Method and system for measuring distance of equipment in ultra-high voltage transformer substation based on radar detection

Abstract

The application relates to the technical field of energy transmission, and discloses an ultra-high voltage substation equipment distance measurement method and system based on radar detection, which comprises the following steps: obtaining equipment reflection signals, positioning and calculating spatial distances and grouping, and determining equipment dense area grouping results; if the number of equipment in the area exceeds a preset equipment number threshold, the priority of the equipment in the area is calculated and sorted, adaptive parameters are determined according to electromagnetic interference level data of high-priority equipment; the time reference of the radar is aligned, a synchronization signal is collected, and after synchronization distance data is obtained, the synchronization distance data is fused with initial equipment position coordinate set data; if the distance deviation in the result exceeds a preset distance deviation threshold, the adaptive parameters are adjusted to re-collect, final high-precision distance information is obtained, a real-time distribution map is generated, the real-time distribution map is matched with a preset fault diagnosis database, the distance abnormality between equipment is determined, and a distance abnormality alarm is generated. The application can realize high-accuracy substation environment ranging.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy transmission, and in particular to a method and system for measuring distances between devices in an ultra-high voltage substation based on radar detection. BACKGROUND

[0002] Currently, in the power system, the ultra-high voltage substation is the core hub of energy transmission, and the accurate measurement of the distribution of its devices is directly related to the stability and safety of the power grid operation. Establishing an intelligent sensing system to accurately grasp the distance information between devices not only optimizes the operation scheduling of the substation, but also improves the efficiency of fault diagnosis and maintenance.

[0003] The traditional method for measuring the distance of devices in an ultra-high voltage substation is mainly through the installation of distance measuring devices on the devices or the use of laser ranging and ultrasonic ranging technology. First, a measurement signal is sent and a reflected signal is received, and then the distance between devices is determined by calculating the propagation time or phase difference of the signal, achieving accurate measurement and monitoring of the distance between devices.

[0004] Due to the interference and dynamic nature of device distribution in the substation environment, the traditional method is difficult to adapt to the complex device distribution, strong electromagnetic interference, and dynamic changes in position in the substation environment, and cannot accurately reflect the device distribution in real time. Therefore, when dealing with multi-target ranging, the lack of coordination mechanism often leads to insufficient accuracy. SUMMARY

[0005] The present application provides a method and system for measuring distances between devices in an ultra-high voltage substation based on radar detection, to solve the problem of insufficient accuracy in existing technology when dealing with multi-target ranging.

[0006] In a first aspect, to solve the above technical problems, the present application provides a method for measuring distances between devices in an ultra-high voltage substation based on radar detection, comprising:

[0007] Collecting and analyzing the reflected signals of devices in the substation to obtain an initial device position coordinate set;

[0008] Based on the initial device position coordinate set, the spatial distances between all devices are calculated, a relative distance matrix is constructed and grouped, and the grouping result of the device-intensive area is determined;

[0009] If the number of devices in the device-intensive area grouping result exceeds the pre-set device number threshold, the initial priority of the devices in the area is calculated and sorted to obtain a priority sorting list;

[0010] Obtain the electromagnetic interference level data of high-priority devices in the priority sorting list, and adjust the radar transmission power and frequency parameters to determine the adaptive parameter configuration;

[0011] Aligning time references of multiple radars, and reacquiring synchronization signals based on the adaptive parameter configuration to obtain synchronized distance data;

[0012] Fusing the synchronized distance data and the initial device position coordinate set to obtain an optimized device distribution coordinate set;

[0013] If the distance deviation in the optimized device distribution coordinate set exceeds a preset distance deviation threshold, adjusting the adaptive parameter configuration and reacquiring the synchronized distance data to determine final high-precision distance information;

[0014] Generating a real-time distribution map through the final high-precision distance information, and matching with a preset fault diagnosis database to determine distance anomalies between devices and generate a distance anomaly alarm.

[0015] In a second aspect, the present application provides an ultra-high voltage substation device distance measurement system based on radar detection, comprising:

[0016] An initial position signal acquisition module is configured to acquire device reflection signals in a substation and analyze the same to obtain an initial device position coordinate set;

[0017] A device dense area grouping module is configured to calculate spatial distances between all devices based on the initial device position coordinate set, construct a relative distance matrix and group the same, and determine a device dense area grouping result;

[0018] A device priority sorting module is configured to calculate initial priorities of devices in the device dense area grouping result and sort the same to obtain a priority sorting list if the number of devices in the device dense area grouping result exceeds a preset device number threshold;

[0019] An adaptive parameter configuration module is configured to acquire electromagnetic interference level data of high-priority devices in the priority sorting list, and adjust radar transmission power and frequency parameters to determine an adaptive parameter configuration;

[0020] A synchronized distance acquisition module is configured to align time references of multiple radars, and reacquire synchronization signals based on the adaptive parameter configuration to obtain synchronized distance data;

[0021] The optimization device distribution coordinate module is configured to fuse the synchronization distance data and the initial device position coordinate set to obtain an optimized device distribution coordinate set, and the fusion of the synchronization distance data and the initial device position coordinate set to obtain the optimized device distribution coordinate set comprises: mapping the synchronization distance data into spatial coordinates to obtain a synchronization device position coordinate set, performing data fusion according to the synchronization device position coordinate set in combination with the initial device position coordinate set, calculating and updating coordinate values to obtain an updated device position coordinate set, calculating a signal-to-noise ratio of each coordinate according to the updated device position coordinate set, determining that the device position coordinate set is valid if the signal-to-noise ratio is greater than a preset signal-to-noise ratio threshold to obtain a valid coordinate set, verifying consistency of time-aligned signal data and the coordinate set according to the valid coordinate set, and performing data fusion processing to obtain the optimized device distribution coordinate set if the consistency is satisfied.

[0022] The high-precision distance information generation module is configured to adjust the adaptive parameter configuration and re-collect the synchronization distance data if a distance deviation in the optimized device distribution coordinate set exceeds a preset distance deviation threshold, and determine final high-precision distance information.

[0023] The distance anomaly alarm generation module is configured to generate a real-time distribution map according to the final high-precision distance information, match the real-time distribution map with a preset fault diagnosis database, determine a distance anomaly between devices, and generate a distance anomaly alarm.

[0024] In a third aspect, the present application further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the multi-point touch signal processing method according to any one of the above.

[0025] In a fourth aspect, the present application further provides a computer readable storage medium comprising a stored computer program, wherein the computer program controls a device where the computer readable storage medium is located to execute the multi-point touch signal processing method according to any one of the above when the computer program is running.

[0026] Compared with the prior art, the present application has the following beneficial effects:

[0027] (1) The application obtains equipment reflection signals through radar detection technology, calculates the preliminary spatial coordinates of each equipment through signal processing and analysis, forms an initial position set, calculates the spatial distances between all equipment based on the initial equipment position coordinate set, constructs a relative distance matrix and groups, and determines the grouping result of the equipment dense area. The measurement interference problem of the equipment dense area of the ultra-high voltage substation is effectively solved, the standardized correlation analysis of equipment distance is realized, and the accuracy of distance measurement is significantly improved.

[0028] (2) Based on the adaptive parameter adjustment technology, the application gives priority to the equipment in the dense area based on equipment importance, distance relationship or other indicators, sorts to determine the key ranging object; collects the electromagnetic interference intensity and type around the key ranging equipment, dynamically adjusts the radar power and frequency according to the interference characteristics, realizes the adaptive adjustment to the environmental change; align multiple radar time references, and based on adaptive parameter configuration, reacquire synchronization signals to obtain synchronized distance data. Realize high-precision synchronization signal acquisition, improve the intelligent level and anti-interference ability of substation equipment distance measurement.

