Electronic map-based visual data research and judgment method and system, medium and product

Abstract

A kind of electronic map-based visual data research and judgment method, system, medium and product, it is related to data processing technical field.In the method, the multi-source heterogeneous data of internet of things sensing device is acquired, heterogeneous identifier is mapped to main identifier, and space coordinates and time stamp are extracted to construct space-time anchor point index table;Region circle selection and time parameter are received on electronic map, data is retrieved based on the index table, generate multiple space-time annotation data sets;With main identifier as key, intersection operation is carried out on each data set, and space-time information is extracted to generate collision result data set;According to space coordinates, point marking is carried out on map and data binding relationship is established, and selected operation is responded to show associated record.Implementation of the technical solution provided in the present application can improve the research and judgment accuracy of visual data in complex multi-region association scenario.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, system, medium, and product for visual data analysis based on electronic maps. Background Technology

[0002] With the rapid development of IoT technology and smart city construction, a large number of front-end sensing devices have been deployed in cities, including video surveillance systems, intelligent access control systems, vehicle checkpoint systems, and WiFi probes. These devices generate massive amounts of sensing data every day, forming a digital mirror of city operations. This multi-source heterogeneous data can record the activity trajectories of people, vehicles, and other target objects, playing a vital role in public safety, urban management, and emergency command. To adapt to the needs of different application scenarios, the analysis and judgment capabilities of IoT sensing data are showing a diversified development trend. Its accuracy and efficiency in complex spatiotemporal scenarios are crucial for ensuring the reliability of analysis results and their practical application value.

[0003] Currently, the analysis methods for IoT sensing data mainly rely on traditional database query techniques. These techniques involve retrieving and filtering data collected by sensing devices based on conditions such as time, location, and object identifiers to obtain target information. This conditional query-based analysis method can identify the activity records of target objects within a specific spatiotemporal range and has been widely used in the field of sensing data analysis. Some advanced systems have also incorporated Geographic Information System (GIS) technology, enabling the display of sensing data distribution within a single area on an electronic map.

[0004] However, due to the complexity of real-world application scenarios and the diversity of analytical needs, data analysis scenarios are becoming increasingly complex and varied. In practical applications, analysis methods based on conditional queries struggle to accurately identify target objects active across regions, and the accuracy of multi-regional correlation analysis is difficult to guarantee. Especially when searching for target objects that simultaneously appear in multiple different regions, existing methods are prone to target object matching biases or even omissions when handling multi-regional data correlations because the query results from multiple regions need to be compared and cross-verified. This reduces the accuracy of analysis of visualized data in complex multi-regional correlation scenarios. Summary of the Invention

[0005] This application provides a method, system, medium, and product for visual data analysis based on electronic maps, which can improve the accuracy of visual data analysis in complex multi-regional scenarios.

[0006] The first aspect of this application provides a method for visual data analysis based on electronic maps, including: Acquire multi-source heterogeneous data collected by front-end IoT sensing devices; The heterogeneous identifiers in the multi-source heterogeneous data are mapped to the main identifiers, and the spatial coordinates and timestamps in the multi-source heterogeneous data are extracted to construct a spatiotemporal anchor index table with the main identifiers, the spatial coordinates and the timestamps as indexes. The system receives selection operations and time range parameters for multiple target areas on an electronic map, retrieves data records that fall within each target area and corresponding time range parameter based on the spatiotemporal anchor index table, and determines the spatiotemporal label of the area corresponding to the data record to obtain multiple spatiotemporal labeled datasets. The spatiotemporal labeled dataset is intersected using the primary identifier as the key to obtain a target object set. The spatiotemporal labels, spatial coordinates, and timestamps corresponding to each primary identifier in the target object set are extracted to generate a collision result dataset. Based on the spatial coordinates in the collision result dataset, points are marked on the electronic map, and a binding relationship is established between the marked points and the collision result dataset. In response to the selection operation of the marked points, the associated target data records are retrieved and displayed according to the binding relationship.

[0007] By adopting the above technical solution, the problem of difficulty in identifying target objects caused by inconsistent data identification formats from different sensing devices is solved by mapping heterogeneous identifiers in multi-source heterogeneous data to primary identifiers. A spatiotemporal anchor index table is constructed by extracting spatial coordinates and timestamps, establishing a complete spatiotemporal association between target objects. When a user selects multiple target areas on an electronic map and sets time range parameters, the system quickly retrieves data records falling within each target area and corresponding time range based on the spatiotemporal anchor index table, and assigns a spatiotemporal label to each data record, forming a spatiotemporal labeled dataset. By performing intersection operations on multiple spatiotemporal labeled datasets using the primary identifier as the key, target objects appearing simultaneously in multiple different areas can be accurately identified, overcoming the matching bias and identification omission problems that easily occur in the comparison of multi-area data using traditional conditional query methods. The target object set obtained from the intersection operation, along with its corresponding spatiotemporal labels, spatial coordinates, and timestamps, constitutes a collision result dataset, completely recording the activity trajectory information of target objects in multiple areas. Based on the spatial coordinates of the collision result dataset, points are marked on the electronic map, and a binding relationship is established between the marked points and the collision result dataset. This allows users to intuitively retrieve and display associated target data records by selecting the marked points, achieving a complete closed loop from multi-regional correlation analysis to visualization. Compared to traditional methods that can only identify target object activity records within a single region or require multiple manual queries and comparisons, this application has higher accuracy and efficiency in identifying and analyzing cross-regional activity target objects, improving the accuracy of judgment of visualized data in complex multi-regional correlation scenarios.

[0008] Optionally, the multi-source heterogeneous data is cleaned and standardized to obtain standard data records; based on the pre-stored mapping relationship, the heterogeneous identifiers corresponding to the same judgment object in the standard data records are associated with the main identifier of the judgment object; the spatial coordinates and timestamps in each of the standard data records are extracted, and the main identifier, the spatial coordinates and the timestamps are bound and stored as spatiotemporal anchors; the spatiotemporal anchors are stored in the database to construct the spatiotemporal anchor index table.

[0009] Optionally, multiple target areas in the electronic map are acquired; for each target area, a drawing operation of the target area is received, and a geographic boundary polygon is determined based on the vertex coordinate sequence of the closed contour line generated by the drawing operation; a time range parameter is received, and the spatial range of the geographic boundary polygon and the time interval of the time range parameter are used as dual constraints to query the spatiotemporal anchor point index table to obtain a data record; the vertex coordinate sequence and the time range parameter are encapsulated into a regional spatiotemporal label and appended to the data record to obtain the spatiotemporal annotation dataset corresponding to the target area; the spatiotemporal annotation datasets corresponding to each target area are used as the multiple spatiotemporal annotation datasets.

