Block chain-based electronic truck scale transaction data tracing method and system

Abstract

The invention specifically relates to an electronic truck scale transaction data tracing method and system based on a block chain, and relates to the technical field of electronic truck scale metering and block chains, and the system comprises a dynamic data collection and self-adaptive real-time calibration module; a digital twin error modeling and measurement uncertainty dynamic evaluation module; a block chain evidence storage and multi-dimensional data security encapsulation module; and an automatic verification and strategy optimization module based on the smart contract. According to the invention, the multi-dimensional technology fusion realizes the leap of the measurement precision quality, the distributed intelligent sensor network is combined with the dual-mode communication to guarantee the stable data transmission, and the distributed balance weight data synthesis unit adopts the weighted least square method to eliminate the unbalance loading error; and the digital twin model integrates full life cycle information, quantizes error source contribution through Monte Carlo simulation, and outputs a precise uncertainty report.

Description

Technical Field

[0001] This invention relates to the fields of electronic truck scale measurement and blockchain technology, and in particular to a blockchain-based method and system for tracing transaction data of electronic truck scales. Background Technology

[0002] In the trade of bulk commodities such as coal, grain, and steel, electronic truck scales, as core weighing equipment, directly determine the fairness of transactions based on the accuracy and reliability of their weighing data. However, existing electronic truck scale systems suffer from several pain points: The data acquisition process is susceptible to environmental interference (temperature, vibration, etc.) and vehicle off-center loading. Traditional fixed compensation methods are not accurate enough, and performance degradation problems such as sensor drift and mechanical fatigue are difficult to detect in a timely manner. Transaction data is mostly stored on centralized servers, which are at risk of being tampered with or lost. In the event of a trade dispute, there is a lack of a credible chain of evidence that can be recognized by the judiciary. Data verification relies on manual review, which is inefficient, and the measurement uncertainty is not deeply linked to the transaction effectiveness mechanism, which can easily lead to performance disputes; The traceability service is limited and cannot provide transparent querying of the entire process from data collection and calibration status to transaction confirmation.

[0003] Blockchain technology, with its decentralized, immutable, and traceable characteristics, provides technical support for solving the problem of trustworthy transaction data; while digital twin technology can map the state of physical devices through virtual simulation, enabling accurate modeling and prediction of errors.

[0004] Therefore, a blockchain-based method and system for tracing electronic truck scale transaction data is proposed to address the aforementioned issues. Summary of the Invention

[0005] The purpose of this invention is to provide a blockchain-based method and system for tracing transaction data of electronic truck scales in order to solve the above-mentioned problems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A blockchain-based electronic truck scale transaction data traceability system includes: The dynamic data acquisition and adaptive real-time calibration module is configured to acquire dynamic weighing data through a distributed intelligent sensor network and an embedded real-time self-calibration engine. The digital twin error modeling and dynamic evaluation module for measurement uncertainty is configured to build a parameterized digital twin model, perform personalized simulations for each weighing, and realize dynamic evaluation of measurement uncertainty and prediction of performance degradation. The blockchain-based evidence storage and multidimensional data security encapsulation module is configured to use a consortium blockchain and national cryptographic algorithms to encapsulate multidimensional weighing data into trusted data packets and solidify hashes. The automatic verification and strategy optimization module based on smart contracts is configured to deploy rule-based smart contract groups to automatically verify data compliance and transaction validity, and optimize measurement strategies through big data analysis.

[0007] Preferably, the dynamic data acquisition and adaptive real-time calibration module specifically includes: Connect and manage a network consisting of multiple digital load cells; It receives data from all sensors in real time and synthesizes the final weight value using the optimal estimation algorithm based on the relative spatial position distribution matrix. It integrates environmental sensors for temperature, humidity, vibration, and tilt angle to monitor interference factors at the weighing site in real time; The built-in environment-error mapping database dynamically calls compensation coefficients based on real-time environment data to correct the original weight signal; The self-calibration engine performs online power-on self-tests; dynamic adaptive filtering; and calibration using micro-weights or excitation responses.

