Vehicle-mounted edge gridding data scheduling system based on combination of block chain and load balancing

Abstract

The invention discloses a vehicle-mounted edge gridding data scheduling system based on combination of a block chain and load balancing. The vehicle-mounted edge gridding data scheduling system comprises a dynamic resource allocation module, an intelligent scheduling module, a data management module and a data security and privacy protection module, the system provided by the invention can dynamically adjust the allocation of calculation and storage resources, and performs data scheduling according to real-time traffic and vehicle state information, thereby realizing efficient vehicle-mounted edge calculation.

Description

Technical Field

[0001] The present invention relates to the fields of vehicle-mounted edge computing, data sharing, load balancing, and encryption authentication, and specifically to a vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing. Background Art

[0002] With the rapid development of the Internet of Things (IoT), artificial intelligence (AI) and edge computing technologies, intelligent transportation systems are gradually becoming an important part of future urban traffic management. In-vehicle edge computing provides low-latency and high-efficiency data processing capabilities by distributing computing resources on edge nodes of vehicles and transportation infrastructure, thereby supporting applications such as autonomous driving, real-time traffic management and intelligent navigation. However, with the development of vehicle-to-everything (V2X) technology, how to effectively schedule data and manage resources has become a problem that needs to be solved.

[0003] Traditional in-vehicle edge computing systems often face problems such as unbalanced load, high data transmission delay, and low resource utilization when facing large amounts of real-time data processing needs. These problems not only affect the performance of the system, but may also lead to reduced reliability and stability of the system. In order to solve these problems, researchers have proposed a variety of data scheduling and load balancing strategies in recent years, but most of these methods fail to fully consider data security and privacy protection issues.

[0004] As a distributed ledger technology, blockchain technology has the characteristics of decentralization, immutability and high security, which can effectively solve the security issues of data during transmission and storage. Applying blockchain technology to the vehicle edge computing system can ensure the integrity and security of data and prevent data from being tampered with and illegally accessed. However, blockchain technology still faces challenges in scalability and performance when processing high-frequency and large-scale data. Summary of the invention

[0005] The purpose of the present invention is to provide a vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing, and the vehicle-mounted edge grid data scheduling system is used to realize data scheduling of vehicle-mounted networks;

[0006] The vehicle network includes multiple central server nodes, multiple vehicle edge nodes, and multiple vehicle terminal device nodes; some or all of these nodes are located in a blockchain;

[0007] The vehicle-mounted edge grid data scheduling system includes a dynamic resource allocation module, an intelligent scheduling module, a data management module, and a data security and privacy protection module;

[0008] The dynamic resource allocation module predicts the resource demand of the node in the future t time period based on the node monitoring index, generates and executes the resource allocation strategy S(t);

[0009] The intelligent scheduling module generates task allocation strategies and makes dynamic adjustments with the goal of minimizing task execution time and resource consumption;

[0010] The grid data management module divides the data of the vehicle-mounted edge grid data scheduling system into grid units for management, thereby achieving distributed storage and fast access to data;

[0011] The data security and privacy protection module protects user privacy and data security through encryption technology, access control mechanism and data anonymization processing.

[0012] Further, the dynamic resource allocation module includes a resource monitoring unit, a resource demand prediction and analysis unit, and a resource scheduling and allocation execution unit;

[0013] The resource monitoring unit collects node monitoring indicators R i =(CPU i ,MEM i ,BW i ,STOR i ) to analyze the resource usage of each node and upload the node monitoring indicators to the central database for storage; CPU i ,MEM i ,BW i ,STOR i Respectively represent CPU usage, memory usage, bandwidth usage, and storage utilization;

[0014] The resource demand prediction and analysis unit uses the node monitoring index as input, uses the resource demand prediction model to predict the resource demand of the node in the future t time period, and generates a resource allocation strategy S(t) according to the prediction result; the resource allocation strategy S(t) includes resource migration, load balancing and priority adjustment strategies;

[0015] The resource demand prediction model includes a time series analysis model and a deep learning model;

[0016] The resource scheduling and allocation execution unit performs dynamic resource scheduling and allocation according to the resource allocation strategy S(t).

[0017] Furthermore, the intelligent scheduling module includes a task initial allocation unit, a task migration unit, and an adaptive adjustment unit;

[0018] The task initial allocation unit sorts the tasks according to their importance and urgency, and allocates the tasks based on the current load and resource capacity of the nodes, so as to allocate a certain amount of time for each task. i Select the resource that can meet the task requirements R i Node n j ;

[0019] The task migration unit monitors the resource usage and task execution status of the nodes in real time during the task execution process. j If the resource usage rate exceeds the preset threshold θ, the task migration mechanism is started to migrate some tasks to nodes with a load less than the preset value;

[0020] The adaptive adjustment unit uses a machine learning algorithm to predict the resource usage and task load changes of each node in the future t period, and continuously adjusts and optimizes the task allocation strategy according to the task execution results and system performance indicators.

[0021] Further, the grid data management module includes a data grid partitioning unit, a data storage location and access strategy optimization unit;

[0022] The data grid division unit divides the entire data space D into multiple grid units according to geographical location and data type, denoted as G = {g1, g2, ..., g M}; g M is the Mth grid unit; M is the number of grid units;

[0023] The data grid division unit stores the data distribution of each grid unit in the corresponding vehicle edge node or cloud storage node;

[0024] The data storage location and access strategy optimization unit dynamically adjusts the data storage location and access strategy according to the access frequency and the node load during the data access process;

[0025] The steps of adjusting the storage location and access strategy of data include: if the data access frequency growth rate of the grid unit is greater than a preset value, the data is migrated to a more efficient storage node to reduce access delay and improve response speed.

[0026] Furthermore, the data security and privacy protection module includes a data encryption and transmission protection unit, an access control and authority management unit, and a data anonymization and privacy protection unit;

[0027] The data encryption and transmission protection unit uses TLS or IPSec encrypted channels for data transmission and transmits data in E(D t ,K t ) as the transmission data D t Encryption method; Kt For transmitting encryption keys;

[0028] The data encryption and transmission protection unit uses an encryption algorithm to encrypt the stored data; the encryption algorithm includes AES and RSA encryption algorithms;

[0029] The access control and authority management unit ensures that only authorized users and devices can access and operate data through access control and authority management mechanisms;

[0030] The data anonymization and privacy protection uses data anonymization and privacy protection technology to protect the privacy of users involved in the data processing process;

[0031] The privacy protection technology is differential privacy technology.

[0032] Furthermore, in the vehicle network, the central server node and the vehicle edge node are connected one-to-many, and the vehicle edge node and the vehicle terminal device node are connected one-to-many.

[0033] The number of devices connected to each vehicle edge node is m is the number of vehicle-mounted edge nodes; n is the number of vehicle-mounted device nodes;

[0034] The number of devices connected to each central server node is k is the number of central server nodes.

[0035] Furthermore, the central server node, the vehicle-mounted edge node, and the vehicle-mounted terminal device all have unique identifiers.

