Method and system for transport driver and vehicle association and management based on two-dimensional code

Abstract

The application discloses a method and system for transport driver and vehicle association and management based on a two-dimensional code, and relates to the technical field of transport management.The application realizes efficient binding and dynamic association of vehicle and driver information through two-dimensional code technology and a hash algorithm, solves the problems of information dispersion and update lag in traditional management, can monitor abnormal behavior in the transport process in real time and generate early warning signals in combination with real-time data synchronization and an abnormal detection algorithm, significantly improves the transport safety management level, and, in addition, through data aggregation and analysis of early warning records, a comprehensive management data set and a visual report are generated, accurate decision support is provided for managers, resource allocation is optimized, and the efficiency and fine level of transport management are comprehensively improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of transportation management, in particular to a method and system for associating and managing a transportation driver with a vehicle based on a two-dimensional code. BACKGROUND

[0002] Transportation management is a core link in the field of modern logistics and is crucial for improving efficiency, ensuring safety and reducing costs. With the rapid development of the logistics industry, the management of transportation vehicles and drivers has become increasingly complex. Traditional management methods have been unable to meet the demands of efficiency and precision. Currently, vehicle and driver management methods that rely on paper documents or simple electronic spreadsheets generally have problems such as information update lag, data isolation and low efficiency of manual verification. These limitations result in cumbersome management processes and make it difficult to monitor vehicle status and driver behavior in real time, thereby affecting transportation safety and efficiency.

[0003] Under this background, the core challenge facing transportation management is how to achieve dynamic association and real-time interaction of vehicle and driver information. First, the information of vehicles and drivers is stored in a scattered manner, and there is a lack of a unified digital entry, resulting in time-consuming and error-prone binding and verification processes. This information fragmentation further leads to inefficiency in data interaction, making it impossible to obtain real-time vehicle driving status, driver behavior or cargo information, which limits the management platform's ability to timely warn of risks. In addition, the lag in data analysis also stems from this, making it difficult for managers to generate accurate performance evaluations or risk predictions based on real-time data, affecting decision-making efficiency. SUMMARY

[0004] The purpose of the present application is to provide a method and system for associating and managing a transportation driver with a vehicle based on a two-dimensional code. The application utilizes two-dimensional code technology to achieve dynamic association and real-time interaction of vehicle and driver information, ensuring transportation safety through real-time monitoring and early warning, and improving the efficiency and refinement level of transportation management.

[0005] The purpose of the present application can be achieved through the following technical solutions:

[0006] The present application provides a method for associating and managing a transportation driver with a vehicle based on a two-dimensional code, comprising the following steps:

[0007] According to the structured data set, a unique identification code is generated using a hash algorithm to obtain a vehicle-driver binding identification set. Then, each binding identification is encoded into a dynamic two-dimensional code using a two-dimensional code generation algorithm to generate a dynamic two-dimensional code image set.

[0008] The dynamic two-dimensional code image set is pushed to the transportation management terminal through a mobile terminal application interface, and the two-dimensional code data is protected using an encrypted transmission protocol to obtain a two-dimensional code data set that can be scanned on a mobile terminal.

[0009] When scanning the two-dimensional code on the mobile phone, the two-dimensional code data uploaded by the scanning device is obtained, the decoding algorithm is used to extract the binding identifier and the timestamp, and the binding identifier that passes the verification is obtained;

[0010] According to the binding identifier that passes the verification, the corresponding vehicle-driver association data is retrieved from the transportation management system database, the real-time data synchronization technology is used to update the vehicle position, the driver behavior and the cargo state, and the real-time transportation state data set is obtained;

[0011] According to the real-time transportation state data set, the abnormal detection algorithm is used to analyze the vehicle driving track deviation and the driver fatigue driving abnormal behavior, and the risk warning data set is obtained;

[0012] According to the risk warning data set, the data pushing technology is used to transmit the warning signal to the management terminal through the mobile phone application interface, and the real-time warning record set is obtained;

[0013] According to the real-time warning record set, the data aggregation technology is used to integrate the vehicle-driver association data, the transportation state and the warning log, and the comprehensive management data set is obtained.

[0014] Further, the vehicle-driver binding identifier set is obtained, specifically including:

[0015] The structured data set is obtained, the vehicle number and the driver identity field are extracted therefrom, the input parameter pair is generated, when the vehicle number or the driver identity field in the input parameter pair is missing, it is marked as an invalid parameter pair, discarded, and the valid input parameter pair set is obtained;

[0016] The hash function H(v, d) is used to process the valid input parameter pair set, to generate a fixed-length unique identifier code, and then through the hash function processing, the unique identifier code set corresponding to each vehicle number and driver identity pair is obtained;

[0017] According to the unique identifier code set, the vehicle-driver binding identifier is generated and stored in the binding identifier set, when there is a duplicate identifier in the binding identifier set, the latest generated identifier is retained by comparing the identifier code value, and the old identifier is discarded, to obtain the de-duplicated binding identifier set;

[0018] According to the de-duplicated binding identifier set, the generation timestamp of each binding identifier is obtained, which is attached to the binding identifier record, and then sorted by timestamp to obtain the binding identifier set arranged in time sequence;

[0019] From the binding identifier set arranged in time sequence, the vehicle number and the driver identity corresponding to each binding identifier are extracted to generate a binding relationship record, when the binding relationship record does not conform to the preset business rule, it is marked as an abnormal record and discarded, and the binding relationship record set that conforms to the rule is obtained;

[0020] According to the rule-compliant binding relationship record set, a final vehicle-driver binding identification data set is generated, and through data verification, the data set integrity is judged to obtain a verified binding identification data set;

[0021] The verified binding identification data set is saved to a database by using a data storage tool, and whether the storage is successful is judged through database indexing to obtain a persistent vehicle-driver binding identification data set.

[0022] Further, a dynamic two-dimensional code image set is generated, specifically including:

[0023] A vehicle-driver binding identification set is obtained, from which each binding identification is extracted to generate data input containing vehicle identification and driver identification, and then a two-dimensional code generation algorithm is used to encode each binding identification, embed a timestamp t and a check bit c, and generate a dynamic two-dimensional code, wherein the timestamp t represents the generation time, and the check bit c is calculated by a hash function H(b, t), and b is the binding identification;

[0024] When the check bit c of the dynamic two-dimensional code is consistent with the expected value, the dynamic two-dimensional code is stored as a first image, and when it is inconsistent, the code is discarded and regenerated;

[0025] Through image processing technology, the first image is compressed to generate a second image, and the readability of the two-dimensional code is maintained, and then according to the second image, a file storage system is used to archive it to an image set to generate a unique file identification;

[0026] When the file identification in the image set matches the binding identification, it is confirmed that the dynamic two-dimensional code generation is completed, the image set is output, each dynamic two-dimensional code in the image set is decoded by a verification algorithm, the timestamp t and the check bit c are extracted, and the validity of the binding identification is judged to obtain a verification result.

