Blockchain-based industrial device resource transaction method, system, device and medium

Abstract

The application relates to the technical field of data processing, and particularly provides an industrial equipment resource transaction method, system and device based on a blockchain and a medium, which comprises the following steps: collecting state data of industrial equipment and converting the state data into blockchain readable data; receiving a task request, extracting a required resource type from the task request; querying a candidate equipment set matched with the required resource type according to a pre-constructed equipment capability topology graph; screening a target equipment with state data reaching a preset standard from the candidate equipment set and uploading the target equipment to the blockchain; and verifying the target equipment based on a preset verification rule by the blockchain, and performing a resource transaction after the verification is passed. The application promotes the transformation of industrial resources from 'private closure' to'sharing economy', and provides a technical base for the digital transformation of the manufacturing industry.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing, and particularly relates to an industrial equipment resource transaction method and system based on a blockchain, an industrial equipment resource transaction device and a medium. BACKGROUND

[0002] Under the development trend of manufacturing cluster, the demand for equipment resource sharing between industrial enterprises is increasingly urgent. The traditional equipment rental mode has three major bottlenecks:

[0003] Lack of trust: the state of equipment (such as machine tool precision, AGV real-time position) depends on manual verification, which is easy to cause delivery disputes;

[0004] Low efficiency of matching: resource supply and demand information is isolated in each enterprise system, and the cross-subject scheduling response delay is more than 48 hours;

[0005] Complex settlement: multi-link manual reconciliation leads to a transaction cost of 15%-20% of the equipment usage fee.

[0006] The existing technology attempts to centrally manage resources through a cloud platform, but exposes the centralized drawbacks: data tampering risks (such as false reporting of equipment idle rate), sensitive process parameter leakage hazards, and platform commissioning to push up transaction costs.

[0007] Although the blockchain technology can build a trusted environment, the high-frequency state data (more than 10+ per second) of industrial equipment and the complex resource matching logic will directly lead to a sharp increase in public chain Gas fees or alliance chain throughput overload. Therefore, it is urgent to develop a decentralized transaction method that takes into account real-time, credibility and economy. SUMMARY

[0008] In view of the above deficiencies of the prior art, the application provides an industrial equipment resource transaction method, system, device and medium based on a blockchain to solve the above technical problems.

[0009] In a first aspect, the application provides an industrial equipment resource transaction method based on a blockchain, comprising:

[0010] Collecting state data of industrial equipment and converting the state data into blockchain readable data;

[0011] Receiving a task request and extracting a demand resource type from the task request;

[0012] Querying a candidate equipment set matched with the demand resource type according to a pre-constructed equipment capability topology graph;

[0013] Screening a target equipment with state data reaching a preset standard from the candidate equipment set and uploading the target equipment to the blockchain;

[0014] The blockchain verifies the target device based on preset verification rules, and performs a resource transaction after verification.

[0015] In an optional implementation, state data of the industrial equipment is collected, including:

[0016] Raw state data of the industrial equipment is collected by using a vibration sensor, a temperature sensor, a current sensor, and a visual sensor.

[0017] Feature data is extracted from the raw state data by using an edge device, and a state of the industrial equipment is identified based on the feature data, to obtain state data.

[0018] In an optional implementation, the state data is converted into blockchain-readable data, including:

[0019] The state data is encapsulated as a standardized declaration, and the standardized declaration is uploaded to the blockchain.

[0020] In an optional implementation, the device capability topology graph includes:

[0021] A node is used to represent each registered industrial equipment.

[0022] An attribute is used to describe an industrial equipment attribute of the node.

[0023] An edge is used to represent a potential collaboration relationship or a physical connection relationship between industrial equipment.

[0024] A location relationship is used to represent a geographical position distance between industrial equipment.

[0025] In an optional implementation, a candidate device set matching the demand resource type is queried according to a pre-constructed device capability topology graph, including:

[0026] Target nodes of the demand resource type are searched from attribute data, and associated nodes having associated edges with the target nodes are obtained.

[0027] The target nodes and the associated nodes are output as the candidate device set.

[0028] In an optional implementation, a target device having state data reaching a preset standard is filtered from the candidate device set, including:

[0029] A task amount is parsed from the task request, and a corresponding standard state is determined based on the task amount.

[0030] The target device having state data reaching the preset standard is filtered from the candidate device set according to the state data of each industrial equipment in the candidate device set and the standard state.

[0031] In an optional embodiment, the blockchain verifies the target device based on preset verification rules, and executes the resource transaction after verification.

