation.

Description

Technical Field

[0001] The present invention relates to the field of electronic scanning technology, and in particular to a design method and system of a beam electronic scanning radar for target deformation detection. Background Art

[0002] There are many methods for measuring target deformation using radar. The traditional synthetic aperture radar is a radar installed on a slide rail or a circular rocker arm. This radar measures target deformation by driving the radar to slide back and forth along the guide rail or rotate periodically with the rocker arm through a motor, increasing the aperture of the radar's transceiver antenna to detect the target with high precision. Due to the limitations of the motor's rotation accuracy or the slide rail positioning accuracy, the target point areas detected before and after cannot completely overlap during the repeated sliding of the radar, which directly affects the radar's target detection accuracy. Therefore, this deformation detection method results in low target deformation detection accuracy. At the same time, conventional SAR radars for detecting target deformation are gradually replaced by new radar deformation detection methods due to their large size, inconvenient use, and low cost performance.

[0003] With the development of electronic technology, phased array radars are applied to the field of target deformation measurement. This method uses the electronic scanning of the antenna radar beam. The radar does not move. The antenna radar beam is changed by controlling the phase information of the antenna radar beam to increase the angle range of the radar covering the target. Since the antenna radar beam is controlled by the signal phase, the angle area of ​​the radar pointing to the target is completely consistent. In this way, the target area detected by the radar at different times will completely overlap, and the accuracy of target deformation detection will not be affected. Its accuracy is obviously higher than that of the traditional SAR deformation detection method. Phased array deformation measurement radar has the advantages of small size and high accuracy, but the disadvantage of phased array deformation detection radar is that the cost is extremely high. Therefore, phased array deformation detection radar cannot be widely promoted and applied in the field of military applications. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a design method of a beam electronic scanning radar for target deformation detection.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: a beam electronic scanning radar design method for target deformation detection comprises the following steps:

[0006] S1: Based on the set transmission frequency, adjust the slow-wave structure antenna parameters, change the offset direction of the radar antenna beam, record the pointing position of the radar antenna beam at each frequency, and generate radar antenna beam offset data;

[0007] S2: analyzing the radar antenna beam deviation data, calculating the pointing position of the radar antenna beam at each frequency, building a data monitoring model, analyzing the change in the pointing position monitoring data corresponding to the radar antenna beam, and obtaining a high-precision measurement result of the target deformation;

[0008] S3: Calculate the deformation relationship with the monitored object according to the pointing position deviation data in the high-precision measurement result of the target deformation, and determine the time and distance quantization parameters of the target deformation to generate the target deformation monitoring result;

[0009] As a further solution of the present invention, the step of acquiring the radar antenna beam deviation data is specifically as follows:

[0010] S111: In a slow-wave structure antenna, the feeding phase difference between two adjacent radiating elements is:

[0011] ,

[0012] in, represents the signal frequency in the feeder, is the signal propagation speed in the feeder, is the feeder length between two adjacent radiating elements, is the pointing deviation correction factor;

[0013] By changing the signal operating frequency in the feeder, the antenna beam can be pointed in different directions, thus realizing the beam electronic scanning function;

[0014] The relationship between the feeding phase difference between the antenna radiating elements and the radar beam pointing angle is:

[0015] ,

[0016] in, is the antenna beam pointing deviation, is the baseline length between two radiation elements;

[0017] S112: According to the antenna beam pointing deviation , using the formula:

[0018] ,

[0019] Calculate the position of the radar beam at each frequency ;

[0020] Where L is the distance from the radar to the monitored object, is the initial offset angle;

[0021] S113: According to the position x of the radar beam at each frequency, the deformation data of the target can be measured as k, using the formula:

[0022] ,

[0023] and

[0024] ,

[0025] Calculate the average deformation of the target measured at the position where the radar antenna beam is pointed and standard deformation deviation , generate radar antenna beam deviation data D, ;

[0026] in, It represents the deformation data obtained by the pointing position of the radar antenna beam at the i-th frequency, where n is the total number of frequency points.

