Expressway large-scale traffic data collaborative scheduling method and device based on block chain, terminal equipment and storage medium

Abstract

The invention is suitable for the field of block chains, and provides a block chain-based highway large-scale traffic data collaborative scheduling method. Comprising the following steps: a server obtains a traffic state prediction model trained by each highway traffic data producer node on a highway traffic data block chain of a target area based on updated traffic event data; acquiring a global traffic state prediction model of the target area based on the traffic state prediction model; and predicting a traffic state corresponding to the traffic live data based on the global traffic state prediction model and the traffic live data uploaded by the user side node. According to the method, the global traffic state prediction model is constructed by transmitting the traffic state prediction model parameters of each highway traffic data producer node after local training through the traffic data block chain, so that the traffic state prediction of each highway traffic data producer node is ensured under the condition that the data is not delivered out of a warehouse. And accurate and effective large-scale highway traffic data collaborative scheduling is realized.

Description

Technical Field

[0001] The present application belongs to the field of blockchain technology, and in particular, relates to a blockchain-based method, device, terminal equipment and storage medium for collaborative scheduling of large-scale highway traffic data. Background Art

[0002] With the acceleration of urbanization, traffic congestion is becoming more and more serious, which brings great challenges to urban operation and residents' travel. In order to effectively solve the problem of traffic congestion, large-scale highway traffic data needs to be analyzed and optimized in real time to achieve the rational allocation and coordinated scheduling of traffic resources, making the traffic system safer, more efficient and more convenient. Due to the widespread popularity of smart devices, most modern cars are equipped with on-board units, sensing functions, global positioning system receivers and dedicated short-range communication modules, the latter allowing vehicles to communicate with each other and with the surrounding environment, such as roadside units, traffic signs, pedestrians, etc. Vehicles can detect traffic conditions and share them with other vehicles to improve the traffic system. For example, the GPS of a vehicle and its speed can be used to propose optimized route suggestions to minimize traffic congestion. In order to achieve coordinated scheduling among large-scale highway traffic data, optimizing the dissemination of highway traffic data and its secure storage and management are key issues, which are essential for establishing a reliable road traffic record history to ensure data integrity, data traceability and availability, and vehicle privacy.

[0003] However, the existing highway traffic data scheduling methods have the following problems, making them difficult to be effectively applied in practice. First, most of the existing highway traffic data scheduling methods are centralized. These technologies rely on the central cloud to store and manage the collected highway traffic data. However, this centralized architecture is prone to privacy issues and bandwidth limitations, which may lead to serious network congestion problems. The centralized architecture is also vulnerable to attacks due to its single point failure vulnerability. Second, the existing highway traffic data collaboration technology often ignores the data island problem. In fact, since these highway traffic data are generated by multiple independent individuals, these individuals are unwilling to share the original data for scheduling analysis in order to protect their own data privacy. This large-scale data island mode greatly increases the difficulty of highway traffic data collaborative scheduling, making it difficult to accurately and effectively carry out data analysis, resulting in the failure of scheduling strategies. Third, the existing highway traffic data scheduling methods ignore the scalability of the task module. Most existing blockchain-based technologies face scalability limitations, such as high confirmation delays and high storage and computing costs. These scalability issues hinder the practical application of this technology. Summary of the invention

[0004] The embodiments of the present application provide a blockchain-based method, apparatus, terminal device, and storage medium for collaborative scheduling of large-scale highway traffic data.

[0005] In the first aspect, an embodiment of the present application provides a blockchain-based method for coordinated scheduling of large-scale highway traffic data, including: obtaining a traffic state prediction model trained by each highway traffic data producer node on the highway traffic data blockchain of the target area based on updated traffic event data; based on the traffic state prediction model, obtaining a global traffic state prediction model of the target area; based on the global traffic state prediction model and the actual traffic data uploaded by the highway traffic data user-end node, predicting the traffic state corresponding to the actual traffic data.

[0006] In a possible implementation of the first aspect, the traffic state prediction model trained by each highway traffic data producer node based on the updated traffic event data includes the following steps: obtaining the traffic flow event data updated by the target highway traffic data producer; using the updated traffic event data to train a pre-prepared traffic state prediction model to obtain an updated traffic state prediction model.