[0032] The smart contract of the blockchain performs compliance verification on the target device according to preset verification rules; the verification rules include machining precision, delivery time limit and geographic location.

[0033] After confirming that the target device passes the verification, a resource transaction task is generated and the resource transaction task is executed.

[0034] In a second aspect, the present application provides an industrial equipment resource transaction system based on a blockchain, comprising:

[0035] A data acquisition module is configured to acquire state data of the industrial equipment and convert the state data into blockchain-readable data.

[0036] A task analysis module is configured to receive a task request and extract a required resource type from the task request.

[0037] A first screening module is configured to query a candidate equipment set matching the required resource type according to a pre-constructed equipment capability topology graph.

[0038] A second screening module is configured to screen a target device whose state data meets a preset standard from the candidate equipment set and upload the target device to the blockchain.

[0039] An on-chain transaction module is configured to verify the target device based on preset verification rules by the blockchain, and execute the resource transaction after verification.

[0040] In a third aspect, a device is provided, comprising:

[0041] A memory is configured to store an industrial equipment resource transaction program based on a blockchain.

[0042] A processor is configured to implement the steps of the industrial equipment resource transaction method based on a blockchain when the industrial equipment resource transaction program based on a blockchain is executed.

[0043] In a fourth aspect, a computer readable medium is provided, and the medium stores an industrial equipment resource transaction program based on a blockchain. When the industrial equipment resource transaction program based on a blockchain is executed by a processor, the steps of the industrial equipment resource transaction method based on a blockchain provided in the first aspect are implemented.

[0044] The beneficial effects of this invention are as follows: the blockchain-based industrial equipment resource trading method, system, equipment, and medium provided by this invention solve the cross-enterprise mutual trust problem and reduce the default rate through on-chain verifiable evidence storage of equipment status (such as on-chain signature of machine tool precision); the off-chain acceleration mechanism based on the equipment capability topology graph enables second-level resource retrieval, improving matching efficiency and utilization of idle equipment; and blockchain-based transactions enhance transaction security and transparency. This invention promotes the transformation of industrial resources from "private and closed" to "sharing economy," providing a technological foundation for the digital transformation of manufacturing. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic flowchart of a method according to an embodiment of the present invention.

[0047] Figure 2 This is a schematic block diagram of a system according to an embodiment of the present invention.

[0048] Figure 3 This is a schematic diagram of the structure of a device provided in an embodiment of the present invention. Detailed Implementation

[0049] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0051] The blockchain-based industrial equipment resource trading method provided in this embodiment of the invention is executed by a computer device, and correspondingly, the blockchain-based industrial equipment resource trading system runs on the computer device.

[0052] Figure 1 This is a schematic flowchart illustrating a method according to an embodiment of the present invention. Wherein,Figure 1 The implementing entity can be a blockchain-based industrial equipment resource trading system. Depending on different needs, the order of steps in this flowchart can be changed, and some can be omitted.

[0053] like Figure 1 As shown, the method includes:

[0054] S1. Collect status data of industrial equipment and convert the status data into blockchain-readable data;

[0055] S2. Receive a task request and extract the required resource type from the task request;

[0056] S3. Query the set of candidate devices that match the required resource type based on the pre-built device capability topology map;

[0057] S4. Select target devices whose status data meets the preset standards from the candidate device set, and upload the target devices to the blockchain;

[0058] S5. The blockchain verifies the target device based on preset verification rules, and executes resource transactions after the verification is successful.

[0059] In one embodiment of the present invention, based on step S1, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0060] S101. Collect raw state data of industrial equipment using vibration sensors, temperature sensors, current sensors and vision sensors.

[0061] To address the complex operating scenarios of industrial equipment, a multi-physics field sensing and monitoring network is constructed to achieve three-dimensional perception of the equipment's status throughout its entire lifecycle. The vibration sensor employs a piezoelectric accelerometer, deployed on key rotating components of the equipment (such as bearing housings and gearboxes). Its sampling frequency is set to 2kHz-10kHz, capturing vibration signals within the 1Hz-5kHz frequency band. A charge amplifier converts the weak vibration signal into a standard 4-20mA current signal. The temperature sensor uses a distributed fiber optic grating sensor, utilizing the Bragg reflection principle to measure temperatures from -40℃ to 120℃, with a spatial resolution of 1m and a measurement accuracy of ±0.5℃, making it particularly suitable for continuous monitoring under high-temperature conditions. The current sensor uses a Hall effect non-contact measuring device to acquire the effective current value of the equipment's main circuit in real time, with a sampling rate of 500Hz and a dynamic measurement range of 0-500A, ensuring accurate capture of motor load changes. The vision sensor uses an industrial CCD camera equipped with an 8mm fixed-focus lens and an LED ring light source, acquiring images of the equipment's surface at a frame rate of 25fps, with a resolution of 2048×1536 pixels. Image grayscale preprocessing eliminates ambient light interference.

