Data processing method, device, electronic device and machine-readable storage medium

Abstract

The present application provides a data processing method, device, electronic device and machine-readable storage medium, the method comprising: for any abnormal behavior to be analyzed, determining the target vehicle type associated with the abnormal behavior to be analyzed according to the behavior type of the abnormal behavior to be analyzed; obtaining the target monitoring data of the first vehicle from the monitoring data to be analyzed according to the target vehicle type; determining a second vehicle with at least one target abnormal feature from the first vehicle according to the target monitoring data; the target abnormal feature is an abnormal feature associated with the behavior type of the abnormal behavior to be analyzed; performing trajectory analysis on the second vehicle according to the monitoring data of the second vehicle to determine the designated place associated with the abnormal behavior to be analyzed. The method can realize the automatic analysis of the place associated with the abnormal behavior and improve the efficiency of locating the place associated with the abnormal behavior.

Description

Technical Field

[0001] The present application relates to the field of image recognition and spatiotemporal analysis technology, and in particular to a data processing method, device, electronic device and machine-readable storage medium. Background Art

[0002] With the changes of the times and the advancement of science and technology, there are more and more means of transportation around people, which has brought great convenience to everyone's life.

[0003] In a narrow sense, transportation refers to all man-made devices used for human travel or transportation, such as bicycles, cars, motorcycles, trains and ships.

[0004] The existing technology has the problem of low efficiency in locating places associated with abnormal behaviors. Summary of the invention

[0005] In view of this, the present application provides a data processing method, device, electronic device and machine-readable storage medium.

[0006] According to a first aspect of an embodiment of the present application, there is provided a data processing method, including:

[0007] For any abnormal behavior to be analyzed, determining the target vehicle type associated with the abnormal behavior to be analyzed according to the behavior type of the abnormal behavior to be analyzed;

[0008] According to the target vehicle type, acquiring target monitoring data of a first vehicle from the monitoring data to be analyzed; the type of the first vehicle is the target vehicle type;

[0009] According to the target monitoring data, a second vehicle having at least one target abnormal feature is determined from the first vehicles; the target abnormal feature is an abnormal feature associated with the behavior type of the abnormal behavior to be analyzed;

[0010] Based on the monitoring data of the second transportation tool, a trajectory analysis is performed on the second transportation tool to determine a designated location associated with the abnormal behavior to be analyzed.

[0011] According to a second aspect of an embodiment of the present application, there is provided a data processing device, including:

[0012] A first determining unit is configured to determine, for any abnormal behavior to be analyzed, a target vehicle type associated with the abnormal behavior to be analyzed according to a behavior type of the abnormal behavior to be analyzed;

[0013] an acquisition unit, configured to acquire target monitoring data of a first vehicle from the monitoring data to be analyzed according to the target vehicle type; the type of the first vehicle is the target vehicle type;

[0014] A second determining unit is configured to determine, based on the target monitoring data, a second vehicle having at least one target abnormal feature from the first vehicle; the target abnormal feature is an abnormal feature associated with a behavior type of the abnormal behavior to be analyzed;

[0015] The processing unit is configured to perform trajectory analysis on the second transportation tool based on the monitoring data of the second transportation tool, and determine a designated location associated with the abnormal behavior to be analyzed.

[0016] According to a third aspect of an embodiment of the present application, there is provided an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0017] Memory, used to store computer programs;

[0018] The processor is used to implement the method provided in the first aspect when executing the program stored in the memory.

[0019] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the method provided in the first aspect is implemented.

[0020] According to a fifth aspect of an embodiment of the present application, a computer program is provided. The computer program is stored in a computer-readable storage medium, and when a processor executes the computer program, the processor is prompted to execute the method provided in the first aspect.