[0027] Further, the verified binding identification is obtained, specifically including:

[0028] The two-dimensional code data collected by the mobile terminal through scanning equipment is obtained, image processing technology is used to pre-process the two-dimensional code image to obtain clear two-dimensional code data, and then a decoding algorithm is used to extract the binding identification and the timestamp from the clear two-dimensional code data to obtain the extracted binding identification and timestamp data;

[0029] According to the extracted timestamp and the current time, the difference between the two is calculated to obtain time difference data, and when the time difference data is less than a preset threshold, the two-dimensional code is determined to be valid, and the valid binding identification is obtained;

[0030] The encrypted algorithm is used to encrypt the effective judgment of the binding identifier, and the encrypted binding identifier data is obtained, and the corresponding user identity information is obtained by matching the encrypted binding identifier data through the pre-established database, and the identity identifier that passes the verification is obtained.

[0031] According to the identity identifier that passes the verification, the binding confirmation information is generated, and the final business binding result is obtained.

[0032] Further, the real-time transportation state data set is obtained, specifically including:

[0033] The real-time data synchronization technology is used to obtain the position information from the vehicle positioning device, update the position field in the vehicle data, obtain the real-time vehicle position data set, and obtain the behavior data through the driver behavior monitoring device, and when the behavior data exceeds the preset threshold, it is marked as abnormal behavior, and the driver behavior analysis data set is obtained.

[0034] The cargo state information is obtained from the cargo sensor, combined with the position information and the behavior data, the cargo state is judged whether it is normal, and the cargo state analysis data set is obtained.

[0035] According to the vehicle position data set, the behavior analysis data set and the cargo state analysis data set, the data fusion processing is executed, and the real-time transportation state data set is generated.

[0036] According to the real-time transportation state data set, the time series analysis algorithm is used to analyze the change trend of the transportation state, and the transportation state trend data set is obtained, and it is judged whether there is an abnormality in the transportation process, if there is an abnormality, an abnormal alarm information is generated, and a transportation abnormality detection result is obtained.

[0037] Further, the risk warning data set is obtained, specifically including:

[0038] According to the real-time transportation state data set, the isolated forest algorithm is used for analysis, the abnormal behavior scores of vehicle trajectory deviation and driver fatigue driving are calculated, and the abnormal score set is obtained, and when the score in the abnormal score set exceeds the preset threshold A, the risk warning signal is generated, and the warning signal set is obtained.

[0039] According to the warning signal set, the time series analysis technology is used to identify the continuity and frequency of abnormal behavior, and the abnormal behavior mode is obtained, and the clustering analysis technology is used to group similar abnormal behaviors according to the abnormal behavior mode, and the abnormal behavior classification set is obtained.

[0040] The classification result is obtained from the abnormal behavior classification set, and the risk warning data set containing the abnormal type and risk level is generated.

[0041] Further, the real-time warning record set is obtained, specifically including:

[0042] Raw data is obtained from a risk early warning data set, preprocessed through data pushing technology to obtain a standardized data set, and when there are outliers in the standardized data set, an abnormal type is determined through an outlier detection algorithm to obtain an abnormal classification result;

[0043] According to the abnormal classification result, an application programming interface is used to transmit the early warning signal to the mobile terminal application program to obtain a transmission completion signal;

[0044] When the mobile terminal application program receives the transmission completion signal, an early warning log containing the abnormal type, occurrence time and geographic location is generated through the management terminal to obtain a log record, and a real-time updating mechanism is used to store the log record to a record set to obtain a real-time early warning record set;

[0045] The latest record is obtained from the real-time early warning record set, the early warning information is transmitted to the management terminal through the push notification mechanism to obtain the final early warning output, and when the final early warning output contains a high-priority abnormality, an instant alarm mechanism is triggered through the application programming interface to obtain an alarm confirmation signal.

[0046] Further, a comprehensive management data set is obtained, specifically including:

[0047] The real-time early warning record set is obtained, data cleaning technology is used to remove duplicate records and missing values to obtain a cleaned record set, and the cleaned record set is grouped through a clustering algorithm to generate a classification data set based on vehicle association and driver association;

[0048] When the transportation state record in the classification data set is complete, the transportation efficiency is calculated using a weighted average method to obtain a transportation efficiency indicator, and when the record is incomplete, the missing values are completed and recalculated to obtain the transportation efficiency indicator;

[0049] According to the transportation efficiency indicator and the early warning log, a decision tree algorithm is used to analyze the driver behavior pattern to generate a driver performance score, and a risk event is extracted from the early warning log, and a frequency statistical method is used to calculate the risk distribution to obtain risk distribution data;

[0050] Through data aggregation technology, the transportation efficiency indicator, the driver performance score and the risk distribution data are integrated to generate a comprehensive management data set, and a visualization technology is used to process the comprehensive management data set to generate an analysis report containing transportation efficiency, driver performance and risk distribution.

[0051] Further, before obtaining the vehicle-driver binding identifier set, including: obtaining vehicle information and driver information from the transportation management system database, using data cleaning technology to standardize the vehicle number, driver identity and driving record fields to generate a uniform format vehicle-driver association data set to obtain a structured data set.

[0052] The application provides a system for two-dimensional code-based transport driver and vehicle association and management, and a method for two-dimensional code-based transport driver and vehicle association and management, comprising the following steps of:

[0053] A two-dimensional code generation module generates a dynamic two-dimensional code of vehicle-driver binding according to a structured data set, generates a vehicle-driver unique binding identifier set by using a hash algorithm, and then encodes each binding identifier into a dynamic two-dimensional code containing a timestamp by using a two-dimensional code generation algorithm to generate a dynamic two-dimensional code image set.

[0054] A data transmission module pushes the dynamic two-dimensional code image set to a transport management terminal through a mobile terminal application interface, and protects the two-dimensional code data by using an encrypted transmission protocol to obtain a two-dimensional code data set that can be scanned on a mobile terminal.