[0062] The sensor network adopts a time-division multiplexing (TDM) data transmission mechanism. Each sensor node transmits raw data to the edge gateway via industrial Ethernet (PROFINET protocol). During transmission, CRC32 checksum is used to ensure data integrity. At the same time, each sensor node has a built-in unique identifier (UUID) so that the raw data carries metadata information such as device location, sensor type and installation orientation, providing a basis for subsequent data traceability.

[0063] S102. Use edge devices to extract feature data from the original state data, and identify the state of the industrial equipment based on the feature data to obtain state data.

[0064] Edge computing nodes are built on a heterogeneous computing architecture (CPU+FPGA) to construct data processing units, employing differentiated feature extraction algorithms for different types of raw data. For vibration signals, the time-domain signal is converted to the frequency domain using Fast Fourier Transform (FFT), and feature parameters such as peak frequency, root mean square (RMS) value, and kurtosis coefficient are extracted. The kurtosis coefficient can effectively characterize the impact characteristics of early faults. Temperature data is extracted using a sliding window method (window size set to 5 minutes) to extract mean, standard deviation, and temperature change rate features, and trend analysis is used to identify the thermal aging trend of the equipment. Current signals are obtained by wavelet packet decomposition to obtain the energy proportion of different frequency bands, with a focus on the 50Hz fundamental component and harmonic distortion rate to determine the load balance of the motor. Visual images are extracted using a convolutional neural network (CNN) to extract texture features, color deviation, and geometric deformation parameters of the equipment surface, and an improved YOLO algorithm is used to achieve real-time detection of surface defects.

[0065] In the state recognition stage, a multi-source data fusion model is constructed, employing the DS evidence theory to perform decision-level fusion of features from various sensors. First, a fuzzy classification system for equipment state is established, dividing it into four levels: normal operation, minor degradation, moderate fault, and severe fault. Each level corresponds to a specific feature threshold range (e.g., the normal range for vibration kurtosis coefficient is 2-3, and a value exceeding 5 indicates a severe fault). During the fusion process, dynamic weights are assigned to different sensors (calculated using the entropy weight method based on feature importance). The final state recognition result is output through a confidence function, with a recognition accuracy exceeding 95% and a recognition latency controlled within 100ms.

[0066] S103. Encapsulate the state data into a standardized statement and upload the standardized statement to the blockchain.

[0067] The standardized declaration of status data is structured and encapsulated using Extensible Markup Language (XML). The declaration includes a unique device code (generated based on the GB / T16679 standard), status identification results (containing four levels of quantized probability values), a feature data digest (generated using the SHA-256 hash algorithm), an edge node digital signature (based on ECC elliptic curve cryptography), and a declaration generation timestamp (accurate to the millisecond level). To achieve cross-platform compatibility, the declaration format follows the device description specifications formulated by the Industrial Internet Consortium (IIC), defining strict data type constraints and node nesting rules through XMLSchema to ensure correct parsing by blockchain nodes from different manufacturers.

[0068] Before uploading to the blockchain, a fragmented upload mechanism is used to process large-scale state data. Standardized claims are divided into fixed-size data packets (e.g., 1MB / slice), each containing a fragment index, total number of fragments, and fragment hash value. The upload process is implemented through the blockchain's P2P network, using a relay transmission mode: edge nodes first send data fragments to the nearest full blockchain node, which verifies the fragment integrity and then forwards them to other nodes until all nodes have completed data synchronization. To improve upload efficiency, a data compression algorithm (such as LZ77) is introduced to reduce data packet size, and a flow control mechanism is used to avoid network congestion.

[0069] The blockchain storage employs a hybrid storage architecture. Core information from standardized declarations (such as state level and digital signature) is written to the main chain, while complete feature data and original data hash values ​​are stored off-chain in an distributed file system (IPFS). The main chain only retains the index hash of the file system, enabling efficient association and verification of on-chain and off-chain data. After uploading, the blockchain node returns confirmation information including block height and transaction ID. Edge devices associate this information with their local state data, forming a complete data traceability chain.