[0029] (3) The application breaks through the static limitation of traditional distance measurement, combines the multi-radar synchronized distance with the initial position coordinates, performs optimization calculation, corrects errors, and improves the equipment position precision; dynamically adjusts the parameters to repeatedly collect, iteratively optimizes the measurement results until the preset precision standard is reached; real-time draw equipment distribution map, combine historical fault data for pattern matching, automatically identify distance anomalies and trigger alarm. The system can dynamically identify equipment position anomalies and generate accurate alarm, greatly improve the reliability and response speed of substation equipment monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 is the flowchart of the ultra-high voltage substation equipment distance measurement method based on radar detection provided by the embodiment of the application;

[0031] Figure 2 is the module schematic diagram of the ultra-high voltage substation equipment distance measurement system based on radar detection provided by the embodiment of the application. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0033] REFERENCE Figure 1The first embodiment of the present application provides a method for measuring the distance of equipment in an ultra-high voltage substation based on radar detection, comprising the following steps:

[0034] S101, collecting the reflection signals of the equipment in the substation and analyzing to obtain an initial equipment position coordinate set;

[0035] S102, calculating the spatial distances between all the equipment based on the initial equipment position coordinate set, constructing a relative distance matrix and grouping, and determining the grouping result of the equipment dense area;

[0036] S103, if the number of equipment in the equipment dense area grouping result exceeds a preset equipment number threshold, calculating the initial priority of the equipment in the area and sorting to obtain a priority sorting list;

[0037] S104, obtaining the electromagnetic interference level data of the high-priority equipment in the priority sorting list, and adjusting the radar transmission power and frequency parameters to determine the adaptive parameter configuration;

[0038] S105, aligning the time reference of multiple radars, and re-collecting the synchronization signals based on the adaptive parameter configuration to obtain synchronized distance data;

[0039] S106, fusing the synchronized distance data and the initial equipment position coordinate set to obtain an optimized equipment distribution coordinate set;

[0040] S107, if the distance deviation in the optimized equipment distribution coordinate set exceeds a preset distance deviation threshold, adjusting the adaptive parameter configuration and re-collecting the synchronized distance data to determine the final high-precision distance information;

[0041] S108, generating a real-time distribution map through the final high-precision distance information, and matching with a preset fault diagnosis database to determine the distance anomaly between equipment and generate a distance anomaly alarm.

[0042] In step S101, the reflection signals of the equipment in the substation are collected and analyzed to obtain an initial equipment position coordinate set, comprising:

[0043] S1011, collecting the reflection signals of the equipment in the substation based on a radar sensor array, extracting the signal strength, and optimizing the signal strength to obtain an initial signal strength data set;

[0044] S1012, calculating the spatial distribution characteristics of the reflection signals according to the initial signal strength data set in combination with preset signal attenuation parameters to determine an initial coordinate set;

[0045] S1013, if the positioning accuracy of the initial coordinate set is lower than a preset positioning accuracy threshold, performing environment interference correction on the signal strength to obtain an optimized coordinate set;

[0046] S1014, performing secondary calibration on the device position according to the optimized coordinate set to obtain the initial device position coordinate set.

[0047] In step S1011, the radar sensor array collects the reflection signal of the device in the substation, extracts the signal strength, and optimizes the signal strength to obtain an initial signal strength data set.

[0048] It should be noted that the optimization of signal strength includes denoising, standardization processing and feature extraction of the original signal; by filtering out environmental noise and interference, the quality and stability of the signal are improved, and then a more accurate and reliable initial signal strength data set is obtained for subsequent distance measurement and device positioning.

[0049] In one implementation, in step S1011, the radar sensor array first transmits a radar signal in the substation and receives the echo signal reflected by the device. Assuming that the original signal strengths received by the three sensors are -72dBm, -77dBm and -83dBm, then wavelet threshold denoising is performed to remove environmental noise, the MVDR algorithm is used to calculate the optimal weight to suppress multipath interference, the Savitzky-Golay filter is selected to smooth the signal strength and the signal-to-noise ratio weighting method is used to optimize the weighted average, and the optimized initial signal strength data set is [−70dBm,−75dBm,−80dBm].

[0050] The standardization processing is performed by the following standardization formula: the difference between the original signal data point and the mean value of the original signal data set is calculated, and the difference is compared with the standard deviation of the original signal data set to obtain the standardized signal data point.

[0051] It should be noted that the standardization processing is performed for each parameter to calculate the mean value and standard deviation. In this embodiment, the parameters of the standardization processing are the signal strength data collected by each sensor of the substation device reflection signal. Then each signal strength data is standardized. The purpose of the standardization processing is to normalize the signal strength collected by different sensors in amplitude and dimension, eliminate the dimension difference and amplitude fluctuation, make the data comparable, and provide a unified basis data for subsequent signal optimization and fusion.

[0052] In step S1012, according to the initial signal strength data set, the spatial distribution characteristics of the reflection signal are calculated in combination with the preset signal attenuation parameters to determine the initial coordinate set.

[0053] It should be noted that, according to the initial signal strength data set and the preset signal attenuation parameter, the signal strength data is first compensated for attenuation calculation, then based on the compensated signal strength value, the inverse distance weighted method is selected for spatial interpolation calculation to generate a three-dimensional spatial distribution feature map, and finally the signal strength extreme point or gradient mutation point is extracted as the target position to determine the initial coordinate set.

[0054] The attenuation compensation calculation formula is:

[0055] ;

[0056] In the formula, is the attenuation coefficient, is the signal propagation distance, is the initial signal strength, and is the compensated signal strength value. It should be noted that all signal strengths must be converted to linear scale (milliwatts, mW) before being substituted into this formula.

[0057] In one implementation, according to the initial signal strength data set (for example, the signal strengths measured by three receiving points are [-70dBm, -75dBm, -80dBm]) and the preset signal attenuation parameter (path loss exponent , reference distance , signal strength at the reference distance ), the signal attenuation formula is used to calculate the distance from each receiving point to the target (for example, m 28.2m m ); based on the known receiving point coordinates [(0, 0), (30, 0), (0, 25)] and the distance , the initial position coordinates (such as (18.4, 12.6)) are obtained by least squares method. When there are more signal strength data sets corresponding to more receiving points and more receiving point coordinate data, more initial position coordinates can be obtained to form an initial coordinate set.

[0058] The signal attenuation formula is a logarithmic distance path loss formula, and the specific formula is:

[0059]

[0060] In the formula, represents the path loss at a distance d, and represents the strength of the signal attenuated during transmission; represents the path loss at a reference distance , and represents the loss value of the known distance measured in the indoor or free space; d represents the actual distance between the receiving point and the transmitting source; wherein, d0 represents a reference distance, is a known reference point; n represents a path loss index (path attenuation coefficient), reflecting the influence of the environment on signal attenuation; wherein, shadow fading represents a shadow fading item, is a random variable, and is usually subject to a normal distribution with a mean of 0, and is used to reflect signal fluctuations due to factors such as building shielding, multipath effects, etc.

[0061] In step S1013, if the positioning accuracy of the initial coordinate set is lower than the preset positioning accuracy threshold, the signal strength is corrected for environmental interference, and an optimized coordinate set is obtained.

[0062] It should be noted that the environmental interference correction refers to a process of correcting and optimizing the collected signal in the case that the signal strength deviates or is abnormal due to environmental factors (such as noise, multipath reflection, electromagnetic interference, obstacle shielding, etc.) during signal measurement. Based on the standard deviation method, abnormal values are removed, noise is filtered through Kalman filtering, and signal attenuation is compensated through the signal attenuation formula, so as to reduce the influence of the environment on the signal quality, thereby improving the accuracy and stability of positioning or signal analysis, and ensuring the reliability of subsequent calculation or judgment.

[0063] In an implementation manner, for example, the coordinate set obtained by initial positioning of a certain device is [(12.5, 18.3), (13.0, 18.7), (50.0, 80.0)], and it is found through calculation that the positioning accuracy is only 3 meters, which is lower than the preset positioning accuracy threshold of 2 meters. At this time, the system corrects the collected signal strength data (such as -70 dBm, -65 dBm and abnormal -90 dBm signals received by the base station) for environmental interference, removes abnormal signals that are greatly affected by strong reflection and shielding, recalculates the signal attenuation formula parameters, and reestimates the device position. The optimized coordinate set obtained after correction is [(12.7, 18.4), (12.9, 18.5), (12.8, 18.3)], and the positioning accuracy is improved to 1.2 meters, thereby meeting the preset requirements.

