An agricultural supply chain data collaborative management method and system

Abstract

The application provides an agricultural supply chain data collaborative management method and system, and relates to the technical field of agricultural supply chain data management. The method comprises the following steps: obtaining the pre-link data state of the batch of products; receiving the temporary receiving information of the batch of products, updating the state of the batch of products in the current link to a temporary state, and generating a to-be-verified task; according to the to-be-verified task, sending a data supplement notification to an upstream participant; matching the temporary state data and the formal data, and performing logical verification; if the matching is successful, the temporary state of the batch of products in the current link is converted to a formal state, and the to-be-verified task is released; if the data verification cannot be completed, an early warning is triggered, and the temporary state and the verification operation log are recorded. The method effectively solves the problem that the physical flow speed and the online data submission speed in the fresh agricultural product supply chain are not matched, and maintains the integrity and rigor of the traceability data.

Description

Technical Field

[0001] This invention relates to the field of agricultural supply chain data management technology, and more specifically, to a method and system for collaborative management of agricultural supply chain data. Background Technology

[0002] In a fresh vegetable supply chain, to achieve end-to-end quality traceability from farm to table, all participants, including planting bases, processing centers, cold chain logistics companies, and sales terminals, must submit data from their respective stages on a unified supply chain collaboration platform. This platform generates a unique traceability code for each batch of vegetable products. Consumers can obtain complete lifecycle information for that batch of products by scanning the traceability code on the product packaging.

[0003] To ensure the integrity and logical accuracy of the data chain, this collaborative platform employs a state node-based process control mechanism. Specifically, the lifecycle of a batch of products is defined as a series of continuous states, such as "sown," "fertilized," "harvested," "transporting," "warehousing," "processing," "shipped," and "shelf-opened." The platform stipulates that data for the next state node can only be entered after the data for the previous state node has been fully submitted. For example, the processing center can only begin submitting data for the "processing" stage, such as washing, cutting, and packaging, once the system shows a batch of vegetables as "warehousing." This design aims to prevent data entry disorder from the outset, ensuring the correctness of the causal and temporal relationships in the traceability information chain.

[0004] This strict sequential control ensures data standardization in most cases. However, in specific business scenarios, the rigidity of this mechanism exposes its inherent flaws. Consider a specific scenario: A large supermarket chain, due to a promotional activity, urgently places a large order for fresh, pre-cut vegetables with a processing center, requiring delivery by the following morning. The processing center then issues raw material procurement instructions to its partner planting base.

[0005] Upon receiving the instructions, the planting base immediately organized personnel to harvest the vegetables. The harvest was completed in the afternoon, and the vegetables were immediately loaded onto trucks. To save time, the truck drivers responsible for cold chain transportation departed immediately after completing the paper handover procedures, heading towards the processing center. Meanwhile, the on-site management personnel at the planting base were unable to return to the office immediately to submit the "harvested" data for this batch of vegetables on the collaborative platform via computer, including harvest time, harvest weight, and responsible person information, because they needed to handle the final harvest work, inventory tools, and arrange the next day's agricultural activities.

[0006] The cold-chain truck arrives at the processing center in the evening. The receiving department of the processing center is ready to receive this batch of vegetables and handle the warehousing. According to the process, the receiver needs to scan the batch traceability code on the transfer note on the collaborative platform and update the status of this batch to "warehoused". However, since the planting base has not submitted the "harvested" data, the status of this batch of products in the platform system remains "fertilized" or an earlier status. According to the control rule of "the next status depends on the previous status" set by the platform, the system rejects the "warehoused" operation request of the processing center and prompts "missing data in the previous link".

[0007] This situation has blocked the business process. The receiver in the processing center cannot complete the digital warehousing operation, which means that the subsequent production planning system cannot obtain the confirmation signal of the raw material arrival and cannot automatically trigger the production work order to arrange the production line for cleaning and cutting. Physically, the cold-chain truck full of fresh vegetables is parked in the unloading area waiting to be unloaded. Every minute of waiting increases the energy consumption of the truck refrigeration system. At the same time, opening and closing the doors to communicate may also cause the cold chain to be interrupted, increasing the risk of vegetable quality deterioration. The receiver can only repeatedly urge the management of the planting base to submit data online as soon as possible by phone. The management of the planting base dozens of kilometers away may have gone home after a day's work and needs to return to the office or find a computer to complete the operation.

[0008] In this process, a rigorous control process designed to ensure data quality has instead become a bottleneck affecting the actual operation efficiency of the supply chain. It not only causes ineffective waiting in the logistics link and increases the operating costs, but more importantly, this physical waiting caused by the delay of data transfer directly threatens the quality of fresh agricultural products, which is contrary to the original intention of establishing the quality traceability system. The rigid control of data transfer by the system fails to adapt to the flexibility and suddenness of business operations in the physical world, resulting in the disconnection and conflict between the digital process and the physical process. Summary of the Invention

[0009] The purpose of the present invention is to provide a method and system for collaborative management of agricultural supply chain data, which effectively solves the problem of the mismatch between the physical transfer speed and the online data submission speed in the fresh agricultural product supply chain, and maintains the integrity and rigor of the traceability data on the premise of ensuring product quality and supply chain efficiency.

[0010] In the first aspect, the present invention provides a method for collaborative management of agricultural supply chain data, including the following steps:

[0011] S1. When receiving a batch of products, obtain the data status of the previous link of this batch of products;

[0012] S2. Based on the obtained status of the preceding stage data, if the preceding stage data is not in a formal state, then receive the temporary receiving information for this batch of products;

[0013] S3. Based on the temporary received information, update the status of the batch of products in the current stage to a temporary status, and generate a pending verification task corresponding to the temporary status;

[0014] S4. Based on the tasks to be verified, send a data supplement notification to the upstream participants responsible for submitting the formal data of the preceding stages;

[0015] S5. After the upstream participant responds to the data supplement notification and submits the formal data of the previous stage, the temporary status data is matched with the formal data according to the batch product traceability code, and the successfully matched data is logically verified.

[0016] S6. Based on the matching results and logical verification results, if the matching is successful and the logical verification is compliant, the temporary status of the batch of products in the current stage will be changed to the formal status, and the pending verification task will be released.

[0017] S7. If data verification cannot be completed within the specified time limit for supplementary entry, an early warning will be triggered, and a temporary status and verification operation log will be recorded.

[0018] The agricultural supply chain data collaborative management method provided by this invention effectively manages the problem of limited digital operation in subsequent links caused by the lag in data submission in the upstream links, while ensuring that the quality of agricultural products is not damaged due to physical waiting and that the efficiency of the supply chain is not reduced due to data blockage. At the same time, it continuously ensures the integrity, time sequence correctness and logical consistency of the traceability data throughout the process.

[0019] Secondly, this invention provides an agricultural supply chain data collaborative management system, comprising:

[0020] The acquisition module is used to acquire the data status of the preceding stages of the batch of products when a batch of products is received.

[0021] The receiving module is used to receive temporary receiving information for this batch of products based on the status of the data obtained from the preceding stages. If the data from the preceding stages is not in a formal state, then the receiving module will receive temporary receiving information for this batch of products.

