Electric power equipment supply chain collaborative management system based on alliance chain

Abstract

The invention relates to the technical field of power equipment supply chain management, and discloses a power equipment supply chain collaborative management system based on an alliance chain, comprising an alliance chain network layer: a distributed network composed of a manufacturer node, a supplier node, a logistics node, a detection node and a grid company node, each node maintaining account book consistency through a consensus algorithm; the intelligent contract execution layer comprises an order processing contract, a logistics tracking contract, a quality acceptance contract and a settlement management contract, and supports automatic execution of a business process; and the data management platform is provided with a distributed storage unit, an encryption transmission unit and an authority control unit and is used for realizing encryption storage and hierarchical access of key data. Full-process automation of order matching, production plan generation and logistics tracking is realized through an intelligent contract execution layer, an AI prediction module can pre-judge a demand peak value in advance based on historical data, a node processing priority is optimized in combination with a dynamic consensus algorithm, and manufacturer order response time is compressed.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment supply chain management, and in particular to a collaborative management system for power equipment supply chain based on an alliance chain. Background Art

[0002] The power equipment supply chain involves multiple links such as raw material procurement, equipment manufacturing, logistics transportation, quality inspection and grid delivery, and has characteristics such as multiple participation methods, complex processes and long cycles. With the rapid growth of smart grid construction and new energy equipment demand, the traditional supply chain management model has been difficult to meet the industry needs. For example, a UHV transformer project needs to coordinate more than 30 suppliers, 5 logistics enterprises and 2 testing institutions. Under the traditional model, the order processing cycle is up to 20 days, and it takes manual access to more than 100 paper documents to trace quality problems after equipment delivery, resulting in an average fault location time of more than 15 days.

[0003] Currently, the industry mainly uses a centralized ERP system for management, relying on manual entry of order information, transmitting inspection reports through email / fax, and the logistics status depends on GPS single-point positioning. Some enterprises have tried to introduce blockchain technology, but there are the following limitations: using the traditional PBFT algorithm, the consensus time exceeds 30 seconds in a scenario of more than 100 nodes, which cannot meet the real-time requirements of power equipment production; the manufacturer system and the power grid company system are independently deployed, and equipment parameters need to be manually entered twice, resulting in a data consistency error rate of 8%; the existing contracts only support simple rules (such as "payment after arrival"), and cannot handle complex quality acceptance logics (such as multi-dimensional parameter comparison).

[0004] The existing technologies have shown significant defects in practical applications: for example, statistics of a provincial power grid company show that the proportion of production plan adjustments due to inconsistent order information reaches 35%, mainly because the centralized system cannot synchronize supplier inventory data in real time; in the investigation of a wind power equipment accident, because the inspection report is not bound to the production batch, it takes 7 days to manually verify more than 2,000 production records; due to the lack of reliable data support for small and medium-sized suppliers, the supply chain financing interest rate is generally 3-5 percentage points higher than the benchmark interest rate. The traditional centralized architecture is difficult to support multi-party collaboration, lacks an effective data trustworthy deposit mechanism, and the smart contract is not deeply integrated with industry knowledge. Therefore, the present invention provides a collaborative management system for power equipment supply chain based on an alliance chain to solve the deficiencies existing in the prior art. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a collaborative management system for power equipment supply chain based on an alliance chain, which solves the problem of poor collaborative processing effect in the whole process of the collaborative management system for power equipment supply chain, resulting in low work efficiency.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A collaborative management system for the power equipment supply chain based on a consortium blockchain, comprising: Consortium blockchain network layer: A distributed network composed of manufacturer nodes, supplier nodes, logistics nodes, inspection nodes, and power grid company nodes, where each node maintains the consistency of the ledger through a consensus algorithm;

[0007] Smart contract execution layer: Includes order processing contracts, logistics tracking contracts, quality acceptance contracts, and settlement management contracts, supporting the automated execution of business processes;

[0008] Data management platform: Equipped with a distributed storage unit, an encrypted transmission unit, and a permission control unit, realizing the encrypted storage and hierarchical access of key data;

[0009] Full-chain traceability system: Based on the blockchain chain structure, records the full life cycle data from raw material procurement to equipment delivery, including a timestamp verification module and a hash verification module;

[0010] Cross-chain interaction interface: Adopts a standardized API protocol to achieve cross-chain data interaction with the State Grid asset system and third-party credit platforms.

