Method, device and equipment for multi-dimensional analysis and monitoring of vehicle violation, and storage medium

Abstract

The invention relates to the technical field of information processing, in particular to a method, a device and equipment for multi-dimensional analysis and monitoring of vehicle violation, and a storage medium, and aims to obtain violation records of express vehicles in real time and monitor vehicle driving states and surrounding environment conditions of the positions in real time. Performing association analysis based on the violation type, the vehicle driving state and the environmental characteristics to obtain a first analysis result, identifying a violation-prone area in which violation points are densely distributed, and analyzing violation distribution conditions of the violation-prone area in different time periods to obtain a second analysis result; a vehicle violation risk prediction model is constructed based on the first analysis result and the second analysis result, violation risks of the monitored vehicle are identified in real time by using the vehicle violation risk prediction model, violation occurrence rules can be more comprehensively and deeply informed, violation risk points of the express vehicle are accurately positioned, risk early warning is performed, and the safety of the express vehicle is improved. The traffic violation probability is effectively reduced, the traffic accident potential is reduced, and the urban road traffic safety level is improved.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a method, device, equipment and storage medium for multi-dimensional analysis and monitoring of vehicle violations. Background Art

[0002] It has become a common phenomenon in the industry for couriers to drive motor vehicles to collect and deliver parcels. However, the problem of motor vehicle violations has become increasingly serious. It is impossible to monitor data such as the frequency of violations, causes of violations, high-incidence areas of violations, and high-incidence time periods of violations. It is difficult to analyze and identify the patterns of vehicle violations, and it is impossible to accurately locate the risk points of violations of courier vehicles. As a result, it is impossible to detect abnormalities in time, and it is impossible to issue reminders to couriers in time to urge them to correct them. This increases the probability of violations, increases the hidden dangers of traffic accidents, and reduces the level of urban road traffic safety. Summary of the Invention

[0003] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide a multi-dimensional analysis and monitoring method, device, equipment and storage medium for vehicle violations that can provide more comprehensive and in-depth insights into the patterns of violations, accurately locate the violation risk points of express vehicles and issue risk warnings, effectively reduce the probability of violations, reduce traffic accident hazards and improve the level of urban road traffic safety.

[0004] The first aspect of the present invention provides a multi-dimensional analysis and monitoring method for vehicle violations, including: obtaining the violation records of express vehicles in real time, the violation records including the license plate number, affiliated outlet, violation time, violation location and violation type, and monitoring the vehicle driving status and the surrounding environment of the express vehicles in real time; extracting the environmental characteristics of the surrounding environment of the location, and performing correlation analysis based on the violation type, the vehicle driving status and the environmental characteristics to obtain a first analysis result; visualizing the violation locations of all express vehicles through geographic information system technology and identifying violation-prone areas with dense distribution of violation points, analyzing the violation distribution of the violation-prone areas in different time periods to obtain a second analysis result; constructing a vehicle violation risk prediction model based on the first analysis result and the second analysis result, and using the vehicle violation risk prediction model to identify the violation risk of the monitored vehicle in real time; generating violation warning information based on the violation risk, and sending the violation warning information to the affiliated outlet corresponding to the monitored vehicle.

[0005] Optionally, in the first implementation manner of the first aspect of the present invention, the real-time acquisition of the violation records of the express delivery vehicles, where the violation records include license plate numbers, affiliated outlets, violation times, violation locations, and violation types, and the real-time monitoring of the vehicle driving status of the express delivery vehicles and the surrounding environment of the locations where they are located, includes: real-time acquisition of the violation records of the express delivery vehicles; parsing the violation records to obtain license plate numbers, affiliated outlets, violation times, violation locations, and violation types; real-time monitoring of the vehicle driving status of the express delivery vehicles and the surrounding environment of the locations where they are located.

[0006] Optionally, in the second implementation manner of the first aspect of the present invention, the extraction of the environmental features of the surrounding environment of the location where the vehicle is located, and the correlation analysis based on the violation type, the vehicle driving status, and the environmental features to obtain a first analysis result, includes: extraction of the environmental features of the surrounding environment of the location where the vehicle is located, encoding the violation type, the vehicle driving status, and the environmental features; generating a candidate item set according to the encoding, calculating the support degree of the candidate item set; screening frequent item sets according to the support degree, extracting rules from the frequent item sets; calculating the confidence degree of the rules, and retaining strong rules according to the confidence degree to obtain a first analysis result.

[0007] Optionally, in the third implementation manner of the first aspect of the present invention, the visualization of the violation locations of all express delivery vehicles by using geographic information system technology and the identification of the high-violation areas where violation points are densely distributed, and the analysis of the violation distribution in the high-violation areas at different time periods to obtain a second analysis result, includes: extracting the longitude and latitude information of the violation locations of all express delivery vehicles from the local database, setting the neighborhood radius and the minimum number of points by using the longitude and latitude information as input data; calculating the number of points in the neighborhood, and if the number of points is greater than or equal to the minimum number of points, marking the longitude and latitude information as a core point; identifying all data points that are density-connected to the core point to form a cluster, and visualizing the cluster in the geographic information system to obtain a high-violation area where violation points are densely distributed; analyzing the violation distribution in the high-violation area at different time periods to obtain a second analysis result.

[0008] Optionally, in the fourth implementation manner of the first aspect of the present invention, the construction of a vehicle violation risk prediction model based on the first analysis result and the second analysis result, and the real-time identification of the violation risks existing in the monitored vehicles by using the vehicle violation risk prediction model, includes: constructing a vehicle violation risk prediction model based on the first analysis result and the second analysis result; using the vehicle violation risk prediction model to calculate the violation risk score of the monitored vehicle in real time; when the violation risk score is greater than a preset first threshold, it is determined that the monitored vehicle has a violation risk.