[0010] Optionally, perform a set intersection operation on each of the spatiotemporal labeled datasets to obtain a set of target objects that exist simultaneously in all spatiotemporal labeled datasets; for each master identifier in the set of target objects, extract the regional spatiotemporal label, spatial coordinates, and timestamp carried by the corresponding data record; use the extracted regional spatiotemporal label, spatial coordinates, and timestamp to jointly constitute the spatiotemporal occurrence attribute of the master identifier; combine each master identifier in the set of target objects and its corresponding spatiotemporal occurrence attribute to generate the collision result dataset.

[0011] Optionally, spatial coordinates are extracted from the collision result dataset, and point markers are generated at the corresponding positions on the electronic map according to the spatial coordinates; the point markers are associated with the corresponding primary identifiers in the collision result dataset to obtain the binding relationship between the point markers and the collision result dataset; in response to a selection operation on the point markers, the target primary identifier corresponding to the selected point markers is extracted according to the binding relationship; based on the target primary identifiers, the associated target data records are retrieved and displayed on the electronic map.

[0012] Optionally, extract multiple target spatial coordinates and target timestamps corresponding to the same primary identifier from the collision result dataset; sort the multiple target spatial coordinates by time according to the time order of the target timestamps to obtain the coordinate sequence of the same primary identifier; connect adjacent spatial coordinates in the coordinate sequence in sequence to generate the activity trajectory line corresponding to the same primary identifier; and overlay and render the activity trajectory line onto the electronic map for display.

[0013] Optionally, based on the geographical distance between adjacent spatial coordinates in the coordinate sequence and the time difference of the corresponding target timestamps, the movement feature parameters of the same primary identifier are calculated; the movement feature parameters are compared with a preset abnormal behavior threshold; if the movement feature parameters are greater than or equal to the preset abnormal behavior threshold, an early warning message for the same primary identifier is generated; the early warning message is associated with the activity trajectory line on the electronic map and highlighted.

[0014] Secondly, embodiments of this application provide a visualization data analysis system based on electronic maps. The visualization data analysis system based on electronic maps includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the visualization data analysis system based on electronic maps to perform the method described in the first aspect and any possible implementation thereof.

[0015] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on an electronic map-based visualization data analysis system, cause the electronic map-based visualization data analysis system to perform the method described in the first aspect and any possible implementation thereof.

[0016] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on an electronic map-based visualization data analysis system, causes the electronic map-based visualization data analysis system to execute the method described in the first aspect and any possible implementation thereof.

[0017] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: By adopting the above technical solution, the problem of difficulty in identifying target objects caused by inconsistent data identification formats from different sensing devices is solved by mapping heterogeneous identifiers in multi-source heterogeneous data to primary identifiers. A spatiotemporal anchor index table is constructed by extracting spatial coordinates and timestamps, establishing a complete spatiotemporal association between target objects. When a user selects multiple target areas on an electronic map and sets time range parameters, the system quickly retrieves data records falling within each target area and corresponding time range based on the spatiotemporal anchor index table, and assigns a spatiotemporal label to each data record, forming a spatiotemporal labeled dataset. By performing intersection operations on multiple spatiotemporal labeled datasets using the primary identifier as the key, target objects appearing simultaneously in multiple different areas can be accurately identified, overcoming the matching bias and identification omission problems that easily occur in the comparison of multi-area data using traditional conditional query methods. The target object set obtained from the intersection operation, along with its corresponding spatiotemporal labels, spatial coordinates, and timestamps, constitutes a collision result dataset, completely recording the activity trajectory information of target objects in multiple areas. Based on the spatial coordinates of the collision result dataset, points are marked on the electronic map, and a binding relationship is established between the marked points and the collision result dataset. This allows users to intuitively retrieve and display associated target data records by selecting the marked points, achieving a complete closed loop from multi-regional correlation analysis to visualization. Compared to traditional methods that can only identify target object activity records within a single region or require multiple manual queries and comparisons, this application has higher accuracy and efficiency in identifying and analyzing cross-regional activity target objects, improving the accuracy of judgment of visualized data in complex multi-regional correlation scenarios. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the visualization data analysis method based on electronic maps disclosed in an embodiment of this application; Figure 2 This is another flowchart illustrating the visualization data analysis method based on electronic maps disclosed in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of a system provided in an embodiment of this application.

[0019] Explanation of reference numerals in the attached drawings: 301, Central Processing Unit; 302, Read-Only Memory; 303, Random Access Memory; 304, Bus; 305, Input / Output Interface; 306, Input Section; 307, Output Section; 308, Storage Section; 309, Communication Section; 310, Driver; 311, Removable Media. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0021] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0022] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0023] This application provides a method for visual data analysis based on electronic maps, referring to... Figure 1 , Figure 1 This is a flowchart illustrating a method for visual data analysis based on electronic maps, provided in an embodiment of this application. The method is applied to a system, which refers to a hardware and software integrated platform capable of executing a program for visual data analysis based on electronic maps. The system can execute a program for visual data analysis based on electronic maps. The method includes steps 101 to 105, as follows: Step 101: Acquire multi-source heterogeneous data collected by front-end IoT sensing devices.

[0024] In this application embodiment, multi-source heterogeneous data refers to sensing data collected from different types of front-end IoT sensing devices with different structures and formats. For example, facial recognition data collected by a video surveillance system includes facial feature codes and captured images; card swipe data collected by a smart access control system includes access card numbers and access control points; vehicle passage data collected by a vehicle checkpoint system includes license plate numbers and vehicle images; and wireless signal data collected by a WiFi probe includes device MAC addresses and signal strength. These data differ in terms of identifier type, data fields, and storage format.

[0025] Specifically, sensing data is acquired from front-end IoT sensing devices deployed in various locations throughout the city via data interfaces. This includes facial recognition records from video surveillance systems (containing face ID, capture time, camera ID, face image path, and latitude / longitude coordinates), card swipe records from smart access control systems (containing access card number, swipe time, access point ID, person's name, and access point location coordinates), vehicle passage records from vehicle checkpoint systems (containing license plate number, vehicle passage time, checkpoint ID, vehicle image path, and checkpoint location coordinates), and detection records from WiFi probes (containing device MAC address, detection time, probe ID, signal strength, and probe location coordinates). All acquired sensing data is stored in a database in chronological order, retaining complete field information and data source identifiers of the original data, providing foundational data support for subsequent data processing and analysis.

[0026] Step 102: Map the heterogeneous identifiers in the multi-source heterogeneous data to the primary identifiers, and extract the spatial coordinates and timestamps from the multi-source heterogeneous data to construct a spatiotemporal anchor index table indexed by the primary identifiers, spatial coordinates, and timestamps.