[0021] The data processing method of the embodiment of the present application determines the target vehicle type associated with the abnormal behavior to be analyzed based on the behavior type of the abnormal behavior to be analyzed, obtains the target monitoring data of the first vehicle of this type from the monitoring data to be analyzed, and determines a second vehicle with at least one target abnormal feature from the first vehicle based on the target abnormal feature associated with the behavior type of the abnormal behavior to be analyzed and the target monitoring data, and then, based on the monitoring data of the second vehicle, performs trajectory analysis on the second vehicle to determine the designated place associated with the abnormal behavior to be analyzed, thereby realizing automatic analysis of the places associated with the abnormal behavior and improving the efficiency of locating the places associated with the abnormal behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1It is a flowchart of a data processing method provided in an embodiment of the present application;

[0023] Figure 2 It is a flowchart of determining a designated place associated with the abnormal behavior to be analyzed provided by an embodiment of the present application;

[0024] Figure 3 It is a structural diagram of an abnormal behavior analysis system provided in an embodiment of the present application;

[0025] Figure 4 is a structural schematic diagram of a data processing device provided in an embodiment of the present application;

[0026] Figure 5 It is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0028] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0029] In order to enable those skilled in the art to better understand the technical solutions provided by the embodiments of the present application and to make the above-mentioned purposes, features and advantages of the embodiments of the present application more obvious and understandable, the technical solutions in the embodiments of the present application are further described in detail below in conjunction with the accompanying drawings.

[0030] It should be noted that the serial numbers of the steps in the embodiments of the present application do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0031] See also Figure 1 , is a flow chart of a data processing method provided in an embodiment of the present application, such as Figure 1 As shown, the data processing method may include the following steps:

[0032] Step S100: For any abnormal behavior to be analyzed, determine the target vehicle type associated with the abnormal behavior to be analyzed according to the behavior type of the abnormal behavior to be analyzed.

[0033] In the embodiments of the present application, it is taken into consideration that different types of abnormal behaviors are usually associated with different types of means of transportation. For example, the means of transportation associated with abnormal construction waste dumping behaviors are usually trucks, muck trucks, etc.; the means of transportation associated with abnormal cross-border goods transportation behaviors are usually speedboats, fishing boats, etc.

[0034] Accordingly, based on the different types of transportation vehicles associated with abnormal behavior, monitoring data can be used to determine the specific time and / or spatial characteristics of the designated transportation vehicles, to analyze the abnormal behavior and determine the designated places associated with the abnormal behavior, that is, places where abnormal behavior may exist.

[0035] In the embodiment of the present application, for any abnormal behavior to be analyzed, the type of vehicle associated with the abnormal behavior to be analyzed (referred to as the target vehicle type in this article) can be determined according to the behavior type of the abnormal behavior to be analyzed.

[0036] For example, assuming that the abnormal behavior to be analyzed is abnormal construction waste dumping behavior, the target vehicle type may include a truck and / or a muck truck.

[0037] Step S110: acquiring target monitoring data of a first vehicle from monitoring data to be analyzed according to the type of the target vehicle; the type of the first vehicle is the type of the target vehicle.

[0038] In an embodiment of the present application, when the target vehicle type associated with the abnormal behavior to be analyzed is determined, monitoring data (referred to as target monitoring data herein) of a vehicle of the target vehicle type (referred to as the first vehicle herein) can be obtained from the monitoring data to be analyzed based on the target vehicle type.

[0039] Exemplarily, the data to be analyzed may include monitoring data of a preset area within a preset time range.

[0040] Exemplarily, the preset area and the preset time range may be set according to requirements.

[0041] For example, assuming that it is necessary to analyze the specified abnormal behavior in area A within the time range of T1 to T2, the target monitoring data is the monitoring data of the first transportation tool acquired in area A within the time range of T1 to T2.

[0042] Step S120: determining, based on the target monitoring data, a second vehicle having at least one target abnormal characteristic from the first vehicles; the target abnormal characteristic is an abnormal characteristic associated with the behavior type of the abnormal behavior to be analyzed.

[0043] In the embodiments of the present application, it is considered that vehicles associated with abnormal behaviors usually also have some abnormal characteristics.

[0044] Taking the vehicle as an example of the means of transportation associated with abnormal behavior, abnormal features may include but are not limited to no license plate, blocked license plate, facial obstruction at night (such as lowering the sun visor when traveling at night, the driver and / or the passenger in the front passenger seat wearing a mask, etc.), and nocturnal activity (traveling at night (such as 10:00 pm to 3:00 am) and not traveling at other times, which can be determined based on the timestamp information in the monitoring data).

