A signal fault identification method and system based on multi-sensor spatio-temporal alignment

Abstract

The present invention relates to the field of data processing, and more specifically, to a signal fault identification method and system based on multi-sensor spatiotemporal alignment, the method comprising: collecting real-time signal strength and distance data from a signal source of a drone; calculating the primary conventional variation degree and distance adjustment factor of the signal strength, and normalizing the product of the two to obtain the conventional variation degree; clustering the signal strength data using a hierarchical clustering algorithm; screening suspected abnormal clusters from the clustering results according to a preset first clipping rule, calculating the average conventional variation degree of the signal strength in each suspected abnormal cluster, marking the cluster whose average conventional variation degree is less than or equal to the conventional threshold as an abnormal cluster, thereby identifying abnormal signal data. The present invention can accurately determine whether a suspected abnormal cluster is truly an abnormal cluster, avoids misjudgment and missed judgment, and improves the accuracy of abnormal signal identification.

Description

Technical Field

[0001] The present invention relates to the field of data processing. More specifically, the present invention relates to a signal fault identification method and system based on multi-sensor spatio-temporal alignment. Background Art

[0002] With the continuous progress of unmanned aerial vehicles (UAVs), they are widely used in many fields such as agricultural monitoring (e.g., monitoring the growth status of crops), environmental monitoring (such as monitoring air quality, water quality, etc.), and geographical surveying (obtaining topographic and geomorphic information, etc.). Among the performance indicators of UAVs, signal stability is particularly crucial. Signal faults, especially abnormally low signal intensity, can easily lead to the loss of contact of UAVs, mission failures, or safety accidents. Therefore, it is very important to quickly identify and effectively locate these signal anomalies, which can significantly reduce the probability of accidents and ensure the smooth completion of UAV missions.

[0003] The prior art, such as the patent application document with the publication number CN116010841A, discloses a signal modulation automatic recognition method based on deep learning. The signal modulation automatic recognition method includes: extracting signal features using wavelet transform; clustering the signal feature data using the agglomerative hierarchical clustering algorithm to classify the signal feature data; setting a generative adversarial network loss function using identity loss and least squares loss; and training a generative adversarial network using the signal features extracted by wavelet transform and the class labels obtained by the agglomerative hierarchical clustering algorithm to obtain the modulation mode.

[0004] In the above signal modulation automatic recognition method, the signal data is clustered using the agglomerative hierarchical clustering algorithm to classify the signals into different categories. However, due to the influence of external interference and other factors, misclassification may occur during the clustering of signal data, resulting in a certain degree of interference and deviation in the clustering results, and causing errors in the judgment of signal states. Summary of the Invention

[0005] To solve the above technical problem that misclassification occurs during the clustering of signal data, resulting in errors in the judgment of signal states, the present invention provides solutions in the following aspects.

[0006] In a first aspect, a signal fault identification method based on multi-sensor spatio-temporal alignment includes:

[0007] Collecting real-time signal intensity data of the UAV during operation and the distance data between the UAV and the signal source, and converting the collected data into digital signals;

[0008] Calculating the primary conventional change degree of the signal intensity data and the distance adjustment factor, and normalizing the product of the primary conventional change degree and the distance adjustment factor to obtain the conventional change degree of the signal intensity data;

[0009] Cluster the signal strength data using the hierarchical clustering algorithm to obtain the clustering result;

[0010] After obtaining the clustering result, screen out all suspected abnormal clusters from the clustering result according to a preset first pruning rule. For each suspected abnormal cluster, calculate the average regular change degree of all signal strength data within the cluster, and mark the suspected abnormal clusters with an average regular change degree less than or equal to the regular threshold as abnormal clusters, thereby identifying all abnormal signal data.

[0011] The present invention first analyzes the collected data, calculates the primary regular change degree and the distance adjustment factor of the signal strength data, and normalizes the product of the two to obtain the regular change degree, comprehensively considering the self-change law of the signal strength and the influence of the distance from the signal source on the signal strength, which can more comprehensively and accurately evaluate the change of the signal strength and provide a more reliable basis for the subsequent identification of abnormal signals; then uses the hierarchical clustering algorithm to cluster the signal strength data, which can automatically group the data according to the similarity of the signal strength data; finally, first screen out suspected abnormal clusters through preset rules (such as cluster size, signal range), and then further verify based on the average regular change degree, which can accurately judge whether the suspected abnormal clusters are really abnormal clusters, avoiding misjudgment and missed judgment, and improving the accuracy of abnormal signal identification.

