Taxi Internet of Vehicles data secure transmission method

Abstract

The invention discloses a taxi Internet of Vehicles data secure transmission method, and relates to the technical field of Internet of Vehicles data transmission, and the method comprises the following components: a digital twin model construction step, a virtual environment data pre-transmission step, a transmission path security verification step, and a real data transmission and comparison detection step. According to the method, the digital twin model of the taxi is constructed, data pre-transmission verification is carried out in the virtual environment, problems possibly occurring in the data transmission process can be found and corrected in advance, meanwhile, a network node risk assessment algorithm based on fuzzy comprehensive evaluation is adopted to carry out safety verification on a data transmission path, and the safety of the data transmission path is improved. The security of a data transmission path is ensured, the security and the reliability of taxi Internet of Vehicles data transmission are remarkably improved through the measures, and the data are effectively prevented from being tampered or leaked in the transmission process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Vehicles data transmission, in particular to a taxi Internet of Vehicles data secure transmission method. BACKGROUND

[0002] With the rapid development of Internet of Vehicles technology, the taxi industry has gradually integrated into the wave of intelligentization and networking. The taxi Internet of Vehicles system realizes real-time monitoring, efficient scheduling and optimized management of vehicle operation state by integrating advanced sensors, communication technology and data analysis capabilities. In this process, the secure transmission of data becomes a key link to ensure the stable operation of the Internet of Vehicles system and the privacy security of passengers. Taxi Internet of Vehicles data not only contains real-time location, speed and other operation information of vehicles, but also may involve personal information and payment data of passengers. Once leaked or tampered, it will cause serious consequences.

[0003] Traditional taxi Internet of Vehicles data transmission methods mainly rely on simple encryption technology and fixed path transmission protocols. These methods have many shortcomings when facing complex and variable network environments and growing security threats. First, traditional encryption technology often cannot cope with new attack methods such as quantum computing, and there is still a risk of data being cracked during transmission. Second, the fixed path transmission protocol lacks flexibility. Once there are security vulnerabilities or network congestion in the transmission path, the data transmission efficiency will be greatly reduced, and even data loss may occur. In addition, traditional methods lack real-time monitoring and dynamic adjustment capabilities for data transmission processes, making it difficult to discover and respond to abnormal situations in the transmission process, such as data tampering and replay attacks. Therefore, traditional technology cannot meet the current needs of taxi Internet of Vehicles data secure transmission.

[0004] In view of the problems of insufficient security and reliability in traditional taxi Internet of Vehicles data transmission methods, the present application proposes a taxi Internet of Vehicles data secure transmission method, which is particularly important. SUMMARY

[0005] The purpose of the present application is to overcome the shortcomings of the prior art and provide a taxi Internet of Vehicles data secure transmission method. It can build a digital twin model of taxis, perform data pre-transmission verification in a virtual environment, and discover and correct potential data transmission problems in advance. At the same time, it uses a network node risk assessment algorithm based on fuzzy comprehensive evaluation to verify the security of the data transmission path, ensuring the security of the data transmission path. In addition, the method combines a dynamic adaptive transmission rate adjustment algorithm and a Markov chain-based interference simulation algorithm to further optimize data transmission efficiency and reduce transmission risks.

[0006] The application provides a taxi Internet of Vehicles data security transmission method, and the specific steps of the method are as follows: The digital twin model construction step is to create a digital twin model for each taxi, collect various parameters of the taxi, including vehicle model, sensor configuration, communication module specification, and historical data of the taxi in different operating states, and use the data to construct a digital twin model of the taxi, which can accurately simulate the actual operation of the taxi and data transmission behavior. The virtual environment data pre-transmission step is to simulate the data transmission process in the virtual environment before actual data transmission, input the data to be transmitted by the taxi into the virtual transmission environment corresponding to the digital twin model, simulate transmission according to the preset transmission path and transmission protocol, monitor the transmission state of the data in real time during the simulation transmission process, including the transmission rate and packet loss rate of the data, and verify the integrity of the data by calculating the hash value of the data to ensure that the data has not been tampered with during the virtual transmission process. The transmission path security verification step is to verify the security of the data transmission path in the virtual environment, analyze the network node risks that may exist on the transmission path, use a network security evaluation algorithm to comprehensively evaluate the transmission path, and if a security risk is found in the transmission path, re-plan the transmission path until the verification is passed. The real data transmission and comparison detection step is to complete the data pre-transmission verification in the virtual environment, perform real data transmission, and simultaneously obtain the transmitted data during the real data transmission process, compare the real transmission data with the results of the digital twin model in the virtual environment, and detect in real time whether the data has been tampered with or is abnormal, and if the data is found to be inconsistent, take appropriate measures immediately, such as interrupting the transmission, re-transmitting the data, or issuing an alarm to the relevant management department.

