A control method for an intelligent transportation system based on automated protocol docking

Abstract

The present invention discloses a control method for an intelligent transportation system based on automated protocol docking, which relates to the field of transportation control technology, including: based on a transportation communication environment archive, real-time monitoring of transportation communication environment parameters, and obtaining a summary of the transportation communication environment; based on a random forest model, building a transportation communication protocol selection model; based on the transportation communication environment summary, obtaining the optimal transportation communication protocol through the transportation communication protocol selection model; based on the optimal transportation communication protocol, performing protocol conversion through an intelligent intermediate, and dynamically evaluating the transportation communication path through a heterogeneous network communication path optimization mechanism to obtain the optimal transportation communication path; based on the optimal transportation communication path, encrypting the transportation communication data through a quantum random number generator, and transmitting the transportation communication data. The present invention improves the communication efficiency, reliability, and security of the intelligent transportation system through the protocol selection mechanism of the random forest model and quantum encryption technology.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic control, and in particular to a control method for an intelligent traffic system based on automated protocol docking. Background Art

[0002] With the rapid development of information technology and traffic management technology, the Intelligent Transportation System (ITS) has become a core component of modern urban traffic management and intelligent services. Through various information technology means, such as sensors, cloud computing, the Internet of Things (IoT), and big data analysis, the Intelligent Transportation System can monitor traffic flow in real time, predict traffic conditions, and optimize the control of traffic lights, thereby improving traffic efficiency, reducing traffic accidents, and reducing energy consumption and pollution. In this process, the selection and management of traffic communication protocols are crucial, because different communication protocols have a direct impact on the reliability, data transmission efficiency, and security of the system. At present, with the emergence of emerging technologies such as vehicle-to-everything (V2X) and autonomous driving, the intelligence of the transportation system continues to improve, especially in the communication needs between vehicles and infrastructure, which puts higher requirements on the adaptability and security of the protocol.

[0003] In the prior art, the Chinese invention patent with publication number CN114255602A discloses a safety protection method and device for traffic signal machines, and the classification number is G08G. The safety protection end can obtain the control data sent by the control end to the traffic signal machine, and parse the control data based on the protocol parsing library. After that, according to the abnormal feature matching library, feature matching is performed on each control content obtained by the parsing. When the control content matches the abnormal feature successfully, the control data is protected according to the protection strategy. Otherwise, the control content is forwarded to the traffic signal machine, so that the traffic signal machine adjusts the display state of the traffic light according to the control content; the control data is parsed by the safety protection end, and abnormal feature matching is performed, which effectively monitors and intercepts malicious control data, avoids major traffic accidents caused by malicious attacks, and ensures the safety of the traffic system. Another Chinese invention patent with authorization announcement number CN109816995B discloses a safe dynamic control method for intelligent traffic lights based on alliance blockchain technology, and the classification number is G08G, including a vehicle-mounted unit, a roadside unit, a traffic department, and an ACP system. The on-board unit sends real-time traffic information to the roadside unit. The pre-selected accounting roadside unit node records the information into the block. The traffic department decrypts and obtains the traffic information from the blockchain, triggers the automatic execution of the smart contract, and the ACP uses the acquired traffic information to dynamically adjust the duration of the traffic light. After the traffic light regulation is completed, the traffic department evaluates the reputation of the on-board unit, and by paying the reputation value, you can enjoy services such as obtaining real-time traffic information. The present invention solves the problem of centralization of traffic signal control, reduces the high degree of human intervention in the process of traffic light management and coordination, and saves financial and material resources. It realizes the coordinated optimization of traffic light management and control, and provides a safe and reliable communication environment for the vehicle-mounted ad hoc network. At the same time, it guarantees the confidentiality, privacy and non-repudiation of information.

