An underwater robot heading data anomaly monitoring method and system

Abstract

The present invention relates to the technical field of data processing, and particularly relates to a method and system for monitoring abnormal heading data of an underwater robot. The method includes: obtaining the heading data of the underwater robot within a predetermined time period; clustering the heading data to obtain a plurality of first clusters; determining the first interaction index value between various indicators during the heading process; clustering the heading data according to the first interaction index value between the various indicators in the first cluster during different heading processes to obtain a second cluster; calculating the abnormal score value of the abnormal data of the indicator data of each indicator in the second cluster; determining the abnormal degree value of each indicator of the underwater robot according to the abnormal score value of the indicator, the number of the second cluster where the indicator data of the indicator is located, and the number of the second cluster, and determining the indicator with the abnormal degree value greater than the first threshold as an abnormal indicator. The present invention improves the detection accuracy of abnormal detection of the underwater robot, and further improves the control accuracy of the underwater robot.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for monitoring abnormal heading data of an underwater robot. Background Art

[0002] Underwater robots, also known as unmanned remote-controlled submersibles, are extreme operation robots that work underwater. As the world pays more and more attention to the marine economy and marine resources gradually become an important competitive resource, the underwater robot industry has ushered in opportunities for rapid development. Underwater robots can not only help humans explore and utilize marine resources more effectively, but also perform various tasks in complex underwater environments, such as seabed mapping, pipeline inspection, and biological research. Therefore, the heading data of underwater robots is crucial to studying the degree of task completion of underwater robots. Therefore, it is necessary to monitor the abnormality of underwater robot industry data.

[0003] In some scenarios, when detecting anomalies in the heading data of an underwater robot, the Local Outlier Factor (LOF) algorithm is usually used to monitor the acquired heading data of the underwater robot for anomalies. However, in actual operation, the underwater environment is complex and changeable. For example, changes in the direction of the water flow, rapid changes in temperature and salinity will cause short-term fluctuations in the heading data, but the fluctuations in the heading data caused by changes in the underwater environment are normal. The LOF algorithm usually misjudges the changes in the heading data caused by fluctuations as anomalies, resulting in errors in the heading control and judgment of the underwater robot. Therefore, when the existing method is used to detect anomalies in the heading data of the underwater robot, the detection accuracy is low, which in turn leads to low control accuracy of the underwater robot. Summary of the invention

[0004] In order to solve the technical problem of low detection accuracy when detecting abnormality of heading data of an underwater robot, the object of the present invention is to provide a method and system for monitoring abnormality of heading data of an underwater robot. The technical scheme adopted is as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for monitoring abnormal heading data of an underwater robot, including: obtaining the heading data of the underwater robot within a predetermined time period, where the heading data includes the index data of multiple indicators; clustering the heading data to obtain a plurality of first clusters, and each first cluster includes the heading data of the same section during multiple heading processes of the underwater robot; determining the first interaction index value between each indicator during the heading process according to the index data of multiple indicators in the heading data within each first cluster; clustering the heading data according to the first interaction index value between the indicators in the first cluster during different heading processes to obtain a second cluster; calculating the abnormal score value of the abnormal data of the index data of each indicator in the second cluster; determining the abnormal degree value of each indicator of the underwater robot according to the abnormal score value of each indicator, the number of the second cluster where the index data of the indicator is located, and the number of the second cluster, and determining the indicator with the abnormal degree value greater than the first threshold as the abnormal indicator.

[0006] Optionally, determining the first interaction index value between each indicator during the heading process according to the index data of multiple indicators in the heading data within each first cluster includes: obtaining the fluctuation curve of the index data of each indicator in each first cluster during multiple heading processes; clustering the index data of each indicator according to the first DTW distance between the fluctuation curves to obtain a plurality of third clusters; determining the target cluster in which there is a positive correlation or negative correlation between the indicators according to the average value of the second DTW distances of the index data of all indicators in the third cluster; determining the second interaction index value between the indicators during the heading process in the target cluster according to the third DTW distance between the fluctuation curves of the indicators during the heading process in the target cluster and the fluctuation amplitude of the fluctuation curves of the indicators; determining the first interaction index value between each indicator during the heading process according to the second interaction index value between the indicators during the heading process in each target cluster.

[0007] Optionally, determining the target cluster in which there is a positive correlation or negative correlation between the indicators according to the average value of the second DTW distances of the index data of all indicators in the third cluster includes: determining that there is a positive correlation between all indicators in the third cluster when the average value of the second DTW distances is less than the second threshold; determining that there is a negative correlation between all indicators in the third cluster when the average value of the second DTW distances is not less than the second threshold.

