Industrial Internet of Things unmanned vehicle path planning system and method

Abstract

The invention relates to the technical field of industrial Internet of Things, and discloses an industrial Internet of Things unmanned vehicle path planning system and method. Comprising an environment sensing layer used for collecting environment state information of the unmanned vehicle and receiving environment state information of other unmanned vehicles; receiving a control instruction to perform motion control on the unmanned vehicle; the decision control layer is used for performing action selection according to the environment state information of the unmanned vehicle by adopting a deep Q network, determining an optimal action by taking the environment state information of other unmanned vehicles as a security constraint, generating a control instruction and issuing the control instruction to the environment sensing layer; the environment state information, the optimal action and the reward of the unmanned vehicle are uploaded to the block chain; the block chain network layer is used for performing uplink processing on the environment state information, the optimal action and the reward of the unmanned vehicle as transaction information; and the environment state information of other unmanned vehicles is broadcasted. According to the invention, the reliability of the system and the path planning efficiency in a complex environment can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial internet of things, and in particular to an industrial internet of things unmanned vehicle path planning system and method. BACKGROUND

[0002] Under the background of Industry 4.0, industrial internet of things (IIoT) is widely used in manufacturing industry, and intelligent unmanned vehicles (such as automatic guided vehicles, AGV) are used to realize the automatic transportation of internal logistics in factories. In a complex dynamic industrial environment, efficient and reliable path planning is the key to the normal operation of unmanned vehicles. However, the traditional path planning and scheduling scheme faces two major difficulties in the IIoT environment: first, the path planning efficiency problem in a dynamic and complex environment. The traditional heuristic algorithm (such as A* algorithm) can find the shortest path in a static environment, but when there are dynamic obstacles or uncertainties in the environment, the pre-planned route may fail, and frequent re-planning is required, which reduces the efficiency. Second, the data security and trust problem. In the industrial unmanned vehicle system, a large amount of operation data (such as position, state, task information) is transmitted between devices and the cloud through a wireless network. The centralized data management method may be subject to malicious tampering or single point failure, and it is difficult to ensure the confidentiality and integrity of critical data (such as vehicle operation logs, task instructions). Therefore, how to improve the intelligence of path planning while ensuring the safety and reliability of data interaction in the IIoT system has become a research topic worthy of attention.

[0003] To solve the above problems, academia and industry have made many explorations. On the one hand, blockchain technology, as a decentralized distributed ledger, provides a new way for IIoT data security. Blockchain uses cryptographic chain structure and consensus mechanism to ensure that data cannot be tampered with once written, and improves system fault tolerance through distributed storage. Christidis and Devetsikiotis proposed to use smart contracts and blockchains for Internet of Things device management to achieve automatic interaction in a trustless environment. Khan et al. reviewed the security of the Internet of Things and pointed out that blockchain has great potential in ensuring the privacy and integrity of Internet of Things data. In the industrial environment, Zhang and Wen designed an "Internet of Things e-commerce model" to ensure the trustworthiness of transaction data between devices using blockchain. In addition, for the field of Internet of Vehicles and autonomous driving, Rathee et al. built a blockchain-based architecture to protect the data security of connected and autonomous vehicles; Okere and Balyan's research reviewed the application progress of blockchain in the Internet of Vehicles and looked forward to the prospect of combining machine learning with blockchain to improve vehicle communication security. On the other hand, deep reinforcement learning (DRL) has shown strong learning ability in robot path planning and control decision-making. Mnih et al. proposed the deep Q network (DQN) algorithm, which proved that agents can approximate the value function through deep neural networks in high-dimensional state space, and can achieve human-level decision-making. Subsequently, a large number of studies have applied deep reinforcement learning to autonomous driving and mobile robot path planning problems. Kiran et al. reviewed the application of deep reinforcement learning in autonomous driving and pointed out that DRL can help autonomous vehicles learn decision-making strategies in complex traffic environments. Recently, there have also been studies that use improved deep reinforcement learning algorithms for mobile robot obstacle avoidance and path planning, achieving higher success rate and robustness than traditional algorithms. For example, Escobar-Naranjo et al. proposed using DQN to optimize robot autonomous navigation in Applied Sciences, achieving improved path planning performance; Chen et al. applied improved DQN to indoor robot local obstacle avoidance path planning, improving efficiency and success rate in complex environments. Zhang et al. recently proposed a path planning method based on the soft actor-critic (SAC) algorithm, which performs well in dynamic narrow environments, demonstrating the application prospects of deep reinforcement learning in complex path planning.

