Product whole-process supervision method and system based on block chain

Abstract

The invention provides a product whole-process supervision method and system based on a block chain, belongs to the field of computers, and solves the problem that the block chain cannot verify the authenticity of input data in a commodity traceability system. The method comprises the following steps: acquiring initial state information after agricultural and sideline products are produced, wherein the initial state information comprises growth data, initial appearance data and initial weight data; obtaining full-process environmental condition parameters of storage, transportation and sales and stage state information of the agricultural and sideline products at each process transfer node, wherein the stage state information comprises stage appearance data and stage weight data; and on the basis of the initial state information or the stage state information and each process environment condition parameter, predicting ideal state information of the agricultural and sideline products at the next process transfer node, so that the agricultural and sideline products can be obtained under the condition that the matching degree of the ideal state information and the recorded stage state information is lower than a preset matching degree. And generating a process environment condition parameter authenticity alarm and recording the process environment condition parameter authenticity alarm to the block chain system.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a blockchain-based product full-process supervision method and system. Background Art

[0002] Blockchain has many advantages in product traceability, such as improving transparency, enhancing data immutability and reducing the risk of fraud. However, it also has some limitations. Blockchain can ensure that data cannot be changed once it is entered, but it cannot guarantee the authenticity of the input data itself. The product traceability system needs to rely on external devices (such as sensors or manual input) to upload data to the blockchain. If the original data is inaccurate or tampered with, the blockchain cannot verify or correct these errors. Summary of the invention

[0003] The embodiments of the present application provide a blockchain-based product full-process supervision method and system, which can solve the problem that blockchain cannot verify the authenticity of input data in the product traceability system.

[0004] The first aspect of the embodiment of the present application provides a product full-process supervision method based on blockchain, including:

[0005] Obtaining initial state information of agricultural and sideline products after they are produced, the initial state information including growth data, initial appearance data, and initial weight data, so as to record the initial state information in the blockchain system;

[0006] Obtaining the environmental condition parameters of the entire process of storage, transportation and sales, as well as the stage status information of the agricultural and sideline products at each process flow node, wherein the stage status information includes stage appearance data and stage weight data, so as to record the environmental condition parameters of the entire process and the stage status information in the blockchain system;

[0007] Based on the initial state information or stage state information and each process environmental condition parameter, the ideal state information of the agricultural and sideline products at the next process flow node is predicted, so that when the matching degree between the ideal state information and the recorded stage state information is lower than the preset matching degree, an authenticity alarm of the process environmental condition parameter is generated and recorded in the blockchain system.

[0008] Optionally, it also includes:

[0009] The shelf life information of the agricultural and sideline products is updated based on the acquired stage status information, wherein the shelf life information includes a shelf life and associated storage conditions.

[0010] Optionally, it also includes:

[0011] When the agricultural and sideline products are transferred to the sales stage, obtaining environmental condition parameters of the sales stage;

[0012] The agricultural and sideline products are divided into quality change time intervals and theoretical quality change states corresponding to each interval based on the environmental condition parameters.

[0013] Optionally, it also includes:

[0014] The recommended selling price of the agricultural and sideline products is adjusted at the start time of the quality change time interval based on the theoretical quality change state corresponding to each interval.

[0015] Optionally, it also includes:

[0016] Before the end of the previous quality change time interval corresponding to the theoretical quality change state that does not meet the selling state, a removal prompt message is sent to the smart terminal device associated with the sales stage of the agricultural and sideline products.

[0017] Optionally, also include:

[0018] Based on the environmental condition parameters, the agricultural and sideline products are divided into quality change time intervals and taste change states corresponding to each interval;

[0019] When a traceability query request based on the traceability identifier is received from the terminal, the current theoretical taste status of the agricultural and sideline products is displayed.

[0020] Optionally, also include:

[0021] When receiving a traceability query request from a terminal based on a traceability identifier, predicting the current theoretical quality status of the agricultural and sideline products based on the initial status information of the agricultural and sideline products and the environmental condition parameters of each process;

[0022] The current theoretical appearance image of the agricultural and sideline products is generated based on the theoretical quality state and the initial state information, so that personnel can judge whether the environmental condition parameters are appropriate and make adjustments at different stages.

[0023] The second aspect of the embodiment of the present application provides a product full-process supervision system based on blockchain, including:

[0024] A first acquisition unit is used to acquire initial state information of agricultural and sideline products after they are produced, wherein the initial state information includes growth data, initial appearance data, and initial weight data, so as to record the initial state information in a blockchain system;

[0025] The second acquisition unit is used to acquire the environmental condition parameters of the entire process of storage, transportation and sales, and the stage status information of the agricultural and sideline products at each process flow node, wherein the stage status information includes stage appearance data and stage weight data, so as to record the environmental condition parameters of the entire process and the stage status information in the blockchain system;

[0026] The analysis unit is used to predict the ideal state information of agricultural and sideline products at the next process flow node based on the initial state information or stage state information and each process environment condition parameter, so as to generate a process environment condition parameter authenticity alarm and record it to the blockchain system when the matching degree between the ideal state information and the recorded stage state information is lower than a preset matching degree.

[0027] A third aspect of an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the processor is used to implement the steps of the above-mentioned blockchain-based full-process product supervision method when executing a computer program stored in the memory.

[0028] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned blockchain-based product full-process supervision method are implemented.

[0029] In summary, the product full-process supervision method based on blockchain provided by the embodiment of the present application obtains the initial state information of agricultural and sideline products after they are produced, and the initial state information includes growth data, initial appearance data and initial weight data, so as to record the initial state information in the blockchain system; obtains the environmental condition parameters of the whole process of storage, transportation and sales, and the stage state information of the agricultural and sideline products at each process flow node, and the stage state information includes stage appearance data and stage weight data, so as to record the environmental condition parameters and stage state information of the whole process in the blockchain system; based on the initial state information or stage state information and each process environmental condition parameter, the ideal state information of agricultural and sideline products at the next process flow node is predicted, so as to generate the process environmental condition parameter authenticity alarm and record it to the blockchain system when the matching degree between the ideal state information and the recorded stage state information is lower than the preset matching degree. Thus, through the mechanisms such as sensor redundancy check, multi-party verification and matching degree detection, the system can detect data anomalies before data is uploaded to the chain, and improve the authenticity and reliability of the data. Through the multi-level alarm mechanism, the system can issue an alarm in time when the environmental conditions and state data are abnormal, prevent the further spread of data errors, and ensure that the problem can be solved in time. All data and alarm records will be recorded in the blockchain to ensure that every link in the process is transparent and cannot be tampered with, which enhances the credibility of the traceability system. The system continuously monitors the data and provides feedback through the alarm mechanism when problems are found, which helps to continuously optimize all links in the supply chain and improve the overall process efficiency and product quality. It solves the problem of tampering or sensor error that may exist before data is uploaded to the chain, and enhances the data authenticity and security of blockchain in the traceability of agricultural and sideline products. At the same time, the tamper-proof characteristics of the blockchain are used to ensure that all data records are transparent and traceable, and improve the overall operation efficiency of the supply chain and the level of product quality assurance.

