Electronic weighing real-time feedback ERP system
By combining intelligent weighing terminals and edge computing processing layers, real-time acquisition and synchronization of weighing data to the ERP system are achieved, solving the problems of data lag and error caused by manual data entry, improving the data accuracy and processing efficiency of the ERP system, and meeting the business risk management needs of enterprises.
Patent Information
- Application Number
- CN202511407457.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-29
- Publication Date
- 2026-01-02
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In existing technologies, weighing data needs to be manually recorded and then manually entered into the ERP system, which leads to data lag, frequent errors, and affects the data accuracy and processing efficiency of the ERP system. Furthermore, it cannot be synchronized in real time, resulting in misjudgments of production plans and inventory problems.
By employing intelligent weighing terminals, edge computing processing layers, and 5G encrypted data transmission layers, the system enables real-time acquisition, processing, and synchronization of weighing data to the ERP system. Combined with dynamic compensation algorithms and anomaly detection, the system ensures data accuracy and uses blockchain technology to create an immutable audit log.
It achieves full-process automation of weighing data, ensuring that inventory data is synchronized with the actual situation, reducing errors, improving business processing efficiency, reducing communication costs and risks, and meeting the needs of internal audit and compliance inspections.
Smart Images
Figure CN121258433A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of ERP system technology, and more specifically, to an electronic weighing real-time feedback ERP system. Background Technology
[0002] Electronic weighing equipment is a key tool in material receiving and dispatching, production and processing, and inventory counting, while ERP system is the core platform for realizing integrated management of enterprise finance, inventory, production, procurement and other business operations.
[0003] Currently, most companies need to manually record and then manually enter weighing data into the ERP system. However, the manual entry process is time-consuming, especially in scenarios with frequent weighing tasks, which can easily lead to data backlog and affect the processing progress of the ERP system. Furthermore, during the manual recording and entry process, problems such as data errors, omissions, and duplicate entries are prone to occur, resulting in discrepancies between the inventory and production data in the ERP system and the actual situation. This can lead to a chain of problems such as misjudgment of production plans, inventory backlog or shortages, and financial accounting deviations. Secondly, weighing data cannot be synchronized to the ERP system in real time, causing a lag in data updates in the ERP system, which in turn affects the accuracy of decision-making.
[0004] Therefore, a new solution is needed to address this problem. Summary of the Invention
[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an electronic weighing real-time feedback ERP system.
[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution: the electronic weighing ERP system includes an intelligent weighing terminal, an edge computing processing layer, a data transmission layer, and an ERP system; The intelligent weighing terminal includes at least one electronic weighing device with data acquisition and edge communication functions. The electronic weighing device has a built-in weighing sensor, a data acquisition module and a communication module. The weighing sensor is used to collect the weight data of the item in real time. The data acquisition module is used to collect the identification information of the item in real time and transmit the weight data and identification information to the edge computing processing layer. The edge computing processing layer includes an edge computing gateway and a data preprocessing module. The data preprocessing module is used to standardize the format of the collected information. The data preprocessing module identifies abnormal weighing data and triggers an abnormal alarm signal through an anomaly detection algorithm. The data transmission layer uses a 5G communication module to establish an encrypted data transmission channel between the edge computing processing layer and the ERP system. The encrypted data transmission channel supports TCP / IP protocol and MQTT IoT communication protocol. The ERP system includes an API interface module, a data mapping module, a business linkage module, an inventory management module, a production execution module, and a procurement management module. The API interface module interfaces with the inventory management module, the production execution module, and the procurement management module. The data mapping module matches and maps standardized information with the data fields of the inventory management module, the production execution module, and the procurement management module. The business linkage module triggers corresponding business operations in the ERP system based on the weighing data. Preferably, the intelligent weighing terminal further includes a human-computer interaction module and a local storage module. The human-computer interaction module is used to display weighing data, abnormal alarm information and operation instructions. The human-computer interaction module supports operators to manually input additional information about the items.