[0007] In a possible implementation of the first aspect, the traffic state prediction model trained by each highway traffic data producer node based on the updated traffic event data further includes the following steps: The updated traffic state prediction model is encrypted using a first encryption function to obtain an encrypted prediction model.

[0008] In a possible implementation of the first aspect, before obtaining the global traffic status prediction model of the target area based on the traffic status prediction model, the following steps are included: decrypting the encrypted prediction model to obtain model parameters of the updated traffic status prediction model; generating a new block on the highway traffic data blockchain based on the model parameters; verifying the new block to obtain a valid block; adding the valid block to the highway traffic data blockchain to obtain a new highway traffic data blockchain.

[0009] In a possible implementation of the first aspect, the verification of the new block to obtain a valid block includes the following steps: obtaining a master node corresponding to the new block, and sending a verification signal to the master node so that the master node broadcasts the new block to the slave nodes; the slave nodes refer to highway traffic data producer nodes other than the master node; when more than a first preset number of slave nodes verify that the new block has passed, a voting start signal is sent to the slave nodes so that the slave nodes vote for the new block; when any of the slave nodes receives a second preset number of voting pass signals, the new block is determined to be valid.

[0010] In a possible implementation of the first aspect, the global traffic state prediction model of the target area is obtained based on the traffic state prediction model, including the following steps: parsing the model parameters of the updated traffic state prediction model in the valid block; obtaining a federal model based on the traffic state prediction model generated by each highway traffic data producer node; updating the federal model according to the model parameters of the updated traffic state prediction model to obtain an updated global traffic state prediction model.

[0011] In a possible implementation of the first aspect, predicting the traffic state corresponding to the real-time traffic data based on the global traffic state prediction model and the real-time traffic data uploaded by the highway traffic data user-end node includes the following steps: inputting the real-time traffic data uploaded by the highway traffic data user-end node into the global traffic state prediction model, and predicting the traffic state corresponding to the real-time traffic data.

[0012] In the second aspect, an embodiment of the present application provides a blockchain-based large-scale highway traffic data collaborative scheduling device, including: a model acquisition module, which is used to obtain the traffic state prediction model trained by each highway traffic data producer node on the highway traffic data blockchain of the target area based on updated traffic event data; a global model acquisition module, which is used to obtain the global traffic state prediction model of the target area based on the traffic state prediction model; a prediction module, which is used to predict the traffic state corresponding to the actual traffic data based on the global traffic state prediction model and the actual traffic data uploaded by the highway traffic data user-end node.

[0013] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: obtaining a traffic state prediction model trained by each highway traffic data producer node on the highway traffic data blockchain of the target area based on updated traffic event data; based on the traffic state prediction model, obtaining a global traffic state prediction model of the target area; based on the global traffic state prediction model and the actual traffic data uploaded by the highway traffic data user-end node, predicting the traffic state corresponding to the actual traffic data.

[0014] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device executes the blockchain-based highway large-scale traffic data collaborative scheduling method as described in any one of the first aspects above.

[0015] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0016] Compared with the prior art, the embodiments of the present application have the following beneficial effects: in the above-mentioned blockchain-based large-scale highway traffic data collaborative scheduling method, the server obtains the traffic state prediction model trained by each highway traffic data producer node on the highway traffic data blockchain of the target area based on the updated traffic event data; based on the traffic state prediction model, the global traffic state prediction model of the target area is obtained; based on the global traffic state prediction model and the actual traffic data uploaded by the highway traffic data user-end node, the traffic state corresponding to the actual traffic data is predicted. This method is used to construct a global traffic state prediction model by transmitting the traffic state prediction model parameters trained locally by each highway traffic data producer node through the highway traffic data blockchain, thereby ensuring that each highway traffic data producer node can achieve accurate and effective large-scale highway traffic data collaborative scheduling without data being stored. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 It is a flowchart of a method for collaboratively dispatching large-scale highway traffic data based on blockchain in one embodiment of the present application; Figure 2 It is a flowchart of the block validity verification step in one embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0020] In one embodiment, Figure 1 As shown, a method for coordinated dispatching of large-scale highway traffic data based on blockchain is provided, comprising the following steps: S101, obtaining a traffic status prediction model trained by each traffic data producer node on the traffic data blockchain of the target area based on the updated traffic event data.