[0070] In one embodiment of the present invention, based on step S2, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0071] The receiving layer adopts a RESTful API interface architecture to receive task requests from the industrial internet platform. The request data packet is encapsulated in JSON format and includes the requester's digital certificate, resource requirement description, quality of service (QoS) parameters (such as response latency threshold and data trustworthiness requirements), and timestamp information. During the parsing process, an entity recognition algorithm (based on a BERT pre-trained model) from the requirement description field is used in natural language processing (NLP) to extract resource type features and construct a multi-dimensional requirement vector.

[0072] For example, for a request for "high-precision CNC machine tool processing resources," the extracted feature vector includes quantitative indicators such as equipment type (CNC machine tool), processing accuracy (≤0.01mm), spindle speed (≥3000r / min), and workload (≤70%). Simultaneously, an ontology is introduced to construct an industrial resource type classification system, matching the extracted demand type with a pre-defined resource classification tree to achieve standardized mapping of demand resource types and ensure semantic consistency in subsequent queries. After parsing, the request data packet undergoes integrity verification (based on SHA-256 hash verification). If the verification fails, a response containing an error code is returned.

[0073] In one embodiment of the present invention, based on step S3, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0074] The equipment capability topology includes: nodes, representing each registered industrial device; attributes, describing the industrial device attributes of the nodes; edges, representing potential collaborative relationships or physical connections between industrial devices; and location relationships, representing the geographical distance between industrial devices.

[0075] Specifically, the device capability topology graph is constructed using a weighted directed graph model, and the technical specifications of its core elements are as follows:

[0076] Node Design: Each node uniquely corresponds to a registered industrial device. The node ID is generated using a hash combination (SHA-3 algorithm) of the device's MAC address and the blockchain account's public key, ensuring global uniqueness. The node attribute system includes two categories: basic attributes and dynamic attributes. Basic attributes are static parameters, covering device model (e.g., "FANUCM-710iC"), rated parameters (power, speed, processing accuracy, etc.), manufacturer, network access time, and hardware configuration (sensor type, computing power level). Dynamic attributes are real-time updated parameters, including current load rate, average failure rate over the past 72 hours, resource response speed (average processing time for historical requests), and trustworthiness score (cumulative evaluation based on blockchain transaction records, ranging from 0 to 100 points).

[0077] Edge weighting mechanism: The existence of an edge is determined by the possibility of collaboration between devices, including physical connections (such as upstream and downstream devices directly connected via an industrial bus) and logical collaboration relationships (such as groups of devices that have historically completed collaborative production tasks). Edge weights are represented by a three-dimensional vector: collaboration efficiency weight (based on the completion quality score of historical collaborative tasks, 0-1), communication latency weight (the average latency of data transmission between devices, in milliseconds), and resource complementarity weight (calculated using cosine similarity to determine the matching degree of device capability vectors, 0-1). Weight values ​​are updated every 24 hours based on the latest collaboration data, and an exponential moving average (EMA) method is used to smooth short-term fluctuations.

[0078] Location relationship quantification: A dual positioning method using latitude and longitude coordinates and the workshop coordinate system is employed. The actual geographical distance (in meters) between equipment is calculated using the Haversine formula, and a distance level classification (level 1-5, with higher levels indicating closer distances) is established based on the physical zoning of the industrial plant (e.g., processing area, assembly area, storage area). The location relationship data is corrected in real time through the plant's IoT positioning system (e.g., UWB indoor positioning) to ensure that the distance error does not exceed ±0.5 meters.

[0079] S301. Search attribute data for target nodes of the required resource type, and obtain associated nodes that have associated edges with the target node.

[0080] Based on the feature vector of the resource type extracted from the task request (e.g., features corresponding to "high-precision turning" include spindle speed ≥ 3000 rpm and positioning accuracy ≤ 0.005 mm), a depth-first search (DFS) based on attribute matching is performed in the topology graph. A heuristic pruning strategy is introduced during the search process: when the matching degree between a node's basic attributes and the requirement vector is lower than a preset threshold (e.g., 60%), the branch search is terminated; for dynamic attributes, hard constraints are set (e.g., load rate ≤ 50%, confidence score ≥ 80 points), and only nodes that satisfy all constraints are retained in the candidate pool.

[0081] To improve search efficiency, a hierarchical indexing mechanism is adopted: equipment is categorized by industry type (e.g., machine tools, robots, logistics equipment) into a primary index, and by core capability parameters (e.g., machining accuracy, load capacity) into a secondary index. Potential matching nodes are quickly located through this index. Search results are sorted by matching degree, calculated using a weighted Euclidean distance formula. The weight of each attribute is dynamically adjusted based on priority requirements (e.g., in precision machining scenarios, the weight of positioning accuracy is set to 0.3, higher than other parameters).