[0030] Correspondingly, the system, electronic device, and computer-readable storage medium provided by the embodiments of the present invention also have the above-mentioned technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A flowchart of a possible blockchain-based product full-process supervision method provided in an embodiment of the present application;

[0032] Figure 2 A schematic structural block diagram of a possible blockchain-based product full-process supervision system provided in an embodiment of the present application;

[0033] Figure 3 A schematic diagram of the hardware structure of a possible blockchain-based product full-process supervision system provided in an embodiment of the present application;

[0034] Figure 4 A schematic structural block diagram of a possible electronic device provided in an embodiment of the present application;

[0035] Figure 5 A schematic structural block diagram of a possible computer-readable storage medium provided for an embodiment of the present application. DETAILED DESCRIPTION

[0036] The embodiments of the present application provide a blockchain-based product full-process supervision method and related equipment, which can solve the problem that blockchain cannot verify the authenticity of input data in the product traceability system.

[0037] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0038] See also Figure 1 , which is a flowchart of a product full-process supervision method based on blockchain provided in an embodiment of the present application, may specifically include:

[0039] S110-S130.

[0040] S110, obtaining initial state information of agricultural and sideline products after production, wherein the initial state information includes growth data, initial appearance data and initial weight data, and recording the initial state information in a blockchain system.

[0041] For example, after the agricultural and sideline products are harvested, it is necessary to obtain the initial status information of the products, including growth data, appearance data and weight data. This initial status information serves as the benchmark data during the product life cycle, but since data collection equipment (such as sensors) may have errors, multiple verification mechanisms and alarm systems must be designed to ensure the accuracy of the initial data.

[0042] Exemplarily, multiple sensors are used to collect the same environmental parameters (such as temperature, humidity, light, etc.) at the same time, and the accuracy of the data is ensured by data comparison. If the sensor data deviates greatly, the system will trigger an alarm and mark the data as abnormal. For the initial appearance data, high-definition camera equipment is used for automatic recording, combined with manual inspection, especially for verification of marginal conditions of product appearance (such as damage or irregular shape). The manually reviewed data is also recorded on the blockchain for audit and traceability. Through accurate electronic weighing, multiple products of the same batch are weighed at the same time, the average value is calculated, and possible abnormal data is excluded. For example, if the weight difference of individual products is large, the system will trigger an alarm, indicating that there may be a problem with the weighing equipment. For example, after a batch of vegetables is picked, multiple sensors are used to monitor the growth environment, and multiple cameras are used to capture appearance images. If the clarity or color of the image collected by a camera is detected to be abnormal, the system will issue an alarm, indicating that the camera needs to be checked or the data needs to be collected again.

[0043] S120, obtaining the environmental condition parameters of the entire process of storage, transportation and sales and the stage status information of the agricultural and sideline products at each process flow node, wherein the stage status information includes stage appearance data and stage weight data, so as to record the environmental condition parameters of the entire process and the stage status information in the blockchain system.

[0044] For example, during the storage, transportation and sales of agricultural and sideline products, environmental condition parameters (such as temperature, humidity, etc.) and status data (such as appearance and weight changes) are obtained at each stage. Since sensors may fail or read inaccurately, the system needs to ensure the reliability of these data through data verification and anomaly detection mechanisms. At each flow node, the sensor data will be automatically calibrated and compared with the data of the previous stage. For example, if the temperature fluctuates greatly during transportation but the sensor does not reflect the change, the system will issue an alarm to indicate that the sensor may fail. The rationality of the data is determined by analyzing the correlation between environmental conditions and product status changes. For example, the weight of the product is normal when the temperature is low, but the weight changes greatly after the temperature suddenly rises, indicating that there may be abnormal storage conditions or sensor problems. The system will judge and issue an alarm based on the matching degree of these data.

[0045] For example, when a batch of fruits is being transported in the cold chain, the sensor records that the temperature fluctuation is too large and the weight of the fruits is much less than the predicted value. At this time, the system will automatically analyze the transportation route, combine the temperature, humidity and weight change data, and issue an alarm to indicate that there may be problems in the transportation section, and it is necessary to check whether the cold chain equipment is operating normally.

[0046] S130, based on the initial state information or stage state information and each process environmental condition parameter, predict the ideal state information of the agricultural and sideline products at the next process flow node, so as to generate a process environmental condition parameter authenticity alarm and record it in the blockchain system when the matching degree between the ideal state information and the recorded stage state information is lower than the preset matching degree.

[0047] Exemplarily, based on the initial state information, stage state information and environmental condition parameters, the system predicts the ideal state of agricultural and sideline products at the next process node through modeling and analysis. If the actual state of the product matches the predicted ideal state less than the preset threshold, it may indicate abnormal sensor data or improper operation, and the system will issue an alarm. Train machine learning models based on a large amount of historical data to predict the normal state of products under different environmental conditions. For example, use data from previous batches under similar environments to predict the weight change or appearance change of products during transportation. Set a reasonable matching threshold, and trigger an alarm when the deviation between the actual data and the predicted data exceeds the threshold. The system can dynamically adjust the threshold based on factors such as product type and environmental conditions to ensure the flexibility and accuracy of detection.

[0048] For example, assuming that a batch of agricultural products is under the expected environmental conditions, the system predicts that the weight loss should not exceed 3%, and the color change should remain within a certain range. If the actual weight drops by 5% when reaching the next node, and the color change is abnormal, the system will issue an alarm, indicating that there may be abnormal environmental parameters during transportation or problems with recorded data.

[0049] It is understandable that by comparing the status information and prediction results of each stage, when the matching degree between the actual data and the ideal state is lower than the threshold, the system will issue an alarm, indicating that there may be abnormal environmental conditions or sensor equipment failure in the process. At the same time, the alarm information will be automatically recorded in the blockchain to ensure that it can be traced and audited later.

[0050] For example, after each data upload, the system automatically compares the rationality between the environmental condition parameters obtained by the sensor and the previous data. If a significant deviation is found in the data, the system will automatically trigger an alarm to prompt the relevant person in charge to check the sensor or operation process. All alarm records, including the time of the alarm, the products involved, environmental condition parameters and status data, will be automatically uploaded to the chain. This ensures that any abnormalities will be documented in the system and will not be tampered with. By analyzing the alarm information, the system can identify nodes with high-frequency failures or data anomalies (such as a certain type of sensor that often makes mistakes) so that they can be optimized and improved in subsequent processes. For example, if a batch of agricultural products is transported during a certain section, the sensor records the temperature outside the set range for many times, the system will continuously issue alarms and record them in the blockchain. The management can check the cold chain equipment for that section of transportation and trace the root cause of the problem based on the blockchain records.