[0021] Among them, the traffic data blockchain can be used on highways. The highway traffic data blockchain is composed of a chain of blocks. Each block contains the data of each target traffic flow event, and the target traffic flow event data includes hash value, traffic event nature, traffic event description, traffic location, signature, data source and traffic event creation time.

[0022] It can be understood that the nodes existing on the highway traffic data blockchain can be divided into nodes that process data uploaded by highway traffic data producers and nodes that process data uploaded by highway traffic data users; highway traffic data producers can be vehicles, pedestrians, traffic lights, etc., and highway traffic data can be collected through sensors, cameras and other equipment; highway traffic data users can be traffic management departments, travel users, and transportation operators.

[0023] Specifically, each highway traffic data producer node trains a pre-trained traffic state prediction model based on the locally updated traffic event data to obtain an updated traffic state prediction model corresponding to the highway traffic data production node; then uses the first encryption function to encrypt the updated traffic state prediction model to obtain an encrypted prediction model, and then sends the encrypted prediction model to the server.

[0024] S102: Acquire a global traffic state prediction model of the target area based on the traffic state prediction model.

[0025] Specifically, after receiving the encrypted prediction model, the server decrypts the encrypted prediction model to obtain the model parameters of the updated traffic status prediction model; based on the model parameters, a new block on the highway traffic data blockchain is generated; the new block is verified to obtain a valid block; the valid block is added to the highway traffic data blockchain to obtain a new highway traffic data blockchain.

[0026] S103, predicting a traffic state corresponding to the real-time traffic data based on the global traffic state prediction model and the real-time traffic data uploaded by the traffic data user terminal node.

[0027] Specifically, the real-time traffic data uploaded by the traffic data user-end node on the highway is input into the global traffic state prediction model to predict the traffic state corresponding to the real-time traffic data.

[0028] In the embodiment of the present application, the server obtains the traffic state prediction model trained by each highway traffic data producer node on the highway traffic data blockchain of the target area based on the updated traffic event data; based on the traffic state prediction model, the global traffic state prediction model of the target area is obtained; based on the global traffic state prediction model and the actual traffic data uploaded by the highway traffic data user terminal node, the traffic state corresponding to the actual traffic data is predicted. This method is used to construct a global traffic state prediction model by transmitting the traffic state prediction model parameters trained locally by each highway traffic data producer node through the highway traffic data blockchain, thereby ensuring that each highway traffic data producer node can achieve accurate and effective large-scale highway traffic data collaborative scheduling without data being stored.

[0029] In one embodiment, the traffic state prediction model trained by each highway traffic data producer node based on the updated traffic event data also includes the following steps, including: encrypting the updated traffic state prediction model using a first encryption function to obtain an encrypted prediction model.

[0030] Specifically, we can define is a highway traffic data set, For the Highway traffic data, ;definition A collection of highway traffic data sources. For the Sources of highway traffic data, ;definition A collection of highway traffic data producers. For the highway traffic data producers, ;definition A collection of highway traffic data users. For the highway traffic data user terminals, ;definition For Get The function returns The result of cleaning, encryption, and hashing; Definition For the general The function of storing evidence on the chain returns Indexes and keys on the blockchain; definitions for access The function returns Amount of digital currency or credits paid; Definition for contribute The function returns Amount of digital currency or points earned; Definition For the highway traffic data sharing platform, it can be expressed as the following formula: in, , , , .

[0031] In this embodiment, the server can use The function encrypts the traffic state prediction model to obtain the encrypted traffic state prediction model. Since the node obtains the traffic state prediction model locally based on its own updated traffic flow event data, it is equivalent to decentralizing the traffic flow event data, ensuring the security of the traffic flow event data. At the same time, the traffic state prediction model obtained by the node is encrypted and then sent to the server, further ensuring the security of the traffic flow event data.

[0032] In one embodiment of the application, Figure 2 As shown, the encrypted prediction model is decrypted to obtain the model parameters of the updated traffic state prediction model, which includes the following steps: S201, decrypt the encrypted prediction model to obtain model parameters of the updated traffic status prediction model.

[0033] S202, generating a new block on the traffic data blockchain based on the model parameters.

[0034] Specifically, after decrypting the encrypted prediction model, the server parses the model parameters of the updated traffic status prediction model, and then generates a new block containing the model parameters according to the data format stored in the block.

