Agricultural supply chain data collaborative management method and system

Abstract

The invention 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: acquiring a front link data state of a batch of products; receiving temporary receiving information of the batch of products, updating the state of the batch of products in the current link into a temporary state, and generating a to-be-cancelled after verification task; sending a data additional recording notification to the upstream participant according to the to-be-cancelled after verification task; matching the temporary state data with the formal data, and performing logic verification; if the matching is successful, converting the temporary state of the batch product in the current link into a formal state, and releasing the to-be-cancelled after verification task; if the data cancel-after-verification is not completed, early warning is triggered, and a temporary state and a cancel-after-verification operation log are recorded. According to the method, the problem that the physical circulation speed in the fresh agricultural product supply chain is not matched with the online data submission speed is effectively solved, and the completeness and preciseness of tracing data are maintained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of agricultural supply chain data management, in particular to an agricultural supply chain data collaborative management method and system. BACKGROUND

[0002] In a fresh vegetable-oriented agricultural product supply chain, in order to realize the whole-process quality traceability from farmland to dining table, each participant, including planting base, processing center, cold chain logistics company and sales terminal, needs to submit the data of each link on a unified supply chain collaborative platform. The platform generates a unique traceability code for each batch of vegetable products. Consumers can obtain the complete life cycle information of the batch of products by scanning the traceability code on the product packaging.

[0003] In order to ensure the integrity of the data chain and the accuracy of the logic, the collaborative platform adopts a flow control mechanism based on state nodes. Specifically, the life cycle of a batch of products is defined as a series of continuous states, such as "has been sown", "has been fertilized", "has been harvested", "in transit", "has been warehoused", "is being processed", "has been shipped", and "has been on the shelf". The platform stipulates that the data of the next state node can only be entered after the data of the previous state node is completely submitted. For example, the processing center can only start submitting the data of the "processing" link, such as cleaning, cutting, packaging, etc., after it queries in the system that the state of a batch of vegetables is "has been warehoused". This design aims to prevent the disorder of data entry sequence and ensure the correctness of the cause-and-effect relationship and time sequence relationship of the traceability information chain.

[0004] This strict sequence control ensures the standardization of data in most cases. However, in specific business scenarios, the rigid characteristics of this mechanism expose its inherent defects. Consider a specific situation: a large supermarket chain urgently issues a large order of fresh vegetables to the processing center due to a promotion activity, and requires delivery before dawn the next day. The processing center immediately issues a raw material procurement instruction to the cooperating planting base.

[0005] After receiving the instruction, the planting base immediately organizes personnel to harvest vegetables. The harvesting operation is completed in the afternoon, and then the truck departs immediately after completing the paper-based handover procedures to the processing center. In order to save time, the truck driver responsible for cold chain transportation departs immediately after completing the paper-based handover procedures, heading for the processing center. At this time, the on-site management personnel of the planting base have not returned to the office in time to submit the "harvested" data of the batch of vegetables on the collaborative platform through the computer, including harvesting time, harvesting weight, responsible person, etc.

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

[0007] This situation causes a blockage of the business process. The receiving clerk of 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 in place, and cannot automatically trigger the production work order to arrange the production line for cleaning and cutting. On the physical level, the cold chain truck full of fresh vegetables stops in the unloading area and cannot be unloaded. Every minute of waiting increases the energy consumption of the truck's refrigeration system, and opening and closing the door for communication may also cause the cold chain to be interrupted, increasing the risk of quality deterioration of the vegetables. The receiving clerk can only repeatedly urge the management personnel of the planting base to submit the data as soon as possible through the phone. The management personnel of the planting base, who are far away, may have returned home after finishing a day's work and need to return to the office or find a computer to complete the operation.

[0008] In this process, a strict control process designed to ensure data quality has become a bottleneck that affects the actual operation efficiency of the supply chain. Not only does it cause ineffective waiting in the logistics link and increase operating costs, but more importantly, this physical waiting caused by data flow delay directly threatens the quality of fresh agricultural products, which is contrary to the original intention of establishing a quality traceability system. The rigid control of the system on data flow does not adapt to the flexibility and suddenness of business operations in the physical world, leading to the disconnection and conflict between digital processes and physical processes. SUMMARY

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

[0010] In a first aspect, the present application provides an agricultural supply chain data collaboration management method, comprising the following steps: S1. When receiving a batch of products, obtaining the state of the previous link data of the batch of products; S2. According to the obtained state of the previous link data, if the previous link data is not in the formal state, receiving the temporary receiving information of the batch of products; S3. According to the temporary receiving information, updating the state of the batch product at the current link to a temporary state, and generating a to-be-verified task corresponding to the temporary state; S4. According to the to-be-verified task, sending a data supplementing notification to an upstream participant responsible for submitting formal data of a previous link; S5. When the upstream participant responds to the data supplementing notification and submits the formal data of the previous link, matching the temporary state data and the formal data according to a batch product trace code, and performing logical verification on the matched data; S6. According to the matching result and the logical verification result, if the matching is successful and the logical verification is consistent, converting the temporary state of the batch product at the current link to a formal state, and releasing the to-be-verified task; S7. If the data verification cannot be completed within a specified supplementing time limit, triggering an early warning, and recording a temporary state and a verification operation log.

[0011] The agricultural supply chain data collaborative management method provided by the application can effectively manage the problem that subsequent link digital operation is limited due to lag of data submission of a previous link, while continuously ensuring integrity, time sequence correctness and logical consistency of whole-process trace data, under the premise that quality of agricultural products is not damaged due to physical waiting, and supply chain efficiency is not reduced due to data blocking.

[0012] In a second aspect, the application provides an agricultural supply chain data collaborative management system, comprising: An acquisition module, configured to acquire a previous link data state of a batch product when the batch product is received; A receiving module, configured to receive temporary receiving information of the batch product if the previous link data is not in a formal state according to the acquired previous link data state; A generation module, configured to update a state of the batch product at a current link to a temporary state according to the temporary receiving information, and generate a to-be-verified task corresponding to the temporary state; A sending module, configured to send a data supplementing notification to an upstream participant responsible for submitting formal data of a previous link according to the to-be-verified task; A matching module, configured to match temporary state data and formal data according to a batch product trace code when the upstream participant responds to the data supplementing notification and submits the formal data of the previous link, and perform logical verification on the matched data; A release module, configured to convert the temporary state of the batch product at the current link to a formal state and release the to-be-verified task if the matching is successful and the logical verification is consistent according to the matching result and the logical verification result; An early warning module, configured to trigger an early warning and record a temporary state and a verification operation log if the data verification cannot be completed within a specified supplementing time limit.