[0008] Preferably, the digital twin error modeling and measurement uncertainty dynamic evaluation module specifically includes: Based on finite element analysis, multibody dynamics, and sensor characteristic curves, a parametric digital model of the weighing instrument's mechanical structure, sensor group, and foundation settlement is established. For each weighing task, real-time data and vehicle characteristic information are received, simulated in a digital twin, and quantitatively analyzed to determine the contribution of each error source to the final measurement result under the current specific vehicle, environment, and weighing instrument condition. Finally, a personalized, real-time updated extended measurement uncertainty report for this measurement is output.

[0009] Preferably, the blockchain-based evidence storage and multi-dimensional data security encapsulation module specifically includes: Encapsulate a complete weighing transaction into a standardized, trusted data packet; The data package includes not only the final weight value, but also: a snapshot of the original sensor data, environmental parameters, an uncertainty report calculated by the digital twin, vehicle identification information, calibration status before and after weighing, operator digital signature, and timestamp.

[0010] Preferably, the method further includes: Encryption algorithms are used to encrypt data packets; Generate the Merkle tree root hash of the entire data packet and key fields; Submit the core hash value, transaction ID, and metadata to the permissioned consortium blockchain network.

[0011] Preferably, the smart contract-based automatic verification and strategy optimization module specifically includes: Deploy a preset number of smart contracts, each corresponding to specific business rules.

[0012] Preferably, the method further includes: Record historical measurement data and uncertainty performance of different cargo types and vehicle models on different weighing instruments; When a user submits an initial measurement strategy for a new task, the optimizer recommends the optimal calibration cycle, vehicle guidance position, and data sampling frequency based on historical big data and digital twin simulations.

[0013] Preferably, the method further includes: The multi-scenario interactive traceability service platform module is configured to provide a one-stop interactive platform, with a full lifecycle traceability map and a visual dashboard as the core, to achieve transparent data query and business ecosystem collaboration.

[0014] Blockchain-based methods for tracing transaction data on electronic truck scales include: Weighing data is collected through a distributed intelligent sensor network, and the initial weight data is generated by combining environmental perception and a self-calibration engine to correct errors. The weighing process is simulated based on a parametric digital twin model, and the measurement uncertainty is dynamically evaluated while the performance degradation trend of the weighing instrument is predicted. The weighing data and environmental parameters are encapsulated into a trusted data packet, encrypted using the national cryptographic algorithm to generate a hash value, and then uploaded to the consortium blockchain to complete the tamper-proof storage. The system automatically verifies the compliance and validity of transaction data through smart contracts, optimizes measurement strategies by combining historical data, and provides end-to-end traceability services.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention achieves a qualitative leap in measurement accuracy through the fusion of multi-dimensional technologies. The distributed intelligent sensor network combined with dual-mode communication ensures stable data transmission. The distributed weighing data synthesis unit uses the weighted least squares method to eliminate off-center loading errors. The digital twin model integrates full lifecycle information and quantifies the contribution of error sources through Monte Carlo simulation, outputting an accurate uncertainty report.

[0016] 2. This invention compares the residuals of twin predictions and actual data through time series regression analysis, identifies degradation trends such as sensor drift in advance, realizes operation and maintenance early warning, reduces equipment failure rate and operation and maintenance costs, and keeps the weighing instrument in the optimal measurement state at all times. Attached Figure Description

[0017] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a system structure diagram of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0018] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0019] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0020] Example 1 Its specific implementation method is combined with the appendix Figure 1 and attached Figure 2 Please provide a detailed explanation.

[0021] Appendix Figure 1 The diagram below illustrates the structure of a blockchain-based electronic truck scale transaction data traceability system provided in this embodiment of the invention. It shows the connection between the dynamic data acquisition and adaptive real-time calibration module and the smart contract-based automatic verification and strategy optimization module, and marks the main functional interaction flow of each module.