[0035] S203, verify the new block and obtain the valid block.

[0036] Specifically, the server obtains the main node corresponding to the new block and sends a verification signal to the main node so that the main node broadcasts the new block to the slave nodes; the slave nodes refer to the highway traffic data producer nodes other than the main node; when more than a first preset number of slave nodes verify that the new block has passed, a voting start signal is sent to the slave nodes so that the slave nodes vote for the new block; when any slave node receives a second preset number of voting pass signals, the new block is determined to be valid.

[0037] S204, adding the valid blocks to the traffic data blockchain to obtain a new highway traffic data blockchain.

[0038] In this embodiment, the blockchain is a linked list composed of blocks, each of which contains a set of descriptive data of traffic flow events (i.e., the atomic data of the system) and a reference to the previous block. A consensus must be reached among the members of the blockchain network to add blocks to the blockchain. Only when most nodes verify that the newly added block is valid can a new block be added to the blockchain, thereby ensuring the privacy of traffic flow event data.

[0039] In one embodiment, verifying a new block and obtaining a valid block includes the following steps: obtaining a master node corresponding to the new block, and sending a verification signal to the master node so that the master node broadcasts the new block to the slave nodes; the slave node refers to a highway traffic data producer node other than the master node; when more than a first preset number of slave nodes verify that the new block has passed, a voting start signal is sent to the slave nodes so that the slave nodes vote for the new block; when any slave node receives a second preset number of voting pass signals, the new block is determined to be valid.

[0040] For example, , , Respectively represent Block, Verification message, voting messages; set , Respectively represent master node, A secondary node; , Indicates the master node Proposing a new block , and broadcast it to all secondary nodes; , Represents a secondary node Verify received blocks , and returns a verification message ; , Represents a secondary node Voting Block , and returns a voting message ; , , Indicates the judgment block A function to determine whether a block is valid. If more than 2 / 3 of the verification messages and voting messages are collected, the block is considered valid. Valid. Master Node Propose new highway traffic data block , and broadcast to all secondary nodes ; Secondary node Received block After verification, if it passes, a verification message is returned , and broadcast to all nodes; when the secondary node After receiving more than 2 / 3 of the verification messages, a vote is conducted. If the votes agree, a voting message is returned. , and broadcast to all nodes; if the secondary node After receiving more than 2 / 3 of the voting messages, the block is considered The valid block model will be added to the blockchain. For the block model that is confirmed to be valid, it will be integrated into the global large model of the server. For the model that is not considered valid due to the vote, the model will not be federated. When the block of the same highway traffic data producer is considered invalid three times in a row, it is considered to be malicious data, and the server will punish it, so that the highway traffic data producer cannot participate in block data aggregation and information acquisition for three consecutive times.

[0041] In one embodiment of the application, the global traffic status prediction model of the target area is obtained based on the traffic status prediction model, including the following steps: parsing the model parameters of the updated traffic status prediction model in the valid block; obtaining a federal model based on the traffic status prediction model generated by each highway traffic data producer node; updating the federal model according to the model parameters of the updated traffic status prediction model to obtain an updated global traffic status prediction model.

[0042] Among them, federated modeling is based on federated learning technology, which implements model iteration and update services while meeting the compliance policies and data value protection requirements of each participant. Each participant in the federated model usually feeds back their own encrypted model parameters to the coordinator of the federated learning modeling, and then the coordinator aggregates the encrypted model parameters and feeds back the aggregated parameters to each participant for federated learning modeling.

[0043] For example, the server selects producers who participate in model training and trains the federated model based on the geographic location, data quality, reputation and other factors of the highway traffic data producers; each party's nodes train the traffic status prediction model locally. For example, a city's traffic management department can use its collected traffic flow, road conditions, accidents and other data to train a model to predict traffic congestion. An autonomous driving vehicle can use its own road traffic information to train its own model in a similar way and share model parameters. A road monitoring camera can use its own traffic condition information to train the model and share model parameters. Each party's nodes encrypt the trained model locally using an encryption function and send it to the server.

[0044] The server aggregates all the model parameters received, and forms new blocks based on each model parameter and adds them to the blockchain for aggregation and synchronization, thereby obtaining a global traffic status prediction model based on blockchain.