[0013] From the above, the agricultural supply chain data collaborative management method provided by the application allows the downstream link to receive and process in advance when the physical goods arrive, effectively avoiding the logistics blockage and physical waiting caused by online data lag, especially for fresh agricultural products with high timeliness. At the same time, it significantly reduces the risk of cold chain interruption, long-term exposure risk and quality deterioration risk of fresh agricultural products at each link of the supply chain due to waiting, which is highly consistent with the original intention of the quality traceability system. In addition, it eliminates the invalid waiting time caused by data submission lag, so that the subsequent production, processing, distribution and other links can start in time, improving the response speed and turnover efficiency of the supply chain, and through the record of temporary state, automatic cancellation process and early warning backtracking mechanism, it ensures that even in the case of temporary asynchronization between physical and digital processes, the final traceability data chain is still complete, time correct and logically consistent. Further, the supply chain collaborative platform is given the flexibility to respond to the suddenness and flexibility of business operations in the physical world under the premise of ensuring data precision, effectively managing the asynchronization between digital and physical processes, enhancing the adaptability of the system, and reducing the waiting time of cold chain trucks and other transportation tools, reducing unnecessary energy consumption and labor costs.

[0014] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent from the description, or can be learned by practice of the present application according to the embodiments. The objects and other advantages of the present application can be achieved and obtained by the structure particularly pointed out in the written description and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A flow chart of the agricultural supply chain data collaborative management method provided by the embodiments of the present application.

[0016] Figure 2 A structural schematic diagram of the agricultural supply chain data collaborative management system provided by the embodiments of the present application.

[0017] Label explanation: 100, acquisition module; 200, receiving module; 300, generation module; 400, sending module; 500, matching module; 600, release module; 700, early warning module. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0019] It should be noted that similar reference numerals and letters refer to like items in the following drawings, and therefore, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", and the like are only used to distinguish description, and cannot be understood as indicating or implying relative importance.

[0020] With reference to the accompanying drawings, Figure 1 the present application provides an agricultural supply chain data collaborative management method, comprising the following steps: S1. When receiving a batch product, obtaining the pre-link data state of the batch product; S2. According to the obtained pre-link data state, if the pre-link data is not in the formal state, receiving the temporary receiving information of the batch product; the temporary receiving information includes the receiving time, the receiving amount and the receiving person in charge; S3. According to the temporary receiving information, updating the state of the batch product in the current link to a temporary state, and generating a to-be-verified task corresponding to the temporary state; S4. According to the to-be-verified task, sending a data supplement notification to an upstream participant responsible for submitting the pre-link formal data; the data supplement notification contains the pre-link formal data to be supplemented and the supplement time limit specified according to the characteristics of agricultural products; the formal data includes the reporting time, the reporting amount and the reporting person in charge; S5. When the upstream participant responds to the data supplement notification and submits the formal data of the pre-link, matching the temporary state data and the formal data according to the batch product traceability code, and performing logical verification on the matched data; S6. According to the matching result and the logical verification result, if the matching is successful and the logical verification is correct, the temporary state of the batch product in the current link is converted to the formal state, and the to-be-verified task is released; S7. If the data verification cannot be completed within the specified supplement time limit, a warning is triggered, and the temporary state and the verification operation log are recorded.

[0021] The pre-stage data status indicates whether the data record of a product at a stage in the supply chain has been completed and confirmed by the system as the final state. It can be implemented by using a state field in the database, a Boolean flag, or an enumeration value, such as "submitted", "to be confirmed", "canceled", etc. The main purpose is to determine whether the current stage can operate based on the data of the previous stage. The temporary receiving information refers to the key information about the current receiving recorded in advance when the product is physically received, but the data of the previous stage has not arrived. It can include the receiving time, receiving quantity, and receiving person in charge. The main purpose is to allow physical operation in advance when the data chain is incomplete, and to provide a basis for subsequent data supplement and cancellation. The temporary state refers to the state of the batch product that has physically entered the current stage, but the data of the previous stage has not been canceled, or the receiving data of the current stage has not been finally confirmed. It can be implemented by using a state field in the database or a specific identifier, such as "to be canceled into warehouse", "temporary into warehouse". The main purpose is to allow the business process to continue, while marking the uncertainty of the data. The to-be-canceled task refers to the task created to ensure that the temporary state finally turns into the formal state, which requires the upstream participants to submit the missing data and perform data comparison and confirmation. It can be implemented by using a work order, to-do list, or specific data record in the task management system. The main purpose is to drive the data supplement process and ensure the integrity and consistency of the data chain. The data supplement notification refers to the message sent by the system to the upstream participants, requiring them to supplement the missing data. It can be implemented by using email, SMS, in-app message, or API call. The main purpose is to actively remind and guide the upstream participants to complete the data submission in a timely manner. The logical verification refers to the process of verifying the consistency between the temporary state data and the formal data that match successfully. It can be implemented by using pre-set business rules, data range checks, or deviation analysis based on historical data, such as checking the deviation between the receiving quantity and the reported quantity. The main purpose is to ensure the accuracy of the data.

[0022] The application provides a kind of agricultural product supply chain data flow conversion management method, its core working principle is in introducing the hierarchical management mechanism of "temporary state" and "official state" of data state, and establishes a set of "data verification channel" and "asynchronous management" framework.Specifically, when the physical goods of the downstream link in the supply chain have arrived, but the official traceability data of the upstream link fails to be submitted in time, the method allows the downstream link to enter the key temporary receiving information through "temporary operation entrance" first, marks the batch of products as "temporary state", so as to avoid the interruption of physical process.The system automatically generates "to-be-verified task" and notifies the upstream responsible party to fill in the missing official data within the preset time limit.Once the upstream data is submitted, the system will automatically trigger the "data verification process", match and check the temporary data and the official data, and automatically convert the "temporary state" to "official state", to ensure the integrity and accuracy of the final traceability data.In addition, the method also includes early warning and backtracking mechanism to deal with the situation of failing to verify in time, so as to effectively manage the temporary asynchrony of physical flow and digital operation under the premise of ensuring the rigor of data, improve the overall efficiency of supply chain and product quality.

[0023] The core innovation of the present application is that by introducing temporary receiving information and temporary state mechanism, combined with asynchronous verification process of to-be-verified task and data re-entry notification, the problem of business process blockage and physical waiting caused by the failure of upstream data to be submitted in time under the traditional data sequence control is solved, the decoupling and cooperation of physical operation and digital record are realized, the operation efficiency of supply chain is improved, and the quality of agricultural products is guaranteed.

[0024] Specifically, the method establishes a data collaborative management mechanism to address the challenge of delayed submission of data in the upstream link of the agricultural supply chain. When a batch of products arrives at the current link and is received, the system first obtains the data state of the upstream link of the batch of products to determine whether it has been in the formal state. If the system identifies that the upstream link data has not been formally submitted, the current link is allowed to receive temporary receiving information of the batch of products, which records the key details of the physical reception. Based on this temporary receiving information, the system updates the state of the batch of products in the current link to a temporary state and synchronously generates a pending verification task. This temporary state allows the downstream business process to start, avoiding physical waiting. Subsequently, the system sends a data supplement notification to the upstream participants according to the generated pending verification task, which specifies the formal data content that needs to be supplemented and the supplement time limit specified according to the characteristics of agricultural products, to prompt the upstream to complete data submission in a timely manner. Once the upstream participants respond to the notification and submit the formal data of the upstream link, the system matches the temporary state data and the formal data according to the batch product traceability code, and performs logical verification on the matched data to ensure data consistency. According to the matching and verification results, if the data is successfully matched and the logical verification meets the preset rules, the system changes the temporary state of the batch of products in the current link to the formal state and releases the corresponding pending verification task, marking the completion of the data verification process. Otherwise, if the data verification cannot be completed within the specified supplement time limit, the system will trigger an early warning mechanism and record the temporary state and verification operation log for subsequent abnormal handling and traceability, thereby ensuring the integrity and traceability of the data chain, while taking into account the flexibility and timeliness of business operations.