[0027] As a further solution of the present invention, the step of acquiring the change amount of the directional position monitoring data corresponding to the analysis radar antenna beam is specifically as follows:

[0028] S211: According to the radar antenna beam deviation data D, the formula is used:

[0029] ,

[0030] Calculate the change in deformation data , get the change data set of shape variable data , ;

[0031] in, represents the radar beam position at the ith frequency, is the frequency interval, is the weight coefficient, which adjusts the sensitivity of the change, log is the natural logarithmic function, is the smoothing parameter;

[0032] S212: Based on the variation data set of the deformation variable data , using the formula:

[0033] ,

[0034] Calculate the change trend data set T for each frequency point, ;

[0035] in, is an adjustment factor used to scale the influence of the changing trend.

[0036] As a further solution of the present invention, the steps of obtaining the high-precision measurement result of the target deformation are specifically as follows:

[0037] S221: Extracting the position change trend of each frequency point from the change trend data set T , using the formula:

[0038] ,

[0039] Calculate offset features ;

[0040] in, express For the change in the transmission frequency f, df represents the change in the transmission frequency f;

[0041] S222: Based on the offset feature , using the formula:

[0042] ,

[0043] Calculate the eigenvector ;

[0044] in, is the weight coefficient, for The value of the pth feature in , where p is the index in the feature vector and z is the total number of features;

[0045] The feature vector Input into the neural network model, using the formula:

[0046] ,

[0047] Calculate the predicted deviation of the radar beam position , and obtain high-precision measurement results of target deformation;

[0048] in, is the weight in the neural network, is the bias term, is the activation function used, is the q-term of the feature vector, q is the neural network weight and feature vector index, and t is the dimension of the input feature vector.

[0049] As a further solution of the present invention, the step of obtaining the deformation relationship between the calculation and the monitored object is specifically:

[0050] S311: Based on the predicted deviation of the radar beam position , using the formula:

[0051] ,

[0052] Calculate the position difference caused by deformation ;

[0053] in, The preset position of the monitored object;

[0054] S312: according to the position difference caused by the deformation , using the formula:

[0055] ,

[0056] Calculate deformation relationship ;

[0057] in, is the deformation sensitivity coefficient.

[0058] As a further solution of the present invention, the step of obtaining the target deformation monitoring result is specifically:

[0059] S321: According to the deformation relationship , using the formula:

[0060] ,

[0061] Calculate the maximum deformation for each deformation variable The ratio of ;

[0062] S322: quantizing the deformation parameters according to the , using the formula:

[0063] ,

[0064] Calculate safety index ,if , then the structure status is Safe, if , the structural state is Alert, and the target deformation monitoring result is obtained.

[0065] A beam electronic scanning radar design system for target deformation detection, the beam electronic scanning radar design system for target deformation detection is used to execute the beam electronic scanning radar design method for target deformation detection, the system comprises:

[0066] The frequency adjustment module adjusts the slow-wave structure antenna parameters based on the given transmission frequency setting, changes the offset direction of the radar beam, records the position of the radar beam at each frequency, and generates radar antenna beam offset data;

[0067] The data analysis module uses a statistical analysis method to analyze the change in the pointing position monitoring data corresponding to the radar antenna beam based on the radar antenna beam deviation data, and generates a high-precision measurement result of the target deformation;

[0068] The neural network model module constructs a neural network model based on the high-precision measurement results of the target deformation, inputs the radar beam position change into the neural network, identifies the radar beam position deviation through model analysis, and generates deformation identification parameters;

[0069] The deformation relationship calculation module calculates the deformation relationship with the monitored object based on the deformation recognition parameters and adopts a geometric analysis method, determines the quantitative parameters of the target deformation, and generates a target deformation monitoring result.