[0036] Furthermore, when any node is started for the first time, it registers itself;

[0037] The registration process is as follows: the node sends a registration request with basic information to the central management system or blockchain network; the central system verifies and records the basic information to ensure the legitimacy and security of the node; the basic information includes a unique identifier, geographic location, and hardware configuration;

[0038] Any node periodically reports its operating status to the central system or blockchain network, including the current load, available resources, and network status.

[0039] Furthermore, the node set in the blockchain is recorded as N = {n1,n2,…,n k}; Each node n i The computing power of C i ≥C min And the storage capacity S i ≥S min ; C min , S min is the lower limit of computing power and storage capacity; i = 1, 2, ..., k;

[0040] The genesis block of the blockchain is denoted as G0; the genesis block stores the initial state of the blockchain and information about all participating nodes;

[0041] The functions of blockchain are realized through smart contracts; the smart contract set is denoted as SC = {sc1, sc2, ..., sc m}, these smart contracts are used to manage node identities, record transactions, and allocate resources.

[0042] Further, it also includes an in-vehicle network update module;

[0043] The vehicle network update module periodically updates the vehicle network based on the node computing capability;

[0044] The update steps include:

[0045] 1) The vehicle network update module divides the vehicle network into multiple grid areas and calculates the computing power of each grid area; if there is a computing power less than the preset threshold C threshold If the grid unit is C, then add vehicle-mounted edge nodes in the grid area to make the computing power of the grid area greater than or equal to C threshold ;

[0046] 2) After adding nodes, the nodes in the vehicle network are divided into different network clusters through the DBSCAN clustering algorithm; in each network cluster, the communication delay between any two nodes is less than the communication delay threshold T max , the distance between any two nodes is greater than the node density threshold D min ;

[0047] The distance d between any two nodes ij As shown below:

[0048]

[0049] in, is the latitude difference between node i and node j, Δλ=λ j -λ i is the difference in longitude, r is the radius of the earth; is the latitude of node j and node i; j , i is the longitude of node j and node i;

[0050] 3) Obtain all identified nodes with load capacity, map all nodes to a geographic information system map, draw network connections between devices on the map, display the communication relationship between nodes, and obtain an updated vehicle network; the device data includes the device's unique identifier, geographic location, load capacity, adjacent node information and current network status;

[0051] The load capacity of the nodes is as follows:

[0052]

[0053] Where D is the load capacity of the node; S cpu S is the available CPU resources of the node; cpu_total is the total CPU resource of the node; S m S is the available memory of the node; m_total is the total memory of the node; S s S is the available storage of the node; s_total is the total storage of the node; S B is the available bandwidth of the node; S B_total is the total node bandwidth; a1, a2, a3, a4 are weight coefficients.

[0054] The technical effect of the present invention is unquestionable. The present invention proposes a vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing. The system uses blockchain technology to ensure the security of data transmission and storage, and optimizes the efficiency of data processing and resource utilization through load balancing strategies.

[0055] The present invention applies blockchain technology to the vehicle-mounted edge computing environment, establishes a decentralized and secure data transmission and storage mechanism, and realizes load balancing through an intelligent scheduling algorithm to improve the overall performance and reliability of the system.

[0056] The system of the present invention can dynamically adjust the allocation of computing and storage resources and perform data scheduling according to real-time traffic and vehicle status information, thereby achieving efficient on-board edge computing.

[0057] At the same time, the present invention combines the security and transparency of blockchain technology to ensure the security and credibility of data during transmission and processing. The present invention can not only improve the performance of the vehicle-mounted edge computing system, but also enhance the security and reliability of the system, providing a new technical solution for the development of intelligent transportation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is the architecture diagram of the vehicle edge scheduling system;

[0059] Figure 2 This is the structural diagram of the vehicle-mounted edge grid scheduling system;

[0060] Figure 3 This is a flowchart of the blockchain and load balancing scheduling algorithm. DETAILED DESCRIPTION

[0061] The present invention is further described below in conjunction with the embodiments, but it should not be understood that the above subject matter of the present invention is limited to the following embodiments. Without departing from the above technical ideas of the present invention, various substitutions and changes are made according to the common technical knowledge and customary means in the art, which should all be included in the protection scope of the present invention.

[0062] Embodiment 1:

[0063] See also Figures 1 to 3 , a vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing, the vehicle-mounted edge grid data scheduling system is used to realize data scheduling of vehicle-mounted networks;

[0064] The vehicle network includes multiple central server nodes, multiple vehicle edge nodes, and multiple vehicle terminal device nodes; some or all of these nodes are located in a blockchain;

[0065] The vehicle-mounted edge grid data scheduling system includes a dynamic resource allocation module, an intelligent scheduling module, a data management module, and a data security and privacy protection module;

[0066] The dynamic resource allocation module predicts the resource demand of the node in the future t time period based on the node monitoring index, generates and executes the resource allocation strategy S(t);

[0067] The intelligent scheduling module generates task allocation strategies and makes dynamic adjustments with the goal of minimizing task execution time and resource consumption;

[0068] The grid data management module divides the data of the vehicle-mounted edge grid data scheduling system into grid units for management, thereby achieving distributed storage and fast access to data;

[0069] The data security and privacy protection module protects user privacy and data security through encryption technology, access control mechanism and data anonymization processing.

[0070] Embodiment 2:

[0071] A vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing, the technical content is the same as that of Example 1, further, the dynamic resource allocation module includes a resource monitoring unit, a resource demand prediction and analysis unit, and a resource scheduling and allocation execution unit;

[0072] The resource monitoring unit collects node monitoring indicators R i =(CPU i ,MEM i ,BW i ,STOR i) to analyze the resource usage of each node and upload the node monitoring indicators to the central database for storage; CPU i ,MEM i ,BW i ,STOR i Respectively represent CPU usage, memory usage, bandwidth usage, and storage utilization;

[0073] The resource demand prediction and analysis unit uses the node monitoring index as input, uses the resource demand prediction model to predict the resource demand of the node in the future t time period, and generates a resource allocation strategy S(t) according to the prediction result; the resource allocation strategy S(t) includes resource migration, load balancing and priority adjustment strategies;

[0074] The resource demand prediction model includes a time series analysis model and a deep learning model;

[0075] The resource scheduling and allocation execution unit performs dynamic resource scheduling and allocation according to the resource allocation strategy S(t).

[0076] Embodiment 3:

[0077] A vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing, the technical content of which is the same as any one of Embodiments 1-2, and further, the intelligent scheduling module includes a task initial allocation unit, a task migration unit, and an adaptive adjustment unit;

[0078] The task initial allocation unit sorts the tasks according to their importance and urgency, and allocates the tasks based on the current load and resource capacity of the nodes, so as to allocate a certain amount of time for each task. i Select the resource that can meet the task requirements R i Node n j ;

[0079] The task migration unit monitors the resource usage and task execution status of the nodes in real time during the task execution process. j If the resource usage rate exceeds the preset threshold θ, the task migration mechanism is started to migrate some tasks to nodes with a load less than the preset value;

[0080] The adaptive adjustment unit uses a machine learning algorithm to predict the resource usage and task load changes of each node in the future t period, and continuously adjusts and optimizes the task allocation strategy according to the task execution results and system performance indicators.