[0027] In this embodiment, the spatiotemporal anchor point index table refers to a data index structure constructed using a primary identifier, spatial coordinates, and a timestamp as index fields, used to represent the activity anchor points of a target object at different times and spatial locations. For example, a record in the spatiotemporal anchor point index table contains the primary identifier P0001, spatial coordinates of longitude 116.4 degrees and latitude 39.9 degrees, a timestamp of May 20, 2024, at 10:23:15, and the face number F123 of the original data source camera A. This record indicates that the target object with the number P0001 was detected by the front-end device at this spatiotemporal location.

[0028] Specifically, a mapping rule table between heterogeneous identifiers and primary identifiers is established. Heterogeneous identifiers such as face IDs, access card numbers, license plate numbers, and device MAC addresses are mapped to a unified primary identifier using an association algorithm. Each sensing record in the multi-source heterogeneous data is traversed, reading the heterogeneous identifiers within the record and searching for the corresponding primary identifier according to the mapping rule table. If the primary identifier does not exist, a new primary identifier is generated and a mapping relationship is established. The latitude and longitude spatial coordinates and collection timestamp are extracted from each sensing record. The primary identifier, spatial coordinates, timestamp, original data type, and original identifier are written as index fields into a spatiotemporal anchor index table. A hash index is built for the spatiotemporal anchor index table based on the primary identifier, a spatial grid index based on the spatial coordinates, and a time series index based on the timestamp, forming a multi-dimensional index structure that supports efficient retrieval.

[0029] In one possible implementation, heterogeneous identifiers in multi-source heterogeneous data are mapped to primary identifiers, and spatial coordinates and timestamps are extracted from the multi-source heterogeneous data to construct a spatiotemporal anchor index table indexed by primary identifiers, spatial coordinates, and timestamps. Specifically, this includes steps 1021-1023, as follows: Step 1021: Clean and standardize the multi-source heterogeneous data to obtain standard data records; based on the pre-stored mapping relationship, associate the heterogeneous identifiers corresponding to the same judgment object in the standard data records with the main identifier of the judgment object.

[0030] In this embodiment of the application, a standard data record refers to a data record that has been cleaned and standardized, and has a uniform format and standardized fields.

[0031] Specifically, the process involves cleaning multi-source heterogeneous data, removing duplicate records, filtering invalid records with missing key fields, and correcting abnormal timestamps and spatial coordinates outside the geographic range. The cleaned data is then standardized by converting the time formats of different data sources to a standard year-month-day-hour-minute-second format, converting various spatial coordinates to latitude and longitude coordinates, and converting identification fields such as equipment numbers to a standardized encoding format, resulting in standardized data records with uniform format. The mapping relationship between heterogeneous identifiers and the primary identifier of the analysis object is retrieved from a pre-stored mapping table. The standard data records are traversed, and heterogeneous identifiers are extracted from each record. The corresponding primary identifier of the analysis object is then found in the mapping table. The found primary identifier is associated and bound to the standard data record by adding a primary identifier field and populating it with the corresponding primary identifier value, thus linking different heterogeneous identifiers corresponding to the same analysis object to a unified primary identifier.

[0032] Step 1022: Extract the spatial coordinates and timestamps from each standard data record, bind and store the main identifier, spatial coordinates and timestamps as spatiotemporal anchors; store the spatiotemporal anchors in the database and construct a spatiotemporal anchor index table.

[0033] In this embodiment of the application, a spatiotemporal anchor point refers to a data unit consisting of a master identifier, spatial coordinates, and a timestamp, which is used to represent the activity record of the object being analyzed at a specific time and spatial location.

[0034] Specifically, all standard data records are traversed, and spatial coordinates and timestamp fields are extracted from each record. The longitude and latitude values ​​from the spatial coordinates are read, and the year, month, day, hour, minute, and second information from the timestamp is retrieved. The primary identifier, extracted spatial coordinates, and timestamp are bound and combined. A spatiotemporal anchor data structure is created, using the primary identifier as the identifier field, the spatial coordinates as the location field, and the timestamp as the time field. The values ​​of these three fields are stored in the same data unit to form the spatiotemporal anchor. The generated spatiotemporal anchors are written to the database. A hash index is built based on the primary identifier to support fast searching of all activity records of a specific analysis object; a two-dimensional spatial index is built based on the spatial coordinates to support regional range queries; and a time series index is built based on the timestamp to support time range queries, forming a spatiotemporal anchor index table that supports multi-dimensional retrieval.

[0035] Step 103: Receive the selection operation and time range parameters for multiple target areas on the electronic map, retrieve the data records that fall within each target area and the corresponding time range parameters based on the spatiotemporal anchor index table, and determine the spatiotemporal label of the area corresponding to the data record to obtain multiple spatiotemporal labeled datasets.

[0036] In this embodiment of the application, a regional spatiotemporal label refers to attribute tagging information used to identify the target region and time range to which a data record belongs. For example, a regional spatiotemporal label includes region number A, region name (crime scene), time start point (May 20, 2024, 22:00), and time end point (May 20, 2024, 23:00). This label indicates that the data record belongs to the crime scene region numbered A and the collection time is between 22:00 and 23:00.

[0037] Specifically, the electronic map interface receives the coordinate sequence of boundary points of the polygonal regions selected by the user, with each polygon corresponding to a target region. It receives the start and end times of each target region as time range parameters. For each target region, data is retrieved sequentially, querying the spatiotemporal anchor index table for data records whose spatial coordinates fall within the polygonal region and whose timestamps are within the time range. It determines whether the spatial coordinates of the data record are inside the polygon by calculating the number of intersections between rays emanating from the coordinate point in any direction and the polygon boundary using the ray casting method. If the number of intersections is odd, the point is inside the polygon. It also determines whether the timestamp of the data record is greater than or equal to the start time and less than or equal to the end time. Regional spatiotemporal labels are added to data records that meet the spatial and temporal conditions. The labels include the target region number, region name, start time of the time range, and end time of the time range. Data records with regional spatiotemporal labels are grouped according to their respective target regions, forming multiple spatiotemporally labeled datasets.

[0038] In one possible implementation, the process involves receiving selection operations and time range parameters for multiple target areas on an electronic map, retrieving data records that fall within each target area and corresponding time range parameter based on a spatiotemporal anchor index table, and determining the spatiotemporal label of the area corresponding to the data record to obtain multiple spatiotemporal labeled datasets. Specifically, this includes steps 1031-1033, as follows: Step 1031: Obtain multiple target areas in the electronic map; for each target area, receive the drawing operation of the target area, and determine the geographic boundary polygon based on the vertex coordinate sequence of the closed contour line generated by the drawing operation.

[0039] In this embodiment of the application, a geographic boundary polygon refers to a polygonal region boundary formed by connecting multiple vertex coordinates, generated based on a closed outline drawn on an electronic map.

