Large weighing apparatus digital twinborn calibration system and traceability method
By combining modular design and digital twin technology with photoelectric arrays and blockchain, efficient calibration and real-time monitoring of large weighing instruments have been achieved, solving the problems of high maintenance costs and easy drift of measurement results in traditional calibration methods, and improving measurement accuracy and reliability.
Patent Information
- Application Number
- CN202511364460.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-23
- Publication Date
- 2025-12-23
AI Technical Summary
Large weighing instruments struggle to maintain high accuracy and reliability under conditions of high throughput, high risk of cheating, and long service life. Traditional calibration methods are costly to maintain, have long cycles, and are prone to drifting results. Traditional monitoring methods also struggle to identify abnormal behavior in real time.
By adopting modular design and digital twin technology, combined with photoelectric arrays and blockchain, and through modular-multi-force point combination comparison method, twin model calibration, Bayesian estimation and AI anomaly detection, the vehicle weighing behavior is monitored in real time, generating an tamper-proof calibration certificate and storing it on the blockchain, realizing modular rapid replacement and real-time alarm.
It significantly improves the maintenance efficiency and measurement accuracy of large weighing instruments, reduces maintenance costs, and enables second-level anomaly identification and transaction freezing, ensuring the credibility and traceability of measurement results.
Smart Images
Figure CN121185403A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of weighing instrument calibration, and particularly relates to a large-scale weighing instrument digital twin calibration system and a traceability method. BACKGROUND
[0002] Large-scale weighing instruments generally refer to whole-vehicle electronic truck scales with a weighing capacity of tens of tons to hundreds of tons and a platform length of tens of meters, and are core measuring instruments for modern logistics, mines, ports, highway toll collection and bulk trade settlement. The measurement performance directly affects trade fairness, transportation safety and national tax revenue. With the rise of Internet of Things, intelligent manufacturing and blockchain technologies, traditional weighing instruments are accelerating the evolution towards digitization, networking and intelligentization, but how to continuously maintain high precision and high credibility in the scene of high throughput, high cheating risk and long service cycle is still an industry pain point.
[0003] The prior art mainly relies on the whole weight method, the weighing vehicle method or the on-site calibration method to complete periodic verification, but the traditional scale body is an integrated steel structure, the sensor, the junction box and the scale platform are welded and fixed, and the interchangeability is poor. Once a sensor fails or the scale body locally deforms, the whole vehicle needs to be returned to the factory or stopped for a long time for maintenance, and the maintenance cost is high. Transporting hundreds of tons of standard weights or scheduling weighing vehicles is limited by roads, weather and traffic control, and periodic verification often takes several days. In addition, factors such as on-site environmental temperature and humidity, foundation settlement and vehicle dynamic impact are difficult to reproduce, resulting in significant drift of the calibration results in subsequent use, long value transmission chain and many error sources. The weighing result can be changed within a few seconds by using methods such as jump weight, impact weight, S walking, hydraulic lifting and wireless remote control, but traditional manual inspection or video monitoring cannot identify abnormal load distribution in real time, and it is difficult to take evidence afterwards, resulting in high supervision cost. SUMMARY
[0004] The purpose of the present application is to provide a large-scale weighing instrument digital twin calibration system and a traceability method to solve the problems raised in the background.