[0007] Further, in order to ensure the quality of the data input into the model and improve the accuracy of the model simulation, the historical data of the taxi operating state collected is preprocessed, and an abnormal data detection method based on density peak clustering is used: first, all the collected taxi operating state data is regarded as a data point set, for each data point in the set, the distance between the data point and all other data points is calculated to determine the local density of the data point, the Euclidean distance is used for distance calculation, and a cutoff distance is set, which is determined by random sampling to determine the appropriate value, and when calculating the local density, the comparison result of the distance between the data points and the cutoff distance is calculated by using an indicator function. Then, the shortest distance of each data point to the data point with greater local density than it is calculated to obtain the distance value of the data point, and then the judgment condition is set to compare the local density and distance value of the data point with the average of the local density and the average of the distance value of all data points. If the local density of a data point is greater than the average of the local density, and the distance value is greater than the average of the distance value, then the data point is determined as a cluster center; the data points that do not meet the condition are determined as abnormal data, which need to be removed from the data set, thereby providing a better and reliable data basis for subsequent construction of a digital twin model.

[0008] Further, in the step of constructing the digital twin model, an improved adaptive weighted fusion modeling algorithm is used to construct the digital twin model for each taxi, specifically, the collected hardware parameter data set of the taxi is , the running state historical data set is , the parameter fusion weight coefficients are defined as and , wherein is determined according to the importance of the hardware parameter to the data transmission, which is obtained by expert scoring combined with the analytic hierarchy process, the higher the importance, the greater the weight; is determined according to the correlation degree of the running state data to the simulation data transmission behavior, the correlation degree is obtained by calculating the Pearson correlation coefficient , , wherein is a data transmission related index, and are the average values of the corresponding data, , and the formula for constructing the digital twin model is: The model can more accurately simulate the actual operation of the taxi and the data transmission behavior.

[0009] Further, in the step of pre-transmitting virtual environment data, a dynamic adaptive transmission rate adjustment algorithm is used to monitor the transmission state of the data, the real-time monitored transmission data volume is , the pre-set transmission data volume threshold is , the network delay is , the network bandwidth is , the transmission rate adjustment coefficient is defined as , When , the transmission data volume is large and the network condition is good, the transmission rate is increased , wherein is a rate increase proportion coefficient, the value range of which is determined by experimental test to be ; when , the transmission rate is reduced , A rate reduction factor, ranging from 0.05 to 0.15, is used to adjust the transmission rate in real time through this algorithm, ensuring the stability and efficiency of data during virtual transmission. At the same time, hash value calculation is combined to verify data integrity, further guaranteeing the quality of data transmission.

[0010] Furthermore, in the virtual environment data pre-transmission step, to more accurately simulate interference factors during data transmission, a Markov chain-based interference simulation algorithm is adopted, assuming the set of interference states during data transmission is... , representing different types of disturbances, determine the state transition probability matrix. ,in Indicates from state Transition to state The probability is obtained by analyzing the statistical patterns of historical interference data. During the simulated transmission process, the next interference state is randomly generated based on the current interference state and the state transition probability matrix. The interference state is introduced into the virtual transmission environment to simulate the transmission of data in a real complex network environment, making the virtual transmission closer to the real scenario and further verifying the security and stability of data transmission.

[0011] Furthermore, in the transmission path security verification step, when verifying the security of the data transmission path, a network node risk assessment algorithm based on fuzzy comprehensive evaluation is adopted, and an evaluation index set is set. These represent the stability, load, and security capabilities of network nodes, respectively; a collection of comments. The corresponding safety risk levels are low, relatively low, relatively high, and high, and the weights of each assessment indicator are determined accordingly. ,in , , The entropy weight method, determined by combining expert experience, has the following formula: , ,in For the first The node of the first Individual indicator values, Constructing a fuzzy relation matrix ,in Indicates the first The first indicator for the first The membership degree of each comment is obtained through expert scoring and statistical analysis, and the comprehensive evaluation vector is calculated. The security risk level of network nodes is determined based on the principle of maximum membership. If there are nodes on the transmission path with a security risk level exceeding the preset threshold, the transmission path is replanned to effectively ensure the security of the data transmission path.