[0040] Specifically, the system displays the geographic area to be selected on the electronic map interface. It receives the user's drawing operations on the target area, acquiring multiple location points clicked or dragged sequentially on the electronic map. The screen pixel coordinates of each location point are converted to corresponding geographic latitude and longitude coordinates. Following the chronological order of the user's drawing operations, the latitude and longitude coordinates of each location point are recorded sequentially, forming a vertex coordinate sequence. The system checks if the start and end points of the vertex coordinate sequence coincide; if not, the start point coordinates are automatically added to the end of the sequence to ensure a closed outline. Based on the vertex coordinate sequence, a geographic boundary polygon is constructed by connecting adjacent vertex coordinates with straight line segments, sequentially connecting all vertices to form a closed polygon boundary. The generated geographic boundary polygon is associated with the target area, recording the vertex coordinate sequence of the polygon and the target area identifier. The drawing operation and polygon generation process are repeated for multiple target areas to obtain the geographic boundary polygons corresponding to each target area.

[0041] Step 1032: Receive the time range parameter, and use the spatial range of the geographic boundary polygon and the time interval of the time range parameter as dual constraints to query the spatiotemporal anchor point index table to obtain the data record.

[0042] In this embodiment of the application, dual constraints refer to query conditions that simultaneously use the spatial range of the geographic boundary polygon and the time range parameter of the time interval to limit the data.

[0043] Specifically, the system receives user-input time range parameters and obtains the start and end times to form the time interval. It extracts vertex coordinate sequences from the geographic boundary polygon, calculates the geographic area covered by the polygon, and determines the minimum longitude, maximum longitude, minimum latitude, and maximum latitude of the polygon's minimum bounding rectangle. It accesses the spatiotemporal anchor point index table, first performing preliminary filtering using spatial indexing to query spatiotemporal anchor points whose spatial coordinates fall within the minimum bounding rectangle. For the initially filtered spatiotemporal anchor points, it performs precise spatial judgment, calculating the positional relationship between the spatial coordinates of the anchor points and the geographic boundary polygon. Using the ray casting method, it emits rays from the spatial coordinates in any direction and counts the number of intersections with the polygon boundary; if the number of intersections is odd, the coordinate point is located inside the polygon. For spatiotemporal anchor points that meet the spatial conditions, it performs time filtering, determining whether the timestamp of the anchor point is greater than or equal to the start time and less than or equal to the end time. Finally, it extracts the data records corresponding to spatiotemporal anchor points that simultaneously satisfy both spatial range and time interval constraints, obtaining the data records that meet the conditions.

[0044] Step 1033: Encapsulate the vertex coordinate sequence and time range parameters into a regional spatiotemporal label and append it to the data record to obtain the spatiotemporal labeled dataset corresponding to the target region; use the spatiotemporal labeled datasets corresponding to each target region as multiple spatiotemporal labeled datasets.

[0045] In this embodiment of the application, the regional spatiotemporal label refers to the identification information formed by encapsulating the vertex coordinate sequence of the target region with the time range parameter, which is used to indicate the spatial region and time range to which the data record belongs.

[0046] Specifically, the process involves reading the vertex coordinate sequence of the geographic boundary polygon corresponding to the target region and receiving the time range parameter. The longitude and latitude values ​​of each vertex in the vertex coordinate sequence are organized sequentially into spatial region description information. The start and end times in the time range parameter are organized into time interval description information. A regional spatiotemporal label data structure is created, encapsulating the spatial region description information and time interval description information into the same label object, adding a unique identifier and region name as additional attributes of the label. The retrieved data records are traversed, and the created regional spatiotemporal label is appended to the end of each data record. Data records with regional spatiotemporal labels are grouped and categorized according to their respective target regions, forming a spatiotemporal labeled dataset corresponding to that target region. The process of encapsulating regional spatiotemporal labels and labeling data records is repeated for other target regions to obtain spatiotemporal labeled datasets corresponding to each target region. All spatiotemporal labeled datasets corresponding to all target regions are combined to form a collection of multiple spatiotemporal labeled datasets.

[0047] Step 104: Perform intersection operation on the spatiotemporal labeled dataset using the primary identifier as the key to obtain the target object set, and extract the regional spatiotemporal label, spatial coordinates and timestamp corresponding to each primary identifier in the target object set to generate the collision result dataset.

[0048] In this embodiment, intersection operation refers to performing a set intersection calculation on multiple spatiotemporally labeled datasets according to their primary identifiers to obtain the target objects that appear simultaneously in all target regions. For example, spatiotemporally labeled dataset A contains primary identifiers P0001, P0002, and P0003, and spatiotemporally labeled dataset B contains primary identifiers P0001, P0003, and P0004. The intersection operation yields primary identifiers P0001 and P0003 that exist simultaneously in both dataset A and dataset B.

[0049] Specifically, the primary identifiers of all data records in each spatiotemporal labeled dataset are extracted to form a primary identifier set. An intersection operation is performed on multiple primary identifier sets. For each primary identifier in the first set, it is checked whether it exists in all other sets. If it does, the primary identifier is added to the intersection result. For each primary identifier in the intersection result, the corresponding data record is searched in each spatiotemporal labeled dataset, and the region spatiotemporal label, spatial coordinates, and timestamp are extracted. The extracted information is aggregated according to the primary identifier. Each primary identifier corresponds to multiple records, and each record contains a region spatiotemporal label and the spatial coordinates and timestamp of the primary identifier in that region. The aggregated data is written to the collision result dataset, where each primary identifier contains its spatiotemporal activity records across all target regions.

[0050] In one possible implementation, the spatiotemporal labeled dataset is intersected using the master identifier as the key to obtain the target object set. The spatiotemporal label, spatial coordinates, and timestamp corresponding to each master identifier in the target object set are then extracted to generate the collision result dataset. Specifically, steps 1041-1043 are included, as follows: Step 1041: Perform set intersection operation on each spatiotemporal labeled dataset to obtain a set of target objects that exist in all spatiotemporal labeled datasets; for each primary identifier in the target object set, extract the regional spatiotemporal label, spatial coordinates and timestamp carried by the corresponding data record.

[0051] In this embodiment of the application, the set intersection operation refers to the operation of performing intersection calculation on each spatiotemporal labeled dataset according to the primary identifier, and extracting the primary identifier and its corresponding data that appear simultaneously in all datasets.

[0052] Specifically, primary identifiers are extracted from all data records in each spatiotemporal labeled dataset, forming primary identifier sets for each dataset. An intersection operation is performed on these primary identifier sets. Each primary identifier in the first set is iterated over, and it is determined whether it exists in all other primary identifier sets. If it exists in all sets, it is added to the intersection result. The primary identifiers in the intersection result are used as the target object set. Data extraction is performed on each primary identifier in the target object set. The corresponding data record for the primary identifier is found in the first spatiotemporal labeled dataset, and the region spatiotemporal label, spatial coordinates, and timestamp are read from the data record. The same process is repeated in the second spatiotemporal labeled dataset. The region spatiotemporal label, spatial coordinates, and timestamp for each target region of the primary identifier are then extracted from the other spatiotemporal labeled datasets. All extracted region spatiotemporal labels, spatial coordinates, and timestamps are associated and stored with the primary identifier.

