A multi-element space-time data causal cascade mode mining method and system

Abstract

The application provides a kind of multivariate spatiotemporal data causal cascade mode mining method and system, it is related to data processing technical field.The method comprises: collecting multivariate spatiotemporal data;Multivariate spatiotemporal data is segmented to time lag perception, obtain multiple subsequences, and multiple subsequences are defined as multivariate spatiotemporal event data set;The causal cascade propagation probability between two different events is established, and the causal cascade mode with the causal cascade propagation probability greater than the set threshold is selected;According to all causal cascade modes mined, a dynamic causal cascade network is constructed;From the dynamic causal cascade network, the regularized causal cascade network is screened according to the frequency of causal cascade mode.The application combines the variable time lag granger causality inference model with the multivariate spatiotemporal cascade mode mining method, designs a variable time lag granger causal cascade mode mining algorithm, which can accurately extract the causal cascade mode in multivariate spatiotemporal data.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for mining causal cascade patterns of multivariate spatiotemporal data. Background Art

[0002] Mining spatiotemporal correlation patterns in multivariate spatiotemporal data is crucial for real-world applications such as intelligent transportation, environmental monitoring, and public safety. A special form of spatiotemporal correlation patterns is the spatiotemporal cascade model. Building on spatial proximity, this model prioritizes the temporal order of spatiotemporal events across multiple spaces, helping to better understand the underlying propagation processes within spatiotemporal data.

[0003] Existing spatiotemporal cascade pattern mining methods are primarily based on time series pattern mining. They extend the associations between patterns in sequence data to multivariate spatiotemporal data, extracting patterns that propagate across time and space based on the autocorrelation and time lag of multivariate spatiotemporal data. Based on how cascading spatiotemporal relationships are considered in pattern mining, existing methods can be broadly divided into two categories: one is spatiotemporal pattern mining methods based on statistical tests, such as ST-MST, which mines spatiotemporal patterns through frequency calculation and clustering, and analyzes valuable patterns based on significance tests. The other is cascade pattern mining methods based on cascade propagation probability and transfer tree models. For example, the CasInf algorithm, which targets traffic congestion events, mines possible cascade patterns based on temporal and spatial proximity of events and constructs a transfer tree to discover the spatiotemporal cascade network with the highest probability.

[0004] However, the aforementioned spatiotemporal cascade pattern mining algorithms ignore the intercorrelations between multiple variables. Furthermore, existing spatiotemporal cascade pattern mining methods construct cascade patterns solely based on the temporal sequence of spatiotemporal events and the frequency of pattern occurrence, ignoring the causal nature of multivariate spatiotemporal data. This results in low reliability of the mined cascade patterns. Furthermore, although pg-Causality combines the advantages of frequent pattern mining and Gaussian Bayesian learning networks, first capturing frequent evolutionary patterns in spatiotemporal data and then filtering out reliable spatiotemporal causal relationships through Bayesian learning to effectively identify spatiotemporal causal paths, the symbolic processing of multivariate spatiotemporal sequences during frequent evolutionary pattern mining cannot effectively mine causal relationships between event sequences. Summary of the Invention

[0005] To address the above issues, the present invention aims to provide a method and system for mining causal cascade patterns in multivariate spatiotemporal data. By combining a variable lag Granger causal inference model with a cascade pattern mining algorithm, a variable lag Granger causality cascade inference algorithm (VLGC-CasInf) is proposed to accurately extract causal cascade patterns in multivariate spatiotemporal data. The technical solution is as follows:

[0006] In one aspect, a method for mining causal cascade patterns in multivariate spatiotemporal data is provided, the method comprising the following steps:

[0007] S1, collect multivariate spatiotemporal data;

[0008] S2, performing time-lag-aware segmentation on the multivariate spatiotemporal data to obtain multiple subsequences, and defining the multiple subsequences as a multivariate spatiotemporal event dataset;

[0009] S3. Establish the probability of causal cascade propagation between two different events and select the causal cascade pattern whose causal cascade propagation probability is greater than the set threshold;

[0010] S4. Construct a dynamic causal cascade network based on all mined causal cascade patterns;

[0011] S5. Filter out the normalized causal cascade network from the dynamic causal cascade network according to the frequency of the causal cascade pattern.

[0012] Optionally, in step S1, the multivariate spatiotemporal data is a spatiotemporal data sequence with spatial information collected by sensors, including environmental monitoring data and traffic data.

[0013] Optionally, step S2 specifically includes:

[0014] Assume that the multivariate spatiotemporal data of P variables observed at N spatial locations within T time points is X; based on the MLVAMTS segmentation algorithm, the multivariate spatiotemporal data X is segmented into multiple subsequences with time lag awareness; the subsequence of the p-th variable at the i-th spatial position in the time interval [ts,te] in the segmentation result is defined as the event ;

[0015] Among them, 1≤i≤N, 1≤p≤P, 1≤ts <te≤T。

[0016] Optionally, in step S3, establishing the causal cascade propagation probability between two different events specifically includes:

[0017] For any two events in a multivariate spatiotemporal event dataset, based on cascade pattern mining, we use the variable-delay Granger causality method to analyze whether there is a causal relationship between the subsequences corresponding to the two events, and calculate the causal cascade propagation probability between the two events.

[0018] The calculation of the causal cascade propagation probability between two events includes cascade probability calculation and causal relationship inference.

[0019] Optionally, in step S3, selecting a causal cascade pattern whose causal cascade propagation probability is greater than a set threshold specifically includes:

[0020] For any spatiotemporal event of variable q in the time interval [ts',te'] at a spatial location j , assuming that the variable p in spatial position i is in the time interval [ts,te] of the spatiotemporal event The probability of causal cascade propagation is the largest and greater than the set threshold γ, then define A causal cascade pattern.

