Vehicle activity statistics method and system

Abstract

The application provides a vehicle activity statistics method and system, the method comprises the following steps: acquiring the toll gate passing vehicle data in real time; constructing an effective license plate library, marking the license plate information with the number of passing different toll gates greater than a first threshold in the toll gate passing vehicle data in a preset time period as effective license plates, and constantly updating the effective license plate library; judging whether the license plate information with the number of passing different toll gates less than a second threshold exists in the effective license plate library, if not, removing it, and according to the updated toll gate passing vehicle data, calculating the vehicle activity in different dimensions; according to the toll gate passing vehicle data, obtaining the activity and similarity of each toll gate to obtain key toll gates, so as to perform data compensation according to the corresponding relationship with the key toll gates when the vehicle activity in different dimensions is abnormal; thus, not only the road traffic travel situation can be reflected from a macro perspective, but also the data fluctuation caused by data quality problems can be reduced.

Description

Technical Field

[0001] This invention relates to the field of transportation technology, and in particular to a method for vehicle activity statistics, a computer-readable storage medium, a computer device, and a vehicle activity statistics system. Background Technology

[0002] Among related technologies, big data and artificial intelligence technologies have been widely applied in the transportation field, which can help traffic management departments and travelers better understand and manage urban traffic, and improve traffic efficiency, safety and service quality. At present, big data technology can comprehensively obtain information on road operation, including road congestion and traffic flow, but it lacks macro-level indicators that can reflect the overall vehicle travel volume, travel frequency and popularity. At the same time, data loss and anomalies caused by equipment, technology, network and extreme weather problems have become common, resulting in large data fluctuations and an inability to stably reflect the road conditions. Summary of the Invention

[0003] This invention aims to at least partially solve one of the technical problems in the aforementioned technologies. Therefore, one objective of this invention is to propose a vehicle activity statistics method that constructs a vehicle activity index system by combining checkpoint vehicle data with an effective license plate database, and reflects the overall activity level through a small number of key checkpoints. This not only provides a macroscopic reflection of road traffic conditions but also reduces data fluctuations caused by data quality issues.

[0004] A second objective of this invention is to provide a computer-readable storage medium.

[0005] The third objective of this invention is to provide a computer device.

[0006] To achieve the above objectives, a first aspect of the present invention proposes a vehicle activity statistics method, comprising the following steps: real-time acquisition of checkpoint vehicle passage data, wherein the checkpoint vehicle passage data includes license plate information and time of passing through the corresponding checkpoint; constructing an effective license plate database, marking license plate information in the checkpoint vehicle passage data within a preset time period that passes through different checkpoints more than a first threshold as effective license plates, and updating the effective license plate database after each preset time period; determining whether license plate information in the checkpoint vehicle passage data that passes through different checkpoints less than a second threshold exists in the effective license plate database, and if not, removing it to obtain updated checkpoint vehicle passage data, and statistically analyzing vehicle activity in different dimensions based on the updated checkpoint vehicle passage data; obtaining the activity and similarity corresponding to each checkpoint in all checkpoints based on the checkpoint vehicle passage data, so as to obtain key checkpoints in all checkpoints based on the activity and similarity, so as to perform data supplementation based on the correspondence between the vehicle activity in different dimensions and the key checkpoints when abnormalities occur.

[0007] According to the vehicle activity statistics method of this invention, firstly, real-time vehicle passage data at checkpoints is acquired, including license plate information and time of vehicles passing through the corresponding checkpoints; then, a valid license plate database is constructed, marking license plate information in the vehicle passage data within a preset time period that passes through different checkpoints more than a first threshold as valid license plates, and updating the valid license plate database after each preset time period; next, it is determined whether license plate information in the vehicle passage data that passes through different checkpoints less than a second threshold exists in the valid license plate database. If not, it is removed to obtain updated vehicle passage data, and the updated data is then used to calculate the vehicle activity statistics. The system collects vehicle activity data from various dimensions. Finally, it obtains the activity level and similarity of each checkpoint based on the checkpoint data. This allows for the identification of key checkpoints based on activity and similarity, enabling data recalculation when abnormalities in vehicle activity occur across different dimensions, by correlating these with the key checkpoints. Thus, by combining checkpoint data with a valid license plate database, a vehicle activity index system is constructed. Furthermore, by using a small number of key checkpoints to reflect the overall activity level, this system not only provides a macro-level view of road traffic conditions but also reduces data fluctuations caused by data quality issues.