[0022] Appendix Figure 2 The flowchart of the blockchain-based electronic truck scale transaction data traceability method provided in this embodiment of the invention illustrates the complete steps from collecting weighing data to optimizing measurement strategies by combining historical data and providing full-process traceability services.

[0023] In this embodiment, it includes: The dynamic data acquisition and adaptive real-time calibration module is configured to achieve high-precision, adaptive environmental compensation dynamic weighing data acquisition through a distributed intelligent sensor network and an embedded real-time self-calibration engine. Specifically, it includes: Connect and manage a network of multiple high-precision digital load cells, each of which is an intelligent node with an independent address and microprocessor, and has built-in temperature compensation and preliminary filtering functions. Based on the distributed weighing theory mathematical model, data from all sensors are received in real time, and the final weight value is synthesized using the optimal estimation algorithm (such as weighted least squares method) according to the pre-calibrated and accurate relative spatial position distribution matrix, effectively eliminating systematic errors caused by vehicle position (off-center loading). Multi-dimensional environmental sensing and compensation unit: integrates environmental sensors such as temperature, humidity, vibration, and tilt angle to monitor interference factors at the weighing site in real time; The built-in environment-error mapping database can dynamically call compensation coefficients based on real-time environment data to correct the original weight signal; Self-calibration engine: Online electrical self-test: Perform zero-point tracking, sensitivity testing, etc. periodically or triggered; Dynamic adaptive filtering: Based on vibration sensor data and historical weighing curves, the filtering algorithm (such as adaptive Kalman filtering) is intelligently selected or adjusted to achieve the best balance between stability and response speed. Miniature weight or excitation response calibration: In conjunction with a miniature calibration device integrated with a standard device, it automatically performs fixed-point calibration during business intervals and feeds back the calibration results in real time to the digital twin model in the digital twin error modeling and measurement uncertainty dynamic evaluation module.

[0024] Sensor nodes communicate with each other via dual-mode industrial Ethernet and wireless sensor network to ensure data transmission redundancy and stability. When a sensor data transmission is abnormal, the system automatically switches to the backup communication link and marks the abnormal node. The spatial location distribution matrix can be periodically calibrated and updated using a laser rangefinder to ensure it matches the actual structure of the weighing instrument; the environmental data sampling frequency is synchronized with the weighing data at 100Hz to ensure the time correlation between environmental interference and the weight signal; The environment-error mapping database is generated through machine learning training on massive amounts of data, improving the correction accuracy compared to traditional fixed compensation; the power-on self-test result serves as a prerequisite for the weighing instrument to be activated, and if the self-test fails, the function is locked and an alarm is triggered; the dynamic filter can be intelligently adjusted according to the vibration conditions and weighing curve, enhancing the filtering strength during high-frequency vibration and improving the response speed when the vehicle is on the scale at a constant speed; the micro calibration device automatically calibrates during business breaks, and the results are fed back to the digital twin model in real time.

[0025] The digital twin error modeling and dynamic evaluation module for measurement uncertainty is configured to build a parameterized digital twin model, perform personalized simulations for each weighing, and realize dynamic evaluation of measurement uncertainty and prediction of performance degradation. Specifically, it includes: Based on finite element analysis, multibody dynamics, and sensor characteristic curves, a parametric digital model of all potential error sources, such as the mechanical structure of the weighing instrument, sensor group, and foundation settlement, is established. For example, the deformation of the load-bearing platform can be modeled as the stiffness function of the mesh nodes, and the nonlinear error of the sensor can be modeled as a polynomial.

[0026] For each weighing task, real-time data (weight, environmental data) and vehicle characteristic information (such as axle type, estimated gross weight, and parking location) are received; Monte Carlo simulation or sensitivity-based analytical propagation algorithm is launched to quickly run thousands of simulations in the digital twin, quantitatively analyze the contribution of each error source to the final measurement result under the current specific vehicle, specific environment, and specific weighing instrument condition, and finally output a personalized, real-time updated extended measurement uncertainty report for this measurement; By comparing the residuals between the predicted data and the actual measured data of the digital twin over a long period of time, machine learning algorithms (such as time series regression analysis) can be used to identify slowly changing performance degradation trends such as sensor drift and mechanical fatigue, and provide early warnings of maintenance needs.