[0022] The generation module is used to update the status of the batch of products in the current stage to a temporary status based on the temporary received information, and generate a corresponding task to be verified.

[0023] The sending module is used to send data supplement notifications to upstream participants responsible for submitting formal data in the preceding stages, based on the tasks to be verified.

[0024] The matching module is used to match temporary status data with formal data based on the batch product traceability code after the upstream participant responds to the data supplement notification and submits the formal data of the previous stage, and to perform logical verification on the successfully matched data.

[0025] The cancellation module is used to change the temporary status of the batch of products in the current stage to the formal status and cancel the pending verification task if the matching result and the logical verification result are successful.

[0026] The early warning module is used to trigger an early warning if data verification is not completed within the specified time limit for supplementary entry, and to record the temporary status and verification operation log.

[0027] As can be seen from the above, the agricultural supply chain data collaborative management method provided by this invention allows downstream links to receive and process physical goods upon arrival, effectively avoiding logistics blockages and physical waiting caused by online data lag, which is especially crucial for time-sensitive fresh agricultural products. Simultaneously, it significantly reduces the risks of cold chain interruption, prolonged exposure, and quality deterioration caused by waiting at various stages of the supply chain, highly aligning with the initial purpose of establishing a quality traceability system. Furthermore, it eliminates invalid waiting time caused by delayed data submission, enabling subsequent production, processing, and distribution stages to start promptly, improving the supply chain's response speed and turnover efficiency. Through temporary status recording, automatic verification processes, and early warning and traceability mechanisms, it ensures that even when physical and digital processes are temporarily asynchronous, the final traceability data chain remains complete, sequentially correct, and logically consistent. Moreover, it empowers the supply chain collaboration platform to flexibly respond to the suddenness and flexibility of business operations in the physical world while ensuring data rigor, effectively managing the asynchronicity between digital and physical processes, enhancing system adaptability, and helping to reduce waiting time for transportation tools such as refrigerated trucks, thereby reducing unnecessary energy consumption and labor costs.

[0028] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0029] Figure 1 A flowchart illustrating an agricultural supply chain data collaborative management method provided in an embodiment of the present invention.

[0030] Figure 2 This is a schematic diagram of an agricultural supply chain data collaborative management system provided in an embodiment of the present invention.

[0031] Label Explanation:

[0032] 100. Acquisition Module; 200. Receiving Module; 300. Generating Module; 400. Sending Module; 500. Matching Module; 600. Deactivating Module; 700. Early Warning Module. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0034] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0035] Reference Appendix Figure 1 This invention provides a method for collaborative management of agricultural supply chain data, comprising the following steps:

[0036] S1. When receiving a batch of products, obtain the status of the preceding process data for that batch of products;

[0037] S2. Based on the obtained data status of the preceding stages, if the preceding stage data is not in a formal state, then receive the temporary receiving information for this batch of products; the temporary receiving information includes the receiving time, the quantity received, and the person in charge of receiving;

[0038] S3. Based on the temporary received information, update the status of the batch of products in the current stage to a temporary status, and generate a pending verification task corresponding to the temporary status;

[0039] S4. Based on the tasks to be verified, send a data supplement notice to the upstream participants responsible for submitting the formal data of the preceding stages; the data supplement notice includes the formal data of the preceding stages to be supplemented and the supplementary data time limit stipulated according to the characteristics of agricultural products; the formal data includes the reporting time, the reporting quantity and the reporting person in charge;

[0040] S5. After the upstream participant responds to the data supplement notification and submits the formal data of the previous stage, the temporary status data is matched with the formal data according to the batch product traceability code, and the successfully matched data is logically verified.

[0041] S6. Based on the matching results and logical verification results, if the matching is successful and the logical verification is compliant, the temporary status of the batch of products in the current stage will be changed to the formal status, and the pending verification task will be released.

[0042] S7. If data verification cannot be completed within the specified time limit for supplementary entry, an early warning will be triggered, and a temporary status and verification operation log will be recorded.

[0043] The status of upstream data indicates whether the data record of a product at a certain stage of the supply chain has been completed and confirmed as the final state by the system. This can be implemented using status fields, Boolean flags, or enumeration values ​​in the database, such as "submitted," "pending confirmation," or "verified." Its main purpose is to determine whether the current stage can proceed based on the data from the upstream stage. Temporary receipt information refers to key information recorded before the upstream data is available when the product is physically received. This information may include the receipt time, quantity received, and the person responsible for receiving the product. Its main purpose is to allow physical operations to proceed in the event of an incomplete data chain and to provide a basis for subsequent data entry and verification. Temporary status indicates that a batch of products has physically entered the current stage, but the data from the upstream stage has not yet been verified, or the received data in the current stage has not been finally confirmed. This can be implemented using status fields or specific identifiers in the database, such as "pending verification upon entry" or "temporary entry." Its main purpose is to allow the business process to continue while marking the uncertainty of the data. Pending verification tasks refer to tasks created to ensure the transition from a temporary to a formal state. These tasks require upstream participants to submit missing data and perform data comparison and confirmation. They can be implemented using work orders, to-do lists, or specific data records in a task management system. Their primary purpose is to drive the data completion process and ensure the integrity and consistency of the data chain. Data entry notifications are messages sent by the system to upstream participants requesting them to complete missing data. These can be implemented via email, SMS, in-app messages, or API calls. Their primary purpose is to proactively remind and guide upstream participants to complete data submission in a timely manner. Logical verification refers to the process of verifying the consistency between successfully matched temporary state data and formal data. This can be implemented using preset business rules, data range checks, or deviation analysis based on historical data. For example, checking the deviation between received and reported data. Its primary purpose is to ensure data accuracy.

[0044] This invention provides a method for managing the data flow of an agricultural product supply chain. Its core working principle lies in introducing a hierarchical management mechanism for data status, distinguishing between "temporary" and "formal" states, and establishing a "data verification channel" and "asynchronous management" framework. Specifically, when physical goods have arrived in the downstream links of the supply chain, but the formal traceability data from the upstream links has not been submitted in time for some reason, this method allows the downstream links to enter key temporary receiving information through a "temporary operation entry," marking the batch of products as "temporary," thereby avoiding interruptions in the physical process. The system then automatically generates a "task to be verified" and notifies the upstream responsible party to supplement the missing formal data within a preset time limit. Once the upstream data is submitted, the system will automatically trigger the "data verification process," matching and verifying the temporary and formal data, and automatically converting the "temporary state" to the "formal state," ensuring the completeness and accuracy of the final traceability data. Furthermore, this method includes early warning and backtracking mechanisms to address situations where verification cannot be completed in time, thereby effectively managing the temporary asynchrony between physical flow and digital operations while ensuring data rigor, improving the overall efficiency of the supply chain and product quality.

[0045] The core innovation of this application lies in the introduction of a temporary information receiving and temporary status mechanism, combined with an asynchronous verification process of pending verification tasks and data supplement notifications. This solves the business process blockage and physical waiting problems caused by the failure to submit data in the preceding stages in a timely manner under the traditional data sequential control, thereby achieving decoupling and collaboration between physical operations and digital records, improving the operational efficiency of the supply chain, and ensuring the quality of agricultural products.