[0012] Preferably, the process of obtaining the primary regular change degree includes:

[0013] Select any signal strength data as the target signal. Taking the target signal as the center, select a set number of data to construct the target sequence of the target signal, calculate the mean value of the absolute values of the differences between the target signal and its adjacent signal strength data, and take the mean value of the absolute values of the differences as the local difference of the target signal; calculate the difference between the maximum value and the minimum value of the signal strength data in the target sequence of the target signal, and take the difference between the maximum value and the minimum value as the overall difference of the target signal;

[0014] Normalize the product of the local difference and the overall difference to obtain the primary regular change degree of the target signal.

[0015] By analyzing the local difference and the overall difference of the target signal, not only can the short-term change trend of the signal be understood, but also the long-term trend or extreme value difference of the signal can be understood. Multiplying and normalizing the local and global differences avoids the limitations of a single index and more comprehensively describes the dynamic change degree of the signal.

[0016] Preferably, the process of obtaining the distance adjustment factor includes:

[0017] In obtaining the historical signal strength data, find the numerical mean of all signal strength data whose distances from the signal source of the UAV at the sampling moments corresponding to the target signal are the same. Take the absolute value of the difference between the target signal and the numerical mean as the first parameter, take the distance of the UAV from the signal source at the sampling moment corresponding to the target signal as the second parameter, and take the ratio of the second parameter to the first parameter as the distance adjustment factor of the target signal.

[0018] By calculating the distance adjustment factor, the target signal is further corrected to more accurately reflect the relationship between the signal strength and the distance of the UAV from the signal source.

[0019] Preferably, the hierarchical clustering is the agglomerative hierarchical clustering algorithm.

[0020] Preferably, after obtaining the abnormal clusters, it further includes:

[0021] For each signal strength data marked as an abnormal cluster, according to the distance data between the UAV and the signal source, find the position information of the signal source where abnormal signal strength appears within a preset distance range, and further formulate a maintenance decision.

[0022] Preferably, the first pruning rule includes: preset a merging distance threshold, and retain the clustering clusters whose merging distances are less than the preset merging distance threshold;

[0023] Preset a threshold for the number of data points within a cluster, and prune the clustering clusters whose number of data points within the cluster is greater than the threshold for the number of data points within the cluster.

[0024] Preferably, the first pruning rule further includes:

[0025] Preset a threshold for the mean value after normalization of the data within a cluster, and prune the clustering clusters whose mean value after normalization of the data within the cluster is greater than the threshold for the mean value after normalization of the data within the cluster.

[0026] By removing abnormal, small-number-of-data-point, and abnormal-mean clustering clusters, the main and representative clustering clusters can be made more obvious.

[0027] Preferably, the process of obtaining the primary conventional change degree includes:

[0028] Select any one signal strength data as the target signal. Taking the target signal as the center, select a set number of data to construct the target sequence of the target signal. Calculate the mean of the absolute values of the differences between the target signal and its adjacent signal strength data, and take this mean of the absolute values of the differences as the local difference of the target signal; calculate the difference between the maximum and minimum values of the signal strength data in the target sequence of the target signal, and take this difference between the maximum and minimum values as the overall difference of the target signal;

[0029] Normalize the local difference and the overall difference, then calculate the Euclidean distance between the processed local difference and the overall difference, and use the Euclidean distance as the primary conventional change degree of the target signal.

[0030] By calculating the Euclidean distance between the processed local difference and the overall difference and using this distance as the primary conventional change degree, it can more comprehensively reflect the difference degree between two vectors (i.e., the normalized local difference and the overall difference), considering the comprehensive influence of the difference in each dimension.

[0031] Preferably, the process of obtaining the distance adjustment factor includes:

[0032] Obtain the numerical mean of the signal intensity data in the historical signal intensity data that are at the same distance from the signal source as the drone at the sampling moment corresponding to the target signal, take the absolute value of the difference between the target signal and the numerical mean as the first parameter, and take the distance between the drone and the signal source at the sampling moment corresponding to the target signal as the second parameter;

[0033] Take the product of the first parameter and the attenuation coefficient as the negative exponent of the exponential function, perform operations on the exponential function, and multiply the obtained result by the second parameter to obtain the distance adjustment factor of the target signal.

[0034] By considering the non-linear characteristic of signal attenuation through the exponential function, it can more accurately reflect the real situation of the signal intensity changing with distance, thereby more precisely correcting the target signal to make it closer to the actual signal propagation.

