A method and device for identifying vehicle overcrowding

Abstract

The present application discloses a method and device for identifying overcrowded vehicles, which relates to the field of big data analysis technology, and includes the following steps: generating a checkpoint-passing time series of each target vehicle based on the checkpoint data, and determining the effective path of each target vehicle based on the checkpoint-passing time series and the set information points; establishing a matching relationship between the road network and the base station coverage area based on the spatial position of the road network and the base station, and determining a number of target users based on the signaling metadata and the matching relationship; generating a travel path for each target user based on the signaling metadata of the target users, and calculating the similarity between each travel path and the effective path respectively; determining whether each target vehicle is overcrowded based on the similarity calculation result and the preset vehicle overcrowding condition. The present application matches the trajectory data of the van in transit with the mobile phone signaling trajectory data of the communication service operator to obtain the suspected illegal vehicles, which provides support for the traffic police department to investigate and deal with them on the spot.

Description

Technical Field

[0001] The present application relates to the field of big data analysis technology, and in particular to a method and device for identifying overcrowded vehicles. Background Art

[0002] Overcrowding in vans is a major risk source for traffic accidents. To effectively manage overcrowded vehicles, traffic police usually conduct manual checks at key intersections during peak passenger flow periods such as holidays. This method is very inconvenient and it is difficult to thoroughly check for cases where passengers get on the bus midway. Other methods of identifying overcrowded vehicles are to add additional equipment to the vehicle, such as cameras, but such methods are not very practical. On the one hand, not all vehicles are equipped with relevant equipment. On the other hand, it is easy for drivers to cover up the actual situation of overcrowding by operating the equipment. Therefore, overcrowding in vans is also highly concealed and organized, making it more difficult to investigate and deal with it by traditional means. Summary of the invention

[0003] The present application provides a method for identifying an overloaded vehicle, aiming to solve the problem of difficulty in detecting overloaded vehicles in the prior art.

[0004] To achieve the above objectives, this application adopts the following technical solutions:

[0005] A method for identifying an overloaded vehicle in the present application comprises the following steps:

[0006] Generate a checkpoint-passing time sequence of each target vehicle according to the checkpoint data, and determine a valid path of each target vehicle based on the checkpoint-passing time sequence and set information points;

[0007] Establishing a matching relationship between a road network and a base station coverage area according to the spatial positions of the road network and the base station, and determining a number of target users according to the signaling metadata and the matching relationship;

[0008] Generate a travel path for each target user according to the signaling metadata of the plurality of target users, and calculate the similarity between each travel path and the valid path respectively;

[0009] Whether each of the target vehicles is overcrowded is determined based on the similarity calculation result and a preset vehicle overcrowding condition.

[0010] Preferably, generating a checkpoint-passing time series of each target vehicle according to the checkpoint data includes:

[0011] Acquire checkpoint data, and clean abnormal data and duplicate record data in the checkpoint data;

[0012] Target vehicle data is screened out from the cleaned data, and the time when each target vehicle passes the checkpoint is sorted according to the target vehicle data.

[0013] Preferably, the determining of the effective path of each target vehicle based on the checkpoint passing time sequence and the set information points includes:

[0014] Map the checkpoint devices to the road network, and calculate the shortest travel distance between each checkpoint device through the shortest path algorithm based on the road network topology;

[0015] Extracting adjacent checkpoint-passing timestamps and corresponding checkpoint positions of each target vehicle from each checkpoint-passing time series, and determining a first driving distance of each target vehicle according to the shortest driving distance and the checkpoint position;

[0016] Calculating a first time difference according to the timestamp, and calculating a travel speed of each target vehicle according to the first travel distance and the corresponding first time difference;

[0017] The stop point of each target vehicle is determined according to the checkpoint position and the driving speed, and the effective path of each target vehicle is determined based on the stop point and the set information point.

[0018] Preferably, the step of determining the stopping point of each target vehicle according to the checkpoint position and the driving speed includes:

[0019] When the checkpoint positions are the same, determining whether each first time difference is greater than a first threshold, and if so, the checkpoint is a stop point;

[0020] When the checkpoint positions are not the same, it is determined whether the driving speeds are less than a second threshold. If so, the current position is a stop point.