[0009] Optionally, in the fifth implementation manner of the first aspect of the present invention, generating a violation warning message based on the violation risk and sending the violation warning message to the affiliated network corresponding to the monitored vehicle includes: generating a violation warning message based on the violation risk; inserting the license plate number and driver contact information corresponding to the monitored vehicle into the violation warning message; and sending the violation warning message to the affiliated network corresponding to the monitored vehicle.

[0010] Optionally, in the sixth implementation manner of the first aspect of the present invention, after generating a violation warning message based on the violation risk and sending the violation warning message to the affiliated network corresponding to the monitored vehicle, it further includes: collecting violation data of multiple express delivery enterprises, calculating the average violation frequency of the entire express delivery industry according to the violation data; setting a second threshold according to the average violation frequency, and regularly counting the number of violations corresponding to the monitored vehicle; when the number of violations is greater than the second threshold, generating a frequent violation record information; encrypting the frequent violation record information to obtain a record encryption information; and uploading the record encryption information to the blockchain.

[0011] The second aspect of the present invention provides a multi-dimensional analysis monitoring vehicle violation device, including: an acquisition monitoring module, configured to acquire the violation records of express delivery vehicles in real time, where the violation records include license plate number, affiliated network, violation time, violation location, and violation type, and to monitor the vehicle driving state and the surrounding environment of the location of the express delivery vehicle in real time; an extraction and analysis module, configured to extract the environmental features of the surrounding environment of the location, and perform correlation analysis based on the violation type, the vehicle driving state, and the environmental features to obtain a first analysis result; an identification and analysis module, configured to visually present the violation locations of all express delivery vehicles through geographic information system technology and identify the easy-violation areas with dense distribution of violation points, and analyze the violation distribution in the easy-violation areas at different time periods to obtain a second analysis result; a construction and identification module, configured to construct a vehicle violation risk prediction model based on the first analysis result and the second analysis result, and use the vehicle violation risk prediction model to identify the violation risks existing in the monitored vehicle in real time; and a generation and sending module, configured to generate a violation warning message based on the violation risk and send the violation warning message to the affiliated network corresponding to the monitored vehicle.

[0012] Optionally, in the first implementation manner of the second aspect of the present invention, the acquisition monitoring module includes: an acquisition unit, configured to acquire the violation records of express delivery vehicles in real time; an analysis unit, configured to analyze the violation records to obtain the license plate number, affiliated network, violation time, violation location, and violation type; and a monitoring unit, configured to monitor the vehicle driving state and the surrounding environment of the location of the express delivery vehicle in real time.

[0013] Optionally, in the second implementation manner of the second aspect of the present invention, the extraction and analysis module includes: an extraction and encoding unit, configured to extract the environmental features of the surrounding environment of the location, and encode the violation type, the vehicle driving state, and the environmental features; a generation and calculation unit, configured to generate a candidate item set according to the encoding, and calculate the support degree of the candidate item set; a screening and extraction unit, configured to screen frequent item sets according to the support degree, and extract rules from the frequent item sets; a calculation and retention unit, configured to calculate the confidence degree of the rules, and retain strong rules according to the confidence degree to obtain a first analysis result.

[0014] Optionally, in the third implementation manner of the second aspect of the present invention, the recognition and analysis module includes: an extraction and setting unit, configured to extract the longitude and latitude information of the violation locations of all express delivery vehicles from a local database, and use the longitude and latitude information as input data to set a neighborhood radius and a minimum number of points; a calculation and marking unit, configured to calculate the number of points in the neighborhood, and if the number of points is greater than or equal to the minimum number of points, mark the longitude and latitude information as a core point; a recognition and presentation unit, configured to recognize all data points connected to the core point in terms of density to form a cluster, and visually present the cluster in a geographic information system to obtain an area prone to violations with a dense distribution of violation points; an analysis unit, configured to analyze the violation distribution of the area prone to violations in different time periods to obtain a second analysis result.

[0015] Optionally, in the fourth implementation manner of the second aspect of the present invention, the construction and recognition module includes: a construction unit, configured to construct a vehicle violation risk prediction model based on the first analysis result and the second analysis result; a calculation unit, configured to use the vehicle violation risk prediction model to calculate the violation risk score of a monitored vehicle in real time; a determination unit, configured to determine that the monitored vehicle has a violation risk when the violation risk score is greater than a preset first threshold.

[0016] Optionally, in the fifth implementation manner of the second aspect of the present invention, the generation and sending module includes: a generation unit, configured to generate a violation warning message based on the violation risk; an insertion unit, configured to insert the license plate number and the driver contact information corresponding to the monitored vehicle into the violation warning message; a sending unit, configured to send the violation warning message to the affiliated network point corresponding to the monitored vehicle.

[0017] Optionally, in the sixth implementation manner of the second aspect of the present invention, it further includes: a collection and calculation module, configured to collect the violation data of multiple express delivery enterprises, and calculate the average violation frequency of the entire express delivery industry according to the violation data; a setting and statistics module, configured to set a second threshold according to the average violation frequency, and regularly count the number of violations corresponding to the monitored vehicle; a generation module, configured to generate frequent violation record information when the number of violations is greater than the second threshold; an encryption module, configured to encrypt the frequent violation record information to obtain record encryption information; an upload module, configured to upload the record encryption information to the blockchain.