[0035] In a second aspect, a signal fault identification system based on multi-sensor spatio-temporal alignment includes: a processor and a memory, and the memory stores computer program instructions, which implement any one of the signal fault identification methods based on multi-sensor spatio-temporal alignment when the computer program instructions are executed by the processor.

[0036] The beneficial effects of the present invention are:

[0037] By fusing signal intensity data and distance data, combining dynamic change quantitative evaluation (conventional change degree) and distance adjustment factor, the evaluation of the signal change degree is made more accurate and comprehensive;

[0038] Then use the hierarchical clustering algorithm to cluster the signal intensity data, and further through multi-step screening and judgment, the false alarm rate can be effectively reduced and the accuracy of signal fault identification can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flowchart of the methods of steps S1 - S4 in a signal fault identification method based on multi-sensor spatio-temporal alignment according to an embodiment of the present invention. Detailed implementation mode

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments.

[0041] Refer to Figure 1 , a signal fault identification method based on multi-sensor spatio-temporal alignment includes steps S1 - S4, specifically as follows:

[0042] S1: Collect the real-time signal strength data of the drone during operation and the distance data between the drone and the signal source, and convert the collected data into digital signals.

[0043] In one embodiment, a wireless communication module is used to collect the signal strength data emitted by the drone device during operation. The wireless communication module can capture the strength of the signal when the drone interacts with the surrounding communication environment, and these data reflect information such as the communication quality of the drone.

[0044] With the help of a GPS module, obtain the positioning data of the drone during operation. The GPS module accurately determines the position of the drone in the geographical space by receiving satellite signals, usually represented in the form of coordinates such as longitude and latitude.

[0045] The collection of the above two types of data needs to be carried out within the same time period to ensure that the signal strength data and the positioning data correspond to each other in the time dimension and can accurately reflect the state of the drone at a specific moment. The collection duration is set to one hour, and the collection frequency is five times per second, that is, five data are collected per second.

[0046] Use an analog-to-digital conversion device to digitally convert the collected signal strength data and positioning data. The function of the analog-to-digital conversion device is to convert analog signals (such as the original signals output by the wireless communication module and the GPS module) into digital signals that can be processed by a computer, and obtain the digital representation forms of these two types of data, which is convenient for subsequent storage, processing, and analysis in a computer system.

[0047] Based on the collected positioning data of the drone during operation, calculate the distance data between the drone and the signal source in real time (such as the formula for calculating the distance between two points based on longitude and latitude).

[0048] S2: Calculate the primary conventional change degree of the signal strength data and the distance adjustment factor, and normalize the product of the primary conventional change degree and the distance adjustment factor to obtain the conventional change degree of the signal strength data.

[0049] When the drone is working, the signal strength data may be low due to the long flight distance. The low signal strength caused by this conventional flight is close to the values of some abnormally low data, making it difficult to directly distinguish. To distinguish these two types of data, first calculate the primary conventional change degree of each signal strength data, and then combine the distance data from the signal source corresponding to the signal strength data and historical data to optimize the primary conventional change degree to obtain the conventional change degree, which is used as the basis for distinguishing between conventional low and abnormally low data.

[0050] In one embodiment, select any one signal strength data as the target signal. Taking the target signal as the center, select at least 5 data on each of its two sides as references, for a total of 10 surrounding reference points, and then construct the target sequence of the target signal. It should be noted that if the number of data on one side is less than 5 (for example, the data is at the start or end of the data segment), make up the deficiency from the other side to ensure that there are a total of 10 surrounding reference points.

[0051] Furthermore, calculate the mean of the absolute values of the differences between the target signal and its adjacent signal strength data, and use this mean of the absolute values of the differences as the local difference of the target signal. Then calculate the difference between the maximum and minimum values of the signal strength data in the target sequence of the target signal, and use it as the overall difference of the target signal.

[0052] Furthermore, normalize the product of the calculated local difference and overall difference to obtain the primary conventional change degree of the target signal. The calculated primary conventional change degree satisfies the relational expression:

[0053]

[0054] In the formula, is the primary conventional change degree of the target signal, is the local difference of the target signal, is the overall difference of the target signal; among them, is the first hyperparameter, and its value is 0.001, which is used to prevent or from taking a value of 0; represents the exponential function with the natural number e as the base.

[0055] It should be noted that if the target signal is at the end or start of the collected data, it may have only one adjacent data point, then is the absolute value of the numerical difference between the target signal and this one adjacent point.