[0007] Preferably, the edge computing processing layer further includes a data verification module, which compares the item identification information with the item data pre-stored in the ERP system's database. The judgment range of the data verification module is ±5% of the weight range. When the data verification module determines that the weighing data is abnormal, it sends an alarm message to the mobile terminal of the management personnel. Preferably, the data transmission layer further includes a data encryption module, which uses the AES-256 encryption algorithm to encrypt the transmitted data. Preferably, the ERP system further includes a data statistical analysis module, which generates weighing efficiency reports, item loss rate analysis reports, and supplier supply quality assessment reports based on historical weighing data and business data of the ERP system.
[0008] Preferred method: Data processing method for electronic weighing real-time feedback ERP system, used to operate the electronic weighing real-time feedback ERP system, including the following steps: S1. Weigh the target object using intelligent weighing equipment to generate corresponding weight data. At the same time, the data acquisition module scans the identification information of the target object, or the operator can manually input it through the human-machine interface. S2. The data acquisition module transmits the weight data and identification information to the edge computing processing layer. The edge computing processing layer parses the weight data and identification information, and after associating and matching the weight data with the item identification information, it encapsulates it into a data packet that conforms to the data format requirements of the ERP system. S3. The edge computing processing layer sends data packets to the ERP system through an encrypted data transmission channel using the TCP / IP protocol or the MQTT IoT communication protocol, while simultaneously receiving data reception status feedback from the ERP system. After receiving the data packet, the S4.ERP system locates the corresponding business object based on the identification information within it. The business objects include purchase orders and production work orders, and then performs the following operations: S4.1. Extract the expected weight data from the business object, compare the real-time collected weight data with the expected weight data, and determine whether the real-time weight is within the allowable tolerance range. S4.2. If the real-time weight is within the allowable tolerance range, the ERP system will mark the purchase order as received and the production work order as material feeding completed, and update the inventory quantity of the corresponding material in the inventory management module in real time. S4.3. If the real-time weight exceeds the allowable tolerance range, the ERP system will trigger an abnormality warning mechanism, display a visual warning message on the operation interface, notify the management personnel via email, SMS or management APP and stop the current work until the management personnel complete the abnormality handling and issue a recovery command through the human-computer interaction interface to continue working. The S5.ERP system stores the weighing results, data collection time, operator identity information, identification information, business object status change records, and anomaly handling records in the ERP system's database, forming an unalterable audit log.
[0009] Preferably, in step S1, the intelligent weighing device has a dynamic weighing compensation function. If the weighing data is unstable, the dynamic compensation algorithm is started, and the final weight data is generated after correction. The data acquisition module supports multi-code association scanning.
[0010] Preferably, in step S4, the anomaly warning mechanism has a tiered processing function: A. If the real-time weight data exceeds the allowable tolerance range but the deviation rate is ≤5%, it is judged as a level one anomaly. The ERP system will only pop up a warning box on the operation interface, allowing the operator to choose to continue execution or pause the review. If the operator chooses to continue execution, the ERP system will record the anomaly information but will not interrupt the work. B. If the real-time weight data exceeds the allowable tolerance range and the deviation rate is >5%, it is judged as a level 2 anomaly. The current work is suspended and the corresponding business object is locked. An alarm message containing details of the abnormal data is sent to the management personnel, and an emergency handling status pops up in the ERP system management interface. Only the management personnel can handle the anomaly after authorization. C. After the exception is handled, the ERP system automatically generates an exception handling report, which records the personnel handling the exception, the handling measures, and the handling results, and stores them in association with the weighing data and exception alarm information.
[0011] Preferred method: In step S5, the audit log uses blockchain notarization technology. Each weighing generates a hash value and uploads it to the enterprise's private blockchain node. The audit log supports multi-dimensional traceability and querying, specifically: A. Enter the business object information to query the time series, associated equipment, operators, and anomaly handling status of all weighing records under this order; B. Enter the code of the intelligent weighing device to query all weighing records, associated business objects and data verification results of the device within a specified time range; C. Input weight anomaly data to trace the cause of the anomaly, the handling process and the final result. The log data stored on the blockchain can only be added, and cannot be modified or deleted.
[0012] In summary, the present invention has the following beneficial effects: 1. Through standardized processing at the edge computing layer, and relying on a 5G encrypted transmission channel to directly connect to the ERP system, it replaces the traditional manual recording and entry mode, realizing the full-process automation of weighing data from collection, processing to synchronization to the ERP system. The data mapping module accurately matches inventory, production, procurement and other business operations, and instantly triggers operations such as receiving confirmation, material feeding completion marking, and inventory quantity update, ensuring the synchronization of inventory data and production work order status in the ERP system with the actual weighing scenario, and solving the problem of data lag in manual entry.