[0070] Compared with the prior art, the advantages and positive effects of the present invention are:

[0071] In the present invention, by adjusting the slow-wave structure antenna parameters, adapting to the set transmission frequency and changing the offset direction of the radar antenna beam, the directional position of the radar antenna beam at each frequency can be accurately recorded and analyzed. At the same time, a data monitoring model is constructed to analyze the change in the directional position monitoring data corresponding to the radar antenna beam, and obtain high-precision measurement results of the target deformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0073] Figure 2 A flow chart of the steps for obtaining radar antenna beam deviation data of the present invention;

[0074] Figure 3 A flow chart of the steps for acquiring the change amount of the pointing position monitoring data corresponding to the radar antenna beam analyzed in the present invention;

[0075] Figure 4 A flow chart of the steps for obtaining high-precision measurement results of target deformation of the present invention;

[0076] Figure 5 A flow chart of the steps of obtaining the deformation relationship between the present invention and the monitored object;

[0077] Figure 6 This is a flow chart of the steps for obtaining the target deformation monitoring results of the present invention. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0079] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0080] See also Figure 1 The present invention provides a technical solution: a beam electronic scanning radar design method for target deformation detection, comprising the following steps:

[0081] S1: Based on the set transmission frequency, adjust the slow-wave structure antenna parameters, change the offset direction of the radar antenna beam, record the pointing position of the radar antenna beam at each frequency, and generate radar antenna beam offset data;

[0082] S2: Analyze the radar antenna beam deviation data, calculate the pointing position of the radar antenna beam at each frequency, build a data monitoring model, analyze the change in the pointing position monitoring data corresponding to the radar antenna beam, and obtain high-precision measurement results of the target deformation;

[0083] S3: Calculate the deformation relationship with the monitored object based on the pointing position deviation data in the high-precision measurement result of the target deformation, and determine the time and distance quantitative parameters of the target deformation to generate the target deformation monitoring result;

[0084] The radar antenna beam offset data includes the center coordinates of the radar beam after frequency adjustment, the change record of the radar beam width at each frequency, and the offset data of the radar beam in the horizontal and vertical directions. The high-precision measurement results of the target deformation include the statistical analysis of the radar beam position offset, the identification results of the offset pattern, and the characteristic description of the radar beam deviation at key frequency points. The target deformation monitoring results include the target's deformation pattern, deformation speed and its changing trend, as well as the spatial distribution map corresponding to the deformation amount.

[0085] See also Figure 2 , the steps for obtaining radar antenna beam deviation data are as follows:

[0086] S111: In a slow-wave structure antenna, the feeding phase difference between two adjacent radiating elements is:

[0087] ,

[0088] in, represents the signal frequency in the feeder, is the signal propagation speed in the feeder, is the feeder length between two adjacent radiating elements, is the pointing deviation correction factor;

[0089] By changing the signal operating frequency in the feeder, the antenna beam can be pointed in different directions, thus realizing the beam electronic scanning function;

[0090] The relationship between the feeding phase difference between the antenna radiating elements and the radar beam pointing angle is:

[0091] ,

[0092] in, is the antenna beam pointing deviation, is the baseline length between two radiation elements;

[0093] S112: Based on the antenna beam pointing deviation , using the formula:

[0094] ,

[0095] Calculate the position of the radar beam at each frequency ;

[0096] Where L is the distance from the radar to the monitored object, is the initial offset angle;

[0097] S113: According to the position x of the radar beam at each frequency, the deformation data of the target can be measured as k, using the formula:

[0098] ,

[0099] and

[0100] ,

[0101] Calculate the average deformation of the target measured at the position where the radar antenna beam is pointed and standard deformation deviation , generate radar antenna beam deviation data D, ;

[0102] in, It represents the deformation data obtained by the pointing position of the radar antenna beam at the i-th frequency, where n is the total number of frequency points.

[0103] The calculation process is as follows:

[0104] Calculate the feed phase difference between two adjacent radiating elements in a slow-wave structure antenna. The calculation of the phase difference is key to the signal frequency, feed line length, signal propagation speed, and pointing deviation correction factor.

[0105] formula:

[0106] ,

[0107] Given parameter: signal frequency , feeder length , signal propagation speed (speed of light), pointing deviation correction factor k=1.1.

[0108] calculate: ,

[0109] Calculates the position of the radar beam at different frequencies, using a given phase difference and the range of the radar to the target.

[0110] formula: ,

[0111] assumed is the initial deviation angle (less than one degree, approximately a small angle), and L is the distance from the radar to the target.