[0025] As a preferred embodiment, the scheme of the present 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 through the handheld terminal. The system queries the "harvested" data state of the batch of vegetables in the planting base. If it is found that the planting base has not submitted the formal data of "harvested" in the platform, the system will not prevent the receiving operation, but prompt the receiving clerk to enter the temporary receiving information, including the receiving time, the receiving amount of vegetables and the identity of the receiving clerk. The system immediately updates the storage state of the batch of vegetables in the processing center to "temporary storage", and automatically generates a "to-be-verified harvested data" task. At the same time, the system sends a data supplement notification to the on-site management personnel of the planting base through WeChat Enterprise or SMS, which contains the traceability code of the batch of vegetables, the harvesting time, the harvesting amount, the harvesting person in charge and other information that need to be supplemented, and sets a supplement time limit according to the preservation period of the vegetables, for example, 2 hours. The processing center can immediately start unloading and preliminary processing. When the planting base management personnel supplements the formal "harvested" data within the specified time limit through the platform, the system automatically matches the temporary receiving amount with the formal harvesting amount according to the traceability code, and performs logical verification, such as checking whether the deviation between the two is within the allowed range. If the verification is passed, the system automatically changes the "temporary storage" state of the batch of vegetables to "officially stored", and cancels the "to-be-verified harvested data" task. If the planting base fails to supplement the data within the time limit, the system will send a warning notice to the relevant persons in charge of the processing center and the planting base, and record the temporary state and unverified log of the batch of data for manual intervention.

[0026] Through the above scheme, the present application solves the problems of business process blockage, physical waiting and deterioration of agricultural products caused by the failure of the previous link data to be submitted in time in the agricultural supply chain. By allowing temporary receiving and state updating when the data is not officially in place, the present application realizes the decoupling of physical operation and digital record, and improves the operation efficiency of the supply chain. At the same time, through the asynchronous data supplement notification and verification mechanism, the integrity and accuracy of the data chain are ensured, and the disconnection and conflict between digital process and physical process are avoided, so as to ensure the freshness of agricultural products and the reliability of traceability information.

[0027] In some embodiments, the specific steps in step S3 include: S3A1. obtaining product type information of the batch of products; S3A2. according to the product type information, obtaining the preservation period parameter and the value density parameter corresponding to the product type by querying the preset product characteristic data; S3A3. according to the receiving amount in the temporary receiving information, the preservation period parameter and the value density parameter, evaluating the priority of the to-be-verified task; S3A4. Update the status of the batch product in the current link to a temporary state, and generate a to-be-verified task corresponding to the temporary state and associate priority information, so that the upstream participant arranges the processing order of the to-be-verified task according to the priority information.

[0028] The preset product characteristic data refers to a set of inherent attributes related to different types of agricultural products, which can be implemented in the form of a database, a configuration file, or a data dictionary, etc., and is used to provide standardized and quantitative basis for the freshness period and value density of the product. The freshness period parameter refers to a quantitative indicator reflecting the length of time that a specific agricultural product maintains its quality and safety under specific storage conditions, which can be represented in the form of days, hours, or specific date ranges, etc. The value density parameter refers to a quantitative indicator reflecting the economic value contained in a unit of mass or volume of a specific agricultural product, which can be represented in the form of price per kilogram, total price per box, or unit volume profit rate, etc. The priority refers to a ranking basis for indicating the urgency and importance of processing the to-be-verified task, which can be represented in the form of a numerical value, a level, or a timestamp, etc. The priority information refers to data associated with the to-be-verified task for guiding its processing order, which can be represented in the form of a priority numerical value, a priority level identifier, or a priority label, etc.

[0029] The present scheme aims to solve how to dynamically evaluate and assign task priorities when generating a to-be-verified task, in combination with the characteristics of agricultural products and the received information, to ensure that the subsequent data processing process can prioritize the processing of urgent or important batches, thereby ensuring the quality of agricultural products and improving the response efficiency of the supply chain. Specifically, when the system prepares to update the state of the batch product to a temporary state according to the temporary received information and generates a to-be-verified task, the product type information of the batch product is first obtained. It is through the identification of the specific product type that the system can carry out differentiated processing according to the inherent characteristics of different agricultural products, laying the foundation for subsequent acquisition of product characteristic data. On this basis, the system acquires the shelf life parameter and value density parameter corresponding to the product type by querying the pre-set product characteristic data according to the obtained product type information. This step is the core, which uses product type information to obtain key parameters directly related to product characteristics, among which the shelf life parameter reflects the timeliness of the product, and the value density parameter reflects the economic value of the product. Through pre-set data query, the accuracy and automation of evaluation are ensured, providing a quantitative basis for subsequent priority evaluation. Subsequently, the system comprehensively considers the receiving amount in the temporary received information, the shelf life parameter and the value density parameter, and evaluates the priority of the to-be-verified task to be generated. The receiving amount reflects the size of the batch product, the shelf life parameter is directly related to the perishability of the product, and the value density parameter is related to the economic value of the product. It is through the comprehensive evaluation of these parameters that the system can intelligently judge which to-be-verified task is more urgent and important, thereby providing a decision basis for subsequent priority processing. Finally, the system updates the state of the batch product at the current link to the temporary state, and generates a to-be-verified task corresponding to the temporary state, while associating the priority information evaluated in the previous step with the task. This processing method enables the system to intelligently predict and sort the subsequent data supplement and verification process while the physical business process continues when the to-be-verified task is generated. When the data supplement notification is sent to the upstream participants, the notification will include the priority of the task. Instead of blindly processing all to-be-verified tasks, the upstream participants can prioritize the processing of batches with short shelf life, high value, or large quantity according to these priority information. This optimizes the workflow of the upstream participants, ensures the timely supplement of critical data, effectively reduces the risks caused by data verification delay, and improves the response speed and efficiency of the entire supply chain. By integrating priority evaluation when generating a to-be-verified task, the present scheme enables the entire data collaboration management method to more finely and intelligently manage the urgency of data supplement while allowing the physical process to continue, thereby effectively solving the problem of delayed data supplement of high-value or perishable agricultural products caused by the lack of priority mechanism, ensuring product quality, and significantly improving the overall operation efficiency of the supply chain.