[0008] In addition, the vehicle activity statistics method proposed in the above embodiments of the present invention may also have the following additional technical features:

[0009] Optionally, after acquiring the vehicle passage data at the checkpoint in real time, the vehicle passage data at the checkpoint is also preprocessed in order to filter out invalid vehicle passage data at the checkpoint.

[0010] Optionally, the different dimensions of vehicle activity include spatial and temporal dimensions, wherein the spatial dimension includes city-wide vehicle activity, regional vehicle activity, and road vehicle activity, and the temporal dimension includes activity at different time granularities and vehicle activity duration.

[0011] Optionally, the activity level and similarity of each checkpoint in all checkpoints are obtained based on the vehicle passage data, so as to obtain key checkpoints in all checkpoints based on the activity level and similarity. This includes: obtaining the activity level of each checkpoint in all checkpoints based on the vehicle passage data to obtain a checkpoint activity level table; obtaining a list of license plates corresponding to each checkpoint, and calculating the intersection and union of the license plate sets between any two checkpoints, so as to obtain the Jaccard similarity coefficient between any two checkpoints based on the intersection and union, to obtain a checkpoint similarity table; associating the checkpoint activity level table and the checkpoint similarity table, and normalizing the checkpoint activity level in the checkpoint activity level table and the Jaccard similarity coefficient in the checkpoint similarity table to obtain a relative joint contribution of activity level; obtaining key checkpoints based on the relative joint contribution of activity level and adding them to the key checkpoint list.

[0012] To achieve the above objectives, a second aspect of the present invention provides a computer-readable storage medium storing a vehicle activity statistics program thereon, which, when executed by a processor, implements the vehicle activity statistics method as described above.

[0013] According to an embodiment of the present invention, a computer-readable storage medium stores a vehicle activity statistics program. When the vehicle activity statistics program is executed by a processor, it implements the vehicle activity statistics method described above. Thus, a vehicle activity index system is constructed by combining checkpoint vehicle data with an effective license plate database, and the overall activity situation is reflected through a small number of key checkpoints. This not only reflects the road traffic situation from a macro perspective but also reduces data fluctuations caused by data quality issues.

[0014] To achieve the above objectives, a third aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the vehicle activity statistics method as described above.

[0015] According to the computer device of the present invention, a vehicle activity statistics program is stored in a memory. When the vehicle activity statistics program is executed by the processor, the above-mentioned vehicle activity statistics method is implemented. Thus, a vehicle activity index system is constructed by combining checkpoint vehicle data with an effective license plate database, and the overall activity situation is reflected through a small number of key checkpoints. This not only reflects the road traffic situation from a macro perspective, but also reduces data fluctuations caused by data quality issues.

[0016] To achieve the above objectives, a fourth aspect of the present invention proposes a vehicle activity statistics system, comprising: an acquisition module for acquiring checkpoint vehicle passage data in real time, wherein the checkpoint vehicle passage data includes license plate information and time of passing through the corresponding checkpoint; a valid license plate database construction module for constructing a valid license plate database, marking license plate information in the checkpoint vehicle passage data within a preset time period that passes through different checkpoints more than a first threshold as valid license plates, and updating the valid license plate database after each preset time period; and a vehicle activity statistics module for determining the number of times the vehicle passes through the checkpoint vehicle passage data passes through different checkpoints. If a license plate with a number of entries at the same checkpoint less than a second threshold exists in the valid license plate database, it is removed to obtain updated checkpoint vehicle passage data. Based on this updated data, vehicle activity is statistically analyzed across different dimensions. A data supplementation module is used to obtain the activity and similarity of each checkpoint from the vehicle passage data. This allows for the identification of key checkpoints based on the activity and similarity, enabling data supplementation when abnormal vehicle activity occurs across different dimensions, according to their correspondence with the key checkpoints.