[0112] Given parameters: , , assuming for .

[0113] calculate: ,

[0114] The target shape and its deviation are calculated using the radar antenna beam position data.

[0115] formula:

[0116] ,

[0117] and

[0118] ,

[0119] The deformation data of the five frequency points are: 5000, 5100, 4950, 5050, 5150m.

[0120] Calculate the average deformation:

[0121] ,

[0122] Calculate the standard deviation:

[0123] ,

[0124] The calculated data can help identify early signs of structural deviations and potential damage. For example, if the actual deformation is significantly higher or lower than the benchmark value, it may indicate that the structure has experienced unusual stress or wear, and further analysis of the cause and appropriate measures are needed. This analysis is crucial to ensuring structural safety, preventing accidents and optimizing maintenance plans, ensuring operational efficiency and long-term sustainability.

[0125] See also Figure 3 ,The specific steps for obtaining the change in the monitoring data of the pointing position corresponding to the radar antenna beam are as follows:

[0126] S211: Based on the radar antenna beam deviation data D, use the formula:

[0127] ,

[0128] Calculate the change in deformation data , get the change data set of shape variable data , ;

[0129] in, represents the radar beam position at the ith frequency, is the frequency interval, is the weight coefficient, which adjusts the sensitivity of the change; log is the natural logarithmic function, which is used for nonlinear adjustment of the change. To smooth the parameters and prevent oversensitivity to small changes;

[0130] S212: According to the change amount data set of the shape variable data , using the formula:

[0131] ,

[0132] Calculate the change trend data set T for each frequency point, ;

[0133] in, is an adjustment factor used to scale the influence of the changing trend.

[0134] The calculation process is as follows:

[0135] Setting parameters , , ;

[0136] Calculate the position change at adjacent frequencies from the given data:

[0137] For the formula: ,

[0138] Setting parameters , =1, =0.001;

[0139] Calculate the first segment change :

[0140] ,

[0141] ,

[0142] ,

[0143] Calculate the second segment change :

[0144] ,

[0145] ,

[0146] get ;

[0147] For the formula: ,

[0148] Setting parameters =0.01;

[0149] Calculate trends :

[0150] ,

[0151] ,

[0152] Calculate trends :

[0153] ,

[0154] It means that the radar beam position changes by 0.00579 meters for every 1GHz increase; Indicates that there is no position change in the second frequency interval.

[0155] See also Figure 4 , the specific steps for obtaining high-precision measurement results of target deformation are:

[0156] S221: Extract the position change trend of each frequency point from the change trend data set T , using the formula:

[0157] ,

[0158] Calculate offset features ;

[0159] in, express For the change in the transmission frequency f, df represents the change in the transmission frequency f;

[0160] S222: Based on the offset feature , using the formula:

[0161] ,

[0162] Calculate the eigenvector ;

[0163] in, is the weight coefficient, for The value of the pth feature in , where p is the index in the feature vector and z is the total number of features;

[0164] The feature vector Input into the neural network model, using the formula:

[0165] ,

[0166] Calculate the predicted deviation of the radar beam position , and obtain high-precision measurement results of target deformation;

[0167] in, is the weight in the neural network, is the bias term, is the activation function used, ensuring that the output is non-negative, is the q-term of the feature vector, q is the neural network weight and feature vector index, and t is the dimension of the input feature vector.

[0168] The calculation process is as follows:

[0169] The known position change trend T is .

[0170] For the formula:

[0171] ,

[0172] ,

[0173] Due to the change in frequency From 1 GHz to 2 GHz, the derivative of the change trend can be approximately expressed as the ratio of T to the frequency change.

[0174] for and , because T does not change further between the two frequency points, so:

[0175] ,

[0176] ,

[0177] For the formula: ,

[0178] Set parameter z=1;

[0179] ,

[0180] ;

[0181] For the formula: ,

[0182] Set parameter t=1;

[0183] ,

[0184] ,

[0185] according to Function definition, the input value is less than or equal to 0, then The output of the function is 0, the result is explained and The results of are all 0, indicating that under this setting, the model prediction has no significant position bias.