[0018] The third aspect of the present invention provides a multi-dimensional analysis monitoring vehicle violation device, where the multi-dimensional analysis monitoring vehicle violation device includes: a memory and at least one processor, and instructions are stored in the memory; at least one of the processors invokes the instructions in the memory to enable the multi-dimensional analysis monitoring vehicle violation device to execute each step of the multi-dimensional analysis monitoring vehicle violation method described in any one of the above.

[0019] The fourth aspect of the present invention provides a computer-readable storage medium, where instructions are stored on the computer-readable storage medium, and when the instructions are executed by a processor, each step of the multi-dimensional analysis monitoring vehicle violation method described in any one of the above is implemented.

[0020] In the technical solution of the present invention, by obtaining the violation records of express delivery vehicles in real time and monitoring the vehicle driving status and the surrounding environment of the location where the express delivery vehicle is located in real time, extracting the environmental characteristics of the surrounding environment of the location, performing correlation analysis based on the violation type, vehicle driving status, and environmental characteristics to obtain a first analysis result, visualizing the violation locations of all express delivery vehicles through geographic information system technology and identifying the high-violation areas where the violation points are densely distributed, analyzing the violation distribution in the high-violation areas at different time periods to obtain a second analysis result, constructing a vehicle violation risk prediction model based on the first analysis result and the second analysis result, and using the vehicle violation risk prediction model to identify the violation risks existing in the monitored vehicle in real time, it is possible to more comprehensively and deeply understand the violation occurrence rules, accurately locate the violation risk points of express delivery vehicles and perform risk early warnings, effectively reduce the probability of violations, reduce potential traffic accident hazards, and improve the urban road traffic safety level. Description of the Drawings

[0021] Figure 1 It is the first flowchart of the multi-dimensional analysis monitoring vehicle violation method provided by the embodiment of the present invention;

[0022] Figure 2 It is the second flowchart of the multi-dimensional analysis monitoring vehicle violation method provided by the embodiment of the present invention;

[0023] Figure 3 The third flowchart of the multi-dimensional analysis and monitoring method for vehicle violations provided by the embodiments of the present invention;

[0024] Figure 4 The fourth flowchart of the multi-dimensional analysis and monitoring method for vehicle violations provided by the embodiments of the present invention;

[0025] Figure 5 A schematic structural diagram of a multi-dimensional analysis and monitoring device for vehicle violations provided by the embodiments of the present invention;

[0026] Figure 6 Another schematic structural diagram of a multi-dimensional analysis and monitoring device for vehicle violations provided by the embodiments of the present invention;

[0027] Figure 7 A schematic structural diagram of a multi-dimensional analysis and monitoring device for vehicle violations provided by the embodiments of the present invention. Detailed implementation manners

[0028] The present invention provides a multi-dimensional analysis and monitoring method, device, equipment and storage medium for vehicle violations, which can more comprehensively and deeply insight into the laws of violations, accurately locate the risk points of violations of express delivery vehicles and conduct risk warnings, effectively reduce the probability of violations, reduce potential traffic accident hazards and improve the level of urban road traffic safety.

[0029] The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order different from that shown or described herein. In addition, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] For ease of understanding, the specific processes of the embodiments of the present invention are described below. Please refer to Figure 1 , an embodiment of the multi-dimensional analysis and monitoring method for vehicle violations in the embodiments of the present invention includes:

[0031] 101. Real-time obtain the violation records of express delivery vehicles. The violation records include license plate numbers, affiliated outlets, violation times, violation locations and violation types, and real-time monitor the vehicle driving status of express delivery vehicles and the surrounding environment conditions of their locations;

[0032] In this embodiment, by docking with the traffic management system, the violation information of the vehicle occurring at a specific time and location is obtained in real time. The express delivery vehicle is equipped with an in-vehicle GPS device, which can obtain information such as the current position, driving route, and speed of the vehicle in real time, and monitor the driving state of the express delivery vehicle and the surrounding environment of its location in real time. By docking with the data of the traffic management system, traffic flow, road construction, accident information, etc. around the vehicle are obtained. By docking with the data of the weather monitoring system, the weather conditions at the real-time position of the vehicle are obtained.

[0033] 102. Extract the environmental characteristics of the surrounding environment of the location, and conduct correlation analysis based on the violation type, vehicle driving state, and environmental characteristics to obtain the first analysis result;

[0034] In this embodiment, the environmental characteristics of the surrounding environment of the location are extracted. The environmental characteristics include road type and structure, traffic signals and signs, traffic flow and density, weather and road surface conditions, and accident-prone areas. Once sufficient environmental characteristics, violation types, and vehicle driving state data are collected, correlation analysis can be conducted based on the violation type, vehicle driving state, and environmental characteristics to obtain the first analysis result.

[0035] 103. Visualize the violation locations of all express delivery vehicles through geographic information system technology and identify the areas prone to violations where violation points are densely distributed, and analyze the violation distribution in the areas prone to violations at different time periods to obtain the second analysis result;

[0036] In this embodiment, through GIS technology, the violation locations of all express delivery vehicles are marked on the map, and each violation point can be represented by a different color or icon. Through point density analysis, it can be determined which geographical areas have relatively dense violation points, and these areas are usually the areas prone to violations. Analyze the violation distribution in the areas prone to violations at different time periods to obtain the second analysis result.

[0037] 104. Build a vehicle violation risk prediction model based on the first analysis result and the second analysis result, and use the vehicle violation risk prediction model to identify the violation risks existing in the monitored vehicle in real time;

[0038] In this embodiment, extract the key features of the first analysis result and the second analysis result, and build a vehicle violation risk prediction model based on the key features. Once the model is trained and verified, use the vehicle violation risk prediction model to identify the violation risks existing in the monitored vehicle in real time.