[0082] Associative node acquisition mechanism: For the selected target node, all directly connected associated nodes (i.e., nodes with edge connections) are obtained by traversing its adjacency matrix. The selection of associated nodes must meet the following collaboration feasibility conditions: collaboration efficiency weight ≥ 0.7, communication latency ≤ 50ms, and location distance level ≤ 3. For devices with indirect connections (via one intermediate node), if the sum of their collaborative path weights with the target node exceeds a preset threshold (e.g., 1.5), they are included in the extended associated node set.

[0083] During the acquisition process, the optimal cooperative path between the associated nodes and the target node is calculated using the shortest path algorithm (Dijkstra's algorithm) in graph theory, and the information of intermediate nodes on the path is recorded to provide a topological basis for subsequent resource scheduling. The final set of associated nodes needs to be checked for redundancy, removing nodes with highly overlapping capabilities with the target node (similarity ≥ 0.8), and retaining devices with strong resource complementarity.

[0084] S302. Output the target node and associated nodes as a set of candidate devices.

[0085] The output results are encapsulated using a standardized data structure, including device node ID, attribute matching degree, associated path weight, location distance level, and dynamic status label (such as "idle" or "to be maintained"), and the integrity of the set information is ensured through digital signature.

[0086] In one embodiment of the present invention, based on step S4, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0087] S401. Parse the task quantity from the task request and determine the corresponding standard state based on the task quantity.

[0088] A multidimensional analytical model for task quantity is constructed by combining semantic parsing and parameter extraction. For discrete manufacturing tasks, the task quantity is parsed into a three-dimensional vector representing the processing batch size (pieces), process complexity (a comprehensive score based on the number of processing features and accuracy level), and time window (task start / end timestamps). For process industry tasks, it is parsed into a three-dimensional vector representing the processing volume (units of mass / volume), process parameter range (e.g., temperature range, pressure threshold), and continuity requirements (allowed number of interruptions). A task ontology library is introduced during the parsing process, and semantic similarity matching (based on the Word2Vec word vector model) is used to standardize the task description, ensuring that similar tasks with different expressions can be mapped to a unified parameter space.

[0089] Standard State Dynamic Generation Mechanism: Based on the mapping relationship between task quantity parameters and equipment capabilities, a standard state space model is constructed. First, a task-equipment capability matrix is ​​established, where matrix elements represent the equipment state parameter thresholds required to complete a unit of task quantity (e.g., requiring spindle vibration ≤5μm / s and temperature fluctuation ≤2℃ for machining one precision part). Standard state vectors are generated through matrix operations. Vector dimensions include equipment load rate (e.g., 70%-80%), operational stability (e.g., vibration amplitude fluctuation range), energy consumption indicators (e.g., unit energy consumption ≤0.5kWh / piece), and process parameter accuracy (e.g., position accuracy ≤±0.01mm). The generation of standard state vectors introduces a flexible boundary mechanism, setting a ±5% tolerance range for non-critical parameters to improve selection flexibility.

[0090] S402. Based on the status data of each industrial device in the candidate device set and the standard status, select target devices whose status data meets the preset standard from the candidate device set.

[0091] A multi-dimensional status assessment index system is constructed, comprising 5 primary indicators and 18 secondary indicators. The operational status dimension includes real-time load rate, temperature anomaly index (deviation rate between current temperature and historical average), and vibration intensity level (based on ISO 10816 standard); the reliability dimension includes predicted MTBF (Mean Time Between Failures), remaining life of key components (estimated through a degradation model), and fault warning level; the quality assurance dimension includes machining accuracy stability (CPK process capability index), product qualification rate trend, and process parameter fluctuation coefficient; the energy efficiency dimension includes unit energy consumption ratio, power factor, and energy recovery efficiency; and the collaborative adaptation dimension includes communication protocol compatibility, data interface matching degree, and task response speed.

[0092] Dynamic weight allocation algorithm: A subjective and objective weighting method combining the Analytic Hierarchy Process (AHP) and entropy weighting is employed to dynamically adjust indicator weights based on task type. For precision machining tasks, the weight of the quality assurance dimension is increased to 0.4, and the weight of the reliability dimension is 0.3; for energy-intensive tasks, the weight of the energy efficiency dimension is increased to 0.5, and the weight of operational status is 0.3. The weight calculation cycle is synchronized with the frequency of task type changes, and an adaptive weight update mechanism ensures the sensitivity of the evaluation system.