[0051] In summary, the product full-process supervision method based on blockchain provided by the above-mentioned embodiment of the present application obtains the initial state information of agricultural and sideline products after they are produced, and the initial state information includes growth data, initial appearance data and initial weight data, so as to record the initial state information in the blockchain system; obtains the environmental condition parameters of the whole process of storage, transportation and sales, and the stage state information of the agricultural and sideline products at each process flow node, and the stage state information includes stage appearance data and stage weight data, so as to record the environmental condition parameters and stage state information of the whole process in the blockchain system; based on the initial state information or stage state information and each process environmental condition parameter, the ideal state information of the agricultural and sideline products at the next process flow node is predicted, so as to generate the process environmental condition parameter authenticity alarm and record it to the blockchain system when the matching degree between the ideal state information and the recorded stage state information is lower than the preset matching degree. Thus, through the mechanisms such as sensor redundancy check, multi-party verification and matching degree detection, the system can detect data anomalies before data is uploaded to the chain, and improve the authenticity and reliability of the data. Through the multi-level alarm mechanism, the system can issue an alarm in time when the environmental conditions and state data are abnormal, prevent the further spread of data errors, and ensure that the problem can be solved in time. All data and alarm records will be recorded in the blockchain to ensure that every link in the process is transparent and cannot be tampered with, which enhances the credibility of the traceability system. The system continuously monitors the data and provides feedback through the alarm mechanism when problems are found, which helps to continuously optimize all links in the supply chain and improve the overall process efficiency and product quality. It solves the problem of tampering or sensor error that may exist before data is uploaded to the chain, and enhances the data authenticity and security of blockchain in the traceability of agricultural and sideline products. At the same time, the tamper-proof characteristics of the blockchain are used to ensure that all data records are transparent and traceable, and improve the overall operation efficiency of the supply chain and the level of product quality assurance.

[0052] In some examples, this also includes:

[0053] The shelf life information of the agricultural and sideline products is updated based on the acquired stage status information, wherein the shelf life information includes a shelf life and associated storage conditions.

[0054] Exemplarily, at each key process node (such as warehousing, transportation, distribution, etc.), the system will obtain the stage status information of agricultural and sideline products at that moment (such as appearance, weight, etc.), and record the environmental condition parameters related to it (such as temperature, humidity, etc.). Stage status information includes whether the appearance of the product has changed (such as darkening of color, damage to the skin), whether the weight has been significantly reduced, etc. This information is captured in real time by sensors or image processing systems. Environmental condition parameters include information such as temperature, humidity, and light during storage or transportation, which are automatically recorded by IoT devices (such as sensors). Environmental conditions have a crucial impact on the shelf life of agricultural and sideline products. For example, during the cold chain transportation of a batch of fresh vegetables, the system monitors the temperature in the vehicle in real time through a temperature sensor. If the temperature is high or exceeds the preset range, combined with the stage status information of the vegetables (such as color and morphological changes), the system can infer that the shelf life of the batch of vegetables may be shortened.

[0055] Exemplarily, the system can predict and update the shelf life of the product through the real-time acquisition of status information and environmental condition parameters. The shelf life is usually affected by transportation and storage conditions. Abnormal temperature and humidity may accelerate the decay of the product, so the shelf life information needs to be adjusted dynamically. At the same time, the system will infer the ideal conditions for subsequent storage based on the current product status and environmental parameters, and give recommended storage environment parameters (such as temperature range, humidity range, etc.) to extend the actual shelf life of the product. The system can dynamically adjust the shelf life of the product based on sensor and image data. For example, if it is detected that the appearance or weight of the product has changed significantly at a certain stage (such as shrinkage or discoloration of vegetables), it may mean that the shelf life needs to be shortened accordingly. Conversely, if the product is stored under good environmental conditions, its shelf life may be relatively extended. The system can predict the change in the shelf life of the product under specific environmental conditions by analyzing a large amount of historical data. For example, if a certain fruit usually stays fresh for 5 days within a certain temperature and humidity range, then when it is detected that the condition is not met, the system will update its shelf life. The system can recommend more suitable storage conditions based on the current stage status information and historical data. For example, if the cold chain temperature is slightly higher, the system may recommend that the product be stored in a lower temperature environment to extend its shelf life. For example, suppose a batch of milk is in transit, and because the temperature of the cold chain system exceeds the standard range, the system detects that the milk packaging has bulged, and the temperature is also higher. Based on this stage status, the system will infer that the shelf life of the milk is shorter than the original date, and update the shelf life information of the batch of products.

[0056] For example, if the system finds that the current environmental conditions or status information deviates greatly from the preset standard storage conditions and status, it will update the shelf life and trigger an alarm to notify the operator or regulator to handle it. The alarm information and the updated records of the shelf life will be synchronized to the blockchain to ensure that all changes are traceable and transparent. The system can automatically update the shelf life based on the actual data and upload the new shelf life record to the blockchain. If the updated shelf life is significantly shortened, the system will trigger an alarm to remind that immediate measures need to be taken (such as speeding up the sale of the product or changing the storage environment). The system can design different levels of alarms. For example, if the shelf life is shortened less, the system will send a low-level alarm and suggest adjusting the storage conditions; if the shelf life is shortened significantly, a high-level alarm may be triggered to remind that emergency measures need to be taken immediately. For example, a batch of fresh fish is stored in a cold chain warehouse, and the temperature sensor records that the warehouse temperature has risen by 5 degrees Celsius, exceeding the suitable temperature range. Through data analysis, the system predicts that the shelf life of this batch of fish will be shortened from the original 7 days to 4 days, and issues an alarm to notify the warehouse management to adjust the temperature, or recommends that the batch of fish be shipped to the sales point as soon as possible.

[0057] For example, each update of the shelf life or adjustment of the storage conditions will be synchronously recorded on the blockchain to ensure the immutability and traceability of the data. This not only improves the transparency of the supply chain, but also provides a basis for subsequent quality accountability. After each shelf life update, the system can record the new shelf life and reasons on the blockchain to ensure that any changes are available for review and traceability. For example, if the shelf life of a batch of products is shortened due to an environmental anomaly, the system will record the event and its associated environmental parameters. The system can verify whether the current storage conditions are consistent with the recommended conditions through the historical data recorded on the blockchain. If not, it can make improvement suggestions or trigger an alarm. For example, for a batch of fruits, if the shelf life is shortened due to high temperatures during transportation, the system will write this information together with the temperature sensor records into the blockchain. This not only ensures the transparency of the data, but also helps with subsequent supervision or accountability, ensuring the transparency of the entire process. Therefore, through the real-time update of the staged status information, the system can dynamically adjust the shelf life to reflect the status of the product under actual storage and transportation conditions, ensuring that the products purchased by consumers are within the safe period. The system does not just record data statically, but recommends more appropriate storage conditions through dynamic analysis, extending the shelf life of products and reducing product loss and waste. When the shelf life or storage conditions of products are abnormal, the system will issue an alarm in time to remind supply chain stakeholders to make adjustments or take measures, thereby improving the efficiency and reliability of supply chain operations. All shelf life updates, storage condition adjustments, and alarm information will be recorded in real time on the blockchain to ensure that data cannot be tampered with and is traceable, enhancing consumer trust and regulatory transparency.