[0046] Specifically, this method addresses the challenge of delayed data submission in the upstream stages of the agricultural supply chain by establishing a data collaborative management mechanism. When a batch of products arrives at the current stage and is received, the system first obtains the data status of the upstream stages to determine if it is in a formal state. If the system identifies that the upstream data has not yet been formally submitted, it allows the current stage to receive temporary reception information for the batch of products, which records key details of the physical reception. Based on this temporary reception information, the system updates the status of the batch of products in the current stage to a temporary state and simultaneously generates a pending verification task. This temporary state allows downstream business processes to start, avoiding physical waiting. Subsequently, based on the generated pending verification task, the system sends a data supplement notification to upstream participants. This notification specifies the formal data content that needs to be supplemented and the supplementation deadline stipulated according to the characteristics of agricultural products, prompting upstream participants to complete data submission in a timely manner. Once the upstream participants respond to the notification and submit the formal data from the upstream stages, the system matches the temporary status data with the formal data according to the batch product traceability code and performs logical verification on the successfully matched data to ensure data consistency. Based on the matching and verification results, if the data matching is successful and the logical verification meets the preset rules, the system will change the temporary status of the batch of products in the current stage to the formal status and release the corresponding pending verification task, marking the completion of the data verification process. Conversely, if data verification is not completed within the specified supplementary entry time limit, the system will trigger an early warning mechanism and record the temporary status and verification operation log for subsequent anomaly handling and traceability, thereby ensuring the integrity and traceability of the data chain while taking into account the flexibility and timeliness of business operations.

[0047] As a preferred embodiment, the solution of this application is implemented as follows: In a fresh agricultural product supply chain management platform, when the receiving department of the processing center receives a batch of vegetables from the planting base, the receiving clerk scans the batch traceability code using a handheld terminal. The system queries the "harvested" data status of the batch of vegetables at the planting base. If it finds that the planting base has not yet submitted official "harvested" data on the platform, the system will not block the receiving operation, but will prompt the receiving clerk to enter temporary receiving information, including the receiving time, the quantity of vegetables received, and the identity of the receiving clerk. The system then updates the warehousing status of the batch of vegetables at the processing center to "temporary warehousing" and automatically generates a "harvesting data pending verification" task. At the same time, the system sends a data supplement notification to the on-site management personnel of the planting base via WeChat or SMS. The notification includes the traceability code of the batch of vegetables, the harvest time to be supplemented, the harvest quantity, the person in charge of harvesting, and other information, and sets a supplementary data supplementation time limit determined according to the shelf life of the vegetables, such as 2 hours. The processing center can immediately begin unloading and preliminary processing. Once the planting base managers have entered the official "harvested" data into the platform within the specified time limit, the system automatically matches the temporarily received quantity with the officially reported harvest quantity based on the traceability code and performs logical verification, such as checking whether the deviation between the two is within the allowable range. If the verification passes, the system automatically changes the "temporary warehousing" status of the batch of vegetables to "officially warehousing" and removes the "pending verification of harvested data" task. If the planting base fails to enter the data within the time limit, the system will send an early warning notification to the relevant personnel at the processing center and the planting base, and record the temporary status and unverified log of the batch of data for manual intervention.

[0048] Through the above-described solution, this application addresses the problems of business process blockage, physical waiting, and agricultural product quality deterioration caused by the untimely submission of data from upstream stages in the agricultural supply chain. This solution decouples physical operations from digital records by allowing temporary data reception and status updates before data is officially available, thereby improving supply chain operational efficiency. Simultaneously, asynchronous data supplementation notification and verification mechanisms ensure the integrity and accuracy of the data chain, preventing disconnects and conflicts between digital and physical processes, thus guaranteeing the freshness of agricultural products and the reliability of traceability information.

[0049] In some embodiments, the specific steps in step S3 include:

[0050] S3A1. Obtain product type information for batch products;

[0051] S3A2. Based on product type information, obtain the shelf life parameters and value density parameters corresponding to the product type by querying preset product characteristic data;

[0052] S3A3. Assess the priority of tasks to be verified based on the amount received, shelf life parameters, and value density parameters in the temporary reception information;

[0053] S3A4. Update the status of the batch of products in the current stage to a temporary status, and generate a pending verification task corresponding to the temporary status and associate it with priority information, so that upstream participants can arrange the processing order of the pending verification tasks according to the priority information.

[0054] Pre-defined product characteristic data refers to a pre-stored set of inherent attributes related to different types of agricultural products. This data can be implemented using databases, configuration files, or data dictionaries, providing standardized quantitative basis for aspects such as shelf life and value density. Shelf life parameters are quantitative indicators reflecting the length of time a specific agricultural product maintains its quality and safety under specific storage conditions. They can be expressed in days, hours, or specific date ranges. Value density parameters are quantitative indicators reflecting the economic value contained in a unit mass or unit volume of a specific agricultural product. They can be expressed in terms of price per kilogram, total price per box, or profit margin per unit volume. Priority refers to the ranking criteria used to indicate the urgency and importance of tasks pending verification. It can be expressed in terms of numerical values, levels, or timestamps. Priority information refers to data associated with tasks pending verification that guides their processing order. It can be expressed in terms of priority values, priority level identifiers, or priority tags.

[0055] This solution aims to address how to dynamically assess and prioritize tasks when generating pending verification tasks, taking into account agricultural product characteristics and received information. This ensures that subsequent data processing prioritizes urgent or important batches, thereby guaranteeing agricultural product quality and improving supply chain responsiveness. Specifically, when the system prepares to update the status of a batch of products to a temporary state and generate pending verification tasks based on temporary received information, it first obtains the product type information for that batch. By identifying the specific product type, the system can differentiate its processing based on the inherent characteristics of different agricultural products, laying the foundation for subsequent acquisition of product characteristic data. Based on this, the system retrieves the corresponding shelf-life and value density parameters for that product type by querying preset product characteristic data. This step is crucial; it utilizes product type information to obtain key parameters directly related to product characteristics. The shelf-life parameter reflects the product's timeliness, while the value density parameter reflects its economic value. Preset data queries ensure the accuracy and automation of the assessment, providing a quantitative basis for subsequent priority evaluation. Subsequently, the system comprehensively considers the received volume in the temporary received information, the acquired shelf-life parameters, and the value density parameters to prioritize the upcoming pending verification tasks. The received volume reflects the scale of the batch of products, the shelf life parameter directly relates to the perishability of the products, and the value density parameter relates to the economic value of the products. Through a comprehensive evaluation of these parameters, the system can intelligently determine which pending verification tasks are more urgent and important, thus providing a basis for subsequent priority processing. Finally, the system updates the status of the batch of products at the current stage to a temporary state and generates a pending verification task corresponding to the temporary state, while associating the previously evaluated priority information with this task. This approach allows the system to intelligently predict and prioritize subsequent data entry and verification processes while the pending verification task is being generated—that is, while the physical business process continues. When a data entry notification is sent to upstream participants, the notification will include the priority of the task. Upstream participants no longer blindly process all pending verification tasks but can prioritize batches with short shelf lives, high value, or large quantities based on this priority information. This optimizes the workflow of upstream participants, ensures timely entry of key data, effectively reduces the risk of delays in data verification, and improves the responsiveness and efficiency of the entire supply chain. By incorporating priority assessment when generating tasks to be verified, this solution enables the entire data collaborative management approach to manage the urgency of data entry more granularly and intelligently while allowing the physical process to continue. This effectively solves the problem of delays in data entry for high-value or perishable agricultural products that may be caused by the lack of a priority mechanism, ensuring product quality and significantly improving the overall operational efficiency of the supply chain.