[0008] Optionally, when the target cluster is the third cluster with a positive correlation, determining the second interaction index value between the indicators in the heading process of the target cluster according to the third DTW distance between the fluctuation curves of the indicators in the heading process of the target cluster and the fluctuation amplitudes of the fluctuation curves of the indicators includes: calculating the first product between the first quantity of all indicators in the third cluster and the second quantity of influencing indicators that have a positive correlation with the indicators in the third cluster; calculating the third DTW distance between the fluctuation curve of the indicator in the target cluster and the fluctuation curve of the influencing indicator that has a positive correlation with the indicator, and normalizing the third DTW distance to obtain the first normalized distance; calculating the absolute value of the first difference between the fluctuation amplitude of the fluctuation curve of the indicator in the target cluster and the fluctuation amplitude of the fluctuation curve of the influencing indicator that has a positive correlation with the indicator, and calculating the reciprocal of the first sum value between the absolute value of the first difference and a preset value; superimposing the second product between the first normalized distance of each indicator in the third cluster and the reciprocal of the first sum value to obtain the first superimposed value; determining the first ratio between the first superimposed value and the first product as the second interaction index value.

[0009] Optionally, when the target cluster is the third cluster with a negative correlation, determining the second interaction index value between the indicators in the heading process of the target cluster according to the third DTW distance between the fluctuation curves of the indicators in the heading process of the target cluster and the fluctuation amplitudes of the fluctuation curves of the indicators includes: calculating the third product between the third quantity of all indicators in the third cluster and the fourth quantity of influencing indicators that have a negative correlation with the indicators in the third cluster; calculating the third DTW distance between the fluctuation curve of the indicator in the target cluster and the fluctuation curve of the influencing indicator that has a negative correlation with the indicator, and normalizing the third DTW distance to obtain the second normalized distance; calculating the reciprocal of the second sum value between the second normalized distance and a preset value; calculating the absolute value of the second difference between the fluctuation amplitude of the fluctuation curve of the indicator in the target cluster and the fluctuation amplitude of the fluctuation curve of the influencing indicator that has a negative correlation with the indicator, and calculating the reciprocal of the third sum value between the absolute value of the second difference and a preset value; superimposing the fourth product between the reciprocal of the second sum value of each indicator in the third cluster and the reciprocal of the third sum value to obtain the second superimposed value; determining the second ratio between the second superimposed value and the third product as the second interaction index value.

[0010] Optionally, determining the first interaction index value between the indicators in the heading process according to the second interaction index value between the indicators in the heading process of each target cluster includes: determining the average value of the second interaction index value between the indicators in the heading process of each target cluster as the first interaction index value.

[0011] Optionally, clustering the course data according to the first interaction index value among the indicators of the course data in the first cluster during different course runs, to obtain a second cluster, including: determining the matching degree value between the first clusters of the course data during different course runs according to the indicators in the first cluster of the course data during different course runs; using the matching degree value to determine the matching quantity of the first clusters of the course data during different course runs; determining the similarity degree value between the course data during different course runs according to the matching quantity, the quantity of the first clusters of the course data during different course runs, and the first interaction index value among the indicators during different course runs; clustering the course data based on the similarity degree value between the course data during different course runs to obtain a second cluster.

[0012] Optionally, determining the matching degree value between the first clusters of the course data during different course runs according to the indicators in the first cluster of the course data during different course runs includes: determining the fifth quantity of the same indicators in the first cluster of the course data during different course runs, and the sixth quantity of the indicators in the first cluster of the course data during different course runs; determining the third ratio between the fifth quantity and the sixth quantity as the matching degree value.

[0013] Optionally, determining the abnormality degree value of each indicator of the underwater robot according to the abnormality score value of each indicator, the number of the second cluster where the indicator data of the indicator is located, and the quantity of the second cluster includes: calculating the fourth ratio between the number of the second cluster where the indicator data of the indicator is located and the quantity of the second cluster; determining the fourth product between the fourth ratio and the abnormality score value of the indicator as the abnormality degree value of the indicator.

[0014] In a second aspect, an embodiment of the present invention provides an underwater robot course data abnormality monitoring system, including: a processor and a memory; wherein, the memory is used for storing a computer program that can run on the processor; the processor is used for executing the program stored on the memory to implement the steps of the underwater robot course data abnormality monitoring method as mentioned in the first aspect.

[0015] The present invention has the following beneficial effects: First, obtain the heading data of the underwater robot within a predetermined time period, where the heading data includes index data of multiple indicators; then cluster the heading data to obtain multiple first clusters, and each first cluster includes the heading data of the same section during multiple heading processes of the underwater robot; secondly, determine the first interaction index value between each indicator during the heading process according to the index data of multiple indicators in the heading data within each first cluster; then cluster the heading data according to the similarity degree value between the heading data in the first cluster during different heading processes to obtain a second cluster; and calculate the anomaly score value of the anomaly data of the index data of each indicator in the second cluster; finally, determine the anomaly degree value of each indicator of the underwater robot according to the anomaly score value of each indicator, the number of the second cluster where the index data of the indicator is located, and the number of the second cluster, and determine the indicator with the anomaly degree value greater than the first threshold as the anomaly indicator.