[0053] Step 1042: The spatiotemporal occurrence attribute of the main identifier is formed by the extracted regional spatiotemporal label, spatial coordinates and timestamp.

[0054] In this embodiment of the application, the spatiotemporal occurrence attribute refers to a set of attributes that describe the activity of the main identifier in each target area, which is composed of regional spatiotemporal labels, spatial coordinates, and timestamps.

[0055] Specifically, for each master identifier in the target object set, the regional spatiotemporal label, spatial coordinates, and timestamp are extracted from the corresponding data records in each spatiotemporal annotation dataset. The regional spatiotemporal label, spatial coordinates, and timestamp of the master identifier in the first target region are combined to form the first spatiotemporal occurrence record; the same combination is performed in the second target region to form the second spatiotemporal occurrence record. This process is repeated sequentially in other target regions. All spatiotemporal occurrence records are arranged in timestamp order to form the spatiotemporal occurrence sequence of the master identifier. Each record in the spatiotemporal occurrence sequence is encapsulated with attributes, adding descriptive fields such as target region identifier, region name, arrival time, departure time, and activity location to each record. The encapsulated spatiotemporal occurrence sequence serves as the spatiotemporal occurrence attribute of the master identifier, containing complete spatiotemporal activity information of the master identifier across all target regions.

[0056] Step 1043: Combine the primary identifiers and corresponding spatiotemporal occurrence attributes in the target object set to generate a collision result dataset.

[0057] In this embodiment of the application, the collision result dataset refers to a data set formed by combining each main identifier in the target object set and its corresponding spatiotemporal occurrence attributes, which is used to represent the judgment objects that appear simultaneously in all target areas and their activity trajectories.

[0058] Specifically, the data structure of the collision result dataset is created, including a master identifier field, a spatiotemporal occurrence attribute field, and a statistical information field. Each master identifier in the target object set is iterated through, and its identifier value and corresponding spatiotemporal occurrence attribute are read. The identifier value of the master identifier is written to the master identifier field of the collision result dataset. The spatiotemporal occurrence attribute of the master identifier is written to the spatiotemporal occurrence attribute field of the collision result dataset, which includes the regional spatiotemporal label, spatial coordinates, and timestamp of the master identifier in each target region. The statistical information of the master identifier is calculated, including the number of target regions in which it appears, the total activity duration, and the total length of the activity trajectory. The calculated statistical information is written to the statistical information field of the collision result dataset. The master identifier, its spatiotemporal occurrence attribute, and statistical information are added as a complete record to the collision result dataset. The data combination and record addition operations are repeated for other master identifiers in the target object set. An index is created for the collision result dataset, using the master identifier as the primary key to create a hash index, generating the final collision result dataset.

[0059] Step 105: Mark points on the electronic map based on the spatial coordinates in the collision result dataset, establish a binding relationship between the marked points and the collision result dataset, and in response to the selection operation of the marked points, retrieve and display the associated target data records according to the binding relationship.

[0060] In this embodiment of the application, dot marking refers to the visual element that marks the activity location of a target object on an electronic map in the form of an icon. For example, a red dot icon is displayed at the position of longitude 116.4 degrees and latitude 39.9 degrees on the electronic map. This icon indicates that a target object has been captured by a sensing device at that spatial coordinate position. Clicking the icon will display detailed activity information of the target object at that position.

[0061] Specifically, the spatial coordinate records of each primary identifier in the collision result dataset are traversed, and the latitude and longitude coordinates are converted into screen pixel coordinates on the electronic map. Icons are drawn at the corresponding pixel positions on the electronic map as markers, with different primary identifiers distinguished by different colors or shapes. A binding relationship is established between the markers and the collision result dataset, associating each marker with its corresponding primary identifier, regional spatiotemporal label, timestamp, and the storage location index of the original data record. User clicks or touches on the markers on the electronic map are monitored; when a selection is detected, the identifier of the selected marker is retrieved. The associated primary identifier is found based on the marker identifier through the binding relationship. The complete activity record of the object in each target area is read from the collision result dataset using the primary identifier. Raw perception data, such as face capture images and vehicle images, are retrieved from the database based on the storage location index of the original data record. The target object's primary identifier, activity trajectory, time series, and capture images are displayed in the electronic map's information panel or pop-up window.

[0062] In one possible implementation, points are marked on an electronic map based on the spatial coordinates in the collision result dataset, establishing a binding relationship between the marked points and the collision result dataset. In response to a selection operation on the marked points, the associated target data records are retrieved and displayed according to the binding relationship. Specifically, this includes steps 1051-1053, as follows: Step 1051: Extract the spatial coordinates from the collision result dataset and generate point labels according to the corresponding positions of the spatial coordinates on the electronic map.

[0063] In this embodiment of the application, the point marking refers to the visual marker points generated at the corresponding locations on the electronic map based on spatial coordinates, which are used to represent the activity records of the object being analyzed at that location.

[0064] Specifically, the spatiotemporal occurrence attributes of each primary identifier are read from the collision result dataset, and all spatial coordinates in the spatiotemporal occurrence attributes are extracted. The extracted spatial coordinates are deduplicated; if multiple primary identifiers appear in the same location, the spatial coordinate is retained and the occurrence count is recorded. The deduplicated list of spatial coordinates is traversed, and the longitude and latitude values ​​of each spatial coordinate are read. Based on the current display scale and projection parameters of the electronic map, the longitude and latitude values ​​are converted into screen pixel coordinates on the electronic map interface. Marker icons are drawn at the converted screen pixel coordinate locations. The marker icons can be circular, thumbtack-shaped, or custom-shaped, and their color depth or size is set according to the number of primary identifiers appearing at that location. A unique label identifier is assigned to each marker icon, and the label identifier is associated with and recorded with the corresponding spatial coordinates. All drawn marker icons are displayed as scattered points on the electronic map, and the display position of the scattered points is dynamically adjusted according to the zoom and pan operations of the electronic map.

[0065] Step 1052: Associate the scattered point labels with the corresponding primary identifiers in the collision result dataset to obtain the binding relationship between the scattered point labels and the collision result dataset; in response to the selection operation of the scattered point labels, extract the target primary identifier corresponding to the selected scattered point label based on the binding relationship.

[0066] In this embodiment of the application, the binding relationship refers to the mapping relationship established by associating the point markers with the corresponding master identifiers in the collision result dataset, which is used to represent the corresponding relationship between the point markers and the data records.