[0056] In a specific implementation scenario, when a batch of fresh agricultural products, such as strawberries, arrives at the processing center and is temporarily received, the system first obtains the product type information of the batch, identifying it as "strawberries." Then, based on the "strawberry" product type information, the system queries a pre-set product characteristic database to obtain the corresponding shelf-life parameter for strawberries, such as "2 days," and its value density parameter, such as "30 yuan per kilogram." Simultaneously, the system obtains the received quantity of the batch of strawberries from the temporary receipt information, such as "500 kilograms." Next, based on this received quantity of 500 kilograms, the 2-day shelf-life parameter, and the value density parameter of 30 yuan per kilogram, the system uses a pre-set priority evaluation algorithm, such as a weighted scoring model, to evaluate the priority of the task to be processed. This algorithm can be set as: Priority Score = (Received Quantity * Value Density Parameter) / Shelf-Life Parameter. In this example, the priority score = (500 * 30) / 2 = 7500. Meanwhile, if a batch of potatoes arrives and is temporarily received, the system identifies its product type as "potatoes," retrieves its shelf life parameter as "30 days," its value density parameter as "3 yuan per kilogram," and its received quantity as "1000 kilograms." Its priority score is then calculated as (1000 * 3) / 30 = 100. By comparison, the priority score of the strawberry batch is significantly higher than that of the potato batch. Ultimately, the system updates the status of this batch of strawberries to a temporary state at this stage and generates a corresponding pending verification task. Simultaneously, it associates the assessed high-priority information (e.g., a priority score of 7500 or marked as "high") with this task. When the system sends a data entry notification to the planting base, this high-priority information will be included in the notification. When processing the pending verification task list, the planting base's managers can prioritize the strawberry batch's entry task, ensuring that the official data for high-value, perishable products is uploaded in a timely manner, thereby avoiding quality degradation and economic losses due to data delays.

[0057] This solution prioritizes and associates tasks with relevant priority information when generating pending verification tasks. This is done by combining batch product type information, pre-defined product characteristic data (including shelf-life and value density parameters), and the received volume in temporary reception information. This allows upstream participants to prioritize data entry based on the urgency and importance of the tasks. This effectively solves the problem of delayed data entry for high-value or perishable agricultural products caused by the lack of a priority assessment mechanism in existing technologies. By prioritizing urgent or important batches, this solution significantly reduces the risk of product quality deterioration, ensures the quality of fresh agricultural products, and improves the data processing efficiency and response speed of the entire supply chain.

[0058] In some embodiments, the specific steps in step S3 include:

[0059] S3B1. Obtain product type information for batch products;

[0060] S3B2. Based on the product type information, obtain the downstream processing characteristic parameters corresponding to the product type;

[0061] S3B3. Generate data uncertainty indication information for batch products in the temporary state based on the received amount in the temporary received information and the downstream processing characteristic parameters;

[0062] S3B4. Update the status of the batch of products in the current stage to a temporary status, and generate a pending verification task corresponding to the temporary status and associate it with data uncertainty indication information; the data uncertainty indication information is used to send early warnings or coordination notices to the business systems of downstream stages.

[0063] Downstream processing characteristic parameters refer to a set of attributes related to the requirements of specific product types in subsequent processing, storage, and distribution stages regarding data accuracy, timeliness, and quantity stability. These parameters can be obtained and represented using pre-defined parameter tables, rule bases, or models based on historical data analysis. Among these, data uncertainty indication information refers to information used to quantify or classify the reliability of received data (especially the received quantity) of a batch of products in a temporary state, as well as the potential impact of this uncertainty on downstream stages. This information can be represented using numerical levels, risk level identifiers, text descriptions, or structured data fields.

[0064] This solution effectively addresses the potential for decision-making errors and resource waste in downstream processes when batches of products are in a temporary state, due to a lack of reliable data information. Specifically, when a batch of products enters the current stage and is temporarily received, the system first acquires the product type information. This is because different types of products have varying requirements for data accuracy and timeliness, and their downstream processing procedures and potential risks also differ. Based on the acquired product type information, the system further acquires downstream processing characteristic parameters corresponding to that product type. These parameters reflect the product's sensitivity to data (especially the received quantity) in downstream stages, such as processing complexity, shelf-life sensitivity, or precise requirements for the quantity and quality of raw materials. For example, for perishable fresh products, downstream processing characteristic parameters may indicate a high requirement for accurate received quantity and strong processing timeliness.

[0065] Subsequently, the system generates data uncertainty indication information for the batch of products in the temporary state based on the received data volume and the acquired downstream processing characteristic parameters. This step is the core of the solution; it comprehensively considers the initial value of the currently received data and the inherent downstream processing requirements of the product, thereby quantitatively or categorizing the reliability and potential impact of the current temporary data. For example, if the received volume is large and the product has high requirements for downstream processing accuracy, the generated data uncertainty indication information may indicate a higher risk.

[0066] Ultimately, the system updates the status of the batch of products at the current stage to a temporary status and generates a corresponding write-off task. Simultaneously, it associates the generated data uncertainty indication information with this write-off task. More importantly, this data uncertainty indication information is used to send warnings or coordination notifications to downstream business systems. This means that downstream systems not only know the data is temporary but also understand the degree of uncertainty and its potential impact, enabling them to proactively adjust production plans, prepare backup plans, or communicate with upstream systems in advance.

[0067] This coordinated series of steps ensures smooth business processes even before formal data submission in the early stages of batch product production, while simultaneously preventing downstream decision-making errors caused by data uncertainty. Compared to basic solutions that merely mark products as temporary, this approach maintains business process continuity while proactively assessing and communicating data uncertainty, enabling downstream systems to conduct more refined risk management and resource allocation. The introduction of this mechanism allows the entire supply chain to achieve more efficient collaboration and more precise risk response when facing data lag, significantly improving the resilience and efficiency of the supply chain.

[0068] In one specific embodiment, when a batch of "organic tomatoes" arrives at the processing center, but the harvest data from its growing base has not yet been formally submitted, the system receives provisional receiving information including the receiving time, the received quantity (e.g., an initial weight of 500 kg), and the person in charge of receiving the tomatoes. At this time, the system performs the following operations:

[0069] First, the system obtains the product type information for this batch of products and identifies it as "organic tomatoes".

[0070] Secondly, based on the product type information of "organic tomatoes," the system queries a pre-set database or rule engine to obtain its corresponding downstream processing characteristic parameters. For example, for "organic tomatoes," the downstream processing characteristic parameters may include "high sensitivity to shelf life," "low processing loss rate," and "high requirements for the accuracy of raw material quantity (for precise ingredient mixing and packaging)."

[0071] Next, based on the received quantity (500 kg) in the temporary reception information and the acquired downstream processing characteristic parameters, the system generates data uncertainty indication information for the batch of products in the temporary state. For example, considering that "organic tomatoes" have high requirements for quantity accuracy and the current data is in a temporary state, the system can generate an indication message such as "Data uncertainty level: medium, it is recommended to reserve a 5% buffer" or "Risk level: yellow warning, there may be a deviation of ±10 kg".