[0039] 105. Generate violation warning information based on the violation risk, and send the violation warning information to the affiliated network point corresponding to the monitored vehicle;

[0040] In this embodiment, a violation risk value is generated. If the violation risk value exceeds the set violation risk value, it indicates that the vehicle has a relatively high violation risk. Based on the violation risk, a violation warning message is generated and sent to the affiliated network point corresponding to the monitored vehicle. Once the warning message is transmitted to the affiliated network point, the corresponding management personnel will respond according to the violation warning message.

[0041] In the embodiment of the present invention, by obtaining the violation records of the express delivery vehicle in real time and monitoring the vehicle driving state and the surrounding environment of the location where the vehicle is located in real time, extracting the environmental characteristics of the surrounding environment of the location, performing correlation analysis based on the violation type, vehicle driving state, and environmental characteristics to obtain a first analysis result, visualizing the violation locations of all express delivery vehicles through geographic information system technology and identifying the high-violation areas where violation points are densely distributed, analyzing the violation distribution in the high-violation areas at different time periods to obtain a second analysis result, constructing a vehicle violation risk prediction model based on the first analysis result and the second analysis result, and using the vehicle violation risk prediction model to identify the violation risks existing in the monitored vehicle in real time, it is possible to more comprehensively and deeply understand the law of violation occurrence, accurately locate the violation risk points of the express delivery vehicle and conduct risk warning, effectively reduce the probability of violation occurrence, reduce potential traffic accident hazards, and improve the safety level of urban road traffic.

[0042] Please refer to Figure 2 , the second embodiment of the method for multi-dimensional analysis of the violation of the monitored vehicle in the embodiment of the present invention includes:

[0043] 201. Obtain the violation records of the express delivery vehicle in real time;

[0044] In this embodiment, by docking with the traffic management system, the violation information of the vehicle occurring at a specific time and location is obtained in real time.

[0045] 202. Analyze the violation records to obtain the license plate number, affiliated network point, violation time, violation location, and violation type;

[0046] In this embodiment, the violation records are analyzed to obtain the license plate number, affiliated network point, violation time, violation location, and violation type, and the analyzed data is structurally processed and organized into the form of a table or a database table.

[0047] 203. Monitor the vehicle driving state and the surrounding environment of the location where the express delivery vehicle is located in real time;

[0048] In this embodiment, the express delivery vehicle is equipped with an on-vehicle GPS device, which can obtain information such as the current position, driving route, and speed of the vehicle in real time, and can monitor the driving state of the express delivery vehicle and the surrounding environment of its location in real time. By docking with the data of the traffic management system, it can obtain traffic flow, road construction, accident information, etc. around the vehicle, and by docking with the data of the weather monitoring system, it can obtain the weather conditions at the real-time position of the vehicle.

[0049] 204. Extract the environmental features of the surrounding environment of the location, and encode the types of violations, vehicle driving states, and environmental features;

[0050] In this embodiment, the environmental features of the surrounding environment of the location are extracted. The environmental features include road type and structure, traffic signals and signs, traffic flow and density, weather and road surface conditions, and accident-prone areas. The types of violations, vehicle driving states, and environmental features are encoded.

[0051] 205. Generate a candidate item set according to the encoding, and calculate the support degree of the candidate item set;

[0052] In this embodiment, the encoding is performed by standardizing the data, converting each possible behavior, state, or feature into a digital identifier. A candidate item set is generated according to the encoding. The candidate item set refers to all possible combinations in a specific data set and is generated by the "frequent item set" algorithm. Different combinations of features or behaviors can be generated from the encoded data, and the support degree of the candidate item set is calculated. The support degree is an important concept in association rule analysis, indicating the occurrence frequency of a certain candidate item set in the data set.

[0053] 206. Screen the frequent item sets according to the support degree, and extract rules from the frequent item sets;

[0054] In this embodiment, the frequent item sets are screened according to the support degree. The frequent item set refers to the item set with a relatively high occurrence frequency in the data set. The process of screening the frequent item sets is first to set a minimum support degree threshold for screening the item sets with a relatively high occurrence frequency in the data set. For example, the minimum support degree is set to 10% (that is, the item set with a support degree greater than or equal to 0.1). By calculating the support degree of each candidate item set, the item sets with a support degree higher than the set threshold are selected as frequent item sets. For example, for the combination of driving behaviors in the transportation data, if the combination of speeding and rapid acceleration appears 200 times in 1000 records, its support degree is 0.2, and the set minimum support degree is 0.15, then this combination will be screened as a frequent item set. After screening out the frequent item sets, the next step is to extract association rules from these frequent item sets.

[0055] 207. Calculate the confidence of the rules, retain the strong rules according to the confidence, and obtain the first analysis result;

[0056] In this embodiment, the confidence of the calculation rules is calculated, and rules with relatively high confidence and lift are selected. A minimum confidence threshold (such as 50%) and a minimum lift threshold (such as 1) are set to screen meaningful association rules. For example, the confidence of the rule "overspeed and sudden acceleration" is 50%, and the lift is 2, indicating that this rule is a relatively strong association rule, and a first analysis result is obtained, which can be further used for prediction and decision-making.

[0057] In the embodiment of the present invention, traffic violation records and vehicle status are obtained in real time to ensure quick response and timely problem handling, covering multi-dimensional data such as license plate numbers, traffic violation details, driving status, and environmental characteristics, providing a comprehensive analysis perspective. Through coding, frequent item set analysis, and rule extraction, automated and intelligent data mining is realized, potential rules and problems can be discovered, effective information is screened through support and confidence, redundant data is reduced, the analysis efficiency and accuracy are improved, and the strong rules and analysis results obtained provide strong support for subsequent decision-making or improvement measures, and can help optimize the driving management of express delivery vehicles.