[0004] However, the existing intelligent transportation system still has certain deficiencies in protocol docking and communication path optimization when controlling traffic lights. First, the selection of traditional communication protocols often relies on fixed rules or preset schemes, and lacks the ability to respond to dynamic traffic environments and communication needs in real time, resulting in the communication protocol may not be able to meet the needs of efficient and secure data transmission in complex traffic environments. In addition, most of the existing systems fail to fully consider encryption and security protection during data transmission, especially when facing potential threats from criminals. They lack effective protection mechanisms and are easily invaded by malicious attackers through vulnerabilities, thereby interfering with or controlling the traffic system. Therefore, how to dynamically select the optimal traffic communication protocol and ensure the security of data transmission has become the key to improving the performance and anti-attack capabilities of the intelligent transportation system. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a control method for an intelligent transportation system based on automated protocol docking, which solves the problems of static selection of traditional communication protocols and insufficient data transmission security.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a control method for a smart transportation system based on automated protocol docking, which includes: an intelligent intermediate body constructs a traffic communication environment archive by collecting traffic communication metadata; based on the traffic communication environment archive, real-time monitoring of traffic communication environment parameters to obtain a traffic communication environment summary; based on a random forest model, a traffic communication protocol selection model is constructed; based on the traffic communication environment summary, an optimal traffic communication protocol is obtained through the traffic communication protocol selection model; based on the optimal traffic communication protocol, a protocol conversion is performed through an intelligent intermediate body, and a traffic communication path optimization mechanism of a heterogeneous network is used to dynamically evaluate the traffic communication path to obtain the optimal traffic communication path; based on the optimal traffic communication path, traffic communication data is encrypting through a quantum random number generator to transmit traffic communication data.

[0009] As a preferred solution of the control method of the intelligent transportation system based on the automatic protocol docking of the present invention, the intelligent intermediate builds a transportation communication environment archive by collecting transportation communication metadata, and the specific steps are as follows:

[0010] The traffic communication metadata includes traffic communication protocol type, network performance indicators, equipment resource parameters and environmental information;

[0011] Clean the traffic and communication metadata and organize them into a unified structured data format to build a traffic and communication environment archive.

[0012] As a preferred solution of the control method of the intelligent transportation system based on the automatic protocol docking of the present invention, wherein: based on the traffic communication environment archive, the traffic communication environment parameters are monitored in real time to obtain the traffic communication environment summary, and the specific steps are as follows:

[0013] Extracting traffic communication environment parameters matching the current timestamp based on the traffic communication environment archive;

[0014] Define monitoring indicators through a combination of demand analysis and analysis of historical data from transportation and communication environment archives;

[0015] Set the normal range, warning threshold, and alarm threshold for each indicator through the network equipment manual;

[0016] Based on the historical data of the traffic and communication environment archive, dynamically adjust and optimize the threshold rules through statistical analysis and define monitoring rules;

[0017] The traffic and communication environment parameters are monitored in real time through monitoring indicators and monitoring rules to obtain a summary of the traffic and communication environment.

[0018] As a preferred solution of the control method of the intelligent transportation system based on automatic protocol docking of the present invention, wherein: based on the random forest model, the traffic communication protocol selection model is constructed, and the specific steps are as follows:

[0019] Extracting historical traffic communication environment features from historical traffic communication environment summaries through feature processing;

[0020] The random forest model is used as the basic model;

[0021] The input layer receives historical traffic and communication environment characteristics;

[0022] The random sampling layer resamples the historical traffic and communication environment characteristics to generate multiple training subsets;

[0023] The feature random selection layer randomly selects some features for each tree;

[0024] The decision tree training layer constructs decision rules based on the training subsets and features;

[0025] The reasoning layer predicts the optimal protocol under the current traffic communication environment;

[0026] The voting layer integrates the prediction results of all trees and determines the final agreement through majority voting;

[0027] The output layer outputs the historical optimal traffic communication protocol;

[0028] Finally, a traffic communication protocol selection model was constructed.

[0029] As a preferred solution of the control method of the intelligent transportation system based on automatic protocol docking of the present invention, wherein: based on the traffic communication environment summary, the optimal traffic communication protocol is obtained through the traffic communication protocol selection model, and the specific steps are as follows:

[0030] Based on the traffic and communication environment summary, the traffic and communication environment features are extracted through feature processing;

[0031] Using the traffic communication protocol selection model, the applicability score of each candidate communication protocol in the current environment is calculated by reasoning about the characteristics of the traffic communication environment. The expression is:

[0032] ;

[0033] in, Is a candidate communication protocol The suitability score of is the total number of decision trees, is the index variable of the decision tree, is the adjustment parameter of information gain, It is The candidate protocol is The average information gain of the tree splits, is the tuning parameter of the depth penalty, It is The candidate protocol is The average depth of a tree, It is The candidate protocol is The average information gain of the tree splits, It is The candidate protocol is The average depth of a tree, is the total number of candidate communication protocols, is the auxiliary index variable of the candidate communication protocol, is the index variable of the candidate communication protocol category being evaluated;

[0034] Based on the applicability scores of candidate communication protocols, the optimal traffic communication protocol is obtained.