[0012] Further, in the transmission path security verification step, when the transmission path is re-planned, a path optimization method based on an improved ant colony algorithm is adopted, the network node set is , the number of ants is , the pheromone matrix is , the initial pheromone concentration is , and the path selection probability formula is defined as: , wherein and are parameters for controlling the importance of pheromone and heuristic information, and the values are determined by experimental optimization; , wherein is the distance between nodes and ; is the set of nodes that can be selected by the ant During the ant search path process, an elite strategy is introduced to increase the pheromone concentration of the better path found, thereby accelerating the convergence speed of the algorithm. At the same time, the maximum number of iterations is set. When the maximum number of iterations is reached or a path that meets the security requirements is found, the algorithm is stopped, and the optimized transmission path is output, thereby improving the efficiency and security of data transmission path planning.

[0013] Further, in the real data transmission and comparison detection step, an improved dynamic time warping algorithm is used for data comparison. The real transmission data sequence is , the virtual transmission result data sequence is , the distance measurement function is defined as , , the cumulative distance matrix is constructed as , , wherein , the time warping path constraint condition is introduced, the maximum time warping degree is set as , to ensure that the time warping path is within a reasonable range, and the final distance between the two data sequences is calculated . The distance threshold is set as . When , it is determined that the data is inconsistent, and measures such as interrupting transmission are immediately taken, thereby improving the accuracy and reliability of data comparison detection.

[0014] Further, in the process of data transmission, the confidentiality of data is ensured by using a secure transmission technology based on homomorphic encryption. The data to be transmitted is , and the data is encrypted using a homomorphic encryption algorithm to obtain ciphertext . During the virtual environment data pre-transmission and real data transmission processes, the ciphertext is operated and transmitted. For the operation of the ciphertext, the addition operation is , multiplication operation So that in the ciphertext state, the processing and verification of data can be completed without decrypting the data, at the data receiving end, the ciphertext is decrypted using the private key to obtain the original data, ensuring that only authorized recipients can obtain the data content, effectively protecting the privacy of taxi Internet of Vehicles data in the transmission process.

[0015] Compared with the prior art, the taxi Internet of Vehicles data secure transmission method has the following beneficial effects: Firstly, the digital twin model of the taxi is constructed, and data pre-transmission verification is performed in the virtual environment, which can discover and correct possible problems in the data transmission process in advance, and the network node risk assessment algorithm based on fuzzy comprehensive evaluation is used to verify the security of the data transmission path, ensuring the security of the data transmission path, which significantly improves the security and reliability of the taxi Internet of Vehicles data transmission, effectively preventing data from being tampered with or leaked during transmission.

[0016] Secondly, in the virtual environment data pre-transmission step, a dynamic adaptive transmission rate adjustment algorithm is used to dynamically adjust the data transmission rate according to real-time network conditions, ensuring the efficiency and stability of data transmission, and through the interference simulation algorithm based on Markov chain, the interference factors in the data transmission process are more accurately simulated, making the virtual transmission environment more close to the real scene, which not only optimizes the data transmission efficiency, but also reduces the risks that may be encountered during data transmission, such as network congestion and data loss.

[0017] Other advantages, objects and features of the present application will be set forth in part in the following specification, and in part will become apparent to those skilled in the art from a consideration of the following description, or can be learned from practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0019] Figure 1 It is a flow chart of a taxi Internet of Vehicles data secure transmission method; Figure 2 It is a core flow diagram of a taxi Internet of Vehicles data secure transmission method. DETAILED DESCRIPTION

[0020] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object of the present application, the specific embodiments, structures, features and effects according to the present application will be described in detail below in combination with the drawings and preferred embodiments.