[0093] Screening Decision Model: An uncertainty decision model is constructed based on cloud model theory, comparing and evaluating equipment status data with standard states. First, a forward cloud generator converts standard state parameters into cloud droplets (expectation Ex, entropy En, hyperentropy He), forming a standard state cloud model. Then, actual equipment status data is mapped to this cloud model, and membership function values ​​are calculated to obtain the compliance degree of each indicator. A weighted summation method is used to calculate the overall compliance degree, setting 0.8 as the screening threshold; equipment with a compliance degree ≥ 0.8 enters the target equipment set. For critical tasks, a redundant screening mechanism is introduced, additionally reserving the top 3 equipment with compliance degrees ≥ 0.7 as backup resources.

[0094] In one embodiment of the present invention, based on step S5, a possible embodiment will be given below, and its specific implementation will be described in a non-limiting manner.

[0095] S501. The blockchain's smart contract performs compliance verification on the target device according to preset verification rules; the verification rules include processing accuracy, delivery time limit, and geographical location.

[0096] Multidimensional verification rule modeling: A formal language (such as Solidity) is used to construct the verification rule system, transforming parameters such as machining accuracy, delivery time, and geographical location into computable logical expressions. Machining accuracy verification rules are defined as triples.<P,T,D> Where P is the set of accuracy parameters (e.g., dimensional tolerance ±0.01mm, surface roughness Ra≤0.8μm), T is the test method (e.g., coordinate measuring machine inspection frequency ≥2 times / hour), and D is the judgment criterion (CPK process capability index ≥1.33); the delivery time limit rule adopts timestamp constraints, defined as a binary tuple.<St,Et> These represent the task start and end times, respectively, with time consistency ensured through a blockchain timestamp service; the geolocation rule is defined as a spatial coordinate range.<C,R> Where C is the mission execution center point (latitude and longitude coordinates), and R is the effective radius (e.g., ≤5km). Location verification is achieved by combining GPS positioning with blockchain evidence storage.

[0097] Rule Execution Engine Design: A dedicated verification engine is designed based on the Ethereum Virtual Machine (EVM) extension, supporting parallel processing of complex rules. The engine adopts a rule chain model, dividing the verification process into three stages: preprocessing, core verification, and result aggregation. The preprocessing stage performs format verification and integrity checks on device status data. The core verification stage executes parallel verification of processing accuracy (calling the on-chain metrology standard contract), delivery time limit (comparing the device's available time window with the task time constraint), and geolocation (calculating the spatial distance between the device's location and the task's center point). The result aggregation stage uses threshold logic; when the pass rate of verification in all three dimensions reaches 80% or higher, the device is deemed to have passed compliance verification.

[0098] Machining accuracy verification mechanism: A verification method combining digital twins and blockchain is adopted. Before executing a task, the equipment must upload a digital twin model of its machining capabilities (including kinematic parameters, dynamic characteristics, thermal deformation coefficient, etc.), and the model parameters are stored on the blockchain using SHA-256 hashing. During task execution, machining process data (such as toolpath and spindle load) is collected in real time. By comparing the actual machining data with the simulation results of the digital twin model, the confidence interval of machining accuracy is calculated. The smart contract calls the on-chain standard contract authorized by the Metrology Institute to compare the calculation results with the accuracy parameters in the verification rules and generate a verification report including uncertainty assessment.

[0099] Delivery time limit verification process: A task timeline management system is built based on a distributed timestamp service (such as an external time source provided by Chainlink). Devices submit available time window declarations on-chain, including start time, end time, and capacity coefficient (processing volume per unit time). The smart contract calculates the theoretical completion time based on the task volume and device capacity coefficient, and compares it with the delivery time limit specified in the rules. A flexible time window mechanism is introduced, allowing ±10% time margin to cope with unforeseen production fluctuations. For cross-enterprise collaborative tasks, a distributed clock synchronization algorithm (such as the GTI protocol) is used to ensure the consistency of time bases among all participants.

[0100] Geographic location verification method: A hybrid positioning verification strategy is adopted, combining GPS positioning, Wi-Fi fingerprint recognition, and blockchain trusted node verification. Devices periodically submit location proofs to the blockchain, which include GPS coordinates, location acquisition timestamps, and digital signatures from at least three trusted anchor points (such as fixed IoT nodes within the factory area). The smart contract first verifies the integrity of the location proof, then calculates the Haversine distance between the device's location and the task execution center point to determine if it is within the effective radius. To prevent location fraud, a spatiotemporal consistency check is introduced to analyze the continuity and rationality of the device's movement trajectory, issuing warnings for abnormal, leaping movements.