[0016] In this way, the embodiment of the present invention can determine the interaction index value between various indicators during the operation of the underwater robot by analyzing the heading data of the underwater robot when working in the same area. Then, determine the similarity between the heading data during different heading processes according to the interaction index value between the indicators of different heading data, so as to cluster the heading data. Then calculate the anomaly score value of the anomaly data of the index data of each indicator in the clustering cluster, and calculate the anomaly degree value of each indicator based on this. Thus, the monitoring of the anomaly indicators is achieved. In this way, it is possible to avoid the situation where the normal fluctuation of the heading data caused by the change of the underwater environment is misjudged as an anomaly, improve the detection accuracy of the anomaly detection of the underwater robot, and further improve the control accuracy of the underwater robot. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of a method for monitoring the anomaly of the heading data of an underwater robot provided by an embodiment of the present invention.

[0019] Figure 2 It is a schematic structural diagram of a device for monitoring the anomaly of the heading data of an underwater robot provided by an embodiment of the present invention.

[0020] Figure 3 It is a schematic structural diagram of a system for monitoring the anomaly of the heading data of an underwater robot provided by an embodiment of the present invention. Detailed Embodiments

[0021] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details a method and system for monitoring abnormal heading data of an underwater robot proposed according to the present invention, including its specific implementation manner, structure, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0023] The following specifically describes the specific solutions of a method and system for monitoring abnormal heading data of an underwater robot provided by the present invention in conjunction with the accompanying drawings.

[0024] Embodiment 1:

[0025] Please refer to Figure 1 , which shows a flowchart of a method for monitoring abnormal heading data of an underwater robot provided by an embodiment of the present invention, including:

[0026] S101, obtain the heading data of the underwater robot within a predetermined time period, where the heading data includes index data of multiple indicators.

[0027] Specifically, in an embodiment of the present invention, the heading data of the underwater robot can be obtained from the terminal database. Among them, the heading data of the underwater robot includes index data such as heading angle, angular velocity, acceleration, and speed. The index data of each index in these heading data together constitute the heading data set of the underwater robot, providing necessary information for the navigation, control, and task execution of the underwater robot. In an embodiment of the present invention, for each underwater robot, it is assumed that the heading data of M sailing processes is obtained in total.

[0028] S102, perform clustering on the heading data to obtain multiple first clusters, where each first cluster includes the heading data of the same section during multiple heading processes of the underwater robot.

[0029] Specifically, when the underwater robot performs tasks in the same section, the corresponding heading data each time is not very different. Therefore, first, the heading data with the same section in the heading data of these M heading processes is clustered into one category, that is, the heading data with the same section is clustered to obtain the first cluster, and the heading data in each first cluster is the heading data in the same section. In an embodiment of the present invention, the subsequent analysis is performed on the heading data in the same category. In an embodiment of the present invention, for the heading data in the same category, it is assumed that there are a total of R times of heading data.

[0030] S103. Determine the first interaction index value between various indicators during the course of determining the heading based on the indicator data of multiple indicators in the heading data of each first cluster.

[0031] Specifically, during each navigation of the underwater robot, the fluctuations between different indicators in the heading data will affect each other. For example, the fluctuation of the heading angle will affect indicators such as acceleration and speed. In the embodiments of the present invention, the heading angle is recorded as the reference indicator, and the acceleration and speed are recorded as the influencing indicators of the reference indicator, that is, the indicators that have a correlation with the heading angle.

[0032] Further, when determining the first interaction index value between various indicators during the course of determining the heading, as an optional embodiment of the present invention, first obtain the fluctuation curves of the indicator data of each indicator in each first cluster during multiple heading processes;

[0033] Then cluster the indicator data of each indicator according to the first DTW distance between the fluctuation curves to obtain multiple third clusters; then determine the target cluster in which there is a positive or negative correlation between the indicators according to the average value of the second DTW distances of the indicator data of all indicators in the third cluster; secondly, determine the second interaction index value between the indicators during the heading process in the target cluster according to the third DTW distance between the fluctuation curves of the indicators during the heading process in the target cluster and the fluctuation amplitudes of the fluctuation curves of the indicators; finally, determine the first interaction index value between the indicators during the heading process according to the second interaction index values between the indicators during the heading process in each target cluster.

[0034] Specifically, in the embodiments of the present invention, obtain the fluctuation curves of the indicator data of each indicator in the first cluster during multiple heading processes, then calculate the DTW distance between the fluctuation curves of each indicator during each heading process, and normalize the calculated DTW distance. Then use the K-means clustering algorithm to cluster all indicators based on the normalized DTW distance, where the number of clustering clusters can be determined by the silhouette coefficient method. After clustering, there are N third clusters in total. It should be noted that the calculation method of the DTW distance and the specific implementation method of determining the number of clustering clusters by the silhouette coefficient method can be referred to the prior art, and the embodiments of the present invention will not be elaborated herein.