[0021] Optionally, step S4 specifically includes:

[0022] The spatial locations and variables of all mined causal cascade patterns are connected in series to construct a dynamic causal cascade network.

[0023] Optionally, step S5 specifically includes:

[0024] Set the frequency threshold min_freq of the causal cascade network connection, calculate the causal cascade pattern with a frequency greater than min_freq in the dynamic causal cascade network in all time periods, and use it as the normalized causal cascade pattern. Connect all normalized causal cascade patterns in series to obtain the normalized causal cascade network.

[0025] In another aspect, a multivariate spatiotemporal data causal cascade pattern mining system is provided for implementing any of the above methods, the system comprising:

[0026] Data acquisition module, used to collect multivariate spatiotemporal data;

[0027] A data processing module is used to perform time-lag aware segmentation on the multivariate spatiotemporal data to obtain multiple subsequences, and define the multiple subsequences as a multivariate spatiotemporal event dataset;

[0028] The causal cascade pattern mining module is used to establish the causal cascade propagation probability between two different events and select the causal cascade patterns whose causal cascade propagation probability is greater than the set threshold; based on all the mined causal cascade patterns, a dynamic causal cascade network is constructed; and from the dynamic causal cascade network, a regularized causal cascade network is screened out according to the frequency of the causal cascade patterns.

[0029] Optionally, the data acquisition module comprises a sensor assembly, a serial port server, a management workstation, an interface server, an information center storage server and a platform core switch.

[0030] The sensor assembly is connected to the management workstation through the serial port server, the management workstation is connected to the interface server, the interface server is connected to the information center storage server through a one-way isolation gateway, the information center storage server is connected to the platform core switch through an optical fiber, and the platform core switch is connected to a plurality of cloud computing nodes and a web server.

[0031] Optionally, the system further comprises a monitoring APP, and the monitoring APP comprises a client, a server and a system management background.

[0032] The client is used for user registration and login, online query, modification and logout; the server is used for registration and login verification and data transmission, addition, modification and deletion functions; and the system management background is used for managing a database.

[0033] In another aspect, an electronic device is provided, and the electronic device comprises:

[0034] a processor;

[0035] a memory, wherein computer readable instructions are stored on the memory, and the computer readable instructions are loaded and executed by the processor to implement the steps of the multi-element spatiotemporal data causal cascade pattern mining method.

[0036] In another aspect, a computer readable storage medium is provided, and the computer readable storage medium stores program codes, and the program codes can be called and executed by a processor to implement the steps of the multi-element spatiotemporal data causal cascade pattern mining method.

[0037] The technical solution provided by the application has at least the following beneficial effects:

[0038] In the embodiment of the application, in order to accurately extract the causal cascade pattern in the multi-element spatiotemporal data, after the time lag perception segmentation is completed, firstly, the existing single-variable cascade pattern mining algorithm is extended to the multi-element spatiotemporal data, secondly, the variable time lag Granger causal inference model is combined with the multi-element spatiotemporal cascade pattern mining method, the causal cascade propagation probability is designed, and the dynamic multi-element spatiotemporal causal cascade pattern in the multi-element spatiotemporal data is mined according to the causal cascade propagation probability, finally, the dynamic causal cascade patterns in different time intervals are integrated into a regular causal cascade network, and a whole representation of the causal cascade relationship in the multi-element spatiotemporal data is provided.

[0039] The application also constructs a multi-element space-time data causal cascade pattern mining system, which is formed by three main parts of data collection, causal cascade pattern mining and monitoring APP, and forms a complete system, thereby providing a beneficial reference for research and development in related fields. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0041] Figure 1 is a flowchart of the multi-element space-time data causal cascade pattern mining method provided by the embodiment of the present application;

[0042] Figure 2 is an example of two cascade networks in a dynamic cascade network set provided by the embodiment of the present application;

[0043] Figure 3 is a conventional causal cascade network mining flowchart provided by the embodiment of the present application;

[0044] Figure 4 is a comparison result schematic diagram of mining patterns under different causal cascade propagation probability thresholds and frequency thresholds provided by the embodiment of the present application;

[0045] Figure 5 is a running time comparison result schematic diagram of multiple algorithms on three actual data sets provided by the embodiment of the present application;

[0046] Figure 6 is a visualization schematic diagram of part of the conventional causal cascade network in the Beijing air pollution data set provided by the embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below in combination with the drawings of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the described embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0048] In the present embodiments, the words "example," "for example," and the like are used herein to mean serving as an instance or illustration. Any embodiment or design described herein as an "example" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word example is intended to present concepts in a concrete manner.

[0049] To accurately extract the causal cascade pattern in multi-variate spatio-temporal data, the present embodiments provide a multi-variate spatio-temporal data causal cascade pattern mining method. After time lag perception segmentation of the multi-variate spatio-temporal data is completed, firstly, the existing single variable cascade pattern mining algorithm is extended to the multi-variate spatio-temporal data, secondly, a variable time lag Granger causality inference model is combined with the multi-variate spatio-temporal cascade pattern mining method, a causal cascade propagation probability is designed, and the dynamic causal cascade pattern in the multi-variate spatio-temporal data is mined according to the causal cascade propagation probability, and finally, the dynamic causal cascade patterns in different time intervals are integrated into a regularized causal cascade network, thereby providing an overall representation of the causal cascade relationship in the multi-variate spatio-temporal data.