[0056] Among them, if the difference between the target signal and the adjacent data points on both sides is small, then intuitively, this numerical change is more in line with the general and conventional change patterns. Therefore, the greater the credibility that the numerical change of the target signal belongs to the conventional change; if the overall difference of the target signal is smaller, it indicates that the numerical difference of the data around the target signal is smaller, indicating that the change of these data is relatively stable and more likely to belong to the conventional fluctuation.

[0057] In summary, when and are smaller, the primary conventional change degree of the target signal is greater.

[0058] In another embodiment, after calculating the local difference and the overall difference of the target signal, the two can also be first normalized (such as Z-score normalization), and then the Euclidean distance between the two is calculated as the primary conventional change degree of the target signal.

[0059] The above-mentioned distance metric more intuitively reflects the and The relative difference between them. The greater the distance, the greater the difference between and and the higher the primary conventional change degree.

[0060] Furthermore, according to the above calculation method of the primary conventional change degree of the target signal, the primary conventional change degrees of all the collected signal strength data are calculated in the same way.

[0061] The possibility that the signal strength data belongs to the conventional change is quantified by calculating the primary conventional change degree. However, the primary conventional change degree of the conventional low signal strength data may also be low. It is not possible to well distinguish the conventional low signal strength data and the abnormally low signal strength data only based on the primary conventional change degree.

[0062] There is usually a certain relationship between the signal strength data when the drone device is working and the distance data from the signal source. That is, the signal strength data will attenuate as the distance between the drone and the signal source increases.

[0063] In order to more accurately evaluate the conventional change degree of the signal strength data, it is necessary to analyze the numerical characteristics of each signal strength data and the corresponding distance data from the signal source. Optimize the primary conventional change degree of each signal strength data to obtain the conventional change degree of each signal strength data.

[0064] In one embodiment, still taking the above target signal as an example, obtain the numerical mean of the signal strength data in the historical signal strength data that are the same as the distance between the drone and the signal source at the sampling moment corresponding to the target signal. Take the absolute value of the difference between the target signal and the calculated numerical mean as the first parameter, take the distance between the drone and the signal source at the sampling moment corresponding to the target signal as the second parameter, and take the ratio of the second parameter to the first parameter as the distance adjustment factor of the target signal.

[0065] The distance adjustment factor of the above target signal satisfies the relationship:

[0066]

[0067] In the formula, is the distance adjustment factor of the target signal, is the second parameter (the distance between the drone and the signal source at the sampling moment corresponding to the target signal), is the first parameter ( is the value of the target signal, is the numerical mean of the signal strength data in the historical signal strength data that are the same as the distance between the drone and the signal source at the sampling moment corresponding to the target signal); where is the second hyperparameter, with a value of 0.01 to prevent the denominator from being zero.

[0068] Among them, The larger it is, if the value of the target signal is low, that is, is larger, then the distance adjustment factor decreases.

[0069] In another embodiment, considering that when the signal difference is small, the change of the adjustment factor is not obvious enough and may not accurately reflect the subtle change of the signal strength.

[0070] Therefore, calculate the distance adjustment factor of the target signal through exponential decay, that is, the relationship is satisfied as:

[0071]

[0072] In the formula, is the distance adjustment factor of the target signal, is the second parameter (the distance between the drone and the signal source at the sampling moment corresponding to the target signal), is the first parameter ( is the value of the target signal, is the numerical mean of the signal strength data in the historical signal strength data that are the same as the distance between the drone and the signal source at the sampling moment corresponding to the target signal); is the attenuation coefficient, which controls the speed at which the adjustment factor decreases with the signal difference.

[0073] Finally, the product of the primary conventional change degree and the distance adjustment factor is normalized to obtain the conventional change degree of the target signal, that is, the relational expression is satisfied as:

[0074]

[0075] In the formula, is the conventional change degree of the target signal, is the primary conventional change degree of the target signal, is the distance adjustment factor of the target signal, represents the normalization process.

[0076] The above reflects the basic change characteristics of the signal itself, while considers the influence of the distance factor on the signal. The multiplication of the two can comprehensively consider the internal change of the signal and the external distance factor, so as to more comprehensively describe the change degree of the signal.

[0077] Furthermore, according to the above calculation method of the conventional change degree of the target signal, the conventional change degree of all signal strength data can be obtained in the same way.

[0078] S3: Use the hierarchical clustering algorithm to cluster the signal strength data to obtain the clustering result.