[0011] Preferably, the consortium blockchain network layer includes the following sub-units:

[0012] Dynamic consensus subsystem: Connected to the consensus algorithm, includes a node weight evaluation module and a faulty node detection module, dynamically adjusting the node voting weight according to the business volume;

[0013] Time synchronization server: Communicatively connected to each node, adopting an accurate time synchronization mechanism based on Beidou satellite time service.

[0014] Preferably, the smart contract execution layer includes the following sub-units:

[0015] Contract exception handling module: Connected to the order processing contract, logistics tracking contract, and quality acceptance contract, sets a timeout rollback mechanism and an exception status capture unit; when the detected data in the quality acceptance contract does not meet the standards, this module automatically triggers a return smart contract and generates a liability traceability report containing the equipment ID and inspection time;

[0016] AI prediction module: Connected to the order processing contract, includes a demand prediction unit and an inventory optimization unit, predicting the supply chain demand based on historical data.

[0017] Preferably, the distributed storage unit of the data management platform is constructed using the IPFS protocol, the encrypted transmission unit uses the national cryptography SM2 algorithm for data encryption, and the permission control unit realizes role-based fine-grained access control.

[0018] Preferably, the full-chain traceability system further includes:

[0019] Multi - dimensional label module: Connected to the chain - like structure, generating digital labels for each device that include model parameters, production batches, and inspection reports, and supporting quick retrieval and verification.

[0020] Preferably, the detection node is configured with:

[0021] Intelligent detection device: Communicatively connected to the quality acceptance contract, automatically collecting device parameters and uploading them to the blockchain.

[0022] Preferably, the logistics node is configured with:

[0023] Beidou positioning module: Connected to the logistics tracking contract, uploading the transportation status to the blockchain in real - time.

[0024] Preferably, the cross - chain interaction interface is configured with a production plan synchronization module, which is connected to the order processing contract, and synchronizes the generated production plan to the State Grid asset system for filing through a standardized API.

[0025] A collaborative management method for the power equipment supply chain based on the consortium chain includes the following steps:

[0026] Step 1. Supplier on - chain deposit:

[0027] The supplier uploads the raw material batch information to the chain through the hash verification module, generating a timestamped deposit record.

[0028] The full - chain traceability system generates an initial traceability node based on this deposit record.

[0029] Step 2. Manufacturer order processing:

[0030] The manufacturer node responds to the hash value of the deposit record in Step 1, triggering the order processing contract.

[0031] Call the AI prediction module to analyze historical data, and automatically match the supplier inventory in combination with the raw material information in Step 1.

[0032] Generate a production plan containing the raw material batch number and synchronize it to the consortium chain.

[0033] Step 3. Logistics status tracking:

[0034] After the production plan is synchronized, the logistics node initializes the transportation task based on this plan.

[0035] Collect the transportation location data in real - time through the Beidou positioning module.

[0036] The logistics tracking contract synchronously updates the transportation status to the blockchain and triggers the intelligent analysis of the transportation route.

[0037] Step 4. Quality inspection and acceptance:

[0038] After the logistics status is updated to "signed for", the inspection agency obtains the transportation completion notice in Step 3;

[0039] Use intelligent inspection equipment to collect equipment parameters and upload them to the quality acceptance contract;

[0040] The contract automatically compares the inspection data with the standard parameters in the production plan in Step 2 and generates an acceptance report.

[0041] Step 5. Settlement and delivery:

[0042] After the acceptance report passes the hash check, the settlement management contract triggers the payment process;

[0043] The power grid company calls the acceptance report in Step 4 and verifies the full life cycle data through the multi-dimensional label module;

[0044] After passing the verification, mark the equipment as "delivered" and update the status of the alliance chain.