[0186] See also Figure 5 , the specific steps for calculating the deformation relationship with the monitored object are:

[0187] S311: Prediction deviation based on radar beam position , using the formula:

[0188] ,

[0189] Calculate the position difference caused by deformation ;

[0190] S312: Based on the position difference caused by deformation , using the formula:

[0191] ,

[0192] Calculate deformation relationship ;

[0193] in, is the deformation sensitivity coefficient, which is usually determined by the physical properties of the material;

[0194] The calculation process is as follows:

[0195] Setting parameters =0.001m, =0, =0, ;

[0196] Calculate the position difference :

[0197] for : ,

[0198] for : ,

[0199] Deformation relationship calculation:

[0200] ,

[0201] ,

[0202] result and Both are 0.00005, indicating that under given conditions, the deformation of the monitored object is very different from the theoretical position. For every increase or decrease of 0.001 meter in the theoretical position, the deformation change is 0.00005. This shows that under the sensitivity setting of the monitoring system, the current deformation change is within an acceptable range.

[0203] See also Figure 6 , the specific steps for obtaining the target deformation monitoring results are:

[0204] S321: Based on deformation relationship , using the formula:

[0205] ,

[0206] Calculate the maximum deformation for each deformation variable The ratio of ;

[0207] S322: According to the quantized deformation parameters , using the formula:

[0208] ,

[0209] Calculate safety index ,if , then the structure status is Safe, if , the structural state is Alert, and the target deformation monitoring result is obtained.

[0210] The calculation process is as follows:

[0211] Setting parameters =0.00005, =0.00005, =0.001;

[0212] Calculate quantitative deformation parameters :

[0213] ,

[0214] ,

[0215] ,

[0216] and Both are 0.05, indicating that the current actual deformation is only 5% of the maximum allowable deformation. This shows that the structure is currently in a safe state and is far from reaching the designed risk threshold.

[0217] Safety Index calculate:

[0218] ,

[0219] ,

[0220] ,

[0221] if , the status is "Safe". , the status is "Alert". Result: and All are 0.95 and are marked as “Safe”. and Both are 0.95, which is much higher than the safety threshold of 0.8, so the structural state can be confirmed to be safe. This shows that the current deformation degree has little effect on the integrity and safety of the structure.

[0222] A beam electronic scanning radar design system for target deformation detection, the beam electronic scanning radar design system for target deformation detection is used to execute the beam electronic scanning radar design method for target deformation detection, the system includes:

[0223] The frequency adjustment module adjusts the slow-wave structure antenna parameters based on the given transmission frequency setting, changes the offset direction of the radar beam, records the position of the radar beam at each frequency, and generates radar antenna beam offset data;

[0224] The data analysis module uses statistical analysis methods based on the radar antenna beam deviation data to analyze the change in the pointing position monitoring data corresponding to the radar antenna beam and generate high-precision measurement results of the target deformation;

[0225] The neural network model module builds a neural network model based on the high-precision measurement results of the target deformation, inputs the radar beam position change into the neural network, identifies the radar beam position deviation through model analysis, and generates deformation recognition parameters;

[0226] The deformation relationship calculation module calculates the deformation relationship with the monitored object based on the deformation recognition parameters and adopts the geometric analysis method, determines the quantitative parameters of the target deformation, and generates the target deformation monitoring results.