[0072] Finally, the system updates the status of this batch of "organic tomatoes" to "temporary warehousing" at the current stage and generates a corresponding task to be verified. Simultaneously, it associates the generated data uncertainty indicator information with this task. This data uncertainty indicator information can then be used to send warnings or coordination notifications to downstream business systems. For example, when the processing center's production planning system receives the temporary warehousing information for this batch of products, it will also receive a warning: "Data uncertainty level: Medium, it is recommended to reserve a 5% buffer." Production planners can adjust the day's production plan accordingly, for example, by reserving some flexibility when scheduling production, or by communicating with the sales department in advance to inform them of potential minor deviations, thereby avoiding production interruptions or product shortages due to data uncertainty.

[0073] This solution proactively acquires product type information and assesses downstream processing characteristics when a batch of products enters a temporary state. It then generates data uncertainty indication information based on the temporary receipt volume and associates it with pending verification tasks. This indication information is used to send early warnings or coordination notifications to downstream business systems. This allows downstream business systems to anticipate potential data uncertainties and their impact on their operations, enabling timely adjustments to production plans, inventory management, or quality control strategies. It avoids decisions based on incomplete information and effectively reduces the risks of decreased production efficiency, resource waste, and product spoilage or quality damage caused by upstream data lag. This solution improves information transparency and collaboration efficiency throughout the supply chain, enhances the system's ability to cope with data uncertainty, and ensures business continuity and stability in the fresh agricultural product supply chain when data flow is disrupted.

[0074] In some embodiments, data uncertainty indication information is used when sending early warnings or coordination notifications to downstream business systems:

[0075] From the data uncertainty indication information, we can parse out the type and degree of data uncertainty;

[0076] To understand the requirements of downstream business systems regarding data accuracy and their ability to handle data uncertainty;

[0077] Based on the type and degree of data uncertainty, the business system's requirements for data accuracy, and the business system's ability to handle data uncertainty, determine the risk assessment content and recommended operational content to be included in the early warning or coordination notice.

[0078] Based on the risk assessment and recommended actions, generate early warnings or coordination notices and send them to the business systems of downstream processes.

[0079] Data uncertainty can be categorized by type, referring to specific issues related to data completeness, accuracy, timeliness, or consistency. It can be represented by predefined codes, enumerated values, or textual descriptions, such as "missing quantity," "questionable quality," "time delay," or "batch information mismatch." The degree of data uncertainty refers to the quantitative or graded assessment of its impact on business processes or product quality. It can be expressed as a numerical range, percentage, grade (e.g., "minor," "moderate," "severe"), or risk level (e.g., "low risk," "medium risk," "high risk"). Downstream business systems' requirements for data accuracy refer to the tolerance range or precision standards for input data when performing their functions. These can be defined using configuration parameters, business rules, or system metadata, such as "accurate to the gram," "allow 5% deviation," or "must be a perfect match." The downstream business system's ability to handle data uncertainty refers to its capacity to automatically or semi-automatically correct, compensate, bypass, or provide alternative solutions when faced with uncertain data. This can be represented by system function identifiers, processing strategy configurations, or historical processing success rates, such as "possesses automatic correction functionality," "supports manual intervention processes," or "can switch to a backup data source." Risk assessment content refers to the analysis and prediction of potential negative impacts caused by data uncertainty. This can be presented using structured text, risk levels, or impact descriptions, such as "may lead to production line shutdowns," "increases product loss risk," or "affects on-time order delivery." Recommended actions refer to specific countermeasures or action plans provided to downstream business systems or operators to address data uncertainty issues. This can be presented using instruction lists, operational steps, or recommended processes, such as "immediately initiate manual verification," "adjust production plans," or "prioritize processing other batches of products."

[0080] Upon receiving a data uncertainty indication message generated by upstream processes, indicating that a batch of products is in a temporary state, this solution no longer simply sends a general warning. Instead, it generates targeted warnings or coordination notifications through a series of collaborative steps. First, the system parses the specific type and quantification of data uncertainty from the received data uncertainty indication message. This parsing process forms the basis for subsequent decision-making, concretizing the abstract data problem and enabling the system to identify its nature and severity. Next, the system proactively acquires or queries the specific requirements of downstream business systems for data accuracy and their capabilities in handling uncertain data. This step introduces contextual information from the downstream business scenario, allowing the system to understand the different recipients' tolerance for data quality and the flexibility of their response strategies. For example, a finishing system with high data accuracy requirements will require different warning content and suggested actions when facing the same data uncertainty compared to a preliminary sorting system with relatively low data accuracy requirements. Subsequently, the system comprehensively analyzes and makes decisions based on four key pieces of information: the parsed data uncertainty type, the data uncertainty level, and the downstream business systems' requirements for data accuracy and their processing capabilities. By integrating this multi-dimensional information, the system can dynamically assess the risks posed by current data uncertainty and determine the risk assessment content and specific operational recommendations to be included in early warnings or coordination notices. For example, for data uncertainty characterized by "missing quantities" and "severe" uncertainty, if downstream systems have "high" requirements for "quantity accuracy" and "limited processing capacity," the system may assess it as "high risk" and recommend "immediately suspending relevant production plans and awaiting manual verification." This decision-making mechanism based on multi-dimensional information integration transforms early warnings from simple notifications into analyses of potential impacts and actionable response strategies, enhancing the decision support capabilities of downstream processes. Ultimately, the system generates early warnings or coordination notices based on these determined risk assessment contents and operational recommendations and sends them to the corresponding downstream business systems. In this way, this solution transforms the raw data uncertainty indication information generated by upstream processes into notices that downstream processes can understand and respond to effectively, containing specific risk analysis and action guidelines. This solves the problems of insufficient information and delayed action caused by sending only general early warning information, improving the collaborative efficiency and risk response capabilities of the entire agricultural supply chain in the face of data uncertainty.

[0081] In a specific implementation scenario, when a processing center receives a batch of vegetables in a temporary state and generates a data uncertainty indication, this indication may include the type of "quantity uncertainty" and the degree of "moderate deviation." Before sending an alert to the subsequent packaging workshop management system, the system first parses this information from the indication. Next, the system queries the packaging workshop management system's configuration and finds that its requirement for product quantity accuracy is "high," because the packaging process requires an accurate bill of materials to control the consumption of packaging materials and the quantity of the final product. Simultaneously, the system learns that the packaging workshop management system's ability to handle data uncertainty is "limited," meaning it lacks the function of automatically adjusting packaging plans or automatically replenishing materials, requiring manual intervention. Based on this information, the system makes a comprehensive judgment: due to the moderate deviation in quantity and the downstream system's high accuracy requirements and limited processing capacity, the system assesses that this uncertainty may lead to the risk of "waste of packaging materials" or "discrepancy in the quantity of the final product." Therefore, the system determined that the risk assessment content to be included in the early warning notification should be "a moderate deviation in the quantity of batch products, which may lead to excessive consumption of packaging materials or insufficient order quantity," and generated the suggested action content as "please manually verify the actual quantity received and adjust the packaging plan or notify the purchasing department according to the actual situation." Finally, the system sends the early warning notification containing these risk assessments and suggested actions to the packaging workshop management system through the enterprise internal message bus or API interface, so that its operators can obtain information in a timely manner and take targeted measures.