[0050] Specifically, as shown in Figure 1 the multi-variate spatio-temporal data causal cascade pattern mining method comprises the following steps:

[0051] S1, collecting multi-variate spatio-temporal data.

[0052] In the present embodiments, the multi-variate spatio-temporal data is a spatio-temporal data sequence with spatial information collected by a sensor, including environmental monitoring data, traffic data, etc.

[0053] S2, performing time lag perception segmentation on the multi-variate spatio-temporal data to obtain a plurality of sub-sequences, and defining the plurality of sub-sequences as a multi-variate spatio-temporal event data set.

[0054] Suppose that the multi-variate spatio-temporal data of P variables observed by N spatial positions within T time intervals is X, ; based on the MVLAMTS segmentation algorithm, the multi-variate spatio-temporal data X is subjected to time lag perception segmentation to divide into a plurality of sub-sequences; the sub-sequence of the pth variable of the ith spatial position in the time interval [ts, te] in the segmentation result is defined as an event . .

[0055] Wherein, 1≤i≤N, 1≤p≤P, 1≤ts<te≤T.

[0056] In the present embodiments, multi-variate causal cascade pattern mining needs to be performed on all time sub-sequences in the multi-variate spatio-temporal data, and the mining result is represented as a causal cascade graph. Table 1 introduces the symbols and meanings involved in the present embodiments.

[0057] Table 1: Symbols and meanings

[0058]

[0059] Among the vast amount of multivariate spatiotemporal events, the present invention focuses more on the important event subsequences. The selection of important events needs to be determined according to the specific practical application field. For example, in the field of air pollution in environmental monitoring, the focus is on pollution events of multiple pollutants. If the air pollution data is segmented into subsequences, If the average pollution concentration value in the sequence exceeds the pollution concentration standard value, then the subsequence is represented as a pollution event subsequence. Based on the above definition of important events, the present invention further defines all multivariate spatiotemporal important events on the location i variable p sequence as the set ,Since the changing trends of different variables in different locations are different, the number of important ,spatiotemporal events in different sequences is different.

[0060] S3. Establish the probability of causal cascade propagation between two different events, and select the causal cascade pattern whose causal cascade propagation probability is greater than the set threshold.

[0061] First, we introduce spatiotemporal cascade pattern mining algorithms and Granger causality models. Most spatiotemporal cascade pattern mining algorithms are based on propagation probabilities and transfer trees. For example, the Cascade Inference Algorithm (CasInf) for single-variable spatiotemporal cascade patterns mines accurate cascade relationships by designing propagation probabilities related to the time and location of events. It then integrates complex cascade relationships into cascade networks using transfer trees.

[0062] Given N spatial position nodes A multivariate spatiotemporal event dataset of a specific variable within T days. The algorithm first extracts a set of dynamic cascade network sets , where c t represents the cascade network extracted on day t, Figure 2 (a) and (b) show examples of cascade networks on day 1 and day 2. Based on this set of extracted dynamic cascade networks C, an implicit normalized cascade network G can be inferred.

[0063] The algorithm determines the cascade relationship based on the temporal proximity and spatial proximity of multi-temporal events, which divides the events at the node r into i The starting time ts i and in r j The starting time ts j The time difference between them is considered as the propagation time. The longer the propagation time, the faster the event is at node r. i to r jthe smaller the propagation probability between them. In addition, due to the effect of spatial distance, the direct influence of adjacent spatial locations is greater than the indirect influence of locations far apart. The time distance and spatial distance are integrated into the exponential model to describe the multivariate spatiotemporal event propagation from location node r i to r j :

[0064] (1)

[0065] where denotes the time difference of the multivariate spatiotemporal event propagation from spatial node r i to r j , d i,j denotes the spatial distance between spatial node r i and r j , a i,j denotes the transition rate, a is a set parameter, and l is the weight of spatial distance in the propagation probability.

[0066] According to the propagation probability calculated from different spatial multivariate spatiotemporal events in the tthday, the maximum propagation probability is selected as the cascade connection to obtain the cascade network c t in the tthday, and all the cascade networks in T days are integrated into a dynamic cascade network set C.

[0067] The algorithm assumes that each set of cascade relationships in the normalized cascade network G is conditionally independent, and thus, the joint probability of a set of cascades in G can be simply represented as follows:

[0068] (2)

[0069] Given the observed multivariate spatiotemporal data and a set of cascade network collections C obtained from a given time span each day, the algorithm converts the problem into a cascade network inference problem based on the likelihood of pairwise propagation of spatiotemporal distance. The goal is to find a network such that:

[0070] (3)

[0071] By optimizing equation (3), the resulting cascade network has the maximum probability to contain and explain the observed dynamic cascade network.

[0072] The Granger causality model is a method for causal inference in time series data. This causal inference model assumes that each timestamp influencing a time series is affected by other time series with a fixed time lag. However, in real applications, a series of timestamps in two time series may interact with each other with non-fixed time lags. Because traditional Granger causality inference algorithms can only analyze causal relationships between two series with fixed time lags, traditional Granger causality tests cannot correctly infer variable-lag Granger causality relationships between different time series for multivariate spatiotemporal data with dynamic time lags.

[0073] To solve this problem, the present invention uses the optimal warping path of dynamic time warping to infer variable-lag causality to resolve the fixed-lag restriction in Granger causality. The definitions involved in the present invention are as follows:

[0074] Definition 1: Time series warp alignment. The warp alignment of time series is to define a warped sequence for two time series x and y ,in Align the timestamps x(t) and y(t') in the two time series.