[0013] 2. The dynamic compensation algorithm can correct unstable weighing data, reducing errors from the source of information collection. The data verification module compares the item identification with the ERP pre-stored data, which can quickly identify abnormal data. The abnormality warning mechanism can be used to handle abnormalities in a graded manner, which can avoid unnecessary business interruption while ensuring the accuracy and reliability of the data.
[0014] 3. The weighing results, collection time, operators, changes in the status of business objects, and records of abnormal handling are compiled into an audit log. The data is traceable and stored immutably throughout the entire process. At the same time, the audit log supports multi-dimensional traceability and query. It not only meets the needs of internal business auditing and accountability, but also provides authoritative and verifiable data evidence for dealing with external compliance inspections, quality disputes and other scenarios, effectively strengthening the enterprise's business risk management capabilities.
[0015] 4. In production, warehousing, and procurement scenarios with frequent weighing tasks, data backlog caused by manual entry is avoided, improving overall business processing efficiency. The preprocessing function of the edge computing processing layer reduces the data processing pressure of the ERP system and optimizes system performance. Real-time data support reduces additional costs caused by inventory misjudgment and production plan adjustments, while blockchain traceability reduces communication and risk costs caused by unclear responsibilities and data disputes. Attached Figure Description
[0016] Figure 1 This is a block diagram of the overall architecture of the present invention; Figure 2 This is a diagram illustrating the data processing steps of the present invention. Detailed Implementation
[0017] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.
[0018] Example: Electronic weighing real-time feedback ERP system, such as Figure 1 As shown, the system includes an electronic weighing ERP system. The electronic weighing ERP system comprises an intelligent weighing terminal, an edge computing processing layer, a data transmission layer, and the ERP system itself. The intelligent weighing terminal includes at least one electronic weighing device with data acquisition and edge communication capabilities. This device has a built-in weighing sensor, data acquisition module, and communication module for real-time acquisition of item weight data and item identification information. Item identification information includes QR code and RFID tag information. The intelligent weighing device has a dynamic weighing compensation function; if the weighing data is unstable, a dynamic compensation algorithm is activated. When the target object is placed on the weighing platform, the device automatically detects the weight. The stability of the data is assessed by determining whether the fluctuation range of five consecutive sampling data is ≤0.02%. If the data is unstable, a dynamic compensation algorithm is activated. Based on the vibration frequency of the platform, such as 50Hz / 60Hz, and the deviation of the object's placement position, the original weight data is compensated and corrected. The final weight data is then generated after correction. The weighing sensor is used to collect the weight data of the items in real time, reducing errors from the source of information collection. The data acquisition module uses a barcode scanner and an RFID reader to simultaneously scan the material barcode and batch barcode of the target object, associating multi-dimensional business identification information such as material code, batch number, and production date with the weight data. The data acquisition module is used to collect the item identification information in real time and transmit the weight data and identification information as raw data to the edge computing processing layer.
[0019] like Figure 1 As shown, the edge computing processing layer includes an edge computing gateway and a data preprocessing module. The data preprocessing module is used to standardize the format of the collected information. The data preprocessing module identifies abnormal weighing data and triggers abnormal alarm signals through an anomaly detection algorithm. The anomaly detection algorithm is based on the 3σ principle outlier detection algorithm to identify abnormal weighing data, such as overweight, underweight, and data jumps. The preprocessing function of the edge computing processing layer reduces the data processing pressure on the ERP system and optimizes system performance. Real-time data support reduces additional costs caused by inventory misjudgments and production plan adjustments. The data transmission layer uses a 5G communication module to establish an encrypted data transmission channel between the edge computing processing layer and the ERP system. The encrypted data transmission channel supports TCP / IP and MQTT IoT communication protocols, with a current data transmission latency of ≤100ms. After standardized processing by the edge computing processing layer, the data is directly connected to the ERP system through the 5G encrypted transmission channel, replacing the traditional manual recording and entry mode. This achieves full automation of the weighing data process from collection and processing to synchronization to the ERP system. In production, warehousing, and procurement scenarios with frequent weighing tasks, it avoids data backlog caused by manual entry and improves overall business processing efficiency.