[0055] A two-dimensional code verification module obtains two-dimensional code data uploaded by a scanning device when scanning the two-dimensional code on the mobile terminal, extracts the binding identifier and the timestamp in the two-dimensional code data by using a decoding algorithm, and obtains a verified binding identifier through a verification operation to ensure the validity and safety of the two-dimensional code.

[0056] A data synchronization module retrieves corresponding vehicle-driver association data from a transport management system database according to the verified binding identifier, and updates vehicle position, driver behavior and cargo state information in real time by using real-time data synchronization technology to obtain a real-time transport state data set.

[0057] An abnormality detection module analyzes the real-time transport state data set, monitors vehicle driving track deviation and driver fatigue driving abnormal behavior by using an abnormality detection algorithm, generates a risk warning signal when the score of the abnormal behavior exceeds a preset threshold, and obtains a risk warning data set.

[0058] A warning pushing module transmits the warning signal to a management terminal by using a data pushing technology through a mobile terminal application interface based on the risk warning data set to generate a warning log containing an abnormal type, a time and a position.

[0059] A data analysis module integrates and processes vehicle-driver association data, transport status and warning logs by using a data aggregation technology according to a real-time warning record set to generate an analysis report containing transport efficiency, driver performance and risk distribution, and obtains a comprehensive management data set.

[0060] The application has the following beneficial effects:

[0061] The application solves the problems of dispersed storage of information and lack of unified digital access in the traditional management mode by dynamically associating the information of the vehicle and the driver through the use of two-dimensional code technology, generates a unique vehicle-driver binding identifier through a hash algorithm, and encodes it into a dynamic two-dimensional code, realizing efficient binding and verification of the vehicle and driver information. The use of two-dimensional code not only simplifies the information verification process, but also solves the problem of information update lag through real-time data synchronization technology, real-time updating of vehicle location, driver behavior and cargo status, realizing dynamic association and real-time interaction of vehicle and driver information, and significantly improving management efficiency and timeliness of data interaction.

[0062] Through real-time data synchronization and abnormal detection algorithm, the abnormal behaviors such as vehicle trajectory deviation and driver fatigue driving are monitored and analyzed in real time. When the abnormal behavior score exceeds the preset threshold, a risk warning signal is generated, and the warning information is transmitted to the management terminal in time through data pushing technology. This real-time monitoring and warning mechanism effectively makes up for the defect that the state of the vehicle and the driver cannot be obtained in real time in the traditional management mode, greatly improves the safety management level in the transportation process, can timely discover and handle potential risks, ensures transportation safety and reduces the accident rate.

[0063] Through data aggregation and analysis of the real-time warning record set, a comprehensive management data set containing transportation efficiency, driver performance and risk distribution is generated, and analysis reports are generated through visualization technology. These reports provide comprehensive and accurate decision support for managers, solve the problem of data analysis lag and difficulty in generating accurate performance evaluation and risk prediction in the traditional management mode, and managers can make scientific and reasonable decisions based on real-time data and analysis results, optimize transportation resource allocation, improve management efficiency and reduce operating costs, thereby comprehensively improving the overall efficiency and fine management level of transportation management. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to better understand and implement, the technical solutions of the present application are described in detail below with reference to the accompanying drawings.

[0065] Figure 1 The flowchart of the method for associating and managing the driver and the vehicle based on two-dimensional code provided by Embodiment 1 of the present application;

[0066] Figure 2 The flowchart of generating a dynamic two-dimensional code image set by the method for associating and managing the driver and the vehicle based on two-dimensional code provided by Embodiment 1 of the present application;

[0067] Figure 3 The flowchart of obtaining a verified binding identifier by the method for associating and managing the driver and the vehicle based on two-dimensional code provided by Embodiment 1 of the present application;

[0068] Figure 4 The structural schematic diagram of the system for associating and managing transport drivers with vehicles based on two-dimensional codes provided in Embodiment 2 of the present application. DETAILED DESCRIPTION

[0069] For further elaboration of the technical means and effects adopted by the present application to achieve the predetermined inventive purpose, exemplary embodiments will be described in detail herein, which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the present application.

[0070] The terms used in the present application are merely for the purpose of describing specific embodiments and are not intended to limit the present application. The singular forms "a," "an," and "the" used in the present application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0071] The specific embodiments, features and effects according to the present application are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0072] Embodiment 1

[0073] Please refer to Figures 1-3 The present embodiment provides a method for associating and managing transport drivers with vehicles based on two-dimensional codes, comprising the following steps:

[0074] S1, generating a unique identification code according to a structured data set using a hash algorithm, wherein the hash function H(v, d) takes the vehicle number v and the driver identification d as input and outputs an identification code of fixed length, obtaining a vehicle-driver binding identification set, and then using a two-dimensional code generation algorithm to encode each binding identification into a dynamic two-dimensional code, wherein the two-dimensional code contains a timestamp and a check bit, and a dynamic two-dimensional code image set is generated;

[0075] Further, the vehicle-driver binding identification set is obtained, specifically comprising:

[0076] Obtaining a structured data set, extracting the vehicle number and driver identification fields from it to generate an input parameter pair, when the vehicle number or driver identification field is missing in the input parameter pair, marking it as an invalid parameter pair and discarding it, obtaining a set of valid input parameter pairs;

[0077] The hash function H(v, d) is used to process the set of valid input parameter pairs to generate a unique identification code of fixed length, and then the hash function is used to obtain a set of unique identification codes corresponding to each vehicle number and driver identity pair;

[0078] According to the set of unique identification codes, a vehicle-driver binding identification is generated and stored in the binding identification set. If there is a duplicate identification in the binding identification set, the latest generated identification is retained by comparing the identification code values, and the old identification is discarded to obtain a de-duplicated binding identification set.

[0079] According to the de-duplicated binding identification set, the generation time stamp of each binding identification is obtained and attached to the binding identification record. Then, the binding identification set is sorted in chronological order by time stamp.

[0080] From the binding identification set sorted in chronological order, the vehicle number and driver identity corresponding to each binding identification are extracted to generate a binding relationship record. If the binding relationship record does not conform to the preset business rules, it is marked as an abnormal record and discarded. The binding relationship record set that conforms to the rules is obtained.

[0081] According to the binding relationship record set that conforms to the rules, a final vehicle-driver binding identification dataset is generated. Then, the data set is checked for integrity to obtain a verified binding identification data set.