[0058] When agricultural and sideline products are transferred to the sales stage, the conditions of the sales environment have a great impact on the product quality. In order to better track the quality changes of products during the sales stage, we can obtain the environmental condition parameters of the sales stage, and predict the quality changes of products based on these parameters, divide different time intervals, and generate theoretical quality change states in each interval.

[0059] In some examples, this also includes:

[0060] When the agricultural and sideline products are transferred to the sales stage, obtaining environmental condition parameters of the sales stage;

[0061] The agricultural and sideline products are divided into quality change time intervals and theoretical quality change states corresponding to each interval based on the environmental condition parameters.

[0062] It is understandable that when agricultural and sideline products enter the sales stage, their quality will be affected by the sales environment (such as temperature, humidity, light and other factors). By using sensors to monitor these environmental condition parameters in real time, the system can accurately obtain the actual situation in the sales environment. These environmental parameters are crucial for predicting the quality changes of products during the sales stage.

[0063] For example, in sales scenarios, especially in sales areas for fresh products, temperature is a key factor affecting shelf life and quality. By installing temperature sensors at the point of sale, the temperature around the product can be monitored in real time. Humidity also has an important impact on perishable products such as fruits and vegetables. Low humidity may cause product dehydration, and high humidity may cause mold. Monitor the humidity of the sales environment through humidity sensors. Some agricultural products may fade or deteriorate in quality under strong light. Light intensity is also an important parameter, especially in some open sales areas, where lighting conditions should be monitored and recorded. For example, in the fresh food area of ​​a supermarket, temperature, humidity, and light sensors are placed to record these environmental parameters. If the temperature exceeds the appropriate range (for example, more than 4°C), the system will record the anomaly and adjust the shelf life forecast of the product.

[0064] It is understandable that based on the environmental conditions during the sales phase, the system will divide different time intervals, which predict the gradual decay of product quality based on changes in environmental conditions. For example, in a high temperature or low humidity environment, the quality of the product will change faster, so it is necessary to divide time into different intervals to track the changes in product quality under different conditions.

[0065] Exemplarily, the system divides the time intervals based on the real-time monitoring data of the sales environment and historical data. For example, under suitable conditions, the product can maintain a certain quality state for 1 or 2 days; but under poor conditions (such as too high temperature), the product may undergo a large quality change within a few hours. Therefore, different time intervals represent different quality stages. If the environmental conditions change suddenly (such as increased temperature or decreased humidity), the system will re-divide the time interval to reflect the faster rate of quality change. This ensures that the quality prediction of the product under actual sales conditions is more accurate. For example, in the refrigerated area of ​​the supermarket, the temperature is maintained between 2°C and 4°C, and the system divides several quality change intervals: the first 48 hours is a period of quality stability, the next 72 hours is a period of slow quality decline, and then enters a period of rapid quality decline. However, if the temperature exceeds 4°C, the system will re-divide the time interval to shorten the duration of each stage.

[0066] It is understandable that after dividing different quality change time intervals, the system will generate theoretical quality change states of the products in each interval based on environmental conditions and product characteristics. Theoretical quality change states refer to the changes in appearance, weight, texture or taste that are expected to occur in products under specific environmental conditions. These theoretical states can be used for quality monitoring at the sales end and to provide consumers with more accurate quality information.

[0067] Exemplarily, the system uses a machine learning model or physical modeling method trained on historical data to predict the quality changes of products in various time intervals under different conditions. For example, when the temperature exceeds 4°C, the system may predict that the fruit will lose its luster, wrinkle its skin, or soften. The system not only predicts the theoretical state, but also monitors the actual quality changes through cameras or sensors, and automatically compares whether the predicted state is consistent with the actual state. If the actual state changes beyond theoretical expectations, the system will issue an alarm and record it in the blockchain for subsequent audits. For example, in a refrigerated sales environment, the system predicts that apples should maintain gloss and hardness in the first 48 hours, but after exceeding this interval, the skin may begin to wrinkle or lose its luster. When the humidity decreases, the system may predict that the apples will lose water, causing the surface to shrink. If the actual detected apple state does not match the prediction, the system will automatically issue an alarm to remind supermarket staff to check the refrigeration equipment.

[0068] It is understandable that while monitoring product quality changes in real time, the system will issue warnings based on the difference between the theoretical quality state and the actual quality change, combined with environmental conditions. When environmental conditions cause product quality changes to accelerate, or when actual product quality changes exceed expectations, the system will remind operators to make appropriate interventions, such as adjusting storage conditions, accelerating sales, etc.

[0069] Exemplarily, the system will issue corresponding alarms when the product enters different quality change intervals. For example, when the product enters a period of rapid quality decline, the system will remind the sales side to give priority to the batch of products. Each alarm information, including environmental condition parameters and actual status changes of the product, will be recorded in the blockchain to ensure that all adjustments and decision-making processes are transparent and traceable. For example, suppose a batch of strawberries is in the process of sales. Because the temperature sensor detects that the temperature of the refrigerator has risen to 8°C, the system predicts that the strawberries will begin to soften and darken in 6 hours. The system will issue an alarm to notify the seller to speed up the processing or adjust the temperature of the refrigerator as soon as possible. All alarms and adjustment operations will be recorded on the blockchain for subsequent auditing and tracking. Therefore, through real-time monitoring and zoning management of environmental condition parameters, the system can more accurately predict the quality changes of products, thereby helping sellers to better manage inventory and reduce losses. When the system detects abnormal environmental conditions or accelerated quality changes, it will issue an alarm in time to ensure that the operator can intervene in time to prevent a significant decline in product quality and protect the consumer experience. Through dynamic monitoring and blockchain records, consumers can obtain more transparent product information, understand the quality of products during the sales stage, and enhance their trust in product quality. Through accurate quality change predictions and timely alerts, the system can help sellers manage the sales of agricultural and sideline products more efficiently and reduce product waste and economic losses caused by quality degradation.

[0070] In some examples, this also includes:

[0071] The recommended selling price of the agricultural and sideline products is adjusted at the start time of the quality change time interval based on the theoretical quality change state corresponding to each interval.

[0072] It is understandable that as the quality of agricultural and sideline products changes, their market value will also change. During the sales stage, by monitoring environmental conditions and product quality status, the system can dynamically adjust the recommended selling price of the product based on the theoretical quality change status of the product in each time interval. This method ensures that the price of the product can more reasonably reflect its current quality status and market demand.

[0073] Exemplary, quality change and price association model: The system establishes a correlation model between product quality and price based on historical data and market analysis. The model can automatically adjust the recommended price according to the theoretical quality status of the product in different intervals (such as appearance changes, weight reduction, etc.). For example, as the product gradually approaches the end of its shelf life or a slight quality decline occurs, the system may recommend a corresponding price reduction. In each quality change time interval, the system sets different recommended prices for the product according to the theoretical quality status. Usually, the product is sold at a higher price during the initial period when the quality is the best. As the quality gradually declines, the system will recommend a gradual reduction in the price to accelerate sales and reduce waste. Each price adjustment will be synchronously recorded on the blockchain to ensure that the price adjustment process is transparent and traceable. Consumers and stakeholders in the supply chain can trace the reasons for the quality changes and environmental conditions behind each price change. For example, when a batch of fresh fruits are just put on the shelves, the appearance and taste are in the best condition, and the system recommends selling them at a higher price. However, as the fruit skin begins to change color or soften slightly after a few days, the system automatically adjusts the recommended price according to the quality change time interval, prompting the seller to reduce the price to accelerate sales. This price adjustment record will be uploaded to the chain at the same time to ensure that the information is open and transparent.