[0045] In one embodiment of the application, the traffic real-time data uploaded by the highway traffic data user terminal node is input into the global traffic state prediction model to predict the traffic state corresponding to the traffic real-time data, including: Specifically, when the global traffic status prediction model performs the prediction function, it applies the trained traffic status prediction model to the actual traffic scenario, outputs the prediction results based on the input real-time traffic data or future traffic data, and displays them to users or decision makers.

[0046] Exemplarily, the massive heterogeneous traffic information data collected comes from different channels and platforms, such as sensors, satellite navigation, smart phones, social media, etc., including road network structure, traffic flow data, traffic event data, weather data, etc. Utilizing the established global highway traffic data model, it is possible to effectively extract hidden features and patterns in the data, automatically establish a complex and accurate traffic prediction paradigm, and dynamically update and optimize it with the iteration of the global model, thereby accurately realizing real-time monitoring and dynamic evaluation of various indicators such as traffic flow, speed, travel time, as well as prediction and early warning of traffic conditions in the future. Utilizing the global highway traffic data model in the blockchain, each node performs data collaborative scheduling. Each blockchain node can obtain highway traffic data from other nodes on the blockchain according to its own authority and needs, and decrypt and verify it. Decryption is performed through the function to achieve this, it returns The original data is verified by function, which returns If the verification passes, the node can use Function to evaluate Utility value, and use it according to your own goals If the node is a highway traffic data user , then it can also Perform optimized scheduling and use Function to pay corresponding digital currency or points to highway traffic data producers All blockchain nodes follow step 3 above to reach consensus and merge, and ensure the blockchain network security, reliability and efficiency.

[0047] In this embodiment, the server can provide optimized traffic scheduling solutions for all nodes based on different goals and needs, and promote the intelligent and sustainable development of urban transportation systems. Among them, for public transportation operators, the frequency, departure interval, and stop points of buses, subways, etc. can be adjusted according to real-time and predicted highway traffic data to improve operational efficiency and passenger satisfaction; for private car owners, the best travel time, route, parking lot, etc. can be selected according to real-time and predicted highway traffic data to save time and fuel consumption; for government departments, reasonable traffic policies, planning and management measures such as traffic restrictions, diversion, and capacity expansion can be formulated according to real-time and predicted highway traffic data to discover and solve traffic problems and challenges, reduce urban traffic pollution and congestion, improve urban traffic efficiency and safety, and enhance the travel experience and quality of life of urban residents. In addition, with the continuous generation and accumulation of massive highway traffic data, it is necessary to regularly update the parameters and weights of the global traffic prediction model to maintain the effectiveness and adaptability of the model. By using test sets or cross-validation methods, the prediction performance of the model, such as accuracy, error, credibility, etc., can be evaluated. If the performance of the model does not meet expectations or there are problems such as overfitting or underfitting, it can be optimized by adjusting the model structure, adding or reducing features, changing the learning rate, etc.

[0048] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0049] Based on the same inventive concept, the embodiment of the present application also provides a device for realizing the above-mentioned blockchain-based large-scale highway traffic data collaborative scheduling. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the blockchain-based highway large-scale traffic data collaborative scheduling device provided below can be referred to the above-mentioned limitations on the blockchain-based highway large-scale traffic data collaborative scheduling method, which will not be repeated here.

[0050] In one embodiment, a blockchain-based highway large-scale traffic data collaborative scheduling device is provided, the device comprising: A model acquisition module is used to obtain the traffic state prediction model trained by each highway traffic data producer node on the highway traffic data blockchain in the target area based on the updated traffic event data; A global model acquisition module, which is used to acquire a global traffic state prediction model of the target area based on the traffic state prediction model; The prediction module is used to predict the traffic state corresponding to the real-time traffic data based on the global traffic state prediction model and the real-time traffic data uploaded by the highway traffic data user terminal node.

[0051] The above-mentioned large-scale highway traffic data collaborative dispatching device based on blockchain, in which the server obtains the traffic state prediction model trained by each highway traffic data producer node on the highway traffic data blockchain of the target area based on the updated traffic event data; based on the traffic state prediction model, the global traffic state prediction model of the target area is obtained; based on the global traffic state prediction model and the actual traffic data uploaded by the highway traffic data user terminal node, the traffic state corresponding to the actual traffic data is predicted. This method is used to construct a global traffic state prediction model by transmitting the traffic state prediction model parameters trained locally by each highway traffic data producer node through the highway traffic data blockchain, thereby ensuring that each highway traffic data producer node can achieve accurate and effective large-scale highway traffic data collaborative dispatching without data being stored.