[0082] The verified binding identification data set is saved to the database using a data storage tool. The storage success is determined by database indexing to obtain a persistent vehicle-driver binding identification data set.

[0083] The hash function H(v, d) is used to combine the vehicle number and driver identity into a unique identification code, ensuring that each combination of vehicle and driver has a unique identification for generating a unique binding identification of vehicle and driver. The hash function H(b, t) generates a check bit for each binding identification, which is used to generate a check bit for the two-dimensional code to ensure the correctness and timeliness of the two-dimensional code.

[0084] Specifically, the vehicle number and driver identity are processed by hash algorithm and two-dimensional code technology to generate a unique vehicle-driver binding identification and encode it into a dynamic two-dimensional code. This process effectively solves the accuracy problem of information association, and through the steps of de-duplication, sorting, verification and storage, the data integrity and persistence are ensured, providing an efficient, safe and dynamic data foundation for transportation management, significantly improving the management efficiency and reliability of information interaction.

[0085] Further, a dynamic two-dimensional code image set is generated, specifically including:

[0086] S11, obtain a vehicle-driver binding identifier set, extract each binding identifier from the set, generate a data input containing a vehicle identifier and a driver identifier, and then use a two-dimensional code generation algorithm to encode each binding identifier, embed a timestamp t and a check bit c, and generate a dynamic two-dimensional code, wherein the timestamp t represents the generation time, and the check bit c is calculated by a hash function H(b, t), b is the binding identifier;

[0087] S12, when the check bit c of the dynamic two-dimensional code is consistent with the expected value, the dynamic two-dimensional code is stored as a first image, and when it is inconsistent, the code is discarded and regenerated;

[0088] S13, compress the first image by image processing technology to generate a second image, maintain the readability of the two-dimensional code, and then use a file storage system to archive the second image to an image set and generate a unique file identifier;

[0089] S14, when the file identifier in the image set matches the binding identifier, it is confirmed that the dynamic two-dimensional code generation is completed, the image set is output, each dynamic two-dimensional code in the image set is decoded by a verification algorithm, the timestamp t and the check bit c are extracted, the validity of the binding identifier is judged, and a verification result is obtained.

[0090] The verification algorithm is a consistency verification algorithm by recalculating the check bit c'=H(b, t) and comparing it with the check bit c embedded in the two-dimensional code, and a timeliness verification is performed by combining whether the difference between the timestamp t and the current system time is within the preset validity period. Both of them are passed to determine that the dynamic two-dimensional code is valid.

[0091] Specifically, a dynamic two-dimensional code image set containing vehicle and driver information is generated. Each two-dimensional code embeds a timestamp and a check bit to ensure its timeliness and uniqueness. After verification, image compression and file storage, the finally output image set not only maintains the readability of the two-dimensional code, but also further confirms the validity of the binding identifier through the verification algorithm. This process realizes efficient generation and management of dynamic two-dimensional codes, provides a safe, reliable and visual information carrier for transportation management, optimizes data storage and verification processes, and improves management efficiency and information interaction accuracy.

[0092] S2, push the dynamic two-dimensional code image set to the transportation management terminal through the mobile terminal application interface, and protect the two-dimensional code data by using an encrypted transmission protocol to obtain a two-dimensional code data set that can be scanned on the mobile terminal;

[0093] Further, the two-dimensional code data set that can be scanned on the mobile terminal specifically includes:

[0094] The dynamic two-dimensional code image set is obtained through the mobile terminal application interface, the image data is encrypted by using the AES encryption algorithm, the encrypted two-dimensional code data is generated, when the integrity check of the encrypted two-dimensional code data is passed, the encrypted two-dimensional code data is pushed to the transportation management terminal through the HTTPS transmission protocol, and the two-dimensional code data of successful transmission is obtained;

[0095] The encrypted two-dimensional code data received by the transportation management terminal is decrypted, the data integrity is verified by using the SHA-256 algorithm, the decrypted two-dimensional code data is generated, when the format of the decrypted two-dimensional code data meets the preset standard, the decrypted two-dimensional code data is stored in the transportation management terminal, and the two-dimensional code data set available for the mobile terminal scanning is generated;

[0096] The two-dimensional code data set is obtained from the transportation management terminal through the mobile terminal application, the two-dimensional code content in the data set is extracted by using a two-dimensional code analysis algorithm, and a displayable two-dimensional code image is generated;

[0097] According to the two-dimensional code image content, when the analysis result is consistent with the preset check code, the two-dimensional code image is displayed on the mobile terminal application, and the dynamic two-dimensional code available for scanning is obtained, when the mobile terminal scans the two-dimensional code image successfully, the transportation management related data is extracted from the two-dimensional code image, and the transportation management data used for business processing is generated.

[0098] Specifically, the secure transmission and efficient management of the dynamic two-dimensional code image set are realized. The two-dimensional code data is encrypted and transmitted by using the AES encryption algorithm and the HTTPS transmission protocol, so that the integrity and security of the data in the transmission process are ensured. After the received data is decrypted and integrity verified on the transportation management terminal, the two-dimensional code data set available for the mobile terminal scanning is generated, the mobile terminal application displays the dynamic two-dimensional code available for scanning through analysis and verification, and extracts the transportation management related data from the two-dimensional code image for business processing. This process not only guarantees the security and accuracy of the data, but also improves the convenience and efficiency of transportation management, realizes the real-time sharing and dynamic interaction of information.

[0099] S3, when scanning the two-dimensional code on the mobile terminal, the two-dimensional code data uploaded by the scanning device is obtained, the decoding algorithm is used to extract the binding identifier and the timestamp, and if the time difference between the timestamp t and the current time is less than the preset threshold T, it is determined that the two-dimensional code is valid, and the verified binding identifier is obtained;

[0100] Further, the verified binding identifier specifically includes:

[0101] S31, the two-dimensional code data collected by the scanning device of the mobile terminal is obtained, the two-dimensional code image is preprocessed by using image processing technology, clear two-dimensional code data is obtained, and then the binding identifier and the timestamp are extracted from the clear two-dimensional code data by using the decoding algorithm, to obtain the extracted binding identifier and timestamp data;

[0102] S32, calculate the difference between the extracted timestamp and the current time to obtain time difference data, and determine that the two-dimensional code is valid when the time difference data is less than a preset threshold, obtaining a valid binding identifier;

[0103] S33, encrypt the valid binding identifier using an encryption algorithm to obtain encrypted binding identifier data, and match the encrypted binding identifier data with the pre-established database to obtain corresponding user identity information, obtaining a verified identity identifier;

[0104] S34, generate a binding confirmation message based on the verified identity identifier to obtain the final business binding result.