[0030] In a specific implementation scenario, when a batch of fresh agricultural products, such as a batch of strawberries, arrives at the processing center and is temporarily received, the system first obtains the product type information of the batch of products, identifying it as "strawberries". Subsequently, the system obtains the corresponding shelf life parameter of strawberries, such as "2 days", and the value density parameter, such as "30 yuan per kilogram", according to the product type information "strawberries" by querying the pre-set product characteristic database. At the same time, the system obtains the receiving amount of the batch of strawberries, such as "500 kilograms", from the temporary receiving information. Then, the system evaluates the priority of the to-be-verified task according to the receiving amount of 500 kilograms, the shelf life parameter of 2 days, and the value density parameter of 30 yuan per kilogram, through a pre-set priority evaluation algorithm, such as a weighted scoring model. The algorithm can be set as: priority score = (receiving amount * value density parameter) / shelf life parameter. In this example, the priority score is (500 * 30) / 2 = 7500. At the same time, if a batch of potatoes arrives and is temporarily received, the system identifies its product type as "potatoes", queries its shelf life parameter as "30 days", value density parameter as "3 yuan per kilogram", and receiving amount as "1000 kilograms". Then its priority score is (1000 * 3) / 30 = 100. By comparison, the priority score of the batch of strawberries is much higher than that of the batch of potatoes. Finally, the system updates the status of the batch of strawberries at the current stage to the temporary state, generates a to-be-verified task corresponding to the temporary state, and associates the high priority information (such as priority score 7500 or marked as "high") with the task. When the system sends the data supplement notification to the planting base, the notification will contain this high priority information. When the management personnel of the planting base processes the to-be-verified task list, they can prioritize the supplement task of the batch of strawberries to ensure that the formal data of high-value and perishable products is uploaded in time, thereby avoiding quality degradation and economic losses caused by delayed data.

[0031] The present scheme combines the product type information of the batch of products, the pre-set product characteristic data (including the shelf life parameter and the value density parameter), and the receiving amount in the temporary receiving information to evaluate the priority of the task and associate the priority information when generating the to-be-verified task. This allows the upstream participants to arrange the processing order of data supplement according to the urgency and importance of the task. This effectively solves the problem of lack of priority evaluation mechanism for to-be-verified tasks in the prior art, which leads to delayed data supplement of high-value or perishable agricultural products. By prioritizing the processing of urgent or important batches, the present scheme can significantly reduce the risk of product quality degradation, ensure the quality of fresh agricultural products, and improve the data processing efficiency and response speed of the entire supply chain.

[0032] In some embodiments, the specific steps in step S3 include: S3B1. Obtain the product type information of the batch of products; S3B2. Obtain downstream processing characteristic parameters corresponding to the product type according to the product type information; S3B3. Generate data uncertainty indication information of the batch product in the temporary state according to the receiving amount in the temporary receiving information and the downstream processing characteristic parameters; S3B4. Update the state of the batch product at the current link to the temporary state, and generate a to-be-verified task corresponding to the temporary state and associate the data uncertainty indication information; the data uncertainty indication information is used to send a warning or coordination notification to the business system of the downstream link.

[0033] The downstream processing characteristic parameters refer to a set of attributes related to the requirements of a specific product type in subsequent processing, storage, distribution, etc. for data accuracy, timeliness, quantity stability, etc. They can be obtained and represented by using a preset parameter table, rule library or model based on historical data analysis. The data uncertainty indication information refers to information for quantitatively or categorically describing the reliability of the receiving data (especially the receiving amount) of the batch product in the temporary state and the potential impact of the uncertainty on the downstream link. It can be represented by using numerical levels, risk level identifiers, text descriptions or structured data fields.

[0034] This scheme effectively solves the problem of decision-making errors and resource waste in the downstream link due to the lack of data reliability information when the batch product is in a temporary state by introducing a generation and transmission mechanism of data uncertainty indication information. Specifically, when the batch product enters the current link and is temporarily received, the system first obtains the product type information of the batch product. This is because different types of products have different requirements for data accuracy and timeliness, and their downstream processing processes and potential risks are also different. Based on the obtained product type information, the system further obtains the downstream processing characteristic parameters corresponding to the product type. These parameters reflect the sensitivity of the product to data (especially the receiving amount) in the downstream link, such as processing complexity, shelf-life sensitivity or accurate requirements for the quantity and quality of raw materials. For example, for perishable fresh products, their downstream processing characteristic parameters may indicate that they have high requirements for receiving quantity accuracy and strong processing timeliness.

[0035] Subsequently, the system generates data uncertainty indication information of the batch product in the temporary state according to the receiving amount in the temporary receiving information and the obtained downstream processing characteristic parameters. This step is the core of the scheme, which comprehensively considers the preliminary value of the current receiving data and the inherent downstream processing requirements of the product, thereby quantitatively or categorically representing the reliability and potential impact of the current temporary data. For example, if the receiving amount is large and the product has high requirements for downstream processing accuracy, the generated data uncertainty indication information may indicate a high risk.

[0036] Ultimately, the system updates the batch's status at the current stage to a temporary one, generates a pending write-off task corresponding to the temporary status, and associates the generated data uncertainty indicator with the pending write-off task. Crucially, this data uncertainty indicator is used to send early warnings or coordination notifications to downstream business systems. This means that downstream systems not only know that the data is temporary, but also understand the degree of uncertainty and potential impact, enabling them to proactively adjust production plans, prepare backup plans, or communicate with upstream systems in advance.

[0037] The coordinated operation of this series of steps allows the business process to proceed smoothly even when the data in the front-end link of the batch product has not been formally submitted, while avoiding blind downstream decision-making due to data uncertainty. Compared with the basic solution of simply marking the product as temporary, this solution maintains the continuity of the business process while actively evaluating and transmitting data uncertainty, allowing downstream systems to perform more refined risk management and resource allocation. The introduction of this mechanism enables the entire supply chain to achieve more efficient collaboration and more accurate risk response when faced with data lags, significantly improving the resilience and efficiency of the supply chain.

[0038] In a specific example, when a batch of "organic tomatoes" arrives at a processing center, but the harvest data from the growing base has not yet been officially submitted, the system will receive provisional receipt information containing the receipt time, quantity (for example, a preliminary weight of 500 kilograms), and the person responsible for receiving the product. At this point, the system will perform the following operations: First, the system obtains the product type information of the batch of products and identifies them as "organic tomatoes".

[0039] Next, based on the product type information for "organic tomatoes," the system queries a pre-set database or rule engine to obtain the corresponding downstream processing characteristic parameters. For example, downstream processing characteristic parameters for "organic tomatoes" might include "high sensitivity to shelf life," "low processing loss rate," and "high requirements for raw material quantity accuracy (for precise batching and packaging)."

[0040] Next, the system generates data uncertainty indicators for the batch in its temporary state based on the quantity received (500 kg) in the temporary receipt information and the acquired downstream processing characteristic parameters. For example, given the high accuracy requirements for the quantity of "organic tomatoes" and the temporary state of the data, the system might generate an indicator such as "Data uncertainty level: medium, recommending a 5% buffer" or "Risk level: yellow warning, possible deviation of ±10 kg."