[0004] In summary, blockchain has unique advantages in data security protection in IIoT unmanned vehicle systems, and deep reinforcement learning (especially DQN) has great potential in unmanned vehicle path planning optimization. However, the current research on the integration of the two is still in its infancy: few documents consider improving the intelligence of path planning and ensuring the trustworthiness of data link at the same time. SUMMARY

[0005] In view of the above problems in the prior art, the present application provides an industrial Internet of Things unmanned vehicle path planning system and method.

[0006] In order to achieve the above-mentioned purposes, the technical scheme adopted by the present application is as follows: In a first aspect, the present application provides an industrial Internet of Things unmanned vehicle path planning system, comprising: An environment perception layer is configured to collect the environment state information of the unmanned vehicle and simultaneously receive the environment state information of other unmanned vehicles issued by a blockchain; the environment state information of the unmanned vehicle and the environment state information of other unmanned vehicles are transmitted to a decision control layer, and the control instructions issued by the decision control layer are received to control the motion of the unmanned vehicle; A decision control layer is configured to receive the environment state information of the unmanned vehicle and the environment state information of other unmanned vehicles transmitted by the environment perception layer, select actions according to the environment state information of the unmanned vehicle using a deep Q network, determine the optimal action by taking the environment state information of other unmanned vehicles as a safety constraint, generate control instructions and issue them to the environment perception layer; and upload the environment state information of the unmanned vehicle, the optimal action and the reward to a blockchain; A blockchain network layer is configured to receive the environment state information of the unmanned vehicle, the optimal action and the reward uploaded by the decision control layer, perform chain processing as transaction information; and broadcast the environment state information of other unmanned vehicles.

[0007] Further, the decision control layer takes the environment state information of other unmanned vehicles as a safety constraint, which is specifically as follows: A safety evaluation function is established wherein the safety evaluation function h takes a value of 1, indicating that the execution of the action may cause a constraint conflict, S is a state space, and A is an action space.

[0008] Further, the decision control layer takes the environment state information of other unmanned vehicles as a safety constraint to determine the optimal action, which is specifically as follows:

[0009] wherein, is the optimal action, is the action at time t, is the action t executed at time t may cause a constraint conflict, is the obstacle avoidance action at state s t .

[0010] Further, the decision control layer obtains a reward after the environment perception layer performs the optimal action and observes a new state, adds a trajectory quadruple of the state, action, reward and new state to an experience replay pool, and periodically samples a small batch of samples in the experience replay pool for gradient descent.

[0011] Further, the decision control layer generates a trajectory quadruple in the digital twin environment and iterates until a termination condition is met.

[0012] Further, the environment perception layer perceives surrounding environment information through sensors and generates a state description by preprocessing raw sensor data by a vehicle-mounted controller, and simultaneously connects with an external positioning base station or a global positioning system to obtain the position of the unmanned vehicle in a global coordinate system.

[0013] Further, the blockchain network layer takes a plurality of unmanned vehicles as light nodes, submits data to adjacent edge blockchain nodes, and the edge blockchain nodes are responsible for participating in consensus and accounting; and predefines vehicle state on-chain contracts and task instruction publishing contracts in the alliance chain, sets data formats and access permissions, and verifies whether the data is legal by using the corresponding contract when the unmanned vehicle sends a transaction to the blockchain and writes it into the ledger.

[0014] Further, the application display layer is further included for receiving the unmanned vehicle operation record pushed by the blockchain network layer, detecting the abnormal behavior of the unmanned vehicle according to the unmanned vehicle operation record, and displaying the state, path and alarm information of the unmanned vehicle.

[0015] Further, the application display layer displays the position of the unmanned vehicle, the planned path and the obstacle distribution in the virtual environment in real time; based on the blockchain data browsing request, the vehicle operation log and transaction details recorded on the chain are displayed in real time, and when the blockchain detects an anomaly, an alarm is given in time through the interface.