[0105] Specifically, the two-dimensional code is scanned through the mobile phone, the binding identifier and timestamp are extracted using image processing and decoding algorithms, and the validity of the two-dimensional code is determined based on the difference between the timestamp and the current time. The binding identifier of the valid two-dimensional code is encrypted and matched with the user identity information in the database, and finally a binding confirmation message is generated. This process ensures the timeliness and security of the two-dimensional code, realizes the rapid and accurate binding of the driver and the vehicle, and improves the efficiency and reliability of transportation management.

[0106] S4, according to the verified binding identifier, retrieve the corresponding vehicle-driver association data from the transportation management system database, and update the vehicle position, driver behavior and cargo status using real-time data synchronization technology to obtain real-time transportation status data set;

[0107] Further, the real-time transportation status data set includes:

[0108] Using real-time data synchronization technology, obtain position information from vehicle positioning equipment, update the position field in vehicle data to obtain real-time vehicle position data set, and obtain behavior data through driver behavior monitoring equipment. When the behavior data exceeds the preset threshold, it is marked as abnormal behavior to obtain driver behavior analysis data set;

[0109] Obtain cargo status information from cargo sensors, combine position information and behavior data to determine whether the cargo status is normal, and obtain cargo status analysis data set;

[0110] According to the vehicle position data set, behavior analysis data set and cargo status analysis data set, perform data fusion processing to generate real-time transportation status data set;

[0111] According to the real-time transportation state data set, a time series analysis algorithm is used to analyze the change trend of the transportation state to obtain a transportation state trend data set, and then it is judged whether there is an abnormality in the transportation process. If there is an abnormality, an abnormality alarm information is generated, and a transportation abnormality detection result is obtained.

[0112] Specifically, the whole transportation process is monitored, abnormal behaviors or states can be found in time, real-time and accurate data support is provided for transportation management, and the safety and management efficiency of the transportation process are improved.

[0113] S5, according to the real-time transportation state data set, an abnormality detection algorithm is used to analyze the vehicle trajectory deviation and driver fatigue driving, and if the score of the abnormal behavior exceeds a preset threshold A, a risk warning signal is generated, and a risk warning data set is obtained;

[0114] Further, the risk warning data set comprises:

[0115] According to the real-time transportation state data set, an isolation forest algorithm is used for analysis, the abnormal behavior scores of vehicle trajectory deviation and driver fatigue driving are calculated, and when the score in the abnormal score set exceeds the preset threshold A, a risk warning signal is generated, and a warning signal set is obtained.

[0116] Among them, the vehicle trajectory related data and driver behavior related data are extracted from the real-time transportation state data set, the driver behavior data includes continuous driving time, operation frequency, steering wheel steering amplitude, heart rate and other behavior monitoring data, and the specific related data is converted into a feature vector that can be processed by the isolation forest algorithm; the feature vector is trained and analyzed by the isolation forest algorithm, a plurality of isolated trees are constructed, the isolation degree of each group of vehicle trajectory data, driver behavior data and normal transportation mode is calculated, that is, the deviation degree of the data in the feature space from the majority of normal data, the higher the isolation degree, the higher the corresponding abnormal behavior score, thereby generating an abnormal score set of vehicle trajectory deviation and driver fatigue driving; then each score in the abnormal score set is compared with the preset threshold A, the threshold is based on statistical analysis of historical normal transportation data, such as the score corresponding to the maximum allowed deviation of normal driving, the behavior feature score under the compliant driving time, etc., if one or more scores exceed the threshold A, it is preliminarily determined that there are abnormal signs of vehicle trajectory deviation or driver fatigue driving.

[0117] According to the warning signal set, the time series analysis technology is used to identify the persistence and frequency of abnormal behaviors, and the abnormal behavior mode is obtained, and then the clustering analysis technology is used to group similar abnormal behaviors according to the abnormal behavior mode, and an abnormal behavior classification set is obtained.

[0118] The classification result is obtained from the abnormal behavior classification set, and a risk warning data set containing abnormal types and risk levels is generated.

[0119] wherein the abnormal type includes: a vehicle trajectory deviation type, specifically, a deviation of a real-time trajectory of a vehicle from a preset planned route exceeding a set range, such as a deviation distance, a deviation time length exceeding a system preset threshold, or a driving speed significantly deviating from a normal speed limit of the road section, such as continuous overspeeding or low-speed road occupation, which belongs to a path abnormality in dynamic driving of the vehicle; and a driver fatigue driving type, which is determined based on data collected by a driver behavior monitoring device, such as a continuous driving time length exceeding a regulatory or enterprise specified time limit, frequent eye closing, head drooping, and slow steering wheel operation during driving, which belong to an abnormality in a driving operation state of the driver;

[0120] The risk level includes: a high priority, corresponding to a serious abnormality directly causing a safety accident, such as a vehicle seriously deviating from a planned route and driving to a no-entry / dangerous area, or a driver showing obvious signs of fatigue loss of control, which requires triggering an immediate alarm and requiring a manager to intervene immediately; a medium priority, corresponding to an abnormality with a safety hazard but not urgent, such as a vehicle slightly deviating from a route, or a driver's continuous driving time length approaching a threshold, which requires generating a warning log and pushing it to a management terminal to remind of subsequent states; and a low priority, corresponding to a slight abnormality with potential risks, such as a vehicle temporarily deviating from a route and quickly correcting, or a driver showing occasional slight operation delay.

[0121] Specifically, by analyzing real-time transportation state data through an abnormality detection algorithm (such as an isolation forest algorithm), vehicle trajectory deviation, driver fatigue driving and other abnormal behaviors can be accurately identified, and a risk warning signal can be generated according to the score of the abnormal behavior. Further, through time series analysis and clustering analysis technology, the continuity and frequency of abnormal behavior are identified and classified, and a risk warning data set containing abnormal type and risk level is generated. This process realizes real-time monitoring and warning of potential risks in the transportation process, provides timely risk prompts for managers, and effectively improves the safety of the transportation process and the initiative of management.