[0005] To achieve the above purpose, the present application provides the following technical solution: a system architecture and a method, the system architecture comprising a physical layer, a twin layer and a service layer; the method comprising the following steps: S1, placing a weighing module in a mobile combined calibration device, and using a "sub-module-multiple force point" combined comparison method to generate a module calibration matrix; S2, in the two states of empty load and known total weight T of the whole vehicle scale, using the twin model to invert the module calibration matrix-system matrix, and correcting the uncertainty through Bayesian estimation; S3, detecting abnormal loading behavior by the photoelectric array, and the twin model calculating the gravity-wheel axle force distribution in real time, and triggering an alarm and freezing the transaction when the deviation from the theoretical model is greater than a threshold value; S4, the calibration certificate, environmental data, sensor drift value is chained after hashing, forming an unalterable "weighing scale digital passport", any node can reproduce the calibration state through on-chain data; As a further preferred of the technical solution: the physical layer is composed of interchangeable weighing module, edge computing gateway, anti-cheating photoelectric array, RFID car number identification device, environmental sensor; As a further preferred of the technical solution: the twin layer adopts a lightweight three-dimensional model and a mechanism-data hybrid model, which maps the stress field, temperature field and sensor output of the physical weighing scale in real time, and dynamically corrects the twin model parameters by fusing the measured data and simulation results through the extended Kalman filtering algorithm; As a further preferred of the technical solution: the service layer includes a metrology cloud platform, a blockchain traceability chain, and an AI anomaly detection engine, wherein the metrology cloud platform provides a remote monitoring interface, which displays the state of the weighing scale, calibration records, and cheating alarm logs in real time, and supports multi-user permission management; wherein the blockchain traceability chain is built using the Hyperledger Fabric framework, and the nodes include the metrology institute, the weighing scale manufacturer, and the user enterprise; the calibration certificate, environmental data, and sensor drift value are chained after SHA-256 hashing, and a timestamp and digital signature are generated to ensure that it cannot be tampered with; wherein the AI anomaly detection engine builds a gravity-axle force distribution detection model based on the improved YOLOv7 algorithm, inputs the axle position data of the photoelectric array and the stress field data of the twin model, and outputs the actual load distribution of the vehicle; when the deviation between the theoretical model and the actual load distribution is greater than the threshold τ, an alarm is triggered and the transaction is frozen; As a further preferred of the technical solution: the specific steps in S1 are: device calibration, the standard force source applies multiple force points on the module surface, the force value range covers 0-100%FS; real-time measurement of module deformation using a laser tracker, combined with force value data to generate module stiffness matrix; and through the least squares method to fit the relationship between force value and deformation, generate module calibration matrix, uncertainty U≤0.008%FS; As a further preferred of the technical solution: the specific steps in S2 are: twin model initialization, input module calibration matrix, simulate stress field distribution under no load, compare with measured data, correct model parameters; then apply known total weight, twin model calculates the theoretical load of each module, compares with measured sensor output, updates system matrix through Bayesian estimation; finally, combine module level uncertainty and system level error, make system uncertainty U≤0.012%FS; As a further preferred of the technical solution: the specific steps in S3 are: record the wheel shaft position, the shielding area and the staying time when the vehicle is on the scale, if abnormal behaviors such as not completely on the scale, stopping in the middle are detected, a preliminary alarm is triggered; then the twin model calculates the vehicle gravity center position and the wheel shaft force distribution according to the measured wheel shaft position and the sensor output, and generates a theoretical load curve; then the theoretical load curve is compared with the standard vehicle database, if the deviation is greater than the threshold τ, a secondary alarm is triggered and the weighing data is frozen, and printing the ticket is prohibited; finally, the wheel shaft position image of the photoelectric array, the stress field nephogram of the twin model and the original data of the sensor are automatically intercepted, packaged as evidence files and chained for storage; As a further preferred of the technical solution: the specific steps in S4 are: during the calibration process, the edge computing gateway real-time acquisition module collects the calibration matrix, the system matrix, the environmental parameters and the sensor drift value; the calibration certificate, the environmental data and the drift value are subjected to SHA-256 hash operation to generate a unique hash value; the hash value, the timestamp and the digital signature are uploaded to the alliance chain through the blockchain API interface to form a quantity transmission traceability chain; any node can download the original data through the on-chain hash value, reproduce the physical state during calibration combined with the twin model, and verify the effectiveness of the calibration result.
[0006] Compared with the prior art, the present application has the following advantages: 1. The present application significantly improves the maintenance efficiency and measurement accuracy of large-scale weighing apparatus through modular design and digital twin technology. The modular weighing unit supports on-site quick replacement, without the need for overall return to the factory, greatly shortening the downtime and reducing maintenance costs. At the same time, the digital twin model maps the physical weighing apparatus state in real time, dynamically corrects errors and maintains high-precision measurement for a long time. In addition, the system uses blockchain technology to store calibration and operation data on the chain to ensure that the data cannot be tampered with, the calibration state can be reproduced at any time, and the credibility of the measurement result is enhanced.