[0016] In a second aspect, the present application provides an industrial Internet of Things unmanned vehicle path planning method, comprising the following steps: The environment perception layer is used to collect the environment state information of the unmanned vehicle and receive the environment state information of other unmanned vehicles issued by the blockchain; and the environment state information of the unmanned vehicle and the environment state information of other unmanned vehicles are transmitted to the decision control layer; The decision control layer receives the environment state information of the unmanned vehicle and the environment state information of other unmanned vehicles transmitted by the environment perception layer, selects an action according to the environment state information of the unmanned vehicle by using a deep Q network, determines an optimal action by taking the environment state information of other unmanned vehicles as a safety constraint, generates a control instruction and issues it to the environment perception layer; and uploads the environment state information of the unmanned vehicle, the optimal action and the reward to the blockchain; The blockchain network layer receives the environmental status information, optimal actions, and rewards of the unmanned vehicle uploaded by the decision control layer, and processes them on the chain as transaction information; and broadcasts the environmental status information of other unmanned vehicles; The environmental perception layer receives control instructions from the decision control layer to control the motion of the unmanned vehicle.

[0017] The present invention has the following beneficial effects: This paper introduces blockchain technology into the industrial unmanned vehicle scheduling system to achieve secure storage and transparent sharing of vehicle status and decision-making data, thereby improving system credibility. It also constructs a DQN-based global path planning algorithm for unmanned vehicles, significantly improving path planning efficiency in complex environments through reinforcement learning. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a framework diagram of an industrial IoT unmanned vehicle path planning system; Figure 2 Flowchart for environmental modeling and data uploading for the perception layer of industrial unmanned vehicles; Figure 3 Flowchart for DQN intelligent decision-making and control execution of industrial unmanned vehicles; Figure 4 A flowchart for on-chain decision-making and data synchronization for industrial IoT unmanned vehicles; Figure 5 It is a flowchart for data display and interaction on the application display layer chain. DETAILED DESCRIPTION

[0019] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0020] like Figure 1 As shown, an embodiment of the present invention provides an industrial Internet of Things unmanned vehicle path planning system, including: The environmental perception layer is used to collect environmental status information of the unmanned vehicle and receive environmental status information of other unmanned vehicles issued by the blockchain; it transmits the environmental status information of the unmanned vehicle and other unmanned vehicles to the decision control layer, and receives control instructions issued by the decision control layer to control the movement of the unmanned vehicle; The decision control layer is used for receiving the environment state information of the unmanned vehicle and the environment state information of other unmanned vehicles transmitted by the environment perception layer, selecting an action according to the environment state information of the unmanned vehicle by using a deep Q network, determining an optimal action by taking the environment state information of other unmanned vehicles as a safety constraint, generating a control instruction and issuing the control instruction to the environment perception layer, and uploading the environment state information of the unmanned vehicle, the optimal action and a reward to the blockchain; The blockchain network layer is used for receiving the environment state information of the unmanned vehicle, the optimal action and the reward uploaded by the decision control layer, performing chain processing as transaction information, and broadcasting the environment state information of other unmanned vehicles.

[0021] In an optional embodiment of the present application, the environment perception layer includes an industrial unmanned vehicle body and various sensors, embedded controllers and the like carried by the industrial unmanned vehicle. The unmanned vehicle perceives surrounding environment information through sensors such as laser radar (LiDAR), ultrasonic wave, camera and the like, and generates a state description by pre-processing raw sensor data by a vehicle-mounted controller. In this embodiment, a 10*10 grid is used to represent a local environment map of the unmanned vehicle, in which obstacles, target points and the position of the unmanned vehicle are marked, and which is used as a state input for reinforcement learning. At the same time, the environment perception layer is also connected with an external positioning base station or a global positioning system to obtain the position of the unmanned vehicle in a global coordinate system. Each unmanned vehicle node is pre-installed with a blockchain client, so that it can upload key information to the blockchain and receive control instructions from the chain or state data of other vehicles, such as Figure 2 As shown in the figure, the solid line represents the communication of the main data flow / writing operation, carries a payload and changes the system state, and the dashed line represents the query / reading operation / dependent call lightweight communication, which does not change the system state.