[0075] Definition 2: Granger causality with variable lag. For time series x and y, assume is the maximum lag (the upper limit of the lag between any two time series that are considered causal). The variable lag Granger causality defines the regression residual at time stamp t for:

[0076] (4)

[0077] in , Δ t >0 is the best alignment sequence P of x and y l The time delay constant in the constant a i 、b i and c i Make the residual set Variance Minimize. If its variance Less than the variance of the sequence y , then define x as time-varying Granger causality leading to y.

[0078] Based on the above theory, an embodiment of the present invention proposes a multivariate spatiotemporal data causal cascade pattern mining algorithm VLGC-CasInf based on variable-delay Granger causality.

[0079] First, establish the probability of causal cascade propagation between two different events. For any two events in the multivariate spatiotemporal event dataset: and ,in Based on cascade pattern mining, the subsequences corresponding to two events are analyzed by the variable time-lag Granger causality method. and Is there a causal relationship between them? And calculate the probability of causal cascade propagation between the two events, as shown in formula (5):

[0080] (5)

[0081] Formula (5) consists of two parts: cascade probability calculation and causal relationship inference.

[0082] 1) Cascade probability middle Represents two events and The difference between the occurrence time ts and ts'; d i,j represents the spatial distance between the locations i and j of two event subsequences. The cascade probability formula considers the temporal and spatial proximity of two events to determine whether there is a potential cascade propagation relationship between the two events.

[0083] 2) Causal Inference Represents two events and Whether there is a time-lag Granger causality relationship between them is calculated as follows:

[0084] (6)

[0085] in For event subsequence The variance of For collection The variance of For events The calculation formula of the Granger causal regression residual with time lag ν is:

[0086] (7)

[0087] in The maximum time lag range set for variable time lag causal inference. The variable time lag alignment sequence is defined in the causal inference process. , where Δ ν >0 is P * The time delay constant of the timestamp ν in the time interval is calculated by Δ ν Get alignment timestamp Based on the analysis of the cascade propagation relationship between events, the variable time-lag Granger causality inference model is used to further screen out events with causal relationships and explore more rigorous and accurate causal cascade relationships.

[0088] Secondly, for any spatiotemporal event of variable q in the time interval [ts',te'] at a spatial location j , assuming that the variable p in spatial position i has a spatiotemporal event in the time interval [ts,te] The probability of causal cascade propagation The maximum value is greater than the set threshold γ, then define is a causal cascade pattern. The causal cascade propagation probability is selected to be greater than the set threshold γ in order to ensure the accuracy of the cascade; selecting nodes and variables with the largest causal cascade propagation probability can ensure the reliability of the obtained causal cascade pattern and avoid the occurrence of cross-level propagation.

[0089] S4. Construct a dynamic causal cascade network based on all the mined causal cascade patterns.

[0090] After mining all the causal cascade patterns in the continuous event subsequence, the spatial locations and variables of all the causal cascade patterns are connected in series to construct a dynamic causal cascade network. Since the mining results of causal cascade patterns in different time intervals are not completely consistent, a series of dynamic causal cascade networks are finally obtained. , where K is the number of disconnected causal cascade networks in the mined multivariate spatiotemporal data.

[0091] S5. Filter out the normalized causal cascade network from the dynamic causal cascade network according to the frequency of the causal cascade pattern.

[0092] Through the above steps, the multivariate spatiotemporal causal cascade patterns of different time periods and their corresponding dynamic causal cascade networks have been mined. Based on the dynamic causal cascade network, the present invention attempts to screen out reliable regularized causal cascade propagation paths according to the frequency of causal cascades and connect them into a regularized causal cascade propagation network G. The process of screening out the regularized causal cascade network from the dynamic causal cascade network set is as follows: Figure 3 The figure includes two parts: a dynamic multivariate spatiotemporal causal cascade network and a conventionalized multivariate spatiotemporal causal cascade network. The dynamic multivariate spatiotemporal causal cascade network section presents the mined dynamic causal cascade patterns and network examples for K different time periods. Due to the different time spans and numbers of event subsequences in different time periods, the resulting dynamic causal cascade network is not completely consistent in terms of time period size, and the causal cascade connections in different time periods change dynamically.

[0093] For example, in Figure 3 In the example of the dynamic causal cascade network of Beijing's air pollution data, the causal cascade pattern in period 1 It means that the pollution event of variable 1 (PM2.5) at location 3 (Guanyuan Monitoring Station) leads to the pollution event of variable 1 (PM2.5) at location 2 (Dongsi Monitoring Station), and the causal cascade pattern in period 2 and This indicates that the pollution event of variable 1 (PM2.5) at location 3 (Guanyuan Monitoring Station) led to the pollution event of variable 2 (NO2) at location 1 (Tongzhou Monitoring Station) and the occurrence of variable 1 (PM2.5) at location 6 (Daxing Monitoring Station). In addition, the causal cascade pattern is partitioned according to its length to intuitively display the length of the causal cascade pattern, such as the cascade 2-pattern in period 2. and 3-mode .

[0094] In order to mine the implicit normalized multivariate spatiotemporal causal cascade network from the dynamically changing patterns and networks, the present invention introduces the concept of causal cascade network connection frequency, sets the frequency threshold min_freq of the causal cascade network connection, calculates the causal cascade pattern with a frequency greater than min_freq in the dynamic causal cascade network in all K time periods, and takes it as the normalized causal cascade pattern. All normalized causal cascade patterns are connected in series to obtain the normalized causal cascade network. Figure 3 As shown in the example of the normalized multivariate spatiotemporal causal cascade network in , the mined normalized causal cascade network is not a single network, but a network collection composed of multiple groups of unconnected normalized cascade networks.