[0020] like Figure 1 As shown, the ERP system includes a customized API interface module, a data mapping module, a business linkage module, an inventory management module, a production execution module, and a procurement management module. The API interface module interfaces with the inventory management module, the production execution module, and the procurement management module. The data mapping module matches and maps standardized information with the data fields of the inventory management module, the production execution module, and the procurement management module. The business linkage module triggers corresponding business operations in the ERP system based on the weighing data, such as automatic inventory quantity updates, production task progress adjustments, and purchase receipt generation. The data mapping module accurately matches inventory, production, and procurement businesses, and instantly triggers operations such as receiving confirmation, material input completion marking, and inventory quantity updates, ensuring the synchronization of inventory data and production work order status in the ERP system with the actual weighing scenario, and solving the problem of data lag caused by manual entry.
[0021] like Figure 1 As shown, the intelligent weighing terminal also includes a human-machine interaction module and a local storage module. The human-machine interaction module displays weighing data, abnormal alarm information, and operation instructions. It allows operators to manually input additional information about the items and obtain the business identification information of the target object. This business identification information includes at least one of the following: purchase order number, material code, production work order number, and batch number. The local storage module caches at least 72 hours of weighing data to prevent data loss due to data transmission interruptions. The edge computing processing layer also includes a data verification module. This module compares the item identification information with the item data pre-stored in the ERP system's database. The judgment range of the data verification module is ±5% of the weight range. When the data verification module determines that the weighing data is abnormal, it sends an alarm message to the mobile terminal of the management personnel. The abnormal warning mechanism has a tiered processing function. A. If the real-time weight data exceeds the allowable tolerance range but the deviation rate is ≤5%, it is judged as a level one anomaly. The ERP system will only pop up a warning box on the operation interface, allowing the operator to choose to continue execution or pause the review. If the operator chooses to continue execution, the ERP system will record the anomaly information but will not interrupt the work. B. If the real-time weight data exceeds the allowable tolerance range and the deviation rate is >5%, it is judged as a level 2 anomaly. The current work is suspended and the corresponding business object is locked. An alarm message containing details of the abnormal data is sent to the management personnel, and an emergency handling status pops up in the ERP system management interface. Only the management personnel can handle the anomaly after authorization. C. After the anomaly is handled, the ERP system automatically generates an anomaly handling report, which records the personnel involved, the handling measures, and the handling results, and stores them in association with the weighing data and anomaly alarm information. It enables rapid identification of abnormal data and graded handling of anomalies through an anomaly early warning mechanism, which can avoid unnecessary business interruptions while ensuring the accuracy and reliability of data.
[0022] like Figure 1 As shown, the data transmission layer also includes a data encryption module. This module uses the AES-256 encryption algorithm to encrypt the transmitted data and the SHA-256 data integrity verification algorithm to ensure that the data is not tampered with during transmission. The ERP system also includes a data statistical analysis module. Based on historical weighing data and the ERP system's business data, this module generates weighing efficiency reports, item loss rate analysis reports, and supplier supply quality assessment reports. These reports can be displayed in visual charts on the ERP system's management interface and are stored as immutable audit logs. The audit logs utilize blockchain technology for evidence storage. Each weighing operation generates a hash value and uploads it to the company's private blockchain node. The audit logs support multi-dimensional traceability and querying, specifically: A. Enter the business object information to query the time series, associated equipment, operators, and anomaly handling status of all weighing records under this order; B. Enter the code of the intelligent weighing device to query all weighing records, associated business objects and data verification results of the device within a specified time range; C. Input weight anomaly data to trace the cause of the anomaly, the handling process and the final result. The log data stored on the blockchain can only be added and cannot be modified or deleted. The audit log is created by recording information such as weighing results, collection time, operators, changes in the status of business objects, and records of abnormal handling. The entire process of data traceability and tamper-proof storage is ensured. At the same time, the audit log supports multi-dimensional traceability queries, which not only meets the needs of internal business audits and accountability, but also provides authoritative and verifiable data evidence for dealing with external compliance inspections and quality disputes. This effectively strengthens the enterprise's business risk management capabilities. Real-time data support reduces the additional costs caused by inventory misjudgments and production plan adjustments, while blockchain traceability reduces communication and risk costs caused by unclear responsibilities and data disputes.
