Track anomaly detection clustering method based on Hamming distance similarity

Abstract

The invention discloses a track anomaly detection clustering method based on Hamming distance similarity. The method comprises the following steps: acquiring track data in a set area and a specified time period and carrying out data preprocessing; carrying out track time sequence information feature extraction on the preprocessed track data based on a long short-term memory (LSTM) network auto-encoder to obtain track features; converting the track features into binary Hash codes based on an iterative quantization Hash algorithm, calculating Hamming distances between the Hash codes of the track features, and calculating an affinity matrix of the track features based on Hamming distance similarity; iterative clustering of the track features is carried out through a dominating set clustering algorithm, and in the iteration process of clustering, the track features which cannot be clustered serve as abnormal tracks to be output. According to the method, the similarity relation can be marked without depending on manual work, the affinity matrix is rapidly and efficiently obtained for clustering, and track anomaly detection in a large-scale data environment is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of track detection, and in particular to a clustering method for track anomaly detection based on Hamming distance similarity. Background Art

[0002] The hash algorithm can map data from a high-dimensional feature space to a low-dimensional Hamming space, occupying less space and with low query complexity. A large number of related works have studied hash algorithms for image or single feature retrieval, among which unsupervised hashing retains the similarity relationship of samples in the original feature space, which is similar to the Euclidean distance used in clustering algorithms, but the amount of similarity calculation is much lower than the Euclidean distance. For clustering, although there is no universal and strict definition, generally for a cluster, the data points within the cluster are highly similar, and the points outside the cluster are highly dissimilar. Graph clustering algorithms such as dominating set clustering use weighted undirected graphs to represent the similarity relationship between data points. By calculating the weight relationship between points within and outside the set, clusters with high internal correlation are calculated. The dominating set clustering algorithm has the advantages of not needing to set the number of cluster centers in advance, not classifying messy points, and allowing cluster overlap.

[0003] At present, the technical implementation ideas for abnormal ship track detection at home and abroad are mainly divided into two categories: 1. Establishing ship behavior distribution and model based on historical ship automatic identification system (AIS) data, and treating samples that do not conform to the normal model as abnormal trajectory samples; 2. Based on artificially set rules, abnormal behaviors are manually calibrated, and those that meet the conditions are abnormal tracks. However, due to the difficulty in obtaining abnormal ship data in the current maritime supervision field and the confidentiality and closure of ship management systems, most studies are based on AIS data to establish corresponding unsupervised models for abnormal track analysis. Summary of the invention

[0004] In order to overcome the defects and shortcomings of the prior art, the present invention provides a track anomaly detection clustering method based on Hamming distance similarity. The present invention performs hash coding on the basis of data cleaning and segmentation and long short-term memory artificial neural network (LSTM) preprocessing of track data sets, proposes an affinity matrix based on Hamming distance similarity, and uses a dominating set clustering algorithm to cluster data according to the affinity matrix to realize ship track anomaly detection. The similarity measurement based on Hamming distance of the present invention has the characteristics of discreteness and fast binary bit calculation, and can quickly and efficiently obtain affinity matrix for clustering without relying on artificially marked similarity relationships, so as to realize track anomaly detection in a large-scale data environment.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The present invention provides a clustering method for track anomaly detection based on Hamming distance similarity, comprising the following steps:

[0007] Obtain track data in the set area and specified time period and perform data preprocessing;

[0008] Based on the long short-term memory network LSTM autoencoder, the track time series information features of the pre-processed track data are extracted to obtain the track features;

[0009] Based on the iterative quantized hash algorithm, the track features are converted into binary hash codes, the Hamming distance between the hash codes of each track feature is calculated, and the affinity matrix of the track features based on the Hamming distance similarity is calculated;

[0010] The track features are iteratively clustered using the dominating set clustering algorithm. During the iterative clustering process, the track features that cannot be clustered are output as abnormal tracks.

[0011] As a preferred technical solution, the acquisition of track data in a set area and a specified time period and data preprocessing specifically includes:

[0012] Obtain the geographic location information of all ships in the set area and specified time period, set filtering conditions, delete and clean the data;

[0013] The AIS data are sorted by ship number and arranged in chronological order to obtain the track data of each ship in the specified time period.