[0007] 2. The present application uses photoelectric array and AI algorithm to monitor vehicle on-scale behavior in real time, identifies abnormalities at a second level and automatically freezes transactions, while solidifying evidence on the chain, effectively curbing fraudulent behavior. In addition, the system builds a unified data platform, provides early warning of potential faults, realizes the transition from passive repair to active maintenance, significantly reduces downtime losses, and improves the operation efficiency and economy of the weighing apparatus. BRIEF DESCRIPTION OF DRAWINGS
[0008] Figure 1 The flowchart of the large-scale weighing apparatus digital twin calibration system and traceability method of the present application Figure One ; Figure 2 The system architecture diagram of the large-scale weighing apparatus digital twin calibration system and traceability method of the present application Figure 3Flow of a large-scale weighing apparatus digital twin calibration system and traceability method Figure Two . DETAILED DESCRIPTION
[0009] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the scope of protection of the present application.
[0010] Embodiment: Please refer to Figure 1 - Figure 3 As shown in the figure, the present application provides a technical solution: including system architecture and method, the system architecture includes physical layer, twin layer and service layer; the method includes the following steps: S1, place the weighing module in the mobile combination calibration device, adopt the "sub-module-multiple force point" combination comparison method, generate the module calibration matrix; S2, in the two states of empty load and known total weight T of the truck scale, use the twin model to inverse the module calibration matrix-system matrix, correct the uncertainty through Bayesian estimation; S3, the photoelectric array detects abnormal on-scale behavior, the twin model calculates the center of gravity-wheel axle force distribution in real time, and triggers an alarm and freezes the transaction when the deviation from the theoretical model is greater than the threshold value; S4, the calibration certificate, environmental data and sensor drift value are chained after being hashed, forming an unalterable "weighing apparatus digital passport", and any node can reproduce the calibration state through the on-chain data; In this embodiment, specifically: the physical layer is composed of interchangeable weighing modules, edge computing gateways, anti-cheating photoelectric arrays, RFID vehicle number recognition devices and environmental sensors. Each interchangeable weighing module has a built-in self-diagnosis digital sensor and has the functions of zero drift automatic compensation and temperature coefficient real-time correction. The modules are connected through high-precision positioning pins to achieve millimeter-level docking. The anti-cheating photoelectric array is composed of 16 groups of infrared reflection sensors to form a grid covering the four corners and the middle of the scale platform. It detects the wheel axle position, shielding area and residence time when the vehicle is on the scale, and triggers an alarm for abnormal behavior.
[0011] In this embodiment, specifically: the twin layer adopts a lightweight three-dimensional model and a mechanism-data hybrid model, which maps the stress field, temperature field and sensor output of the physical weighing instrument in real time, fuses the measured data and simulation results through an extended Kalman filtering algorithm, and dynamically corrects the twin model parameters; the lightweight three-dimensional model constructs a scale body geometric model based on laser scanning point cloud data, reduces the number of model surfaces to less than 5% of the original model through a surface reduction algorithm, adapts to edge computing resources, the mechanism-data hybrid model integrates a mechanical simulation module of finite element analysis to simulate load distribution, adopts a linear elastic assumption, and material property parameters are updated in real time through module-level calibration, a temperature field prediction model is constructed based on an LSTM neural network, environmental temperature and historical sensor data are input, and temperature gradient distribution of each module is output to correct measurement errors caused by thermal expansion; In this embodiment, specifically: the service layer includes a metrology cloud platform, a blockchain traceability chain, and an AI anomaly detection engine, wherein the metrology cloud platform provides a remote monitoring interface, displays the state of the weighing instrument, calibration records, and cheating alarm logs in real time, and supports multi-user permission management; wherein the blockchain traceability chain adopts a Hyperledger Fabric framework to build a consortium chain, nodes include the metrology institute, weighing instrument manufacturers, and user enterprises, calibration certificates, environmental data, and sensor drift values are chained after SHA-256 hashing, time stamps and digital signatures are generated to ensure that they cannot be tampered with; wherein the AI anomaly detection engine constructs a gravity-axle force distribution detection model based on an improved YOLOv7 algorithm, inputs the axle position data of the photoelectric array and the stress field data of the twin model, and outputs the actual load distribution of the vehicle; when the deviation from the theoretical model is greater than a threshold τ, an alarm is triggered and transactions are frozen; In this embodiment, specifically: the specific steps in S1 are: calibrating the device, making the standard force source apply multiple force points on the module surface, and covering the force value range of 0-100% FS; using a laser tracker to measure the deformation of the module in real time, generating a module stiffness matrix in combination with the force value data; and generating a module calibration matrix by fitting the relationship between the force value and the deformation through the least squares method, with an uncertainty U≤0.008% FS; In this embodiment, specifically: the specific steps in S2 are: initializing the twin model, inputting the module calibration matrix, simulating