[0035] Further, after classifying multiple third clusters, calculate the average value of the second DTW distances of the index data of all the indexes in the third cluster. When determining the correlation between indexes based on this average value, as an optional embodiment of the present invention, when the average value of the second DTW distances is less than the second threshold, it is determined that there is a positive correlation between all the indexes in the third cluster; when the average value of the second DTW distances is not less than the second threshold, it is determined that there is a negative correlation between all the indexes in the third cluster. Among them, the second threshold can be determined according to the actual situation. In the embodiment of the present invention, the value here is 0.5. If the average value of the second DTW distances of the index data of all the indexes in the third cluster is less than 0.5, it means that there is a positive correlation between all the indexes in this third cluster, otherwise it means that there is a negative correlation between all the indexes in this third cluster.

[0036] Further, when determining the second interaction index value between the indexes in the heading process of the target cluster, if the target cluster is the third cluster with a positive correlation, as an optional embodiment of the present invention, first calculate the first product between the first quantity of all the indexes in the third cluster and the second quantity of the influencing indexes that have a positive correlation with the indexes in the third cluster; then calculate the third DTW distance between the fluctuation curves of the indexes in the target cluster and the fluctuation curves of the influencing indexes that are positively correlated with the indexes, and normalize the third DTW distance to obtain the first normalized distance; secondly, calculate the absolute value of the first difference between the fluctuation amplitudes of the fluctuation curves of the indexes in the target cluster and the fluctuation amplitudes of the fluctuation curves of the influencing indexes that are positively correlated with the indexes, and calculate the reciprocal of the first sum value between the absolute value of the first difference and the preset value; and superimpose the second products between the first normalized distance of each index in the third cluster and the reciprocal of the first sum value to obtain the first superimposed value; finally, determine the first ratio between the first superimposed value and the first product as the second interaction index value.

[0037] Specifically, taking the heading data of the r-th heading process as an example in the embodiment of the present invention, cluster the heading data of the r-th heading process according to the above embodiment of the present invention to obtain multiple third clusters. For the index data of each index in the n-th third cluster (assuming that there is a positive correlation between the indexes in the n-th third cluster), the second interaction index value between its indexes can be calculated by the following formula:

[0038]

[0039] In the above formula, represents the second interaction index value between the indexes in the n-th cluster in the heading data of the r-th heading process. It represents the number of reference indicators in the nth cluster. J represents the number of indicators affected by the ith reference indicator, that is, the second quantity of the influencing indicators that have a positive correlation with the reference indicators in the third cluster. It represents a normalization function, which is used to perform normalization processing. It represents the third DTW distance between and It represents the fluctuation curve of the ith reference indicator in the nth cluster in the heading data of the rth heading process. It represents the fluctuation curve of the jth indicator affected by the ith reference indicator in the nth cluster in the heading data of the rth heading process. It represents the fluctuation amplitude of the fluctuation curve of the ith reference indicator in the nth cluster in the heading data of the rth heading process. It represents the fluctuation amplitude of the fluctuation curve of the jth indicator affected by the ith reference indicator in the nth cluster in the heading data of the rth heading process. The larger the value of , the more similar the change trends between the two indicators. At the same time, the smaller the value of

[0040] , the more similar the change amplitudes between the two indicators. Therefore, the larger the value of , the larger the second interaction indicator value between the various indicators in the rth heading process. Further, when determining the second interaction indicator value between the indicators in the heading process of the target cluster, if the target cluster is the third cluster with a negative correlation, as an optional embodiment of the present invention, calculate the third product between the third quantity of all indicators in the third cluster and the fourth quantity of the influencing indicators that have a negative correlation with the indicators in the third cluster; calculate the third DTW distance between the fluctuation curve of the indicators in the target cluster and the fluctuation curve of the influencing indicators that are negatively correlated with the indicators, and normalize the third DTW distance to obtain the second normalized distance; calculate the reciprocal of the second sum value between the second normalized distance and the preset value; calculate the absolute value of the second difference between the fluctuation amplitude of the fluctuation curve of the indicators in the target cluster and the fluctuation amplitude of the fluctuation curve of the influencing indicators that are negatively correlated with the indicators, and calculate the reciprocal of the third sum value between the absolute value of the second difference and the preset value; superimpose the fourth product between the reciprocal of the second sum value of each indicator in the third cluster and the reciprocal of the third sum value to obtain the second superimposed value; determine the second ratio between the second superimposed value and the third product as the second interaction indicator value.