[0082] This solution analyzes the type and degree of data uncertainty and, combined with the data accuracy requirements and processing capabilities of downstream business systems, determines the risk assessment content and recommended actions to be included in early warning or coordination notices. This allows for the generation and delivery of targeted early warning or coordination notices. This solves the problem of traditional general early warning notices being too vague, making it difficult for downstream systems or operators to respond effectively. By providing specific risk assessments and actionable recommendations, downstream processes can quickly understand the potential impact of data uncertainty and obtain clear action guidelines, thereby avoiding resource waste or product loss and improving the response efficiency and decision-making accuracy of all links in the supply chain.

[0083] In some embodiments, the specific steps in step S5 include:

[0084] S51. Obtain product type information and corresponding transportation conditions information for the batch of products;

[0085] S52. Based on the product type information, query the natural wear characteristic parameters corresponding to the product type;

[0086] S53. Based on the natural loss characteristic parameters, transportation condition information, and the reported amount in the official data, calculate the allowable deviation threshold range between the temporary state data and the official data;

[0087] S54. Compare the received amount in the temporary status data with the reported amount in the formal data, and calculate the deviation value;

[0088] S55. Based on the comparison result between the deviation value and the deviation threshold range, determine whether the successfully matched data meets the logical verification requirements; if the deviation value is within the deviation threshold range, determine that the successfully matched data meets the logical verification requirements; if the deviation value exceeds the deviation threshold range, determine that the successfully matched data does not meet the logical verification requirements, and generate data anomaly indication information; the data anomaly indication information is used for subsequent anomaly handling procedures.

[0089] Product type information refers to data used to identify the specific type of a batch of products, such as the variety of vegetables or the type of fruit. This can be implemented using product codes, product names, or classification labels. Transportation condition information refers to data describing the environment in which a batch of products is transported, such as transportation time, average temperature, humidity, and whether refrigeration is required. This can be implemented using structured data fields, sensor records, or logistics document information. Natural loss characteristic parameters refer to a set of data on the natural loss patterns that may occur under specific transportation conditions and are related to a specific product type. This can be implemented using a pre-defined loss rate table, a statistical model based on historical data, or expert rules. Deviation threshold range refers to the range of quantitative differences allowed between temporary and final data during logical verification. This range is considered a reasonable and acceptable loss range and can be represented as a numerical range, a percentage range, or a combination of upper and lower limits. Data anomaly indication information refers to information used to mark successfully matched data that does not meet the requirements of logical verification. This can be implemented using a Boolean flag, an error code, or a text description containing anomaly details.

[0090] This solution, when logically verifying successfully matched data, goes beyond simple quantity consistency checks. It introduces a quantitative consideration of the natural losses of agricultural products, making the verification process more aligned with real-world business scenarios. Specifically, firstly, by acquiring product type information and corresponding transportation conditions for each batch of products, the system can identify key factors influencing losses during the distribution process. Product type determines its inherent perishability, while transportation conditions directly affect the degree of loss. Based on this fundamental information, the system further queries the natural loss characteristic parameters corresponding to that product type. These parameters, either preset or based on historical data accumulation, reflect the loss patterns of different agricultural products under different conditions, providing a scientific basis for subsequent loss assessment. Furthermore, the system dynamically calculates the allowable deviation threshold range between temporary and official data using the acquired natural loss characteristic parameters, transportation condition information, and reported quantities from the official data. This calculation process simulates reasonable losses that may occur during actual product distribution, thus setting a flexible and realistic verification standard and avoiding misjudgments caused by normal losses. Subsequently, the system compares the received data in the temporary state data with the reported data in the formal data to calculate the actual deviation value. Finally, based on the comparison result of this deviation value with the pre-calculated deviation threshold range, the system determines whether the successfully matched data meets the logical verification requirements. If the actual deviation value falls within the allowable deviation threshold range, the data is considered to meet the logical verification requirements, allowing the batch of products to smoothly transition from the temporary state to the formal state in the current stage and releasing the pending verification task. Conversely, if the deviation value exceeds the range, the data is explicitly determined to not meet the logical verification requirements, and a data anomaly indication message is generated to trigger subsequent anomaly handling procedures. In this way, this solution incorporates the objectively existing natural losses in the agricultural product supply chain into the data verification model, making the data verification process more accurate and intelligent, avoiding false alarms for normal business losses, and significantly improving the efficiency and accuracy of data processing.

[0091] In one specific embodiment, when the system receives temporary receipt information for a batch of lettuce and matches it with the corresponding formally reported data, a logical verification process is initiated. First, the system obtains the product type information as "lettuce" from the batch's metadata and transportation condition information from logistics records or sensor data, such as a transportation duration of "24 hours" and an average temperature of "5 degrees Celsius." Next, based on the "lettuce" product type information, the system queries a preset agricultural product loss database for the corresponding natural loss characteristic parameters. For example, the natural loss rate at 5 degrees Celsius is approximately 2% to 4% per 24 hours. Subsequently, the system calculates the allowable deviation threshold range based on these natural loss characteristic parameters, the 24-hour transportation condition information, and the reported quantity of 1000 kg of lettuce in the formal data. For example, based on a loss rate of 2% to 4%, the system can calculate that the allowable loss is 20 kg to 40 kg, thus determining that the deviation between the received quantity and the reported quantity should be between -40 kg and -20 kg (i.e., the received quantity can be 20 to 40 kg less than the reported quantity). Next, the system compares the received quantity in the temporary status data, such as 970 kg, with the reported quantity of 1000 kg in the official data, calculating a deviation value of -30 kg. Finally, the system compares the -30 kg deviation value with the calculated deviation threshold range (-40 kg to -20 kg). Since -30 kg falls within this range, the system can determine that the successfully matched data meets the logical verification requirements, thus allowing the batch of lettuce to be changed to the official status. If the received quantity is 900 kg and the deviation value is -100 kg, exceeding the allowable range, the system will generate a data anomaly indication message, such as an error code marked "quantity anomaly," and trigger the corresponding anomaly handling process, such as notifying manual verification.

[0092] This solution addresses the problem of traditional verification mechanisms failing to accurately reflect actual agricultural product losses by incorporating a quantitative consideration of natural losses into the logical verification process. By acquiring product type and transportation condition information and querying natural loss characteristic parameters accordingly, the system can dynamically calculate a deviation threshold range that aligns with actual conditions. This ensures that even reasonable, acceptable quantity deviations can be correctly identified as meeting logical verification requirements, thus avoiding misjudgments and unnecessary anomaly handling processes caused by normal losses. Therefore, this solution significantly improves the efficiency and accuracy of data verification, reduces manual intervention costs, and ensures the effectiveness of system alerts, enabling it to more accurately identify genuine business problems rather than frequently reporting "false anomalies."