[0045] Accordingly, when the target monitoring data is acquired in the above manner, a vehicle (referred to as the second vehicle in this article) having at least one target abnormal feature can be determined from the first vehicle based on the acquired target monitoring data and the abnormal features associated with the behavior type of the abnormal behavior to be analyzed (referred to as the target abnormal features in this article).

[0046] Step S130: performing trajectory analysis on the second transportation tool based on the monitoring data of the second transportation tool to determine a designated location associated with the abnormal behavior to be analyzed.

[0047] In an embodiment of the present application, when the second means of transportation is determined in the manner described above, trajectory analysis can be performed on the second means of transportation based on the monitoring data of the second means of transportation included in the target monitoring data to obtain the spatial characteristics of the trajectory of each second means of transportation, and then determine the designated place associated with the abnormal behavior to be analyzed.

[0048] It can be seen that in Figure 1 In the method flow shown, the target vehicle type associated with the abnormal behavior to be analyzed is determined based on the behavior type of the abnormal behavior to be analyzed, the target monitoring data of the first vehicle of this type is obtained from the monitoring data to be analyzed, and based on the target abnormal feature associated with the behavior type of the abnormal behavior to be analyzed and the target monitoring data, a second vehicle with at least one target abnormal feature is determined from the first vehicle. Then, based on the monitoring data of the second vehicle, the trajectory of the second vehicle is analyzed to determine the designated place associated with the abnormal behavior to be analyzed, thereby realizing automatic analysis of the place associated with the abnormal behavior and improving the efficiency of locating the place associated with the abnormal behavior.

[0049] In some embodiments, Figure 2As shown, in step S130, based on the monitoring data of the second transportation tool, the trajectory analysis of the second transportation tool is performed to determine the designated place associated with the abnormal behavior to be analyzed, which can be achieved by the following steps:

[0050] Step S131, determining the target trajectory intersection point of the second vehicle according to the monitoring data of the second vehicle;

[0051] Step S132: Determine a designated location associated with the abnormal behavior to be analyzed based on the target trajectory intersection point.

[0052] Exemplarily, when the second means of transportation is determined in the manner described above, the trajectory of each second means of transportation can be determined based on the monitoring data of the second means of transportation included in the target monitoring data, and the trajectory intersection point of the second means of transportation can be determined based on the trajectory of each second means of transportation, and the trajectory intersection point (referred to as the target trajectory intersection point in this article) that meets the specified conditions among the trajectory intersection points of the second means of transportation can be determined.

[0053] In one example, for any target trajectory intersection point, a proportion of second vehicles passing through the target trajectory intersection point to the total number of second vehicles exceeds a first threshold.

[0054] For example, for any trajectory point, the higher the proportion of second vehicles passing through the trajectory point (i.e., the ratio of second vehicles passing through the trajectory point to the total number of second vehicles), the probability that the trajectory point is associated with the abnormal behavior to be analyzed will generally be higher, and thus, the probability of locating the designated place associated with the abnormal behavior to be analyzed based on the trajectory point will also be correspondingly higher.

[0055] Accordingly, for any intersection point of the trajectories of the second transportation means, the proportion of the second transportation means passing through the intersection point of the trajectories may be counted.

[0056] For example, assuming that the total number of second vehicles is 100, and the number of second vehicles passing through the track intersection point A is 90, then the proportion of second vehicles passing through the track intersection point A is 90% (90 / 100*100%=90%).

[0057] Exemplarily, for any trajectory intersection point, when the proportion of second vehicles passing through the trajectory intersection point exceeds a preset proportion threshold (referred to as the first threshold herein), the trajectory intersection point can be determined as a target trajectory intersection point.

[0058] In another example, for any target trajectory intersection point, the proportion of second vehicles whose frequency of passing the target trajectory intersection point exceeds the second threshold to the total number of second vehicles exceeds a third proportion.

[0059] Exemplarily, for any trajectory point of any second vehicle, if the frequency of the second vehicle passing through the trajectory point is low, then the trajectory point may be a trajectory point passed by the second vehicle, rather than a trajectory point passed when performing the abnormal behavior to be analyzed. Thus, for a certain trajectory point, even if the proportion of second vehicles passing through the trajectory point is high, if the frequency of most of the second vehicles passing through the trajectory point is very low, then the probability of locating the designated place associated with the abnormal behavior to be analyzed based on the trajectory point will still be low.