[0045] Preferably, in Step 4, if the inspection data does not conform to the standard parameters in Step 2, the exception handling module automatically triggers the return intelligent contract and synchronously generates a responsibility traceability report to all nodes; the production plan generated in Step 2 is synchronously updated to the State Grid asset system through the production plan synchronization module.

[0046] The present invention provides a collaborative management system for the power equipment supply chain based on the alliance chain. It has the following beneficial effects:

[0047] 1. The system of the present invention realizes the full-process automation of order matching, production plan generation, and logistics tracking through the intelligent contract execution layer. The AI prediction module can predict the demand peak in advance based on historical data, and optimize the node processing priority in combination with the dynamic consensus algorithm, so as to compress the order response time of the manufacturer; the logistics node uploads data in real time through Beidou positioning, and automatically updates the transportation status in cooperation with the intelligent contract, reducing the manual input error rate.

[0048] 2. The present invention adopts a dual verification mechanism of hash check and time stamp through the full-chain traceability system to ensure that data such as raw material batches, production process parameters, and inspection reports cannot be tampered with. The multi-dimensional label module generates a digital label containing more than 20 key parameters for each device, supporting the power grid company to quickly verify the full life cycle information by scanning the code. This mechanism improves the accuracy of quality problem positioning to the specific production station, and the responsibility determination accuracy rate reaches 100%.

[0049] 3. Through the cross-chain interaction interface and the standardized API protocol, the present invention realizes real-time data synchronization with the State Grid asset system and the third-party credit investigation platform. After the production plan is generated, it is automatically synchronized to the power grid filing system, shortening the approval cycle; the supplier credit score is updated in real time, reducing the supply chain financing interest rate. The acceptance reports of the inspection agencies are directly stored on the chain, reducing the per-order cost of repeated inspections among enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a system interaction flow chart of the present invention;

[0051] Figure 2 It is a work step flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] Next, in combination with the drawings of the specification of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] Embodiment 1:

[0054] Please refer to the appendix Figure 1 , the embodiment of the present invention provides a collaborative management system for the power equipment supply chain based on the consortium blockchain, including the construction of the consortium blockchain network layer: selecting servers with high-performance computing and large-capacity storage capabilities as each node device. Specifically, the server is configured as follows: Intel Xeon Platinum 8380 processor, with 40 cores and 80 threads, which can meet the complex operation requirements; 128GB DDR4 3200MHz memory to ensure fast data reading and processing; 2TB NVMe SSD solid-state drive to provide high-speed data storage and access.

[0055] At the manufacturer node, due to its heavy production plan operation and order processing tasks, a cache acceleration card is additionally configured to improve the data reading and writing speed. The supplier node focuses on the storage and management of raw material inventory data and is equipped with a redundant disk array (RAID5) to ensure data security and reliability. The logistics node needs to process a large amount of location data in real time, so the network transmission module is optimized and a 10 Gigabit Ethernet network card is adopted to ensure the high efficiency of data transmission. The inspection node has high requirements for the accuracy and stability of inspection data, and is configured with a high-precision clock synchronization device to ensure accurate recording of inspection time. As the terminal of the supply chain, the power grid company node needs to interface with multiple external systems, so it has strong network communication capabilities and security protection devices.

[0056] Install an optimized underlying framework for the consortium blockchain, such as custom development based on Hyperledger Fabric. When configuring the dynamic consensus subsystem, carefully set the relevant parameters of the node weight evaluation module and the faulty node detection module. The business volume statistical period is set from 0:00 to 24:00 every morning. This time period covers the business activities of all links in the supply chain in one day, and can comprehensively and accurately count the business processing volume of each node. The weight adjustment threshold is set to trigger the recalculation of the node weight when the change in the node's business volume exceeds 20% or the fluctuation of the average response time exceeds 30%.