[0227] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for designing a beam electronic scanning radar for target deformation detection, characterized in that: The following steps are involved: Based on the set transmission frequency, adjust the slow-wave structure antenna parameters, change the offset direction of the radar antenna beam, record the pointing position of the radar antenna beam at each frequency, and generate radar antenna beam offset data; The steps for acquiring the radar antenna beam deviation data are specifically as follows: In a slow-wave structure antenna, the feeding phase difference between two adjacent radiating elements is for: ， in, represents the signal frequency in the feeder, is the signal propagation speed in the feeder, is the feeder length between two adjacent radiating elements, is the pointing deviation correction factor; By changing the signal operating frequency in the feeder, the antenna beam can be pointed in different directions, thus realizing the beam electronic scanning function; Feed phase difference between two adjacent radiating elements The relationship with the radar beam pointing angle is: ， in, is the antenna beam pointing deviation, is the baseline length between two radiation elements; According to the antenna beam pointing deviation , using the formula: ， Calculate the position of the radar beam at each frequency ; in, is the distance from the radar to the monitored object, is the initial offset angle; According to the position of the radar beam at each frequency , the deformation data of the radar antenna beam pointing position can be measured, using the formula: ， and ， Calculate the average deformation of the target measured at the position where the radar antenna beam is pointed and standard deformation deviation , generate radar antenna beam deviation data D, ; in, It represents the deformation data obtained from the radar antenna beam pointing position at the i-th frequency, where n is the total number of frequency points; Analyze the radar antenna beam deviation data, calculate the pointing position of the radar antenna beam at each frequency, build a data monitoring model, analyze the change in the pointing position monitoring data corresponding to the radar antenna beam, and obtain a high-precision measurement result of the target deformation; The step of acquiring the change amount of the pointing position monitoring data corresponding to the analysis radar antenna beam is specifically as follows: According to the radar antenna beam deviation data D, the formula is used: ， Calculate the change in deformation data , get the change data set of shape variable data , ; in, is the frequency interval, is the weight coefficient, which adjusts the sensitivity of the change, log is the natural logarithmic function, is the smoothing parameter; According to the variation data set of the deformation variable data , using the formula: ， Calculate the change trend data set T for each frequency point, ; in, is the adjustment factor used to scale the influence of the changing trend; The steps for obtaining the high-precision measurement result of the target deformation are specifically as follows: Extract the position change trend of each frequency point from the change trend data set T , using the formula: ， Calculate offset features ; in, express For the change in the transmission frequency f, df represents the change in the transmission frequency f; According to the offset characteristics , using the formula: ， Calculate the eigenvector ; in, is the weight coefficient, for The value of the pth feature in , where p is the index in the feature vector and z is the total number of features; The feature vector Input into the neural network model, using the formula: ， Calculate the predicted deviation of the radar beam position , and obtain high-precision measurement results of target deformation; in, is the weight in the neural network, is the bias term, is the activation function used, is the q-term of the feature vector, q is the neural network weight and feature vector index, and t is the dimension of the input feature vector; According to the pointing position deviation data in the high-precision measurement result of the target deformation, the deformation relationship with the monitored object is calculated, and the time and distance quantization parameters of the target deformation are determined to generate the target deformation monitoring result; The steps of obtaining the deformation relationship between the calculation and the monitored object are specifically as follows: The predicted deviation of the radar beam position is , using the formula: ， Calculate the position difference caused by deformation ; in, The preset position of the monitored object; According to the position difference caused by the deformation , using the formula: ， Calculate deformation relationship ; in, is the deformation sensitivity coefficient; The steps for obtaining the target deformation monitoring result are specifically as follows: According to the deformation relationship , using the formula: ， Calculate the maximum deformation for each deformation variable The ratio of ; According to the quantified deformation parameter , using the formula: ， Calculate safety index ,if , then the structure status is Safe, if , the structural state is Alert, and the target deformation monitoring result is obtained.

2. Design system of beam electronic scanning radar for target deformation detection, characterized in that: According to the method for designing a beam electronic scanning radar for target deformation detection according to claim 1, the system comprises: The frequency adjustment module adjusts the slow-wave structure antenna parameters based on the given transmission frequency setting, changes the offset direction of the radar beam, records the position of the radar beam at each frequency, and generates radar antenna beam offset data; The data analysis module uses a statistical analysis method to analyze the change in the pointing position monitoring data corresponding to the radar antenna beam based on the radar antenna beam deviation data, and generates a high-precision measurement result of the target deformation; The neural network model module constructs a neural network model based on the high-precision measurement results of the target deformation, inputs the radar beam position change into the neural network, identifies the radar beam position deviation through model analysis, and generates deformation identification parameters; The deformation relationship calculation module calculates the deformation relationship with the monitored object based on the deformation recognition parameters and adopts a geometric analysis method, determines the quantitative parameters of the target deformation, and generates a target deformation monitoring result.

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