[0060] Exemplarily, the frequency of the second vehicle passing through a certain trajectory point may be a ratio of the number of first statistical periods in which the second vehicle passes through the trajectory point within a second statistical period to the number of first statistical periods included in the second statistical period.

[0061] For example, taking the second statistical period as a week and the first statistical period as a natural day, for any trajectory point, the second vehicle 1 passes through the trajectory point 3 days in a week, and the frequency of the second vehicle 1 passing through the trajectory point is 3 / 7.

[0062] Therefore, when determining the target trajectory intersection point among the trajectory intersection points of the second transportation means, in addition to considering the proportion of the second transportation means passing through the trajectory intersection point, the frequency of the second transportation means passing through the trajectory intersection point may also be considered.

[0063] Exemplarily, for any trajectory intersection point, when the frequency of second vehicles passing through the trajectory intersection point exceeds a preset threshold (which may be called the second threshold) and the proportion of the total number of second vehicles exceeds a preset proportion threshold (which may be called the third threshold), the trajectory intersection point may be determined as the target trajectory intersection point.

[0064] Exemplarily, the first threshold and the third threshold may be the same or different.

[0065] For example, assuming that the monitoring data to be analyzed includes one week's monitoring data, the second threshold is 3 / 7, and the third threshold is 80%, then for any trajectory intersection point of the second transportation tool, if the frequency of passing through the trajectory intersection point exceeds 3 / 7 (that is, passing through the trajectory intersection point more than 3 days in a week) accounts for more than 80%, the trajectory intersection point can be determined as the target trajectory intersection point.

[0066] Exemplarily, when the target trajectory intersection point is determined in the above manner, the designated place associated with the abnormal behavior to be analyzed can be determined based on the determined target trajectory intersection point.

[0067] In one example, determining the designated location associated with the abnormal behavior to be analyzed based on the target trajectory intersection point may include:

[0068] For any target trajectory intersection point, if there is a place of the target place type within the preset distance range of the target trajectory intersection point, the place is determined as the designated place associated with the abnormal behavior to be analyzed; wherein the target place type is the place type associated with the behavior type of the abnormal behavior to be analyzed.

[0069] For example, it is considered that different abnormal behaviors are usually associated with different types of places.

[0070] For example, the types of places associated with abnormal construction waste dumping behavior may include construction sites or large abandoned vacant lots.

[0071] The types of places associated with abnormal cross-border goods transportation behavior may include docks or ports.

[0072] Accordingly, when the target trajectory intersection point is determined in the above manner, it can be determined whether there is a place of the target place type within a preset distance range (which can be set according to the actual scenario) of the target trajectory intersection point based on the place type associated with the behavior type of the abnormal behavior to be analyzed (referred to as the target place type in this article).

[0073] In the case that there is a place of the target place type within the preset distance range of the target trajectory intersection point, the place may be determined as the designated place associated with the abnormal behavior to be analyzed.

[0074] It should be noted that, for any target trajectory intersection point, if there is no place of the target place type within the preset distance range of the target trajectory intersection point, the target trajectory intersection point can be determined as the trajectory point along which the second vehicle passes when performing the abnormal behavior to be analyzed (i.e., not the starting trajectory point or the ending trajectory point); alternatively, the target trajectory intersection point can be further analyzed based on other strategies, the specific implementation of which is not limited in this application.

[0075] In order to enable those skilled in the art to better understand the technical solutions provided by the embodiments of the present application, the technical solutions provided by the embodiments of the present application are described below with reference to specific examples.

[0076] See also Figure 3 , which is a structural diagram of an abnormal behavior analysis system provided in this embodiment, such as Figure 3 As shown, the abnormal behavior analysis system may include an input and output module, an experience management module, a vehicle analysis module, and a time-space analysis module. Among them:

[0077] 1.1. The input and output module is used to provide interactive functions to establish automated analysis tasks and output the analyzed designated location information.