[0093] In some embodiments, the specific steps in step S53 include:

[0094] S531. Obtain information on the packaging type, loading and unloading method, and transport vehicle characteristics of batch products;

[0095] S532. Based on the packaging type information, loading and unloading method information, and transport vehicle characteristic information, query the preset non-natural loss impact parameters;

[0096] S533. Based on the non-natural loss impact parameters and the reported quantities in the official data, calculate the first estimated loss amount of batch products caused by non-natural factors during the circulation process.

[0097] S534. Based on natural loss characteristic parameters, transportation condition information and reported quantities in official data, calculate the second estimated loss of batch products due to natural factors during the circulation process.

[0098] S535. Calculate the total estimated loss that a batch of products can have during the circulation process based on the first estimated loss and the second estimated loss.

[0099] S536. Based on the total estimated loss and the reported amount in the official data, determine the allowable deviation threshold range between the temporary state data and the official data.

[0100] In this scheme, packaging type information indicates the packaging materials, form, or structure used for the batch of products, which can be implemented through text descriptions, codes, or pre-set classification labels. Loading and unloading method information indicates the loading and unloading operations used during the batch's circulation, which can be implemented through manual operation, mechanical assistance, or automated equipment operation. Transport vehicle characteristic information indicates the physical properties and operational characteristics of the vehicles or containers used to transport the batch of products, which can be implemented using parameters such as vehicle type, container type, internal environmental control capabilities, or shock absorption performance. Non-natural loss impact parameters indicate pre-set values ​​or models related to packaging type, loading and unloading method, and transport vehicle characteristics, used to quantify non-natural losses, which can be implemented using loss rate coefficients, loss estimation formulas, or statistical models based on historical data. The first estimated loss amount indicates the expected amount of loss of the batch of products during circulation due to non-natural factors (such as physical collisions, compression, friction, etc.), which can be calculated based on the product of the non-natural loss impact parameters and the reported amount, or through table lookup. The second estimated loss indicates the expected amount of loss of a batch of products during its circulation due to natural factors (such as respiration, transpiration, and microbial activity). This can be estimated using calculations or models based on natural loss characteristic parameters, transportation condition information, and reported quantities. The total estimated loss indicates the total expected loss of a batch of products during its circulation, caused by both natural and unnatural factors. This can be achieved by simply summing or weighted averaging the first and second estimated losses. The deviation threshold range indicates the permissible range of difference between provisional and official data. This can be achieved using a percentage range or absolute value range calculated based on the total estimated loss and reported quantities.

[0101] This solution refines the calculation process for the deviation threshold range, incorporating losses caused by non-natural factors. This allows the deviation threshold to more comprehensively cover various reasonable losses that fresh agricultural products may encounter during the supply chain, improving the comprehensiveness and accuracy of the verification and effectively solving the misjudgment problem caused by insufficient consideration of non-natural losses in existing solutions. Specifically, when calculating the allowable deviation threshold range between provisional data and official data, the system first obtains information on the batch product's packaging type, loading and unloading method, and transport vehicle characteristics. This information is crucial for identifying and quantifying non-natural losses, as different packaging, loading and unloading methods, and transport vehicles have varying impacts on the physical damage to the product. Based on this information, the system queries preset non-natural loss impact parameters, which provide a basis for quantifying the impact of specific non-natural factors on product losses. Subsequently, based on these non-natural loss impact parameters and the reported quantities in the official data, the system calculates the first estimated loss amount of the batch product caused by non-natural factors during the circulation process. This step enables the system to quantify and consider losses that may occur during packaging, loading and unloading, and transportation, compensating for the shortcomings of only considering natural losses. Meanwhile, to ensure continuous consideration of natural product losses, the system combines natural loss characteristic parameters, transportation condition information, and reported quantities in the official data to calculate a second estimated loss due to natural factors during the batch of products' circulation. By combining the first and second estimated losses, the total estimated loss that is permissible during the batch of products' circulation is calculated, providing a more comprehensive and realistic loss expectation. Finally, based on this total estimated loss and the reported quantities in the official data, the permissible deviation threshold range between the temporary data and the official data is determined. Through the above steps, the determined deviation threshold range can more accurately reflect the reasonable losses that may exist in actual business operations. This allows the system to more accurately determine whether the data meets the requirements in subsequent logical verification, avoiding misjudging normal and acceptable losses as data anomalies, thereby reducing unnecessary anomaly handling processes and improving the accuracy of data verification and the system's operating efficiency. This refined processing of deviation threshold calculation makes the data collaborative management method of the entire agricultural supply chain more reliable and practical in the data matching and logical verification stages, effectively reducing business interruptions and resource waste caused by data differences, thereby improving the digital management level and responsiveness of the entire supply chain.

[0102] In a specific embodiment, suppose a batch of fresh tomatoes is transported from a planting base to a processing center. When calculating the allowable deviation threshold range between provisional data and formal data, the system first obtains information about the packaging type, loading and unloading method, and transport vehicle characteristics of the batch of tomatoes. For example, it can be obtained that the batch of tomatoes is packaged in "plastic turnover boxes," the loading and unloading method is "manual handling," and it is transported by "ordinary vans." Subsequently, based on this information, the system queries preset non-natural loss impact parameters. For example, in a preset database, it can be found that "plastic turnover box packaging" may result in a preset crushing loss rate under "manual handling," while "ordinary vans" may result in a preset bumping loss rate during transportation. Based on these non-natural loss impact parameters and the reported quantities in the formal data, the system calculates the first estimated loss amount of the batch of tomatoes due to non-natural factors during the circulation process. At the same time, based on the product type information of the tomatoes, the system queries its corresponding natural loss characteristic parameters, and combines this with the transportation condition information and the reported quantities in the formal data to calculate the second estimated loss amount of the batch of tomatoes due to natural factors during the circulation process. Next, the system sums the calculated first and second estimated losses to obtain the total estimated loss that a batch of tomatoes can tolerate during its circulation. Finally, based on this total estimated loss and the reported amount in the official data, the system determines the allowable deviation threshold range between the temporary data and the official data. For example, if the total estimated loss is 20 kg and the officially reported amount is 1000 kg, the deviation threshold range can be determined to be between 0 and 20 kg, or between 0 and 2%. Thus, when the difference between the temporary received amount and the officially reported amount falls within this range, the system will determine that the data meets the logical verification requirements, avoiding misjudging normal losses as abnormal.

[0103] This solution obtains information on the packaging type, loading and unloading methods, and transport vehicle characteristics of batch products. Based on this, it calculates the first estimated loss due to non-natural factors, and then combines this with the second estimated loss due to natural factors to determine the total estimated loss. This, in turn, establishes the allowable deviation threshold range between provisional and official data. This method makes the calculated deviation threshold range more accurate and comprehensive, fully considering the reasonable losses of fresh agricultural products during the supply chain flow caused by non-natural factors such as packaging, loading and unloading, and transport vehicles. Therefore, this solution effectively avoids misjudging normal non-natural losses as data anomalies, significantly reducing the system's misjudgment rate and unnecessary anomaly handling processes, thereby improving the accuracy of data verification and the system's operational efficiency.

[0104] Reference Appendix Figure 2 This invention provides an agricultural supply chain data collaborative management system, comprising:

[0105] The acquisition module 100 is used to acquire the data status of the preceding stages of the batch of products when receiving a batch of products.