[0022] In an optional embodiment of the present application, the decision control layer is composed of an edge computing gateway or a local server and a reinforcement learning algorithm module, and is responsible for path planning decision and vehicle motion control. A deep Q network decision module is deployed on an edge computing device, which is used to calculate an optimal action according to the state provided by the environment perception layer. Specifically, the environment perception layer uploads the state to the decision module, and the DQN neural network outputs the Q value of each candidate action according to the input state, and the system selects the action with the maximum Q value . If - If the greedy policy requires exploration, a random action is selected with a small probability. After the action is selected, the speed and steering instructions are sent to the vehicle via the motion control unit (e.g., AGV controller) to execute. At the same time, the decision-making layer records the information related to the decision (state, decision, reward, etc.) to provide data for training. It is worth noting that at the initial stage of system deployment, the DQN model needs to be trained through a large number of simulations to achieve optimal performance before being used in actual operation. In actual operation, the DQN decision-making module can also continuously learn online - adding new interaction data collected in actual operation to the experience pool, constantly fine-tuning and optimizing the strategy, so as to adapt to environmental changes. The decision-making control layer also includes safety monitoring logic: when the path given by the DQN is obviously dangerous (e.g., a collision is predicted to occur), the safety rule module intervenes to correct or emergency stop to ensure system safety and stability, as shown in FIG. 8. Figure 3

[0023] Under the reinforcement learning framework, the vehicle path planning can be modeled as a Markov Decision Process (MDP). MDP is usually represented as a five-tuple , where is the state space, representing the set of possible states of the vehicle; is the action space, corresponding to the set of actions that the vehicle can perform (e.g., forward, backward, turn, etc. discrete actions, or continuous speed and steering control); is the state transition probability, i.e., the probability distribution of transitioning from state to the next state after performing action ; is the immediate reward function, mapping state-action pairs to a numerical reward; is the discount factor, weighing the relative importance of immediate rewards and future rewards. In the path planning task, the state can be defined as the pose of the vehicle in the environment or the surrounding perception information (e.g., position coordinates on a grid map, or laser radar scan results, etc.), and the target state is the predetermined destination point. The action is the navigation control instruction, such as discrete forward, backward, left, or right movement by one step. The design of the immediate reward directly affects the learning effect: a common approach is to define a positive reward (e.g., +100) when reaching the target, a negative reward (e.g., -100) when colliding with obstacles, and a small negative reward (e.g., -1) for each step to encourage shorter paths, which is used to balance path length. Through the above modeling, the vehicle path planning problem is transformed into a reinforcement learning problem of finding the strategy with the maximum cumulative reward on the MDP. The agent (vehicle) tries different actions by continuously interacting with the environment and updates the strategy according to the rewards obtained, eventually learning the optimal strategy that maximizes the long-term return (e.g., reaching the target and taking the shortest path).​

[0024] The deep reinforcement learning algorithm used in this embodiment is the Deep Q Network (DQN). DQN is a combination of the value iteration algorithm Q-learning and a deep neural network, which is used to approximate the optimal state-action value function. In this embodiment, the state of the unmanned vehicle navigation task is input into a multi-layer neural network, the Q value of each action in the action space is output, and then the Q value of each action in the action space is output according to the -The greedy strategy selects the action with the largest Q value. Neural network parameters The goal is to minimize the loss function by repeated gradient descent training, and to satisfy the Bellman optimal equation. During the training process, DQN introduces two key mechanisms: (1) Experience replay: each step sample generated by the interaction between the unmanned vehicle and the simulation environment Stored in the experience pool, where r is the reward; randomly sample small batches of data from it during training, which breaks the data correlation and improves training stability. (2) Target network: Maintain a delayed updated target Q network, the target network parameters Used to calculate the target value y: ; in, For the next action; The current network parameters are copied to the target network only after a fixed number of steps to avoid divergence in the training process. Through these techniques, DQN can gradually converge on high-dimensional decision-making problems such as unmanned vehicle path planning. It is important to pay attention to the impact of network structure and hyperparameters on algorithm performance: the study used a fully connected neural network with two hidden layers to estimate Q values ​​in the implementation. Each layer contains 128 ReLU activated neurons to balance expressive power and computational overhead; the learning rate is set to 0.001, the experience pool capacity is 10,000, and the discount factor is 0. , Greedy Initial The value is gradually decayed to 0.1 to ensure sufficient exploration in the early stages and full utilization in the later stages. During training, a parameter update is performed after each interaction step, and the target network synchronizes parameters every 500 steps. This setting has been proven in experiments to achieve stable convergence and achieve optimal policies.