[0041] Finally, the system updates the state of the batch of "organic tomatoes" at the current link to "temporary warehousing", generates a corresponding task to be checked and voided, and associates the generated data uncertainty indication information with the task to be checked and voided. This data uncertainty indication information can then be used to send early warnings or coordination notifications to the business systems of downstream links. For example, when the production planning system of the processing center receives the temporary warehousing information of this batch of products, it will also receive the early warning "data uncertainty level: medium, suggest reserving 5% buffer". The production planner can adjust the production plan for the day accordingly, for example, reserve some flexibility in production or communicate with the sales department in advance to inform them of the possible small deviations, so as to avoid production interruptions or product shortages caused by data uncertainty.

[0042] The present scheme actively obtains product type information and evaluates its downstream processing characteristics when the batch product enters the temporary state, then generates data uncertainty indication information according to the temporary receiving amount, associates it with the task to be checked and voided, and uses the indication information to send early warnings or coordination notifications to the business systems of downstream links. This enables the business systems of downstream links to be aware of the potential uncertainty of the data and its impact on their own business in advance, so that they can adjust production plans, inventory management or quality control strategies in a timely manner, avoid making decisions based on incomplete information, and effectively reduce the risk of production efficiency decline, resource waste, and product loss or quality damage caused by upstream data lag. The present scheme improves the information transparency and coordination efficiency of the entire supply chain, enhances the system's ability to cope with data uncertainty, and ensures the business continuity and stability of the fresh agricultural product supply chain when data flow is not smooth.

[0043] In some embodiments, the data uncertainty indication information is used to send early warnings or coordination notifications to the business systems of downstream links when: the type of data uncertainty and the degree of data uncertainty are parsed from the data uncertainty indication information; the requirement of the business system of the downstream link for data accuracy and the processing capacity of the business system for data uncertainty are obtained; the risk assessment content and the recommended operation content contained in the early warning or coordination notification are determined according to the type of data uncertainty, the degree of data uncertainty, the requirement of the business system for data accuracy, and the processing capacity of the business system for data uncertainty; the early warning or coordination notification is generated according to the risk assessment content and the recommended operation content, and is sent to the business system of the downstream link.

[0044] The type of data uncertainty refers to the specific category of issues existing in the data in terms of integrity, accuracy, timeliness, or consistency, which can be represented by predefined codes, enumerated values, or textual descriptions, such as "quantity missing", "quality questionable", "time delayed", or "batch information mismatch". The degree of data uncertainty refers to the quantitative or graded assessment of the impact of data uncertainty on business processes or product quality, which can be represented by numerical ranges, percentages, grades (such as "slight", "medium", "severe"), or risk levels (such as "low risk", "medium risk", "high risk"). The requirement of downstream business systems for data accuracy refers to the error range or accuracy standard that a specific business system can tolerate when executing its functions, which can be defined by configuration parameters, business rules, or system metadata, such as "accurate to the gram", "allow 5% deviation", or "must match completely". The processing capacity of downstream business systems for data uncertainty refers to the ability of a specific business system to automatically or semi-automatically correct, compensate, bypass, or provide alternative solutions when facing uncertain data, which can be represented by system function identification, processing strategy configuration, or historical processing success rate, such as "with automatic correction function", "support manual intervention process", or "switch to backup data source". The risk assessment content refers to the analysis and prediction of the potential negative impact of data uncertainty, which can be presented by structured text, risk level, or impact description, such as "may cause production line to stop", "increase the risk of product loss", or "affect the on-time delivery of orders". The recommended operation content refers to the specific response measures or action plans provided for downstream business systems or operating personnel to deal with data uncertainty, which can be presented by instruction list, operation steps, or recommended process, such as "immediately start manual verification", "adjust production plan", or "give priority to processing other batches of products".

[0045] After receiving the data uncertainty indication information generated by the upstream link indicating that the batch product is in a temporary state, the scheme no longer simply sends a general warning, but generates a targeted warning or coordination notification through a series of coordination steps. First, the system parses the specific type and quantitative degree of data uncertainty from the received data uncertainty indication information. This parsing process is the basis for subsequent decision-making, which concretizes abstract data problems, enabling the system to identify the nature and severity of the problem. Next, the system actively obtains or queries the specific requirements of the downstream link business system for data accuracy and its ability to handle uncertain data. This step introduces the context information of the downstream business scenario, enabling the system to understand the tolerance of different recipients for data quality and the flexibility of their response strategies. For example, a precision machining system that requires high data accuracy and a preliminary sorting system that requires relatively low data accuracy will have different warning content and recommended operations when faced with the same data uncertainty. Subsequently, the system analyzes and decides on the four key pieces of information: the type of data uncertainty, the degree of data uncertainty, and the requirements and processing capacity of the downstream business system for data accuracy. Through the fusion of this multi-dimensional information, the system can dynamically assess the risks that the current data uncertainty may pose and determine the risk assessment content and specific recommended operation content that should be included in the warning or coordination notification. For example, for "quantity missing" and "serious" data uncertainty, if the downstream system has "high" requirements for "quantity accuracy" and "limited processing capacity", the system may assess it as "high risk" and recommend "immediately suspend related production plans and wait for manual verification". This multi-dimensional information fusion-based decision-making mechanism enables the warning to be more than just a notification, but also includes an analysis of potential impacts and executable response strategies, enhancing the decision-making support capabilities of the downstream link. Finally, the system generates a warning or coordination notification based on the determined risk assessment content and recommended operation content, and sends it to the corresponding downstream link business system. In this way, the scheme converts the original data uncertainty indication information generated by the upstream link into a notification that the downstream link can understand and effectively respond to, containing specific risk analysis and action guidelines, thereby solving the problem of insufficient information and delayed action caused by sending general warning information, and improving the coordination efficiency and risk response capabilities of the entire agricultural supply chain when facing data uncertainty.

[0046] In a specific implementation scenario, when the processing center receives a batch of vegetables in a temporary state and generates data uncertainty indication information, the indication information can include the type of "quantity uncertainty" and the degree of "moderate deviation". When preparing to send a warning notification to the subsequent packaging plant management system, the system first parses these information from the indication information. Then, the system queries the configuration of the packaging plant management system and finds that its accuracy requirement for product quantity is "high" because the packaging process needs accurate bill of materials to control the consumption of packaging materials and the quantity of final products. At the same time, the system understands that the processing capacity of the packaging plant management system for data uncertainty is "limited", that is, it does not have the function of automatically adjusting the packaging plan or automatically supplementing materials, and needs manual intervention. Based on these information, the system makes a comprehensive judgment: since there is a moderate deviation in the quantity and the downstream system has a high accuracy requirement but limited processing capacity, the system assesses that this uncertainty may cause the risk of "packaging material waste" or "quantity of final products not meeting the order". Therefore, the system determines that the risk assessment content included in the warning notification should be "there is a moderate deviation in the quantity of the batch product, which may cause the over-consumption of packaging materials or the shortage of order quantity", and generates the suggested operation content as "please manually check the actual quantity of arrival, and adjust the packaging plan or notify the procurement department according to the actual situation". Finally, the system sends the warning notification containing the risk assessment and suggested operation content to the packaging plant management system through the enterprise internal message bus or API interface, so that the operating personnel of the system can obtain the information in time and take targeted measures.