[0081] Embodiment 4:

[0082] A vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing, the technical content of which is the same as any one of Embodiments 1-3, and further, the grid data management module includes a data grid division unit, a data storage location and access strategy optimization unit;

[0083] The data grid division unit divides the entire data space D into multiple grid units according to geographical location and data type, denoted as G = {g1, g2, ..., g M}; g M is the Mth grid unit; M is the number of grid units;

[0084] The data grid division unit stores the data distribution of each grid unit in the corresponding vehicle edge node or cloud storage node;

[0085] The data storage location and access strategy optimization unit dynamically adjusts the data storage location and access strategy according to the access frequency and the node load during the data access process;

[0086] The steps of adjusting the storage location and access strategy of data include: if the data access frequency growth rate of the grid unit is greater than a preset value, the data is migrated to a more efficient storage node to reduce access delay and improve response speed.

[0087] Embodiment 5:

[0088] A vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing, the technical content of which is the same as any one of Embodiments 1-4, and further, the data security and privacy protection module includes a data encryption and transmission protection unit, an access control and authority management unit, and a data anonymization and privacy protection unit;

[0089] The data encryption and transmission protection unit uses TLS or IPSec encrypted channels for data transmission and transmits data in E(D t ,K t ) as the transmission data D t Encryption method; K t For transmitting encryption keys;

[0090] The data encryption and transmission protection unit uses an encryption algorithm to encrypt the stored data; the encryption algorithm includes AES and RSA encryption algorithms;

[0091] The access control and authority management unit ensures that only authorized users and devices can access and operate data through access control and authority management mechanisms;

[0092] The data anonymization and privacy protection uses data anonymization and privacy protection technology to protect the privacy of users involved in the data processing process;

[0093] The privacy protection technology is differential privacy technology.

[0094] Embodiment 6:

[0095] A vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing, the technical content of which is the same as any one of Examples 1-5. Furthermore, in the vehicle-mounted network, the central server node and the vehicle-mounted edge node are connected one-to-many, and the vehicle-mounted edge node and the vehicle-mounted terminal device node are connected one-to-many.

[0096] The number of devices connected to each vehicle-mounted edge node is m is the number of vehicle-mounted edge nodes; n is the number of vehicle-mounted device nodes;

[0097] The number of devices connected to each central server node is k is the number of central server nodes.

[0098] Embodiment 7:

[0099] A vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing, the technical content of which is the same as any one of Examples 1-6. Furthermore, the central server node, the vehicle-mounted edge node, and the vehicle-mounted terminal device all have a unique identifier.

[0100] Embodiment 8:

[0101] A vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing, the technical content of which is the same as any one of Embodiments 1-7, and further, any node performs self-registration when it is started for the first time;

[0102] The registration process is as follows: the node sends a registration request with basic information to the central management system or blockchain network; the central system verifies and records the basic information to ensure the legitimacy and security of the node; the basic information includes a unique identifier, geographic location, and hardware configuration;

[0103] Any node periodically reports its operating status to the central system or blockchain network, including the current load, available resources, and network status.

[0104] Embodiment 9:

[0105] A vehicle-mounted edge grid data scheduling system based on blockchain and load balancing, the technical content is the same as any one of embodiments 1-8, further, the node set in the blockchain is recorded as N = {n1, n2, ..., n k}; Each node n i The computing power of C i ≥C min And the storage capacity S i ≥S min ; Cmin , S min is the lower limit of computing power and storage capacity; i = 1, 2, ..., k;

[0106] The genesis block of the blockchain is denoted as G0; the genesis block stores the initial state of the blockchain and information about all participating nodes;

[0107] The functions of blockchain are realized through smart contracts; the smart contract set is denoted as SC = {sc1, sc2, ..., sc m}, these smart contracts are used to manage node identities, record transactions, and allocate resources.

[0108] Embodiment 10:

[0109] A vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing, the technical content of which is the same as any one of Embodiments 1-9, and further includes a vehicle-mounted network update module;

[0110] The vehicle network update module periodically updates the vehicle network based on the node computing capability;

[0111] The update steps include:

[0112] 1) The vehicle network update module divides the vehicle network into multiple grid areas and calculates the computing power of each grid area; if there is a computing power less than the preset threshold C threshold If the grid unit is C, then add vehicle-mounted edge nodes in the grid area to make the computing power of the grid area greater than or equal to C thresho;d ;

[0113] 2) After adding nodes, the nodes in the vehicle network are divided into different network clusters through the DBSCAN clustering algorithm; in each network cluster, the communication delay between any two nodes is less than the communication delay threshold T max , the distance between any two nodes is greater than the node density threshold D min ;

[0114] The distance d between any two nodes ij As shown below:

[0115]

[0116] in, is the latitude difference between node i and node j, Δλ=λ j -λ i is the difference in longitude, r is the radius of the earth; is the latitude of node j and node i; j , i is the longitude of node j and node i;

[0117] 3) Obtain all identified nodes with load capacity, map all nodes to a geographic information system map, draw network connections between devices on the map, display the communication relationship between nodes, and obtain an updated vehicle network; the device data includes the device's unique identifier, geographic location, load capacity, adjacent node information and current network status;

[0118] The load capacity of the nodes is as follows:

[0119]

[0120] Where D is the load capacity of the node; S cpu S is the available CPU resources of the node; cpu_total is the total CPU resource of the node; S m S is the available memory of the node; m_total is the total memory of the node; S s S is the available storage of the node; s_total is the total storage of the node; S B is the available bandwidth of the node; S B_total is the total node bandwidth; a1, a2, a3, a4 are weight coefficients.