[0025] set up is the state space; For the action space (such as moving forward , turn wait); :By deep Q network (neural network parameters ) approximation of the action-value function; : exploration coefficient; Sample Collection , t is the time, N is the number of samples; the experience replay pool (capacity ).

[0026] 1 Action selection: given moment Perception state , the decision module first calculates ; ; in is the action space capacity; selected action After that, the corresponding control instruction It is sent to the unmanned vehicle through the motion controller, For the moment The expected longitudinal speed of the unmanned vehicle, For the moment Expected steering control amount, It is the action-control mapping function.

[0027] 2 Numerical safety constraints: There is a security assessment function , where the security evaluation function h takes the value of 1, indicating execution May result in constraint violations (such as predicted collisions).

[0028] The final action performed is ; in An emergency stop or a rule-based avoidance strategy may be recommended.

[0029] 3. Online Learning: Get rewards after execution And observe the new state ,Will And periodically sample small batches of samples Perform gradient descent: ; in, is the learning rate, For the neural network parameters The gradient operator, is the number of mini-batch samples, is the discount factor, are the target network parameters.

[0030] 4 Offline pre-training: In the digital twin environment Internally generated trajectory , iterate the above formula until the verification index satisfies ; for a preset tolerance) and then deployed to the actual system for online fine-tuning; wherein is a desired operator, is an optimal action value function.

[0031] In an optional embodiment of the present application, the blockchain network layer is composed of a number of blockchain nodes forming a consortium chain, which is used to store and share system data. The nodes of the consortium chain are deployed at key locations of the industrial network, such as the central control room server, edge servers in each major workshop, and the unmanned vehicles themselves (when resources are sufficient). Each node runs a blockchain consensus client and participates in the maintenance of the ledger together. As shown in Figure 4 , a typical blockchain network topology is depicted: a number of unmanned vehicles act as light nodes, submitting data to adjacent edge blockchain nodes, which are responsible for participating in consensus and recording. A series of smart contracts (chaincode) are predefined in the consortium chain, such as "vehicle state on-chain contract", "task instruction publishing contract", etc., which specify data formats and access permissions. When an unmanned vehicle sends a transaction (e.g. uploads path information) to the blockchain, the corresponding contract will verify whether the data is legal (such as signature, permission) and write it into the ledger. Through the blockchain network, a trust intermediary for data sharing is formed between the decision control layer and the perception layer, and between different vehicles: all data obtained by the nodes comes from the consensused blockchain ledger copy, ensuring "consistency and authenticity". For example, when multiple unmanned vehicles need to coordinate to avoid collision, they can make decisions by querying the other party's recent position and speed information on the chain, without directly trusting the data sent by the other party. For example, management personnel can query the historical running track of any vehicle through the blockchain without worrying about the log being tampered with. It should be emphasized that the real-time impact of blockchain is fully considered in the design: not all sensor data is on-chain, but only valuable information for global coordination and traceability (such as decision sequence, accident alarm, etc.) is selected to reduce data volume and consensus pressure. A large amount of sensor data related to local real-time control is still transmitted through a point-to-point network and processed directly by the vehicle controller to ensure millisecond-level response. The blockchain layer provides macro data credibility support for the system, rather than replacing the existing control network.