[0095] The reason why the multivariate causal cascade patterns and networks mined from multivariate spatiotemporal data change dynamically over time is that many factors in space are not constant. However, the sources of many spatiotemporal events are generally fixed, so there are often strong conventional paths implied in the dynamic cascade patterns. Therefore, in the present invention, the concept of frequency is used to mine conventional multivariate causal cascade networks from these dynamic causal cascade patterns. In this embodiment of the present invention, the causal cascade connections of all events at location i with variable p and all events at location j with variable q are calculated. The frequency is as follows:

[0096] (8)

[0097] in is the number of causal cascade patterns in which events propagate from variable p in position i to variable q in position j in the dynamic causal cascade network, K is the number of causal cascade networks mined from multivariate spatiotemporal data, if If the frequency threshold min_freq is greater than the frequency threshold, then the cascade relationship from the variable q in position j to the variable p in position i in the causal cascade network is defined as a frequent causal cascade relationship, which is used as a connection in the regularized causal cascade network. The frequent causal cascade connections in the dynamic causal cascade network are screened out and the regularized causal cascade network G is constructed.

[0098] In an embodiment of the present invention, in order to accurately extract causal cascade patterns in multivariate spatiotemporal data, after completing the time-lag-aware segmentation, the existing single-variable cascade pattern mining algorithm is first extended to multivariate spatiotemporal data. Secondly, the variable-lag Granger causal inference model is combined with the multivariate spatiotemporal cascade pattern mining method, and the causal cascade propagation probability is designed to mine the dynamic multivariate spatiotemporal causal cascade patterns in the multivariate spatiotemporal data based on it. Finally, the dynamic causal cascade patterns of different time intervals are integrated into a normalized causal cascade network, providing a holistic representation of the causal cascade relationship in the multivariate spatiotemporal data.

[0099] The specific implementation steps of the VLGC-CasInf algorithm are shown in Algorithm 1.

[0100]

[0101] Algorithm 1 mainly consists of two parts: first, dynamic multivariate spatiotemporal causal cascade patterns are mined from the time-delay-aware segmentation results of multivariate spatiotemporal data, and then regularized multivariate spatiotemporal causal cascade patterns and networks are further screened on this basis. In the process of mining dynamic causal cascade patterns, important spatiotemporal events must first be scanned from the segmentation results. The complexity is O(nPN), where n is the number of subsequences of the time-delay-aware segmentation. Secondly, the causal relationship and cascade propagation probability between important spatiotemporal events are mined on the spatiotemporal event dataset. This process requires mining for each variable at each location, and the search complexity is O(mP 2 N 2 ), where m is the average number of important spatiotemporal events in each sequence; at the same time, the complexity of calculating cascade propagation probability and causal inference mainly lies in causal inference, which has a complexity of O(T / n). Therefore, the time complexity of mining dynamic multivariate spatiotemporal causal cascade patterns is O(mP 2 N 2 T / n). In the second part of mining the normalized multivariate spatiotemporal causal cascade pattern, it is necessary to calculate the frequency of each pattern in the dynamic cascade pattern, and its time complexity is O(mk), where k is the number of cascade network edges. In summary, the time complexity of the entire algorithm 1 is O((mP 2 N 2 T / n)+mk).

[0102] Correspondingly, the embodiment of the application also provides a multi-element spatio-temporal data causal cascade mode mining method and system.

[0103] A data acquisition module is configured to acquire multi-element spatio-temporal data.

[0104] A data processing module is configured to perform time lag perception segmentation on the multi-element spatio-temporal data to obtain a plurality of sub-sequences, and define the plurality of sub-sequences as a multi-element spatio-temporal event data set.

[0105] A causal cascade mode mining module is configured to establish a causal cascade propagation probability between two different events, select a causal cascade mode with a causal cascade propagation probability greater than a set threshold, construct a dynamic causal cascade network according to all the mined causal cascade modes, and filter out a regularized causal cascade network from the dynamic causal cascade network according to the frequency of the causal cascade mode.

[0106] The system of the embodiment can be used to perform Figure 1 The technical solutions of the method embodiment have similar implementation principles and technical effects, and thus will not be described here.

[0107] Further, the data acquisition module includes a sensor assembly, a serial port server, a management workstation, an interface server, an information center storage server, and a platform core switch.

[0108] The sensor assembly is connected to the management workstation through the serial port server, the management workstation is connected to the interface server, the interface server is connected to the information center storage server through a one-way isolation gateway, the information center storage server is connected to the platform core switch through an optical fiber, and the platform core switch is connected to a plurality of cloud computing nodes and a web server.

[0109] The data acquisition module is a support system responsible for sensor monitoring data acquisition, uses C++ as a development language, and embeds a plurality of IEC 60870-5 101, 102, 103, 104, Modbus, CDT, DISA, and other data communication protocols; the modeling conforms to the interface reference model, the common information model (CIM), and the component interface specification (CIS) requirements in IEC 61970, conforms to international standards, can be seamlessly integrated with each system as middleware, and realizes the access of system data such as monitoring systems, comprehensive energy management and control systems, metering, fault analysis, and alarm pushing. The system supports the access of a plurality of devices and has the parsing capability of a plurality of protocols.

[0110] The data acquisition module utilizes a two-tier architecture: a single integrated management and control system and a cloud-based centralized monitoring system. The integrated management and control system collects real-time operational monitoring data, enabling local data monitoring, historical data sampling and storage, and uploading critical real-time data to the cloud-based centralized monitoring system. The cloud-based centralized monitoring system acquires real-time monitoring data. The communication protocol between the two-tier systems can utilize the IEC 104 power standard or other protocols. Real-time data collection frequency supports sub-seconds as required by the protocol, and supports various modes including variable upload, cyclic upload, and call-up.