[0122] S6, according to the risk warning data set, a data pushing technology is used to transmit the warning signal to the management terminal through a mobile terminal application program interface to generate a warning log containing the abnormal type, time and location, and obtain a real-time warning record set;

[0123] Further, the real-time warning record set specifically includes:

[0124] The original data is obtained from the risk warning data set, the data is preprocessed through a data pushing technology to obtain a standardized data set, when there is an abnormal value in the standardized data set, an abnormality detection algorithm is used to determine the abnormal type to obtain an abnormal classification result;

[0125] According to the anomaly classification result, an application programming interface is used to transmit the early warning signal to the mobile terminal application program to obtain a transmission completion signal;

[0126] When the mobile terminal application program receives the transmission completion signal, a management terminal generates an early warning log containing the type of anomaly, the occurrence time and the geographic location, obtains a log record, and uses a real-time updating mechanism to store the log record in a record set to obtain a real-time early warning record set;

[0127] The latest record is obtained from the real-time early warning record set, the early warning information is transmitted to the management terminal through a push notification mechanism to obtain a final early warning output, and when the final early warning output contains a high-priority anomaly, an instant alarm mechanism is triggered through the application programming interface to obtain an alarm confirmation signal.

[0128] Specifically, the rapid transmission and real-time updating of early warning information are realized, ensuring that the manager can obtain the abnormal situation in the transportation process in a timely manner. At the same time, for high-priority anomalies, the system reminds the manager through the instant alarm mechanism, further improving the response speed and emergency handling capability of transportation management.

[0129] S7、According to the real-time early warning record set, a data aggregation technique is used to integrate vehicle-driver association data, transportation status and early warning logs to generate an analysis report containing transportation efficiency, driver performance and risk distribution, and obtain a comprehensive management data set.

[0130] Further, the comprehensive management data set specifically includes:

[0131] The real-time early warning record set is obtained, a data cleaning technique is used to remove duplicate records and missing values to obtain a cleaned record set, and a clustering algorithm is used to group the cleaned record set to generate a classification data set based on vehicle association and driver association;

[0132] When the transportation status record in the classification data set is complete, a weighted average method is used to calculate the transportation efficiency to obtain a transportation efficiency indicator, and when the record is not complete, the missing values are completed and recalculated to obtain the transportation efficiency indicator;

[0133] According to the transportation efficiency indicator and the early warning log, a decision tree algorithm is used to analyze the driver behavior pattern to generate a driver performance score, and a frequency statistical method is used to calculate the risk distribution to obtain risk distribution data from the risk events extracted from the early warning log;

[0134] The calculation of the driver performance score includes: extracting efficiency type features such as punctuality rate, transportation time standard rate, oil consumption / energy consumption indicators from the transportation efficiency indicators, extracting abnormal type frequency associated with the driver from the early warning log, such as fatigue driving, track deviation warning number, abnormal level distribution high / medium / low priority ratio, abnormal handling timeliness and other safety type features, standardizing and classifying the features to form a feature vector input for the decision tree algorithm; then taking the historical driver performance level as the label, such as excellent, good, etc., training the decision tree model according to the feature vector, letting the model learn the association rules between the features and the performance level and determine the weight of each feature, such as the fatigue driving warning weight is higher than the slight track deviation, and the punctuality rate weight is higher than the energy consumption indicator; finally, input the real-time feature vector of the driver to be scored into the trained model, and the model divides the driver into the corresponding performance level through layer-by-layer judgment, and converts it into a basic score according to the grade mapping rule, and the grade mapping rule includes excellent 90-100 points, good 80-89 points, etc., to generate the driver comprehensive performance score.

[0135] Through data aggregation technology, transportation efficiency indicators, driver performance scores and risk distribution data are integrated to generate a comprehensive management data set. Visualization technology is used to process the comprehensive management data set to generate analysis reports containing transportation efficiency, driver performance and risk distribution.

[0136] Specifically, through data aggregation and analysis technology, real-time early warning record sets are integrated with vehicle-driver association data, transportation status and other information to generate a comprehensive management data set containing transportation efficiency, driver performance and risk distribution, and presented in the form of a visual report. This process realizes comprehensive analysis and efficient display of transportation management data, providing accurate decision support for managers, helping to optimize transportation resource allocation and improve management efficiency and transportation safety.

[0137] Further, before obtaining the vehicle-driver binding identifier set, it includes: obtaining vehicle information and driver information from the transportation management system database, using data cleaning technology to standardize the vehicle number, driver identity, driving record and other fields, generating a unified format vehicle-driver association data set, and obtaining a structured data set.

[0138] Further, obtaining the structured data set specifically includes: obtaining vehicle number, driver identity and driving record raw data from the transportation management system database, using batch extraction technology to obtain an initial data set, using data cleaning technology to remove duplicates and fill in missing values in the vehicle number, driver identity and driving record in the initial data set, and obtaining a cleaned data set.

[0139] When there are non-standard formats in the vehicle number or driver identity in the cleaning data set, the standardization processing is performed through regular expression matching and conversion rules to obtain a standardized data set;

[0140] According to the vehicle number and driver identity in the standardized data set, a mapping relationship between the vehicle and the driver is established by using an association algorithm to obtain a vehicle-driver association data set, and the vehicle-driver association data set is adjusted to a predefined unified format by using a format conversion technique, including a vehicle number, a driver identity, and a driving record field, to obtain a formatted data set;

[0141] The integrity and consistency of the formatted data set are checked by using a data verification technique, and if data anomalies are found, the cleaning step is reprocessed to obtain a verification data set, and the verification data set is saved to a structured data table by using a database storage technique, and an index optimization storage structure is used to obtain a structured data set.

[0142] Embodiment 2

[0143] Please refer to Figure 4 The embodiment provides a system for associating and managing transport drivers and vehicles based on a two-dimensional code, and a method for associating and managing transport drivers and vehicles based on a two-dimensional code, which comprises:

[0144] A two-dimensional code generation module generates a dynamic two-dimensional code for vehicle-driver binding according to the structured data set, generates a unique binding identifier set for vehicle-driver by using a hash algorithm, and encodes each binding identifier into a dynamic two-dimensional code containing a timestamp by using a two-dimensional code generation algorithm to generate a dynamic two-dimensional code image set;

[0145] A data transmission module pushes the dynamic two-dimensional code image set to a transport management terminal through a mobile terminal application interface, and protects the two-dimensional code data by using an encrypted transmission protocol to obtain a two-dimensional code data set that can be scanned on a mobile terminal;

[0146] A two-dimensional code verification module obtains two-dimensional code data uploaded by a scanning device when a two-dimensional code is scanned on a mobile terminal, extracts the binding identifier and timestamp therefrom by using a decoding algorithm, and obtains a verified binding identifier after a verification operation to ensure the validity and security of the two-dimensional code;

[0147] A data synchronization module retrieves corresponding vehicle-driver association data from a transport management system database according to the verified binding identifier, and updates vehicle position, driver behavior, and cargo status information in real time by using real-time data synchronization technology to obtain a real-time transport status data set;

[0148] An abnormality detection module analyzes the real-time transportation state data set, adopts an abnormality detection algorithm, monitors vehicle driving track deviation and driver fatigue driving abnormal behavior, generates a risk early warning signal when a score of the abnormal behavior detected exceeds a preset threshold, and obtains a risk early warning data set.