the stress field distribution under no load, comparing with the measured data, and correcting the model parameters; then applying a known total weight, the twin model calculates the theoretical load of each module, compares with the measured sensor output, and updates the system matrix through Bayesian estimation; finally, combining the module-level uncertainty and the system-level error, the system uncertainty U≤0.012% FS; In this embodiment, specifically: the specific steps in S3 are: recording the axle position, shielding area and stay time when the vehicle is on the scale, if abnormal behavior such as not completely on the scale, stopping in the middle, etc. is detected, a preliminary alarm is triggered; then the twin model calculates the vehicle's center of gravity position and the distribution of each axle force according to the measured axle position and sensor output, and generates a theoretical load curve; then compare the theoretical load curve with the standard vehicle database, if the deviation is greater than the threshold τ, trigger the secondary alarm and freeze the weighing data, prohibit printing the ticket; finally, automatically intercept the axle position image of the photoelectric array, the stress field nephogram of the twin model, and the sensor raw data, package them as evidence files and chain them for storage; In this embodiment, specifically: the specific steps in S4 are: during calibration, the edge computing gateway real-time acquisition module collects the module calibration matrix, system matrix, environmental parameters, and sensor drift value; perform SHA-256 hash operation on the calibration certificate, environmental data, and drift value to generate a unique hash value; upload the hash value, timestamp, and digital signature to the consortium chain through the blockchain API interface to form a quantity transmission traceability chain; any node can download the original data through the on-chain hash value, reproduce the physical state during calibration by combining the twin model, and verify the effectiveness of the calibration result; through the technical route of module calibration matrix-system matrix-twin model-blockchain storage, a quantity value transmission closed loop from the module level to the system level is realized.
[0012] Working principle or structural principle: After the mobile combination calibration device is in place, first clamp the to-be-tested weighing module according to the unified interface, use the standard force source to apply force on the module surface in stages, and at the same time, the laser tracker captures the deformation data in real time, and generates the module calibration matrix through least squares fitting; then install the calibrated module into the truck scale, first start the digital twin model in the empty state, invert the module matrix to the system matrix through extended Kalman filtering and compare it with the measured value, correct the model parameters, then apply the known total weight, and again invert and Bayesian estimate until the system level uncertainty converges; when the vehicle is normally on the scale, the photoelectric array continuously acquires the axle position and shielding information, the twin model calculates the center of gravity-axle force curve in real time and compares it with the standard vehicle database, if the deviation is out of limit, an alarm is triggered and the transaction is frozen, at the same time, the photoelectric image, stress nephogram, and sensor raw data are packaged as evidence; the calibration process and subsequent operation data are collected by the edge gateway, and after SHA-256 hashing, timestamping and digital signing, they are written into the consortium chain to form an unalterable "weighing instrument digital passport"; at any time, authorized nodes can download the data package through the on-chain hash, load the same version of the twin model, reproduce the historical calibration state, complete remote verification or audit, and realize the whole life cycle closed loop traceability from the module to the system, from the scene to the cloud, and from the present to the future.
[0013] It will be apparent to those skilled in the art that the application is not limited to the details of the above-exemplified embodiments and that the application can be implemented in other particular forms without departing from the spirit or essential characteristics of the application. The presently disclosed embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. No reference to an item in the claims to provide support for discarding any other specified element of the application. Furthermore, it should be understood that although the description is made on embodiments, not every embodiment contains only one independent technical solution, and the description is made in this way only for the sake of clarity, and those skilled in the art should consider the description as a whole, and the technical solutions in each embodiment can also be combined appropriately to form other embodiments that those skilled in the art can understand.
Claims
1. A digital twin calibration system and traceability method for large-scale weighing instruments, characterized in that, The system architecture includes a physical layer, a twin layer, and a service layer; the method includes the following steps: S1. Place the weighing module in the mobile combined calibration device and use the "sub-module-multi-force point" combined comparison method to generate a module calibration matrix; S2. Under the two conditions of empty vehicle scale and known total weight T, the calibration matrix-system matrix is calibrated using the twin model inversion module, and the uncertainty is corrected by Bayesian estimation. S3. The photoelectric array detects abnormal weighing behavior, and the twin model calculates the center of gravity-wheel axle force distribution in real time. When the deviation from the theoretical model is greater than the threshold, an alarm is triggered and the transaction is frozen. S4, calibration certificate, environmental data, and sensor drift values are hashed and uploaded to the blockchain to form an immutable "weighing instrument digital passport". Any node can reproduce the calibration status through the data on the blockchain.