[0111] Furthermore, the system also includes a monitoring APP, which includes: a client, a server and a system management background.

[0112] The client is used for user registration and login, online query, modification and logout; the server is used for registration and login verification as well as data transmission, addition, modification and deletion; the system management background is used to manage the database.

[0113] The client is developed and designed using the MUI front-end framework, and HTML5, CSS, and JavaScript are used for front-end development. It is used for user registration and login, online query, modification, and logout. The server is developed using the ThinkJS server-side framework and paired with a MySQL database. It is used for registration and login verification, as well as data transmission, addition, modification, and deletion. The system management backend is developed using HTML5, CSS, and JavaScript for database management.

[0114] The monitoring app is easy to use and features a simple and beautiful interface. It provides real-time monitoring, allowing registered users to access the system from anywhere via their mobile phone. The system provides automatic query and display functions, as well as user registration information management capabilities.

[0115] The multivariate spatiotemporal data causal cascade pattern mining system constructed by the present invention consists of three main parts: data collection, causal cascade pattern mining, and monitoring APP, which are interconnected to form a complete system, providing a useful reference for research and development in related fields.

[0116] For experimental validation, we used three real-world datasets from the traffic and air pollution domains to evaluate the proposed algorithm. All experiments were conducted using the Python 3.8 compiler on a Windows 10 operating system, a 2.30 GHz processor, and 16 GB of RAM.

[0117] The three datasets include:

[0118] (1) Air-BJ: Air-BJ contains the PM2.5, PM10, NO2, and O3 concentration data of 34 air pollution monitoring stations in Beijing in 2018, with a sampling period of 1 hour.

[0119] For air pollution data, the present invention focuses on pollution events in the data. According to the Ambient Air Quality Standard (GB3095-2012), the PM2.5 pollution concentration standard is 75mg / m 3 , PM10 pollution concentration standard is 100mg / m 3 The NO2 pollution concentration standard is 40mg / m 3 , the O3 pollution concentration standard is 100mg / m 3 When the average pollutant concentration in a subsequence exceeds the pollution concentration standard, the subsequence is defined as a pollution event subsequence.

[0120] (2) PeMSD7: The PeMSD7 dataset contains traffic flow data collected by 228 sensors on roads in seven districts of California from May to June 2012, with a sampling period of 5 minutes.

[0121] (3) PeMSD8: PeMSD8 contains traffic flow data collected from July to August 2016 by 170 sensors on 8 roads in the San Bernardino area, with a sampling period of 5 minutes.

[0122] For traffic data, if the car speed is less than 20km / h, it is defined as congestion. In the segmented subsequences of the traffic dataset, the subsequence where congestion occurs is defined as the congestion event subsequence.

[0123] In order to evaluate the effectiveness of the mining algorithm described in the present invention, a time window γ w If the method predicts the next location and variable where pollution (congestion) will occur within a given time window, it is considered a hit. In this invention, the probability of occurrence is used to indicate the effectiveness of the cascade pattern discovered by the model. The calculation formula for the probability of occurrence is as follows:

[0124] (9)

[0125] in Represents the prediction data in the cascade network The number of correct predictions of the propagation relationship between Indicates the number of times that important spatiotemporal events occur in variable p at location i in the historical data. In order to verify the correctness of the cascade pattern generated by the method, the present invention defines score as an evaluation criterion.

[0126] (10)

[0127] Among them EG is the set of edges in the cascade network G, is the number of edges in the causal cascade network.

[0128] In addition, in order to analyze the operation efficiency of the algorithm, the average running time of cascade mode mining is used as an evaluation indicator.

[0129] In order to verify the effectiveness of the algorithm, the following spatiotemporal cascade pattern mining algorithm and spatiotemporal association pattern mining algorithm were selected to compare and analyze the performance of the algorithm.

[0130] 1) CasInf. This algorithm first discretizes traffic data, models the influence of traffic propagation, and uses a data-driven approach to mine cascade patterns.

[0131] 2) ST-MST: This algorithm mines temporal patterns from multivariate spatiotemporal data using information theory and uses statistical significance tests to identify valuable patterns.

[0132] 3) pg-Causality. This algorithm combines frequent pattern mining with Bayesian learning. It first mines frequent patterns and then uses a Gaussian Bayesian network-based model to encode spatiotemporal causal relationships.

[0133] 4) GC-CasInf. The proposed VLGC-CasInf algorithm replaces the variable-delay Granger causal inference part with the traditional Granger causal inference method.

[0134] In order to evaluate the impact of two important parameters introduced in the VLGC-CasInf algorithm, the causal cascade propagation probability threshold γ and the frequency threshold min_freq, on the accuracy of the mined patterns, the present invention compares the score values ​​calculated by the VLGC-CasInf algorithm on three data sets when the causal cascade propagation probability threshold is 0.2 to 0.9 (frequency threshold min_freq = 0.5) and the frequency threshold is 0.1 to 0.8 (causal cascade propagation probability threshold γ = 0.7). The experimental results are as follows: Figure 4 As shown in (a) and (b).

[0135] from Figure 4 From the results in (a), we can see that the causal cascade patterns mined by the VLGC-CasInf algorithm are significantly affected by the causal cascade propagation probability threshold γ. When the value of γ is small, the accuracy of the patterns mined by the algorithm is low, and it is difficult to mine reliable causal cascade patterns. When the value of γ is between 0.7 and 0.8, the accuracy of the mining pattern tends to be flat. Therefore, selecting γ=0.7 can mine more reliable causal cascade patterns. Selecting too large a value will filter out some reliable patterns. Figure 4From the results in (b), we can see that the causal cascade pattern mined by the VLGC-CasInf algorithm is less affected by the frequency threshold min_freq, and the impact on accuracy tends to be flat when the frequency threshold is greater than 0.5. Therefore, selecting the frequency threshold min_freq=0.5 can mine a reliable normalized causal cascade network.