[0074] It is understandable that the starting moment refers to the moment when the system detects that the quality of the product has entered a new range of changes. At each starting moment, the system will adjust the recommended selling price based on the theoretical quality status of the current product. This mechanism ensures that the selling price of the product can reflect its quality changes in real time, making the pricing more reasonable. For example, on the third day of a batch of strawberries being sold in cold storage, the system detected that the temperature fluctuated, and the strawberry skin began to soften slightly, entering a time period of declining quality. At this time, the system issued a "revelation" to prompt the seller to reduce the selling price from the original 40 yuan per kilogram to 30 yuan. At the same time, information on price changes will be recorded in the blockchain to ensure that consumers and sellers can query the basis for the adjustment.

[0075] It is understandable that the recommended selling price is not only related to the quality change status of the product, but also to other factors such as market demand, inventory level, and promotion strategy. Therefore, when adjusting the selling price based on the quality change status, the system can also take these other factors into consideration and further optimize the pricing strategy. For example, on the eve of the Spring Festival, a batch of fresh oranges entered the quality decline range, and the system originally recommended a 5% price reduction. However, due to the high market demand, the system analyzed that it was more advantageous to maintain the original price, so it recommended to maintain the selling price unchanged and mark it as "Spring Festival Special" to increase sales. At the same time, all price decisions and reasons will be recorded in the blockchain to ensure transparency. Therefore, by real-time monitoring of product quality changes and environmental conditions, the system can accurately adjust the selling price to ensure that the product is sold at a reasonable price during the best period and maximize profits. When the product enters the quality decline range, the system promptly recommends price reduction promotions to accelerate sales and reduce waste caused by expiration or damage. The process of recording the price change through the blockchain ensures that all relevant parties can trace the reasons for each pricing decision, thereby improving the transparency and credibility of the entire supply chain. Consumers can understand the basis for product price adjustments and be confident that product quality changes are handled reasonably, which enhances their trust in the product.

[0076] Considering that the quality of agricultural and sideline products will gradually change with time and environmental conditions during the sales stage, when the quality of the product is close to the state where it can no longer be sold, timely sending out a reminder to remove the product from the shelves can prevent the product from expiration or serious quality degradation from affecting the consumer experience or causing losses. By monitoring quality changes in real time and sending a reminder to remove the product from the shelves to relevant smart terminal devices before the state that does not meet the sales conditions comes, it helps merchants to deal with inventory in a timely manner.

[0077] In some examples, this also includes:

[0078] Before the end of the previous quality change time interval corresponding to the theoretical quality change state that does not meet the selling state, a removal prompt message is sent to the smart terminal device associated with the sales stage of the agricultural and sideline products.

[0079] It is understandable that during the agricultural and sideline product sales phase, the system will use sensors and environmental monitoring equipment to detect the product status and environmental conditions (such as temperature, humidity, etc.) in real time. Based on this data, the system can predict changes in product quality and determine in advance when the product will enter a state that is no longer suitable for sale. These states may include deterioration in appearance, significant weight loss, expiration of shelf life, etc.

[0080] Exemplarily, the system sets minimum selling standards for different agricultural and sideline products. For example, fruits cannot be obviously rotten or faded, and vegetables cannot wilt or lose too much water. When the quality of the product is close to these minimum standards, the system will issue a reminder to remove it from the shelves. The system predicts the quality change state of each product based on sensors and historical data. For example, by analyzing temperature and humidity, the system can predict when a batch of fruits will show signs of rot, and then remind operators to remove them from the shelves in advance. Different environmental conditions can affect the deterioration rate of products. The system predicts when it will enter an unsalable state by associating environmental conditions (such as high temperature and high humidity) with the status of the product. For example, a batch of strawberries is sold in a cold storage area. The system detects that the temperature rises to 8°C and predicts that the strawberries will begin to soften and change color within 24 hours, exceeding the saleable state. According to the quality standards, the system prompts that the batch of strawberries will not meet the sales requirements and is ready to send a notice to remove them from the shelves.

[0081] It is understandable that when a product is close to being no longer suitable for sale, the system will send a removal reminder message to the smart terminal devices associated with the sales stage (such as the store manager's mobile phone, tablet, sales system, etc.), reminding relevant personnel to remove the product in time. This mechanism can ensure that the product is processed before the quality deteriorates and reduce unnecessary losses.

[0082] Exemplarily, before the system detects that the product quality is close to the critical value of the selling standard (usually at the end of the previous quality change interval), it sends an advance warning to the smart terminal device to prompt that the product is about to be removed from the shelves. This advance notice gives the seller enough time to handle the product, such as accelerating promotions, arranging for removal from the shelves, etc. The system is connected to the smart terminal device through the network, such as the manager's mobile phone, sales terminal, warehousing system, etc., and automatically sends a reminder message for removal from the shelves. The message includes information such as the batch of products that are about to be removed from the shelves, the predicted removal time, and the reason for removal from the shelves, so that the operator can take timely action. The prompt message will contain detailed product information, the expected removal time, the current environmental conditions, and the quality change status of the product, so that the manager can make a judgment. For example, it prompts that strawberries will not be suitable for continued sale within 12 hours due to softening in appearance. For example, a batch of bananas are sold at room temperature, and the system detects that the ambient temperature is higher than the appropriate level, and the color of the bananas gradually turns black, and it is expected that they will no longer be suitable for sale within the next 6 hours. The system automatically sends a prompt message to the supermarket manager's smart terminal to remind them to prepare to remove this batch of bananas from the shelves.

[0083] Understandably, the timing and implementation of the removal notice need to be adjusted flexibly to ensure that the product is processed in time before reaching the limit of the selling standard. By sending out reminder messages in advance, managers can take different measures according to the actual situation, such as early promotion, changing storage conditions, or quickly removing the product from the shelves to avoid the product being sold after it has deteriorated.