[0149] An early warning pushing module, based on the risk early warning data set, adopts a data pushing technology, transmits the early warning signal to a management terminal through a mobile terminal application program interface, and generates an early warning log containing an abnormal type, time and location;

[0150] A data analysis module, according to the real-time early warning record set, adopts a data aggregation technology to integrate and process vehicle-driver association data, transportation state and early warning logs, generates an analysis report containing transportation efficiency, driver performance and risk distribution, and obtains a comprehensive management data set, thereby providing strong support for transportation management decision-making.

[0151] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above with a preferred embodiment, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content without departing from the scope of the technical solution of the present application, and any equivalent embodiments with equivalent changes or modifications are still within the scope of the present application.

Claims

1. A method for transport driver and vehicle association and management based on two-dimensional code, characterized in that: The method comprises the following steps: According to the structured data set, a unique identification code is generated using a hash algorithm to obtain a vehicle-driver binding identification set, and then each binding identification is encoded into a dynamic two-dimensional code using a two-dimensional code generation algorithm to generate a dynamic two-dimensional code image set; Through a mobile terminal application interface, the dynamic two-dimensional code image set is pushed to a transportation management terminal, and an encryption transmission protocol is used to protect the two-dimensional code data to obtain a two-dimensional code data set that can be scanned on a mobile terminal; When the two-dimensional code is scanned on the mobile terminal, the two-dimensional code data uploaded by the scanning device is obtained, and a decoding algorithm is used to extract the binding identification and timestamp to obtain a verified binding identification; According to the verified binding identification, the corresponding vehicle-driver association data is retrieved from the transportation management system database, and real-time data synchronization technology is used to update the vehicle position, driver behavior and cargo status to obtain a real-time transportation status data set; According to the real-time transportation status data set, an abnormality detection algorithm is used to analyze the vehicle driving track deviation and driver fatigue driving abnormal behavior to obtain a risk warning data set; According to the risk warning data set, a data pushing technology is used to transmit the warning signal to the management terminal through the mobile terminal application interface to obtain a real-time warning record set; According to the real-time warning record set, a data aggregation technology is used to integrate the vehicle-driver association data, transportation status and warning log to obtain a comprehensive management data set; The comprehensive management data set comprises: The real-time warning record set is obtained, a data cleaning technology is used to remove duplicate records and missing values to obtain a cleaned record set, and a clustering algorithm is used to group the cleaned record set to generate a classification data set based on vehicle association and driver association; When the transportation status record in the classification data set is complete, a weighted average method is used to calculate the transportation efficiency to obtain a transportation efficiency indicator, and when the record is incomplete, the missing values are completed and recalculated to obtain the transportation efficiency indicator; According to the transportation efficiency indicator and the warning log, a decision tree algorithm is used to analyze the driver behavior pattern to generate a driver performance score, and a risk event is extracted from the warning log, and a frequency statistical method is used to calculate the risk distribution to obtain risk distribution data; Through a data aggregation technology, the transportation efficiency indicator, driver performance score and risk distribution data are integrated to generate a comprehensive management data set, and a visualization technology is used to process the comprehensive management data set to generate an analysis report containing transportation efficiency, driver performance and risk distribution.

2. The method for two-dimensional code-based transport driver and vehicle association and management according to claim 1, characterized in that: The vehicle-driver binding identification set comprises: The structured data set is obtained, and the vehicle number and driver identity fields are extracted therefrom to generate an input parameter pair, and when the vehicle number or driver identity field in the input parameter pair is missing, it is marked as an invalid parameter pair and discarded to obtain a set of valid input parameter pairs; A hash function H(v, d) is used to process the set of valid input parameter pairs to generate a fixed-length unique identification code, and then a hash function is used to process the set of unique identification codes corresponding to each vehicle number and driver identity pair. According to the unique identification code set, a vehicle-driver binding identification is generated and stored in a binding identification set. When there is a duplicate identification in the binding identification set, the latest generated identification is retained by comparing the identification code values, and the old identification is discarded, to obtain a deduplicated binding identification set. According to the deduplicated binding identification set, the generation time stamp of each binding identification is obtained and attached to the binding identification record, and then the binding identification set is sorted in chronological order by time stamp. From the binding identification set sorted in chronological order, the vehicle number and driver identity corresponding to each binding identification are extracted to generate a binding relationship record. When the binding relationship record does not conform to the preset business rules, it is marked as an abnormal record and discarded, to obtain a binding relationship record set that conforms to the rules. According to the binding relationship record set that conforms to the rules, a final vehicle-driver binding identification dataset is generated, and data verification is performed to determine the integrity of the dataset, to obtain a verified binding identification dataset. The verified binding identification dataset is saved to a database using a data storage tool, and the storage success is determined by database indexing to obtain a persistent vehicle-driver binding identification dataset.

3. The method for two-dimensional code-based transportation driver and vehicle association and management according to claim 1, characterized in that: A dynamic two-dimensional code image set is generated, specifically including: The vehicle-driver binding identification set is obtained, and each binding identification is extracted to generate a data input containing vehicle identification and driver identification. Then, a two-dimensional code generation algorithm is used to encode each binding identification, embed a time stamp t and a check bit c, and generate a dynamic two-dimensional code, where the time stamp t represents the generation time, and the check bit c is calculated by a hash function H(b, t), where b is the binding identification. When the check bit c of the dynamic two-dimensional code is consistent with the expected value, the dynamic two-dimensional code is stored as a first image. When it is not consistent, the code is discarded and regenerated. The first image is compressed by image processing technology to generate a second image, maintaining the readability of the two-dimensional code. Then, according to the second image, a file storage system is used to archive it to an image set, generating a unique file identification. When the file identification in the image set matches the binding identification, it is confirmed that the dynamic two-dimensional code generation is complete, and the image set is output. The verification algorithm is used to decode each dynamic two-dimensional code in the image set to extract the time stamp t and the check bit c, and determine the validity of the binding identification, to obtain the verification result.