[0035] As a preferred solution of the control method of the intelligent transportation system based on the automatic protocol docking of the present invention, wherein: based on the optimal transportation communication protocol, the protocol conversion is performed through the intelligent intermediate, and the specific steps are as follows:

[0036] Based on the optimal traffic communication protocol, the core characteristics of the protocol are extracted through intelligent intermediates;

[0037] The intelligent intermediate uses the supported protocol lists of the sender and receiver to convert the protocol targets according to the core characteristics of the protocol. The expression is:

[0038] ;

[0039] in, is the protocol conversion rate, It is The task weight factor of a time slice is is the computing resources and time required for protocol conversion, is the total number of protocol fields, It is The difference between the protocol fields, is the index variable of the protocol field, is the total number of time slices, is the index variable of the time slice, is the sending protocol, It is the receiving end protocol.

[0040] As a preferred solution of the control method of the intelligent transportation system based on the automatic protocol docking of the present invention, wherein: the traffic communication path is dynamically evaluated through the heterogeneous network communication path optimization mechanism to obtain the best traffic communication path, and the specific steps are as follows:

[0041] Obtain the performance parameters of heterogeneous network communication paths through traffic communication environment archives;

[0042] Based on the protocol conversion rate, the communication quality score of the traffic communication path is dynamically evaluated, and the expression is:

[0043] ;

[0044] in, It is The communication quality scores of the traffic communication paths, is the bandwidth weight coefficient, is the delay weight coefficient, is the weight coefficient of packet loss rate, is the weight coefficient of the jitter range, is the weight coefficient of the protocol conversion rate, It is The delay value of the traffic communication path, It is The bandwidth value of the traffic communication path, It is The packet loss rate of the traffic communication path, It is The jitter range of the traffic communication path, is the total number of traffic communication paths, is the index variable of the traffic communication path;

[0045] The communication quality scores of all traffic communication paths are compared to obtain the best traffic communication path.

[0046] As a preferred solution of the control method of the intelligent transportation system based on the automatic protocol docking of the present invention, wherein: based on the optimal traffic communication path, the traffic communication data is encrypted by a quantum random number generator to transmit the traffic communication data, and the specific steps are as follows:

[0047] Generate high entropy random keys through quantum random number generator and distribute them securely to both communicating parties;

[0048] Encrypt traffic communication data using quantum keys combined with symmetric encryption algorithms;

[0049] Transmit encrypted traffic communication data through the best traffic communication path and monitor network status in real time to dynamically optimize the path;

[0050] The receiving end decrypts the encrypted traffic communication data using the shared key to restore the traffic communication data.

[0051] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the control method of the intelligent transportation system based on automated protocol docking as described in the first aspect of the present invention.

[0052] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the control method for an intelligent transportation system based on automated protocol docking as described in the first aspect of the present invention.

[0053] The beneficial effects of the present invention are: by collecting traffic communication metadata and building a traffic communication environment archive through an intelligent intermediate, combined with a protocol selection mechanism based on a random forest model, the communication protocol and path are dynamically optimized to ensure that the traffic system can flexibly and accurately select the optimal communication solution in a complex and dynamic traffic environment. At the same time, quantum encryption technology is used to ensure the security of data transmission, prevent external attacks, and enhance the system's anti-interference ability and communication stability. Overall, the present invention improves the communication efficiency, reliability and security of the intelligent transportation system, optimizes the signal control and data transmission process, and significantly enhances the intelligence and security of traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0055] Figure 1 This is a flow chart of a control method for an intelligent transportation system based on automated protocol docking in Example 1.

[0056] Figure 2 This is a flowchart summarizing the process of obtaining the traffic communication environment in Example 1. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0058] Example 1

[0059] Reference Figure 1 and Figure 2, which is the first embodiment of the present invention, and provides a control method for an intelligent transportation system based on automatic protocol docking, comprising the following steps:

[0060] S1. The intelligent intermediate builds a traffic and communication environment archive by collecting traffic and communication metadata.

[0061] It should be noted that the intelligent intermediary is an intermediate layer component used to connect and coordinate the communicating parties. It is responsible for collecting traffic communication metadata, processing protocol conversion, monitoring the communication environment, and realizing data transmission optimization and management in heterogeneous networks.