[0136] In order to evaluate the accuracy of VLGC-CasInf in mining regularized multivariate spatiotemporal causal cascade patterns, the score values ​​calculated by the patterns mined by various algorithms and the overall average values ​​of the three datasets are compared. The results are shown in Table 2. In the experimental setting, the causal cascade propagation probability threshold γ = 0.7 and the frequency threshold min_freq = 0.5 are set for VLGC-CasInf and the ablation comparison algorithm GC-CasInf according to the results of parameter evaluation. In the experiment, the prediction time window γ is set for the Air-BJ dataset. w The prediction time window γ for the two traffic datasets is 6 hours. w It should be noted that since the CasInf algorithm can only mine single-variable cascade patterns, the present invention uses the CasInf algorithm to mine the Air-BJ dataset, extracting a corresponding cascade network for each variable and obtaining a set of cascade networks for all variables.

[0137] Table 2 Comparison of the scores of mining patterns of various algorithms on three real data sets

[0138]

[0139] The results in Table 2 show that VLGC-CasInf outperforms existing pattern mining algorithms on all three datasets and overall. Comparing the results on the Air-BJ dataset and two traffic datasets reveals that the CasInf algorithm, because it can only mine cascade relationships in univariate data, exhibits the worst performance on this multivariate spatiotemporal dataset. However, on the two univariate spatiotemporal datasets of traffic data, its performance is similar to that of the ST-MST algorithm. Furthermore, VLGC-CasInf, GC-CasInf, and pg-Causality, by adding causal inference mechanisms to pattern mining, are able to mine more accurate spatiotemporal causal relationships. VLGC-CasInf, which uses variable-lag causal inference, achieves significantly better accuracy than GC-CasInf, which uses traditional Granger inference, and pg-Causality, which uses Gaussian Bayesian inference, demonstrating the effectiveness of variable-lag Granger causal inference in mining causal cascade patterns. However, the CasInf and ST-MST algorithms only mine relevant patterns from spatiotemporal proximity and statistical methods, focusing on the spatiotemporal patterns of frequently interacting neighboring spaces, and ignoring the correlation between the changing trends of spatiotemporal events, resulting in large deviations in their prediction results.

[0140] To evaluate the efficiency of VLGC-CasInf, we compare its running time with three other comparison algorithms and ablation comparison algorithms on three real data sets, using the average running time of spatiotemporal pattern mining as a metric. The experimental results are as follows: Figure 5 shown.

[0141] like Figure 5 As shown, compared to the univariate spatiotemporal cascade pattern mining algorithm CasInf, VLGC-CasInf takes longer to run because it considers the changing trend of the sequence during variable-lag causal inference. Furthermore, due to the added variable-lag awareness process in variable-lag Granger causality inference, the VLGC-CasInf algorithm requires more runtime compared to the traditional Granger causality algorithm GC-CasInf. However, compared to the ST-MST and pg-Causality algorithms, the algorithm first uses cascade patterns to mine patterns that may have cascade relationships and then calculates causal patterns from them, significantly improving its efficiency. While the pg-Causality algorithm captures frequently evolving patterns through frequent pattern mining, its use of Gaussian Bayesian networks to mine causal relationships increases its computational complexity. The ST-MST algorithm not only mines frequent itemsets but also uses clustering algorithms to cluster similar itemsets. It then uses statistical methods to identify significant patterns in different spaces. Due to its complex algorithmic steps, its computational complexity is the highest and it consumes the most computational time compared to other algorithms.

[0142] In the embodiment of the present invention, an example of a normalized multivariate spatiotemporal causal cascade network is as follows: Figure 6 As shown in Figure 2. Taking the conventional multivariate spatiotemporal causal cascade network mined from the Beijing air pollution dataset as an example, we select the part of the network centered on the PM2.5 pollutants at the Temple of Heaven pollution monitoring station in the causal cascade network, as shown in Figure 2. Figure 6 As shown in (a). In each event node Z in the figure i,p In the data, locations 2, 3, 11, 13, 15, 18, and 33 represent the Temple of Heaven, Guanyuan, Gucheng, Daxing, Tongzhou, Mentougou, and South Third Ring Road pollution monitoring stations, respectively, and variables 1, 2, and 3 represent PM2.5, PM10, and NO2 pollutants, respectively. Figure 6 The causal cascade network in (a) is visualized on the actual Beijing map as follows Figure 6 The result of (b).

[0143] In summary, in order to extract dynamic spatiotemporal correlation information and normalized spatiotemporal correlation relationships in multivariate spatiotemporal data to mine the propagation process of spatiotemporal events, after completing the time-lag-aware segmentation, it is also necessary to effectively mine the spatiotemporal events obtained by segmentation. The present invention combines the variable time-lag Granger causal inference model with the cascade pattern mining algorithm to propose a multivariate spatiotemporal causal cascade pattern mining algorithm VLGC-CasInf. The algorithm first extends the existing single-variable cascade pattern mining algorithm to multivariate, and uses the idea of ​​cascade pattern mining to discover the cascade propagation probability between the occurrence time and location of spatiotemporal events between different spaces and different variables in multivariate spatiotemporal data. It further uses variable time-lag Granger causality to infer whether there is a reliable causal relationship between these spatiotemporal events, thereby mining dynamic causal cascade patterns. In order to further realize the tracing of spatiotemporal events, a normalized causal cascade pattern and a normalized causal cascade network are integrated on the basis of the dynamic causal cascade pattern.