[0058] Please refer to Figure 3 , the third embodiment of the multi-dimensional analysis method for monitoring vehicle traffic violations in the embodiment of the present invention includes:

[0059] 301. Extract the longitude and latitude information of the traffic violation locations of all express delivery vehicles from the local database, and use the longitude and latitude information as input data to set the neighborhood radius and the minimum number of points;

[0060] In this embodiment, a data table containing the traffic violation location information of express delivery vehicles is extracted from the local database. The data table includes a vehicle ID field, a traffic violation time field, a traffic violation location longitude field, and a traffic violation location latitude field, and the longitude and latitude information is used as input data to set the neighborhood radius and the minimum number of points.

[0061] 302. Calculate the number of points within the neighborhood. If the number of points is greater than or equal to the minimum number of points, mark the longitude and latitude information as a core point;

[0062] In this embodiment, the number of points within the neighborhood is calculated. In spatial data analysis, a neighborhood refers to other points that can be included within a certain fixed radius around a certain point (such as the longitude and latitude of the traffic violation location). If the number of points is greater than or equal to the minimum number of points, mark the longitude and latitude information as a core point.

[0063] 303. Identify all data points that are density-connected to the core points to form a cluster, and visually present the cluster in a geographic information system to obtain an area prone to traffic violations with a dense distribution of traffic violation points;

[0064] In this embodiment, through a spatial clustering algorithm (such as DBSCAN) or a similar algorithm, all points with relatively high density (i.e., points within the neighborhood of core points) are identified as "connected data points". These points may be the direct neighbors of core points or "border points" connected to core points through other neighborhood points. Connecting these density points can form a cluster, and the cluster is visually presented in a geographic information system to generate a map of a hot spot area of violations, obtaining an area prone to violations where violation points are densely distributed.

[0065] 304. Analyze the distribution of violations in the area prone to violations during different time periods to obtain a second analysis result;

[0066] In this embodiment, a clustering algorithm is used to analyze violation points during different time periods to explore whether there is a highly concentrated period of violation behavior. For each area prone to violations, the violation behavior is statistically analyzed and visualized by time period to check whether there is an obvious change trend. For example, violations may be frequent in some areas during the peak commuting periods and less frequent during other time periods. Through the above analysis, the characteristics of the violation distribution during different time periods are obtained, thereby identifying the concentrated areas of violation behavior during specific time periods to obtain a second analysis result.

[0067] 305. Construct a vehicle violation risk prediction model based on the first analysis result and the second analysis result;

[0068] In this embodiment, the key features of the first analysis result and the second analysis result are extracted, and a vehicle violation risk prediction model is constructed based on the key features.

[0069] 306. Use the vehicle violation risk prediction model to calculate the violation risk score of the monitored vehicle in real time;

[0070] In this embodiment, once the model is trained and verified, the vehicle violation risk prediction model is used to calculate the violation risk score of the monitored vehicle in real time.

[0071] 307. When the violation risk score is greater than a preset first threshold, it is determined that the monitored vehicle has a violation risk;

[0072] In this embodiment, the preset first threshold is a critical point of the risk score. Once the risk score of the monitored vehicle exceeds the preset first threshold, it is determined that the monitored vehicle has a violation risk.

[0073] In the embodiments of the present invention, through clustering analysis and visualization techniques, geographical areas with frequent violations can be identified, laying a foundation for further risk prediction. Combining with time period analysis, the violation distribution in different time periods can be dynamically tracked, and potential risk changes can be identified in a timely manner. By constructing a violation risk prediction model, the violation risks of vehicles can be accurately predicted and quantified, avoiding manual intervention. Through the violation risk scoring mechanism, a warning can be issued immediately when the vehicle reaches the preset risk threshold, facilitating quick action. The entire process relies on big data analysis, forming a systematic and intelligent decision support system from data extraction to risk prediction.

[0074] Please refer to Figure 4 , the fourth embodiment of the method for multi-dimensional analysis and monitoring of vehicle violations in the embodiments of the present invention includes:

[0075] 401. Generate violation warning information based on violation risks;

[0076] In this embodiment, generating violation warning information based on violation risks includes predicting the types of risks that occur and the violation risk levels.

[0077] 402. Insert the license plate number of the monitored vehicle and the driver's contact information into the violation warning information;

[0078] In this embodiment, the driver's contact information (such as mobile phone number) comes from vehicle registration information, insurance company records, or registration information during vehicle annual inspection, and the license plate number of the monitored vehicle and the driver's contact information are inserted into the violation warning information.

[0079] 403. Send the violation warning information to the affiliated network point corresponding to the monitored vehicle;

[0080] In this embodiment, the violation warning information is sent to the affiliated network point corresponding to the monitored vehicle through text messages, emails, or internal systems.

[0081] 404. Collect violation data of multiple express delivery enterprises, and calculate the average violation frequency of the entire express delivery industry according to the violation data;

[0082] In this embodiment, establish a data sharing mechanism and cooperation agreement with major express delivery companies, collect violation data of multiple express delivery enterprises, ensure the synchronization of the frequency and time of collecting data from each express delivery enterprise, and calculate the average violation frequency of the entire express delivery industry according to the violation data.