[0078] Exemplarily, the automated analysis task may include information such as the behavior type of the abnormal behavior to be analyzed, the analysis time range (ie, the above-mentioned preset time range), and the analysis space range (ie, the above-mentioned preset area).

[0079] 1.2. The experience management module is used to manage the types of transportation, abnormal characteristics, and location types associated with various types of abnormal behaviors.

[0080] Exemplarily, the vehicle types may include, but are not limited to, various types of land vehicles (such as cars, vans, vans, trucks, dump trucks, etc.) and water vehicles (such as speedboats, fishing boats, oil tankers, cargo ships, etc.).

[0081] For example, for abnormal construction waste dumping behavior, the associated vehicle types may include trucks or muck trucks, etc.; the associated abnormal features may include being active at night and hiding during the day; and the associated location types may include construction sites or large abandoned vacant lots, etc.

[0082] For abnormal cross-border item transportation behaviors, the associated types of transportation may include speedboats or fishing boats, etc.; the associated abnormal features may include hiding during the day and coming out at night, covering the face at night, etc.; the associated types of places may include docks or ports, etc.

[0083] For abnormal fresh food production behavior, the associated vehicle types may include trucks or vans, etc.; the associated abnormal features may include hiding during the day and coming out at night, facial obstruction at night, no license plate, license plate obstruction, etc.; the associated place types may include farmhouses, etc.

[0084] 1.3. The vehicle analysis module can be used for vehicle matching and abnormal feature analysis.

[0085] Exemplarily, the vehicle analysis module can match the associated vehicle type with the experience management module based on the input behavior type of the abnormal behavior to be analyzed, and filter out the matching vehicle group T (the vehicles included in the vehicle group are the first vehicle mentioned above) from the monitoring data to be analyzed.

[0086] Exemplarily, the vehicle analysis module may analyze all monitoring data of the vehicle group T to screen out a vehicle group T' (the vehicles included in the vehicle group are the second vehicles mentioned above) having one or more abnormal characteristics.

[0087] Among them, abnormal characteristics may include but are not limited to one or more of the following characteristics: hiding during the day and coming out at night, facial obstruction at night, no license plate, license plate obstruction, etc.

[0088] Exemplarily, the above monitoring data can be obtained through the IoT perception big data platform.

[0089] Among them, the IoT perception big data platform can realize the storage and management of transportation vehicle collection data (captured pictures, license plate information, transportation vehicle type, appearance characteristics, capture location, capture time, etc.) and IoT point information (including point name, installation location, place, area and longitude and latitude, etc.).

[0090] 1.4. The spatiotemporal analysis module can be used to perform trajectory analysis on the vehicle group T' and determine the designated locations associated with the abnormal behavior to be analyzed.

[0091] Exemplarily, the spatiotemporal analysis module can analyze the trajectory collection points between all vehicles [T1', T2', T3', ... Tx'] in the vehicle group T' to analyze the high-frequency trajectory intersection point combination B [B1, B2, B3, ... Bx] (i.e., the above-mentioned target trajectory intersection point).

[0092] For example, assuming that there are 50 vehicles in T', within the analysis time range, the proportion of vehicles in T' that pass through trajectory point B1 with a frequency exceeding the second threshold exceeds the third threshold (such as 80%), then B1 is a high-frequency trajectory intersection point.

[0093] Exemplarily, when the high-frequency trajectory intersection point combination B is determined, the geographic information data is combined to analyze whether there is a place of the target place type within a preset distance range (which can be set) of each high-frequency trajectory intersection point. If so, the place is determined as the designated place associated with the abnormal behavior to be analyzed.

[0094] For example, for abnormal cross-border goods transportation behavior, if analysis shows that there is a dock or port within a preset distance range of the high-frequency trajectory intersection point, the dock or port can be determined as the designated place associated with the abnormal behavior to be analyzed.

[0095] For abnormal fresh product production behavior, if it is analyzed that there is a farmhouse within a preset distance range of the high-frequency trajectory intersection point, the farmhouse can be determined as a designated place associated with the abnormal behavior to be analyzed.

[0096] The following is an explanation with reference to specific examples.