[0027] The constructed parametric digital twin model not only integrates basic data such as finite element analysis, but also incorporates information from the entire lifecycle of the weighing instrument. This includes factory calibration parameters, maintenance records, and long-term load loss data, forming a dynamically updated digital image. For polynomial modeling of sensor nonlinear errors, basic coefficients are preset according to the differences in sensor models, and the error model is optimized by reverse iteration through the actual residual of each weighing, so that the error model is more in line with the specific hardware characteristics.

[0028] In a single weighing simulation, the system synchronously simulates the dynamic force process of a vehicle being weighed (such as starting impact and braking vibration), and corrects the simulation parameters by combining real-time environmental data. The final uncertainty report not only includes numerical results, but also marks the contribution ratio of each error source, providing clear measurement basis for both parties in the transaction.

[0029] The blockchain evidence storage and multi-dimensional data security encapsulation module is configured to use a consortium blockchain and national cryptographic algorithms to encapsulate multi-dimensional weighing data into a trusted data packet and solidify the hash, providing judicial-grade tamper-proof evidence storage. Specifically, it includes: Encapsulate a complete weighing transaction into a standardized, trusted data packet; The data package includes not only the final weight value, but also: a snapshot of the original sensor data, environmental parameters, uncertainty reports calculated by the digital twin, vehicle identification information (such as license plate image hashes), calibration status before and after weighing, operator digital signature, timestamps, etc.

[0030] The raw sensor data in the trusted data packet is stored in a frame structure. Each frame contains four core pieces of information: sensor ID, acquisition timestamp, raw analog quantity, and preliminary filter value. Adjacent frames are checked using CRC to ensure transmission integrity. In addition to the license plate image hash, the vehicle identification information also includes the vehicle's VIN code and the encrypted digest of the driver's identity authentication information, realizing the association and binding of vehicle-cargo-person information.

[0031] At the same time, the data packet will include the current operating status code of the weighing instrument (such as normal, pending calibration, under warning) and the business scenario label of this weighing (such as purchase weighing, sales weighing, inventory counting), providing a scenario-based basis for subsequent smart contract verification and traceability query.

[0032] The data packet encapsulation process introduces a data correlation verification mechanism: The final weight value is associated with the corresponding original sensor data block and environmental parameter set through a hash algorithm, forming a chain structure of result-source data.

[0033] After encapsulation, the system will automatically generate a unique identifier for the data packet (composed of a timestamp, weighing instrument number, and random sequence). This identifier will serve as the core index for subsequent blockchain uploading. At the same time, the data packet will reserve extended fields to support the access of personalized data according to industry needs (such as cargo temperature data in hazardous chemical transportation and humidity data in fresh food transportation), thereby improving scenario adaptability.

[0034] Encryption algorithms are used to encrypt data packets; Generate the Merkle tree root hash of the entire data packet and key fields; Submit the core hash value, transaction ID, and metadata to the permissioned consortium blockchain network; The on-chain nodes are composed of authoritative or relevant parties such as metrology institutions, market regulatory departments, and the companies of both parties to the transaction. They adopt efficient consensus mechanisms such as PBFT to ensure the efficiency of evidence storage and the credibility of consensus. Complete, massive data packets are stored in IPFS or regulated cloud storage, with their content-addressed hashes stored only on the blockchain.

[0035] This on-chain-off-chain collaborative storage model solves the problems of high cost and low efficiency in blockchain storage while ensuring data immutability.

[0036] The national cryptographic algorithm combination used includes the SM4 symmetric encryption algorithm (used for overall data packet encryption) and the SM2 asymmetric encryption algorithm (used for digital signatures and key exchange). The key is generated by the transaction parties and regulatory agencies, and key fragments are managed through threshold signature technology. The keys are automatically rotated periodically and are never recorded on the blockchain to avoid the risk of key leakage. When constructing a Merkle tree, data is layered according to importance. Core transaction data such as the final weight value and uncertainty report are treated as independent branches. The hash of key fields is extracted separately and used to form a double verification with the hash of the overall data packet, thereby improving the efficiency of data tampering detection.