[0017] According to an embodiment of the present invention, a vehicle activity statistics system acquires checkpoint vehicle passage data in real time through an acquisition module. The checkpoint vehicle passage data includes license plate information and time of passage through the corresponding checkpoint. A valid license plate database construction module constructs a valid license plate database, marking license plates that have passed through more than a first threshold number of different checkpoints within a preset time period as valid license plates, and updates the valid license plate database after each preset time period. A vehicle activity statistics module determines whether license plates that have passed through fewer than a second threshold number of different checkpoints exist in the valid license plate database; if not, they are removed to obtain updated checkpoint vehicle passage data. The updated checkpoint vehicle data is used to statistically analyze vehicle activity across different dimensions. The data supplementation module obtains the activity level and similarity of each checkpoint from all checkpoints based on the vehicle data. This allows for the identification of key checkpoints based on activity level and similarity, enabling data supplementation when abnormalities occur in vehicle activity across different dimensions, based on their correspondence with key checkpoints. Thus, by combining checkpoint vehicle data with an effective license plate database, a vehicle activity index system is constructed. Furthermore, a small number of key checkpoints reflect the overall activity level, providing a macro-level view of road traffic conditions and reducing data fluctuations caused by data quality issues.

[0018] In addition, the vehicle activity statistics system proposed in the above embodiments of the present invention may also have the following additional technical features:

[0019] Optionally, it also includes a preprocessing module for preprocessing the checkpoint vehicle passage data in order to filter out invalid checkpoint vehicle passage data.

[0020] Optionally, the different dimensions of vehicle activity include spatial and temporal dimensions, wherein the spatial dimension includes city-wide vehicle activity, regional vehicle activity, and road vehicle activity, and the temporal dimension includes activity at different time granularities and vehicle activity duration.

[0021] Optionally, the data supplementation module is further configured to: obtain the activity level of each checkpoint in all checkpoints based on the vehicle passage data to obtain a checkpoint activity level table; obtain a list of license plates corresponding to each checkpoint, and calculate the intersection and union of the license plate sets between any two checkpoints, so as to obtain the Jaccard similarity coefficient between any two checkpoints based on the intersection and union, to obtain a checkpoint similarity table; associate the checkpoint activity level table and the checkpoint similarity table, and normalize the checkpoint activity level in the checkpoint activity level table and the Jaccard similarity coefficient in the checkpoint similarity table to obtain a relative joint contribution of activity level; obtain key checkpoints based on the relative joint contribution of activity level, and add them to the key checkpoint list. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the vehicle activity statistics method according to an embodiment of the present invention;

[0023] Figure 2 This is a flowchart illustrating a vehicle activity statistics method according to an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of a vehicle activity index system with different dimensions according to an embodiment of the present invention;

[0025] Figure 4 This is a block diagram of a vehicle activity statistics system according to an embodiment of the present invention. Detailed Implementation

[0026] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0027] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the invention to those skilled in the art.

[0028] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0029] Figure 1 This is a flowchart illustrating the vehicle activity statistics method according to an embodiment of the present invention. Figure 1 As shown, the vehicle activity statistics method of this invention includes the following steps:

[0030] S101 acquires real-time vehicle passage data at checkpoints, including license plate information and time of passage through the corresponding checkpoint.

[0031] It should be noted that there are multiple checkpoints, distributed in various locations within a city. When a vehicle passes through a checkpoint, the checkpoint's identification information, the corresponding license plate information, and the time the vehicle passed through the checkpoint will be saved, thus obtaining a checkpoint vehicle passage data.

[0032] As an example, after acquiring the vehicle passage data at the checkpoint in real time, the vehicle passage data is also preprocessed to filter out invalid vehicle passage data.

[0033] It should be noted that the obtained checkpoint vehicle data may contain issues such as unrecognizable license plates, missing checkpoint passage time information, or duplicate checkpoint identification information. Therefore, it is necessary to remove checkpoint vehicle data with these issues to improve data integrity.

[0034] S102, construct a valid license plate database, mark license plate information that passes through different checkpoints more than the first threshold in the checkpoint vehicle data within a preset time period as valid license plates, and update the valid license plate database after each preset time period.