[0021] Preferably, before determining a plurality of target users according to the signaling metadata of the plurality of initial users and the matching relationship, the method further includes:

[0022] The original signaling data of a plurality of initial users are acquired, and the original signaling data are preprocessed, wherein the preprocessing includes filtering and noise reduction, to obtain signaling metadata.

[0023] Preferably, the determining whether each of the target vehicles is overcrowded based on the similarity calculation result and the preset vehicle overcrowding condition includes:

[0024] It is determined whether each similarity calculation result is greater than a third threshold value. If so, the corresponding target user is used as the passenger of the corresponding target vehicle to obtain the number of passengers of each target vehicle.

[0025] A vehicle overload identification device, comprising:

[0026] A generation module, used to generate a checkpoint-passing time sequence of each target vehicle according to the checkpoint data, and determine a valid path of each target vehicle based on the checkpoint-passing time sequence and set information points;

[0027] A matching module, used to establish a matching relationship between a road network and a base station coverage area according to the spatial positions of the road network and the base station, and determine a number of target users according to the signaling metadata and the matching relationship;

[0028] A calculation module, used to generate a travel path of each target user according to the signaling metadata of the plurality of target users, and respectively calculate the similarity between each travel path and a valid path;

[0029] The judgment module is used to determine whether each of the target vehicles is overcrowded based on the similarity calculation result and the preset vehicle overcrowding condition.

[0030] Preferably, the generating module comprises:

[0031] A mapping unit, used to map the checkpoint device to the road network, and calculate the shortest travel distance between each checkpoint device by using the shortest path algorithm based on the road network topology;

[0032] An extraction unit is used to extract the adjacent checkpoint passing timestamps and corresponding checkpoint positions of each target vehicle from each checkpoint passing time series, and determine the first driving distance of each target vehicle according to the shortest driving distance and the checkpoint position;

[0033] a calculation unit, configured to calculate a first time difference according to the timestamp, and calculate a travel speed of each target vehicle according to the first travel distance and the corresponding first time difference;

[0034] The determination unit is used to determine the stop point of each target vehicle according to the checkpoint position and the driving speed, and determine the effective path of each target vehicle based on the stop point and the set information point.

[0035] An electronic device comprises a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a method for identifying an overloaded vehicle as described in any one of the above.

[0036] A computer-readable storage medium storing a computer program, wherein the computer program enables a computer to implement a method for identifying an overloaded vehicle as described in any one of the above when executed.

[0037] The present invention has the following beneficial effects:

[0038] This application adopts a multi-source data fusion approach to integrate monitoring data, license plate data, mobile phone signaling data, POI data and road network data to establish a database of on-the-go van driving trajectories, and matches the on-the-go van vehicle trajectory data with the mobile phone signaling trajectory data of the communication service operator to obtain suspected illegal vehicles, providing support for the traffic police department to investigate and punish them. At the same time, the results of the analysis of the vehicle checkpoint trajectory combined with mobile phone signaling are unique and accurate, with strong promotion and high universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0040] Figure 1 This is a first flow chart of a method for identifying an overloaded vehicle implemented in the present application;

[0041] Figure 2 This is a second flow chart of a method for identifying an overloaded vehicle implemented in the present application;

[0042] Figure 3 It is the base station mapped to the road schematic diagram in this application;

[0043] Figure 4 It is the principle diagram of target user identification in this application;

[0044] Figure 5 This is a schematic diagram of a vehicle overcrowding identification device implemented in the present application;

[0045] Figure 6 It is a schematic diagram of an electronic device for implementing a method for identifying an overloaded vehicle in the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0047] The terms "first", "second", etc. in the claims and specification of the present application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable under appropriate circumstances. This is merely a way of distinguishing objects with the same properties in the embodiments of the present application. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, so that a process, method, system, product or apparatus that includes a series of units is not necessarily limited to those units, but may include other units not expressly listed or inherent to these processes, methods, products or apparatus.