[0064] In step S1014, according to the optimized coordinate set, the device position is calibrated again, and the initial device position coordinate set is obtained.

[0065] In an implementation, for example, for positioning of equipment in a factory building, the optimized coordinate set is [(10.5, 20.3), (11.2, 19.8), (10.8, 20.1)], and the system performs the following steps for secondary calibration of the set: first, calculate the mean (10.83, 20.07) of these coordinates as a reference position, eliminate abnormal points deviating from the mean by more than a preset abnormal distance threshold (such as 0.5 meters), then combine the historical installation position of the equipment (such as a preset position of (11.0, 20.0)) to correct the spatial deviation, adjust the coordinates to be close to the preset position (such as coordinates (10.95, 20.05)), and combine the time series data to judge the stability of the position, to finally determine a more accurate and stable initial equipment position coordinate set, improving the positioning accuracy and reliability.

[0066] In step S102, based on the initial equipment position coordinate set, the spatial distances between all equipment are calculated, a relative distance matrix is constructed and grouped, and a device dense area grouping result is determined, including:

[0067] S1021, obtain initial coordinate data from the initial equipment position coordinate set, perform data cleaning and standardization processing on the initial coordinate data, and obtain a standardized coordinate data set;

[0068] S1022, according to the standardized coordinate data set, calculate the spatial distance between each pair of equipment, and generate a relative distance matrix containing the spatial distance;

[0069] S1023, according to the relative distance matrix, cluster and group the equipment, calculate the intra-cluster density and contour coefficient of each group, and obtain a preliminary grouping result;

[0070] S1024, if the contour coefficient of the preliminary grouping result is lower than a preset contour coefficient threshold, modify the number of clusters or change the position of the initial cluster center, recalculate the grouping, and obtain an optimized device dense area grouping result.

[0071] In step S1021, obtain initial coordinate data from the initial equipment position coordinate set, perform data cleaning and standardization processing on the initial coordinate data, and obtain a standardized coordinate data set.

[0072] In an implementation, assume the initial device location coordinate set is [(12.5, 18.3), (13.0, 18.7), (120.0, 300.0), (12.8, 18.5)], where (120.0, 300.0) is obviously abnormal from the rest of the initial device location coordinates. The system first performs data cleaning on the data, eliminating the abnormal coordinate point that exceeds the spatial boundary (50.0, 50.0) preset based on the actual substation size, to obtain the cleaned coordinate set [(12.5, 18.3), (13.0, 18.7), (12.8, 18.5)]. Then, the mean and standard deviation of the x and y dimensions are calculated for the remaining coordinate data, for example, the x mean is 12.77, the standard deviation is 0.25, the y mean is 18.5, and the standard deviation is 0.20, and the coordinate data is converted into a dimensionless normalized coordinate data set using the standardization formula, to obtain the normalized coordinate data set such as [(−1.08,−1.00), (0.92,1.00), (0.16,0.00)], which is convenient for subsequent unified processing and analysis.

[0073] In step S1022, according to the normalized coordinate data set, the spatial distance between each pair of devices is calculated to generate a relative distance matrix containing the spatial distance.

[0074] In an implementation, assume that the normalized coordinate data set contains three device points, A(0.5,-1.0), B(1.0,0.0), and C(-0.5,0.5). The system calculates the spatial distance between each pair of devices according to the Euclidean distance formula, for example, the distance between devices A and B is .

[0075] The final relative distance matrix is as follows:

[0076]

[0077] The matrix represents the spatial distance between all pairs of devices, with the diagonal line being 0 (the distance from a device to itself), and the remaining elements being symmetrically distributed (e.g., the distance from A to B is the same as from B to A).

[0078] In step S1023, according to the relative distance matrix, the devices are clustered and grouped, and the intra-cluster density and silhouette coefficient of each group are calculated to obtain a preliminary grouping result.

[0079] In an implementation, the system first clusters the devices based on the distance matrix using the K-means algorithm, and divides the devices into two groups: the first group is {A, B}, and the second group is {C, D}. Then, the distance between all pairs of points in each cluster is calculated according to the Euclidean distance formula, and the formula is:

[0080]

[0081] wherein, is the intra-cluster average distance of the kth cluster (i.e., a measure of intra-cluster density), reflecting the average distance between member points in the cluster, and the smaller the value, the tighter the members in the cluster; is the number of data points contained in the kth cluster; is the vector representation of the ith and jth data points in the kth cluster; is the distance between the ith and jth data points in the kth cluster.

[0082] The intra-cluster density of each cluster is obtained, for example, the average distance in the first group is about 1.1, and the average distance in the second group is about 1.2, indicating that the members in the cluster are relatively close. Then, according to the formula:

[0083]

[0084] is the silhouette coefficient of data point i, used to measure the clustering effect of the point, with a value range of [-1, 1], and the closer to 1, the more reasonable the clustering of point i, and the closer to the points in the cluster and the farther from the points in other clusters, and the negative value indicates that point i may be misclassified; is the average distance between point i and other points in its cluster, reflecting the tightness of the point in its own cluster; is the average distance from point i to its nearest neighboring cluster, reflecting the separation degree of the point from the neighboring cluster.

[0085] The silhouette coefficient is obtained, assuming that the silhouette coefficient of the first group is 0.75 and that of the second group is 0.78, both indicating good clustering effect. Finally, the system outputs the two clusters as the preliminary grouping result, facilitating further analysis and processing.

[0086] In step S1024, if the silhouette coefficient of the preliminary grouping result is lower than a preset silhouette coefficient threshold, the number of clusters is modified or the position of the initial cluster center is changed, the grouping is recalculated, and the optimized device dense area grouping result is obtained.

[0087] It should be noted that "cluster" is a basic concept in cluster analysis, referring to a set of data points with high similarity and close distance between each other. By performing clustering operation, devices can be automatically grouped according to distance or features to form several "clusters", helping to identify device dense areas or similar categories.

[0088] In an implementation, assuming that the devices are divided into 3 clusters in the preliminary grouping, the calculated silhouette coefficient is 0.45, which is lower than the preset threshold 0.6, indicating that the clustering effect is not ideal, and the devices in the cluster are not close enough or the devices between clusters are not separated enough. Therefore, the system increases the number of clusters to 4, or randomly selects different initial cluster center positions, and repeatedly performs the above adjustment to reduce the influence of different initial points and different cluster numbers on the result. Then, the K-means algorithm is re-run, and a new silhouette coefficient is calculated. For example, after adjusting the cluster number to 4, the new silhouette coefficient is increased to 0.72, indicating that the grouping is more reasonable, and the device-intensive area is optimized and divided, thereby obtaining more accurate and stable device clustering results.

[0089] In step S103, if the number of devices in the device-intensive area grouping result exceeds the preset device number threshold, the initial priority of the devices in the area is calculated and sorted to obtain a priority sorting list, including:

[0090] S1031, if the number of devices in the device-intensive area grouping result exceeds the preset device number threshold, the service weight, power level and historical ranging success rate of each device in the cluster are obtained, the initial priority of the devices in the cluster is generated by weighted calculation, and an initial priority list is obtained.

[0091] S1032, the real-time electromagnetic interference data of the devices in the cluster is obtained, and compared with the preset electromagnetic interference threshold. If the interference exceeds the electromagnetic interference threshold, the priority of the corresponding device is reduced, and an adjusted priority list is generated.

[0092] S1033, according to the adjusted priority list, the communication resource of the devices in the cluster is allocated, a resource scheduling sequence is generated, and the final device communication priority sorting list is obtained.

[0093] In step S1031, if the number of devices in the device-intensive area grouping result exceeds the preset device number threshold, the service weight, power level and historical ranging success rate of each device in the cluster are obtained, the initial priority of the devices in the cluster is generated by weighted calculation, and an initial priority list is obtained.