[0121] Embodiment 11:

[0122] A vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing, by deploying vehicle-mounted edge nodes and building a blockchain network, combined with dynamic resource allocation and intelligent scheduling algorithms, real-time scheduling of grid data processing tasks, and data encryption and access control mechanisms to protect data security, to achieve an efficient and secure vehicle-mounted edge computing system. All devices involved in the present invention can connect to the network and have completed time synchronization. The communication nodes are composed of lightweight vehicle-mounted nodes in the automotive network environment, road facility operation nodes in the road network system, edge-side devices responsible for network communication transfer in the road network system, and full nodes of the data center that processes the Internet of Vehicles business. Different nodes use two collaborative working modes to jointly achieve secure data sharing, thereby preventing network attacks and ensuring data security. The specific solution includes the following steps:

[0123] Step 1: Deploy vehicle-mounted edge nodes

[0124] Step 1.1 Mesh device network

[0125] Due to the large number of devices in the Internet of Vehicles system, and the presence of mobile devices similar to vehicle-mounted devices whose physical location and online status change at any time, if there are a large number of such devices at the same time, if the entire Internet of Vehicles system is to be used as a computing cluster and the computing power of the entire computing cluster is used, the centralized network structure cannot meet the needs of large-scale task scheduling for the entire cluster. At the same time, it may cause a large number of vehicle-mounted devices to upload data to the same central server, resulting in a decrease in the computing power of the server equipment and low data sharing efficiency. In order to balance the load pressure of the central server, the present invention introduces a vehicle-mounted edge node that is specifically used to establish a connection with the vehicle-mounted edge node, thereby reducing the load pressure of the central server and improving the load coordination capability of the entire vehicle network computing cluster. Assume that the number of vehicle-mounted devices is n, denoted by T1, T2···T n , assuming the number of edge terminals is m,E1,E2···E m , then the number of devices connected to each edge node is Assume that the number of central servers is k, C1, C2 · · · C k , then the number of devices connected to each central node is By arranging all nodes into a network plane, with one-to-many connections between the central node and the edge node, and one-to-many connections between the edge node and the vehicle-mounted node, a network topology diagram consisting of vehicle-mounted nodes, edge nodes and central nodes can be formed.

[0126] Based on the network topology formed above, it can be converted into a two-dimensional network topology map, and through reasonable grid division, all devices in the grid can be arranged in order to form a two-dimensional grid map. This allows the vehicle-mounted edge nodes to form a structured layout geographically and logically to optimize data processing and resource management, and improve the overall efficiency and reliability of the system.

[0127] Step 1.2 Calculate the load capacity of the node

[0128] In the two-dimensional grid diagram mentioned above, actively identifying devices and calculating their load capacity is a key step to ensure efficient system operation and reasonable resource allocation. This process can be described in detail in the following steps.

[0129] First, the devices need to be actively identified and a unique identifier is assigned to each device. Each in-vehicle edge node needs to be assigned a unique identifier (ID), which can be achieved by combining hardware features (such as MAC address) and software features (such as the unique ID generated when the node is registered). The assignment of a unique identifier not only ensures the uniqueness of the node, but also facilitates subsequent management and tracking. At the same time, when the node is started for the first time, it self-registers. The registration process includes the node sending a registration request containing basic information such as its unique identifier, geographic location, hardware configuration, etc. to the central management system or blockchain network. The central system verifies and records this information to ensure the legitimacy and security of the node. The node needs to regularly report its operating status to the central system or blockchain network, including the current load situation, available resources, and network status. This periodic status report ensures that the system monitors each node in real time and can respond to any abnormal situation in a timely manner.

[0130] Then, all nodes in the grid need to perform load capacity assessment to determine the size of the computing load that the node can bear. Each on-board edge node needs to monitor its own load in real time, including CPU usage, memory usage, storage space usage, network bandwidth utilization, and battery power. The monitoring program inside the node can use system calls or monitoring tools provided by the operating system to obtain this data. Based on real-time monitoring data, the node needs to perform load capacity assessment. The assessment can be achieved by defining a set of standard calculation formulas or models. For example, the load capacity of the node can be evaluated by the following simplified formula:

[0131]

[0132] The weight of each resource can be adjusted according to system requirements. The result of this evaluation formula can be used as a metric for node load capacity.

[0133] The system needs to dynamically adjust task allocation and resource usage based on the load capacity assessment results of the nodes. Nodes with higher load capacity can be assigned more tasks, while nodes with lower load capacity need to reduce the amount of tasks or be prioritized for resource scheduling. This dynamic load balancing ensures the overall efficient operation of the system and avoids overloading or idleness of some nodes. The load capacity assessment results of all nodes need to be recorded in the central system or blockchain network to facilitate global resource management and optimization. The system should update these records regularly to reflect the latest node status and load conditions to ensure the accuracy and timeliness of scheduling decisions.

[0134] Through the above steps, actively identifying the device and calculating its load capacity not only helps the efficient operation of the vehicle edge computing system, but also realizes the reasonable allocation and dynamic adjustment of resources, improving the overall performance and reliability of the system. The real-time status and load capacity information of each node provides a comprehensive perspective for the system, ensuring the optimization of data scheduling and resource management.

[0135] Step 1.3 Check the regional computing power

[0136] First, a comprehensive computing capacity test is performed on each grid area. This step requires the deployment of a real-time monitoring system that can continuously collect performance data of each vehicle-mounted edge node. Assume that the performance indicators of each node n include CPU usage C n 、Memory usage R n , Storage space utilization S n , Network bandwidth usage BW n And the average response time RT of the node n Through these performance indicators, the system can evaluate the load and processing capacity of each node. We define the comprehensive computing capacity of each grid unit as g for:

[0137]

[0138] Among them, ω1, ω2, ω3, ω4, ω5 are the weight coefficients of each performance index, reflecting the relative importance of each index to the comprehensive computing capability. g Below the preset threshold C threshold After the detection is completed, the system generates a detailed report listing the grid areas that need to be optimized and their specific performance bottlenecks.

[0139] Step 1.4 Adjust the number of nodes

[0140] After identifying the areas with poor computing power, it is necessary to add new vehicle-mounted edge nodes in these areas. The selection of new nodes should be based on the previous test results, and hardware devices with high computing power and communication capabilities should be selected. Assume that the computing power of the new node is C NEW , select an appropriate geographical location L for node deployment to ensure that the new node can effectively cover the entire vulnerable area and provide the necessary computing and storage resources. After the new node is installed, it is configured and initialized, including operating system installation, edge computing platform deployment, and network connection testing. After the configuration is completed, the new node needs to be integrated with the existing system and the node identifier ID is registered through the self-registration mechanism. NEW, hardware information and location data are sent to the central management system for verification and authentication. After authentication, the new node can participate in actual computing and data processing tasks. Through real-time monitoring and dynamic load balancing, the system transfers some tasks to the new node to optimize the computing power of the entire grid area. The new comprehensive computing power C g 'for:

[0141] C g ′=C g +C NEW

[0142] Ensure load balancing and efficient operation. In this way, the overall performance of areas with poor computing power can be gradually improved to achieve balanced development of the system and resource optimization.

[0143] Step 1.5 Divide the network into sub-clusters

[0144] First, it is necessary to determine the criteria for dividing network nodes, which are mainly based on the geographical location of the nodes, communication delay, node density and physical distance. By using geographic information system (GIS) data combined with actual traffic and communication network conditions, the relative positions and communication paths between nodes can be identified. A maximum communication delay threshold T is set. max and the minimum node density threshold D min , to ensure that the nodes in each network cluster can communicate efficiently and have sufficient resources. In order to accurately divide the network cluster, it is necessary to calculate the physical distance d between nodes ij and communication delay t ij . Physical distance d ij It can be calculated using the Haversing formula using the node's geographic coordinates (longitude and latitude):

[0145]

[0146] Among them, Δφ=φj-φi is the latitude difference between node i and node j, Δλ=λ j -λ i is the longitude difference, and r is the radius of the earth. Based on these distance data, a distance matrix D is constructed, where D ij Represents the distance between node i and node j.