[0057] The formula for calculating the node weight is:

[0058]

[0059] Among them, W i is the voting weight of node i, T i is the historical business processing volume of node i (unit: transactions per day), R i is the average response time of node i (unit: ms), α and β are weight coefficients (α + β = 1, configured according to industry characteristics), represents the sum of the historical business processing volumes of n nodes in the whole network, represents the sum of the average response times of n nodes in the whole network. When calculating, the business processing volume and response time of each node are statistically calculated every day, and the node weight is obtained by weighted summation after normalization processing. When reaching a consensus, the voting weight value is allocated according to the weight, so as to ensure that when there are large changes in the business volume or response time, the node weight is adjusted in time to ensure the fairness and efficiency of the consensus.

[0060] Deploy a time synchronization server, and connect it to the Beidou satellite time service system through a high-precision satellite time receiving device. During the time synchronization process, a precise time calibration algorithm is adopted to ensure that the time synchronization error of each node is less than 1 ms. The specific implementation method is that the time synchronization server sends a time calibration signal to each node every 10 minutes. After receiving the signal, each node adjusts according to its own clock deviation, so as to ensure the time consistency of the entire consortium blockchain network.

[0061] For the intelligent contract execution layer, develop and deploy intelligent contracts, and use Solidity language to write the code for order processing contracts, logistics tracking contracts, quality acceptance contracts, settlement management contracts, contract exception handling modules, and AI prediction modules.

[0062] In the order processing contract, the order matching algorithm adopts a combination of rule-based and machine learning methods. First, according to the preset rules, such as the specifications, prices, delivery dates, etc. of raw materials, suppliers that meet the basic requirements are screened out. Then, using the machine learning model, based on data such as the historical supply records and credit evaluations of suppliers, a comprehensive score is given to the suppliers, and the supplier with the highest score is selected for order matching.

[0063] In the AI prediction module, the demand prediction unit adopts an algorithm that combines time series analysis and neural networks to comprehensively analyze historical order data, market demand data, macroeconomic data, etc., and predicts the demand for power equipment in the next period of time.

[0064] The prediction formula is:

[0065]

[0066] Among them, is the predicted value of the t-th period, is the actual value of the (t - 1)-th period, α is the data smoothing coefficient (0 ≤ α ≤ 1), and β is the trend adjustment coefficient (0 ≤ β ≤ 1). When calculating, first initialize the base value and trend value, then iteratively calculate the predicted values of each period, and finally output the demand prediction curve for the next 3 - 6 months. The inventory optimization unit then determines the optimal safety inventory level according to the demand prediction results, combined with factors such as inventory costs and stockout costs, using an optimization algorithm.

[0067] The inventory optimization algorithm formula is:

[0068]

[0069] Among them, SS is the safety inventory, z is the Z value corresponding to the service level (for example, when the service level is 95%, z = 1.645), σ is the demand standard deviation (unit: units / week), and L is the lead time (unit: weeks). When calculating, first calculate the standard deviation of the historical demand data, then determine the Z value according to the target service level, and finally calculate the safety inventory in combination with the lead time.

[0070] In the logistics tracking contract, it is clear that the trigger condition for updating the logistics status is that when the location data uploaded by the Beidou positioning module changes and the change distance exceeds 1 kilometer, or when the transportation time exceeds the preset time interval (such as 1 hour), the logistics status is automatically updated. At the same time, the logistics tracking contract triggers the intelligent analysis function of the transportation route. The intelligent analysis function of the transportation route adopts an optimization algorithm based on real-time data, combines traffic big data and map information, and conducts real-time evaluation and optimization of the transportation route.

[0071] The algorithm formula is:

[0072]

[0073] Constraints:

[0074]

[0075] x ij ∈ {0, 1}

[0076] where c ij is the transportation cost from node i to j, and x ij is a binary variable (0 / 1) indicating whether to select the path i → j. When solving, a genetic algorithm is used for heuristic solution, and the path is dynamically adjusted in combination with real-time traffic data.

[0077] In the quality acceptance contract, the quality acceptance criteria are detailed. For example, for a power transformer, it is stipulated that the deviation of its winding resistance shall not exceed ±2%, and the no-load loss shall not exceed ±5% of the standard value, etc.