[0021] Embodiment one The digital twin model construction step: to create a digital twin model for a taxi participating in the morning peak operation, first collect the hardware parameters of the car, including the type of on-board sensor, the running performance of the communication module, the processing capacity of the on-board terminal, etc., and collect its running state historical data, covering the driving speed, engine running state, real-time positioning information, fuel consumption data, etc. during the past three months of morning peak period, when preprocessing these running state historical data, an abnormal data detection method based on density peak clustering is used to accurately identify and eliminate abnormal data caused by temporary equipment failure or extreme environmental interference, to ensure that the data used for modeling is true and reliable, then an improved adaptive weighted fusion modeling algorithm is used to construct the model, the collected hardware parameter data set of the taxi is , the running state historical data set is , the parameter fusion weight coefficients are defined as and , wherein determined according to the importance of the hardware parameters to the data transmission; determined according to the correlation degree of the running state data to the simulated data transmission behavior, the correlation degree is obtained by calculating the Pearson correlation coefficient , , wherein is the data transmission related index, and are the mean values of the corresponding data, , the formula for constructing the digital twin model is: The algorithm will effectively fuse various data according to the actual influence degree of the hardware parameters on the data transmission, and the correlation degree of the running state data to the simulated transmission behavior, and finally form a digital twin model that can accurately reflect the characteristics of the taxi during the morning peak period, providing an accurate virtual carrier for subsequent simulation transmission.

[0022] Virtual environment data pre-transmission step: before the start of the morning peak, input the real-time data to be transmitted by the taxi during operation, such as the current accurate position, the state information whether the passenger is loaded, the fault warning signal of each system of the vehicle, etc. into the virtual transmission environment corresponding to the digital twin model, simulate the transmission according to the preset transmission path from the on-board terminal of the taxi to the city taxi dispatching center and the communication protocol agreed by both parties, in this simulation process, a dynamic adaptive transmission rate adjustment algorithm is used to monitor the transmission state in real time, and the real-time monitored transmission data volume is The preset threshold for the amount of data transmitted is Network latency is Network bandwidth is Define the transmission rate adjustment factor , ,when At times, when the amount of data to be transmitted is large and the network conditions are good, it is necessary to increase the transmission rate. ,in For the rate increase proportional coefficient; when At that time, reduce the transmission rate , To reduce the rate, for example, when the data volume suddenly surges at the beginning of the morning rush hour but the network condition is good, the transmission rate is automatically increased to ensure timely data transmission; while when the network shows signs of congestion, the rate is appropriately reduced to ensure transmission stability. At the same time, an interference simulation algorithm based on Markov chains is used to simulate various network interference situations common during the morning rush hour, such as signal congestion caused by too many connected devices at base stations in the city center, and signal strength fluctuations caused by tall buildings blocking the signal. This verifies whether the data can remain intact under these complex interference environments, detects potential data loss or corruption problems in advance, and prepares for actual transmission.

[0023] Transmission path security verification steps: In a virtual environment, a comprehensive security verification is performed on the preset transmission path from the taxi to the dispatch center. A network node risk assessment algorithm based on fuzzy comprehensive evaluation is adopted, and a set of evaluation indicators is set. These represent the stability, load, and security capabilities of network nodes, respectively; a collection of comments. The corresponding safety risk levels are low, relatively low, relatively high, and high, and the weights of each assessment indicator are determined accordingly. ,in , , The entropy weight method, determined by combining expert experience, has the following formula: , ,in For the first The node of the first Individual indicator values, Construct a fuzzy relation matrix ,in Indicates the first The first indicator for the first The membership degree of each comment is used to calculate the comprehensive evaluation vector. According to the maximum membership principle, the security risk level of the network node is determined, if it is found that a certain node on a path has a load that is too high due to too many connected devices during the morning rush hour, and the risk level exceeds the preset security threshold, then a path optimization method based on an improved ant colony algorithm is used to re-plan the transmission path, which can quickly search and select a node with low load, good stability and strong security protection capability to form a new transmission path until a path that meets the security requirements is found, ensuring the safety of the data transmission path.