[0084] Exemplarily, the system dynamically adjusts the time of the delisting reminder according to the speed of product quality change. For example, if the environmental conditions suddenly change and cause the product to deteriorate faster, the system will issue a delisting notice in advance; conversely, if the environmental conditions return to normal, the system can postpone the delisting time. While sending the delisting reminder message, the system can combine the inventory situation and sales strategy to recommend appropriate promotional activities. For example, a limited-time discount can be initiated before the product is removed from the shelf to quickly clean up products that are about to expire or have deteriorated in quality. For retail places with automated warehousing or smart shelves, the system can be linked with the warehousing or shelf system to automatically perform the delisting operation. For example, when the system issues a delisting notice, the smart shelf automatically removes the corresponding product to reduce manual intervention. For example, the system monitors a batch of vegetables that are about to fail to meet the sales conditions in the supermarket and sends a delisting reminder to the manager 24 hours in advance. At the same time, the system recommends initiating a limited-time promotion to clean up this batch of vegetables as soon as possible, and gives a discount suggestion in the prompt message. The manager can decide whether to follow the suggestion. Therefore, by monitoring the quality changes of the product in real time, the system issues a delisting reminder when the product is close to not meeting the sales standards, ensuring that the product is handled in time before it deteriorates to avoid affecting the consumer experience. Advance removal reminders and linked smart terminal devices help sellers manage inventory more efficiently and reduce losses caused by product quality degradation. By recording each removal operation through blockchain, the transparency and traceability of the supply chain are greatly improved. Consumers and regulators can view the removal records and reasons of products at any time to enhance trust. The advance warning mechanism provides sellers with a time window to quickly deal with products that are about to fail to meet sales standards through promotions or discounts, reducing waste and increasing profits.

[0085] In some examples, this also includes:

[0086] Based on the environmental condition parameters, the agricultural and sideline products are divided into quality change time intervals and taste change states corresponding to each interval;

[0087] When a traceability query request based on the traceability identifier is received from the terminal, the current theoretical taste status of the agricultural and sideline products is displayed.

[0088] It is understandable that the quality of agricultural and sideline products not only affects their appearance, weight and shelf life, but also has a direct impact on the taste. Environmental conditions (such as temperature, humidity, light, etc.) play a vital role in the change of taste. By monitoring these environmental parameters, the system can predict the taste changes of the product and divide them into different time intervals.

[0089] Exemplarily, the system predicts the quality change of the product and its impact on the taste based on the real-time monitored environmental conditions (such as temperature, humidity, etc.) and historical data. In each quality change time interval, the taste change of the product will have different performances. For example, the fruit may taste better at the optimal storage temperature, but may become soft or overripe at a higher temperature. The system combines product characteristics and historical data, and uses machine learning models or experience-based rules to predict the theoretical taste state of the product in different time intervals. For example, for apples, the system can predict that it will maintain a crisp and sweet taste for the first 5 days under optimal storage conditions, and then gradually become soft and waxy. Different quality states directly affect the taste of the product. For example, vegetables may become dry and hard after losing water, and the sugar content of fruits may change with storage time. The system predicts the change in taste based on these quality parameters and divides the specific time periods by partitioning. For example, when a batch of mangoes is stored at low temperature, the system divides several quality change time intervals: the 1st to 3rd day is the best taste interval, with firm flesh and moderate sweetness; the 4th to 6th day is the interval of slightly softening taste; after the 7th day, it enters the stage of relatively soft taste. The theoretical taste status corresponding to each interval will be updated in real time for consumers to query.

[0090] It is understandable that when consumers use smart terminals (such as mobile phones, tablets, etc.) to query the status of a batch of agricultural and sideline products through the traceability system, the system can show users the theoretical taste status of the product based on the current environmental conditions and quality change time interval of the product. This provides consumers with more detailed information, helping them understand the current quality and taste of the product and make better purchasing decisions.

[0091] Exemplarily, each batch of agricultural and sideline products is attached with a unique traceability identifier (such as a QR code, RFID tag, etc.). Consumers scan the identifier to send a query request to the traceability system. The system will query the real-time data of the batch of products to display its current theoretical taste status. The system will display the theoretical taste status of the product to consumers based on the quality change time interval of the product and the current environmental conditions. This information may include changes in the hardness, sweetness, acidity, juice content, etc. of the product to help consumers choose products that better suit their taste. The system uses sensors and IoT devices to update the quality change information of agricultural and sideline products in real time. When consumers issue a traceability query, the system will instantly calculate the theoretical taste status of the batch of products and display it to the user. For example, when consumers buy strawberries in a supermarket, they use their mobile phones to scan the traceability QR code on the strawberry packaging. After receiving the query request, the system displays the current theoretical taste status of the strawberries: "The strawberries currently have a firm taste and moderate sweetness, suitable for immediate consumption." If the strawberries have been stored for a long time, the system will prompt "The strawberries have a soft taste and are suitable for making jam or as a dessert ingredient."

[0092] It is understandable that the theoretical taste state is the result of the prediction based on the current environmental conditions and quality change range of the product. The system calculates the taste changes of the product under different environmental conditions through data models to ensure that the theoretical state is as close to the actual situation as possible. At the same time, the system can also continuously optimize and adjust these prediction models based on consumer query feedback.

[0093] For example, the system can use a large amount of historical data (such as the impact of temperature and humidity changes on the taste of the product) to predict the taste changes under specific conditions through machine learning models. For example, how does the sweetness or acidity of fruits change at different temperatures and how does it affect their taste when eaten. Consumers can provide feedback on the actual taste of the product after querying the product traceability information. The feedback data collected by the system will be used to continuously optimize the prediction model of taste changes. For example, if a certain fruit is predicted to be "crisp and sweet" in the theoretical state, but the user feedback shows that the actual taste is soft, the system can adjust the parameters of the model to improve the prediction accuracy. The theoretical taste state can be displayed not only in text descriptions, but also in visual forms such as charts. For example, using sweetness curves, hardness graphs, etc. helps consumers understand the taste changes of products more intuitively. For example, in the system, consumers can see the taste change curve of mangoes: the sweetness gradually increases from the first to the third day of storage, and the hardness remains high. After the fourth day, the sweetness reaches a peak but the hardness decreases. Through visual display, consumers can clearly understand the current state of the product and make purchasing decisions. Therefore, by real-time monitoring of environmental conditions and quality changes, the system can provide consumers with the theoretical taste status of the product, help them understand the current taste of the product, and make more accurate purchasing decisions. The traceability system increases the transparency of the supply chain and enhances consumers' trust in product quality and supply chain management by displaying product quality and taste change data. Consumers can choose products that meet taste requirements based on the real-time status of the product, reduce situations that do not meet expectations, and improve shopping experience and satisfaction. Through consumer feedback data, the system can continuously optimize the taste state prediction model, improve the accuracy of the prediction, and then optimize product management and sales strategies. In the sales and consumption process of agricultural and sideline products, taste is one of the important factors affecting consumer experience and purchasing decisions. By monitoring the environmental conditions and quality change status of the product, the system can predict and divide the taste changes of agricultural and sideline products in different time intervals. This can not only help sellers optimize sales strategies, but also provide consumers with more detailed product quality information.

[0094] In some examples, this also includes:

[0095] When receiving a traceability query request from a terminal based on a traceability identifier, predicting the current theoretical quality status of the agricultural and sideline products based on the initial status information of the agricultural and sideline products and the environmental condition parameters of each process;

[0096] The current theoretical appearance image of the agricultural and sideline products is generated based on the theoretical quality state and the initial state information, so that personnel can judge whether the environmental condition parameters are appropriate and make adjustments at different stages.