[0067] Specifically, all generated point markers are traversed, and the marker identifier and corresponding spatial coordinates of each point marker are read. Based on the spatial coordinates of the point markers, all master identifiers whose spatiotemporal occurrence attributes contain those spatial coordinates are searched in the collision result dataset. The list of found master identifiers is associated and bound with the marker identifiers of the point markers, establishing a one-to-many mapping relationship between point markers and master identifiers. The mapping relationship is stored in a binding relationship table, where records include fields such as point marker identifier, master identifier list, and spatial coordinates. User interaction operations are monitored on the electronic map, capturing user click, touch, or hover events on point markers. When a selection operation on a point marker is detected, the marker identifier of the selected point marker is obtained. The list of master identifiers corresponding to this marker identifier is searched in the binding relationship table, and the master identifier value in the list is extracted as the target master identifier. If the master identifier list contains multiple master identifiers, all master identifiers are used as the target master identifiers for subsequent processing.

[0068] Step 1053: Retrieve the associated target data records based on the target master identifier and display the target data records on the electronic map.

[0069] In this embodiment of the application, the target data record refers to a complete record containing detailed activity information and original perception data of the object being assessed, retrieved from the data source based on the target master identifier.

[0070] Specifically, the process reads the target primary identifier corresponding to the selected marker point; if multiple target primary identifiers exist, they are processed one by one. Based on the target primary identifier, the complete record corresponding to that primary identifier is queried in the collision result dataset, and the spatiotemporal occurrence attributes and statistical information are extracted from the record. From the spatiotemporal occurrence attributes, the regional spatiotemporal label, spatial coordinates, and timestamp of the primary identifier in each target area are obtained. Based on the original data source identifier in the regional spatiotemporal label, the original sensing data corresponding to the heterogeneous identifier associated with that primary identifier is queried in the original database. Multimedia resources such as face capture images, vehicle capture images, and video clips are retrieved from the original sensing data, along with structured data such as access control card swipe records and WiFi detection records. The record information in the collision result dataset is integrated with the retrieved original sensing data to form a target data record containing the primary identifier, spatiotemporal activity trajectory, original images, and detailed records. An information display panel or pop-up window is created on the electronic map to format and display the content of the target data record, showing the activity trajectory of the object under analysis, the location markers at each time point, the captured images, and the detailed record details.

[0071] In the above embodiments, basic collision result dataset generation functions were achieved through multi-source heterogeneous data association, identifier collision detection, and spatiotemporal information extraction. To further enhance the visualization capabilities of data analysis and reduce the workload of manual analysis of massive spatiotemporal data, this application also provides a visualization data analysis method based on electronic maps. This method constructs a coordinate sequence by extracting multiple target spatial coordinates and target timestamps corresponding to the same primary identifier, sequentially connecting adjacent spatial coordinates to generate activity trajectory lines, and overlaying and rendering them onto an electronic map for visualization. It also combines motion feature parameter calculation and abnormal behavior threshold comparison mechanisms to perform real-time detection of trajectory anomalies and highlight early warning information, enabling the system to more intuitively and efficiently handle the needs of behavior trajectory analysis, abnormal pattern recognition, and dynamic monitoring and early warning in complex spatiotemporal scenarios. The following section combines... Figure 2 The visualization data analysis method based on electronic maps in the embodiments of this application is described below: Please see Figure 2 This is another flowchart illustrating a visualization data analysis method based on electronic maps in this application embodiment.

[0072] Step 201: Extract the spatial coordinates and timestamps of multiple targets corresponding to the same primary identifier in the collision result dataset.

[0073] In this embodiment of the application, target spatial coordinates and target timestamps refer to the latitude and longitude coordinates of the same primary identifier appearing at different locations and their corresponding times of occurrence, extracted from the collision result dataset.

[0074] Specifically, the collision results dataset is accessed, and the primary identifier field of all data records in the dataset is read. The primary identifiers are iterated through, and the same primary identifier to be analyzed is selected as the extraction target. All associated records of this primary identifier in the collision results dataset are retrieved, and spatial coordinates and timestamps are extracted from the spatiotemporal occurrence attributes of each associated record. The longitude and latitude values ​​in the spatial coordinates are read and recorded as the target spatial coordinates of this primary identifier at a certain location. The year, month, day, hour, minute, and second information in the timestamps are read and recorded as the target timestamp corresponding to the target spatial coordinates. The spatial coordinates and timestamps of this primary identifier at other locations are extracted sequentially, forming multiple paired data sets of target spatial coordinates and target timestamps. The extracted target spatial coordinates and target timestamps are stored in key-value pairs, with the target spatial coordinates as the location attribute and the target timestamp as the time attribute, maintaining a one-to-one correspondence. The extraction results are validated for completeness, ensuring that each target spatial coordinate has a corresponding target timestamp, and data items with missing timestamps or abnormal coordinate values ​​are removed.

[0075] Step 202: Sort the spatial coordinates of multiple targets by time according to the time order of the target timestamps to obtain the coordinate sequence of the same primary identifier.

[0076] In this embodiment of the application, the coordinate sequence refers to an ordered list of spatial coordinates formed by arranging multiple target spatial coordinates with the same primary identifier in chronological order according to the target timestamp.

[0077] Specifically, the process involves reading paired data of multiple target spatial coordinates and target timestamps corresponding to the same primary identifier. The target timestamp is extracted from each pair, and the year, month, day, hour, minute, and second values ​​are parsed. All target timestamps are converted to a unified time base value, and the difference in seconds between each timestamp and the initial base time is calculated. The converted time base values ​​are sorted in ascending order using either quicksort or mergesort algorithms. Based on the sorted time base value order, the positions of the corresponding target spatial coordinates are adjusted. The target spatial coordinate corresponding to the first timestamp is placed at the beginning of the sequence, the target spatial coordinate corresponding to the second timestamp is placed in the second position, and so on, arranging all target spatial coordinates in chronological order. The sorted target spatial coordinates are then compiled into an ordered list, where the first element is the spatial coordinate of the earliest occurrence of the primary identifier, and the last element is the spatial coordinate of the latest occurrence. Index numbers are added to the coordinate sequence, assigning sequential numbers to each target spatial coordinate in the sequence starting from zero, forming a complete coordinate sequence for the same primary identifier.

[0078] Step 203: Connect adjacent spatial coordinates in the coordinate sequence in sequence to generate the activity trajectory line corresponding to the same primary identifier.

[0079] In this embodiment of the application, the activity trajectory line refers to a set of continuous line segments that represent the movement path of the same primary identifier, generated by sequentially connecting adjacent spatial coordinates in a coordinate sequence.