[0062] S1.1 Traffic communication metadata includes traffic communication protocol type, network performance indicators, equipment resource parameters and environmental information.

[0063] S1.2. Clean the traffic and communication metadata and organize them into a unified structured data format to build a traffic and communication environment archive.

[0064] It should be noted that the transportation communication metadata was cleaned by missing value processing and outlier removal;

[0065] Unify the fields and units of cleaned traffic communication metadata;

[0066] Convert numeric fields to floating point types and categorical fields to strings;

[0067] Organize all records into a unified structured data format according to a hierarchical framework;

[0068] The formatted transportation communication metadata are integrated by timestamp to build a transportation communication environment archive.

[0069] S2. Based on the traffic and communication environment archive, monitor the traffic and communication environment parameters in real time and obtain the traffic and communication environment summary.

[0070] S2.1. Extracting traffic communication environment parameters matching the current timestamp based on the traffic communication environment archive.

[0071] S2.2. Define monitoring indicators by combining demand analysis with historical data analysis of transportation and communication environment archives.

[0072] It should be noted that the monitoring indicators are defined by combining demand analysis with historical data analysis of the traffic and communication environment archives. The specific process is as follows:

[0073] By analyzing communication scenarios and business needs, we can identify the core requirements for communication performance and select key communication parameters based on different scenarios (such as remote control, video transmission, and IoT monitoring). For example, low latency is the focus of real-time scenarios, and low packet loss rate and high bandwidth are the core requirements for video transmission.

[0074] By analyzing historical data in the traffic and communication environment archives, the distribution range, fluctuation patterns and abnormal conditions of communication parameters are statistically analyzed to identify key indicators that have a greater impact on performance. For example, by calculating the average, maximum and abnormal frequency of the parameters, it is possible to verify whether the monitoring indicators selected in the demand analysis are reasonable. At the same time, new problem indicators (such as abnormal fluctuations in latency and packet loss rate in specific scenarios) are discovered to optimize and improve the selection of monitoring indicators.

[0075] S2.3. Set the normal range, warning threshold and alarm threshold for each indicator through the network equipment manual.

[0076] It should be noted that the recommended values, maximum values, and tolerance ranges of equipment performance parameters are obtained through the network equipment manual, and the normal range, warning threshold, and alarm threshold are set for each monitoring indicator based on actual operating requirements.

[0077] S2.4. Based on the historical data of the traffic and communication environment archives, dynamically adjust and optimize the threshold rules through statistical analysis and define monitoring rules.

[0078] It should be noted that the trend changes and abnormal frequencies are derived through statistical analysis of historical data of the transport and communication environment archives (e.g. time series analysis, comparison of distribution patterns, statistics of abnormal times, etc.);

[0079] Dynamically adjust threshold rules based on trend changes and abnormal frequencies to define more practical monitoring rules.

[0080] S2.5. Monitor the traffic and communication environment parameters in real time through monitoring indicators and monitoring rules to obtain a summary of the traffic and communication environment.

[0081] S3. Based on the random forest model, a traffic communication protocol selection model is constructed.

[0082] S3.1. Extract historical traffic and communication environment features from historical traffic and communication environment summaries through feature processing.

[0083] S3.2, random forest model as the basic model;

[0084] The input layer receives historical traffic and communication environment characteristics;

[0085] The random sampling layer resamples the historical traffic and communication environment characteristics to generate multiple training subsets;

[0086] The feature random selection layer randomly selects some features for each tree;

[0087] The decision tree training layer constructs decision rules based on the training subsets and features;

[0088] The reasoning layer predicts the optimal protocol under the current traffic communication environment;

[0089] The voting layer integrates the prediction results of all trees and determines the final agreement through majority voting;

[0090] The output layer outputs the historical optimal traffic communication protocol;

[0091] Finally, a traffic communication protocol selection model was constructed.

[0092] It should be noted that the advantage of building a traffic communication protocol selection model based on the random forest model is that the random forest has strong robustness and generalization ability by integrating the prediction results of multiple decision trees, and can effectively avoid the overfitting problem of a single model. The random sampling and feature random selection mechanism enables the model to make full use of historical traffic communication environment feature data and explore the applicability of different communication protocols in various environments, thereby improving the accuracy and adaptability of protocol selection. At the same time, the majority voting mechanism ensures the stability of the prediction results, enabling the model to respond flexibly in complex and dynamic communication environments, thereby improving the communication efficiency and reliability of the intelligent transportation system.