[0047] The present scheme can determine the risk assessment content and suggested operation content included in the warning or coordination notification by analyzing the type and degree of data uncertainty, and combining the accuracy requirement and processing capacity of the downstream business system, and then generate and send the warning or coordination notification with targeted content. This solves the problem that the content of traditional general warning notification is too general, making it difficult for downstream systems or operating personnel to respond effectively. By providing specific risk assessment and actionable suggestions, the downstream 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 accuracy of each link in the supply chain.

[0048] In some embodiments, the specific steps in step S5 include: S51. Obtain product type information of the batch product and transportation condition information corresponding to the batch product; S52. Query the natural loss characteristic parameters corresponding to the product type according to the product type information; S53. Calculate the allowed deviation threshold range between the temporary state data and the formal data according to the natural loss characteristic parameters, the transportation condition information, and the reported quantity in the formal data; S54. Comparing the received amount in the temporary state data with the reported amount in the formal data, a deviation value is calculated; S55. According to the comparison result of the deviation value and the deviation threshold range, it is determined whether the matched successful data meets the logical verification requirement; if the deviation value is within the deviation threshold range, it is determined that the matched successful data meets the logical verification requirement; if the deviation value exceeds the deviation threshold range, it is determined that the matched successful data does not meet the logical verification requirement, and data anomaly indication information is generated; the data anomaly indication information is used for subsequent abnormal processing process.

[0049] The product type information refers to data used to identify the specific type of batch product, such as the variety of vegetables, the category of fruits, etc., which can be implemented by product code, product name or classification label. The transportation condition information refers to data describing the environment of batch products during transportation, such as transportation duration, average temperature, humidity, whether refrigerated, etc., which can be implemented by structured data field, sensor record or logistics document information. The natural loss characteristic parameter refers to a set of data related to a specific product type and the natural loss law that may occur under specific transportation conditions, which can be implemented by a pre-set loss rate table, a statistical model based on historical data or expert experience rules. The deviation threshold range refers to the interval of quantity difference between the temporary state data and the formal data allowed in the logical verification, which is considered as a reasonable and acceptable loss range, which can be represented by a numerical range, a percentage interval or a combination of upper and lower limits. The data anomaly indication information refers to information used to mark that the matched successful data does not meet the logical verification requirement, which can be implemented by a Boolean flag, an error code or a text description containing abnormal details.

[0050] The scheme is no longer limited to simple quantity consistency judgment when logical verification is performed on the matched data, but introduces quantitative consideration of natural loss of agricultural products, so that the verification process is more in line with the actual business scenario. Specifically, first, by obtaining the product type information of the batch product and the transportation condition information corresponding to the batch product, the system can identify the key factors affecting the loss of the batch product in the circulation process. The product type determines its inherent vulnerability, while the transportation condition directly affects the degree of loss. Based on this basic information, the system further queries the natural loss characteristic parameters corresponding to the product type according to the product type information. These parameters are pre-set or based on historical data accumulation, reflecting the loss law of different agricultural products under different conditions, providing a scientific basis for subsequent loss assessment. On this basis, the system dynamically calculates the allowed deviation threshold range between the temporary state data and the formal data using the obtained natural loss characteristic parameters, transportation condition information and the reported quantity in the formal data. This calculation process simulates the reasonable loss that may occur in the actual circulation of products, thereby setting a flexible and realistic verification standard, avoiding false positives due to normal loss. Subsequently, the system compares the received quantity in the temporary state data with the reported quantity in the formal data, and calculates the actual deviation value. Finally, the system determines whether the matched data meets the logical verification requirements according to the comparison result of the deviation value and the pre-calculated deviation threshold range. If the actual deviation value falls within the allowed deviation threshold range, the data is considered to meet the logical verification requirements, so that the temporary state of the batch product at the current link can be smoothly converted to the formal state, and the pending verification task is released. Otherwise, if the deviation value exceeds the range, it is determined that the data does not meet the logical verification requirements, and data anomaly indication information is generated to trigger the subsequent exception handling process. In this way, the scheme incorporates the natural loss objectively existing in the agricultural product supply chain into the data verification model, making the data verification process more accurate and intelligent, avoiding false positives for normal business loss, and significantly improving the efficiency and accuracy of data processing.

[0051] In a specific embodiment, when the system receives the temporary receiving information of a batch of leafy vegetables and matches it to the corresponding formal reporting data, it will start the logical verification process. First, the system can obtain the product type information of the batch product as "leafy vegetables" from the product's metadata, and obtain the transportation condition information such as the transportation duration of "24 hours" and the average temperature of "5 degrees Celsius" from the logistics records or sensor data. Then, according to the product type information of "leafy vegetables", the system queries the natural loss characteristic parameters corresponding to leafy vegetables in the pre-set agricultural product loss database, for example, the natural loss rate per 24 hours at 5 degrees Celsius is about 2% to 4%. Subsequently, the system can calculate the allowable deviation threshold range according to these natural loss characteristic parameters, 24 hours of transportation condition information and the reported amount of 1000 kg of leafy vegetables in the formal data. For example, based on the loss rate of 2% to 4%, the system can calculate the allowable loss amount as 20 kg to 40 kg, thereby determining that the deviation between the receiving amount and the reported amount should be between -40 kg to -20 kg (i.e. the receiving amount can be 20 to 40 kg less than the reported amount). After that, the system compares the receiving amount in the temporary state data, for example 970 kg, with the reported amount 1000 kg in the formal data, and calculates the deviation value of -30 kg. Finally, the system compares the deviation value of -30 kg with the calculated deviation threshold range (-40 kg to -20 kg). Since -30 kg falls within the range, the system can determine that the matched data meets the logical verification requirements, thereby allowing the status of the batch of leafy vegetables to be formal. If the receiving amount is 900 kg, the deviation value is -100 kg, which exceeds the allowable range, the system will generate data anomaly indication information, such as an error code labeled "quantity anomaly", and trigger the corresponding abnormal processing process, such as notifying manual verification.

[0052] The present scheme solves the problem that the traditional verification mechanism cannot accurately reflect the actual loss of agricultural products by introducing the quantitative consideration of the natural loss of agricultural products in the logical verification. By obtaining the product type information and transportation condition information, and querying the natural loss characteristic parameters accordingly, the system can dynamically calculate the deviation threshold range that meets the actual situation. This allows even reasonable quantity deviations within the acceptable range to be correctly identified by the system as meeting the logical verification requirements, thereby avoiding false positives and unnecessary abnormal processing processes caused by normal loss. Therefore, the present scheme significantly improves the efficiency and accuracy of data verification, reduces the cost of manual intervention, and ensures the effectiveness of system warnings, enabling it to more accurately identify real business problems rather than frequently reporting "false anomalies".