[0101] S502. Confirm that the target device passes verification, generate a resource transaction task, and execute the resource transaction task.

[0102] Formal definition of transaction tasks: A blockchain transaction proposal mechanism is used to encapsulate verified target device information into a standardized transaction structure. The transaction proposal includes elements such as task description (e.g., processing content, quality requirements), resource requirements (equipment type, quantity), economic parameters (unit price, payment method), time constraints (start / deadline), and quality assurance clauses (e.g., handling of defective products). Each element is digitally signed to ensure authenticity, and transaction data is organized using a Merkle tree structure to generate a transaction root hash for on-chain storage.

[0103] Smart contract execution process: Efficient off-chain transaction execution is achieved using StateChannel technology, while ensuring on-chain security. Both parties first lock transaction margins on-chain, then exchange task execution data (such as processing progress reports and quality inspection data) through the StateChannel in multiple rounds. Each round of data exchange is confirmed with a digital signature, forming an immutable execution record. When the task completion reaches a preset milestone (e.g., 50%, 100%), the on-chain settlement contract is triggered, automatically calculating the amount due based on the execution record and transferring funds. The entire process employs zero-knowledge proof technology to protect commercially sensitive information, only publicly displaying necessary settlement results on the blockchain.

[0104] Dispute Resolution Mechanism: A decentralized arbitration system based on blockchain is constructed. When the two parties to a transaction disagree on the execution result, the arbitration process is automatically triggered. The system first invokes the dispute resolution clauses in the smart contract and makes a preliminary judgment according to preset rules. If a consensus cannot be reached, an expert arbitration mechanism is initiated, randomly selecting 3-5 experts from a pool of industry experts registered on the blockchain to form an arbitration committee. The experts conduct independent evaluations by accessing the execution records stored on the blockchain. A decision is valid if the vote exceeds 2 / 3, and the decision is automatically written to the blockchain and executed.

[0105] In some embodiments, the blockchain-based industrial equipment resource trading system may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the blockchain-based industrial equipment resource trading system may be stored in the memory of a computer device and executed by at least one processor to perform (see details). Figure 1 (Description) Functionality of industrial equipment resource trading based on blockchain.

[0106] In this embodiment, the blockchain-based industrial equipment resource trading system can be divided into multiple functional modules according to its functions, such as... Figure 2As shown. The module referred to in this invention is a series of computer program segments that can be executed by at least one processor and perform a fixed function, and is stored in memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0107] The data acquisition module is used to collect status data of industrial equipment and convert the status data into blockchain-readable data.

[0108] The task parsing module is used to receive task requests and extract the required resource types from the task requests;

[0109] The first filtering module is used to query a set of candidate devices that match the required resource type based on a pre-built device capability topology map;

[0110] The second filtering module is used to filter out target devices whose status data meets the preset standards from the candidate device set and upload the target devices to the blockchain;

[0111] The on-chain transaction module is used by the blockchain to verify the target device based on preset verification rules, and execute resource transactions after successful verification.

[0112] Figure 3 The blockchain-based industrial equipment resource trading method provided in this application embodiment can be applied to equipment. Those skilled in the art will understand that the equipment structure involved in the embodiments of this invention does not constitute a limitation on the equipment. Equipment may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, equipment includes, but is not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Equipment may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0113] The device 300 may include a processor 310, a memory 320, and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will understand that the server structure shown in the figure does not constitute a limitation of the present invention. It may be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0114] The memory 320 can be used to store execution instructions of the processor 310. The memory 320 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the device 300 is able to perform some or all of the steps in the above method embodiments.

[0115] The processor 310 serves as the control center of the storage device, connecting various parts of the electronic device via various interfaces and lines. It executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 310 may consist only of a central processing unit (CPU). In this embodiment of the invention, the CPU may have a single processing core or include multiple processing cores.

[0116] The communication unit 330 is used to establish a communication channel, enabling the storage device to communicate with other devices. It can receive user data sent by other devices or send user data to other devices.

[0117] The present invention also provides a computer medium, wherein the computer medium may store a program, which, when executed, may include some or all of the steps provided in the embodiments of the present invention. The medium may be a magnetic disk, an optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0118] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a medium such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other medium capable of storing program code. It includes several instructions to cause a computer device (which may be a personal computer, a server, or a second device, network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0119] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

[0120] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or modules may be electrical, mechanical, or other forms.