[0093] S4. Based on the traffic communication environment summary, the optimal traffic communication protocol is obtained through the traffic communication protocol selection model.

[0094] S4.1. Based on the traffic and communication environment summary, the traffic and communication environment features are extracted through feature processing.

[0095] S4.2. Using the traffic communication protocol selection model, the applicability score of each candidate communication protocol in the current environment is calculated by reasoning about the characteristics of the traffic communication environment. The expression is:

[0096] ;

[0097] in, Is a candidate communication protocol The suitability score of is the total number of decision trees, is the index variable of the decision tree, is the adjustment parameter of information gain, It is The candidate protocol is The average information gain of the tree splits, is the tuning parameter of the depth penalty, It is The candidate protocol is The average depth of a tree, It is The candidate protocol is The average information gain of the tree splits, It is The candidate protocol is The average depth of a tree, is the total number of candidate communication protocols, is the auxiliary index variable of the candidate communication protocol, is the index variable of the candidate communication protocol category being evaluated.

[0098] S4.3. Based on the applicability scores of the candidate communication protocols, an optimal traffic communication protocol is obtained.

[0099] Specifically, the suitability score of each candidate protocol is ,By comparing the suitability scores of all candidate protocols, the candidate ,protocol with the highest suitability score is selected as the optimal ,traffic communication protocol.

[0100] S5. Based on the optimal traffic communication protocol, protocol conversion is performed through an intelligent intermediate.

[0101] S5.1. Based on the optimal traffic communication protocol, extract the core characteristics of the protocol through intelligent intermediates.

[0102] S5.2, the intelligent intermediate uses the supported protocol lists of the sender and receiver to convert the protocol according to the core characteristics of the protocol. The expression is:

[0103] ;

[0104] in, is the protocol conversion rate, It is The task weight factor of a time slice is is the computing resources and time required for protocol conversion, is the total number of protocol fields, It is The difference between the protocol fields, is the index variable of the protocol field, is the total number of time slices, is the index variable of the time slice, is the sending protocol, It is the receiving end protocol.

[0105] It should be noted that The specific expression is:

[0106] ;

[0107] in, is the complexity weight of the protocol field, It is the fixed overhead of protocol conversion.

[0108] The specific expression is:

[0109] Δ f ( W s , W d )= | W s [f] - W d [f]| max( W s [f], W d [f]) ;

[0110] in, is the sending protocol, It is the receiving end protocol.

[0111] S6. Through the heterogeneous network communication path optimization mechanism, the traffic communication path is dynamically evaluated to obtain the optimal traffic communication path.

[0112] S6.1. Obtain the performance parameters of heterogeneous network communication paths through traffic communication environment archives.

[0113] It should be noted that performance parameters include bandwidth, delay, packet loss, jitter range and protocol conversion rate.

[0114] S6.2. Based on the protocol conversion rate, the communication quality score of the traffic communication path is dynamically evaluated. The expression is:

[0115] ;

[0116] in, It is The communication quality scores of the traffic communication paths, is the bandwidth weight coefficient, is the delay weight coefficient, is the weight coefficient of packet loss rate, is the weight coefficient of the jitter range, is the weight coefficient of the protocol conversion rate, It is The delay value of the traffic communication path, It is The bandwidth value of the traffic communication path, It is The packet loss rate of the traffic communication path, It is The jitter range of the traffic communication path, is the total number of traffic communication paths, is the index variable of the traffic communication path.

[0117] It should be noted that the calculation process of the expression is as follows:

[0118] For a certain traffic communication path, calculate the weighted combination of its performance parameters and substitute the result into the exponential function to obtain the score of the path. Perform the same calculation for all possible traffic communications and sum the scores of all traffic communication paths. Use the sum of the scores as the normalized denominator and divide the score of the current traffic communication path by the total score to obtain the communication quality score of the traffic communication path.

[0119] S6.3. Compare the communication quality scores of all traffic communication paths to obtain the best traffic communication path.

[0120] S7. Based on the optimal traffic communication path, the traffic communication data is encrypted through the quantum random number generator to transmit the traffic communication data.