[0094] It should be noted that the business weights are derived from the business management module or scheduling system of the device or system, and are determined based on the business policies such as the type, importance, and service level (SLA) of the business undertaken by the device. They are dynamically adjusted based on historical load, business priority, or the operator's business allocation rules. The power level is information from the device's own battery management system, which periodically or in real time reports the current remaining power percentage or status to the management platform. The historical ranging success rate comes from the device's operation logs or ranging performance monitoring system. It is calculated by the system by statistically analyzing the ratio of the number of times the device successfully completed the positioning or ranging task to the total number of attempts within a certain time range. This data is recorded in the background database or performance management module to evaluate the ranging reliability of the device.

[0095] In one implementation, assuming the number of devices in a cluster is 8, exceeding a preset device threshold of 5, the system collects the service weight, battery level, and historical ranging success rate for each device. For example, device A has a service weight of 0.9, a battery level of 0.7, and a historical ranging success rate of 0.8; device B has weights of 0.6, 0.9, and 0.95, and so on. Setting the weight coefficients to service weight 0.5, battery level 0.3, and ranging success rate 0.2, the initial priority of device A is... The initial priority of device B is Through this weighted calculation, the system generates an initial priority list for all devices within the cluster, which is used for subsequent resource scheduling and task allocation optimization.

[0096] In step S1032, real-time electromagnetic interference data of the devices within the cluster is obtained and compared with a preset electromagnetic interference threshold. If the interference exceeds the electromagnetic interference threshold, the priority of the corresponding device is reduced, and an adjusted priority list is generated.

[0097] It should be noted that the electromagnetic interference specifically includes various electromagnetic wave signals generated by the equipment during operation, which may interfere with the normal operation of other electronic equipment. Specifically, this manifests in various forms such as interference from radio frequency signals emitted by radar, communication equipment, etc., electromagnetic noise, signal interference caused by frequency overlap, and electromagnetic radiation leakage, thereby affecting the signal transmission quality between devices and the stability of system performance.

[0098] In one implementation, suppose there are three devices (A, B, and C) in a cluster. Their real-time electromagnetic interference (EMI) data are 85dB, 92dB, and 78dB, respectively, with a preset threshold of 90dB. The system compares the data with the threshold and finds that device B (92dB) exceeds the threshold, requiring a reduction in its priority. If the original priorities of the devices are A: 0.8, B: 0.7, and C: 0.6, then an interference penalty coefficient (e.g., 0.9) is applied to device B, adjusting its priority to 0.7 × 0.9 = 0.63. The final adjusted priority list is generated as follows: A (0.8), C (0.6), B (0.63), with the following priority order: This optimizes the scheduling sequence of highly interference-prone devices.

[0099] In step S1033, communication resources are allocated to the devices within the cluster according to the adjusted priority list, a resource scheduling sequence is generated, and the priority sorting list for final device communication is obtained.

[0100] It should be noted that a resource scheduling sequence refers to a dynamically generated list of resource allocation orders based on device or task priorities, real-time requirements, and system constraints. Its core function is to optimize overall system performance by allocating resources such as communication, computing, or spectrum in an orderly manner.

[0101] In one implementation, suppose there are four devices A, B, C, and D in a cluster, with adjusted priorities of A: 0.85, B: 0.75, C: 0.65, and D: 0.55, respectively. The system allocates communication resources to the highest-priority device according to this priority list, generating a resource scheduling sequence [A, B, C, D]. For example, device A first obtains channel time slot 1, device B obtains time slot 2, and so on, thus forming the final device communication priority order. This ensures that high-priority devices receive priority communication support in resource allocation, improving overall network performance and resource utilization.

[0102] In step S104, electromagnetic interference level data of high-priority devices in the priority ranking list is obtained, and radar transmit power and frequency parameters are adjusted to determine adaptive parameter configuration, including:

[0103] S1041, Obtain the high-priority device identifier from the priority sorting list, collect the electromagnetic interference level data of the high-priority device, and obtain the electromagnetic interference level dataset;

[0104] S1042, if the interference value in the electromagnetic interference level dataset exceeds the preset high-priority device interference threshold, then the radar transmission power of the high-priority device is dynamically adjusted to generate an adjusted radar transmission power value.

[0105] S1043, Based on the adjusted radar transmit power value, select the communication frequency of the high-priority device and determine the optimized frequency parameters;

[0106] S1044, by integrating the optimized frequency parameters and the adjusted radar transmit power value, an adaptive parameter configuration for the high-priority device is generated.

[0107] In step S1041, the high-priority device identifier is obtained from the priority sorting list, and the electromagnetic interference level data of the high-priority device is collected to obtain the electromagnetic interference level dataset.

[0108] In one implementation, assuming the top three high-priority devices in the priority ranking list are device A, device B, and device C, the system first obtains the identifiers of these three devices, and then collects their electromagnetic interference level data and device status data in real time. For example, the interference intensity of device A is 88dB, the interference intensity of device B is 92dB, and the interference intensity of device C is 85dB. These data are then aggregated to form an electromagnetic interference level dataset.

[0109] In step S1042, if the interference value in the electromagnetic interference level dataset exceeds the preset high-priority device interference threshold, the radar transmission power of the high-priority device is dynamically adjusted to generate an adjusted radar transmission power value.

[0110] In one implementation, assuming a preset electromagnetic interference threshold of 90dB for high-priority devices, and the interference value of device A is 95dB, exceeding the threshold, the radar transmit power of device A is dynamically reduced based on the level of interference. For example, the original power is adjusted from 100mW to 90mW (a 10% decrease) to reduce the impact of interference, generating an adjusted radar transmit power value of 90mW, thereby ensuring the stability of radar performance and communication quality.

[0111] It should be noted that the dynamic reduction refers to the system dynamically adjusting the transmission power according to a pre-set stepped scale (such as reducing by 10% when the electromagnetic interference level of the device exceeds a preset threshold, and reducing by 20% when it exceeds 0-5dB, etc.) when the electromagnetic interference level exceeds 5-10dB. This aims to effectively reduce the impact of electromagnetic interference on system performance, while ensuring the stability of radar detection and communication quality, and achieving a balance between interference suppression and performance assurance.

[0112] In step S1043, the communication frequency of the high-priority device is selected based on the adjusted radar transmit power value to determine the optimized frequency parameters.

[0113] In one implementation, assuming the adjusted radar transmit power of device A is 90mW, the communication frequency is selected to avoid frequency bands prone to interference, taking into account the power reduction. For example, if the original frequency corresponding to a radar transmit power of 100mW before adjustment was 3.5GHz, considering the decrease in transmit power after adjustment and environmental interference, the system adjusts the frequency to 3.45GHz to optimize signal quality and interference suppression. The final optimized frequency parameter is 3.45GHz.

[0114] In step S1044, the adaptive parameter configuration of the high-priority device is generated by integrating the optimized frequency parameters and the adjusted radar transmit power value.

[0115] It should be noted that the adaptive parameter configuration includes: signal attenuation parameters, such as path loss index and reference distance signal strength, used to describe the attenuation characteristics during signal transmission; environmental interference correction parameters, adjustment coefficients used to compensate for environmental factors such as multipath effects and noise interference; sensor array parameters, such as the number, position, and angle of sensors, which are related to data acquisition; positioning accuracy threshold, a preset positioning error tolerance range used to determine whether further correction is needed; filtering and optimization parameters, such as filter type, window size, and weighting coefficients, used for optimizing signal strength; and iteration count and convergence criteria, used as control parameters during automatic adjustment to ensure the stability and effectiveness of adaptive adjustment.

[0116] In one implementation, assuming that for device A, the optimized frequency parameter is 3.45 GHz and its corresponding optimized radar transmit power is 90 mW, the current operating environment information and priority rules of device A are first collected to determine the impact of frequency and power on communication performance and interference level. Then, for the joint optimization problem of frequency and power, a Lagrangian function containing objective function and constraints is constructed, the ratio of frequency and power is adjusted, and the optimal solution under constraints is obtained to achieve the goal of minimizing interference and maximizing communication performance, thus forming an adaptive parameter combination for the device.