[0078] The formula for the quality acceptance parameter comparison algorithm is:

[0079] If δ > γ, it is determined as abnormal;

[0080] where x i is the value of the i-th detection parameter, μ i is the standard parameter mean, σ i is the standard parameter standard deviation, and γ is the abnormality threshold (e.g., γ = 3 represents the 3σ principle). When calculating, first calculate the Z-score value of the detection parameter, and then compare it with the threshold to determine whether it is abnormal. When it is abnormal, the return contract is triggered.

[0081] In the settlement management contract, according to the payment method and time node agreed in the contract, the payment process is automatically triggered. For example, the payment is completed within 7 working days after the equipment is accepted.

[0082] In the contract exception handling module, an exception status capture unit is set up to monitor the exception situation in real time during the contract execution process. When it is detected that the detection data in the quality acceptance contract does not meet the standards, the exception handling module automatically triggers the return smart contract and generates a responsibility traceability report containing detailed information such as equipment ID, detection time, problem description, and unqualified parameters.

[0083] Deploy the developed smart contract to the consortium blockchain network. During the deployment process, optimize the bytecode of the contract to reduce the contract's storage space and execution time. After deployment, conduct comprehensive testing and debugging. Adopt a method that combines black-box testing and white-box testing. Black-box testing mainly verifies whether the functions of the contract meet the expectations. For example, test whether the order processing contract can correctly match suppliers, and whether the logistics tracking contract can accurately update the logistics status. White-box testing delves into the contract code to check the logical correctness and potential vulnerabilities of the code. For example, check whether the acceptance criteria in the quality acceptance contract are correctly implemented, and whether the payment process in the settlement management contract is secure and reliable. Through repeated testing and debugging, ensure the stability and reliability of the smart contract.

[0084] Construction of the data management platform: Build a distributed storage system based on the IPFS protocol, configure multiple data storage nodes, which are distributed in different geographical locations to improve the availability and disaster resistance of data. Each storage node uses a high-performance server equipped with a large-capacity hard disk, such as a 4TB enterprise-class mechanical hard disk. At the same time, to improve the efficiency of data storage and retrieval, adopt the distributed hash table (DHT) technology to disperse the data storage on each node and quickly locate the storage location of the data through the hash algorithm.

[0085] Configure data access interfaces to provide RESTful APIs to facilitate access to the stored data by each node. During the data upload process, fragment the data, split large files into multiple small data blocks, and store them on different nodes respectively to improve the parallelism and reliability of data storage. When downloading data, according to the hash value of the data, quickly obtain the storage location of the data block through DHT and download the data blocks from multiple nodes simultaneously to achieve fast data recovery.

[0086] Integrate the national cryptographic SM2 algorithm library to implement the function of data encrypted transmission. Before data transmission, encrypt the data. Adopt the public-key encryption method of the SM2 algorithm to encrypt the data with the public key of the receiving party to ensure the security of the data during the transmission process. When storing data, encrypt sensitive data, such as the purchase price of raw materials and the business secrets of suppliers. Adopt the symmetric encryption method of the SM2 algorithm to encrypt and decrypt the data with the same key to improve the security of data storage.

[0087] Develop a permission control unit to set different data access permissions according to different roles (such as manufacturers, suppliers, power grid companies, etc.). Adopt the Role-Based Access Control (RBAC) model to assign corresponding permission sets to each role. For example, manufacturers have read and write permissions for data such as production plans and raw material inventories, suppliers have read and write permissions for data such as their own raw material information and order status, and power grid companies have query and verification permissions for the full life cycle data of power equipment, but do not have modification permissions. Through strict permission control, ensure the security and privacy of data.

[0088] Development of the full-chain traceability system and cross-chain interaction interface: Develop the full-chain traceability system to implement the functions of the timestamp verification module, hash verification module, and multi-dimensional label module. The timestamp verification module adopts the blockchain-based timestamp service and uses the immutable feature of the blockchain to add an accurate timestamp to each evidence record. The hash verification module performs hash calculations on data such as raw material batch information, production process parameters, and inspection reports to generate a unique hash value and stores the hash value on the blockchain. During data verification, recalculate the hash value of the data and compare it with the hash value stored on the blockchain to ensure the integrity and authenticity of the data.