[0014] As a preferred technical solution, the long short-term memory network LSTM autoencoder extracts track time series information features from the preprocessed track data, specifically including:

[0015] The preprocessed track data is input into the long short-term memory network LSTM network, mapped to the hidden layer to generate track hidden features, and then it is restored from the hidden layer through the long short-term memory network LSTM to map it back to track data of the same dimension, and the track hidden features are extracted, which can be specifically expressed as:

[0016] D = encoder (P)

[0017] P′=decoder(D)

[0018] Among them, P represents the preprocessed track data, D is the track hidden feature, P' is the restored track data, encoder(.) and decoder(.) are the encoder and decoder LSTM networks of the autoencoder respectively.

[0019] As a preferred technical solution, the long short-term memory network LSTM autoencoder uses the mean square error loss function to calculate the error between the input and output of each track data. The mean square error loss function is expressed as:

[0020]

[0021] Among them, d is the characteristic dimension of the track at each time point, n is the number of time points set in a track, and p q and p q ' are the original input data at the qth time point of the track and the data restored by the encoder.

[0022] As a preferred technical solution, the track features are converted into binary hash codes based on an iterative quantized hash algorithm, specifically including:

[0023] The principal component analysis of the track characteristics is performed to obtain the first k eigenvectors, and the orthogonal rotation matrix R is randomly generated to balance the information in the eigenvectors. The quantization error Q is minimized to perform iterative quantization optimization, which is specifically expressed as:

[0024]

[0025] Where D represents the track characteristics, W represents the number of feature vectors, and B is the hash code of the track data.

[0026] As a preferred technical solution, the affinity matrix of the track features based on the Hamming distance similarity is calculated, which specifically includes:

[0027] The number of different digits of the hash code of each track feature is calculated as the Hamming distance. The affinity in the affinity matrix based on the Hamming distance similarity is expressed as:

[0028]

[0029] Among them, a ij is the affinity between track features i and j, Ham ij is the Hamming distance between two track features i and j, and σ is the preset parameter for affinity calculation.

[0030] As a preferred technical solution, iterative clustering of track features is performed using a dominating set clustering algorithm, specifically including:

[0031] Use the affinity matrix A based on Hamming distance similarity composed of affinity elements sim =(a ij ), perform iterative clustering of track features, expressed as:

[0032]

[0033] Among them, y i and(·) i represents the weight and corresponding vector value of the ith track, t represents the number of iterative calculations in one clustering, and y is the vector composed of the weights of all track feature data;

[0034] The weights of all N tracks in the first clustering are initialized as For each track feature:

[0035]

[0036] Among them, N T Represents the total number of unclustered samples in the Tth clustering. When T=1 is the first clustering, N1=N.

[0037] The present invention also provides a track anomaly detection clustering system based on Hamming distance similarity, which is used to implement the above-mentioned track anomaly detection clustering method based on Hamming distance similarity, and the system includes: a track data acquisition module, a data preprocessing module, a feature extraction module, a hash coding module, a Hamming distance calculation module, an affinity matrix calculation module, an iterative clustering module, and an abnormal track output module;

[0038] The track data acquisition module is used to acquire track data in a set area and a specified time period;

[0039] The data preprocessing module is used to perform data preprocessing on the track data;

[0040] The feature extraction module is used to extract track time series information features from the preprocessed track data based on the long short-term memory network LSTM autoencoder to obtain track features;

[0041] The hash coding module is used to convert the track characteristics into binary hash codes based on an iterative quantized hash algorithm;

[0042] The Hamming distance calculation module is used to calculate the Hamming distance between the hash codes of each track feature;

[0043] The affinity matrix calculation module is used to calculate the affinity matrix of the track features based on the Hamming distance similarity;

[0044] The iterative clustering module is used to perform iterative clustering of track features through a dominating set clustering algorithm;

[0045] The abnormal track output module is used to output track features that cannot be clustered as abnormal tracks during the iterative process of clustering.

[0046] The present invention also provides a computer-readable storage medium storing a program, wherein when the program is executed by a processor, the above-mentioned track anomaly detection clustering method based on Hamming distance similarity is implemented.