[0083] 405. Set a second threshold according to the average violation frequency, and regularly count the number of violations corresponding to the monitored vehicle;

[0084] In this embodiment, a second threshold is set according to the average violation frequency, and the number of violations corresponding to the monitored vehicle is regularly counted. By collecting and updating the violation data of each enterprise in real time, the number of violations of each vehicle can be calculated regularly (such as monthly or quarterly) and compared with the set second threshold to determine whether it exceeds the standard.

[0085] 406. When the number of violations is greater than the second threshold, a frequent violation record information is generated;

[0086] In this embodiment, when the number of violations is greater than the second threshold, it indicates that the vehicle's violation exceeds the standard, and a frequent violation record information is generated.

[0087] 407. Encrypt the frequent violation record information to obtain record encrypted information;

[0088] In this embodiment, the asymmetric encryption algorithm is used to encrypt the frequent violation record information to obtain record encrypted information. Once the encryption is completed, data verification is performed on the record encrypted information to ensure that the encrypted data does not lose information or is tampered with.

[0089] 408. Upload the record encrypted information to the blockchain;

[0090] In this embodiment, through the transaction mechanism of the blockchain, the record encrypted information is uploaded. For example, in Ethereum, a transaction can be sent to trigger a smart contract for data storage, and this transaction will be verified and finally written into the blockchain.

[0091] In the embodiment of the present invention, by generating violation warning information in a timely manner and sending it to relevant outlets, the violation risk can be quickly responded to and timely processing can be ensured. By counting the violation data of multiple express delivery enterprises, the overall situation of the industry can be accurately evaluated, providing a reference for the risk management of monitored vehicles. By setting thresholds and regularly counting the number of violations, the system can intelligently identify vehicles with frequent violations and perform targeted processing. Encrypting and uploading the frequent violation records to the blockchain ensures the security, immutability, and transparency of information, helps to improve the fairness and trust of management. The entire process relies on automated data processing and encrypted uploading, improving the efficiency and accuracy of violation risk management and reducing manual intervention.

[0092] The multi-dimensional analysis method for monitoring vehicle violations in the embodiment of the present invention is described above. Next, the multi-dimensional analysis device for monitoring vehicle violations in the embodiment of the present invention will be described. Please refer to Figure 5 , an embodiment of the multi-dimensional analysis device for monitoring vehicle violations in the embodiment of the present invention includes:

[0093] An acquisition and monitoring module 501, configured to acquire in real time the violation records of express delivery vehicles. The violation records include license plate numbers, affiliated outlets, violation times, violation locations, and violation types, and to monitor in real time the vehicle driving status of express delivery vehicles and the environmental conditions around their locations;

[0094] An extraction and analysis module 502, configured to extract the environmental features of the environmental conditions around the location, and perform correlation analysis based on the violation type, vehicle driving status, and environmental features to obtain a first analysis result;

[0095] An identification and analysis module 503, configured to visually present the violation locations of all express delivery vehicles through geographic information system technology and identify the areas prone to violations where violation points are densely distributed, and analyze the violation distribution in the areas prone to violations at different time periods to obtain a second analysis result;

[0096] A construction and identification module 504, configured to construct a vehicle violation risk prediction model based on the first analysis result and the second analysis result, and use the vehicle violation risk prediction model to identify in real time the violation risks existing in the monitored vehicles;

[0097] A generation and sending module 505, configured to generate violation warning information based on the violation risks and send the violation warning information to the affiliated outlets corresponding to the monitored vehicles.

[0098] In this embodiment, by acquiring in real time the violation records of express delivery vehicles and monitoring in real time the vehicle driving status of express delivery vehicles and the environmental conditions around their locations, extracting the environmental features of the environmental conditions around the location, performing correlation analysis based on the violation type, vehicle driving status, and environmental features to obtain a first analysis result, visually presenting the violation locations of all express delivery vehicles through geographic information system technology and identifying the areas prone to violations where violation points are densely distributed, analyzing the violation distribution in the areas prone to violations at different time periods to obtain a second analysis result, constructing a vehicle violation risk prediction model based on the first analysis result and the second analysis result, and using the vehicle violation risk prediction model to identify in real time the violation risks existing in the monitored vehicles, it is possible to more comprehensively and deeply understand the laws of violations, accurately locate the violation risk points of express delivery vehicles and conduct risk warnings, effectively reduce the probability of violations, reduce potential traffic accident hazards, and improve the safety level of urban road traffic.

[0099] Please refer to Figure 6 , another embodiment of the multi-dimensional analysis monitoring vehicle violation device in the embodiment of the present invention includes:

[0100] An acquisition and monitoring module 501, configured to acquire in real time the violation records of express delivery vehicles. The violation records include license plate numbers, affiliated outlets, violation times, violation locations, and violation types, and to monitor in real time the vehicle driving status of express delivery vehicles and the environmental conditions around their locations;

[0101] The extraction and analysis module 502 is used to extract the environmental features of the surrounding environment of the location, and perform correlation analysis based on the violation type, vehicle driving state, and environmental features to obtain the first analysis result;

[0102] The identification and analysis module 503 is used to visually present the violation locations of all express delivery vehicles through geographic information system technology, identify the high-violation areas with dense distribution of violation points, and analyze the violation distribution in the high-violation areas at different time periods to obtain the second analysis result;

[0103] The construction and identification module 504 is used to construct a vehicle violation risk prediction model based on the first analysis result and the second analysis result, and use the vehicle violation risk prediction model to identify the violation risks existing in the monitored vehicles in real time;

[0104] The generation and sending module 505 is used to generate a violation warning message based on the violation risk and send the violation warning message to the affiliated network point corresponding to the monitored vehicle;

[0105] In this embodiment, the acquisition and monitoring module 501 includes: an acquisition unit 5011, which is used to acquire the violation records of express delivery vehicles in real time; an analysis unit 5012, which is used to analyze the violation records to obtain the license plate number, affiliated network point, violation time, violation location, and violation type; a monitoring unit 5013, which is used to monitor the vehicle driving state and the surrounding environment of the location of the express delivery vehicle in real time.