[0089] The multi-dimensional label module generates a digital label for each device containing more than 20 key parameters such as model parameters, production batches, inspection reports, raw material sources, and key process parameters during production. For example, for a high-voltage switchgear, its digital label contains the model of the switchgear, rated voltage, rated current, production batch number, raw material supplier information, hash value of the factory inspection report, and key assembly process parameters during production. Through the multi-dimensional label, support the power grid company to quickly retrieve and verify the full life cycle information of the device by scanning the code or entering the device ID, etc.

[0090] Develop a cross-chain interaction interface to achieve docking with the State Grid asset system and the third-party credit information platform. Adopt the standardized API protocol to ensure the compatibility and stability of data interaction. When docking with the State Grid asset system, configure the parameters of the production plan synchronization module. For example, set the synchronization frequency to immediately synchronize every time a new production plan is generated; customize the data format conversion rules according to the requirements of the State Grid asset system to ensure that the production plan data can be accurately transmitted to the State Grid asset system.

[0091] When docking with the third-party credit information platform, establish a data security transmission channel, adopt encryption technology and identity authentication mechanism to ensure the security and reliability of data transmission. Through the cross-chain interaction interface, obtain information such as the credit score and credit record of the supplier to provide a decision-making basis for the execution of smart contracts. For example, in the order processing contract, decide whether to give it preferential cooperation rights or adjust the cooperation conditions according to the credit score of the supplier.

[0092] Example 2:

[0093] Please refer to the appendix Figure 2 , the embodiment of the present invention provides a collaborative management method for the power equipment supply chain based on the consortium blockchain, including the following steps:

[0094] Supplier on-chain certification stage: Before the supplier uploads the raw material batch information to the blockchain, it first sorts out and enters the detailed information of the raw materials, including the origin, specifications, quantity, production date, quality inspection report, etc. of the raw materials. After the entry is completed, the hash verification module calculates the hash value of the raw material batch information using the SHA-256 hash algorithm to generate a unique hash value.

[0095] Upload the generated hash value and the detailed information of the raw material batch to the consortium blockchain network. After receiving the data, the consortium blockchain network automatically adds an accurate timestamp to it. The generation of the timestamp is based on the timestamp service of the blockchain to ensure the accuracy and immutability of the time. After generating the certification record, the whole-chain traceability system generates an initial traceability node according to the certification record, and records the initial information of the raw materials, including the basic information, hash value, timestamp, etc. of the raw materials. The generation of the initial traceability node adopts a chain structure and is connected to the nodes in subsequent production, logistics, inspection and other links to form a complete traceability chain.

[0096] Manufacturer order processing stage: The manufacturer node triggers the order processing contract by listening to the consortium blockchain network in real time. When a new certification record hash value is detected. After the order processing contract is started, it immediately calls the AI prediction module. The AI prediction module first cleans and preprocesses the historical order data to remove abnormal data and noise data. Then, using the time series analysis algorithm, it analyzes the trends and seasonal changes of the historical order data, combines external factors such as market demand data and macroeconomic data, and uses a neural network model for training and prediction to predict the demand for power equipment in the next period of time.

[0097] According to the prediction results of the AI prediction module, the order processing contract automatically matches the supplier inventory. In the matching process, first, according to preset rules, such as the specifications, prices, delivery dates, etc. of the raw materials, suppliers that meet the basic requirements are screened out. Then, using a machine learning model, based on data such as the historical supply records and credit evaluations of the suppliers, the suppliers are comprehensively scored, and the supplier with the highest score is selected for order matching. After determining the supplier, a production plan containing detailed information such as raw material batch numbers, production quantities, production process requirements, delivery times, etc. is generated.

[0098] Synchronize the generated production plan to the alliance chain network so that all links in the supply chain can obtain production plan information in real time. At the same time, through the production plan synchronization module of the cross-chain interaction interface, synchronize the production plan to the State Grid asset system for filing in real time. During the synchronization process, perform format conversion and encryption processing on the production plan data to ensure the accuracy and security of the data. After receiving the production plan, the State Grid asset system stores and backs it up, and audits and supervises the production plan.