[0047] The present invention also provides a computing device, comprising a processor and a memory for storing a program executable by the processor, wherein when the processor executes the program stored in the memory, the above-mentioned track anomaly detection clustering method based on Hamming distance similarity is implemented.

[0048] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0049] (1) The present invention cleans and divides the ship trajectory segments, improves the quality of track features through the LSTM autoencoder, and thus improves the subsequent anomaly detection effect and clustering efficiency.

[0050] (2) The current clustering algorithm has the problems of requiring the preset number of clusters, long clustering time for large-scale data, and the need to give pairwise similarity. The present invention calculates hash codes for time-series continuous track features. Based on the characteristics of hash algorithms such as discreteness and high efficiency, it can quickly obtain an affinity matrix based on Hamming distance similarity in the case of large-scale unlabeled data for subsequent anomaly detection clustering.

[0051] (3) The present invention automatically clusters unsupervised ship tracks, leaving behind non-clusterable ship tracks to achieve anomaly identification, and improves the clustering algorithm based on the Hamming distance, thereby enhancing the algorithm's data mining capability and analysis speed, enabling it to more accurately identify complex actual marine track situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic diagram of the implementation architecture of the track anomaly detection clustering method based on Hamming distance similarity of the present invention;

[0053] Figure 2 A schematic diagram of clustering of track data examples used in the present invention;

[0054] Figure 3 The figure is a schematic diagram of an abnormal track detected by the present invention and an example of one of the tracks. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] Example 1

[0057] like Figure 1As shown, this embodiment provides a track anomaly detection clustering method based on Hamming distance similarity, comprising the following steps:

[0058] S1: First, the track data of each ship in a specific area (such as a river or sea basin) and a specified time period are cleaned and segmented, and then the autoencoder with a long short-term memory network (LSTM) structure is used to extract the time series information features of the track data. The data processing includes the following steps:

[0059] S11: Extraction and cleaning of ship data in target sea area;

[0060] Use the data from the ship automatic identification system AIS to obtain the geographic location information of all ships in a specified sea area (sea area near a river or sea) during a certain period of time, including the longitude, latitude, speed, and direction of travel of each ship at each time point. Delete and clean the data that does not contain information or is obviously wrong, including: stationary ship data points with a speed of 0, track data outside the longitude and longitude of the specified sea area, track data with incorrect format, data with an absolute value of longitude exceeding 180°, an absolute value of latitude exceeding 90°, and a heading less than 0° or greater than or equal to 360°. Figure 2 As shown, the geographical location information of a river basin near a certain river or sea is determined, that is, the tracks that are not within the scope of this geographical location information are removed.

[0061] S12: Ship track data segmentation and construction;

[0062] The AIS data is sorted by ship number and arranged in chronological order to obtain the track data of each ship within the specified time period. In order to detect track anomalies and prevent the distance and time span of a single track from being too large, for ships with too long time intervals, they are divided into multiple separate tracks according to a certain number of time points. A track is assumed to consist of n time point data, which can be set to 100 time points. In each segmented track, each time point of the data contains the ship's longitude, latitude, speed, direction of travel, and the time interval between the current time point and the previous time point. One track is denoted as P, and the data at the qth time point is denoted as p q .

[0063] S13: Extract track time series information features based on long short-term memory network LSTM autoencoder;

[0064] The autoencoder based on the long short-term memory network LSTM is used to capture the time series information of the track data. The overall structure of the autoencoder is: the track data is used as input to map to the low-dimensional hidden layer through the LSTM network to generate track hidden features, and then it is restored from the hidden layer through the long short-term memory network LSTM to map to the track data of the same dimension, so as to extract the hidden features that can represent the input track data information:

[0065] D = encoder (P)

[0066] P′=decoder(D)

[0067] Where D is the hidden feature of the track, P' is the restored track data, encoder(.) and decoder(.) are the encoder and decoder LSTM networks of the autoencoder respectively.