[0117] In step S105, the time references of multiple radars are aligned, and the synchronization signal is re-acquired based on the adaptive parameter configuration to obtain synchronized range data, including:

[0118] S1051: Timestamp data is acquired from multiple radar sensors, and each sensor node exchanges timestamp data with each other through the network to obtain a set of timestamps for each sensor.

[0119] S1052, calculate the clock deviation between each sensor and the reference clock based on the set of timestamps of each sensor, and correct it to obtain a unified correction time reference;

[0120] S1053, if the unified correction time reference meets the preset time reference threshold, then the synchronization signal is re-acquired to obtain time-aligned signal data.

[0121] S1054, the time-aligned signal data is fused with the preset sensor accuracy weight data, and data consistency is checked to obtain synchronized distance data.

[0122] In step S1051, timestamp data is acquired from multiple radar sensors, and each sensor node exchanges timestamp data with each other through the network to obtain a set of timestamps for each sensor.

[0123] In one implementation, in a radar collaborative monitoring scenario, the system first synchronously collects timestamp data from three radar sensor nodes. For example, node A records a timestamp of 12:00:05.230, node B records 12:00:05.235, and node C records 12:00:05.228. Subsequently, each node broadcasts its own timestamp through the local area network and receives data from other nodes, and finally aggregates to generate a timestamp set (such as [12:00:05.230, 12:00:05.235, 12:00:05.228]).

[0124] In step S1052, the clock deviation between each sensor and the reference clock is calculated based on the set of timestamps of each sensor, and then corrected to obtain a unified correction time reference.

[0125] In one implementation, assuming there are three radar sensor nodes with timestamps of Node A: 12:00:05.230, Node B: 12:00:05.235, and Node C: 12:00:05.228, and a reference clock time of 12:00:05.200, the clock deviations between each node and the reference clock are calculated as follows: Node A deviation + 30 milliseconds, Node B deviation + 35 milliseconds, and Node C deviation + 28 milliseconds. Then, based on these deviation values, the corresponding deviations are subtracted from the timestamps of each node to perform clock correction, unifying them to the reference clock standard. After correction, the time for Node A is 12:00:05.200, the time for Node B is 12:00:05.200, and the time for Node C is 12:00:05.200, resulting in a unified corrected time standard, ensuring that all sensor data is processed synchronously under the same time standard.

[0126] In step S1053, if the unified correction time reference meets the preset time reference threshold, the synchronization signal is re-acquired to obtain time-aligned signal data.

[0127] In one implementation, it is assumed that after clock deviation correction, the three sensor nodes have a unified correction time base of 12:00:05.200 (reference clock), and the preset time base threshold is ±1 millisecond. If the corrected timestamps of nodes A, B, and C are 12:00:05.200, 12:00:05.200, and 12:00:05.200 respectively, all meeting the preset time base threshold (deviation ≤ 1 millisecond), then the system re-acquires the synchronization signal: node A sends a synchronization pulse at 12:00:05.300, and nodes B and C receive and record the pulse at 12:00:05.300 and 12:00:05.300 respectively, obtaining time-aligned signal data (both timestamped at 12:00:05.300), ensuring strict synchronization in subsequent data processing.

[0128] In step S1054, the time-aligned signal data is fused with the preset sensor accuracy weight data, and data consistency is checked to obtain synchronized distance data.

[0129] In one implementation, assume that three radar sensors (A, B, and C) measure the target distance at 12:00:05:300 after time alignment as follows: A = 10.2 meters (accuracy weight 0.9), B = 10.5 meters (accuracy weight 0.8), and C = 9.8 meters (accuracy weight 0.7). First, weighted data fusion is performed, yielding a synchronized distance of (10.2 × 0.9 + 10.5 × 0.8 + 9.8 × 0.7) / (0.9 + 0.8 + 0.7) ≈ 10.18 meters. Next, data consistency is checked. If the preset deviation threshold is ±0.5 meters, the deviations between each measurement and the fused value are checked (A deviation 0 meters, B deviation +0.3 meters, C deviation -0.4 meters). If none exceed the threshold, the synchronized distance data is output as 10.18 meters and marked as a valid result. If a sensor's deviation exceeds the limit (e.g., B measures 10.5 meters), the corresponding data is discarded and re-fused.

[0130] In step S106, if the synchronized distance data and the initial device location coordinate set are fused to obtain the optimized device distribution coordinate set, it includes:

[0131] S1061, The synchronized distance data is mapped to spatial coordinates to obtain a set of synchronization device location coordinates;

[0132] S1062, Based on the set of location coordinates of the synchronization device, and combined with the set of location coordinates of the initial device, data fusion is performed to calculate and update the coordinate values, thereby obtaining the updated set of device location coordinates;

[0133] S1063, Based on the updated set of device location coordinates, calculate the signal strength and noise ratio of each coordinate. If the signal strength and noise ratio is greater than a preset signal-to-noise ratio threshold, then determine that the set of device location coordinates is valid and obtain a valid set of coordinates.

[0134] S1064, based on the effective coordinate set, verify the consistency between the time-aligned signal data and the coordinate set. If the consistency is satisfied, perform data fusion processing to obtain an optimized device distribution coordinate set.

[0135] In step S1061, the synchronized distance data is mapped to spatial coordinates to obtain a set of synchronization device location coordinates.

[0136] In one implementation, the synchronized distance data is assumed to be the distances from the target point to three sensors: sensor A: 5.0 meters, sensor B: 6.0 meters, and sensor C: 5.5 meters, respectively. The known spatial coordinates of these three sensors are A (0,0,0), B (10,0,0), and C (0,10,0). Using trilateration (distance measurement combined with sensor coordinates), the spatial coordinates of the target point are calculated to be (3,4,1), thus completing the mapping from distance data to spatial coordinates and ultimately forming a set of synchronization device position coordinates containing that point.

[0137] In step S1062, data fusion is performed based on the set of synchronous device location coordinates and the initial set of device location coordinates to calculate and update coordinate values, thereby obtaining the updated set of device location coordinates.

[0138] In one implementation, assume the initial position coordinates of a device are (10, 20, 5), and the corresponding coordinates in the set of synchronized device position coordinates are (10.2, 19.8, 5.2). Combining these two sets of coordinates and their confidence weights (initial position weight 0.6, synchronized position weight 0.8), a weighted average is used for data fusion.

[0139] The updated coordinates are [(10×0.6+10.2×0.8) / (0.6+0.8),(20×0.6+19.8×0.8) / (0.6+0.8),(5×0.6+5.2×0.8) / (0.6+0.8)], which is (10.11,19.89,5.11).

[0140] This fusion method yields an updated set of device location coordinates, which more accurately reflects the current actual location of the device, achieving effective fusion and dynamic updating of initial data and real-time synchronized data.

[0141] In step S1063, based on the updated set of device location coordinates, the signal strength and noise ratio of each coordinate are calculated. If the signal strength and noise ratio is greater than a preset signal-to-noise ratio threshold, the set of device location coordinates is determined to be valid, and a valid set of coordinates is obtained.

[0142] In one implementation, assume the updated device location coordinate set contains three coordinate points with signal strengths of 30dBm, 28dBm, and 25dBm, and a noise level of 3dBm, while the preset signal-to-noise ratio (SNR) threshold is 20dB. The SNR of each coordinate is calculated as follows: point 1 is 27dB (30-3), point 2 is 25dB (28-3), and point 3 is 22dB (25-3), all greater than the 20dB threshold. Therefore, these three coordinate points are deemed valid, and a valid coordinate set is obtained.

[0143] In step S1064, the consistency between the time-aligned signal data and the coordinate set is verified according to the effective coordinate set. If the consistency is satisfied, data fusion processing is performed to obtain the optimized device distribution coordinate set.