[0106] The receiving module 200 is used to receive temporary receiving information for the batch of products if the data of the preceding stage is not in a formal state, based on the status of the data of the preceding stage.

[0107] The generation module 300 is used to update the status of the batch of products in the current stage to a temporary status based on the temporary received information, and generate a task to be verified corresponding to the temporary status.

[0108] The sending module 400 is used to send a data supplement notification to the upstream participant responsible for submitting the formal data of the preceding stage, based on the task to be verified.

[0109] The matching module 500 is used to match temporary status data with formal data based on the batch product traceability code after the upstream participant responds to the data supplement notification and submits the formal data of the previous stage, and to perform logical verification on the successfully matched data.

[0110] The release module 600 is used to change the temporary status of the batch of products in the current stage to the formal status and release the pending verification task if the matching result and the logical verification result are successful.

[0111] The early warning module 700 is used to trigger an early warning if data verification is not completed within the specified time limit for supplementary entry, and to record the temporary status and verification operation log.

[0112] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0113] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for collaborative data management in the agricultural supply chain, characterized in that, Includes the following steps: S1. When receiving a batch of products, obtain the status of the preceding process data for that batch of products; S2. Based on the obtained status of the preceding stage data, if the preceding stage data is not in a formal state, then receive the temporary receiving information for this batch of products; S3. Based on the temporary received information, update the status of the batch of products in the current stage to a temporary status, and generate a pending verification task corresponding to the temporary status; S4. Based on the tasks to be verified, send a data supplement notification to the upstream participants responsible for submitting the formal data of the preceding stages; S5. After the upstream participant responds to the data supplement notification and submits the formal data of the previous stage, the temporary status data is matched with the formal data according to the batch product traceability code, and the successfully matched data is logically verified. S6. Based on the matching results and logical verification results, if the matching is successful and the logical verification is compliant, the temporary status of the batch of products in the current stage will be changed to the formal status, and the pending verification task will be released. S7. If data verification cannot be completed within the specified time limit for supplementary entry, an early warning will be triggered, and a temporary status and verification operation log will be recorded.

2. The agricultural supply chain data collaborative management method according to claim 1, characterized in that, Temporary received information includes the amount received.

3. The agricultural supply chain data collaborative management method according to claim 1, characterized in that, The data supplement notice includes the formal data of the preceding stages to be supplemented and the supplementary data time limit stipulated according to the characteristics of agricultural products.

4. The agricultural supply chain data collaborative management method according to claim 2, characterized in that, The specific steps in step S3 include: S3A1. Obtain product type information for batch products; S3A2. Based on product type information, obtain the shelf life parameters and value density parameters corresponding to the product type by querying preset product characteristic data; S3A3. Assess the priority of tasks to be verified based on the amount received, shelf life parameters, and value density parameters in the temporary reception information; S3A4. Update the status of the batch of products in the current stage to a temporary status, and generate a pending verification task corresponding to the temporary status and associate it with priority information, so that upstream participants can arrange the processing order of the pending verification tasks according to the priority information.

5. The agricultural supply chain data collaborative management method according to claim 2, characterized in that, The specific steps in step S3 include: S3B1. Obtain product type information for batch products; S3B2. Based on the product type information, obtain the downstream processing characteristic parameters corresponding to the product type; S3B3. Generate data uncertainty indication information for batch products in the temporary state based on the received amount in the temporary received information and the downstream processing characteristic parameters; S3B4. Update the status of the batch of products in the current stage to a temporary status, and generate a pending verification task corresponding to the temporary status and associate it with data uncertainty indication information; the data uncertainty indication information is used to send early warnings or coordination notices to the business systems of downstream stages.

6. The agricultural supply chain data collaborative management method according to claim 5, characterized in that, Data uncertainty indication information is used when sending early warnings or coordination notices to downstream business systems: From the data uncertainty indication information, we can parse out the type and degree of data uncertainty; To understand the requirements of downstream business systems regarding data accuracy and their ability to handle data uncertainty; Based on the type and degree of data uncertainty, the business system's requirements for data accuracy, and the business system's ability to handle data uncertainty, determine the risk assessment content and recommended operational content to be included in the early warning or coordination notice. Based on the risk assessment and recommended actions, generate early warnings or coordination notices and send them to the business systems of downstream processes.

7. The agricultural supply chain data collaborative management method according to claim 2, characterized in that, Official data includes reported figures.

8. The agricultural supply chain data collaborative management method according to claim 7, characterized in that, The specific steps in step S5 include: S51. Obtain product type information and corresponding transportation conditions information for the batch of products; S52. Based on the product type information, query the natural wear characteristic parameters corresponding to the product type; S53. Based on the natural loss characteristic parameters, transportation condition information, and the reported amount in the official data, calculate the allowable deviation threshold range between the temporary state data and the official data; S54. Compare the received amount in the temporary status data with the reported amount in the formal data, and calculate the deviation value; S55. Based on the comparison result between the deviation value and the deviation threshold range, determine whether the successfully matched data meets the logical verification requirements; if the deviation value is within the deviation threshold range, determine that the successfully matched data meets the logical verification requirements; if the deviation value exceeds the deviation threshold range, determine that the successfully matched data does not meet the logical verification requirements, and generate data anomaly indication information; the data anomaly indication information is used for subsequent anomaly handling procedures.

9. The agricultural supply chain data collaborative management method according to claim 8, characterized in that, The specific steps in step S53 include: S531. Obtain information on the packaging type, loading and unloading method, and transport vehicle characteristics of batch products; S532. Based on the packaging type information, loading and unloading method information, and transport vehicle characteristic information, query the preset non-natural loss impact parameters; S533. Based on the non-natural loss impact parameters and the reported quantities in the official data, calculate the first estimated loss amount of batch products caused by non-natural factors during the circulation process. S534. Based on natural loss characteristic parameters, transportation condition information and reported quantities in official data, calculate the second estimated loss of batch products due to natural factors during the circulation process. S535. Calculate the total estimated loss that a batch of products can have during the circulation process based on the first estimated loss and the second estimated loss. S536. Based on the total estimated loss and the reported amount in the official data, determine the allowable deviation threshold range between the temporary state data and the official data.

10. An agricultural supply chain data collaborative management system, characterized in that, include: The acquisition module is used to acquire the data status of the preceding stages of the batch of products when a batch of products is received. The receiving module is used to receive temporary receiving information for this batch of products based on the status of the data obtained from the preceding stages. If the data from the preceding stages is not in a formal state, then the receiving module will receive temporary receiving information for this batch of products. The generation module is used to update the status of the batch of products in the current stage to a temporary status based on the temporary received information, and generate a corresponding task to be verified. The sending module is used to send data supplement notifications to upstream participants responsible for submitting formal data in the preceding stages, based on the tasks to be verified. The matching module is used to match temporary status data with formal data based on the batch product traceability code after the upstream participant responds to the data supplement notification and submits the formal data of the previous stage, and to perform logical verification on the successfully matched data. The cancellation module is used to change the temporary status of the batch of products in the current stage to the formal status and cancel the pending verification task if the matching result and the logical verification result are successful. The early warning module is used to trigger an early warning if data verification is not completed within the specified time limit for supplementary entry, and to record the temporary status and verification operation log.

Images