[0147] Using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm, nodes can be divided into different network clusters. The DBSCAN algorithm identifies high-density areas and divides clusters by setting ∈ (maximum distance between nodes) and minPts (minimum number of nodes required to form a cluster). First, randomly select an unvisited node and check all nodes in its ∈ neighborhood. If the number of nodes in the neighborhood is greater than or equal to minPts, a cluster is formed, and all unvisited nodes in the cluster are marked as visited, and continue to check the nodes in its neighborhood. Repeat the above steps until all nodes are visited and classified. After the initial division is completed, the cluster results need to be optimized and verified. The communication delay and data transmission efficiency of the nodes in the cluster are verified by simulation and actual measurement to ensure efficient communication of each cluster. Finally, the divided network cluster information is recorded in the central management system or blockchain network, and these records are regularly updated and maintained to reflect the latest node status and cluster structure. Through these steps, the structure of the vehicle edge computing system is optimized, and the data transmission efficiency and resource utilization are improved.

[0148] Step 1.6 Reconstruct the grid map

[0149] First, collect data on all identified and load-capable devices, including the device's unique identifier, geographic location (longitude and latitude), load capacity, adjacent node information, and current network status. This data needs to be kept updated through regular status reports to ensure its accuracy and timeliness. Then, based on this geographic location data, map all devices onto a geographic information system (GIS) map, using software such as ArcGIS or QGIS to convert the longitude and latitude of the device into coordinate points on the map. To improve visualization, different colors and sizes can be used to identify nodes, with nodes with higher load capacity represented by larger green dots and nodes with lower load capacity represented by smaller red dots.

[0150] Next, draw the network connections between devices on the map to show the communication relationship between nodes. Construct the adjacency matrix AAA, where A ij =1 indicates that there is a direct communication connection between node i and node j, A ij=0 means there is no direct connection. Use the adjacency matrix to draw the connection lines between devices to reflect the actual communication links, and use different colors or thicknesses to indicate the quality or bandwidth of the connection. According to the clustering results, divide the nodes into different network clusters, and display each cluster on the map with a different color or bounding box to clearly distinguish the scope of each cluster. Ensure that the connection lines of the nodes in the cluster are visually distinguished, such as the connection lines within the same cluster are represented by solid lines, and the connection lines between different clusters are represented by dotted lines. The system should have dynamic update and interaction functions, automatically update the node status and connection status through real-time monitoring data, and use dynamic data visualization tools (such as D3.js, Leaflet, etc.) to achieve real-time update and interactive operations of nodes and connections. Users can click on the node to view detailed information, or select a specific area to zoom in and browse in detail to ensure the efficient operation of the vehicle edge computing system.

[0151] Step 2: Build a blockchain network

[0152] Step 2.1 Node deployment and configuration

[0153] First, select appropriate devices as nodes of the blockchain network. These devices can be vehicle-mounted edge nodes, roadside infrastructure, or cloud servers. Set the node set to N = {n1, n2, …, n k}, each node n i You need to install blockchain software, such as Hyperledger Fabric or Ethereum, and perform the necessary configuration. This includes setting the node's identity ID i , distribute the encryption key K i , configure network parameters and chain code (smart contract). Ensure that all nodes have sufficient computing and storage capabilities to support the operation of the blockchain. At the same time, a secure communication channel needs to be established between nodes, using TLS (Transport Layer Security) or VPN (Virtual Private Network) to protect the security of data transmission. Let C i and S i Represents node n i The computing power and storage capacity ensure that C i ≥C min And S i ≥S min .

[0154] Step 2.2 Initialize the blockchain

[0155] After the nodes are deployed and configured, create a blockchain network. Define the initial configuration of the blockchain, including the setting of the Genesis Block, and set the Genesis Block to G0. The Genesis Block contains the initial state of the blockchain network and information about all participating nodes. Next, run the blockchain software on each node, start the blockchain network, and perform initialization operations to synchronize all nodes to the Genesis Block. During the initialization process, set a consensus mechanism, such as PoW (Proof of Work), PoS (Proof of Stake), or PBFT (Practical Byzantine Fault Tolerance), to ensure that the network can reach consensus and process transactions. The choice of consensus mechanism M affects the security and efficiency of the blockchain, for example:

[0156] M=PoW or M=PoS or M=PBFT

[0157] Step 2.3 Deploy smart contract and test

[0158] Finally, deploy smart contracts to realize the specific functions of the blockchain network. Set the smart contract set to SC = {sc1, sc2, ..., sc m}, these contracts can be used to manage node identities, record transactions, allocate resources, etc. After the smart contract is deployed, comprehensive functional testing and performance testing are carried out. The testing content includes verifying the correctness of the smart contract V(sc i ), detect the efficiency of transaction processing E(sc i ), evaluate the stability and security of the network. By simulating real scenarios, ensure that smart contracts can operate normally and that the blockchain network can process data efficiently. After the test is completed, the blockchain network will be officially enabled, and daily maintenance and monitoring will be carried out to ensure its continued stable operation.

[0159] Step 3: Dynamic resource allocation

[0160] Step 3.1 Node deployment and configuration

[0161] First, a real-time monitoring system is established to collect and analyze the resource usage of each node. The monitoring indicators include CPU usage, memory usage, bandwidth usage, and storage utilization. i The resource usage can be expressed as a vector R i =(CPU i ,MEM i ,BW i ,STOR i ). Through distributed monitoring tools such as Prometheus or Zabbix, this data is collected regularly (for example, every second) and stored in a central database. Ensure the real-time and accuracy of data collection so that the resource usage status in the network can be reflected in a timely manner.

[0162] Step 3.2 Resource Demand Forecast and Analysis

[0163] Based on the collected real-time data, machine learning algorithms are used to predict resource requirements.

[0164] Assume that the resource requirement of each node is D i (t)=(CPU d,i (t),MEM d,i (t),BW d,i (t),STOR d,i (t)) to predict resource requirements in the future. Commonly used prediction models include time series analysis (such as ARIMA model) and deep learning models (such as LSTM network). Through prediction, the resource requirements of each node at the future time t are determined, and whether there is a shortage or surplus of resources. Based on the prediction results, a resource allocation strategy S(t) is generated to adjust the resource allocation of each node.