[0068] It is expected that the hidden features can represent the corresponding track data, so that the restored track data is the same as the corresponding track data. The autoencoder uses the mean square error loss function L m Calculate the error between the input and output for each track:

[0069]

[0070] Where d is the characteristic dimension of the track at each time point, i.e. 5, n is the number of time points set in a track, i.e. 100, and p is q and p q ' are the original input data at the qth time point of the track and the data restored by the autoencoder. This embodiment uses the gradient descent method to train the autoencoder. After the training of the autoencoder network is completed, the track data is input into the encoder encoder(.) to obtain the track hidden features, that is, the track features used for subsequent encoding.

[0071] S2: Use the iterative quantized hash algorithm to convert the extracted ship track features into binary hash codes, calculate the Hamming distance between the hash codes of each track feature, and then calculate the affinity matrix of the track data based on the Hamming distance similarity, including the following steps:

[0072] S21: Iterative quantized hash coding calculation of track characteristics;

[0073] Perform principal component analysis on the track feature D to obtain its first k eigenvectors, for example, k can be set to 64, denoted as W. Randomly generate an orthogonal rotation matrix R to balance the information in the eigenvector, and then minimize the following quantization error Q for iterative quantization optimization:

[0074]

[0075] Where B is the hash code of the track data.

[0076] In iterative quantization optimization, R is first fixed, so D, W, and R are known to calculate the optimal hash code B = sgn(DWR), where sgn(.) is a sign function that records data greater than 0 as 1 and the rest as -1. Then fix B and use the singular value decomposition technique to calculate the optimal R. After multiple iterations of optimization, the optimal hash code B can be obtained. During the iteration process, the optimal solution is obtained when the hash code and the rotation matrix values ​​do not change during the update. Generally, an approximate optimal solution can be obtained when the number of iterations reaches 50.

[0077] The huge original data space is mapped to the Hamming space through the hash function, so that the probability that similar data in the original space (in the present invention, points with similar distances in the track feature space) are also similar in the new Hamming space is very high, and vice versa is very small, so that the hash codes of similar track features are also similar, and the hash codes of dissimilar track features are very different.

[0078] S22: Calculate the affinity matrix of track features based on Hamming distance similarity;

[0079] The Hamming distance is the number of different bits in the hash code of each track feature, that is, the degree of difference in the hash code. The greater the distance, the smaller the affinity. An element in the affinity matrix based on the Hamming distance similarity is expressed as the following formula, which is used to calculate the affinity between track features:

[0080]

[0081] In the formula, a ij is the affinity between two points i and j, Ham ij is the Hamming distance between two track features i and j, σ is the parameter for affinity calculation, which is an empirical value and is generally set to the maximum value of the Hamming distance between the data. The matrix diagonal is set to 0, that is, when i=j, the corresponding element of the affinity matrix is ​​0.

[0082] S3: Finally, the affinity matrix based on the Hamming distance similarity of the track features is used to realize the automatic iterative clustering of the tracks through the dominating set clustering algorithm. During the iterative clustering process, the conventional track data with high similarity that can be clustered is removed, and the track data that cannot be clustered is detected as abnormal tracks. It includes the following contents:

[0083] After calculating the affinity matrix based on the Hamming distance similarity, the dominating set clustering algorithm is used to leave the abnormal data samples without clustering and identify abnormal tracks. The track features and the affinity matrix are regarded as a graph, the values ​​of the track features are the points in the graph, and the affinity matrix is ​​the edge in the graph. The track feature set that meets the dominating set condition is considered to have a high degree of similarity. In the actual large amount of track data, the proportion of normal tracks far exceeds that of abnormal tracks, and the normal navigation routes of ships in reality tend to be fixed. After iterative clustering, abnormal tracks that are different from normal routes and cannot form a high-aggregation set will be identified in the algorithm. The track data is processed through cyclic iterative calculations. Every time a dominating set with a high degree of aggregation is found, this part of the track data is removed from the set, and then the next round of search is performed. After several cyclic clusterings, there are still data that cannot be clustered, which are abnormal data.

[0084] Use the affinity matrix A based on Hamming distance similarity composed of affinity elements sim =(a ij ), through iterative calculation of the following formula, the ship tracks can be automatically clustered:

[0085]

[0086] In the formula, y i and(·) i represents the weight and corresponding vector value of the ith track, t represents the number of iterative calculations in a clustering, and the maximum value of t is limited to 1000. Generally, the weight does not change after the update within the maximum value, that is, the iteration is completed. y is the vector composed of the weights of all track feature data. The weights of all N tracks in the first clustering are initialized to That is, for each track feature:

[0087]

[0088] Where, N T Represents the total number of unclustered samples during the Tth clustering, that is, N1=N during the first clustering T=1.