[0121] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0123] Although the present invention has been described in detail with reference to the accompanying drawings and preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made to the embodiments of the present invention by those skilled in the art without departing from the spirit and essence of the invention, and such modifications or substitutions should all be within the scope of the present invention. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should also be covered within the protection scope of the present invention.

Claims

1. A blockchain-based method for trading industrial equipment resources, characterized in that, include: Collect status data from industrial equipment and convert the status data into blockchain-readable data; Receive a task request and extract the required resource type from the task request; Based on the pre-built equipment capability topology map, query the set of candidate devices that match the required resource type; Select target devices whose status data meets the preset standards from the candidate device set, and upload the target devices to the blockchain; Blockchain verifies target devices based on preset verification rules, and executes resource transactions after successful verification; Converting the state data into blockchain-readable data includes: The state data is encapsulated into a standardized statement, and the standardized statement is uploaded to the blockchain; The device capability topology diagram includes: Nodes are used to represent each registered industrial device; Attributes, used to describe the industrial equipment attributes of a node; Edges are used to represent potential collaborative or physical connections between industrial devices; Locational relationship, used to represent the geographical distance between industrial equipment; Based on a pre-built device capability topology map, a set of candidate devices matching the required resource type is queried, including: Search attribute data for target nodes of the required resource type, and obtain associated nodes that have associated edges with the target node; Output the target node and associated nodes as a set of candidate devices; Target devices whose status data meets preset standards are selected from the candidate device set, including: The task quantity is parsed from the task request, and the corresponding standard state is determined based on the task quantity; Based on the status data of each industrial device in the candidate device set and the standard status, target devices whose status data meets the preset standard are selected from the candidate device set.

2. The method according to claim 1, characterized in that, Collect status data from industrial equipment, including: Raw state data of industrial equipment is collected using vibration sensors, temperature sensors, current sensors, and vision sensors. By using edge devices to extract feature data from the raw state data, and based on the feature data to identify the state of the industrial equipment, state data is obtained.

3. The method according to claim 1, characterized in that, Blockchain verifies target devices based on preset verification rules. Once verification is successful, resource transactions are executed, including: The blockchain's smart contract performs compliance verification on the target device according to preset verification rules; these verification rules include processing accuracy, delivery time limit, and geographical location. Once the target device is verified, a resource transaction task is generated and executed.

4. A blockchain-based industrial equipment resource trading system, characterized in that, include: The data acquisition module is used to collect status data of industrial equipment and convert the status data into blockchain-readable data. The task parsing module is used to receive task requests and extract the required resource types from the task requests; The first filtering module is used to query a set of candidate devices that match the required resource type based on a pre-built device capability topology map; The second filtering module is used to filter out target devices whose status data meets the preset standards from the candidate device set and upload the target devices to the blockchain; The on-chain transaction module is used by the blockchain to verify the target device based on preset verification rules, and execute resource transactions after the verification is successful. Converting the state data into blockchain-readable data includes: The state data is encapsulated into a standardized statement, and the standardized statement is uploaded to the blockchain; The device capability topology diagram includes: Nodes are used to represent each registered industrial device; Attributes, used to describe the industrial equipment attributes of a node; Edges are used to represent potential collaborative or physical connections between industrial devices; Locational relationship, used to represent the geographical distance between industrial equipment; Based on a pre-built device capability topology map, a set of candidate devices matching the required resource type is queried, including: Search attribute data for target nodes of the required resource type, and obtain associated nodes that have associated edges with the target node; Output the target node and associated nodes as a set of candidate devices; Target devices whose status data meets preset standards are selected from the candidate device set, including: The task quantity is parsed from the task request, and the corresponding standard state is determined based on the task quantity; Based on the status data of each industrial device in the candidate device set and the standard status, target devices whose status data meets the preset standard are selected from the candidate device set.

5. A blockchain-based industrial equipment resource trading device, characterized in that, include: A storage device for storing blockchain-based industrial equipment resource transaction procedures; A processor is configured to implement the steps of the blockchain-based industrial equipment resource trading method as described in any one of claims 1-3 when executing the blockchain-based industrial equipment resource trading program.

6. A computer-readable medium storing a computer program, characterized in that, The readable medium stores a blockchain-based industrial equipment resource trading program, which, when executed by a processor, implements the steps of the blockchain-based industrial equipment resource trading method as described in any one of claims 1-3.

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