[0080] Specifically, the system reads the target spatial coordinates from the coordinate sequence, obtaining the longitude and latitude values ​​of the first target spatial coordinate as the trajectory starting point. It then reads the longitude and latitude values ​​of the second target spatial coordinate, connecting them with a straight line segment to form the first trajectory segment. The same process is repeated for the third target spatial coordinate, connecting them with a straight line segment to form the second trajectory segment. This process is repeated for each subsequent target spatial coordinate, connecting each coordinate with its preceding coordinate to generate a corresponding trajectory segment. All trajectory segments are then joined end-to-end according to the coordinate sequence to form a continuous active trajectory line. Line type attributes are set for the active trajectory line, defining display parameters such as line thickness, color, and style. A start marker is placed at the starting point of the active trajectory line, an end marker at the ending point, and a time node is marked in the middle of the trajectory line based on the target timestamp. Finally, the generated active trajectory line is associated with the same primary identifier to form a complete active trajectory line corresponding to that primary identifier.

[0081] Step 204: Overlay and render the activity trajectory lines onto the electronic map for display.

[0082] In this embodiment of the application, overlay rendering refers to the process of visually displaying the activity trajectory line as a layer overlaid on the electronic map.

[0083] Specifically, the process involves reading the activity trajectory lines corresponding to the same primary identifier and obtaining the spatial coordinates of the start and end points of each trajectory segment. Based on the current projection parameters and display scale of the electronic map, the longitude and latitude values ​​of each spatial coordinate in the activity trajectory line are converted into screen pixel coordinates on the electronic map interface. A new trajectory layer is created on the vector layer or overlay layer of the electronic map, and the stacking order of the trajectory layers is set so that they are above the map base. On the trajectory layer, each trajectory segment is drawn sequentially according to the converted screen pixel coordinates, and a straight line drawing algorithm is used to connect adjacent pixel coordinates into line segments. A rendering style is set for the drawn activity trajectory lines, defining the line segment width as three pixels, the color as blue, and the transparency as 80%. Marker points are drawn at key nodes of the activity trajectory lines, and the marker points use circular icons to indicate the dwell time of the primary identifier at that location. The rendered activity trajectory lines are displayed on the electronic map, maintaining the correct geographical location correspondence as the electronic map is zoomed and panned. Interactive functionality is added to the activity trajectory lines, responding to user clicks or hovering actions on the trajectory lines to display detailed information about the primary identifier.

[0084] In one possible implementation, the activity trajectory line is overlaid and rendered onto an electronic map for display. Following this step, steps 2041-2043 are further included, as follows: Step 2041: Calculate the movement feature parameters of the same primary identifier based on the geographical distance between adjacent spatial coordinates in the coordinate sequence and the time difference of the corresponding target timestamps.

[0085] In the embodiments of this application, the movement feature parameter refers to a numerical index that describes the movement pattern of the same primary identifier, calculated based on the geographical distance between adjacent spatial coordinates in the coordinate sequence and the time difference of the corresponding target timestamp.

[0086] Specifically, the process involves reading the first and second target spatial coordinates from the coordinate sequence and extracting their longitude and latitude values. The geographical distance between the two spatial coordinates is calculated using a spherical distance calculation method, treating the Earth as a sphere with a radius of 6,371 meters. The length of the great circle arc on the sphere is calculated based on the difference in longitude and latitude between the two points. The target timestamps corresponding to the first and second target spatial coordinates are read, and the time difference between the two timestamps is calculated and converted to seconds. The geographical distance is divided by the time difference to obtain the average movement speed of the primary identifier between the two locations. The geographical distances and time differences between other adjacent spatial coordinates in the coordinate sequence are calculated sequentially, and the average movement speed of each segment is calculated. The maximum, minimum, and average values ​​of all movement speeds are collected as components of the movement characteristic parameters. The azimuth change between adjacent spatial coordinates is calculated, and the turning angle is calculated based on the direction angles of the preceding and following trajectory segments. The maximum and average values ​​of the turning angles are collected and used as the direction change index of the movement characteristic parameters. The movement speed index and the direction change index are combined to form the complete movement characteristic parameters for the same primary identifier.

[0087] Step 2042: Compare the movement feature parameters with the preset abnormal behavior threshold; if the movement feature parameters are greater than or equal to the preset abnormal behavior threshold, generate a warning message with the same master identifier.

[0088] In this embodiment of the application, the warning information refers to the warning data generated when the movement feature parameter is greater than or equal to a preset abnormal behavior threshold, which is used to remind the same primary identifier of abnormal movement behavior.

[0089] Specifically, the system reads preset abnormal behavior thresholds, including speed and direction change thresholds. The speed threshold is set to 20 meters per second, meaning movement exceeding this speed is considered abnormally fast movement. The direction change threshold is set to 120 degrees, meaning movement with a turning angle exceeding this value is considered abnormal turning behavior. The system extracts the average and maximum movement speeds from the movement feature parameters and compares the average speed with the speed threshold. If the average speed is greater than or equal to the speed threshold, the main identifier is determined to have abnormal speed behavior. The system also extracts the maximum turning angle from the movement feature parameters and compares it with the direction change threshold. If the maximum turning angle is greater than or equal to the direction change threshold, the main identifier is determined to have abnormal direction behavior. When a speed or direction abnormality is detected, a warning message data structure is created. The warning message records the identifier value, abnormality type, abnormality occurrence time, and abnormal location coordinates of the same main identifier. A warning text description is generated, explaining the main identifier's movement speed in meters per second or its turning angle in degrees, and indicating the specific value exceeding the threshold. The generated warning message is stored and prepared for display.

[0090] Step 2043: Associate the warning information with the activity trajectory line on the electronic map and highlight it.

[0091] In this embodiment of the application, highlighting refers to the process of associating the warning information with the activity trajectory line on the electronic map and highlighting it with a striking visual style.

[0092] Specifically, the generated warning information is read, and the same primary identifier and abnormal location coordinates recorded in the warning information are extracted. The activity trajectory line corresponding to this primary identifier is located on the electronic map, and the location of the abnormality is marked on the activity trajectory line according to the abnormal location coordinates. The rendering style of the trajectory line segment at the abnormal location is modified: the line color is changed from blue to red, the line width is increased from three pixels to five pixels, and the line style is changed from a solid line to a blinking animation effect. A warning icon is added at the abnormal location coordinates, using an exclamation mark or warning sign graphic style, with an icon size of thirty pixels. The text content of the warning information is associated with the warning icon; when the user clicks or hovers over the warning icon, a pop-up information box displays the warning text. A blinking effect is set for the abnormal segment of the activity trajectory line, with the trajectory line color alternating between red and yellow twice per second. A list of warning information is displayed in the sidebar or bottom panel of the electronic map, showing all primary identifiers exhibiting abnormal behavior and their abnormality types. A link function is added to the list items of the warning information; clicking on a list item automatically zooms the electronic map to the activity trajectory line location of the corresponding primary identifier. Place the highlighted active trajectory line above other trajectory lines to ensure that abnormal trajectories have the highest display priority.

[0093] The following describes a visualization data analysis system based on an electronic map from the perspective of hardware processing in an embodiment of this invention. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the structure of a visualization data analysis system based on electronic maps in an embodiment of this application.