[0041] Specifically, in the embodiment of the present invention, taking the heading data of the r-th heading process as an example, the heading data of the r-th heading process is clustered according to the above embodiment of the present invention to obtain a plurality of third clusters. For the index data of each index in the n-th third cluster (assuming that there is a negative correlation between the indicators in the n-th third cluster), the second interaction index value between the indicators can be calculated by the following formula:

[0042]

[0043] In the above formula, represents the second interaction index value between the indicators in the n-th cluster of the heading data in the r-th heading process. represents the number of reference indicators in the n-th cluster. represents the number of indicators affected by the i-th reference indicator, that is, the fourth quantity of the influencing indicators that have a negative correlation with the reference indicators in the third cluster. represents a normalization function, which is used to perform normalization processing on represents and the third DTW distance between them. represents the fluctuation curve of the i-th reference indicator in the n-th cluster of the heading data in the r-th heading process. represents the fluctuation curve of the j-th indicator affected by the i-th reference indicator in the n-th cluster of the heading data in the r-th heading process. represents the fluctuation amplitude of the fluctuation curve of the i-th reference indicator in the n-th cluster of the heading data in the r-th heading process. represents the fluctuation amplitude of the fluctuation curve of the j-th indicator affected by the i-th reference indicator in the n-th cluster of the heading data in the r-th heading process.

[0044] Furthermore, when determining the first interaction index value between the indicators in the heading process, the average value of the second interaction index values between the indicators in the heading process in each target cluster is determined as the first interaction index value.

[0045] Specifically, the embodiment of the present invention uses the following formula to calculate the first interaction index value between the indicators in the heading data of the r-th heading process:

[0046]

[0047] In the above formula, represents the first interaction index value between the indicators in the heading data of the r-th heading process. N represents the number of clustering clusters formed by all indicators. ​It represents the second interaction index value among the various indicators in the nth cluster of the heading data during the rth heading process. It should be noted that if there is a negative correlation among the various indicators, then in the above formula is replaced by .

[0048] S104. Cluster the heading data based on the first interaction index value among the various indicators of the heading data in the first cluster during different heading processes to obtain the second cluster.

[0049] Specifically, in the above embodiments of the present invention, the interaction index value among the various indicators in each heading data in the nth cluster is calculated. Since these multiple heading data are obtained when working in the same area. Therefore, the fluctuation differences of the various indicators in the heading data of different times should be relatively small, and the distribution of the positive correlation indicators and negative correlation indicators should also be the same. Therefore, by analyzing the similarity degree of the first interaction index value among the various indicators in the heading data of different times in the nth cluster and the similarity of the index clustering results, the similarity degree value of the heading data of different times is determined, and the heading data is clustered based on this to obtain the second cluster.

[0050] Further, when clustering the heading data based on the first interaction index value among the various indicators of the heading data in the first cluster during different heading processes to obtain the second cluster, as an optional embodiment of the present invention, first determine the matching degree value between the first clusters of the heading data of different heading processes according to the indicators in the first cluster of the heading data during different heading processes; then use the matching degree value to determine the matching quantity of the first clusters of the heading data of different heading processes; then, according to the matching quantity, the quantity of the first clusters of the heading data of different heading processes, and the first interaction index value among the various indicators in different heading processes, determine the similarity degree value between the heading data of different heading processes; finally, cluster the heading data based on the similarity degree value between the heading data of different heading processes to obtain the second cluster.

[0051] Specifically, in the embodiments of the present invention, since the clustering results of the various indicators in the heading data during different heading processes in the nth cluster may be different, it is first necessary to match the clustering clusters in the clustering results of the various indicators in the heading data of different times. As an optional embodiment of the present invention, first determine the fifth quantity of the same indicators in the first cluster of the heading data during different heading processes, and the sixth quantity of the indicators in the first cluster of the heading data in the heading data of different times; then determine that the third ratio between the fifth quantity and the sixth quantity is the matching degree value.

[0052] More specifically, in the embodiment of the present invention, taking the heading data of the p-th time and the heading data of the q-th time in the n-th cluster as an example, the clustering clusters are first matched. Among them, the mathematical formula for the matching degree value between the a-th cluster in the heading data of the p-th heading process and the b-th cluster in the heading data of the q-th heading process is:

[0053]

[0054] In the above formula, represents the matching degree value between the a-th cluster in the heading data of the p-th heading process and the b-th cluster in the heading data of the q-th heading process. represents the sixth quantity of the indicators in the a-th cluster in the heading data of the q-th heading process. represents the fifth quantity of the same indicators between the a-th cluster in the heading data of the p-th heading process and the b-th cluster in the heading data of the q-th heading process.

[0055] Among them, if the value of is greater than the predetermined threshold. For example, if the predetermined threshold is 0.5, if is greater than 0.5, it means that the matching is successful. Otherwise, it means that the matching is unsuccessful. If there are multiple clusters in the a-th cluster of the p-th heading data that match the heading data of the q-th time, then the cluster with the largest value is recorded as the matching cluster. According to the above method, each cluster in the p-th heading data in the n-th cluster is matched with each cluster in the q-th heading data. Suppose that after the matching is completed, a total of clusters with the matching quantity are matched.