[0121] S7.1. Generate a high-entropy random key through a quantum random number generator and distribute it securely to both communicating parties.

[0122] Specifically, a high-entropy random key is generated by a quantum random number generator, and the key is securely transmitted to the communicating parties through a quantum key distribution protocol.

[0123] S7.2. Use quantum keys combined with symmetric encryption algorithms to encrypt traffic communication data.

[0124] Specifically, the sending end uses the symmetric encryption algorithm One-Time Pad to generate ciphertext by bit-by-bit XORing the traffic communication data and the key. The ciphertext is transmitted to the receiving end through the optimal traffic communication path. The receiving end uses the shared key to restore the original data through the corresponding decryption algorithm to complete the encrypted communication.

[0125] S7.3. Transmit the encrypted traffic communication data through the optimal traffic communication path, and monitor the network status in real time to dynamically optimize the path.

[0126] Specifically, the encrypted traffic communication data is transmitted through the current optimal traffic communication path, and the network status is monitored in real time, including performance parameters such as bandwidth, delay, and packet loss rate. If path performance degradation or abnormality is detected, the dynamic optimization mechanism is immediately triggered to re-evaluate the quality of the traffic communication path and switch to the new optimal traffic communication path.

[0127] S7.4. The receiving end decrypts the encrypted traffic communication data through the shared key to complete the restoration of the traffic communication data, thereby realizing the docking of the automation protocol.

[0128] This embodiment also provides a computer device, which is applicable to a control method of an intelligent transportation system based on automated protocol docking, and includes: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a control method of an intelligent transportation system based on automated protocol docking as proposed in the above embodiment.

[0129] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0130] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a control method for a smart transportation system based on automated protocol docking as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0131] In summary, the present invention achieves this by: using an intelligent intermediate to collect traffic communication metadata and construct a traffic communication environment archive, combining a protocol selection mechanism based on a random forest model, dynamically optimizing communication protocols and paths, and ensuring that the traffic system can flexibly and accurately select the optimal communication solution in a complex and dynamic traffic environment. At the same time, quantum encryption technology is used to ensure the security of data transmission, prevent external attacks, and enhance the system's anti-interference ability and communication stability. Overall, the present invention improves the communication efficiency, reliability, and security of the intelligent transportation system, optimizes the signal control and data transmission process, and significantly enhances the intelligence and security of traffic management.

[0132] It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A control method for an intelligent transportation system based on automated protocol docking, characterized in that: include: The intelligent intermediate builds a traffic and communication environment archive by collecting traffic and communication metadata; Based on the traffic and communication environment archive, real-time monitoring of traffic and communication environment parameters is carried out to obtain a summary of the traffic and communication environment; Based on the random forest model, a traffic communication protocol selection model is constructed; Based on the traffic communication environment summary, the optimal traffic communication protocol is obtained through the traffic communication protocol selection model; Based on the optimal traffic communication protocol, the protocol conversion is performed through the intelligent intermediate, and the traffic communication path is dynamically evaluated through the heterogeneous network communication path optimization mechanism to obtain the optimal traffic communication path; Based on the optimal traffic communication path, the traffic communication data is encrypted through the quantum random number generator to transmit traffic communication data; The above-mentioned steps of obtaining the optimal traffic communication protocol based on traffic communication environment summary and traffic communication protocol selection model are as follows: Based on the traffic and communication environment summary, the traffic and communication environment features are extracted through feature processing; Using the traffic communication protocol selection model, the applicability score of each candidate communication protocol in the current environment is calculated by reasoning about the characteristics of the traffic communication environment. The expression is: ； in, Is a candidate communication protocol The suitability score of is the total number of decision trees, is the index variable of the decision tree, is the adjustment parameter of information gain, It is The candidate protocol is The average information gain of the tree splits, is the tuning parameter of the depth penalty, It is The candidate protocol is The average depth of a tree, It is The candidate protocol is The average information gain of the tree splits, It is The candidate protocol is The average depth of a tree, is the total number of candidate communication protocols, is the auxiliary index variable of the candidate communication protocol, is the index variable of the candidate communication protocol category being evaluated; Based on the applicability scores of candidate communication protocols, the optimal traffic communication protocol is obtained.