[0079] In one embodiment, the signal strength data corresponding to every 5 minutes on the time axis is selected and clustered according to the existing technology of the agglomerative hierarchical clustering algorithm to obtain a preliminary clustering result. The agglomerative hierarchical clustering algorithm is a commonly used clustering method, which gradually merges similar data points to form clusters.

[0080] S4: After obtaining the clustering result, all suspected abnormal clusters are screened out from the clustering result according to the preset first pruning rule. For each suspected abnormal cluster, calculate the average conventional change degree of all signal strength data in the cluster, and mark the suspected abnormal cluster with an average conventional change degree less than or equal to the conventional threshold as an abnormal cluster, and then identify all abnormal signal data.

[0081] In the clustering of signal strength data, the abnormal clusters usually contain fewer data points. This is because abnormal data is often sporadic and isolated, and does not have the aggregation like normal data. For example, when the drone is flying normally, the signal strength will remain relatively stable, and the data points will be concentrated in a certain range to form a normal cluster; while when there is interference or equipment failure, a small number of abnormally low signal strength data points may be generated to form an abnormal cluster.

[0082] In one embodiment, the preliminary clustering result is pruned according to three preset thresholds. The specific pruning process is as follows:

[0083] Set the merging distance threshold during the clustering process (set to 2 in the embodiments of the present invention). If the merging distance between two clusters is greater than 2.0, it is considered that they should not be merged.

[0084] In addition, since the number of data points in abnormal clusters is usually small, a threshold for the number of data points within a cluster (set to 5 in the embodiments of the present invention) can also be set to trim the data clusters with an excessive number of data points.

[0085] It should be noted that since abnormally low signal strength values need to be detected, a threshold for the mean value of the data within a cluster after normalization (set to 0.5 in the embodiments of the present invention) is set to distinguish between data clusters with relatively large and small signal strength values, and trim the data clusters with relatively large values.

[0086] After trimming the preliminary clustering results with the three thresholds set above, the resulting clusters are suspected abnormal clusters.

[0087] After the first trimming above, although some suspected abnormal clusters are obtained, they may contain some interference items. In actual signal strength data, even if the number of data points is small and the normalized mean value is low, it does not necessarily mean that it is abnormal data. In some cases, the change in signal strength may be caused by normal environmental factors or minor fluctuations in the operating state of the device. For example, during the flight of a drone, due to factors such as minor adjustments in the flight attitude and slight interference from the surrounding environment, the signal strength may show a short-term and regular low situation.

[0088] Through secondary trimming, we can further screen out those clusters with a relatively small average degree of regular change, and these clusters are more likely to be caused by abnormal reasons such as equipment failures and severe interference resulting in low signal strength.

[0089] In one embodiment, calculate the average degree of regular change of all signal strength data within all suspected abnormal clusters, and set a regular threshold (set to 0.35 in the embodiments of the present invention). If the average degree of regular change within a suspected abnormal cluster is greater than the regular threshold, it indicates that the credibility of the data within this cluster belonging to the small signal strength data caused by regular change is greater, and then such clusters need to be trimmed. After this trimming, the final abnormal clusters are obtained.

[0090] After the above two trimmings, the finally obtained clusters are marked as abnormal clusters.

[0091] After identifying all the abnormal clusters, according to the communication range of the drone, the effective coverage range of the signal source, and practical experience, set a preset distance range. For example, if the effective communication radius of the drone is 500 meters, then the preset distance range can be set to 500 meters.

[0092] Traverse the signal strength data of each abnormal cluster and check the distances between the corresponding drones and various signal sources. If the distance between a certain signal source and the drone is within the preset distance range, record the location information of the signal source. The location information may include the longitude and latitude coordinates of the signal source, the specific address, etc.

[0093] Based on the locations and distributions of the abnormal signal sources, formulate a reasonable maintenance plan. For example, if the abnormal signal sources are relatively concentrated, a centralized maintenance operation can be arranged to improve the maintenance efficiency.

[0094] The system includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a signal fault identification method based on multi-sensor spatio-temporal alignment according to the first aspect of the present invention is implemented.

[0095] The system also includes other components well-known to those skilled in the art, such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be elaborated here.

[0096] It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of this invention patent shall be subject to the appended claims.