[0097] It is understandable that during the life cycle of agricultural and sideline products, different environmental conditions (such as temperature, humidity, light, etc.) will affect their quality status. The system can predict the current theoretical quality status of the product by collecting these environmental condition data in real time and combining them with the initial status information of the product. This process can help determine whether the product is still at its best quality or whether it has deteriorated due to unsuitable environmental conditions.

[0098] Exemplarily, when agricultural and sideline products are picked or produced, the system will collect initial state information as a baseline for subsequent judgment of quality changes. The initial state information includes information such as the weight, appearance, color, and size of the product. The system uses sensors and other equipment to monitor the environmental condition parameters (such as temperature, humidity, vibration, etc.) of the product in real time at various stages such as transportation, storage, and sales. These data are used to infer the quality changes of the product at each stage. Based on the initial state information of the product and the environmental conditions at each stage, the system uses algorithms (such as physical modeling or machine learning models) to predict the current theoretical quality state of the product. For example, excessively high temperatures may cause the softening of fruits, and low humidity may cause the loss of water in vegetables. For example, for a batch of strawberries, the system records the appearance and weight of the strawberries when they are initially picked. Subsequently, during the cold chain transportation and sales process, the system continues to monitor temperature and humidity. If the system detects that the temperature is too high and the transportation time of the strawberries is long, the system can predict that the quality of the strawberries will deteriorate, and softening and color changes may occur.

[0099] It is understandable that, based on the theoretical quality status of agricultural and sideline products, the system can generate the current theoretical appearance image of the product to help operators intuitively view the potential appearance changes of the product. By comparing the theoretical appearance image with the actual product, the operator can determine whether the environmental conditions are reasonable and adjust the storage or sales environment as needed to ensure that the product maintains the best quality.

[0100] Exemplarily, the system uses initial state information (such as the appearance, color, and size of the product) as a reference, and generates a theoretical appearance image of the product by comparing the current environmental condition parameters with the theoretical quality state. For example, high temperature may cause the fruit to darken and soften; low humidity may cause wrinkles or loss of gloss on the surface of vegetables. Using image processing and simulation technology, the system can synthesize theoretical appearance images based on predicted quality changes. These images not only show the appearance changes of the product, but also show the potential changes of the product under different conditions to help personnel make judgments. The system can compare the generated theoretical appearance image with the image of the actual product. If it is found that the appearance of the actual product is significantly different from the predicted theoretical appearance (which may indicate abnormal environmental conditions or quality degradation beyond expectations), the system will issue an alarm to remind the operator to check the environmental parameters and make adjustments. For example, for a batch of apples, the system records its surface gloss and color in the initial state. As the apples are stored at room temperature for a longer time, the system predicts that the theoretical quality state of the apples is slight discoloration and the skin begins to lose gloss. The system generates a theoretical appearance image based on this prediction and compares it with the actual image of the apples. If it is found that the actual discoloration of apples is more serious than predicted, the system will remind the operator to adjust the storage temperature or humidity.

[0101] It is understandable that by comparing the theoretical appearance image with the current theoretical quality status, the operator can intuitively judge whether the product is too greatly affected by environmental conditions. If the actual appearance of the product is significantly different from the generated theoretical appearance image, it may indicate that the environmental conditions are not suitable. The operator can adjust the environmental conditions in a timely manner according to the system's prompts to ensure the stability of product quality.

[0102] Exemplarily, the system automatically analyzes the difference and provides adjustment suggestions by comparing the theoretical appearance image with the image of the actual product. For example, if the theoretical appearance shows that the product is beginning to soften, but the actual product shows a more serious quality degradation, the system will prompt that the temperature needs to be lowered or the humidity needs to be increased. In an environment with automated control (such as smart warehousing, cold chain logistics, etc.), the system can automatically adjust the environmental conditions according to the prediction results. For example, if the system predicts that the fruit will deteriorate quickly at the current temperature, the system can automatically lower the temperature of the refrigerator to delay the quality degradation. If the environment does not support automatic control, the system will remind the operator to manually adjust the environmental parameters through an intelligent terminal (such as a mobile phone, tablet, warehouse management system). The system will provide specific adjustment suggestions, such as lowering the temperature, increasing the humidity, or shortening the sales time. For example, in a cold chain warehouse, the system detects that the temperature is slightly higher than the standard and predicts that this will cause the lemon skin to soften and lose its luster. The generated theoretical appearance image shows a slight discoloration, but the actual detected lemon appearance discoloration is more serious. The system will automatically remind the warehouse manager to lower the temperature to prevent further deterioration of the lemon.

[0103] It is understandable that when consumers or other participants in the supply chain use the traceability system to query the product status, the system not only displays the product's initial status and current environmental conditions, but also displays the theoretical quality status and theoretical appearance images. This allows users to more intuitively understand the current status of the product and helps to determine whether the product's quality meets expectations.

[0104] Exemplarily, consumers or managers can send query requests to the system by scanning the traceability QR code, RFID tag or other identification. The system will display the initial state of the product, the current theoretical quality state and the generated theoretical appearance image. The system will display the theoretical appearance image of the current product to the querying user. This can help consumers intuitively understand whether the current appearance state of the product is close to the optimal state, or help supply chain managers determine whether the product is suitable for continued sale or needs environmental adjustment. The system will also display data such as the historical environmental conditions and quality change curves of the product, so that the querying user can understand the state changes of the product from production to sales, ensuring information transparency and traceability. For example, in a supermarket, a consumer scans the traceability QR code of a batch of mangoes. The system displays the initial state, current quality state (if the flesh is soft and the sweetness increases) and theoretical appearance image (slight discoloration) of the mango. Consumers can judge whether the mango meets their needs based on this information and make a purchase decision. As a result, the system can predict the theoretical quality state of the product based on environmental conditions and initial state information, helping operators to adjust the environment in time to prevent product deterioration. By generating theoretical appearance images, operators and consumers can intuitively understand the state changes of the product. This helps to more accurately determine whether the product needs environmental adjustment or is suitable for purchase. The system can automatically generate environmental adjustment suggestions and even make adjustments directly in the automated environment to help managers optimize the storage and sales conditions of products. Through the traceability system, all parties in the supply chain can query the product's status data, theoretical appearance images and environmental history, enhancing the transparency of the supply chain and product quality control.

[0105] The above describes the product full-process supervision method based on blockchain in the embodiment of the present application. The following describes the product full-process supervision system based on blockchain in the embodiment of the present application.

[0106] See also Figure 2 In the embodiments of the present application, an embodiment of a product full-process supervision system based on blockchain is described, which may include:

[0107] The first acquisition unit 201 is used to acquire initial state information of agricultural and sideline products after they are produced, wherein the initial state information includes growth data, initial appearance data and initial weight data, so as to record the initial state information in the blockchain system;

[0108] The second acquisition unit 202 is used to acquire the environmental condition parameters of the entire process of storage, transportation and sales, and the stage status information of the agricultural and sideline products at each process flow node, wherein the stage status information includes stage appearance data and stage weight data, so as to record the environmental condition parameters of the entire process and the stage status information in the blockchain system;

[0109] The analysis unit 203 is used to predict the ideal state information of the agricultural and sideline products at the next process flow node based on the initial state information or the stage state information and each process environment condition parameter, so as to generate a process environment condition parameter authenticity alarm and record it to the blockchain system when the matching degree between the ideal state information and the recorded stage state information is lower than a preset matching degree.