[0037] The consortium blockchain adopts a dynamic admission and behavior audit mechanism. New nodes must be confirmed by multi-signature from at least 3 existing authoritative nodes (such as provincial metrology institutes and leading enterprises in the industry). The daily operation logs of nodes (such as data reading and writing, consensus voting) are stored on the blockchain in real time. For off-chain storage, IPFS storage adopts a data sharding and multi-node backup strategy, with shard hashes linked to blockchain indexes. Regulatory cloud storage deploys a disaster recovery system in a different location. At the same time, data retention periods are set through smart contracts. Expired data is recorded and destroyed using fragmented blockchain evidence after confirmation by multiple parties, thus balancing storage security and compliance.

[0038] The automatic verification and strategy optimization module based on smart contracts is configured to deploy rule-based smart contract groups to automatically verify data compliance and transaction validity, and optimize measurement strategies through big data analysis. Specifically, it includes: Multi-condition triggered smart contract group: Deploy a series of smart contracts, each corresponding to specific business rules, for example: Data Consistency Verification Contract: When new data is uploaded to the blockchain, the weight reported by Module 1 is automatically compared with the range of the predicted value of the digital twin model. If it exceeds the reasonable residual threshold, the data is marked as pending review and an alarm is triggered.

[0039] The transaction validity contract stipulates that the weighing result will only be marked as valid by the contract and automatically generate a unique blockchain transaction certificate if the measurement uncertainty of a weighing is less than the threshold agreed in the contract (such as 0.1%), the weighing instrument calibration status is within the validity period, and there are no abnormal alarms.

[0040] Contract confirmation by multiple parties: After the buyer, seller and regulator have all digitally signed the transaction certificate with their private keys, the contract will automatically update the transaction status to final confirmation, and the fund settlement process can then be initiated.

[0041] Smart contract groups support dynamic rule configuration. Both parties to the transaction can customize core parameters such as data consistency residual threshold and measurement uncertainty qualification standard according to the value of goods and the trade agreement. Parameter modifications must be confirmed by digital signatures of both parties and stored on the blockchain. The data consistency verification contract adds a secondary review mechanism. After the first alarm is triggered, it automatically retrieves historical data on similar working conditions for auxiliary judgment. If anomalies are still found, it will be pushed to the regulatory node for intervention to avoid misjudgment affecting transaction efficiency.

[0042] The transaction validity contract and the blockchain notarization module work together in real time. Once the contract is marked as valid, the core hash of the data packet is automatically extracted to generate a transaction certificate with a timestamp. The certificate contains a verifiable digital watermark (linked to the identity information of both parties to the transaction). Multiple parties have confirmed that the contract supports cross-terminal signature interaction. Buyers, sellers, and regulators can complete private key signing through various devices such as hardware dongles and mobile security terminals. The signing process is executed offline, with only the signature result uploaded to the blockchain, balancing security and ease of operation. At the same time, the contract has a built-in automatic timeout reminder mechanism to avoid transaction delays caused by one party's procrastination.

[0043] Record historical measurement data and uncertainty performance of different types of goods (such as coal, grain, and steel) and different vehicle types on different weighing instruments; When a user submits an initial measurement strategy for a new task, the optimizer recommends the optimal calibration cycle, vehicle guidance position, data sampling frequency, etc., based on historical big data and digital twin simulation, forming a personalized strategy package and feeding it back to the user to achieve continuous optimization of the measurement process.

[0044] The dimensions of historical data recording have been further expanded. In addition to cargo type, vehicle type, and weighing instrument, key factors such as weighing time period, environmental fluctuation range, and sensor working time have also been included. The data is classified and stored in a multi-dimensional manner according to static and dynamic weighing scenarios with high / medium / low accuracy requirements, thus building a structured data warehouse. The optimizer has a built-in multi-objective optimization algorithm that balances measurement accuracy, operation efficiency, and equipment wear and tear costs when recommending the optimal strategy. For example, for high-frequency light-load weighing scenarios, it recommends extending the calibration cycle and increasing the sampling frequency to balance efficiency and accuracy.