[0048] The first embodiment of the present application provides a method for identifying an overloaded vehicle. Figure 1 and Figure 2 As shown, the following steps are included:

[0049] S110, generating a checkpoint-passing time sequence of each target vehicle according to the checkpoint data, and determining a valid path of each target vehicle based on the checkpoint-passing time sequence and set information points;

[0050] S120, establishing a matching relationship between a road network and a base station coverage area according to the spatial positions of the road network and the base station, and determining a number of target users according to the signaling metadata and the matching relationship;

[0051] S130, generating a travel path for each target user according to the signaling metadata of the plurality of target users, and respectively calculating a similarity between each travel path and a valid path;

[0052] S140: Determine whether each of the target vehicles is overcrowded based on the similarity calculation result and a preset vehicle overcrowding condition.

[0053] First, the checkpoint data is obtained, where the checkpoint data refers to the vehicle driving data collected by the checkpoint equipment installed on the road, mainly including the vehicle entry and exit time, license plate number, vehicle model, color and other information, and then the obtained checkpoint data is pre-processed by data cleaning, data screening and data sorting to obtain the target vehicle data, where the target vehicle refers to a vehicle with the main purpose of carrying people. In this embodiment, the target vehicle is a van. Then, the time series of each target vehicle passing the checkpoint can be determined based on the target vehicle data. The time series not only records the timestamp of the target vehicle passing the checkpoint, but also includes the corresponding checkpoint position. The driving speed of each target vehicle can be calculated through the adjacent checkpoint timestamps and corresponding checkpoint positions of each target vehicle. When the checkpoint positions recorded adjacently are the same, if the difference between the two timestamps is greater than the first threshold value, in this embodiment, the first threshold value is 1200s, then it is determined that the checkpoint is the corresponding If the target vehicle's stop point is not the same location, it is determined whether the target vehicle's driving speed is less than a second threshold. In this embodiment, the second threshold VL=max{Vp5,5}, and the maximum value of the 5% quantile Vp5 and 5km / h under the driving speed is taken, wherein Vp5 (5% quantile) is a symbol, and the quantile is obtained by sorting the data by size, multiplying the percentage by the number of data to obtain the order of the data, and then taking the data corresponding to the order. If so, the current location is determined to be a stop point, and the effective path of each target vehicle is determined in combination with the pre-selected information point, i.e., the POI point. In this embodiment, the effective path refers to the travel path whose starting and ending points of the vehicle are within the POI set. For example, if the starting point is a labor market (POI point) and the end point is a temporary employment enterprise (POI point), then such a driving path is a valid path, and the stop point included between the starting point and the end point is the intermediate point on the valid path.

[0054] Next, the original signaling data of multiple initial users is obtained through the data interface opened by the operator, and the data is preprocessed. In this embodiment, the preprocessing mainly includes filtering and noise reduction, that is, removing the ping-pong data and drift data in the original signaling data, and then reducing the noise of the filtered data to obtain the signaling metadata. Then, the road network model is used to analyze the spatial position of the road network and the base station, and a matching relationship between the base station coverage area and the road network road is established accordingly, and the base station switching section is determined. The surrounding base stations are selected in the base station database according to the coordinate information obtained by the map mark. In addition, there are often factories, schools and other crowded areas on both sides of the road. In these crowded areas, mobile communication operators will deploy a large number of indoor sites. Compared with the outdoor sites covering the roads, the signals of these indoor sites are generally weaker and are not the main information covering the roads. Interference removal processing should be performed, such as Figure 3As shown, the road network within the coverage of the outdoor base station is called the online area, and the area outside the coverage of the outdoor base station is called the offline area. According to the signaling metadata, it can be determined whether each initial user is an online user, that is, a target user. In this embodiment, Figure 4 As shown, first determine whether the signaling metadata meets the following conditions: 1) X consecutive initial user signaling metadata belong to the online area, where X is 3; 2) The distance that the initial user has traveled is greater than Y kilometers, where Y is 4KM; 3) The speed of the initial user is greater than Z kilometers per hour, where Z is 60KM / H. It should be noted that the values ​​of X, Y and Z can be changed according to the actual situation and are not limited. When the signaling metadata of an initial user meets the above conditions, it is determined to be a target user. If not, it will continue to be tracked. Once its signaling metadata meets the above conditions, it will also be identified as a target user. Since residents will carry out activities for specific purposes at the travel endpoints of each trip, that is, they stay for a long time, the corresponding mobile phone location trajectory will form a dense point cluster, which is quite different from the sparse scattered points during the trip. The density-based spatial clustering algorithm can be used to specifically cluster the trajectory points. The travel endpoints with large point space density are identified in the form of point clusters. Preferably, in this embodiment, DBSCAN is used to calculate the stay points of each target user, so as to obtain the travel path of each target user.