[0023] like Figure 1 As shown, the data processing method of the electronic weighing real-time feedback ERP system, used to operate the electronic weighing real-time feedback ERP system, includes the following steps: S1. Weigh the target object using intelligent weighing equipment to generate corresponding weight data. At the same time, the data acquisition module scans the identification information of the target object, or the operator can manually input it through the human-machine interface. S2. The data acquisition module transmits the weight data and identification information to the edge computing processing layer. The edge computing processing layer parses the weight data and identification information, and after associating and matching the weight data with the item identification information, it encapsulates it into a data packet that conforms to the data format requirements of the ERP system. S3. The edge computing processing layer sends data packets to the ERP system through an encrypted data transmission channel using the TCP / IP protocol or the MQTT IoT communication protocol, while simultaneously receiving data reception status feedback from the ERP system. After receiving the data packet, the S4.ERP system locates the corresponding business object based on the identification information within it. The business objects include purchase orders and production work orders, and then performs the following operations: S4.1. Extract the expected weight data from the business object, compare the real-time collected weight data with the expected weight data, and determine whether the real-time weight is within the allowable tolerance range. S4.2. If the real-time weight is within the allowable tolerance range, the ERP system will mark the purchase order as received and the production work order as material feeding completed, and update the inventory quantity of the corresponding material in the inventory management module in real time. S4.3. If the real-time weight exceeds the allowable tolerance range, the ERP system will trigger an abnormality warning mechanism, display a visual warning message on the operation interface, notify the management personnel via email, SMS or management APP and stop the current work until the management personnel complete the abnormality handling and issue a recovery command through the human-computer interaction interface to continue working. The S5.ERP system stores the weighing results, data collection time, operator identity information, identification information, business object status change records, and anomaly handling records in the ERP system's database, forming an unalterable audit log.
[0024] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.
Claims
1. An electronic weighing real-time feedback ERP system, including an electronic weighing ERP system, characterized in that: The electronic weighing ERP system includes an intelligent weighing terminal, an edge computing processing layer, a data transmission layer, and an ERP system. The intelligent weighing terminal includes at least one electronic weighing device with data acquisition and edge communication functions. The electronic weighing device has a built-in weighing sensor, a data acquisition module and a communication module. The weighing sensor is used to collect the weight data of the item in real time. The data acquisition module is used to collect the identification information of the item in real time and transmit the weight data and identification information to the edge computing processing layer. The edge computing processing layer includes an edge computing gateway and a data preprocessing module. The data preprocessing module is used to standardize the format of the collected information. The data preprocessing module identifies abnormal weighing data and triggers an abnormal alarm signal through an anomaly detection algorithm. The data transmission layer uses a 5G communication module to establish an encrypted data transmission channel between the edge computing processing layer and the ERP system. The encrypted data transmission channel supports TCP / IP protocol and MQTT IoT communication protocol. The ERP system includes an API interface module, a data mapping module, a business linkage module, an inventory management module, a production execution module, and a procurement management module. The API interface module interfaces with the inventory management module, the production execution module, and the procurement management module. The data mapping module matches and maps standardized information with the data fields of the inventory management module, the production execution module, and the procurement management module. The business linkage module triggers corresponding business operations in the ERP system based on the weighing data.
2. The electronic weighing real-time feedback ERP system according to claim 1, characterized in that: The intelligent weighing terminal also includes a human-computer interaction module and a local storage module. The human-computer interaction module is used to display weighing data, abnormal alarm information and operation instructions. The human-computer interaction module supports operators to manually input additional information about the items.
3. The electronic weighing real-time feedback ERP system according to claim 1, characterized in that: The edge computing processing layer also includes a data verification module, which compares the item identification information with the item data pre-stored in the ERP system's database. The judgment range of the data verification module is ±5% of the weight range. When the data verification module determines that the weighing data is abnormal, it sends an alarm message to the mobile terminal of the management personnel.