[0032] ​This embodiment uses blockchain for data storage and interaction within the unmanned vehicle system. In the designed system, the blockchain acts as a distributed database, storing critical data during the unmanned vehicle's operation, such as the route generated by each path planning session, vehicle mileage and energy consumption information, and records of important control command issuance. This data is encapsulated as transactions, which are packaged and agreed upon by nodes in the blockchain network. This research employs a consortium chain architecture, where the nodes participating in bookkeeping are composed of industrial users (such as factory servers and AGV management terminals) and are not open to the public. The consortium chain uses the Byzantine Fault Tolerance (PBFT) algorithm to reach consensus. Nodes vote on which transactions to include in the block through mutual communication, eliminating the time-consuming proof-of-work required by public chains. At regular intervals or when a certain number of transactions is reached, the block-producing node packages the transactions into blocks and broadcasts them to other nodes for verification. Once a majority of bookkeeping nodes reach consensus on the block's contents, the block is added to the chain. Blocks are linked in chronological order, and each block contains the hash value of the previous block, ensuring the chain's immutability. The study parallels the process of uploading vehicle data to the blockchain with the autonomous vehicle's navigation decision-making process: each time the autonomous vehicle completes a plan or reaches a key milestone (such as passing a critical node), a transaction is generated and sent to the blockchain. Because PBFT (Publicly Tokenized Forwarding) has low confirmation latency (typically within seconds) in small-scale consortium chains, it does not significantly impact the autonomous vehicle's real-time control. With the blockchain, all participants in the system (including the monitoring center and vehicle controllers) maintain an identical copy of the ledger. Any attempt to tamper with the data will be rejected due to a hash check failure, significantly improving data credibility and system security. Furthermore, the blockchain's transparent and traceable nature enables managers to easily audit vehicle routes and operation records, improving operational management efficiency. Furthermore, the study also implements permission control to protect sensitive data privacy: Access rights are set for each transaction type in the blockchain, limiting access to detailed vehicle data to authorized nodes and limiting access to essential summary information to other nodes. Overall, the designed blockchain data model provides a secure, shared data foundation for the autonomous vehicle system, ensuring the reliable operation of the upper-layer path planning algorithm.

[0033] In an optional embodiment of the present application, the application display layer provides a monitoring and human-computer interaction interface of the system, including function modules such as simulation visualization, state monitoring and data analysis. In the simulation stage, a visualization interface is developed, which can display the position of the unmanned vehicle, the planned path and the distribution of obstacles in the virtual environment in real time. The interface also integrates blockchain data browsing function, and the manager can view the vehicle operation log and transaction details recorded on the chain, such as the route of a certain vehicle in a certain time period, whether it has stopped abnormally, etc. Another role of the application layer is to provide decision reference and alarm notification to the operator when actually deployed: when the blockchain detects an anomaly (for example, a vehicle does not travel according to the planned route for a long time or the data on the chain is inconsistent), the interface will alarm in time. Through friendly visualization and interaction design, the application layer improves the usability of the system, enabling the operator to intuitively understand the running status of the unmanned vehicle and the data recorded by the blockchain, thereby enhancing the degree of trust in the system.

[0034] In summary, the layers of the system architecture of the present application work closely together: the perception layer provides the environment state, the decision control layer uses the DQN algorithm to output the optimized path and control the vehicle, the blockchain layer ensures the reliable sharing of decision data and vehicle state, and the application layer transparently presents the entire system operation to the user. Such design realizes a closed loop of "data - decision - execution - evidence": the unmanned vehicle executes the optimal strategy given by the intelligent algorithm, and at the same time, every key data has a traceable record, ensuring the unity of running efficiency and safety, as shown in Figure 5

[0035] The present application provides an industrial Internet of Things unmanned vehicle path planning method, comprising the following steps: The environment state information of the unmanned vehicle is collected by the environment perception layer, and the environment state information of other unmanned vehicles is received from the blockchain; the environment state information of the unmanned vehicle and the environment state information of other unmanned vehicles are transmitted to the decision control layer; The environment state information of the unmanned vehicle and the environment state information of other unmanned vehicles transmitted by the environment perception layer are received by the decision control layer, the action selection is performed according to the environment state information of the unmanned vehicle by using the deep Q network, the optimal action is determined by taking the environment state information of other unmanned vehicles as a safety constraint, the control instruction is generated and sent to the environment perception layer; and the environment state information of the unmanned vehicle, the optimal action and the reward are uploaded to the blockchain; The environment state information of the unmanned vehicle, the optimal action and the reward uploaded by the decision control layer are received by the blockchain network layer, and are processed as transaction information for on-chain processing; and the environment state information of other unmanned vehicles is broadcasted; The motion control of the unmanned vehicle is performed by the environment perception layer receiving the control instruction sent by the decision control layer.

[0036] The system of the present application will be analyzed in combination with specific experimental design.​

[0037] The experimental platform is shown in Table 1 Table 1 Experimental platform configuration table

[0038] Dataset and comparison methods 1. Proposed-DQN: Improved DQN, experience replay 10k, two hidden layers 128 neurons, ε-greedy decay 1→0.1.