[0094] It should be noted that, Figure 3 The structure of the visualization data analysis system based on electronic maps shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0095] like Figure 3As shown, a visualization data analysis system based on electronic maps includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes according to programs stored in Read-Only Memory (ROM) 302 or programs loaded from storage section 308 into Random Access Memory (RAM) 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0096] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0097] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0098] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0099] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0100] Specifically, the visualization data analysis system based on electronic maps in this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the visualization data analysis method based on electronic maps provided in the above embodiment.

[0101] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the electronic map-based visualization data analysis system described in the above embodiments; or it may exist independently and not assembled into the electronic map-based visualization data analysis system. The storage medium carries one or more computer programs, which, when executed by a processor of the electronic map-based visualization data analysis system, enable the electronic map-based visualization data analysis system to implement the electronic map-based visualization data analysis method based on IoT data encryption transmission provided in the above embodiments.

Claims

1. A method for visual data analysis based on electronic maps, characterized in that, The method includes: Acquire multi-source heterogeneous data collected by front-end IoT sensing devices; The heterogeneous identifiers in the multi-source heterogeneous data are mapped to the main identifiers, and the spatial coordinates and timestamps in the multi-source heterogeneous data are extracted to construct a spatiotemporal anchor index table with the main identifiers, the spatial coordinates and the timestamps as indexes. The system receives selection operations and time range parameters for multiple target areas on an electronic map, retrieves data records that fall within each target area and corresponding time range parameter based on the spatiotemporal anchor index table, and determines the spatiotemporal label of the area corresponding to the data record to obtain multiple spatiotemporal labeled datasets. The spatiotemporal labeled dataset is intersected using the primary identifier as the key to obtain a target object set. The spatiotemporal labels, spatial coordinates, and timestamps corresponding to each primary identifier in the target object set are extracted to generate a collision result dataset. Based on the spatial coordinates in the collision result dataset, points are marked on the electronic map, and a binding relationship is established between the marked points and the collision result dataset. In response to the selection operation of the marked points, the associated target data records are retrieved and displayed according to the binding relationship.

2. The method according to claim 1, characterized in that, The step of mapping heterogeneous identifiers in the multi-source heterogeneous data to primary identifiers, and extracting spatial coordinates and timestamps from the multi-source heterogeneous data to construct a spatiotemporal anchor index table indexed by the primary identifiers, the spatial coordinates, and the timestamps includes: The multi-source heterogeneous data is cleaned and standardized to obtain standard data records; Based on the pre-stored mapping relationship, the heterogeneous identifiers corresponding to the same judgment object in the standard data records are associated with the main identifier of the judgment object; Extract the spatial coordinates and timestamps from each of the standard data records, and bind and store the main identifier, spatial coordinates and timestamps as spatiotemporal anchors; The spatiotemporal anchor points are stored in the database to construct the spatiotemporal anchor point index table.

3. The method according to claim 1, characterized in that, The process involves receiving selection operations and time range parameters for multiple target areas on an electronic map, retrieving data records falling within each target area and corresponding time range parameter based on the spatiotemporal anchor index table, and determining the spatiotemporal label of the area corresponding to the data record, thereby obtaining multiple spatiotemporal labeled datasets, including: Obtain multiple target areas from the electronic map; For each target region, a drawing operation for the target region is received, and a geographic boundary polygon is determined based on the vertex coordinate sequence of the closed contour line generated by the drawing operation. Receive time range parameters, and use the spatial range of the geographic boundary polygon and the time interval of the time range parameters as dual constraints to query the spatiotemporal anchor point index table to obtain data records; The vertex coordinate sequence and the time range parameter are encapsulated into a regional spatiotemporal label and appended to the data record to obtain the spatiotemporal labeled dataset corresponding to the target region; The spatiotemporal annotation datasets corresponding to each target region are used as the plurality of spatiotemporal annotation datasets.

4. The method according to claim 1, characterized in that, The process involves performing an intersection operation on the spatiotemporal labeled dataset using the primary identifier as the key to obtain a target object set. Then, the spatiotemporal labels, spatial coordinates, and timestamps corresponding to each primary identifier in the target object set are extracted to generate a collision result dataset, including: Perform a set intersection operation on each of the aforementioned spatiotemporal labeled datasets to obtain a set of target objects that exist simultaneously in all spatiotemporal labeled datasets; For each primary identifier in the target object set, extract the regional spatiotemporal label, spatial coordinates, and timestamp carried by the corresponding data record; The spatiotemporal occurrence attributes of the main identifier are constituted by the extracted regional spatiotemporal labels, spatial coordinates, and timestamps. The collision result dataset is generated by combining the main identifiers and corresponding spatiotemporal occurrence attributes in the target object set.

5. The method according to claim 1, characterized in that, The step of marking points on the electronic map based on the spatial coordinates in the collision result dataset, establishing a binding relationship between the marked points and the collision result dataset, and in response to a selection operation on the marked points, retrieving and displaying the associated target data records according to the binding relationship, includes: Extract the spatial coordinates from the collision result dataset, and generate point markers at the corresponding locations on the electronic map according to the spatial coordinates; The point markers are associated with the corresponding primary identifiers in the collision result dataset to obtain the binding relationship between the point markers and the collision result dataset. In response to the selection operation of the scattered point marker, the target primary identifier corresponding to the selected scattered point marker is extracted according to the binding relationship; Based on the target primary identifier, retrieve the associated target data record and display the target data record on the electronic map.

6. The method according to claim 1, characterized in that, After generating the collision result dataset, the following is also included: Extract the spatial coordinates and timestamps of multiple targets corresponding to the same primary identifier from the collision result dataset; According to the time order of the target timestamps, the spatial coordinates of the multiple targets are sorted by time to obtain the coordinate sequence of the same primary identifier; By sequentially connecting adjacent spatial coordinates in the coordinate sequence, an activity trajectory line corresponding to the same primary identifier is generated; The activity trajectory line is overlaid and rendered onto the electronic map for display.

7. The method according to claim 6, characterized in that, After overlaying and rendering the activity trajectory line onto the electronic map for display, the method further includes: Based on the geographical distance between adjacent spatial coordinates in the coordinate sequence and the time difference of the corresponding target timestamps, the movement characteristic parameters of the same primary identifier are calculated. The movement feature parameters are compared with a preset abnormal behavior threshold. If the movement feature parameter is greater than or equal to the preset abnormal behavior threshold, then a warning message for the same primary identifier is generated. The warning message is associated with the activity trajectory line on the electronic map and highlighted.

8. A visualization data analysis system based on electronic maps, characterized in that, The electronic map-based visualization data analysis system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the electronic map-based visualization data analysis system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is run on the electronic map-based visualization data analysis system, the electronic map-based visualization data analysis system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the electronic map-based visualization data analysis system, the electronic map-based visualization data analysis system performs the method as described in any one of claims 1-7.

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