[0052] Each module in the above-mentioned blockchain-based highway large-scale traffic data collaborative dispatching device can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above modules.

[0053] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A blockchain-based highway large-scale traffic data collaborative scheduling method, the method is applied to a server, characterized in that: The method comprises: Obtain the traffic status prediction model trained by each highway traffic data producer node on the highway traffic data blockchain in the target area based on the updated traffic event data; Based on the traffic state prediction model, obtaining a global traffic state prediction model of the target area; Based on the global traffic state prediction model and the real-time traffic data uploaded by the highway traffic data user terminal node, the traffic state corresponding to the real-time traffic data is predicted.

2. The method for coordinated dispatching of large-scale highway traffic data based on blockchain as claimed in claim 1 is characterized in that The traffic state prediction model trained by each highway traffic data producer node based on the updated traffic event data includes the following steps: Obtain the traffic flow event data updated by the target highway traffic data producer; The updated traffic event data is used to train a pre-predicted traffic state prediction model to obtain an updated traffic state prediction model.

3. The method for coordinated dispatching of large-scale highway traffic data based on blockchain as claimed in claim 2 is characterized in that: The traffic state prediction model trained by each highway traffic data producer node based on the updated traffic event data is also The following steps are involved, including: The updated traffic state prediction model is encrypted using a first encryption function to obtain an encrypted prediction model.

4. The method for coordinated dispatching of large-scale highway traffic data based on blockchain as claimed in claim 3 is characterized in that: Before acquiring the global traffic state prediction model of the target area based on the traffic state prediction model, the following steps are included: Decrypting the encrypted prediction model to obtain model parameters of the updated traffic state prediction model; Based on the model parameters, generating a new block on the highway traffic data blockchain; Verifying the new block to obtain a valid block; The valid block is added to the highway traffic data blockchain to obtain a new highway traffic data blockchain.

5. The method for coordinated dispatching of large-scale highway traffic data based on blockchain as claimed in claim 4 is characterized in that: The verifying of the new block to obtain a valid block includes the following steps: Acquire a master node corresponding to the new block, and send a verification signal to the master node, so that the master node broadcasts the new block to the slave nodes; the slave nodes refer to the highway traffic data producer nodes other than the master node; When more than a first preset number of slave nodes have verified the new block, a voting start signal is sent to the slave nodes to enable the slave nodes to vote for the new block; When any of the slave nodes receives a second preset number of voting approval signals, the new block is determined to be valid.

6. The method for coordinated dispatching of large-scale highway traffic data based on blockchain as claimed in claim 4 is characterized in that: The step of acquiring the global traffic state prediction model of the target area based on the traffic state prediction model comprises the following steps: parsing model parameters of the updated traffic state prediction model in the valid block; Based on the traffic state prediction models generated by the highway traffic data producer nodes, a federation model is obtained; The federated model is updated according to the model parameters of the updated traffic state prediction model to obtain an updated global traffic state prediction model.

7. The method for coordinated dispatching of large-scale highway traffic data based on blockchain as claimed in claim 1, characterized in that: The method of predicting the traffic state corresponding to the live traffic data based on the global traffic state prediction model and the live traffic data uploaded by the highway traffic data user terminal node comprises the following steps: The real-time traffic data uploaded by the highway traffic data user terminal node is input into the global traffic state prediction model to predict the traffic state corresponding to the real-time traffic data.

8. A blockchain-based highway large-scale traffic data collaborative dispatching device, characterized in that: The device comprises: A model acquisition module is used to obtain the traffic state prediction model trained by each highway traffic data producer node on the highway traffic data blockchain in the target area based on the updated traffic event data; A global model acquisition module, which is used to acquire a global traffic state prediction model of the target area based on the traffic state prediction model; The prediction module is used to predict the traffic state corresponding to the real-time traffic data based on the global traffic state prediction model and the real-time traffic data uploaded by the highway traffic data user terminal node.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images