[0089] After multiple iterations of calculation, when the weight of each track does not change, the weight of some track feature data is higher than 0, while the weight of the rest of the track data is 0, which means that these track data have a high degree of aggregation compared with all other data and can be removed as a group of regular tracks. After the algorithm removes multiple groups of regular tracks through multiple cycles of clustering, abnormal tracks will remain and cannot be clustered. Track data that cannot be clustered or has too few clusters are marked as abnormal tracks to monitor ship track anomalies. Figure 3As shown in Figure 2, the tracks that cannot be clustered are abnormal tracks. The specific situation of one track is as follows: Figure 3 As shown in , there is a big difference from the smooth curve of ships sailing on normal routes.

[0090] Example 2

[0091] This embodiment provides a track anomaly detection clustering system based on Hamming distance similarity, which is used to implement the track anomaly detection clustering method based on Hamming distance similarity in the above embodiment 1. The system includes: a track data acquisition module, a data preprocessing module, a feature extraction module, a hash coding module, a Hamming distance calculation module, an affinity matrix calculation module, an iterative clustering module, and an abnormal track output module;

[0092] In this embodiment, the track data acquisition module is used to acquire track data in a set area and a specified time period;

[0093] In this embodiment, the data preprocessing module is used to perform data preprocessing on the track data;

[0094] In this embodiment, the feature extraction module is used to extract track time series information features from the preprocessed track data based on the long short-term memory network LSTM autoencoder to obtain track features;

[0095] In this embodiment, the hash coding module is used to convert the track features into binary hash codes based on an iterative quantized hash algorithm;

[0096] In this embodiment, the Hamming distance calculation module is used to calculate the Hamming distance between the hash codes of each track feature;

[0097] In this embodiment, the affinity matrix calculation module is used to calculate the affinity matrix of the track features based on the Hamming distance similarity;

[0098] In this embodiment, the iterative clustering module is used to perform iterative clustering of track features by using a dominating set clustering algorithm;

[0099] In this embodiment, the abnormal track output module is used to output track features that cannot be clustered as abnormal tracks during the iterative process of clustering.

[0100] Example 3

[0101] This embodiment provides a storage medium, which may be a storage medium such as ROM, RAM, disk, or CD. The storage medium stores one or more programs. When the program is executed by a processor, the track anomaly detection clustering method based on Hamming distance similarity of Embodiment 1 is implemented.

[0102] Example 4

[0103] This embodiment provides a computing device, which can be a desktop computer, a laptop computer, a smart phone, a PDA handheld terminal, a tablet computer or other terminal device with a display function. The computing device includes a processor and a memory, and the memory stores one or more programs. When the processor executes the program stored in the memory, the track anomaly detection clustering method based on Hamming distance similarity of Example 1 is implemented.

[0104] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A clustering method for track anomaly detection based on Hamming distance similarity, characterized in that: The steps include: Obtain track data in the set area and specified time period and perform data preprocessing; Based on the long short-term memory network LSTM autoencoder, the track time series information features of the pre-processed track data are extracted to obtain the track features; Based on the iterative quantized hash algorithm, the track features are converted into binary hash codes, the Hamming distance between the hash codes of each track feature is calculated, and the affinity matrix of the track features based on the Hamming distance similarity is calculated; The track features are iteratively clustered using the dominating set clustering algorithm. During the iterative clustering process, the track features that cannot be clustered are output as abnormal tracks.

2. The track anomaly detection clustering method based on Hamming distance similarity according to claim 1 is characterized in that: The step of obtaining the track data in the set area and the specified time period and performing data preprocessing specifically includes: Obtain the geographic location information of all ships in the set area and specified time period, set filtering conditions, delete and clean the data; The AIS data are sorted by ship number and arranged in chronological order to obtain the track data of each ship in the specified time period.