[0099] Logistics status tracking phase: After receiving the production plan, the logistics node initializes the transportation task based on the plan. First, select a suitable transportation route and transportation tool according to the delivery time and location in the production plan. Then, install a Beidou positioning module on the transportation vehicle to ensure that the position data of the transportation vehicle can be collected in real time. When installing the Beidou positioning module, debug and calibrate the module to ensure its positioning accuracy and data transmission stability.

[0100] The Beidou positioning module collects the position data of the transportation vehicle in real time and uploads the position data to the logistics tracking contract every 10 minutes. The logistics tracking contract synchronizes and updates the transportation status to the blockchain according to the received position data. When the position of the transportation vehicle changes and the change distance exceeds 1 kilometer, or the transportation time exceeds the preset time interval (such as 1 hour), the logistics status is automatically updated, such as from "in transit" to "resting on the way" or "about to arrive", etc. At the same time, the logistics tracking contract triggers the intelligent analysis function of the transportation route, and optimizes the transportation route according to real-time traffic data, road conditions information, etc., to improve transportation efficiency.

[0101] The intelligent analysis function of the transportation route adopts an optimization algorithm based on real-time data, combines traffic big data and map information, and conducts real-time evaluation and optimization of the transportation route. For example, when it is detected that there is congestion on the front section of the road, automatically adjust the transportation route and select an alternative route with better road conditions. During the optimization process, consider factors such as transportation cost, transportation time, and cargo safety, and comprehensively determine the optimal transportation route. At the same time, feedback the optimized transportation route information to the driver of the transportation vehicle in real time to ensure the smooth progress of the transportation task.

[0102] Quality inspection and acceptance phase: When the logistics status is updated to "signed for", the inspection agency receives a transportation completion notice through the alliance chain network. After receiving the notice, the inspection agency prepares intelligent inspection equipment, calibrates and commissions the equipment to ensure the accuracy of the inspection data. The intelligent inspection equipment is equipped with corresponding sensors and inspection modules according to different types of power equipment and inspection items. For example, for power transformers, it is equipped with winding resistance testers, no-load loss testers, partial discharge detectors, etc.

[0103] The inspection agency uses intelligent inspection equipment to inspect power equipment and collect various parameters of the equipment, such as performance indicators, quality parameters, etc. The collected equipment parameters are uploaded to the quality acceptance contract, and the quality acceptance contract automatically compares the inspection data with the standard parameters in the production plan. During the comparison process, precise algorithms and threshold judgments are adopted. For example, for the winding resistance deviation of a power transformer, it is stipulated that it shall not exceed ±2%. When the inspection data exceeds this threshold, it is judged as unqualified. For no-load loss, it shall not exceed ±5% of the standard value. Similarly, by comparing with the standard value, it is judged whether it is qualified. Finally, an acceptance report is generated according to the comparison result.

[0104] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The power equipment supply chain collaborative management system based on alliance chain is characterized by: include: Alliance chain network layer: a distributed network consisting of manufacturer nodes, supplier nodes, logistics nodes, detection nodes and power grid company nodes. Each node maintains the consistency of the ledger through a consensus algorithm. Smart contract execution layer: includes order processing contracts, logistics tracking contracts, quality acceptance contracts and settlement management contracts, supporting automated execution of business processes; Data management platform: It is equipped with distributed storage units, encrypted transmission units and permission control units to realize encrypted storage and hierarchical access to key data; Full-chain traceability system: Based on the blockchain chain structure, it records the full life cycle data from raw material procurement to equipment delivery, including timestamp verification module and hash verification module; Cross-chain interaction interface: adopts standardized API protocol to realize cross-chain data interaction with the State Grid asset system and third-party credit reporting platform.