[0055] Finally, the locality in-between polylines (LIP) algorithm is used to calculate the similarity between each travel path and the effective path. This is a prior art and will not be described in detail here. In combination with the preset vehicle overcrowding condition, it is determined whether each target vehicle is overcrowded. Specifically, when the similarity between the travel path and the effective path is greater than the third threshold value, in this embodiment, the third threshold value is 85%. The corresponding target user is regarded as the passenger of the target vehicle, so as to obtain the number of passengers of each target vehicle, and then see whether the number of passengers of each target vehicle exceeds 10% of its rated number of passengers. The rated number of passengers is determined by the vehicle model. At the same time, it is determined whether the overcrowding behavior of each target vehicle in the past month is greater than 8 times. Among them, the numbers such as 10% and 8 can be changed according to the actual situation. If a certain target vehicle meets the above conditions, the target vehicle is set as a suspected vehicle and its suspected index is determined. In this embodiment, the suspected index is the average value of the similarity of the mobile phone signaling target user path*100, and the corresponding effective path is used as the suspected path.

[0056] In some embodiments, generating a checkpoint-passing time series of each target vehicle according to the checkpoint data includes:

[0057] Acquire checkpoint data, and clean abnormal data and duplicate record data in the checkpoint data;

[0058] Target vehicle data is screened out from the cleaned data, and the time when each target vehicle passes the checkpoint is sorted according to the target vehicle data.

[0059] In practice, due to weather reasons such as haze and rain, as well as problems with equipment installation angles, the monitoring and identification system cannot achieve performance under ideal conditions. It is necessary to analyze the actual quality of the cross-section data. Specifically, the checkpoint data must be cleaned, screened, and sorted. Data cleaning is mainly aimed at abnormal data and duplicate records in the checkpoint data, and data screening is mainly aimed at non-passenger vehicles such as trucks, that is, deleting the data corresponding to all non-target vehicles in the checkpoint data. Data sorting is mainly aimed at the checkpoint-passing time of a single vehicle, in preparation for the identification of a single vehicle's stop point.

[0060] In some embodiments, determining the effective path of each of the target vehicles based on the checkpoint time sequence and the set information points includes:

[0061] Map the checkpoint devices to the road network, and calculate the shortest travel distance between each checkpoint device through the shortest path algorithm based on the road network topology;

[0062] Extracting adjacent checkpoint-passing timestamps and corresponding checkpoint positions of each target vehicle from each checkpoint-passing time series, and determining a first driving distance of each target vehicle according to the shortest driving distance and the checkpoint position;

[0063] Calculating a first time difference according to the timestamp, and calculating a travel speed of each target vehicle according to the first travel distance and the corresponding first time difference;

[0064] The stop point of each target vehicle is determined according to the checkpoint position and the driving speed, and the effective path of each target vehicle is determined based on the stop point and the set information point.

[0065] There are two cases for stop point identification: 1) The end point of the previous trip and the starting point of the next trip are at the same location, that is, the start and end points of the trip are at the same location; 2) The end point of the previous trip and the starting point of the next trip are at different locations, that is, the start and end points of the trip are at different locations.

[0066] The basic idea of ​​the stop point judgment is to judge whether to stop between two trajectory points based on the speed. The specific judgment process is as follows:

[0067] Map the bayonet devices to the road network, and obtain the shortest driving distances between various bayonet devices through the shortest path algorithm based on the road network topology; extract the passing bayonet timestamps and corresponding bayonet positions of adjacent records of the target vehicles from the passing bayonet time series, and calculate the first driving distance d and time difference t of each target vehicle in combination with the shortest driving distances between various bayonet devices to obtain the driving speed v = d / t. Since the volatility of traffic flow within a day causes the travel time to vary with time, a statistical time window is set. It is considered that the travel time data within the same time window is stable. In this embodiment, the time window from 0 to 6 o'clock is set to 60 minutes, and the time window from 6 to 24 o'clock is set to 30 minutes, and the lower limit value VL of the driving speed is set to max{Vp5, 5}. Finally, for the case where the front and rear records are at the same position, if t>1200s, then this bayonet is a stop point. For the case where they are not at the same position, if v<VL, then the current position is a stop point. Combining with the pre-selected information points, that is, POI points, the effective paths of each target vehicle can be determined.