[0144] In one implementation, assume the effective coordinate set contains three device location coordinates: (10, 20, 5.0), (15, 22, 5.5), and (13, 21, 5.2). The time-aligned signal data indicates that the device signal positions measured at the same time point are (10.2, 19.8, 4.8), (14.8, 21.9, 5.7), and (13.1, 21.2, 5.0). The system calculates the spatial distance errors between these two sets to be 2 meters, 2 meters, and 3 meters, with a preset error threshold of 5 meters. All these values ​​satisfy the consistency condition. Therefore, the weights of these three sets of coordinates are determined by inverse error ratio, and a weighted fusion process is performed to obtain an optimized set of device distribution coordinates, such as (101, 199, 49), (149, 219.5, 56), and (130.5, 211, 51).

[0145] In step S107, if the distance deviation in the optimized device distribution coordinate set exceeds a preset distance deviation threshold, the adaptive parameter configuration is adjusted and the synchronized distance data is re-acquired to determine the final high-precision distance information, including:

[0146] S1071, Obtain the synchronized distance dataset from the optimized device distribution coordinate set;

[0147] S1072, Obtain distance deviation data based on the synchronized distance dataset; if the distance deviation exceeds a preset synchronized distance deviation threshold, adjust the adaptive parameter configuration to obtain an optimized parameter set.

[0148] S1073, Based on the optimized parameter set, the distance of the device distribution is re-collected, the location information of each device is updated, and the final high-precision distance information is obtained;

[0149] In step S1071, the synchronized distance dataset is obtained from the optimized device distribution coordinate set.

[0150] In one implementation, assume that the distance data for a device in the optimized device distribution coordinate set are as follows: Radar 1 measures 10.2 meters (timestamp: 12:00:00.100), Radar 2 measures 9.8 meters (timestamp: 12:00:00.120), and Radar 3 measures 10.5 meters (timestamp: 12:00:00.095). To ensure time synchronization of the multi-radar data, the system first uniformly timestamps these initial distance data, for example, adjusting them to 12:00:00.100. Then, it processes the signals from Radar 2 and Radar 3 using linear interpolation to compensate for time differences and different sampling rates, ultimately obtaining time-aligned and synchronized distance datasets, such as Radar 1: 10.2 meters, Radar 2 (after resampling): 9.8 meters, and Radar 3 (after resampling): 10.5 meters. These synchronized distance datasets are all under the unified timestamp 12:00:00.100, ensuring time and data synchronization of the multi-radar data acquisition.

[0151] In step S1072, distance deviation data is obtained based on the synchronized distance dataset. If the distance deviation exceeds a preset synchronized distance deviation threshold, the adaptive parameter configuration is adjusted to obtain an optimized parameter set.

[0152] In one implementation, assuming the measured distances of a certain device in the synchronized distance dataset are 50.0 meters measured by radar 1, 53.5 meters by radar 2, and 49.8 meters by radar 3, the calculated maximum distance deviation is 3.7 meters, and the preset synchronization distance deviation threshold is 2 meters. Since the deviation of 3.7 meters exceeds the synchronization distance deviation threshold, the system automatically adjusts the adaptive parameter configuration, such as increasing time synchronization accuracy, adjusting the sampling frequency, or adjusting filtering parameters. After parameter optimization, measurement data is collected again, and the distance deviation is reduced to 1.5 meters, below the synchronization distance deviation threshold, thereby obtaining an optimized parameter set and ensuring the synchronization and accuracy of multi-radar data.

[0153] In step S1073, the distances of the device distribution are re-acquired based on the optimized parameter set, the location information of each device is updated, and the final high-precision distance information is obtained.

[0154] In one implementation, assuming the synchronized distance data is 10.18 meters, after optimization using an inverse error weighting method, reference points with smaller errors receive higher weights, and those with larger errors receive lower weights. The resulting parameter set determines the weights of the device's distance to three nearby reference points as 0.4, 0.35, and 0.25, respectively. Based on these weights, the system re-acquires the distances between the device and these three reference points, measuring distances of 10.2 meters, 9.8 meters, and 10.5 meters, respectively. The updated high-precision device position is calculated using a weighted fusion method, with the adjusted position corresponding to a distance of 10.2 × 0.4 + 9.8 × 0.25 + 10.5 × 0.35, or 10.205 meters. This step ensures that the data is corrected by incorporating weights, updating the device's spatial coordinates, thereby obtaining more accurate and stable final high-precision distance information.

[0155] In step S108, a real-time distribution map is generated using the final high-precision distance information and matched with a preset fault diagnosis database to determine abnormal distances between devices and generate a distance anomaly alarm, including:

[0156] S1081, Generate a real-time coordinate distribution map based on the final high-precision distance information;

[0157] S1082, compare the real-time coordinate distribution map with the expected location range in the preset fault diagnosis database, and mark the coordinate distribution that exceeds the expected location range as a preliminary abnormal distance between devices.

[0158] S1083, if the deviation between the device location corresponding to the preliminary abnormal device distance and the expected location range exceeds the dynamic threshold, it is marked as a confirmed abnormal device distance; the dynamic threshold is calculated by the standard deviation of historical data;

[0159] S1084, if the distance between the determined devices is abnormal, then generate a distance abnormality alarm message based on the coordinate position of the device with the abnormal distance and the deviation value, and add a timestamp to generate the distance abnormality alarm.

[0160] In step S1081, a real-time coordinate distribution map is generated based on the final high-precision distance information.

[0161] In one implementation method, based on the final high-precision distance information (e.g., the distance from the target device to reference point A is 12.3 meters, the distance to reference point B is 8.7 meters, and the distance to reference point C is 15.1 meters), the system first assigns fixed weights to the multi-source distance data (e.g., laser ranging weight 0.6, UWB weight 0.4), and obtains the fused distance through weighted calculation; secondly, based on the reference point coordinates (A(0,0), B(15,0), C(7.5,13)) and the fused distance, the target coordinates (7.8, 6.2) are solved using the trilateration formula; then, based on the ranging error (±0.3m) and the geometric sensitivity matrix, the error ellipse parameters (major axis 0.4m, minor axis 0.2m, rotation angle 30°) are calculated; finally, the solved target coordinates (7.8, 6.2) and the error ellipse are superimposed on the map, and a timestamp (e.g., 10:00:00) is added to output the real-time coordinate distribution map of the target device.

[0162] It should be noted that the process of overlaying the target coordinates and the error ellipse onto the map includes: transforming the target point (7.8, 6.2) from the local coordinate system to the global coordinate system of the map; generating ellipse contour points through parametric equations based on the error ellipse parameters (major axis 0.4m, minor axis 0.2m, rotation angle 30°); marking the coordinate positions with solid red dots, drawing the ellipse contour with semi-transparent blue fill, and labeling the confidence level (e.g., 95%).

[0163] In step S1082, the real-time coordinate distribution map is compared with the expected location range in the preset fault diagnosis database. For coordinate distributions that exceed the expected location range, they are marked as preliminary abnormal distances between devices.

[0164] In one implementation, based on the real-time coordinate distribution of the target device (e.g., coordinates (7.8, 6.2), (8.0, 6.4), (7.9, 6.3)) and the preset expected location range in the fault diagnosis database (e.g., defined as a circular area centered at (8.0, 6.0) with a radius of 0.5 meters), the system uses the Euclidean distance calculation formula to determine whether the coordinates exceed this range point by point: if the distance from the center exceeds 0.5 meters, it is considered abnormal. Simultaneously, it compares with historical fault patterns (e.g., historical anomalies where the distance deviation between devices exceeded 0.4 meters and device signal loss occurred), and marks coordinate distributions that meet the conditions as "preliminary device distance anomaly" for further diagnosis and processing.

[0165] In step S1083, if the deviation between the device location corresponding to the preliminary device distance anomaly and the expected location range exceeds a dynamic threshold, it is marked as a device distance anomaly; the dynamic threshold is calculated using the standard deviation of historical data.