4. The method for two-dimensional code-based transportation driver and vehicle association and management according to claim 1, characterized in that: The verified binding identification is obtained, specifically including: The two-dimensional code data collected by the scanning device on the mobile phone is obtained, and the two-dimensional code image is preprocessed using image processing technology to obtain clear two-dimensional code data. Then, the binding identification and time stamp are extracted from the clear two-dimensional code data by a decoding algorithm to obtain the extracted binding identification and time stamp data. According to the extracted time stamp and the current time, the difference between the two is calculated to obtain time difference data. When the time difference data is less than a preset threshold, the two-dimensional code is determined to be valid, and the valid binding identification is obtained. The valid binding identifier is encrypted using an encryption algorithm to obtain encrypted binding identifier data. Then, the encrypted binding identifier data is matched with a pre-established database to obtain the corresponding user identity information and obtain the verified identity identifier. Based on the verified identity, a binding confirmation message is generated, resulting in the final business binding result.

5. The method for two-dimensional code-based transportation driver and vehicle association and management according to claim 1, characterized in that: The real-time transportation status dataset is obtained, specifically including: Using real-time data synchronization technology, location information is obtained from vehicle positioning devices, the location field in vehicle data is updated, and a real-time vehicle location dataset is obtained. Then, behavior data is obtained through driver behavior monitoring devices. When the behavior data exceeds a preset threshold, it is marked as abnormal behavior, and a driver behavior analysis dataset is obtained. Cargo status information is obtained from cargo sensors, and combined with location information and behavioral data to determine whether the cargo status is normal, thus obtaining a cargo status analysis dataset. Based on the vehicle location dataset, behavior analysis dataset, and cargo status analysis dataset, data fusion processing is performed to generate a real-time transportation status dataset. Based on the real-time transportation status dataset, a time series analysis algorithm is used to analyze the changing trend of transportation status, obtain a transportation status trend dataset, and then determine whether there are any abnormalities in the transportation process. If there are abnormalities, an abnormality alarm message is generated, and the transportation abnormality detection result is obtained.

6. The method for two-dimensional code-based transportation driver and vehicle association and management according to claim 1, characterized in that: The risk warning dataset obtained includes: The isolated forest algorithm is used to analyze the real-time transportation status dataset to calculate the abnormal behavior scores of vehicle trajectory deviation and driver fatigue driving, and obtain an abnormal score set. When the scores in the abnormal score set exceed the preset threshold A, a risk warning signal is generated, and a warning signal set is obtained. Based on the early warning signal set, time series analysis technology is used to identify the persistence and frequency of abnormal behavior to obtain abnormal behavior patterns. Then, based on the abnormal behavior patterns, cluster analysis technology is used to group similar abnormal behaviors to obtain an abnormal behavior classification set. The classification results are obtained from the abnormal behavior classification set to generate a risk warning dataset containing the abnormality type and risk level.

7. The method for two-dimensional code-based transportation driver and vehicle association and management according to claim 1, characterized in that: The real-time early warning record set is obtained, specifically including: Raw data is obtained from the risk warning dataset, and the data is preprocessed using data push technology to obtain a standardized dataset. When there are outliers in the standardized dataset, the outlier type is determined by an anomaly detection algorithm to obtain the anomaly classification result. Based on the anomaly classification results, the warning signal is transmitted to the mobile application using the application programming interface, and a transmission completion signal is obtained. When the mobile application receives the transmission completion signal, the management terminal generates an early warning log containing the anomaly type, occurrence time and geographical location, obtains the log record, and then uses a real-time update mechanism to store the log record into the record set, thus obtaining the real-time early warning record set; The latest records are retrieved from the real-time early warning record set, and the early warning information is transmitted to the management terminal through a push notification mechanism to obtain the final early warning output. When the final early warning output contains high-priority anomalies, the instant alarm mechanism is triggered through the application programming interface to obtain an alarm confirmation signal.

8. The method for two-dimensional code-based transportation driver and vehicle association and management according to claim 1, characterized in that: Before obtaining the vehicle-driver binding identification set, the method comprises: obtaining vehicle information and driver information from a transportation management system database, using data cleaning techniques to standardize vehicle number, driver identification and driving record fields, generating a uniform format vehicle-driver association dataset, and obtaining a structured data set.

9. A system for two-dimensional code-based transport driver and vehicle association and management for implementing the two-dimensional code-based transport driver and vehicle association and management method according to any one of claims 1 to 8, characterized in that: Comprise: A two-dimensional code generation module generates a dynamic two-dimensional code for vehicle-driver binding based on the structured data set, generates a unique binding identification set for the vehicle-driver using a hash algorithm, and then uses a two-dimensional code generation algorithm to encode each binding identification into a dynamic two-dimensional code containing a timestamp, generating a dynamic two-dimensional code image set; A data transmission module pushes the dynamic two-dimensional code image set to a transportation management terminal through a mobile application interface, and uses an encrypted transmission protocol to protect the two-dimensional code data, obtaining a two-dimensional code data set that can be scanned on a mobile phone; A two-dimensional code verification module obtains two-dimensional code data uploaded by a scanning device when scanning a two-dimensional code on a mobile phone, uses a decoding algorithm to extract the binding identification and timestamp, and obtains a verified binding identification after verification, ensuring the effectiveness and security of the two-dimensional code; A data synchronization module retrieves corresponding vehicle-driver association data from the transportation management system database based on the verified binding identification, uses real-time data synchronization technology to update vehicle location, driver behavior and cargo status information in real time, and obtains a real-time transportation status data set; An anomaly detection module analyzes the real-time transportation status data set, uses an anomaly detection algorithm to monitor vehicle driving trajectory deviation and driver fatigue driving abnormal behavior, and generates a risk warning signal when the abnormal behavior score exceeds a preset threshold, obtaining a risk warning data set; An early warning push module uses data push technology to transmit the warning signal to the management terminal through the mobile application interface based on the risk warning data set, and generates a warning log containing the abnormal type, time and location; A data analysis module uses data aggregation technology to integrate and process vehicle-driver association data, transportation status and warning logs based on the real-time warning record set, generates an analysis report containing transportation efficiency, driver performance and risk distribution, and obtains a comprehensive management data set.

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