[0053] In some embodiments, the specific steps in step S53 include: S531. Obtain the packaging type information, loading and unloading method information, and transportation vehicle characteristic information of the batch product; S532. According to the packaging type information, the loading and unloading method information, and the transportation carrier characteristic information, query the preset non-natural loss influence parameter; S533. According to the non-natural loss influence parameter and the reported quantity in the formal data, calculate the first estimated loss quantity of the batch product in the circulation process caused by non-natural factors; S534. According to the natural loss characteristic parameter, the transportation condition information, and the reported quantity in the formal data, calculate the second estimated loss quantity of the batch product in the circulation process caused by natural factors; S535. According to the first estimated loss quantity and the second estimated loss quantity, calculate the total estimated loss quantity of the batch product in the circulation process allowed to exist; S536. According to the total estimated loss quantity and the reported quantity in the formal data, determine the deviation threshold range allowed to exist between the temporary state data and the formal data.

[0054] In this scheme, the packaging type information indicates the packaging material, form or structure used by the batch product, which can be realized by text description, coding or preset classification label. The loading and unloading method information indicates the loading and unloading operation method adopted by the batch product in the circulation process, which can be realized by manual operation, mechanical assistance or automatic equipment operation, etc. The transportation carrier characteristic information indicates the physical properties and operating characteristics of the vehicle or container used for transporting the batch product, which can be realized by vehicle type, container type, internal environment control capability or shock absorption performance, etc. The non-natural loss influence parameter indicates the preset numerical value or model related to packaging type, loading and unloading method and transportation carrier characteristics, which can be used to quantify non-natural loss, which can be realized by loss rate coefficient, loss quantity estimation formula or statistical model based on historical data. The first estimated loss quantity indicates the estimated loss quantity of the batch product in the circulation process caused by non-natural factors (such as physical collision, extrusion, friction, etc.), which can be realized by multiplication calculation or table lookup based on non-natural loss influence parameter and reported quantity. The second estimated loss quantity indicates the estimated loss quantity of the batch product in the circulation process caused by natural factors (such as respiration, transpiration, microbial activity, etc.), which can be realized by calculation or model estimation based on natural loss characteristic parameter, transportation condition information and reported quantity. The total estimated loss quantity indicates the total estimated loss quantity of the batch product in the circulation process allowed to exist, which is caused by natural factors and non-natural factors, which can be realized by simple addition or weighted average of the first estimated loss quantity and the second estimated loss quantity. The deviation threshold range indicates the difference range allowed to exist between the temporary state data and the formal data, which can be realized by percentage range or absolute value range calculated based on the total estimated loss quantity and the reported quantity.

[0055] The scheme can more comprehensively cover various types of reasonable losses that may be encountered by fresh agricultural products in the supply chain during circulation by refining the calculation process of the deviation threshold range and taking into account the losses caused by non-natural factors, thereby improving the comprehensiveness and accuracy of the verification, and effectively solving the misjudgment problem caused by insufficient consideration of non-natural losses in the existing scheme. Specifically, when calculating the deviation threshold range allowed to exist between the temporary state data and the formal data, first, the packaging type information, loading and unloading method information, and transportation vehicle characteristic information of the batch product are obtained. These information are the key inputs for identifying and quantifying non-natural losses, because different packaging, loading and unloading methods, and transportation vehicles have different physical loss effects on products. Based on these obtained information, the system queries the pre-set non-natural loss impact parameters, which provide the basis for quantifying the impact of specific non-natural factors on product loss. Subsequently, according to these non-natural loss impact parameters and the reported quantity in the formal data, the first estimated loss quantity of the batch product caused by non-natural factors during circulation is calculated. This step enables the system to quantify and consider the loss that may occur during packaging, loading and unloading, and transportation, making up for the shortcomings of only considering natural loss. At the same time, in order to ensure the continuous consideration of the natural loss of the product, the system combines the natural loss characteristic parameters, transportation condition information, and the reported quantity in the formal data to calculate the second estimated loss quantity of the batch product caused by natural factors during circulation. By integrating the first estimated loss quantity and the second estimated loss quantity, the total estimated loss quantity allowed to exist during circulation of the batch product is calculated, which provides a more comprehensive and more realistic loss expectation. Finally, according to the total estimated loss quantity and the reported quantity in the formal data, the deviation threshold range allowed to exist between the temporary state data and the formal data is determined. Through the above steps, the deviation threshold range determined can more accurately reflect the reasonable loss that may exist in actual business. This enables the system to more accurately judge whether the data meets the requirements in subsequent logical verification, avoiding the misjudgment of normal and acceptable loss as data anomaly, thereby reducing unnecessary abnormal processing procedures and improving the accuracy of data verification and the efficiency of the system. This fine processing of the deviation threshold calculation makes the entire agricultural supply chain data collaboration management method more reliable and practical in data matching and logical verification, effectively reducing business interruptions and resource waste caused by data differences, thereby improving the digital management level and response capability of the entire supply chain.

[0056] In a specific embodiment, it is assumed that a batch of fresh tomatoes is transported from a planting base to a processing center. When calculating the allowable deviation threshold range between the temporary state data and the formal data, the system first obtains the packaging type information, loading and unloading method information, and transportation vehicle characteristic information of the batch of tomatoes. For example, it can be obtained that the batch of tomatoes is packaged with "plastic turnover boxes", the loading and unloading method is "manual handling", and the transportation is carried out by "ordinary van". Then, the system queries the preset non-natural loss influence parameters according to the obtained information. For example, in the preset database, it can be queried that "plastic turnover box packaging" may cause a preset extrusion loss rate under "manual handling", and "ordinary van" may cause a preset jolt loss rate during transportation. Based on these non-natural loss influence parameters and the reported quantity in the formal data, the system calculates a first estimated loss quantity of the batch of tomatoes caused by non-natural factors in the circulation process. At the same time, the system queries the natural loss characteristic parameters corresponding to the product type information of the tomatoes, and combines the transportation condition information and the reported quantity in the formal data to calculate a second estimated loss quantity of the batch of tomatoes caused by natural factors in the circulation process. Next, the system adds the first estimated loss quantity and the second estimated loss quantity to obtain the total estimated loss quantity of the batch of tomatoes allowed to exist in the circulation process. Finally, the system determines the allowable deviation threshold range between the temporary state data and the formal data according to the total estimated loss quantity and the reported quantity in the formal data. For example, if the total estimated loss quantity is 20 kg and the formal reported quantity is 1000 kg, the deviation threshold range can be determined to be between 0 and 20 kg, or between 0 and 2%. In this way, when the difference between the temporary received quantity and the formal reported quantity falls within this range, the system will judge that the data meets the logical verification requirements, avoiding misjudging normal loss as abnormal.