[0045] The personalized strategy package includes directly executable parameter configuration files and visual operation guides, supporting one-click import into the weighing instrument control system for automatic activation; the optimization module and digital twin model are linked in real time, automatically comparing the strategy execution effect with the expected target when new transaction data is generated. If the measurement uncertainty exceeds the optimization threshold or the operation efficiency does not meet expectations, a second optimization is immediately triggered, generating an iteratively updated strategy package; it also supports users to manually provide feedback on the applicability of the strategy.

[0046] The multi-scenario interactive traceability service platform module is configured to provide a one-stop interactive platform, with a full lifecycle traceability map and a visual dashboard as its core, enabling transparent data querying and business ecosystem collaboration, specifically including: One-stop traceability portal: provides multiple access methods including Web, APP, and API.

[0047] Users (cargo owners, vehicle owners, and regulators) can instantly access the full lifecycle traceability map of a transaction by scanning a QR code or entering the transaction number.

[0048] The diagram clearly displays key information and evidence from all stages, including vehicle weighing, data collection, environmental conditions, digital twin assessment, blockchain packaging and on-chain processing, and legal confirmation by all parties, in a timeline format.

[0049] Visualized digital twin dashboard: Opens up partial digital twin views to authorized users (such as weighing instrument maintenance personnel) to display the status of each virtual sensor of the weighing instrument in real time, force cloud diagrams, pie charts of current uncertainty, etc., making the invisible measurement process transparent and visible.

[0050] Cross-platform data interconnection service: The platform provides standardized API interfaces that can be seamlessly integrated with enterprise ERP systems, logistics management platforms, and market supervision platforms.

[0051] It supports the automatic generation of electronic bills and settlement statements based on transaction vouchers, and triggers subsequent logistics, payment, and invoicing processes, truly integrating into the industrial internet ecosystem; Public oversight and integrity records: Under the premise of anonymization, the periodic verification results, historical performance evaluations, and complaint handling records of weighing instruments are made public to form a socially co-governed integrity measurement ecosystem.

[0052] Example 2 Please see Figure 2 A blockchain-based method for tracing transaction data on electronic truck scales includes the following components: Weighing data is collected through a distributed intelligent sensor network, and high-precision initial weight data is generated by combining environmental perception and a self-calibration engine to correct errors. The weighing process is simulated based on a parametric digital twin model, and the measurement uncertainty is dynamically evaluated while the performance degradation trend of the weighing instrument is predicted. Weighing data, environmental parameters and other multi-dimensional information are encapsulated into a trusted data packet, encrypted with the national cryptographic algorithm to generate a hash value, and uploaded to the consortium blockchain to complete the tamper-proof storage. The system automatically verifies the compliance and validity of transaction data through smart contracts, optimizes measurement strategies by combining historical data, and provides end-to-end traceability services.

[0053] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0054] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0055] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely for distinguishing one entity or operation from another, and does not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "include," "contain," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the statement "includes a…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0056] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0057] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0058] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0059] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0060] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0061] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0062] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A blockchain-based electronic truck scale transaction data traceability system, characterized in that, include: The dynamic data acquisition and adaptive real-time calibration module is configured to acquire dynamic weighing data through a distributed intelligent sensor network and an embedded real-time self-calibration engine. The digital twin error modeling and dynamic evaluation module for measurement uncertainty is configured to build a parameterized digital twin model, perform personalized simulations for each weighing, and realize dynamic evaluation of measurement uncertainty and prediction of performance degradation. The blockchain-based evidence storage and multidimensional data security encapsulation module is configured to use a consortium blockchain and national cryptographic algorithms to encapsulate multidimensional weighing data into trusted data packets and solidify hashes. The automatic verification and strategy optimization module based on smart contracts is configured to deploy rule-based smart contract groups to automatically verify data compliance and transaction validity, and optimize measurement strategies through big data analysis.