2. The control method of the intelligent transportation system based on the automated protocol docking according to claim 1, characterized in that: The intelligent intermediate collects traffic communication metadata and builds a traffic communication environment archive. The specific steps are as follows: The traffic communication metadata includes traffic communication protocol type, network performance indicators, equipment resource parameters and environmental information; Clean the traffic and communication metadata and organize them into a unified structured data format to build a traffic and communication environment archive.

3. The control method of the intelligent transportation system based on automated protocol docking according to claim 2, characterized in that: The specific steps of monitoring the traffic communication environment parameters in real time and obtaining the traffic communication environment summary based on the traffic communication environment archive are as follows: Extracting traffic communication environment parameters matching the current timestamp based on the traffic communication environment archive; Define monitoring indicators through a combination of demand analysis and analysis of historical data from transportation and communication environment archives; Set the normal range, warning threshold, and alarm threshold for each indicator through the network equipment manual; Based on the historical data of the traffic and communication environment archive, dynamically adjust and optimize the threshold rules through statistical analysis and define monitoring rules; The traffic communication environment parameters are monitored in real time through monitoring indicators and monitoring rules to obtain a summary of the traffic communication environment.

4. The control method of the intelligent transportation system based on automated protocol docking according to claim 3, characterized in that: The traffic communication protocol selection model is constructed based on the random forest model. The specific steps are as follows: Extracting historical traffic communication environment features from historical traffic communication environment summaries through feature processing; The random forest model is used as the basic model; The input layer receives historical traffic and communication environment characteristics; The random sampling layer resamples the historical traffic and communication environment characteristics to generate multiple training subsets; The feature random selection layer randomly selects some features for each tree; The decision tree training layer constructs decision rules based on the training subsets and features; The reasoning layer predicts the optimal protocol under the current traffic communication environment; The voting layer integrates the prediction results of all trees and determines the final agreement through majority voting; The output layer outputs the historical optimal traffic communication protocol; Finally, a traffic communication protocol selection model was constructed.

5. The control method of the intelligent transportation system based on the automatic protocol docking according to claim 4 is characterized in that: The specific steps of performing protocol conversion based on the optimal traffic communication protocol through an intelligent intermediate are as follows: Based on the optimal traffic communication protocol, the core characteristics of the protocol are extracted through intelligent intermediates; The intelligent intermediate uses the supported protocol lists of the sender and receiver to convert the protocol targets according to the core characteristics of the protocol. The expression is: ； in, is the protocol conversion rate, It is The task weight factor of a time slice is is the computing resources and time required for protocol conversion, is the total number of protocol fields, It is The difference between the protocol fields, is the index variable of the protocol field, is the total number of time slices, is the index variable of the time slice, is the sending protocol, It is the receiving end protocol.

6. The control method of the intelligent transportation system based on automated protocol docking according to claim 5, characterized in that: The specific steps of dynamically evaluating the traffic communication path and obtaining the best traffic communication path through the heterogeneous network communication path optimization mechanism are as follows: Obtain the performance parameters of heterogeneous network communication paths through traffic communication environment archives; Based on the protocol conversion rate, the communication quality score of the traffic communication path is dynamically evaluated, and the expression is: ； in, It is The communication quality scores of the traffic communication paths, is the bandwidth weight coefficient, is the delay weight coefficient, is the weight coefficient of packet loss rate, is the weight coefficient of the jitter range, is the weight coefficient of the protocol conversion rate, It is The delay value of the traffic communication path, It is The bandwidth value of the traffic communication path, It is The packet loss rate of the traffic communication path, It is The jitter range of the traffic communication path, is the total number of traffic communication paths, is the index variable of the traffic communication path; The communication quality scores of all traffic communication paths are compared to obtain the best traffic communication path.

7. The control method of the intelligent transportation system based on automated protocol docking according to claim 6, characterized in that: Based on the optimal traffic communication path, the traffic communication data is encrypted by a quantum random number generator to transmit traffic communication data. The specific steps are as follows: Generate high entropy random keys through quantum random number generator and distribute them securely to both communicating parties; Encrypt traffic communication data using quantum keys combined with symmetric encryption algorithms; Transmit encrypted traffic communication data through the best traffic communication path and monitor network status in real time to dynamically optimize the path; The receiving end decrypts the encrypted traffic communication data using the shared key to restore the traffic communication data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the control method of the intelligent transportation system based on automation protocol docking as described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the control method of the intelligent transportation system based on automation protocol docking as described in any one of claims 1 to 7 are implemented.

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