[0035] In other words, after obtaining a certain amount of vehicle passage data at checkpoints, it can be determined whether the number of times the same license plate information passes through different checkpoints is greater than the first threshold based on the license plate information in the checkpoint vehicle passage data. If so, the license plate information is marked as a valid license plate, and the valid license plate database is updated every preset time interval.

[0036] It should be noted that due to factors such as weather, equipment, and algorithms, license plates may be misidentified at checkpoints, including incorrect license plate recognition and the identification of non-physical vehicles. Directly calculating the activity level of vehicle passage data at checkpoints would lead to an overestimation of the data. Therefore, it is necessary to further filter the data by building an effective license plate database.

[0037] As a specific implementation, firstly, daily checkpoint vehicle passage data is acquired. After deduplication, the number of checkpoints passed by each license plate on that day is counted to form a daily vehicle passage statistics result, which is then saved to the HDFS file system. Then, each month, the daily vehicle passage statistics results saved for the past three months are read, and license plate numbers that have passed through more than 3 different checkpoints in the past three months are calculated and marked as valid license plates. The newly added valid license plates from the valid license plates marked each month are added to the constructed valid license plate database to obtain the latest valid license plate database.

[0038] It should be noted that for a given city, the number of valid license plates increases slowly because the number of vehicles moving within that city is relatively fixed.

[0039] S103, determine whether the license plate information of vehicles that have passed through different checkpoints less than the second threshold in the checkpoint vehicle data exists in the valid license plate database. If not, remove it to obtain the updated checkpoint vehicle data, and calculate vehicle activity in different dimensions based on the updated checkpoint vehicle data.

[0040] In other words, when calculating vehicle activity in different dimensions, the corresponding checkpoint vehicle data is obtained according to the needs of different dimensions. It is then determined whether the number of times each license plate information in the obtained checkpoint vehicle data passes through different checkpoints is less than a second threshold. If it is less than the second threshold, it is then determined whether the license plate information exists in the valid license plate database. If it does not exist, it is removed; if it exists, it is retained, thus obtaining updated checkpoint vehicle data, so that vehicle activity can be statistically analyzed based on the updated checkpoint vehicle data.

[0041] It should be noted that if the number of times each license plate in the obtained vehicle passage data passes through different checkpoints is greater than or equal to the second threshold, then there is no need to compare it with the valid license plate database, and it can be directly used in subsequent calculations.

[0042] As a specific implementation, the vehicle passage data of the checkpoints for the day is obtained. After removing duplicate data, the number of checkpoints passed through that day is counted. If the number of checkpoints passed through by a vehicle is equal to 1, it is compared with the current valid license plate database. If it is in the valid license plate database, it is included in the subsequent calculations. If it is not, it is marked as an invalid license plate and removed.

[0043] As an example, such as Figure 3 As shown, vehicle activity in different dimensions includes spatial and temporal dimensions. The spatial dimension includes citywide vehicle activity, regional vehicle activity, and road vehicle activity, while the temporal dimension includes activity at different time granularities and vehicle activity duration.

[0044] It should be noted that (1) the citywide vehicle activity level is calculated as follows: obtain all valid license plate data from all checkpoints, deduplicate the data by license plate number, and calculate the citywide vehicle activity level by counting the number of duplicate license plates; (2) the regional vehicle activity level is calculated as follows: construct a checkpoint list corresponding to the region as needed, such as dividing according to administrative regions or cultural environment, forming regions such as administrative regions, scenic spots, business districts, and entry and exit channels, filter all checkpoint data in the region, filter the data by valid license plate number, deduplicate the data by license plate number, count the number of duplicate license plates, and calculate the vehicle activity level of different regions; (3) the road vehicle activity level is calculated as follows: construct a checkpoint equipment list corresponding to the road, filter all checkpoint data in a certain road, filter the data by valid license plate number, and calculate the data by license plate number. (3) Deduplication is performed, the number of deduplicated license plates is counted, and the vehicle activity of all roads is calculated; (4) The vehicle activity of different granularities is calculated as follows: the vehicle activity is calculated according to different granularities, such as hour, 30 minutes, etc., and all passing vehicle data within the time range are filtered out. After the effective license plate is filtered, the data is deduplicated according to the license plate number, the number of deduplicated license plates is counted, and the vehicle activity of different time granularities is calculated; (5) The vehicle activity duration is calculated as follows: the vehicle trajectory is calculated based on the checkpoint data. After the effective license plate is filtered, the number of hours of each trajectory point of each vehicle is recorded according to the license plate number. The number of hours is deduplicated, the number of deduplicated vehicles is counted, and the vehicle activity duration is obtained. The activity duration of all vehicles is grouped and counted to obtain the distribution of the number of vehicles with different activity durations.