4. The electronic weighing real-time feedback ERP system according to claim 1, characterized in that: The data transmission layer also includes a data encryption module, which uses the AES-256 encryption algorithm to encrypt the transmitted data.
5. The electronic weighing real-time feedback ERP system according to claim 1, characterized in that: The ERP system also includes a data statistics and analysis module, which generates weighing efficiency reports, item loss rate analysis reports, and supplier supply quality assessment reports based on historical weighing data and business data from the ERP system.
6. A data processing method for an electronic weighing real-time feedback ERP system, used to operate the electronic weighing real-time feedback ERP system according to any one of claims 1-5, comprising the following steps: S1. Weigh the target object using intelligent weighing equipment to generate corresponding weight data. At the same time, the data acquisition module scans the identification information of the target object, or the operator can manually input it through the human-machine interface. S2. The data acquisition module transmits the weight data and identification information to the edge computing processing layer. The edge computing processing layer parses the weight data and identification information, and after associating and matching the weight data with the item identification information, it encapsulates it into a data packet that conforms to the data format requirements of the ERP system. S3. The edge computing processing layer sends data packets to the ERP system through an encrypted data transmission channel using the TCP / IP protocol or the MQTT IoT communication protocol, while simultaneously receiving data reception status feedback from the ERP system. After receiving the data packet, the S4.ERP system locates the corresponding business object based on the identification information within it. The business objects include purchase orders and production work orders, and then performs the following operations: S4.
1. Extract the expected weight data from the business object, compare the real-time collected weight data with the expected weight data, and determine whether the real-time weight is within the allowable tolerance range. S4.
2. If the real-time weight is within the allowable tolerance range, the ERP system will mark the purchase order as received and the production work order as material feeding completed, and update the inventory quantity of the corresponding material in the inventory management module in real time. S4.
3. If the real-time weight exceeds the allowable tolerance range, the ERP system will trigger an abnormality warning mechanism, display a visual warning message on the operation interface, notify the management personnel via email, SMS or management APP and stop the current work until the management personnel complete the abnormality handling and issue a recovery command through the human-computer interaction interface to continue working. The S5.ERP system stores the weighing results, data collection time, operator identity information, identification information, business object status change records, and anomaly handling records in the ERP system's database, forming an unalterable audit log.
7. The data processing method of the electronic weighing real-time feedback ERP system according to claim 6, characterized in that: In step S1, the intelligent weighing device has a dynamic weighing compensation function. If the weighing data is unstable, the dynamic compensation algorithm is started, and the final weight data is generated after correction. The data acquisition module supports multi-code association scanning.
8. The data processing method of the electronic weighing real-time feedback ERP system according to claim 6, characterized in that: In step S4, the anomaly warning mechanism has a tiered processing function: A. If the real-time weight data exceeds the allowable tolerance range but the deviation rate is ≤5%, it is judged as a level one anomaly. The ERP system will only pop up a warning box on the operation interface, allowing the operator to choose to continue execution or pause the review. If the operator chooses to continue execution, the ERP system will record the anomaly information but will not interrupt the work. B. If the real-time weight data exceeds the allowable tolerance range and the deviation rate is >5%, it is judged as a level 2 anomaly. The current work is suspended and the corresponding business object is locked. An alarm message containing details of the abnormal data is sent to the management personnel, and an emergency handling status pops up in the ERP system management interface. Only the management personnel can handle the anomaly after authorization. C. After the exception is handled, the ERP system automatically generates an exception handling report, which records the personnel handling the exception, the handling measures, and the handling results, and stores them in association with the weighing data and exception alarm information.
9. The data processing method of the electronic weighing real-time feedback ERP system according to claim 6, characterized in that: In step S5, the audit log uses blockchain notarization technology. Each weighing data point generates a hash value and is uploaded to the company's private blockchain node. The audit log supports multi-dimensional traceability and querying, specifically: A. Enter the business object information to query the time series, associated equipment, operators, and anomaly handling status of all weighing records under this order; B. Enter the code of the intelligent weighing device to query all weighing records, associated business objects and data verification results of the device within a specified time range; C. Input weight anomaly data to trace the cause of the anomaly, the handling process and the final result. The log data stored on the blockchain can only be added, and cannot be modified or deleted.