[0039] 2. PF-DDQN: The latest multi-AGV reinforcement learning algorithm, reports a 92.62% improvement in DDQN path superiority.

[0040] 3. A* (Dynamic-Replan): Classic A* with dynamic obstacles, i.e., recalculation.

[0041] 4. Env-Weighted A*: Industry commonly used improved A* with grid weights.

[0042] Each algorithm was independently run in a scenario with 100 random start and end points and an obstacle density of 15%; Proposed-DQN / PF-DDQN was first trained offline for 3k episodes, and then inference testing was performed.

[0043] Evaluation indicators 1. Success rate Success (%) 2. Average path length Len (grid) 3. Average planning delay Time (ms / step) 4. Collision rate Crash (%) 5. Blockchain TPS / confirmation delay (only for Proposed-DQN to record key decisions on-chain) Public benchmark: Fabric 4 peer peak reached 1619 TPS; single-transaction confirmation latency ≈ 2 s with BatchTimeout=2 s setting. Figure 1

[0044] Core experimental results Path planning performance is shown in Table 2 Table 2 Comparison of performance of different algorithms

[0045] As shown in Table 2, the results are consistent with the latest DQN report of 93% success rate in dynamic obstacle scenarios, and further improvements have been made in path length and collision rate. Real-time decision-making at the level of 0.52 ms fully meets the requirements of a 50 Hz control cycle; compared with A* replanning, it saves two orders of magnitude of inference time.

[0046] Blockchain subsystem performance is shown in Table 3 Table 3 Blockchain performance demonstration table

[0047] As shown in Table 3, the system delay remains <1.5 s with a load far below the Fabric peak of 1619 TPS, without any packet loss or rollback. Tampering tests show that single-node private modification of the ledger is immediately rejected by consensus, meeting the design goal of “non-tamperable”.

[0048] Resource overhead Edge server: CPU peak 46%, GPU video memory 2.3 GB.

[0049] Peer node: memory increment ≈ 180 MB / h, chain size linear growth 0.9 MB / h, which can be controlled to <5 GB through a semi-annual archiving strategy.

[0050] Conclusion Effectiveness: Proposed-DQN is superior to the three control lines in success rate, path length, and real-time performance, with a 13.5 pp increase in success rate compared to traditional A*, and higher than the average level of 93% in recent literature.

[0051] Reliability: The measured confirmation delay of the blockchain is 1.3-1.5 s, which is lower than the official recommended threshold of 2 s, ensuring the industrial logistics state consistency requirement of ≤3 s.

[0052] Ablative observation: If experience replay or target network is closed, the success rate drops to 88%, verifying the necessity of the network design in Section 3.

[0053] Engineering value: The system resource utilization is <50% under 4-car concurrency, and does not require additional hardware to be deployed in a medium-sized manufacturing workshop.

[0054] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 An apparatus for performing a function specified by a flow or a plurality of flows and / or blocks Figure 1 An apparatus for performing a function specified by a flow or a plurality of flows and / or blocks

[0055] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 one or more flow or block Figure 1 one or more blocks or steps of the flow.

[0056] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 one or more flow or block ​ one or more blocks or steps of the flow.

[0057] The principles and implementations of the present application are described in the specific embodiments, the above examples are only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation and application range will be changed, and the above description should not be understood as the limitation of the present application.

[0058] Those skilled in the art will realize that the embodiments described herein are for the purpose of understanding the principles of the present application and should be understood as not limited to such specific embodiments and examples. Those skilled in the art can make various other specific modifications and combinations according to the technical inspiration disclosed in the present application without departing from the spirit of the present application, and these modifications and combinations are still within the scope of the present application.

Claims

1. An industrial Internet of Things unmanned vehicle path planning system, characterized in that: include: The environmental perception layer is used to collect the environmental status information of the unmanned vehicle and receive the environmental status information of other unmanned vehicles issued by the blockchain; it transmits the environmental status information of the unmanned vehicle and other unmanned vehicles to the decision control layer, and receives the control instructions issued by the decision control layer to control the movement of the unmanned vehicle; The decision-making control layer receives the environmental status information of the unmanned vehicle and other unmanned vehicles transmitted by the environmental perception layer, uses a deep Q network to select actions based on the environmental status information of the unmanned vehicle, and uses the environmental status information of other unmanned vehicles as a safety constraint to determine the optimal action. It then generates control instructions and sends them to the environmental perception layer. It also uploads the environmental status information of the unmanned vehicle, the optimal action, and the reward to the blockchain. The blockchain network layer is used to receive the environmental status information, optimal actions, and rewards of the unmanned vehicle uploaded by the decision control layer, and process them on the chain as transaction information; it also broadcasts the environmental status information of other unmanned vehicles.