[0045] S104. Obtain the activity level and similarity of each checkpoint in all checkpoints based on the vehicle passage data, so as to identify the key checkpoints in all checkpoints based on the activity level and similarity, so as to perform data supplementation based on the correspondence between the vehicle activity level in different dimensions and the key checkpoints when abnormalities occur.

[0046] It should be noted that since the definition of activity level is the number of unique vehicles, and considering the current density of checkpoint equipment, a vehicle is likely to pass through more than one checkpoint during its journey. Therefore, to calculate the overall vehicle activity level of the city, it is not necessary to calculate all checkpoints in the city. Instead, we can use the "set coverage" problem to find the smallest number of key checkpoints that can cover all vehicles. These checkpoints can approximately reflect the overall activity level of the city. By using the data from these key checkpoints, we can estimate and complete missing and abnormal data, ensuring the stability and reliability of the data.

[0047] As an example, the activity level and similarity of each checkpoint in all checkpoints are obtained based on vehicle passage data, so as to identify key checkpoints among all checkpoints based on activity level and similarity. This includes: obtaining the activity level of each checkpoint in all checkpoints based on vehicle passage data to obtain a checkpoint activity level table; obtaining a list of license plates corresponding to each checkpoint, and calculating the intersection and union of the license plate sets between any two checkpoints, so as to obtain the Jaccard similarity coefficient between any two checkpoints based on the intersection and union, to obtain a checkpoint similarity table; associating the checkpoint activity level table and the checkpoint similarity table, and normalizing the checkpoint activity level in the checkpoint activity level table and the Jaccard similarity coefficient in the checkpoint similarity table to obtain the relative joint contribution of activity level; and identifying key checkpoints based on the relative joint contribution of activity level and adding them to the key checkpoint list.

[0048] As a specific example:

[0049] I. Calculate checkpoint activity

[0050] Obtain vehicle traffic data for a specific day, calculate the vehicle activity level for each checkpoint, and store it as a checkpoint activity table.

[0051] II. Calculating checkpoint similarity

[0052] (1) Group the vehicle data by checkpoint and obtain the license plate list corresponding to each checkpoint;

[0053] (2) Calculate the size of the intersection and union of the license plate sets between any two checkpoints;

[0054] (3) Calculate and obtain the Jaccard similarity coefficient between any two checkpoints.

[0055]

[0056] Here, A and B represent the sets of license plates that pass through the checkpoint. The larger the Jaccard similarity coefficient, the higher the similarity between the two sets.

[0057] III. Calculation of key checkpoints

[0058] (1) Link the checkpoint activity table and the checkpoint similarity table;

[0059] (2) Normalize the checkpoint activity level and Jaccard similarity coefficient.

[0060]

[0061]

[0062] Where, x act x represents the normalized value of checkpoint activity.jaccard This represents the normalized value of the Jaccard similarity coefficient.

[0063] (3) Calculate the relative joint contribution of activity.

[0064] β a|b =α×x act +(1-α)×x jaccard(a|b)

[0065] Where, β a|b This represents the combined contribution of checkpoints a and b; the higher the value, the greater the contribution of a and b to the overall activity level.

[0066] (4) Obtain key checkpoints

[0067] a. Construct an empty list as the key checkpoint list.

[0068] b. Obtain the checkpoint device with the highest activity level at a single checkpoint.

[0069] c. Calculate and obtain the checkpoint with the highest joint contribution, and add it to the list of key checkpoints.