[0165] Step 3.3 Resource Scheduling and Allocation Execution

[0166] Dynamic resource scheduling and allocation are performed based on the predicted resource allocation strategy S(t). Scheduling strategies can include resource migration, load balancing, and priority adjustment. Specifically, container technology (such as Docker) or virtualization technology (such as KVM) is used to migrate resources from nodes with lighter loads to nodes with heavier loads. For example, suppose that from node n i To node n j Migrate CPU resources ΔCPU, then the adjusted resource allocation can be expressed as:

[0167] R′ i (t)=(CPU i -ΔCPU,MEM i ,BW i ,STOR i )

[0168] R′ j (t)=(CPU j +ΔCPU,MEM j ,BW j ,STOR j )

[0169] By performing resource migration and adjustment, ensure that the resource utilization of each node remains within a reasonable range to avoid resource waste and performance bottlenecks. At the same time, monitor the delay and overhead in the resource scheduling process to ensure that the scheduling process is efficient and has minimal impact on system performance. The execution of resource allocation strategies should include automation and intelligence, and use automated operation and maintenance tools (such as Kubernetes) to achieve dynamic scheduling and allocation of resources to meet the efficient operation requirements of the vehicle edge network.

[0170] Step 4: Intelligent Scheduling Algorithm

[0171] The intelligent scheduling algorithm aims to optimize the allocation and execution efficiency of tasks in the vehicle edge network. By combining load balancing, real-time data analysis and machine learning technology, the intelligent scheduling algorithm can dynamically adjust the task allocation strategy to improve resource utilization and network performance. The algorithm needs to consider multiple factors, including the computing power of the node, the current load, the network bandwidth, the task priority and the latency requirements. The main goal is to minimize the task execution time and resource consumption while ensuring the stability and reliability of the system.

[0172] Step 4.1 Task priority classification and initial allocation

[0173] First, the tasks are classified according to their importance and urgency. Set the task set to be T = {t1, t2, …, t m}, each task t i With priority p i and resource requirements R i =(CPU i ,MEM i ,BW i ,STOR i ). According to the priority p i Sort the tasks and assign high priority tasks to nodes with sufficient resources. The initial allocation strategy is based on the current load and resource capacity of the nodes. Assume that the node set is N = {n1, n2, ..., n k}, for each task t i Select the most suitable node n j , so that n j Able to meet the resource requirements of the task R i .

[0174] Step 4.2 Real-time monitoring and task migration

[0175] During the task execution process, the resource usage and task execution status of the node are monitored in real time. Data is collected through the distributed monitoring system, including the node's CPU usage, memory usage, network bandwidth, and storage utilization. Set the node n j The resource status is R j =(CPUj ,MEM j ,BW j ,STOR j ). If a node n is detected j If the resource usage rate exceeds the preset threshold θ, the task migration mechanism is started to migrate some tasks to nodes n with lighter loads. l During the migration process, it is necessary to ensure the status and data integrity of the task and reduce delays and overhead during the migration process.

[0176] Step 4.3 Machine Learning Prediction and Optimization

[0177] Use machine learning algorithms to predict resource usage and task load changes of each node in the future. Assume that each node n j The resource requirement is

[0178] D j (t)=(CPU d,j (t),MEM d,j (t),BW d,j (t),STOR d,j (t)),

[0179] Predictions are made through time series analysis or deep learning models (such as LSTM). Based on the prediction results, the task allocation strategy is adjusted, and resource scheduling and optimization are performed in advance. The goal of the algorithm is to minimize the task execution time T i and resource consumption C i , while improving the overall performance and stability of the system.

[0180] Step 4.4 Feedback mechanism and adaptive adjustment

[0181] Introduce a feedback mechanism to continuously adjust and optimize the scheduling algorithm based on task execution results and system performance indicators. Collect data such as task completion time, resource utilization efficiency, and network latency as the basis for algorithm optimization. Use reinforcement learning methods to find the optimal scheduling strategy through continuous experiments and adjustments. Set the reward function R(t), give positive or negative feedback based on the improvement of system performance indicators, adjust the algorithm parameters, and achieve adaptive optimization. Ultimately, the intelligent scheduling algorithm can dynamically adapt to changes in the network environment and improve the task execution efficiency and resource utilization of the vehicle edge network.

[0182] Step 5: Grid Data Management

[0183] Grid data management aims to optimize the data storage and transmission efficiency in the vehicle edge network. By dividing the data into grid units for management, it can achieve distributed storage and fast access to data. Grid data management needs to consider the geographical location of the data, the access frequency and the resource conditions of the storage nodes to ensure efficient use and reliable transmission of data.

[0184] Step 5.1: Data Grid Partitioning and Distributed Storage

[0185] First, the entire data space is divided into multiple grid cells according to geographical location and data type. Set the data space D and divide it into G = {g1, g2, …, g n} grid units. Each grid unit g i Contains data within a specific area, allocated according to the geographical location and type of data. Using Geographic Information System (GIS) tools, the data is mapped to the corresponding grid cells.

[0186] Then, the data of each grid unit is distributed and stored in the corresponding vehicle edge node or cloud storage node. Set the node set N = {n1, n2, ..., n k}, according to the storage capacity and geographical location of the node, the grid unit g i The data is stored in the most suitable node n j Ensure that data storage is evenly distributed to avoid overloading of certain nodes. At the same time, to improve data reliability and access speed, data redundancy and replication technologies can be used to store key data on multiple nodes.

[0187] Step 5.2: Data access optimization and dynamic adjustment

[0188] In the data access process, the data storage location and access strategy are dynamically adjusted according to the access frequency and node load. Set the data access set A = {a1, a2, ..., a m}, each data access a i Contains the visited grid cell g i and data item d i By monitoring the data access frequency f(a i ) and the resource usage of the node R j =(CPU j ,MEM j ,BW j ,STOR j ) to determine whether the data storage location needs to be adjusted.

[0189] If the data access frequency of some grid cells increases significantly, migrate the data to more efficient storage nodes to reduce access latency and improve response speed. Use machine learning algorithms to predict future data access patterns and perform pre-scheduling and optimization based on the prediction results. For example, suppose grid cell g i The access frequency prediction is f(g i ,t), the data storage strategy can be adjusted according to the prediction results. Through these optimization measures, efficient management and dynamic adjustment of grid data can be achieved, ensuring efficient and reliable data transmission and storage in the vehicle edge network.

[0190] Step 6: Data security and privacy protection

[0191] Data security and privacy protection are crucial in the vehicle edge network, ensuring the confidentiality, integrity and availability of data during transmission and storage. By adopting encryption technology, access control mechanism and data anonymization processing, user privacy and data security can be effectively protected.

[0192] Step 6.1: Data encryption and transmission protection

[0193] First, encryption technology is used to protect data transmission and storage. For data transmission, TLS (Transport Layer Security) or IPSec (Internet Protocol Security) encryption channels are used to ensure that data is not eavesdropped or tampered with during network transmission. t The encryption method is E(D t ,K t ), where K t In terms of data storage, encryption algorithms such as AES (Advanced Encryption Standard) or RSA (RSA encryption algorithm) are used to encrypt the stored data. s The encryption method is E(D s ,K s ), where K s To store encryption keys. Encryption technology is used to ensure the confidentiality and integrity of data during transmission and storage.