[0057] The present scheme determines the total estimated loss quantity by obtaining the packaging type information, loading and unloading method information, and transportation vehicle characteristic information of the batch of products, calculating the first estimated loss quantity caused by non-natural factors, and combining the second estimated loss quantity caused by natural factors, and then determines the allowable deviation threshold range between the temporary state data and the formal data. This method makes the calculated deviation threshold range more accurate and comprehensive, and can fully consider the reasonable loss of fresh agricultural products caused by non-natural factors such as packaging, loading and unloading, and transportation vehicles in the supply chain circulation. Therefore, the present scheme can effectively avoid misjudging normal non-natural loss as data anomaly, significantly reduce the misjudgment rate and unnecessary abnormal processing flow of the system, thereby improving the accuracy of data verification and the running efficiency of the system.

[0058] Reference is made to the accompanying drawings Figure 2 The present application provides an agricultural supply chain data collaborative management system, comprising: The acquisition module 100 is configured to acquire a preceding link data state of the batch product when the batch product is received. The receiving module 200 is configured to receive temporary receiving information of the batch product according to the acquired preceding link data state, if the preceding link data is not in the formal state. The generation module 300 is configured to update a state of the batch product in a current link to a temporary state according to the temporary receiving information, and generate a to-be-verified task corresponding to the temporary state. The sending module 400 is configured to send a data supplementing notification to an upstream participant responsible for submitting the formal data of the preceding link according to the to-be-verified task. The matching module 500 is configured to match the temporary state data and the formal data according to a batch product traceability code when the upstream participant responds to the data supplementing notification and submits the formal data of the preceding link, and perform a logical check on the matched data. The release module 600 is configured to convert the temporary state of the batch product in the current link to a formal state and release the to-be-verified task according to a matching result and a logical check result, if the matching is successful and the logical check is consistent. The early warning module 700 is configured to trigger an early warning and record a temporary state and a verification operation log if the data verification cannot be completed within a specified supplementing time limit.

[0059] In this document, the terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0060] The above only describes the embodiments of the present application and is not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A collaborative management method for agricultural supply chain data, characterized in that: The following steps are involved: S1. When receiving a batch of products, obtain the data status of the preceding links of the batch of products; S2. Based on the status of the acquired pre-process data, if the pre-process data is not in a formal state, the temporary receipt information of the batch of products is received; S3. Update the status of the batch product in the current link to a temporary status based on the temporary receipt information, and generate a pending write-off task corresponding to the temporary status; S4. Send a data re-entry notice to the upstream party responsible for submitting the official data for the previous stage based on the pending verification tasks; S5. When the upstream participant responds to the data re-entry notification and submits the official data from the previous stage, the temporary status data is matched with the official data based on the batch product traceability code, and the matched data is logically verified; S6. Based on the matching results and logic verification results, if the match is successful and the logic verification is met, the temporary status of the batch product in the current link is changed to the official status, and the pending write-off task is released; S7. If data verification is not completed within the specified re-entry time limit, an early warning will be triggered and the 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: The temporary reception information includes the reception amount.

3. The agricultural supply chain data collaborative management method according to claim 1, characterized in that: The data re-entry notice includes the official data of the preceding links to be re-entered and the re-entry time limit specified according to the characteristics of the 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. Get product type information of batch products; S3A2 according to the product type information, by querying the preset product characteristics data, obtain the product type corresponding to the shelf life parameters and value density parameters; S3A3. Evaluate the priority of pending write-off tasks based on the volume, shelf life, and value density parameters in the temporary receipt information. S3A4. Update the status of the batch product in the current link to a temporary status, generate pending verification tasks corresponding to the temporary status, and associate priority information so that upstream participants can arrange the processing order of pending verification tasks based on 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. Get product type information of batch products; S3B2. According to the product type information, obtain the downstream processing characteristic parameters corresponding to the product type; S3B3 based on the amount of temporary receiving information received and downstream processing characteristic parameters, generate batch product data uncertainty indication information in a temporary state; S3B4. Update the status of the batch product in the current link to a temporary status, 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 notifications to the business systems of downstream links.

6. The agricultural supply chain data collaborative management method according to claim 5, characterized in that: Data uncertainty indication information is used to send warnings or coordination notifications to downstream business systems: From the data uncertainty indication information, the type of data uncertainty and the degree of data uncertainty are analyzed; Obtain the data accuracy requirements of downstream business systems and their ability to handle data uncertainty; Determine the risk assessment content and recommended actions to be included in the warning or coordination notice 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; Based on the risk assessment content and recommended operation content, an early warning or coordination notification is generated and sent to the business system of the downstream link.

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

8. The agricultural supply chain data collaborative management method according to claim 7, characterized in that: The specific steps in step S5 include: S51. Get product type information of batch products and transportation condition information corresponding to batch products; S52. According to the product type information, query the natural loss characteristic parameters corresponding to the product type; S53. Calculate the allowable deviation threshold between the temporary status data and the official data based on the natural loss characteristic parameters, transportation condition information, and the reported quantity in the official data; S54. Compare the amount received in the temporary status data with the amount reported in the official 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 logic verification requirements; if the deviation value is within the deviation threshold range, determine that the successfully matched data meets the logic verification requirements; if the deviation value exceeds the deviation threshold range, determine that the successfully matched data does not meet the logic verification requirements, and generate data anomaly indication information; the data anomaly indication information is used for subsequent exception handling processes.

9. The agricultural supply chain data collaborative management method according to claim 8, characterized in that: The specific steps in step S53 include: S531. Obtaining information on the packaging type, loading and unloading methods, and transport vehicle characteristics of the product batch; S532. Query the preset non-natural loss impact parameters based on the packaging type information, loading and unloading method information, and transport vehicle characteristics information; S533. Calculate the first estimated loss amount of the batch product due to non-natural factors during the circulation process based on the non-natural loss impact parameter and the reported amount in the official data; S534. Calculate the second estimated loss amount of the batch product due to natural factors during the circulation process based on the natural loss characteristic parameters, transportation condition information, and the reported amount in the official data; S535. Calculate the total estimated loss allowed for a batch of products during circulation based on the first estimated loss and the second estimated loss. S536. Determine the allowable deviation threshold range between the temporary status data and the official data based on the total estimated loss amount and the reported amount in the official data.

10. An agricultural supply chain data collaborative management system, characterized in that: include: The acquisition module is used to obtain the data status of the preceding links of the batch of products when receiving the batch of products; The receiving module is used to receive the temporary receiving information of the batch of products according to the status of the acquired data of the preceding link, if the data of the preceding link is not in the formal status; The generation module is used to update the status of the batch product in the current link to a temporary status based on the temporary receipt information, and generate a pending write-off task corresponding to the temporary status; The sending module is used to send data supplementary recording notifications to upstream participants responsible for submitting formal data in the preceding stages based on pending write-off tasks; The matching module is used to match the temporary status data with the official data according to the batch product traceability code after the upstream party responds to the data supplement notification and submits the official data of the previous link, and performs logical verification on the successfully matched data; The release module is used to change the temporary status of the batch product in the current link to the official status and release the pending write-off task based on the matching results and logic verification results. If the match is successful and the logic verification is met, The early warning module is used to trigger an early warning if data verification is not completed within the specified supplementary recording time limit, and record the temporary status and verification operation log.

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