[0106] In this embodiment, the extraction and analysis module 502 includes: an extraction and coding unit 5021, which is used to extract the environmental features of the surrounding environment of the location and code the violation type, vehicle driving state, and environmental features; a generation and calculation unit 5022, which is used to generate a candidate item set according to the code and calculate the support degree of the candidate item set; a screening and extraction unit 5023, which is used to screen the frequent item sets according to the support degree and extract rules from the frequent item sets; a calculation and retention unit 5024, which is used to calculate the confidence of the rules and retain the strong rules according to the confidence to obtain the first analysis result.

[0107] In this embodiment, the recognition and analysis module 503 includes: an extraction and setting unit 5031, configured to extract the longitude and latitude information of the violation locations of all express delivery vehicles from the local database, and use the longitude and latitude information as input data to set the neighborhood radius and the minimum number of points; a calculation and marking unit 5032, configured to calculate the number of points within the neighborhood, and if the number of points is greater than or equal to the minimum number of points, mark the longitude and latitude information as a core point; an identification and presentation unit 5033, configured to identify all data points that are density-connected to the core points to form a cluster, and visually present the cluster in a geographic information system to obtain an area prone to violations with a dense distribution of violation points; and an analysis unit 5034, configured to analyze the violation distribution of the area prone to violations in different time periods to obtain a second analysis result.

[0108] In this embodiment, the construction and recognition module 504 includes: a construction unit 5041, configured to construct a vehicle violation risk prediction model based on the first analysis result and the second analysis result; a calculation unit 5042, configured to use the vehicle violation risk prediction model to calculate the violation risk score of the monitored vehicle in real time; and a determination unit 5043, configured to determine that the monitored vehicle has a violation risk when the violation risk score is greater than a preset first threshold.

[0109] In this embodiment, the generation and sending module 505 includes: a generation unit 5051, configured to generate a violation warning message based on the violation risk; an insertion unit 5052, configured to insert the license plate number and the driver contact information corresponding to the monitored vehicle into the violation warning message; and a sending unit 5053, configured to send the violation warning message to the affiliated network point corresponding to the monitored vehicle.

[0110] In this embodiment, it further includes: a collection and calculation module 506, configured to collect the violation data of multiple express delivery enterprises and calculate the average violation frequency of the entire express delivery industry according to the violation data; a setting and statistics module 507, configured to set a second threshold according to the average violation frequency and regularly count the number of violations corresponding to the monitored vehicle; a generation module 508, configured to generate a frequent violation record information when the number of violations is greater than the second threshold; an encryption module 509, configured to encrypt the frequent violation record information to obtain an encrypted record information; and an upload module 510, configured to upload the encrypted record information to the blockchain.

[0111] Above Figure 5 And Figure 6 The multi-dimensional analysis and monitoring vehicle violation device in the embodiment of the present invention has been described in detail from the perspective of modular functional entities. Next, the multi-dimensional analysis and monitoring vehicle violation device in the embodiment of the present invention will be described in detail from the perspective of hardware processing.

[0112] Figure 7FIG. 0 is a schematic structural diagram of a multi-dimensional analysis and monitoring vehicle violation device provided by an embodiment of the present invention. The multi-dimensional analysis and monitoring vehicle violation device 600 may vary greatly due to different configurations or performances, and may include one or more processors (central processing units, CPUs) 610 (for example, one or more processors) and a memory 620, and one or more storage media 630 (for example, one or more mass storage devices) storing application programs 633 or data 632. Among them, the memory 620 and the storage media 630 may be transient storage or persistent storage. The program stored in the storage media 630 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the multi-dimensional analysis and monitoring vehicle violation device 600. Further, the processor 610 may be configured to communicate with the storage media 630 and execute a series of instruction operations in the storage media 630 on the multi-dimensional analysis and monitoring vehicle violation device 600 to implement the steps of the multi-dimensional analysis and monitoring vehicle violation method provided by the above method embodiments.

[0113] The multi-dimensional analysis and monitoring vehicle violation device 600 may further include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input / output interfaces 660, and / or one or more operating systems 631, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, and so on. Those skilled in the art can understand that Figure 7 the shown structure of the multi-dimensional analysis and monitoring vehicle violation device does not constitute a limitation on the multi-dimensional analysis and monitoring vehicle violation device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0114] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer, the computer is caused to execute the steps of the multi-dimensional analysis and monitoring vehicle violation method.

[0115] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, devices, or units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0116] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0117] Finally, it should be noted that the above are only preferred examples of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A multi-dimensional analysis method for monitoring vehicle violations, characterized in that, Including: Obtaining the violation records of express delivery vehicles in real time, where the violation records include license plate numbers, affiliated outlets, violation times, violation locations, and violation types, and monitoring the vehicle driving status of the express delivery vehicles and the environmental conditions around their locations in real time; Extracting the environmental features of the environmental conditions around the location, and performing correlation analysis based on the violation type, the vehicle driving status, and the environmental features to obtain a first analysis result; Visualizing the violation locations of all express delivery vehicles through geographic information system technology and identifying areas prone to violations with dense distribution of violation points, and analyzing the violation distribution in the areas prone to violations at different time periods to obtain a second analysis result; Constructing a vehicle violation risk prediction model based on the first analysis result and the second analysis result, and using the vehicle violation risk prediction model to identify the violation risks existing in the monitored vehicles in real time; Generating violation warning information based on the violation risks and sending the violation warning information to the affiliated outlets corresponding to the monitored vehicles.