[0194] Step 6.2: Access Control and Permission Management

[0195] Secondly, implement strict access control and permission management mechanisms to ensure that only authorized users and devices can access and operate data. Use role-based access control (RBAC) or attribute-based access control (ABAC) models to assign different access rights based on the user's role or attribute. Set the user set U = {u1,u2,…,u m}, each user u i Assign permission set P according to its role or attribute i ={pi1 ,p i2 ,…,p in}. Implement access control lists (ACLs) on nodes, record the access rights of each user or device, and verify and authorize each data access request. Prevent unauthorized access and data leakage through strict access control.

[0196] Step 6.3: Data anonymization and privacy protection

[0197] Finally, data anonymization and privacy protection technologies are used to ensure that user privacy is effectively protected during data processing. Technologies such as data desensitization (such as de-identification, pseudo-anonymization) and differential privacy are used to remove or blur sensitive information before processing user data. r And processed data D a , through the anonymization function A(D r ) Generate anonymized data D a , ensuring that the processed data cannot be directly associated with an individual. Applying differential privacy technology, adding noise ∈ to the data ensures that the query results do not leak individual privacy. The differential privacy model can be expressed as D dp =D+∈. Through these privacy protection measures, the privacy and security of user data during processing and analysis are ensured.

[0198] Data security and privacy protection in the vehicle edge network are achieved through the three sub-steps of data encryption and transmission protection, access control and permission management, and data anonymization and privacy protection. Advanced encryption technology is used to protect data transmission and storage, strict access control mechanisms prevent unauthorized access, and data anonymization and privacy protection technologies ensure that user privacy is not leaked during data processing. The above measures are applied in combination to effectively protect data security and user privacy in the vehicle edge network.

[0199] Embodiment 12:

[0200] An application of the vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing as described in any one of embodiments 1-11 comprises the following steps:

[0201] Step 1: Deploy vehicle edge nodes

[0202] Deploying computing nodes on vehicles and traffic infrastructure is the foundation of the entire system. Each vehicle-mounted edge node n i Equipped with necessary sensors, processors and storage devices for real-time data collection, processing and storage. In order to ensure coverage of the entire target area, we grid all devices and divide them into grid units G = {g1, g2, …, g nAdditional vehicle-mounted edge nodes are added to areas of the grid with poor computing power to compensate for the lack of computing resources. Each node is marked according to its geographical location and computing power, and its computing power C is set. i and storage capacity S i , ensure that C i ≥C min And S i ≥S min At the same time, the network nodes in the adjacent areas are divided into different network clusters. By actively identifying the devices and recording their load capacity, a regional network diagram is finally drawn to show the node distribution and load conditions, such as Figure 1 The system architecture diagram before the grid is shown. Figure 2 The gridded system structure diagram is shown in FIG.

[0203] Step 2: Build a blockchain network

[0204] Building a blockchain network involves connecting multiple vehicle-mounted edge nodes N = {n1, n2, ..., n k}Organized into a decentralized distributed system. Each node installs blockchain software, such as Hyperledger Fabric or Ethereum, and configures its identity ID i , encryption key K i and chaincode. The initial configuration includes setting up the genesis block G0, which contains the initial state of the network and node information. After starting the blockchain, all nodes synchronize to the genesis block and run the selected consensus mechanism, such as PoW, PoS, or PBFT. Through these mechanisms, all data transactions are verified and recorded to ensure data integrity and security. Secure communication channels are established between nodes through TLS or VPN to prevent data from being eavesdropped or tampered with during transmission.

[0205] Step 3: Dynamic resource allocation

[0206] Dynamic resource allocation adjusts the computing and storage resources of each node through real-time monitoring and prediction to ensure system load balancing. First, establish a real-time monitoring system to regularly collect node resource usage information.

[0207] R i =(CPU i ,MEM i ,BW i ,STOR i )

[0208] Then, use machine learning algorithms to predict future resource requirements

[0209] MEM_{d,i}(t),BW_{d,i}(t),STOR_{d,i}(t)),

[0210] Generate resource allocation strategy S(t). According to the strategy, dynamically adjust resource allocation through container or virtualization technology, such as from node n i To node n j Migrate resource ΔCPU, the resource status after adjustment is

[0211] R′ i (t)=(CPU i -ΔCPU,MEM i ,BW i ,STOR i )

[0212] and R j ′(t)=(CPU j +ΔCPU,MEM j ,BW j ,STOR j ).

[0213] Step 4: Intelligent Scheduling Algorithm

[0214] The intelligent scheduling algorithm uses machine learning to analyze historical data and real-time status, predict data traffic and computing requirements, and achieve efficient scheduling. Task set T = {t1, t2, …, t m} By priority p i Sort and allocate to nodes with sufficient resources. Monitor task execution status and node load in real time. If it is detected that the node resource usage exceeds the threshold θ, start task migration and migrate the task to a node with a lighter load. Use time series analysis or LSTM model to predict node resource requirements

[0215] D j (t)=(CPU d,j (t),MEM d,j (t),BW d,j (t),STOR d,j (t)),

[0216] And adjust the task allocation strategy based on the prediction results. Introduce a feedback mechanism to continuously optimize the scheduling strategy through reinforcement learning to improve the overall system performance and resource utilization.

[0217] Step 5: Grid Data Management

[0218] Grid data management realizes distributed data storage and collaborative processing by dividing the vehicle edge nodes into multiple grid units. The data space D is divided into G = {g1, g2, …, g n} grid units, and the nodes in each unit collaborate to process and store data. Data is distributed and stored in the corresponding vehicle edge nodes or cloud storage nodes N = {n1, n2, ..., nk}, allocate nodes according to their storage capacity and geographical location to ensure balanced data storage and efficient access. During data access, according to the access frequency f(a i ) and node resource status R j Dynamically adjust data storage location and use machine learning algorithms to predict future access patterns f(g i ,t), optimize data storage strategies to ensure the efficiency and reliability of data management.

[0219] Step 6: Data security and privacy protection

[0220] Data security and privacy protection are achieved through encryption technology, access control and data anonymization. Data transmission uses TLS or IPSec encrypted channels to ensure that data is not eavesdropped or tampered with. t The encryption method is E(D t ,K t ). Data storage uses AES or RSA encryption algorithm to store data D s The encryption method is E(D s ,K s ). Access control uses RBAC or ABAC model to assign permissions based on user roles or attributes. i , implement access control list (ACL) verification and authorization on the node. Data anonymization uses desensitization and differential privacy technology to remove or blur sensitive information before processing user data. The anonymization function A(D r ) Generate data D a , the differential privacy model is represented by D dp =D+∈. Through these measures, the security of data transmission and storage and the protection of user privacy are ensured.

Claims

1. A vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing, characterized by: The vehicle-mounted edge grid data scheduling system is used to realize data scheduling of the vehicle network. The vehicle network includes multiple central server nodes, multiple vehicle edge nodes, and multiple vehicle terminal device nodes; some or all of these nodes are located in a blockchain; The vehicle-mounted edge grid data scheduling system includes a dynamic resource allocation module, an intelligent scheduling module, a data management module, and a data security and privacy protection module; The dynamic resource allocation module predicts the resource demand of the node in the future t time period based on the node monitoring index, generates and executes the resource allocation strategy S(t); The intelligent scheduling module generates task allocation strategies and makes dynamic adjustments with the goal of minimizing task execution time and resource consumption; The grid data management module divides the data of the vehicle-mounted edge grid data scheduling system into grid units for management, thereby achieving distributed storage and fast access to data; The data security and privacy protection module protects user privacy and data security through encryption technology, access control mechanism and data anonymization processing.