[0068] In this embodiment, a driving track database of the target minibus is established relying on the video bayonet data, and then the vehicle track data of the target minibus is matched with the mobile phone signaling track data of the communication service operator to determine the illegal suspect vehicles, which can provide support for the on-site investigation by the traffic police department. At the same time, the results obtained by combining the vehicle bayonet track and the mobile phone signaling have uniqueness and more accurate data.

[0069] An overloaded vehicle identification device provided in the second embodiment of the present application, as Figure 5 shown, includes the following modules:

[0070] A generation module 10 for generating the passing bayonet time series of each target vehicle according to the bayonet data, and determining the effective path of each target vehicle based on the passing bayonet time series and the set information points;

[0071] A matching module 20 for establishing a matching relationship between the road network roads and the base station coverage area according to the spatial positions of the road network and the base station, and determining a number of target users according to the signaling metadata and the matching relationship;

[0072] A calculation module 30 for generating the travel paths of each target user according to the signaling metadata of the number of target users, and calculating the similarity between each travel path and the effective path respectively;

[0073] A judgment module 40 for determining whether each target vehicle is overloaded based on the similarity calculation result and the preset vehicle overloading condition.

[0074] In some embodiments, the generation module 10 specifically includes:

[0075] A mapping unit 11 is used to map the checkpoint devices to the road network and calculate the shortest travel distance between each checkpoint device by using the shortest path algorithm based on the road network topology;

[0076] The extraction unit 12 is used to extract the adjacent checkpoint passing timestamps and corresponding checkpoint positions of each target vehicle from each checkpoint passing time series, and determine the first driving distance of each target vehicle according to the shortest driving distance and the checkpoint position;

[0077] a calculation unit 13, configured to calculate a first time difference according to the timestamp, and calculate a travel speed of each target vehicle according to the first travel distance and the corresponding first time difference;

[0078] The determination unit 14 is used to determine the stop point of each target vehicle according to the checkpoint position and the driving speed, and determine the effective path of each target vehicle based on the stop point and the set information point.

[0079] As shown in the figure, the present application also provides an electronic device, including a memory 601 and a processor 602, wherein the memory 601 is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor 602 to implement the above-mentioned method for identifying an overloaded vehicle.

[0080] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the electronic device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0081] The present application also provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to implement the above-mentioned method for identifying an overloaded vehicle when executed.

[0082] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 601 and executed by the processor 602, and the I / O interface transmission of data is completed by the input interface 605 and the output interface 606 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0083] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may include, but is not limited to, a memory 601 and a processor 602. Those skilled in the art may understand that this embodiment is only an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components, or a combination of certain components, or different components. For example, the computer device may also include an input device 607, a network access device, a bus, etc.

[0084] The processor 602 may be a central processing unit (CPU), or other general-purpose processors 602, digital signal processors 602 (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor 602 may be a microprocessor 602 or the processor 602 may be any conventional processor 602, etc.

[0085] The memory 601 may be an internal storage unit of a computer device, such as a hard disk or memory of the computer device. The memory 601 may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory 601 may also include both an internal storage unit of the computer device and an external storage device. The memory 601 is used to store computer programs and other programs and data required by the computer device. The memory 601 may also be used to temporarily store in the output device 608, and the aforementioned storage media include various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory ROM603, a random access memory RAM604, a disk or an optical disk.