2. The multi-dimensional analysis and monitoring method for vehicle violations according to claim 1, wherein The obtaining the violation records of express delivery vehicles in real time, where the violation records include license plate numbers, affiliated outlets, violation times, violation locations, and violation types, and monitoring the vehicle driving status of the express delivery vehicles and the environmental conditions around their locations in real time, includes: Obtaining the violation records of express delivery vehicles in real time; Parsing the violation records to obtain license plate numbers, affiliated outlets, violation times, violation locations, and violation types; Monitoring the vehicle driving status of the express delivery vehicles and the environmental conditions around their locations in real time.

3. The multi-dimensional analysis and monitoring method for vehicle violations according to claim 1, wherein The extracting the environmental features of the environmental conditions around the location, and performing correlation analysis based on the violation type, the vehicle driving status, and the environmental features to obtain a first analysis result, includes: Extracting the environmental features of the environmental conditions around the location, and encoding the violation type, the vehicle driving status, and the environmental features; Generating candidate item sets according to the encoding, and calculating the support degrees of the candidate item sets; Screening frequent item sets according to the support degrees, and extracting rules from the frequent item sets; Calculating the confidence degrees of the rules, and retaining strong rules according to the confidence degrees to obtain a first analysis result.

4. The multi-dimensional analysis and monitoring method for vehicle violations according to claim 1, characterized in that, The visualizing the violation locations of all express delivery vehicles through geographic information system technology and identifying areas prone to violations with dense distribution of violation points, and analyzing the violation distribution in the areas prone to violations at different time periods to obtain a second analysis result, includes: Extracting the longitude and latitude information of the violation locations of all express delivery vehicles from the local database, and setting the neighborhood radius and the minimum number of points with the longitude and latitude information as input data; Calculating the number of points in the neighborhood, and if the number of points is greater than or equal to the minimum number of points, marking the longitude and latitude information as a core point; Identifying all data points density-connected to the core point to form a cluster, and visualizing the cluster in the geographic information system to obtain an area prone to violations with dense distribution of violation points; Analyzing the violation distribution in the area prone to violations at different time periods to obtain a second analysis result.

5. The multi-dimensional analysis and monitoring method for vehicle violations according to claim 1, wherein Construct a vehicle violation risk prediction model based on the first analysis result and the second analysis result, and use the vehicle violation risk prediction model to identify the violation risks of the monitored vehicle in real time, including: Construct a vehicle violation risk prediction model based on the first analysis result and the second analysis result; Use the vehicle violation risk prediction model to calculate the violation risk score of the monitored vehicle in real time; When the violation risk score is greater than a preset first threshold, it is determined that the monitored vehicle has a violation risk.

6. The multi-dimensional analysis and monitoring method for vehicle violations according to claim 1, characterized in that, Generate a violation warning message based on the violation risk, and send the violation warning message to the affiliated network point corresponding to the monitored vehicle, including: Generate a violation warning message based on the violation risk; Insert the license plate number and driver contact information corresponding to the monitored vehicle into the violation warning message; Send the violation warning message to the affiliated network point corresponding to the monitored vehicle.

7. The multi-dimensional analysis and monitoring method for vehicle violations according to claim 1, wherein After generating a violation warning message based on the violation risk and sending the violation warning message to the affiliated network point corresponding to the monitored vehicle, it further includes: Collect the violation data of multiple express delivery enterprises, and calculate the average violation frequency of the entire express delivery industry according to the violation data; Set a second threshold according to the average violation frequency, and regularly count the number of violations corresponding to the monitored vehicle; When the number of violations is greater than the second threshold, generate frequent violation record information; Encrypt the frequent violation record information to obtain record encryption information; Upload the record encryption information to the blockchain.

8. A multi-dimensional analysis and monitoring device for vehicle violations, characterized in that, Including: An acquisition monitoring module for obtaining the violation records of express delivery vehicles in real time. The violation records include license plate numbers, affiliated network points, violation times, violation locations, and violation types, and for monitoring the vehicle driving status and the surrounding environment of the location where the vehicle is located in real time; An extraction and analysis module for extracting the environmental characteristics of the surrounding environment of the location, and performing correlation analysis based on the violation type, the vehicle driving status, and the environmental characteristics to obtain a first analysis result; An identification and analysis module for visually presenting the violation locations of all express delivery vehicles through geographic information system technology and identifying the high-violation areas where violation points are densely distributed, and analyzing the violation distribution in the high-violation areas at different time periods to obtain a second analysis result; A construction and identification module for constructing a vehicle violation risk prediction model based on the first analysis result and the second analysis result, and using the vehicle violation risk prediction model to identify the violation risks of the monitored vehicle in real time; A generation and sending module for generating a violation warning message based on the violation risk and sending the violation warning message to the affiliated network point corresponding to the monitored vehicle.

9. A multi-dimensional analysis and monitoring vehicle violation device, characterized in that, The multi-dimensional analysis monitoring vehicle violation device includes: a memory and at least one processor, and instructions are stored in the memory; At least one of the processors calls the instructions in the memory so that the multi-dimensional analysis monitoring vehicle violation device executes each step of the multi-dimensional analysis monitoring vehicle violation method described in any one of claims 1-7.

10. A computer-readable storage medium, on which instructions are stored, characterized in that, When the instruction is executed by a processor, it implements each step of the multi-dimensional analysis and monitoring method for vehicle violations as described in any one of claims 1-7.

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