2. The large-scale weighing instrument digital twin calibration system and traceability method according to claim 1, characterized in that, The physical layer consists of interchangeable weighing modules, edge computing gateways, anti-cheating photoelectric arrays, RFID vehicle number identification devices, and environmental sensors.
3. The large-scale weighing instrument digital twin calibration system and traceability method according to claim 2, characterized in that, The twin layer employs a lightweight 3D model and a mechanism-data hybrid model to map the stress field, temperature field, and sensor output of the physical weighing instrument in real time. It dynamically corrects the twin model parameters by fusing measured data and simulation results through an extended Kalman filter algorithm.
4. The large-scale weighing instrument digital twin calibration system and traceability method according to claim 3, characterized in that, The service layer includes a metrology cloud platform, a blockchain traceability chain, and an AI anomaly detection engine. The metrology cloud platform provides a remote monitoring interface that displays the weighing instrument status, calibration records, and cheating alarm logs in real time, and supports multi-user permission management. The blockchain traceability chain is a consortium blockchain built using the Hyperledger Fabric framework. Nodes include metrology institutes, weighing instrument manufacturers, and user companies. Calibration certificates, environmental data, and sensor drift values are hashed using SHA-256 and uploaded to the chain to generate timestamps and digital signatures, ensuring immutability. The AI anomaly detection engine is based on the improved YOLOv7 algorithm to build a center of gravity-wheel axle force distribution detection model. It takes the wheel axle position data of the photoelectric array and the stress field data of the twin model as input, and outputs the actual load distribution of the vehicle. When the deviation from the theoretical model is greater than the threshold τ, an alarm is triggered and the transaction is frozen.
5. The large-scale weighing instrument digital twin calibration system and traceability method according to claim 4, characterized in that, The specific steps in S1 are as follows: calibrate the device by applying multiple force points on the module surface using a standard force source, with the force value range covering 0-100%FS; use a laser tracker to measure the module deformation in real time, and generate a module stiffness matrix by combining the force value data; and use the least squares method to fit the relationship between the force value and the deformation to generate a module calibration matrix with an uncertainty U≤0.008%FS.
6. The large-scale weighing instrument digital twin calibration system and traceability method according to claim 5, characterized in that, The specific steps in S2 are as follows: initialize the twin model, input the module calibration matrix, simulate the stress field distribution under no-load conditions, compare with the measured data, and correct the model parameters; then apply the known total weight, calculate the theoretical load of each module in the twin model, compare with the measured sensor output, and update the system matrix through Bayesian estimation; finally, combine the module-level uncertainty and the system-level error to make the system uncertainty U≤0.012%FS.
7. The large-scale weighing instrument digital twin calibration system and traceability method according to claim 6, characterized in that, The specific steps in S3 are as follows: The photoelectric array records the wheel axle position, obstruction area, and dwell time when the vehicle is placed on the scale. If abnormal behavior such as incomplete weighing or stopping midway is detected, a preliminary alarm is triggered. Then, the twin model calculates the vehicle's center of gravity position and the force distribution of each wheel axle based on the measured wheel axle position and sensor output, and generates a theoretical load curve. Subsequently, the theoretical load curve is compared with the standard vehicle database. If the deviation is greater than the threshold τ, a secondary alarm is triggered and the weighing data is frozen, and the printing of receipts is prohibited. Finally, the wheel axle position image of the photoelectric array, the stress field cloud map of the twin model, and the original sensor data are automatically captured, packaged into evidence files, and stored on the blockchain.
8. The large-scale weighing instrument digital twin calibration system and traceability method according to claim 7, characterized in that, The specific steps in S4 are as follows: During the calibration process, the edge computing gateway collects the calibration matrix, system matrix, environmental parameters, and sensor drift values in real time; and performs SHA-256 hash operation on the calibration certificate, environmental data, and drift values to generate a unique hash value. Hash values, timestamps, and digital signatures are uploaded to the consortium blockchain via the blockchain API interface to form a traceability chain. Any node can download the original data through the on-chain hash value, and combine it with the twin model to reproduce the physical state during calibration and verify the validity of the calibration results.