[0070] Repeat the above process until the number of checkpoints in the key checkpoint list reaches the set value.

[0071] It should be noted that, when the number of key checkpoints selected varies, the proportion of the activity level of the selected checkpoints relative to the city's total activity level is shown in the table below:

[0072] The proportion of checkpoints to all checkpoints 0.7％ 3.5％ 7.1％ 10.6％ 14.1％ The selected checkpoint's activity level is calculated as a percentage of the city's total activity level. 18.3％ 33.6％ 44.9％ 53.5％ 58.8％

[0073] It should be noted that, as can be seen from the table above, the small number of checkpoints obtained through the key checkpoint acquisition method can largely reflect the overall activity level.

[0074] In addition, when a significant anomaly is detected in the overall data, i.e., the vehicle activity in different dimensions, the system checks whether the key checkpoint data is normal. If the key checkpoint data is normal, the overall activity is deduced from the activity of the key checkpoints. If the key checkpoint data is abnormal, the system finds the checkpoint most similar to the abnormal key checkpoint according to the checkpoint similarity table, recalculates the activity of the key checkpoint, and deduces the overall activity based on the ratio.

[0075] In summary, such as Figure 2As shown, the process first acquires vehicle passage data at checkpoints, then cleans and preprocesses the data, constructs an effective license plate database for further filtering, and then calculates an activity index based on the filtered data. If an anomaly occurs in the activity index, key checkpoint data is acquired for supplementary calculation. Thus, based on checkpoint vehicle passage data, a vehicle activity index is constructed to comprehensively reflect the overall urban traffic situation. Simultaneously, an improved greedy algorithm is used to identify "key checkpoints," and data from these key checkpoints is used to estimate and supplement missing and abnormal data, ensuring data stability and reliability. This provides traffic management departments or other urban management departments with macro-level travel data, offering data support for decision-making.

[0076] In addition, this invention also proposes a computer-readable storage medium storing a vehicle activity statistics program, which, when executed by a processor, implements the vehicle activity statistics method as described above.

[0077] According to an embodiment of the present invention, a computer-readable storage medium stores a vehicle activity statistics program. When the vehicle activity statistics program is executed by a processor, it implements the vehicle activity statistics method described above. Thus, a vehicle activity index system is constructed by combining checkpoint vehicle data with an effective license plate database, and the overall activity situation is reflected through a small number of key checkpoints. This not only reflects the road traffic situation from a macro perspective but also reduces data fluctuations caused by data quality issues.

[0078] In addition, this invention also proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the vehicle activity statistics method described above.

[0079] According to the computer device of the present invention, a vehicle activity statistics program is stored in a memory. When the vehicle activity statistics program is executed by the processor, the above-mentioned vehicle activity statistics method is implemented. Thus, a vehicle activity index system is constructed by combining checkpoint vehicle data with an effective license plate database, and the overall activity situation is reflected through a small number of key checkpoints. This not only reflects the road traffic situation from a macro perspective, but also reduces data fluctuations caused by data quality issues.

[0080] Figure 4 The present invention is a schematic block diagram of a vehicle activity statistics system according to an embodiment of the present invention. The vehicle activity statistics system includes: an acquisition module 10, an effective license plate database construction module 20, a vehicle activity statistics module 30, and a data supplementation module 40.

[0081] The system comprises the following modules: an acquisition module for acquiring real-time vehicle passage data at checkpoints, including license plate information and time of passage; a valid license plate database construction module for building a valid license plate database, marking license plates that have passed through more than a first threshold number of different checkpoints within a preset time period as valid license plates, and updating the database after each preset time period; a vehicle activity statistics module for determining whether license plates that have passed through less than a second threshold number of different checkpoints exist in the valid license plate database, removing them if they do not, and obtaining updated checkpoint passage data, and calculating vehicle activity in different dimensions based on the updated data; and a data supplementation module for acquiring the activity and similarity of each checkpoint in all checkpoints based on the vehicle passage data, so as to identify key checkpoints in all checkpoints, and perform data supplementation based on the correspondence between vehicle activity and key checkpoints when anomalies occur in different dimensions of vehicle activity.