[0166] It should be noted that the dynamic threshold is calculated using the standard deviation of historical data. The specific process is as follows: First, the distance deviation values ​​between devices at multiple time points under normal operating conditions are collected to form a set of historical deviation data. Then, the standard deviation of this set of historical deviation data is calculated to reflect the fluctuation range of the deviation. Finally, this standard deviation is used as the dynamic threshold. When the real-time detected device position deviation exceeds this dynamic threshold, it indicates an abnormal deviation, and the system marks the distance between the devices as abnormal. In this way, by dynamically adjusting the threshold based on the standard deviation of historical data, it can adapt to changes in the environment and device status, improving the accuracy and robustness of anomaly detection.

[0167] If an anomaly in the initial distance between devices is detected, for example, a real-time observed distance deviation of 0.7 meters, while the preset static threshold is 0.5 meters and the standard deviation calculated from historical data is 0.1 meters, the following judgments are made sequentially: First, it is determined whether the real-time deviation of 0.7 meters exceeds the static threshold of 0.5 meters; the result is that it does. Second, the dynamic threshold is calculated, which is the static threshold plus three times the standard deviation, i.e., 0.5 + 3 × 0.1 = 0.8 meters. The deviation of 0.7 meters is compared to see if it exceeds the dynamic threshold of 0.8 meters; the result is that it does not. According to the judgment rules, an anomaly is only marked when both the static and dynamic thresholds are exceeded simultaneously. Since only the static threshold is exceeded, the anomaly is not marked as a confirmed anomaly for the time being. If the deviation is 0.85 meters, then both are exceeded simultaneously, and the system will mark the anomaly as "confirmed anomaly in the distance between devices".

[0168] In step S1084, if the distance between the determined devices is abnormal, a distance abnormality alarm message is generated based on the coordinate position of the device with the abnormal distance and the deviation value, and a timestamp is added to generate the distance abnormality alarm.

[0169] When the system detects an anomaly in the distance between devices, for example, device A's actual coordinates are (8.6, 6.3), while its expected coordinates are (8.0, 6.0), with a deviation of 0.67 meters, the system generates an anomaly alarm message. This message includes the device identifier "Device A," the deviation value of 0.67 meters, and the location information. Subsequently, the system obtains the current system time, such as "2024-06-15 14:23:05," and combines this timestamp with the alarm message to form a complete anomaly alarm record. Ultimately, this alarm includes the specific coordinates, deviation value, and timestamp, ensuring that each alarm has accurate spatiotemporal information for easy tracking and processing.

[0170] In summary, this invention discloses a radar-based method for measuring the distance to equipment in ultra-high voltage substations, comprising:

[0171] Collect and analyze reflected signals from equipment within the substation to obtain an initial set of equipment position coordinates;

[0172] Based on the initial set of device location coordinates, calculate the spatial distance between all devices, construct a relative distance matrix and group them, and determine the grouping results of densely populated areas of devices.

[0173] If the number of devices in the densely populated area grouping result exceeds the preset device number threshold, the initial priority of the devices in the area is calculated and sorted to obtain a priority sorting list;

[0174] Obtain electromagnetic interference level data of high-priority devices in the priority ranking list, and adjust radar transmit power and frequency parameters to determine adaptive parameter configuration;

[0175] The time references of multiple radars are aligned, and the synchronization signal is reacquired based on the adaptive parameter configuration to obtain synchronized range data;

[0176] By fusing the synchronized distance data with the initial set of device location coordinates, an optimized set of device distribution coordinates is obtained;

[0177] If the distance deviation in the optimized device distribution coordinate set exceeds the preset distance deviation threshold, the adaptive parameter configuration is adjusted and the synchronized distance data is re-acquired to determine the final high-precision distance information;

[0178] A real-time distribution map is generated using the final high-precision distance information and matched with a preset fault diagnosis database to determine abnormal distances between devices and generate a distance anomaly alarm.

[0179] Reference Figure 2 The second embodiment of the present invention provides a radar-based distance measurement system for ultra-high voltage substation equipment, comprising:

[0180] Initial position signal acquisition module: used to acquire reflected signals from equipment within the substation, analyze them, and obtain the initial set of equipment position coordinates;

[0181] Equipment-dense area grouping module: used to calculate the spatial distance between all devices based on the initial set of device location coordinates, construct a relative distance matrix and group them, and determine the equipment-dense area grouping results;

[0182] Device priority sorting module: If the number of devices in the grouping result of the densely populated device area exceeds a preset device number threshold, calculate the initial priority of the devices in the area and sort them to obtain a priority sorting list;

[0183] Adaptive parameter configuration module: used to obtain electromagnetic interference level data of high-priority devices in the priority ranking list, and adjust radar transmit power and frequency parameters to determine adaptive parameter configuration;

[0184] Synchronization range acquisition module: used to align the time bases of multiple radars and reacquire synchronization signals based on the adaptive parameter configuration to obtain synchronized range data;

[0185] Optimized device distribution coordinate module: used to fuse the synchronized distance data with the initial device position coordinate set to obtain an optimized device distribution coordinate set;

[0186] High-precision distance information generation module: used to adjust the adaptive parameter configuration and re-collect the synchronized distance data if the distance deviation in the optimized device distribution coordinate set exceeds a preset distance deviation threshold, in order to determine the final high-precision distance information;

[0187] Distance Anomaly Alarm Module: Used to generate a real-time distribution map based on the final high-precision distance information, match it with a preset fault diagnosis database, determine the distance anomaly between devices, and generate a distance anomaly alarm.

[0188] It should be noted that the radar-based ultra-high voltage substation equipment distance measurement system provided in this embodiment of the invention is used to execute all the process steps of the radar-based ultra-high voltage substation equipment distance measurement method in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0189] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a radar-based ultra-high voltage substation equipment distance measurement system. When the processor executes the computer program, it implements the steps in the aforementioned radar-based ultra-high voltage substation equipment distance measurement method embodiments, for example... Figure 1 The step S101 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the initial position signal acquisition module, etc.

[0190] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0191] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0192] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0193] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0194] The above specific embodiments have further described in detail the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and do not limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for measuring the distance to equipment in ultra-high voltage substations based on radar detection, characterized in that, include: Collect and analyze reflected signals from equipment within the substation to obtain an initial set of equipment position coordinates; Based on the initial set of device location coordinates, calculate the spatial distance between all devices, construct a relative distance matrix and group them, and determine the grouping results of densely populated areas of devices. If the number of devices in the densely populated area grouping result exceeds the preset device number threshold, the initial priority of the devices in the area is calculated and sorted to obtain a priority sorting list; Obtain electromagnetic interference level data of high-priority devices in the priority ranking list, and adjust radar transmit power and frequency parameters to determine adaptive parameter configuration; The time references of multiple radars are aligned, and the synchronization signal is reacquired based on the adaptive parameter configuration to obtain synchronized range data; By fusing the synchronized distance data with the initial set of device location coordinates, an optimized set of device distribution coordinates is obtained; The process of fusing the synchronized distance data with the initial set of device location coordinates to obtain an optimized set of device distribution coordinates includes: The synchronized distance data is mapped to spatial coordinates to obtain a set of synchronization device location coordinates; Based on the set of location coordinates of the synchronous device, data fusion is performed in combination with the set of location coordinates of the initial device to calculate and update the coordinate values, thereby obtaining the updated set of device location coordinates. Based on the updated set of device location coordinates, the signal strength and noise ratio of each coordinate are calculated. If the signal strength and noise ratio is greater than a preset signal-to-noise ratio threshold, the set of device location coordinates is determined to be valid, and a valid set of coordinates is obtained. Based on the effective coordinate set, verify the consistency between the time-aligned signal data and the coordinate set. If the consistency is satisfied, perform data fusion processing to obtain an optimized device distribution coordinate set. If the distance deviation in the optimized device distribution coordinate set exceeds the preset distance deviation threshold, the adaptive parameter configuration is adjusted and the synchronized distance data is re-acquired to determine the final high-precision distance information; A real-time distribution map is generated using the final high-precision distance information and matched with a preset fault diagnosis database to determine abnormal distances between devices and generate a distance anomaly alarm.