[0024] Real data transmission and comparison detection step: After completing the data pre-transmission verification in the virtual environment, real data transmission is formally carried out during the morning rush hour. In the transmission process, real-time synchronization is performed to obtain the real data of the transmission, and an improved dynamic time warping algorithm is used to compare the real transmission data with the results of the virtual environment transmission of the digital twin model in detail. Let the real transmission data sequence be , the virtual transmission result data sequence be , the distance metric function be defined as , , the cumulative distance matrix be constructed as , , wherein , the time warping path constraint condition be introduced, the maximum time warping degree be set as , the final distance between the two data sequences be calculated as , the distance threshold be set as , when , it is determined that the data is inconsistent, and measures such as interrupting the transmission are immediately taken to detect whether the data has been tampered with or abnormal situations have occurred during transmission (for example, the passenger status information is maliciously modified during transmission, or the vehicle fault warning signal changes without reason), and at the same time, in order to protect the privacy of the data, a secure transmission technology based on homomorphic encryption is used during the entire transmission process. Let the data to be transmitted be , the data is encrypted using a homomorphic encryption algorithm to obtain the ciphertext , the ciphertext is operated and transmitted during the virtual environment data pre-transmission and real data transmission process. For the operation of the ciphertext, the addition operation is , and the multiplication operation is The transmitted data is encrypted, so that the data always exists in the form of ciphertext during transmission and processing. Only the authorized scheduling center can decrypt the ciphertext using the private key to obtain the original data content after receiving the data, ensuring that the data cannot be obtained by unauthorized third parties. If inconsistencies are found through comparison (for example, the virtual transmission result shows that the vehicle state is normal, but the real transmitted data has an unexplained abnormal fault code, and the two cannot be matched), measures are immediately taken to interrupt the transmission, and technical personnel are notified in a timely manner to troubleshoot and handle the problem, ensuring the accuracy and security of data transmission.

[0025] Embodiment two Digital twin model construction steps: Create a digital twin model for a taxi that plans to travel long distances from city A to city B. First, collect the vehicle's hardware parameters, including the signal reception range of the long-distance dedicated communication antenna, the adaptation performance of the vehicle-mounted data transmission module and the endurance battery, the accuracy level of the positioning system, etc. Collect its running state historical data, including the average speed in the past six months of cross-city travel, signal reception strength in different road sections (highway, national road, rural road), running stability of the vehicle in different altitude areas, environmental conditions when historical faults occur, etc. Use the density peak clustering-based anomaly data detection method to eliminate abnormal values caused by extreme weather (such as signal instantaneous interruption records caused by heavy rain), temporary equipment failure (such as jump data generated by short-term sensor failure), etc. Ensure that the data used for modeling is real and effective. Then, use the improved adaptive weighted fusion modeling algorithm to construct the model. This algorithm will consider the importance of hardware parameters on long-distance data transmission (such as the signal reception capability of the communication antenna being more important than the accuracy of the positioning system), and the correlation between running state data and simulated transmission behavior (correlation determined by Pearson correlation coefficient, such as the correlation between the degree of shaking during high-speed driving and the data transmission packet loss rate). Fuse various data to form a digital twin model. This model can accurately simulate the data transmission characteristics of the vehicle during cross-city travel, providing reliable virtual mapping for subsequent virtual transmission.

[0026] Virtual environment data pre-transmission step: Before the vehicle departs, input the real-time data that needs to be transmitted during its cross-city journey into the virtual transmission environment corresponding to the digital twin model. Perform simulated transmission according to the preset cross-city transmission path (passing through the edge base station of City A, multiple highway service area base stations along the way, and the entrance base station of City B) and the general vehicle networking communication protocol. During the simulation process, use a dynamic self-adaptive transmission rate adjustment algorithm to monitor the transmission state. This algorithm can flexibly adjust the transmission rate based on the network bandwidth of different road segments (such as sufficient bandwidth in highway service areas and limited bandwidth in remote rural areas), real-time transmission data volume (such as smaller data volume for reporting information and larger data volume for fault warning), and network delay conditions. For example, increase the rate when the bandwidth is sufficient and the data volume is large to shorten the transmission time, and reduce the rate when the bandwidth is tight to avoid data loss. At the same time, use a Markov chain-based interference simulation algorithm to simulate common network interference during cross-city travel (such as signal shielding in highway tunnels, signal fluctuations during network standard switching in different cities, and signal reflection interference caused by mountainous terrain). This verifies the integrity of the data in a complex interference environment and identifies potential transmission vulnerabilities in advance.