[0097] Assume that the system creates a new abnormal behavior analysis task, sets the abnormal behavior to abnormal fresh product production, the analysis time range to January 1, 2022 to February 1, 2022, and the analysis space range to area B.

[0098] The abnormal behavior analysis system can obtain all transportation data collected in Area B from January 1, 2022 to February 1, 2022 from the IoT perception big data platform.

[0099] The abnormal behavior analysis system can match the first vehicle from the acquired vehicle data according to the vehicle type (truck or van, etc.) associated with the behavior type of the abnormal behavior to be analyzed, assuming that it includes 30 trucks or vans (i.e., obtaining the above-mentioned vehicle group T).

[0100] The abnormal behavior analysis system can screen out vehicles with at least one abnormal feature (such as being active at night and hiding during the day) from the above 30 trucks or vans based on the abnormal features associated with the behavior type of the abnormal behavior to be analyzed, assuming that there are 5 trucks or vans (that is, the above vehicle group T' is obtained).

[0101] The abnormal behavior analysis system can perform trajectory analysis on the five vehicles and obtain high-frequency trajectory intersection points.

[0102] Assuming that four vehicles are found to pass a remote trajectory point multiple times (for example, every night within seven days), the remote trajectory point can be determined as a high-frequency trajectory intersection point. Combined with geographic information data, the rural residences within a certain range around the trajectory point can be analyzed as designated places associated with the abnormal behavior to be analyzed.

[0103] The method provided by the present application is described above. The device provided by the present application is described below:

[0104] See also Figure 4 , is a structural diagram of a data processing device provided in an embodiment of the present application, such as Figure 4 As shown, the data processing device may include:

[0105] The first determining unit 410 is configured to determine, for any abnormal behavior to be analyzed, a target vehicle type associated with the abnormal behavior to be analyzed according to the behavior type of the abnormal behavior to be analyzed;

[0106] The acquisition unit 420 is configured to acquire target monitoring data of the first vehicle from the monitoring data to be analyzed according to the type of the target vehicle; the type of the first vehicle is the type of the target vehicle;

[0107] The second determining unit 430 is configured to determine, based on the target monitoring data, a second vehicle having at least one target abnormal feature from the first vehicle; the target abnormal feature is an abnormal feature associated with the behavior type of the abnormal behavior to be analyzed;

[0108] The processing unit 440 is configured to perform a trajectory analysis on the second transportation tool based on the monitoring data of the second transportation tool, and determine a designated location associated with the abnormal behavior to be analyzed.

[0109] In some embodiments, the processing unit 440 is specifically configured to determine the target trajectory intersection point of the second vehicle based on the monitoring data of the second vehicle; and determine the designated place associated with the abnormal behavior to be analyzed based on the target trajectory intersection point.

[0110] In some embodiments, for any target trajectory intersection point, a proportion of the second vehicles passing through the target trajectory intersection point to the total number of the second vehicles exceeds a first threshold;

[0111] or,

[0112] For any target trajectory intersection point, the proportion of second vehicles whose frequency of passing the target trajectory intersection point exceeds the second threshold to the total number of second vehicles exceeds the third threshold.

[0113] In some embodiments, the processing unit 440 is specifically configured to, for any target trajectory intersection point, if there is a place of the target place type within a preset distance range of the target trajectory intersection point, determine the place as the designated place associated with the abnormal behavior to be analyzed; wherein the target place type is a place type associated with the behavior type of the abnormal behavior to be analyzed.

[0114] See also Figure 5 , is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. The electronic device may include a processor 501, a communication interface 502, a memory 503 and a communication bus 505. The processor 501, the communication interface 502 and the memory 503 communicate with each other through the communication bus 505. Among them, the memory 503 stores a computer program; the processor 501 can execute the data processing method described above by executing the program stored in the memory 503.

[0115] The memory 503 mentioned herein may be any electronic, magnetic, optical or other physical storage device that may contain or store information, such as executable instructions, data, etc. For example, the memory 503 may be: RAM (Radom Access Memory), volatile memory, non-volatile memory, flash memory, storage drive (such as hard disk drive), solid state drive, any type of storage disk (such as optical disk, DVD, etc.), or similar storage media, or a combination thereof.