[0086] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for identifying an overloaded vehicle, characterized in that: The following steps are included Generate a checkpoint-passing time sequence of each target vehicle according to the checkpoint data, and determine a valid path of each target vehicle based on the checkpoint-passing time sequence and set information points; Establishing a matching relationship between a road network and a base station coverage area according to the spatial positions of the road network and the base station, and determining a number of target users according to the signaling metadata and the matching relationship; Generate a travel path for each target user according to the signaling metadata of the plurality of target users, and calculate the similarity between each travel path and the valid path respectively; Determining whether each of the target vehicles is overcrowded based on the similarity calculation result and the preset vehicle overcrowding condition; Wherein, determining the effective path of each target vehicle based on the checkpoint passing time sequence and the set information points includes: Map the checkpoint devices to the road network, and calculate the shortest travel distance between each checkpoint device through the shortest path algorithm based on the road network topology; Extracting adjacent checkpoint-passing timestamps and corresponding checkpoint positions of each target vehicle from each checkpoint-passing time series, and determining a first driving distance of each target vehicle according to the shortest driving distance and the checkpoint position; Calculating a first time difference according to the timestamp, and calculating a travel speed of each target vehicle according to the first travel distance and the corresponding first time difference; The stop point of each target vehicle is determined according to the checkpoint position and the driving speed, and the effective path of each target vehicle is determined based on the stop point and the set information point.

2. A method for identifying an overloaded vehicle according to claim 1, characterized in that: The method of generating a checkpoint-passing time series of each target vehicle according to the checkpoint data includes: Acquire checkpoint data, and clean abnormal data and duplicate record data in the checkpoint data; Target vehicle data is screened out from the cleaned data, and the time when each target vehicle passes the checkpoint is sorted according to the target vehicle data.

3. A method for identifying an overloaded vehicle according to claim 1, characterized in that: Determining the stopping point of each target vehicle according to the checkpoint position and the driving speed includes: When the checkpoint positions are the same, determining whether each first time difference is greater than a first threshold, and if so, the checkpoint is a stop point; When the checkpoint positions are not the same, it is determined whether the driving speeds are less than a second threshold. If so, the current position is a stop point.

4. The method for identifying an overloaded vehicle according to claim 1, characterized in that: Before determining a plurality of target users according to the signaling metadata of the plurality of initial users and the matching relationship, the method further includes: The original signaling data of a plurality of initial users are acquired, and the original signaling data are preprocessed, wherein the preprocessing includes filtering and noise reduction, to obtain signaling metadata.

5. The method for identifying an overloaded vehicle according to claim 1, characterized in that: The determining whether each of the target vehicles is overcrowded based on the similarity calculation result and the preset vehicle overcrowding condition includes: It is determined whether each similarity calculation result is greater than a third threshold value. If so, the corresponding target user is used as the passenger of the corresponding target vehicle to obtain the number of passengers of each target vehicle.

6. A vehicle overload identification device, characterized in that: include: A generation module, used to generate a checkpoint-passing time sequence of each target vehicle according to the checkpoint data, and determine a valid path of each target vehicle based on the checkpoint-passing time sequence and set information points; A matching module, used to establish a matching relationship between a road network and a base station coverage area according to the spatial positions of the road network and the base station, and determine a number of target users according to the signaling metadata and the matching relationship; A calculation module, used to generate a travel path of each target user according to the signaling metadata of the plurality of target users, and respectively calculate the similarity between each travel path and a valid path; A judgment module, used for determining whether each of the target vehicles is overcrowded based on the similarity calculation result and a preset vehicle overcrowding condition; Wherein, the generating module comprises: A mapping unit, used to map the checkpoint device to the road network, and calculate the shortest travel distance between each checkpoint device by using the shortest path algorithm based on the road network topology; An extraction unit is used to extract the adjacent checkpoint passing timestamps and corresponding checkpoint positions of each target vehicle from each checkpoint passing time series, and determine the first driving distance of each target vehicle according to the shortest driving distance and the checkpoint position; a calculation unit, configured to calculate a first time difference according to the timestamp, and calculate a travel speed of each target vehicle according to the first travel distance and the corresponding first time difference; The determination unit is used to determine the stop point of each target vehicle according to the checkpoint position and the driving speed, and determine the effective path of each target vehicle based on the stop point and the set information point.

7. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a method for identifying an overloaded vehicle as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables a computer to implement a method for identifying an overloaded vehicle as claimed in any one of claims 1 to 5 when executed.

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