[0027] Transmission path safety verification step: In the virtual environment, verify the safety of the preset cross-city transmission path. Use a network node risk assessment algorithm based on fuzzy comprehensive evaluation to evaluate each network node on the path from three core indicators: node stability (such as whether there are frequent offline records for remote area base stations), load condition (such as whether the number of connected devices at service area base stations during holidays is saturated), and security protection capability (such as whether the node has protection mechanisms against data tampering and illegal intrusion). After evaluation, divide the node risk level into four levels: low, relatively low, relatively high, and high. If a mountainous area base station on a certain path is found to have poor stability due to equipment aging, and the risk level exceeds the preset threshold, use a path optimization method based on an improved ant colony algorithm to re-plan the path. This method can quickly search for available nodes in the surrounding area, select nodes with high stability, low load, and strong protection capabilities to form a new transmission path, and introduce an elite strategy to speed up the search efficiency of high-quality paths until a path that meets the safety requirements is found, ensuring that the data transmission path is always safe and reliable during the cross-city journey.

[0028] Real data transmission and comparison detection step: after completing the virtual pre-transmission verification, the vehicle departs and begins real data transmission. During the transmission process, real-time synchronization is performed to obtain the real transmission data, and an improved dynamic time warping algorithm is used to compare it with the virtual transmission results. This algorithm can effectively handle sequence mismatch problems caused by transmission delays and differences in data sampling frequencies, accurately detect whether there is tampering or abnormalities in the data (such as malicious modification of location information leading to deviation from the actual driving route, tampering with oil quantity data to hide low oil quantity risks, etc.), and at the same time, use a homomorphic encryption-based secure transmission technology to encrypt the transmission data throughout the process. The ciphertext can be directly operated during transmission and processing (such as oil quantity data accumulation and location information comparison), without the need for decryption. Only the authorized city B dispatch center can obtain the original data by decrypting it with a private key, ensuring that the data is not stolen by unauthorized parties during cross-city transmission. If the comparison finds that the data is inconsistent (such as virtual transmission showing normal oil quantity, but real transmission data showing a sudden drop in oil quantity without a reasonable cause), the transmission is immediately interrupted, and a warning message is sent to the driver's terminal, notifying technical personnel to investigate the transmission link and vehicle status, ensuring the accuracy and security of data transmission during cross-city travel.

[0029] 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, it is not intended to limit the present application. Any person skilled in the art can make minor changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, without departing from the scope of the technical solution of the present application. Any simplification, modification, equivalent change and modification of the above embodiments made in accordance with the technical essence of the present application, without departing from the technical solution of the present application, are still within the scope of the present application.

Claims

1. A taxi Internet of Vehicles data secure transmission method, characterized in that, The specific steps of the method are: Digital twin model construction step: create a digital twin model for each taxi, collect various parameters of the taxi, and use these data to construct a digital twin model of the taxi; Virtual environment data pre-transmission step: before actual data transmission, simulate the data transmission process in the virtual environment, input the data to be transmitted by the taxi into the corresponding virtual transmission environment of the digital twin model, and perform simulated transmission according to the preset transmission path and transmission protocol. In the simulation transmission process, the transmission state of the data is monitored in real time, including the transmission rate and packet loss rate indicators, and the integrity of the data is verified; Transmission path security verification step: in the virtual environment, the security of the data transmission path is verified, the network node risks that may exist on the transmission path are analyzed, and the network security evaluation algorithm is used to comprehensively evaluate the transmission path. If security risks are found in the transmission path, the transmission path is re-planned until the verification is passed; Real data transmission and comparison detection step: after completing the data pre-transmission verification in the virtual environment, real data transmission is performed. In the real data transmission process, the transmitted data is synchronously acquired, and the real transmitted data is compared with the result of the digital twin model in the virtual environment. Real-time detection is performed to determine whether the data is tampered with or abnormal. If the data is found to be inconsistent, appropriate measures are immediately taken.

2. The method of claim 1, wherein, In the process of constructing the digital twin model, the collected taxi running state historical data is preprocessed, and an abnormal data detection method based on density peak clustering is used: first, all the collected taxi running state data is regarded as a data point set. For each data point in the set, the local density of the data point is determined by calculating the distance between it and all other data points. The distance is calculated using the Euclidean distance method, and a cutoff distance is set. The cutoff distance is determined by random sampling. When calculating the local density, an indicator function is used to calculate the distance between the data points based on the comparison of the distance and the cutoff distance; Next, the shortest distance from each data point to the data point with greater local density is calculated to obtain the distance value of the data point. Then, the local density and distance value of the data point are compared with the average local density and average distance value of all data points, respectively. If the local density of a data point is greater than the average local density, and the distance value is greater than the average distance value, the data point is determined to be a clustering center. Data points that do not meet the condition are determined to be abnormal data and need to be removed from the data set.