3. The track anomaly detection clustering method based on Hamming distance similarity according to claim 1, characterized in that: The method of extracting track time series information features from the preprocessed track data based on the long short-term memory network LSTM autoencoder specifically includes: The preprocessed track data is input into the long short-term memory network LSTM network, mapped to the hidden layer to generate track hidden features, and then it is restored from the hidden layer through the long short-term memory network LSTM to map it back to track data of the same dimension, and the track hidden features are extracted, which can be specifically expressed as: D = encoder (P) P'=decoder(D) Among them, P represents the preprocessed track data, D is the track hidden feature, P' is the restored track data, encoder(.) and decoder(.) are the encoder and decoder LSTM networks of the autoencoder respectively.

4. The track anomaly detection clustering method based on Hamming distance similarity according to claim 1, characterized in that: The long short-term memory network LSTM autoencoder uses the mean square error loss function to calculate the error between the input and output of each track data. The mean square error loss function is expressed as: Among them, d is the characteristic dimension of the track at each time point, n is the number of time points set in a track, and p q and p q ' are the original input data at the qth time point of the track and the data restored by the encoder.

5. The track anomaly detection clustering method based on Hamming distance similarity according to claim 1, characterized in that: The track features are converted into binary hash codes based on the iterative quantized hash algorithm, including: The principal component analysis of the track characteristics is performed to obtain the first k eigenvectors, and the orthogonal rotation matrix R is randomly generated to balance the information in the eigenvectors. The quantization error Q is minimized to perform iterative quantization optimization, which is specifically expressed as: Where D represents the track characteristics, W represents the number of feature vectors, and B is the hash code of the track data.

6. The track anomaly detection clustering method based on Hamming distance similarity according to claim 1, characterized in that: Calculate the affinity matrix of track features based on Hamming distance similarity, including: The number of different digits of the hash code of each track feature is calculated as the Hamming distance. The affinity in the affinity matrix based on the Hamming distance similarity is expressed as: Among them, a ij is the affinity between track features i and j, Ham ij is the Hamming distance between two track features i and j, and σ is the preset parameter for affinity calculation.

7. The track anomaly detection clustering method based on Hamming distance similarity according to claim 6 is characterized in that: Iterative clustering of track features is performed using the dominating set clustering algorithm, including: Use the affinity matrix A based on Hamming distance similarity composed of affinity elements sim =(a ij ), perform iterative clustering of track features, expressed as: Among them, y i and(·) i represents the weight and corresponding vector value of the ith track, t represents the number of iterative calculations in one clustering, and y is the vector composed of the weights of all track feature data; The weights of all N tracks in the first clustering are initialized as For each track feature: Among them, N T Represents the total number of unclustered samples in the Tth clustering. When T=1 is the first clustering, N1=N.

8. A clustering system for track anomaly detection based on Hamming distance similarity, characterized in that: The system is used to implement the track anomaly detection clustering method based on Hamming distance similarity as described in any one of claims 1 to 7, comprising: a track data acquisition module, a data preprocessing module, a feature extraction module, a hash coding module, a Hamming distance calculation module, an affinity matrix calculation module, an iterative clustering module, and an abnormal track output module; The track data acquisition module is used to acquire track data in a set area and a specified time period; The data preprocessing module is used to perform data preprocessing on the track data; The feature extraction module is used to extract track time series information features from the preprocessed track data based on the long short-term memory network LSTM autoencoder to obtain track features; The hash coding module is used to convert the track characteristics into binary hash codes based on an iterative quantized hash algorithm; The Hamming distance calculation module is used to calculate the Hamming distance between the hash codes of each track feature; The affinity matrix calculation module is used to calculate the affinity matrix of the track features based on the Hamming distance similarity; The iterative clustering module is used to perform iterative clustering of track features through a dominating set clustering algorithm; The abnormal track output module is used to output track features that cannot be clustered as abnormal tracks during the iterative process of clustering.

9. A computer-readable storage medium storing a program, characterized in that: When the program is executed by a processor, the track anomaly detection clustering method based on Hamming distance similarity as described in any one of claims 1 to 7 is implemented.

10. A computing device comprising a processor and a memory for storing a program executable by the processor, characterized in that: When the processor executes the program stored in the memory, the track anomaly detection clustering method based on Hamming distance similarity as described in any one of claims 1 to 7 is implemented.

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