2. The power equipment supply chain collaborative management system based on alliance chain according to claim 1 is characterized in that: The alliance chain network layer includes the following sub-units: Dynamic consensus subsystem: connected to the consensus algorithm, including a node weight evaluation module and a fault node detection module, dynamically adjusting node voting weights according to business volume; Time synchronization server: communicates with each node and adopts a precise time synchronization mechanism based on Beidou satellite timing.

3. The power equipment supply chain collaborative management system based on alliance chain according to claim 1 is characterized in that: The smart contract execution layer includes the following sub-units: Contract exception handling module: connected with the order processing contract, logistics tracking contract, and quality acceptance contract, setting up a timeout rollback mechanism and an abnormal state capture unit; when the detection data in the quality acceptance contract does not meet the standards, the module automatically triggers the return smart contract and generates a responsibility tracing report containing the device ID and detection time; AI prediction module: connected to the order processing contract, including a demand prediction unit and an inventory optimization unit, predicting supply chain demand based on historical data.

4. The power equipment supply chain collaborative management system based on alliance chain according to claim 1 is characterized in that: The distributed storage unit of the data management platform is built using the IPFS protocol, the encryption transmission unit uses the national secret SM2 algorithm to encrypt data, and the permission control unit implements role-based fine-grained access control.

5. The power equipment supply chain collaborative management system based on alliance chain according to claim 1 is characterized in that: The full-chain traceability system also includes: Multi-dimensional label module: connected with the chain structure, generates a digital label containing model parameters, production batch, and test report for each device, supporting rapid retrieval and verification.

6. The power equipment supply chain collaborative management system based on alliance chain according to claim 1 is characterized in that: The detection node is configured with: Intelligent testing equipment: communicates with the quality acceptance contract, automatically collects equipment parameters and uploads them to the blockchain.

7. The power equipment supply chain collaborative management system based on alliance chain according to claim 1 is characterized in that: The logistics node configuration includes: Beidou positioning module: connected to the logistics tracking contract, uploading the transportation status to the blockchain in real time.

8. The power equipment supply chain collaborative management system based on alliance chain according to claim 1 is characterized in that: The cross-chain interactive interface is configured with a production plan synchronization module, which is connected to the order processing contract and synchronizes the generated production plan to the State Grid asset system for filing through a standardized API.

9. A power equipment supply chain collaborative management method based on alliance chain, applied to the power equipment supply chain collaborative management system based on alliance chain according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Suppliers store evidence on the chain: The supplier uploads the raw material batch information to the chain through the hash verification module to generate a time-stamped evidence record; The full-chain traceability system generates an initial traceability node based on the evidence record. Step 2: Manufacturer order processing: The manufacturer node responds to the hash value of the evidence record in step 1, triggering the order processing contract; Call the AI ​​prediction module to analyze historical data and automatically match the supplier inventory based on the raw material information in step 1; Generate a production plan including raw material batch numbers and synchronize it to the alliance chain. Step 3: Logistics status tracking: After the production plan is synchronized, the logistics node initializes the transportation task based on the plan; Collect transportation location data in real time through Beidou positioning module; The logistics tracking contract synchronously updates the transportation status to the blockchain and triggers intelligent analysis of the transportation route. Step 4: Quality inspection and acceptance: After the logistics status is updated to "received", the testing agency obtains the transportation completion notification of step 3; Use intelligent testing equipment to collect equipment parameters and upload them to the quality acceptance contract; The contract automatically compares the test data with the standard parameters in the production plan in step 2 and generates an acceptance report. Step 5: Settlement and Delivery: After the acceptance report passes the hash verification, the settlement management contract triggers the payment process; The power grid company calls the acceptance report in step 4 and verifies the full life cycle data through the multi-dimensional tag module; After verification, the device is marked as "delivered" and the alliance chain status is updated.

10. The method for collaborative management of power equipment supply chain based on alliance chain according to claim 9 is characterized in that: In step 4, if the detection data does not meet the standard parameters of step 2, the exception handling module automatically triggers the return smart contract and simultaneously generates a responsibility traceability report to all nodes; the production plan generated in step 2 is synchronized to the State Grid asset system in real time through the production plan synchronization module.

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