Claims

1. A signal fault identification method based on multi-sensor spatio-temporal alignment, characterized in that Including: Collecting real-time signal strength data and distance data between the drone and the signal source during the operation of the collecting drone, and converting the collected data into digital signals; Calculating the primary conventional change degree of the signal strength data and the distance adjustment factor, and normalizing the product of the primary conventional change degree and the distance adjustment factor to obtain the conventional change degree of the signal strength data; Using the hierarchical clustering algorithm to cluster the signal strength data to obtain the clustering result; After obtaining the clustering result, screening out all suspected abnormal clusters from the clustering result according to the preset first pruning rule. For each suspected abnormal cluster, calculating the average conventional change degree of all signal strength data within the cluster, and marking the suspected abnormal cluster with an average conventional change degree less than or equal to the conventional threshold as an abnormal cluster, thereby identifying all abnormal signal data; The obtaining process of the primary conventional change degree includes: Selecting any signal strength data as the target signal, centering on the target signal, selecting a set number of data to construct the target sequence of the target signal, calculating the mean value of the absolute value of the difference between the target signal and its adjacent signal strength data, and taking the mean value of the absolute value of the difference as the local difference of the target signal; calculating the difference between the maximum value and the minimum value of the signal strength data in the target sequence of the target signal, and taking the difference between the maximum value and the minimum value as the overall difference of the target signal; normalizing the product of the local difference and the overall difference to obtain the primary conventional change degree of the target signal; or selecting any signal strength data as the target signal, centering on the target signal, selecting a set number of data to construct the target sequence of the target signal, calculating the mean value of the absolute value of the difference between the target signal and its adjacent signal strength data, and taking the mean value of the absolute value of the difference as the local difference of the target signal; calculating the difference between the maximum value and the minimum value of the signal strength data in the target sequence of the target signal, and taking the difference between the maximum value and the minimum value as the overall difference of the target signal; Performing standardization processing on the local difference and the overall difference, calculating the Euclidean distance between the processed local difference and the overall difference, and taking the Euclidean distance as the primary conventional change degree of the target signal.

2. The signal fault recognition method based on multi-sensor spatio-temporal alignment according to claim 1, wherein The obtaining process of the distance adjustment factor includes: Obtaining the numerical mean value of all signal strength data with the same distance between the drone and the signal source at the sampling moment corresponding to the target signal in the historical signal strength data, taking the absolute value of the difference between the target signal and the numerical mean value as the first parameter, taking the distance between the drone and the signal source at the sampling moment corresponding to the target signal as the second parameter, and taking the ratio of the second parameter to the first parameter as the distance adjustment factor of the target signal.

3. The signal fault identification method based on multi-sensor spatio-temporal alignment according to claim 2, wherein The hierarchical clustering is the agglomerative hierarchical clustering algorithm.

4. A signal fault identification method based on multi-sensor spatio-temporal alignment according to claim 3, characterized in that, After obtaining the abnormal cluster, it further includes: For each signal strength data marked as an abnormal cluster, according to the distance data between the drone and the signal source, finding out the signal source position information where abnormal signal strength appears within the preset distance range, and further formulating a maintenance decision.

5. The signal fault recognition method based on multi-sensor spatio-temporal alignment according to claim 4, wherein, The first pruning rule includes: presetting a merging distance threshold, and retaining the clustering clusters with a merging distance less than the preset merging distance threshold; Preset the threshold of the number of data points in the cluster, and trim the clustering clusters where the number of data points in the cluster is greater than the threshold of the number of data points in the cluster.

6. A signal fault identification method based on multi-sensor spatio-temporal alignment according to claim 5, characterized in that The first trimming rule further includes: Preset the threshold of the mean value of the data in the cluster after normalization, and trim the clustering clusters where the mean value of the data in the cluster after normalization is greater than the threshold of the mean value of the data in the cluster after normalization.

7. A signal fault identification method based on multi-sensor spatio-temporal alignment according to claim 1, characterized in that The process of obtaining the distance adjustment factor includes: Obtain the numerical mean of the signal intensity data with the same distance from the signal source of the UAV at the sampling moment corresponding to the target signal in the historical signal intensity data, take the absolute value of the difference between the target signal and the numerical mean as the first parameter, and take the distance of the UAV from the signal source at the sampling moment corresponding to the target signal as the second parameter; Take the product of the first parameter and the attenuation coefficient as the negative exponent of the exponential function, perform operations on the exponential function, and multiply the obtained result by the second parameter to obtain the distance adjustment factor of the target signal.

8. A signal fault identification system based on multi-sensor spatio-temporal alignment, characterized in that, Include: A processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the signal fault identification method based on multi-sensor spatio-temporal alignment according to any one of claims 1-7 is implemented.

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