2. The blockchain-based electronic truck scale transaction data traceability system according to claim 1, characterized in that, The dynamic data acquisition and adaptive real-time calibration module specifically includes: Connect and manage a network consisting of multiple digital load cells; It receives data from all sensors in real time and synthesizes the final weight value using the optimal estimation algorithm based on the relative spatial position distribution matrix. It integrates environmental sensors for temperature, humidity, vibration, and tilt angle to monitor interference factors at the weighing site in real time; The built-in environment-error mapping database dynamically calls compensation coefficients based on real-time environment data to correct the original weight signal; The self-calibration engine performs online power-on self-tests; dynamic adaptive filtering; and calibration using micro-weights or excitation responses.

3. The blockchain-based electronic truck scale transaction data traceability system according to claim 1, characterized in that, The digital twin error modeling and dynamic measurement uncertainty assessment module specifically includes: Based on finite element analysis, multibody dynamics, and sensor characteristic curves, a parametric digital model of the weighing instrument's mechanical structure, sensor group, and foundation settlement is established. For each weighing task, real-time data and vehicle characteristic information are received, simulated in a digital twin, and quantitatively analyzed to determine the contribution of each error source to the final measurement result under the current specific vehicle, environment, and weighing instrument condition. Finally, a personalized, real-time updated extended measurement uncertainty report for this measurement is output.

4. The blockchain-based electronic truck scale transaction data traceability system according to claim 1, characterized in that, The blockchain-based evidence storage and multi-dimensional data security encapsulation module specifically includes: Encapsulate a complete weighing transaction into a standardized, trusted data packet; This data packet contains not only the final weight value, but also: Original sensor data snapshots, environmental parameters, uncertainty reports calculated by digital twins, vehicle identification information, calibration status before and after weighing, operator digital signatures, and timestamps.

5. The blockchain-based electronic truck scale transaction data traceability system according to claim 4, characterized in that, Also includes: Encryption algorithms are used to encrypt data packets; Generate the Merkle tree root hash of the entire data packet and key fields; Submit the core hash value, transaction ID, and metadata to the permissioned consortium blockchain network.

6. The blockchain-based electronic truck scale transaction data traceability system according to claim 1, characterized in that, The automatic verification and strategy optimization module based on smart contracts specifically includes: Deploy a preset number of smart contracts, each corresponding to specific business rules.

7. The blockchain-based electronic truck scale transaction data traceability system according to claim 6, characterized in that, Also includes: Record historical measurement data and uncertainty performance of different cargo types and vehicle models on different weighing instruments; When a user submits an initial measurement strategy for a new task, the optimizer recommends calibration cycles, vehicle guidance positions, and data sampling frequencies based on historical big data and digital twin simulations.

8. The blockchain-based electronic truck scale transaction data traceability system according to claim 1, characterized in that, Also includes: The multi-scenario interactive traceability service platform module is configured to provide a one-stop interactive platform, with a full lifecycle traceability map and a visual dashboard as the core, to achieve transparent data query and business ecosystem collaboration.

9. A blockchain-based method for tracing transaction data of electronic truck scales, comprising the blockchain-based electronic truck scale transaction data tracing system according to any one of claims 1-8, characterized in that, include: Weighing data is collected through a distributed intelligent sensor network, and the initial weight data is generated by combining environmental perception and a self-calibration engine to correct errors. The weighing process is simulated based on a parametric digital twin model, and the measurement uncertainty is dynamically evaluated while the performance degradation trend of the weighing instrument is predicted. The weighing data and environmental parameters are encapsulated into a trusted data packet, encrypted using the national cryptographic algorithm to generate a hash value, and then uploaded to the consortium blockchain to complete the tamper-proof storage. The system automatically verifies the compliance and validity of transaction data through smart contracts, optimizes measurement strategies by combining historical data, and provides end-to-end traceability services.

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