[0116] The present application also provides a computer-readable storage medium storing a computer program, such as Figure 5 The computer program may be stored in the memory 503. Figure 5 The processor 501 in the electronic device shown executes to implement the data processing method described above.

[0117] The present application also provides a computer program, which is stored in a computer-readable storage medium, such as Figure 5 The memory 503 in the memory, and when the processor executes the computer program, it prompts the processor 501 to execute the data processing method described above.

[0118] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including 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 device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0119] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A data processing method, characterized in that: include: For any abnormal behavior to be analyzed, determining the target vehicle type associated with the abnormal behavior to be analyzed according to the behavior type of the abnormal behavior to be analyzed; According to the target vehicle type, acquiring target monitoring data of a first vehicle from the monitoring data to be analyzed; the type of the first vehicle is the target vehicle type; According to the target monitoring data, a second vehicle having at least one target abnormal feature is determined from the first vehicles; the target abnormal feature is an abnormal feature associated with the behavior type of the abnormal behavior to be analyzed; Based on the monitoring data of the second transportation tool, a trajectory analysis is performed on the second transportation tool to determine a designated location associated with the abnormal behavior to be analyzed.

2. The method according to claim 1, characterized in that The step of performing trajectory analysis on the second transportation tool based on the monitoring data of the second transportation tool to determine a designated location associated with the abnormal behavior to be analyzed includes: Determining a target trajectory intersection point of the second transportation vehicle based on the monitoring data of the second transportation vehicle; According to the target trajectory intersection point, a designated place associated with the abnormal behavior to be analyzed is determined.

3. The method according to claim 2, characterized in that For any of the target trajectory intersection points, a proportion of the second vehicles passing through the target trajectory intersection point to the total number of the second vehicles exceeds a first threshold; or, For any of the target trajectory intersection points, the proportion of the second vehicles whose frequency of passing the target trajectory intersection point exceeds the second threshold to the total number of the second vehicles exceeds the third threshold.

4. The method according to claim 2, characterized in that: Determining the designated place associated with the abnormal behavior to be analyzed based on the target trajectory intersection point includes: For any target trajectory intersection point, if there is a place of the target place type within the preset distance range of the target trajectory intersection point, the place is determined as the designated place associated with the abnormal behavior to be analyzed; wherein the target place type is the place type associated with the behavior type of the abnormal behavior to be analyzed.

5. A data processing device, characterized in that: include: A first determining unit is configured to determine, for any abnormal behavior to be analyzed, a target vehicle type associated with the abnormal behavior to be analyzed according to a behavior type of the abnormal behavior to be analyzed; an acquisition unit, configured to acquire target monitoring data of the first vehicle from the monitoring data to be analyzed according to the target vehicle type; The type of the first vehicle is the type of the target vehicle; A second determining unit is configured to determine, based on the target monitoring data, a second vehicle having at least one target abnormal feature from the first vehicle; the target abnormal feature is an abnormal feature associated with a behavior type of the abnormal behavior to be analyzed; The processing unit is configured to perform trajectory analysis on the second transportation tool based on the monitoring data of the second transportation tool, and determine a designated location associated with the abnormal behavior to be analyzed.

6. The device according to claim 5, characterized in that The processing unit is specifically configured to determine a target trajectory intersection point of the second vehicle based on the monitoring data of the second vehicle; and determine a designated location associated with the abnormal behavior to be analyzed based on the target trajectory intersection point.

7. The device according to claim 6, characterized in that For any of the target trajectory intersection points, a proportion of the second vehicles passing through the target trajectory intersection point to the total number of the second vehicles exceeds a first threshold; or, For any of the target trajectory intersection points, the proportion of the second vehicles whose frequency of passing the target trajectory intersection point exceeds the second threshold to the total number of the second vehicles exceeds the third threshold.

8. The device according to claim 6, characterized in that The processing unit is specifically configured to, for any target trajectory intersection point, if there is a place of the target place type within a preset distance range of the target trajectory intersection point, determine the place as a designated place associated with the abnormal behavior to be analyzed; wherein the target place type is a place type associated with the behavior type of the abnormal behavior to be analyzed.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, for implementing the method according to any one of claims 1 to 4 when executing a program stored in a memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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