2. The method for measuring the distance to UHV substation equipment based on radar detection according to claim 1, characterized in that, The process involves collecting and analyzing reflected signals from equipment within the substation to obtain an initial set of equipment location coordinates, including: The initial signal strength dataset is obtained by collecting reflected signals from equipment in the substation using a radar sensor array, extracting the signal strength, and optimizing the signal strength. Based on the initial signal strength dataset and a preset signal attenuation parameter, the spatial distribution characteristics of the reflected signal are calculated, and the initial coordinate set is determined. If the positioning accuracy of the initial coordinate set is lower than the preset positioning accuracy threshold, then the signal strength is corrected for environmental interference to obtain an optimized coordinate set. Based on the optimized coordinate set, the device position is calibrated a second time to obtain the initial device position coordinate set.

3. The method for measuring the distance to UHV substation equipment based on radar detection according to claim 1, characterized in that, Based on the initial set of device location coordinates, the spatial distance between all devices is calculated, a complete relative distance matrix is ​​constructed and grouped, and the grouping results for densely populated device areas are determined, including: Initial coordinate data is obtained from the initial device location coordinate set, and the initial coordinate data is cleaned and standardized to obtain a standardized coordinate dataset. Based on the normalized coordinate dataset, calculate the spatial distance between each pair of devices and generate a relative distance matrix containing the spatial distance; Based on the relative distance matrix, the devices are clustered and grouped, and the intra-cluster density and silhouette coefficient of each group are calculated to obtain preliminary grouping results; If the contour coefficient of the preliminary grouping result is lower than the preset contour coefficient threshold, then the number of clusters is modified or the position of the initial cluster center is changed, the grouping is recalculated, and the optimized grouping result of the dense equipment area is obtained.

4. The method for measuring the distance to UHV substation equipment based on radar detection according to claim 1, characterized in that, If the number of devices in the densely populated area grouping result exceeds a preset device number threshold, then the initial priority of the devices in the area is calculated and sorted to obtain a priority ranking list, including: If the number of devices in the densely populated area grouping results exceeds the preset device number threshold, then the service weight, power level and historical ranging success rate of each device in the cluster are obtained, and the initial priority of the devices in the cluster is generated by weighted calculation to obtain the initial priority list. The system acquires real-time electromagnetic interference data of devices within the cluster and compares it with a preset electromagnetic interference threshold. If the interference exceeds the electromagnetic interference threshold, the priority of the corresponding device is reduced, and an adjusted priority list is generated. Based on the adjusted priority list, communication resources are allocated to the devices within the cluster, a resource scheduling sequence is generated, and the final priority ranking list for device communication is obtained.

5. The method for measuring the distance to UHV substation equipment based on radar detection according to claim 1, characterized in that, The step of obtaining electromagnetic interference level data of high-priority devices in the priority ranking list, adjusting radar transmit power and frequency parameters, and determining adaptive parameter configuration includes: Obtain the high-priority device identifier from the priority sorting list, collect the electromagnetic interference level data of the high-priority devices, and obtain the electromagnetic interference level dataset; If the interference value in the electromagnetic interference level dataset exceeds the preset high-priority device interference threshold, the radar transmission power of the high-priority device is dynamically adjusted to generate an adjusted radar transmission power value. Based on the adjusted radar transmit power value, the communication frequency of the high-priority device is selected, and the optimized frequency parameters are determined. By integrating the optimized frequency parameters and the adjusted radar transmit power value, an adaptive parameter configuration for the high-priority device is generated.

6. The method for measuring the distance to UHV substation equipment based on radar detection according to claim 1, characterized in that, The process of aligning the time bases of multiple radars and re-acquiring synchronization signals based on the adaptive parameter configuration to obtain synchronized range data includes: Timestamp data is acquired from multiple radar sensors, and each sensor node exchanges timestamp data with each other through the network to obtain a set of timestamps for each sensor. The clock deviation between each sensor and the reference clock is calculated based on the set of timestamps of each sensor, and then corrected to obtain a unified correction time reference. If the unified correction time reference meets the preset time reference threshold, then the synchronization signal is re-acquired to obtain time-aligned signal data. The time-aligned signal data is fused with preset sensor accuracy weight data, and data consistency is verified to obtain synchronized distance data.

7. The method for measuring the distance to UHV substation equipment based on radar detection according to claim 1, characterized in that, If the distance deviation in the optimized device distribution coordinate set exceeds a preset distance deviation threshold, the adaptive parameter configuration is adjusted and the synchronized distance data is re-acquired to determine the final high-precision distance information, including: Obtain the synchronized distance dataset from the optimized device distribution coordinate set; Distance deviation data is obtained based on the synchronized distance dataset. If the distance deviation exceeds a preset synchronized distance deviation threshold, the adaptive parameter configuration is adjusted to obtain an optimized parameter set. Based on the optimized parameter set, the distances of the device distribution are re-collected, the location information of each device is updated, and the final high-precision distance information is obtained.

8. The method for measuring the distance to UHV substation equipment based on radar detection according to claim 1, characterized in that, The process of generating a real-time distribution map using the final high-precision distance information and matching it with a preset fault diagnosis database to determine abnormal distances between devices and generate a distance anomaly alarm includes: Based on the final high-precision distance information, a real-time coordinate distribution map is generated; The real-time coordinate distribution map is compared with the expected location range in the preset fault diagnosis database. For coordinate distributions that exceed the expected location range, they are marked as preliminary abnormal distances between devices. If the deviation between the device location corresponding to the initial abnormal inter-device distance exceeds the dynamic threshold, it is marked as an abnormal inter-device distance; the dynamic threshold is calculated using the standard deviation of historical data. If an abnormal distance is found between the determined devices, a distance abnormality alarm message is generated based on the coordinate position of the device with the abnormal distance and the deviation value, and a time stamp is added to generate the distance abnormality alarm.

9. A radar-based distance measurement system for ultra-high voltage substation equipment, characterized in that, include: Initial position signal acquisition module: used to acquire reflected signals from equipment within the substation, analyze them, and obtain the initial set of equipment position coordinates; Equipment-dense area grouping module: used to calculate the spatial distance between all devices based on the initial set of device location coordinates, construct a relative distance matrix and group them, and determine the equipment-dense area grouping results; Device priority sorting module: If the number of devices in the grouping result of the densely populated device area exceeds a preset device number threshold, calculate the initial priority of the devices in the area and sort them to obtain a priority sorting list; Adaptive parameter configuration module: used to obtain electromagnetic interference level data of high-priority devices in the priority ranking list, and adjust radar transmit power and frequency parameters to determine adaptive parameter configuration; Synchronization range acquisition module: used to align the time bases of multiple radars and reacquire synchronization signals based on the adaptive parameter configuration to obtain synchronized range data; The optimized device distribution coordinate module is used to fuse the synchronized distance data with the initial device position coordinate set to obtain an optimized device distribution coordinate set. The fusion of the synchronized distance data and the initial device position coordinate set to obtain the optimized device distribution coordinate set includes: mapping the synchronized distance data to spatial coordinates to obtain a synchronized device position coordinate set; performing data fusion based on the synchronized device position coordinate set and the initial device position coordinate set; calculating and updating coordinate values ​​to obtain an updated device position coordinate set; calculating the signal strength and noise ratio of each coordinate based on the updated device position coordinate set; if the signal strength and noise ratio is greater than a preset signal-to-noise ratio threshold, determining that the device position coordinate set is valid and obtaining a valid coordinate set; and verifying the consistency between the time-aligned signal data and the coordinate set based on the valid coordinate set. If the consistency is satisfied, performing data fusion processing to obtain the optimized device distribution coordinate set. High-precision distance information generation module: used to adjust the adaptive parameter configuration and re-collect the synchronized distance data if the distance deviation in the optimized device distribution coordinate set exceeds a preset distance deviation threshold, in order to determine the final high-precision distance information; Distance Anomaly Alarm Module: Used to generate a real-time distribution map based on the final high-precision distance information, match it with a preset fault diagnosis database, determine the distance anomaly between devices, and generate a distance anomaly alarm.

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