[0144] The invention also constructs a multivariate spatiotemporal data causal cascade pattern mining system. The system consists of three main parts: data collection, causal cascade pattern mining, and monitoring APP, which are interconnected to form a complete system, providing useful reference for research and development in related fields.

[0145] In an exemplary embodiment, the present invention further provides an electronic device, comprising:

[0146] processor;

[0147] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are loaded and executed by the processor, the steps of the above-mentioned multivariate spatiotemporal data causal cascade pattern mining method are implemented.

[0148] In an exemplary embodiment, the present invention further provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the steps of the above-described method for mining causal cascade patterns in multivariate spatiotemporal data. For example, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device.

[0149] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements but also other elements not explicitly listed, or also includes elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the element.

[0150] References in the specification to "one embodiment," "an embodiment," "exemplary embodiments," "some embodiments," etc. indicate that the described embodiments may include a particular feature, structure, or characteristic, but not every embodiment necessarily includes that particular feature, structure, or characteristic. In addition, when a particular feature, structure, or characteristic is described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement that feature, structure, or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0151] It should be understood that the term "and / or" as used herein simply describes an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the related objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0152] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0153] It should be understood that the size of the sequence number of each process described above does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the application.

[0154] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the above-described apparatus embodiments are merely schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0155] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0156] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0157] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the present application that essentially contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0158] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0159] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for mining causal cascade patterns in multivariate spatiotemporal data, characterized by: The following steps are involved: S1, collect multivariate spatiotemporal data; In step S1, the multivariate spatiotemporal data is a spatiotemporal data sequence with spatial information collected by sensors, including environmental monitoring data and traffic data; S2, performing time-lag-aware segmentation on the multivariate spatiotemporal data to obtain multiple subsequences, and defining the multiple subsequences as a multivariate spatiotemporal event dataset; The step S2 specifically includes: Assumptions N The spatial location T Observed within a moment P The multivariate spatiotemporal data of variables is X ; Based on MLVAMTS segmentation algorithm for multivariate spatiotemporal data X Perform time-delay-aware segmentation and divide it into multiple subsequences; i The spatial position p variables in the time interval [ ts , te ] is defined as an event ; Where 1≤ i ≤ N , 1≤ p ≤ P , 1≤ ts < te ≤ T ; S3. Establish the probability of causal cascade propagation between two different events and select the causal cascade pattern whose causal cascade propagation probability is greater than the set threshold; In step S3, establishing the probability of causal cascade propagation between two different events specifically includes: For any two events in a multivariate spatiotemporal event dataset, based on cascade pattern mining, we use the variable-delay Granger causality method to analyze whether there is a causal relationship between the subsequences corresponding to the two events, and calculate the causal cascade propagation probability between the two events. Wherein, the calculation of the causal cascade propagation probability between two events includes cascade probability calculation and causal relationship inference; In step S3, selecting a causal cascade pattern with a causal cascade propagation probability greater than a set threshold specifically includes: For any spatial position j Variables in q In the time interval [ ts' , te' ]'s space-time events , assuming the spatial position i Variables in p In the time interval [ ts , te ]'s space-time events The probability of causal cascade propagation is the largest and greater than the set threshold γ , then define It is a causal cascade model; S4. Construct a dynamic causal cascade network based on all mined causal cascade patterns; The step S4 specifically includes: Connect the spatial locations and variables of all mined causal cascade patterns in series to construct a dynamic causal cascade network; S5. Filtering out a regularized causal cascade network from the dynamic causal cascade network according to the frequency of the causal cascade pattern; The step S5 specifically includes: Set the frequency threshold min_ of causal cascade network connections freq Calculate the frequency of dynamic causal cascade networks greater than min_ freq The causal cascade pattern is taken as the regularized causal cascade pattern, and all regularized causal cascade patterns are connected in series to obtain a regularized causal cascade network.

2. A multivariate spatiotemporal data causal cascade pattern mining system, the system being used to implement the method according to claim 1, characterized in that: The system comprises: Data acquisition module, used to collect multivariate spatiotemporal data; A data processing module is used to perform time-lag aware segmentation on the multivariate spatiotemporal data to obtain multiple subsequences, and define the multiple subsequences as a multivariate spatiotemporal event dataset; The causal cascade pattern mining module is used to establish the causal cascade propagation probability between two different events and select the causal cascade patterns whose causal cascade propagation probability is greater than the set threshold; based on all the mined causal cascade patterns, a dynamic causal cascade network is constructed; and from the dynamic causal cascade network, a regularized causal cascade network is screened out according to the frequency of the causal cascade patterns.

3. The multivariate spatiotemporal data causal cascade pattern mining system according to claim 2, characterized in that: The data acquisition module includes a sensor component, a serial port server, a management workstation, an interface server, an information center storage server, and a platform core switch; Among them, the sensor component is connected to the management workstation through the serial port server, the management workstation is connected to the interface server, the interface server is connected to the information center storage server through a one-way isolation gateway, the information center storage server is connected to the platform core switch through optical fiber, and the platform core switch is connected to multiple cloud computing nodes and web servers.

4. The multivariate spatiotemporal data causal cascade pattern mining system according to claim 2, characterized in that: The system also includes a monitoring APP, which includes: a client, a server and a system management background; The client is used for user registration and login, online query, modification and logout; the server is used for registration and login verification as well as data transmission, addition, modification and deletion; the system management background is used to manage the database.

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