2. The industrial Internet of Things unmanned vehicle path planning system according to claim 1, characterized in that: The decision control layer uses the environmental status information of other unmanned vehicles as safety constraints. Specifically: Establishing a security assessment function , where the security evaluation function h takes the value of 1, indicating that the action is executed in state s This may lead to constraint conflicts, where S is the state space and A is the action space.

3. The industrial Internet of Things unmanned vehicle path planning system according to claim 2, characterized in that: The decision control layer uses the environmental status information of other unmanned vehicles as safety constraints to determine the optimal action: in, For the optimal action, is the action at time t, For state s t Execute an action This may lead to constraint conflicts. For state s t Obstacle avoidance action.

4. The industrial Internet of Things unmanned vehicle path planning system according to claim 1, characterized in that: After the decision control layer performs the optimal action in the environment perception layer, it obtains rewards and observes the new state. It adds the trajectory quadruple of state, action, reward and new state to the experience replay pool, and periodically samples small batches of samples in the experience replay pool for gradient descent.

5. The industrial Internet of Things unmanned vehicle path planning system according to claim 1, characterized in that: The decision control layer generates trajectory quadruples in the digital twin environment and iterates until the termination condition is met.

6. The industrial Internet of Things unmanned vehicle path planning system according to claim 1, characterized in that: The environmental perception layer perceives the surrounding environment information through sensors, and the on-board controller preprocesses the raw sensor data to generate a state description; at the same time, it connects with the external positioning base station or global positioning system to obtain the position of the unmanned vehicle in the global coordinate system.

7. The industrial Internet of Things unmanned vehicle path planning system according to claim 1, characterized in that: The blockchain network layer uses several unmanned vehicles as light nodes, submitting data to nearby edge blockchain nodes, which are responsible for participating in consensus accounting; and pre-defines the vehicle status chain contract and task instruction release contract in the alliance chain, setting the data format and access rights. When the unmanned vehicle sends a transaction to the blockchain, it uses the corresponding contract to verify whether the data is legal and write it into the ledger.

8. The industrial Internet of Things unmanned vehicle path planning system according to claim 1, characterized in that: It also includes an application display layer, which is used to receive the unmanned vehicle operation records pushed by the blockchain network layer, detect abnormal behavior of the unmanned vehicle based on the unmanned vehicle operation records, and display the unmanned vehicle status, path and alarm information.

9. The industrial Internet of Things unmanned vehicle path planning system according to claim 1, characterized in that: The application display layer displays the position of the unmanned vehicle, planned path and obstacle distribution in the virtual environment in real time; based on the blockchain data browsing request, it displays the vehicle operation log and transaction details recorded on the chain in real time, and issues timely alarms through the interface when the blockchain detects an anomaly.

10. A path planning method for an industrial Internet of Things unmanned vehicle, characterized in that: The following steps are involved: The environmental perception layer collects the environmental status information of the unmanned vehicle and receives the environmental status information of other unmanned vehicles issued by the blockchain. The environmental status information of the unmanned vehicle and other unmanned vehicles is transmitted to the decision control layer. The decision control layer receives the environmental status information of the unmanned vehicle and other unmanned vehicles transmitted by the environmental perception layer. A deep Q network is used to select actions based on the environmental status information of the unmanned vehicle. The optimal action is determined using the environmental status information of other unmanned vehicles as a safety constraint, and control instructions are generated and sent to the environmental perception layer. The environmental status information of the unmanned vehicle, the optimal action, and the reward are uploaded to the blockchain. The blockchain network layer receives the environmental status information, optimal actions, and rewards of the unmanned vehicle uploaded by the decision control layer, and processes them on the chain as transaction information; and broadcasts the environmental status information of other unmanned vehicles; The environmental perception layer receives control instructions from the decision control layer to control the motion of the unmanned vehicle.

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