[0056] Furthermore, when determining the similarity degree value, in the embodiment of the present invention, taking the heading data of the p-th time and the heading data of the q-th time in the n-th cluster as an example, the following formula is specifically used to calculate the similarity degree value between the heading data of different heading processes:

[0057]

[0058] In the above formula, represents the similarity degree value between the heading data of the p-th heading process and the heading data of the q-th heading process. represents the number of clustering clusters of the indicators in the heading data of the p-th heading process. represents the number of clustering clusters of the indicators in the heading data of the q-th heading process. represents the number of matching between the clustering clusters of the indicators in the heading data of the p-th heading process and the clustering clusters of the indicators in the heading data of the q-th heading process. represents the first interaction index value between the indicators in the p-th heading data. Represents the first interaction index value between the indicators in the qth heading data. The smaller the value of The larger the value of is, the more similar the number of clusters of the indicators in the p-th heading data and the q-th heading data are, and the more similar the indicator data of each cluster of the clustering results are, that is, the more similar the clustering results of the indicators in the two heading data are. The smaller the value of indicates that the interaction indicators between the indicators in the p-th heading data and the q-th heading data are also similar. The larger the value of , the greater the similarity between the p-th heading data and the q-th heading data.

[0059] Further, according to the above embodiment of the present invention, the similarity value between the p-th heading data and the q-th heading data can be calculated. Then, the k-menas method is used to cluster all the M-times of heading data based on the similarity value, wherein the distance between different times of heading data is calculated by To quantify, the number of clusters is determined by the silhouette coefficient method, wherein the use of the k-menas method for clustering and the use of the silhouette coefficient method to determine the number of clusters can be referred to the prior art, and the embodiments of the present invention will not be repeated here. Assume that there are R second clusters after clustering is completed, and then sort them in order from small to large according to the number of heading data in the second cluster. Because the more heading data in the cluster, the more the change in the heading data shows the characteristics of the cluster when the underwater robot is working in this area. Therefore, when performing abnormal data analysis later, the higher the weight value of the heading data in the cluster.

[0060] S105, calculating the abnormal score value of the abnormal data of the indicator data of each indicator in the second cluster.

[0061] Specifically, the embodiment of the present invention can use the LOF method to calculate the abnormal data in the time series data of each indicator of each heading data in each second cluster. The average of the LOF scores of all the abnormal data calculated is recorded as the abnormal score value of the abnormal data of each indicator. It is worth noting that the use of the LOF method to calculate abnormal data can refer to the prior art, and the embodiment of the present invention will not be repeated here.

[0062] S106, determining the abnormality level of each indicator of the underwater robot according to the abnormality score value of each indicator, the number of the second cluster where the indicator data of the indicator is located, and the number of the second clusters, and determining the indicator whose abnormality level value is greater than the first threshold as an abnormal indicator.

[0063] Specifically, the first threshold can be determined according to the actual situation, and in the embodiments of the present invention, its value is 0.7. An index with an abnormal degree value greater than 0.7 is recorded as an abnormal index, indicating that the index has an abnormality during the operation of the underwater robot, and professionals are required to check the parts and functions corresponding to the index of the underwater robot to ensure the safe operation of the underwater robot.

[0064] Further, when calculating the abnormal degree values of each index of the underwater robot, as an optional embodiment of the present invention, first calculate the fourth ratio between the number of the second cluster where the index data of the index is located and the number of the second cluster; then determine the fourth product between the fourth ratio and the abnormal score value of the index as the abnormal degree value of the index.

[0065] Specifically, the embodiments of the present invention use the following formula to calculate the abnormal degree value:

[0066]

[0067] In the formula, represents the abnormal degree value of the nth index during the operation of the underwater robot. represents the number of clustering clusters of all heading data. represents the number of the cluster where the mth sorted heading data is located, and this number is a natural number greater than or equal to 1. represents the abnormal score value of the nth index in the mth heading data. The larger the value of the greater the weight value contributed by the mth heading data.

[0068] The embodiments of the present invention can determine the interaction index values between various indexes during the operation of the underwater robot by analyzing the heading data of the underwater robot when working in the same area. Then, according to the interaction index values between the indexes of different heading data, the similarity between the heading data during different heading processes is determined, so as to cluster the heading data. Then, the abnormal score values of the abnormal data of the index data of each index in the clustering cluster are calculated, and the abnormal degree values of each index are calculated accordingly. Thus, the monitoring of abnormal indexes is achieved. In this way, the normal fluctuations of the heading data caused by the changes in the underwater environment can be avoided from being misjudged as abnormal, the detection accuracy of the abnormal detection of the underwater robot is improved, and the control accuracy of the underwater robot is further improved.