[0082] As an example, the vehicle activity statistics system also includes a preprocessing module for preprocessing the checkpoint vehicle passage data in order to filter out invalid checkpoint vehicle passage data.

[0083] As an example, vehicle activity in different dimensions includes spatial and temporal dimensions. The spatial dimension includes citywide vehicle activity, regional vehicle activity, and road vehicle activity, while the temporal dimension includes activity at different time granularities and vehicle activity duration.

[0084] As an example, the data supplementation module 40 is further configured to: obtain the activity level of each checkpoint in all checkpoints based on the vehicle passage data to obtain a checkpoint activity level table; obtain a list of license plates corresponding to each checkpoint, and calculate the intersection and union of the license plate sets between any two checkpoints, so as to obtain the Jaccard similarity coefficient between any two checkpoints based on the intersection and union, to obtain a checkpoint similarity table; associate the checkpoint activity level table and the checkpoint similarity table, and normalize the checkpoint activity level in the checkpoint activity level table and the Jaccard similarity coefficient in the checkpoint similarity table to obtain the relative joint contribution of activity level; obtain key checkpoints based on the relative joint contribution of activity level, and add them to the key checkpoint list.

[0085] It should be noted that the explanations and descriptions of the aforementioned embodiments of the vehicle activity statistics method also apply to the vehicle activity statistics system of this embodiment, and will not be repeated here.

[0086] According to an embodiment of the present invention, a vehicle activity statistics system acquires checkpoint vehicle passage data in real time through an acquisition module. The checkpoint vehicle passage data includes license plate information and time of passage through the corresponding checkpoint. A valid license plate database construction module constructs a valid license plate database, marking license plates that have passed through more than a first threshold number of different checkpoints within a preset time period as valid license plates, and updates the valid license plate database after each preset time period. A vehicle activity statistics module determines whether license plates that have passed through fewer than a second threshold number of different checkpoints exist in the valid license plate database; if not, they are removed to obtain updated checkpoint vehicle passage data. The updated checkpoint vehicle data is used to statistically analyze vehicle activity across different dimensions. The data supplementation module obtains the activity level and similarity of each checkpoint from all checkpoints based on the vehicle data. This allows for the identification of key checkpoints based on activity level and similarity, enabling data supplementation when abnormalities occur in vehicle activity across different dimensions, based on their correspondence with key checkpoints. Thus, by combining checkpoint vehicle data with an effective license plate database, a vehicle activity index system is constructed. Furthermore, a small number of key checkpoints reflect the overall activity level, providing a macro-level view of road traffic conditions and reducing data fluctuations caused by data quality issues.

[0087] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0092] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0093] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0094] In the description of this invention, it should be understood that 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 number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0095] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0096] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0097] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms should not be construed as necessarily referring to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0098] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for statistically analyzing vehicle activity, characterized in that, Includes the following steps: Real-time acquisition of vehicle passage data at checkpoints, wherein the vehicle passage data includes license plate information and time of passing through the corresponding checkpoint; Construct an effective license plate database, mark license plate information that passes through different checkpoints more than a first threshold in the checkpoint vehicle data within a preset time period as effective license plates, and update the effective license plate database after each preset time period; Determine whether the license plate information of vehicles that have passed through different checkpoints less than the second threshold in the checkpoint vehicle data exists in the valid license plate database. If it does not exist, remove it to obtain updated checkpoint vehicle data, and calculate vehicle activity in different dimensions based on the updated checkpoint vehicle data. Based on the vehicle passage data at all checkpoints, the activity level and similarity of each checkpoint are obtained, so as to identify the key checkpoints among all checkpoints based on the activity level and similarity, so that when the vehicle activity level in different dimensions is abnormal, data supplementation can be performed based on the correspondence between the vehicle activity level and the key checkpoints. Specifically, the activity level and similarity of each checkpoint are obtained based on the vehicle passage data, so as to identify key checkpoints among all checkpoints based on the activity level and similarity, including: Based on the vehicle passage data at all checkpoints, the activity level of each checkpoint is obtained to generate a checkpoint activity level table. Obtain the list of license plates corresponding to each checkpoint, and calculate the intersection and union of the license plate sets between any two checkpoints, so as to obtain the Jaccard similarity coefficient between any two checkpoints based on the intersection and union, and thus obtain the checkpoint similarity table. The checkpoint activity table and the checkpoint similarity table are associated. The checkpoint activity in the checkpoint activity table and the Jaccard similarity coefficient in the checkpoint similarity table are normalized respectively to obtain the relative joint contribution of activity. Key checkpoints are identified based on the relative joint contribution of the activity level and added to the key checkpoint list; The relative joint contribution of activity is calculated according to the following formula: in, Representative checkpoint and The higher the joint contribution, the better. and The greater the contribution to overall activity, This represents the normalized value of checkpoint activity. This represents the normalized Jaccard similarity coefficient between checkpoints a and b.