3. The method of claim 1, wherein, The improved adaptive weighted fusion modeling algorithm is adopted to construct the digital twin model when the digital twin model is created for each taxi in the digital twin model construction step. Specifically, the collected hardware parameter data set of the taxi is , the running state historical data set is , the parameter fusion weight coefficients are defined as and , wherein is determined according to the importance of the influence of the hardware parameter on the data transmission; is determined according to the correlation degree of the running state data to the simulation data transmission behavior, and the correlation degree is obtained by calculating the Pearson correlation coefficient , , wherein is the data transmission related index, and are the mean values of the corresponding data, , and the formula for constructing the digital twin model is .

4. The method of claim 1, wherein, The virtual environment data pre-transmission step adopts a dynamic adaptive transmission rate adjustment algorithm to monitor the transmission state of the data, wherein the real-time monitored transmission data amount is , the preset transmission data amount threshold is , the network delay is , the network bandwidth is , the transmission rate adjustment coefficient is defined as , , when , the transmission data amount is large and the network condition is good, the transmission rate is increased , wherein is a rate increase proportion coefficient; when , the transmission rate is decreased , is a rate decrease proportion coefficient.

5. The method of claim 1, wherein, During the virtual environment data pre-transmission step, interference factors in the simulation data transmission process are simulated by using a Markov chain-based interference simulation algorithm, interference state sets in the data transmission process are determined as , representing different types of interference, a state transition probability matrix is determined, wherein represents a probability of transition from state to state . In the simulation transmission process, the next interference state is randomly generated according to the current interference state and the state transition probability matrix, the interference state is introduced into the virtual transmission environment, and the transmission of data in the actual complex network environment is simulated.

6. The method of claim 1, wherein, In the transmission path security verification step, when verifying the security of the data transmission path, a network node risk assessment algorithm based on fuzzy comprehensive evaluation is adopted, and an evaluation index set is set. These represent the stability, load, and security capabilities of network nodes, respectively; a collection of comments. The corresponding safety risk levels are low, relatively low, relatively high, and high, and the weights of each assessment indicator are determined accordingly. ,in , , The entropy weight method, determined by combining expert experience, has the following formula: , ,in For the first The node of the first Individual indicator values, Constructing a fuzzy relation matrix ,in Indicates the first The first indicator for the first The membership degree of each comment is used to calculate the comprehensive evaluation vector. The security risk level of network nodes is determined based on the principle of maximum membership. If there are nodes on the transmission path whose security risk level exceeds the preset threshold, the transmission path is replanned.

7. The method of claim 1, wherein, In the transmission path security verification step, the path optimization method based on improved ant colony algorithm is adopted, the network node set is , the number of ants is , the pheromone matrix is , the initial pheromone concentration is , the path selection probability formula is defined as: , wherein and are parameters for controlling the importance of pheromone and heuristic information; , wherein is the distance between nodes and ; is the node set that can be selected by the ant In the process of ant searching path, the elite strategy is introduced, the pheromone concentration of the better path found is additionally increased to accelerate the convergence speed of the algorithm, at the same time, the maximum iteration number is set, when the maximum iteration number is reached or the path meeting the security requirement is found, the algorithm is stopped, and the optimized transmission path is output.

8. The method of claim 1, wherein, The real data transmission and contrast detection step, using improved dynamic time warping algorithm for data comparison, set real transmission data sequence is , virtual transmission result data sequence is , define distance metric function , , construct cumulative distance matrix , , wherein , introduce time warping path constraint condition, set the maximum time bending degree is , calculate the final distance of two data sequences , set distance threshold , when , determine that the data is inconsistent, immediately take the transmission measures.

9. The method of claim 1, wherein, The security of the data in the data transmission process is guaranteed by using a homomorphic encryption-based secure transmission technology, wherein the data to be transmitted is , the data is encrypted by using a homomorphic encryption algorithm to obtain ciphertext , and the ciphertext is operated and transmitted in the virtual environment data pre-transmission and real data transmission processes. For the operation of the ciphertext, the addition operation , and the multiplication operation , the data processing and verification can be completed in the ciphertext state without decrypting the data. At the data receiving end, the ciphertext is decrypted by using a private key to obtain the original data, so that only the authorized receiver can obtain the data content.

Images