[0069] Embodiment 2:

[0070] Corresponding to the underwater robot heading data anomaly monitoring method provided in the above embodiments, based on the same technical concept, an embodiment of the present invention further provides an underwater robot heading data anomaly monitoring device. This underwater robot heading data anomaly monitoring system is used to execute the above underwater robot heading data anomaly monitoring method. Figure 2 FIG. is a schematic structural diagram of an underwater robot heading data anomaly monitoring device provided by an embodiment of the present invention, as Figure 2 shown. The device 200 includes: an acquisition module 201, configured to acquire the heading data of the underwater robot within a predetermined time period, where the heading data includes the index data of multiple indexes; a clustering module 202, configured to cluster the heading data to obtain a plurality of first clusters, and each first cluster includes the heading data of the same section during multiple heading processes of the underwater robot; a determination module 203, configured to determine the first interaction index value between each index during the heading process according to the index data of multiple indexes in the heading data within each first cluster; the clustering module 202 is further configured to cluster the heading data according to the first interaction index value between each index in the heading data in the first cluster during different heading processes to obtain a second cluster; a calculation module 204, configured to calculate the anomaly score value of the anomaly data of the index data of each index in the second cluster; the determination module 203 is further configured to determine the anomaly degree value of each index of the underwater robot according to the anomaly score value of each index, the number of the second cluster where the index data of the index is located, and the number of the second cluster, and determine the index with the anomaly degree value greater than the first threshold as the anomaly index.

[0071] Embodiment Three:

[0072] Corresponding to the underwater robot heading data anomaly monitoring method provided in the above embodiments, based on the same technical concept, an embodiment of the present invention further provides an underwater robot heading data anomaly monitoring system. This underwater robot heading data anomaly monitoring system is used to execute the above underwater robot heading data anomaly monitoring method. Figure 3 FIG. is a schematic structural diagram of an underwater robot heading data anomaly monitoring system provided by an embodiment of the present invention, as Figure 3 shown. The underwater robot heading data anomaly monitoring system may vary greatly due to configuration or performance, and may include one or more processors 301 and a memory 302. The memory 302 is used to store a computer program that can run on the processor 301. The processor 301 is configured to execute the program stored in the memory 302 to implement each step in the above Figure 1 method embodiment. Among them, the memory 302 can be short-term storage or persistent storage. The application program stored in the memory 302 may include one or more modules (not shown in the figure), and each module may include a series of computer-executable instructions for the underwater robot heading data anomaly monitoring system.

[0073] Furthermore, the processor 301 can be configured to communicate with the memory 302 and execute a series of computer-executable instructions in the memory 302 on the underwater robot heading data anomaly monitoring system. The underwater robot heading data anomaly monitoring system may further include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input / output interfaces 305, and one or more keyboards 306.

[0074] Specifically, in this embodiment, the underwater robot heading data anomaly monitoring system includes a processor, a communication interface, a memory, and a communication bus; wherein, the processor, the communication interface, and the memory complete communication with each other through the bus; the memory is used to store computer programs; the processor is used to execute the programs stored on the memory to implement each step in the above Figure 1 method embodiments, and has the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present invention will not be described in detail here.

[0075] It should be noted that the underwater robot heading data anomaly monitoring system provided by the embodiments of the present invention and the underwater robot heading data anomaly monitoring method provided by the embodiments of the present invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned underwater robot heading data anomaly monitoring method and has the same or similar beneficial effects. The repeated parts will not be described again.

[0076] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0077] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. An underwater robot heading data anomaly monitoring method, characterized in that, The method for monitoring abnormal heading data of the underwater robot includes: Obtain the heading data of the underwater robot within a predetermined time period, where the heading data includes index data of multiple indicators; Cluster the heading data to obtain a plurality of first clusters, and each of the first clusters includes the heading data of the same section during multiple heading processes of the underwater robot; Determine the first interaction index value between each indicator during the heading process according to the index data of multiple indicators in the heading data within each first cluster; Cluster the heading data according to the first interaction index value between the indicators in the first cluster during different heading processes to obtain a second cluster; Calculate the abnormal score value of the abnormal data of the index data of each indicator in the second cluster; Determine the abnormal degree value of each indicator of the underwater robot according to the abnormal score value of each indicator, the number of the second cluster where the index data of the indicator is located, and the number of the second cluster, and determine the indicator with the abnormal degree value greater than the first threshold as the abnormal indicator.

2. The underwater robot heading data anomaly monitoring method according to claim 1, wherein The determining the first interaction index value between each indicator during the heading process according to the index data of multiple indicators in the heading data within each first cluster includes: Obtain the fluctuation curve of the index data of each indicator in each first cluster during multiple heading processes; Cluster the index data of each indicator according to the first DTW distance between the fluctuation curves to obtain a plurality of third clusters; Determine the target cluster with a positive or negative correlation relationship between the indicators according to the average value of the second DTW distances of the index data of all indicators in the third cluster; Determine the second interaction index value between the indicators during the heading process in the target cluster according to the third DTW distance between the fluctuation curves of the indicators during the heading process in the target cluster and the fluctuation amplitude of the fluctuation curves of the indicators; Determine the first interaction index value between each indicator during the heading process according to the second interaction index value between the indicators during the heading process in each target cluster.

3. The underwater robot heading data anomaly monitoring method according to claim 2, characterized in that, The determining the target cluster with a positive or negative correlation relationship between the indicators according to the average value of the second DTW distances of the index data of all indicators in the third cluster includes: When the average value of the second DTW distance is less than the second threshold, determine that there is a positive correlation relationship between all indicators in the third cluster; When the average value of the second DTW distance is not less than the second threshold, determine that there is a negative correlation relationship between all indicators in the third cluster.