2. According to claim 1, a vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing is characterized in that: The dynamic resource allocation module includes a resource monitoring unit, a resource demand prediction and analysis unit, and a resource scheduling and allocation execution unit; The resource monitoring unit collects node monitoring indicators R i =(CPU i ,MEM i ,BW i ,STOR i ) to analyze the resource usage of each node and upload the node monitoring indicators to the central database for storage; CPU i ,MEM i ,BWi i ,STOR i Respectively represent CPU usage, memory usage, bandwidth usage, and storage utilization; The resource demand prediction and analysis unit uses the node monitoring index as input, uses the resource demand prediction model to predict the resource demand of the node in the future t time period, and generates a resource allocation strategy S(t) according to the prediction result; the resource allocation strategy S(t) includes resource migration, load balancing and priority adjustment strategies; The resource demand prediction model includes a time series analysis model and a deep learning model; The resource scheduling and allocation execution unit performs dynamic resource scheduling and allocation according to the resource allocation strategy S(t).

3. According to claim 1, a vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing is characterized in that: The intelligent scheduling module includes a task initial allocation unit, a task migration unit, and an adaptive adjustment unit; The task initial allocation unit sorts the tasks according to their importance and urgency, and allocates the tasks based on the current load and resource capacity of the nodes, so as to allocate a certain amount of time for each task. i Select the resource that can meet the task requirements R i Node n j ; The task migration unit monitors the resource usage and task execution status of the nodes in real time during the task execution process. j If the resource usage rate exceeds the preset threshold θ, the task migration mechanism is started to migrate some tasks to nodes with a load less than the preset value; The adaptive adjustment unit uses a machine learning algorithm to predict the resource usage and task load changes of each node in the future t period, and continuously adjusts and optimizes the task allocation strategy according to the task execution results and system performance indicators.

4. According to claim 1, a vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing is characterized in that: The grid data management module includes a data grid division unit, a data storage location and access strategy optimization unit; The data grid division unit divides the entire data space D into multiple grid units according to geographical location and data type, denoted as G = {g1, g2, ..., g M }; g M is the Mth grid unit; M is the number of grid units; The data grid division unit stores the data distribution of each grid unit in the corresponding vehicle edge node or cloud storage node; The data storage location and access strategy optimization unit dynamically adjusts the data storage location and access strategy according to the access frequency and the node load during the data access process; The steps of adjusting the storage location and access strategy of data include: if the data access frequency growth rate of the grid unit is greater than a preset value, the data is migrated to a more efficient storage node to reduce access delay and improve response speed.

5. According to claim 1, a vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing is characterized in that: The data security and privacy protection module includes a data encryption and transmission protection unit, an access control and authority management unit, and a data anonymization and privacy protection unit; The data encryption and transmission protection unit uses TLS or IPSec encrypted channels for data transmission and transmits data in E(D t ,K t ) as the transmission data D t Encryption method; K t For transmitting encryption keys; The data encryption and transmission protection unit uses an encryption algorithm to encrypt the stored data; the encryption algorithm includes AES and RSA encryption algorithms; The access control and authority management unit ensures that only authorized users and devices can access and operate data through access control and authority management mechanisms; The data anonymization and privacy protection uses data anonymization and privacy protection technology to protect the privacy of users involved in the data processing process; The privacy protection technology is differential privacy technology.

6. According to claim 1, a vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing is characterized in that: In the vehicle network, the central server node and the vehicle edge node are connected one-to-many, and the vehicle edge node and the vehicle terminal device node are connected one-to-many. The number of devices connected to each vehicle edge node is m is the number of vehicle-mounted edge nodes; n is the number of vehicle-mounted device nodes; The number of devices connected to each central server node is k is the number of central server nodes.

7. According to claim 1, a vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing is characterized in that: The central server node, vehicle-mounted edge node, and vehicle-mounted terminal device all have unique identifiers.

8. According to claim 1, a vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing is characterized in that: When any node is started for the first time, it registers itself; The registration process is as follows: the node sends a registration request with basic information to the central management system or blockchain network; the central system verifies and records the basic information to ensure the legitimacy and security of the node; the basic information includes a unique identifier, geographic location, and hardware configuration; Any node periodically reports its operating status to the central system or blockchain network, including the current load, available resources, and network status.

9. According to claim 1, a vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing is characterized in that: The node set in the blockchain is recorded as N = {n1,n2,…,n k }; Each node n i The computing power of C i ≥C min And the storage capacity S i ≥S min ; C min , S min is the lower limit of computing power and storage capacity; i = 1, 2, ..., k; The genesis block of the blockchain is denoted as G0; the genesis block stores the initial state of the blockchain and information about all participating nodes; The functions of blockchain are realized through smart contracts; the smart contract set is denoted as SC = {sc1, sc2, ..., sc m }, these smart contracts are used to manage node identities, record transactions, and allocate resources.

10. According to claim 1, a vehicle-mounted edge grid data scheduling system based on the combination of blockchain and load balancing is characterized in that: It also includes the vehicle network update module; The vehicle network update module periodically updates the vehicle network based on the node computing capability; The update steps include: 1) The vehicle network update module divides the vehicle network into multiple grid areas and calculates the computing power of each grid area; if there is a computing power less than the preset threshold C threshold If the grid unit is C, then add vehicle-mounted edge nodes in the grid area to make the computing power of the grid area greater than or equal to C thresho;d ; 2) After adding nodes, the nodes in the vehicle network are divided into different network clusters through the DBSCAN clustering algorithm; in each network cluster, the communication delay between any two nodes is less than the communication delay threshold T max , the distance between any two nodes is greater than the node density threshold D min ; The distance d between any two nodes ij As shown below: in, is the latitude difference between node i and node j, Δλ=λ j -λ i is the difference in longitude, r is the radius of the earth; is the latitude of node j and node i; j , i is the longitude of node j and node i; 3) Obtain all identified nodes with load capacity, map all nodes to a geographic information system map, draw network connections between devices on the map, display the communication relationship between nodes, and obtain an updated vehicle network; the device data includes the device's unique identifier, geographic location, load capacity, adjacent node information and current network status; The load capacity of the nodes is as follows: Where D is the load capacity of the node; S cpu S is the available CPU resources of the node; cpu_total is the total CPU resource of the node; S m S is the available memory of the node; m_total is the total memory of the node; S s S is the available storage of the node; s_total is the total storage of the node; S B is the available bandwidth of the node; S B_total is the total node bandwidth; a1, a2, a3, a4 are weight coefficients.

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