2. The vehicle activity statistics method as described in claim 1, characterized in that, After acquiring vehicle passage data at checkpoints in real time, the data is preprocessed to filter out invalid data.

3. The vehicle activity statistics method as described in claim 2, characterized in that, The different dimensions of vehicle activity include spatial and temporal dimensions. The spatial dimension includes city-wide vehicle activity, regional vehicle activity, and road vehicle activity. The temporal dimension includes activity at different time granularities and vehicle activity duration.

4. A computer-readable storage medium, characterized in that, It stores a vehicle activity statistics program, which, when executed by the processor, implements the vehicle activity statistics method as described in any one of claims 1-3.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the vehicle activity statistics method as described in any one of claims 1-3.

6. A vehicle activity statistics system, characterized in that, include: The acquisition module is used to acquire vehicle passage data at checkpoints in real time, wherein the vehicle passage data includes license plate information and time of passing through the corresponding checkpoint; The effective license plate database construction module is used to construct an effective license plate database, mark license plate information that passes through different checkpoints more than a first threshold in the checkpoint vehicle data within a preset time period as effective license plates, and update the effective license plate database after each preset time period. The vehicle activity statistics module is used to determine whether the license plate information of vehicles that have passed through different checkpoints less than a second threshold in the checkpoint vehicle data exists in the valid license plate database. If it does not exist, it is removed to obtain updated checkpoint vehicle data, and vehicle activity in different dimensions is calculated based on the updated checkpoint vehicle data. The data supplementation module is used to obtain the activity level and similarity of each checkpoint in all checkpoints based on the vehicle passage data of the checkpoints, so as to obtain the key checkpoints in all checkpoints based on the activity level and similarity, so as to perform data supplementation based on the correspondence between the vehicle activity level in different dimensions and the key checkpoints when abnormalities occur. The data supplementation module is further configured to: obtain the activity level of each checkpoint in all checkpoints based on the vehicle passage data to obtain a checkpoint activity level table; obtain a list of license plates corresponding to each checkpoint and calculate the intersection and union of the license plate sets between any two checkpoints to obtain the Jaccard similarity coefficient between the two checkpoints based on the intersection and union to obtain a checkpoint similarity table; associate the checkpoint activity level table and the checkpoint similarity table, and normalize the checkpoint activity level in the checkpoint activity level table and the Jaccard similarity coefficient in the checkpoint similarity table to obtain a relative joint contribution of activity level; and obtain key checkpoints based on the relative joint contribution of activity level and add them to the key checkpoint list. The relative joint contribution of activity is calculated according to the following formula: in, Representative checkpoint and The higher the joint contribution, the better. and The greater the contribution to overall activity, This represents the normalized value of checkpoint activity. This represents the normalized Jaccard similarity coefficient between checkpoints a and b.

7. The vehicle activity statistics system as described in claim 6, characterized in that, It also includes a preprocessing module for preprocessing the checkpoint vehicle passage data in order to filter out invalid checkpoint vehicle passage data.

8. The vehicle activity statistics system as described in claim 7, characterized in that, The different dimensions of vehicle activity include spatial and temporal dimensions. The spatial dimension includes city-wide vehicle activity, regional vehicle activity, and road vehicle activity. The temporal dimension includes activity at different time granularities and vehicle activity duration.

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