4. The underwater robot heading data anomaly monitoring method according to claim 2, wherein, When the target cluster is the third cluster with a positive correlation relationship, the determining the second interaction index value between the indicators during the heading process in the target cluster according to the third DTW distance between the fluctuation curves of the indicators during the heading process in the target cluster and the fluctuation amplitude of the fluctuation curves of the indicators includes: Calculate the first product between the first quantity of all indicators in the third cluster and the second quantity of the influencing indicators that have a positive correlation relationship with the indicators in the third cluster; Calculate the third DTW distance between the fluctuation curve of the index in the target cluster and the fluctuation curve of the influencing index that has a positive correlation with the index, and normalize the third DTW distance to obtain the first normalized distance; Calculate the absolute value of the first difference between the fluctuation amplitude of the fluctuation curve of the index in the target cluster and the fluctuation amplitude of the fluctuation curve of the influencing index that has a positive correlation with the index, and calculate the reciprocal of the first sum value between the absolute value of the first difference and a preset value; Superimpose the second products between the first normalized distances of the respective indexes in the third cluster and the reciprocal of the first sum value to obtain a first superimposed value; Determine the first ratio between the first superimposed value and the first product as the second interaction index value.

5. The underwater robot heading data anomaly monitoring method according to claim 2, wherein When the target cluster is the third cluster with a negative correlation, the determining the second interaction index value between the respective indexes in the heading process in the target cluster according to the third DTW distance between the fluctuation curves of the respective indexes in the heading process in the target cluster and the fluctuation amplitudes of the fluctuation curves of the respective indexes includes: Calculate the third product between the third quantity of all indexes in the third cluster and the fourth quantity of the influencing index that has a negative correlation with the indexes in the third cluster; Calculate the third DTW distance between the fluctuation curve of the index in the target cluster and the fluctuation curve of the influencing index that has a negative correlation with the index, and normalize the third DTW distance to obtain the second normalized distance; Calculate the reciprocal of the second sum value between the second normalized distance and a preset value; Calculate the absolute value of the second difference between the fluctuation amplitude of the fluctuation curve of the index in the target cluster and the fluctuation amplitude of the fluctuation curve of the influencing index that has a negative correlation with the index, and calculate the reciprocal of the third sum value between the absolute value of the second difference and the preset value; Superimpose the fourth products between the reciprocal of the second sum value and the reciprocal of the third sum value of the respective indexes in the third cluster to obtain a second superimposed value; Determine the second ratio between the second superimposed value and the third product as the second interaction index value.

6. The method for abnormal monitoring of the heading data of an underwater robot according to claim 2, wherein The determining the first interaction index value between the respective indexes in the heading process according to the second interaction index values between the respective indexes in the heading process in each target cluster includes: Determine the average value of the second interaction index values between the respective indexes in the heading process in each target cluster as the first interaction index value.

7. The method for abnormal monitoring of the heading data of an underwater robot according to claim 1, wherein The clustering the heading data according to the first interaction index values between the respective indexes in the first cluster of the heading data in different heading processes to obtain the second cluster includes: Determine the matching degree value between the first clusters of the heading data of different heading processes according to the indexes in the first cluster of the heading data in different heading processes; Use the matching degree value to determine the matching quantity of the first clusters of the heading data of different heading processes; Determine the similarity degree value between the course data of different course processes according to the matching quantity, the quantity of the first clusters of the course data in different course processes, and the first interaction index value between each index in different course processes; Cluster the course data based on the similarity degree value between the course data of different course processes to obtain a second cluster.

8. The method for monitoring abnormal heading data of an underwater robot according to claim 7, wherein, The determining the matching degree value between the first clusters of the course data of different course processes according to the indexes in the first clusters of the course data in different course processes includes: Determine the fifth quantity of the same indexes in the first clusters of the course data in different course processes, and the sixth quantity of the indexes in the first clusters of the course data in different course processes; Determine that the third ratio between the fifth quantity and the sixth quantity is the matching degree value.

9. The method for abnormal monitoring of the heading data of an underwater robot according to claim 1, characterized in that, The determining the abnormality degree value of each index of the underwater robot according to the abnormal score value of each index, the number of the second cluster where the index data of the index is located, and the quantity of the second cluster includes: Calculate the fourth ratio between the number of the second cluster where the index data of the index is located and the quantity of the second cluster; Determine that the fourth product between the fourth ratio and the abnormal score value of the index is the abnormality degree value of the index.

10. An underwater robot heading data anomaly monitoring system, characterized in that, The abnormal monitoring system for the course data of the underwater robot includes: a processor and a memory; wherein, the memory is used for storing a computer program that can run on the processor; the processor is used for executing the program stored on the memory to implement the steps of the method for abnormal monitoring of the course data of the underwater robot according to any one of claims 1-9.

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