[0110] In summary, the product full-process supervision system based on blockchain provided in the above embodiment obtains the initial state information of agricultural and sideline products after they are produced, and the initial state information includes growth data, initial appearance data and initial weight data, so as to record the initial state information in the blockchain system; obtains the environmental condition parameters of the whole process of storage, transportation and sales, and the stage state information of the agricultural and sideline products at each process flow node, and the stage state information includes stage appearance data and stage weight data, so as to record the environmental condition parameters and stage state information of the whole process in the blockchain system; based on the initial state information or stage state information and each process environmental condition parameter, the ideal state information of agricultural and sideline products at the next process flow node is predicted, so as to generate the process environmental condition parameter authenticity alarm and record it to the blockchain system when the matching degree between the ideal state information and the recorded stage state information is lower than the preset matching degree. Thus, through the mechanisms such as sensor redundancy check, multi-party verification and matching degree detection, the system can detect data anomalies before data is uploaded to the chain, and improve the authenticity and reliability of the data. Through the multi-level alarm mechanism, the system can issue an alarm in time when the environmental conditions and state data are abnormal, prevent the further spread of data errors, and ensure that the problem can be solved in time. All data and alarm records will be recorded in the blockchain to ensure that every link in the process is transparent and cannot be tampered with, which enhances the credibility of the traceability system. The system continuously monitors the data and provides feedback through the alarm mechanism when problems are found, which helps to continuously optimize all links in the supply chain and improve the overall process efficiency and product quality. It solves the problem of tampering or sensor error that may exist before data is uploaded to the chain, and enhances the data authenticity and security of blockchain in the traceability of agricultural and sideline products. At the same time, the tamper-proof characteristics of the blockchain are used to ensure that all data records are transparent and traceable, and improve the overall operation efficiency of the supply chain and the level of product quality assurance.

[0111] In some examples, this also includes:

[0112] Based on the determined ideal placement position, the distribution terminal displays a suggested placement order and the ideal placement position of the product storage space that matches the delivery vehicle.

[0113] above Figure 2 The product full-process supervision system based on blockchain in the embodiment of the present application is described from the perspective of modular functional entities. The product full-process supervision system based on blockchain in the embodiment of the present application is described in detail from the perspective of hardware processing. Please refer to Figure 3 , an embodiment of a product full-process supervision system 300 based on blockchain in an embodiment of the present application includes:

[0114] An input device 301, an output device 302, a processor 303 and a memory 304, wherein the number of the processor 303 can be one or more. Figure 3 In some embodiments of the present application, the input device 301, the output device 302, the processor 303 and the memory 304 may be connected via a bus or other means, wherein: Figure 3 The example of connecting through bus is taken in the following.

[0115] Wherein, by calling the operation instruction stored in the memory 304, the processor 303 is used to perform the following steps:

[0116] Obtaining initial state information of agricultural and sideline products after they are produced, the initial state information including growth data, initial appearance data, and initial weight data, so as to record the initial state information in the blockchain system;

[0117] Obtaining the environmental condition parameters of the entire process of storage, transportation and sales, as well as the stage status information of the agricultural and sideline products at each process flow node, wherein the stage status information includes stage appearance data and stage weight data, so as to record the environmental condition parameters of the entire process and the stage status information in the blockchain system;

[0118] Based on the initial state information or stage state information and each process environmental condition parameter, the ideal state information of the agricultural and sideline products at the next process flow node is predicted, so that when the matching degree between the ideal state information and the recorded stage state information is lower than the preset matching degree, an authenticity alarm of the process environmental condition parameter is generated and recorded in the blockchain system.

[0119] By calling the operation instructions stored in the memory 304, the processor 303 is also used to execute Figure 1 Any method in the corresponding embodiment.

[0120] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of an electronic device provided in an embodiment of the present application.

[0121] like Figure 4 As shown, an embodiment of the present application provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0122] Obtaining initial state information of agricultural and sideline products after they are produced, the initial state information including growth data, initial appearance data, and initial weight data, so as to record the initial state information in the blockchain system;

[0123] Obtaining the environmental condition parameters of the entire process of storage, transportation and sales, as well as the stage status information of the agricultural and sideline products at each process flow node, wherein the stage status information includes stage appearance data and stage weight data, so as to record the environmental condition parameters of the entire process and the stage status information in the blockchain system;

[0124] Based on the initial state information or stage state information and each process environmental condition parameter, the ideal state information of the agricultural and sideline products at the next process flow node is predicted, so that when the matching degree between the ideal state information and the recorded stage state information is lower than the preset matching degree, an authenticity alarm of the process environmental condition parameter is generated and recorded in the blockchain system.

[0125] In the specific implementation process, when the processor 420 executes the computer program 411, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0126] Since the electronic device introduced in this embodiment is a device used to implement a blockchain-based product full-process supervision system in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation method of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0127] See also Figure 5 , Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present application.

[0128] like Figure 5 As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 511 is stored. When the computer program 511 is executed by a processor, the following steps are implemented:

[0129] Obtaining initial state information of agricultural and sideline products after they are produced, the initial state information including growth data, initial appearance data, and initial weight data, so as to record the initial state information in the blockchain system;

[0130] Obtaining the environmental condition parameters of the entire process of storage, transportation and sales, as well as the stage status information of the agricultural and sideline products at each process flow node, wherein the stage status information includes stage appearance data and stage weight data, so as to record the environmental condition parameters of the entire process and the stage status information in the blockchain system;

[0131] Based on the initial state information or stage state information and each process environmental condition parameter, the ideal state information of the agricultural and sideline products at the next process flow node is predicted, so that when the matching degree between the ideal state information and the recorded stage state information is lower than the preset matching degree, an authenticity alarm of the process environmental condition parameter is generated and recorded in the blockchain system.

[0132] By calling the operation instructions stored in the memory 304, the processor 303 is also used to execute Figure 1 Any method in the corresponding embodiment.

[0133] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of an electronic device provided in an embodiment of the present application.

[0134] like Figure 4 As shown, an embodiment of the present application provides an electronic device, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0135] Obtaining initial state information of agricultural and sideline products after they are produced, the initial state information including growth data, initial appearance data, and initial weight data, so as to record the initial state information in the blockchain system;

[0136] Obtaining the environmental condition parameters of the entire process of storage, transportation and sales, as well as the stage status information of the agricultural and sideline products at each process flow node, wherein the stage status information includes stage appearance data and stage weight data, so as to record the environmental condition parameters of the entire process and the stage status information in the blockchain system;

[0137] Based on the initial state information or stage state information and each process environmental condition parameter, the ideal state information of the agricultural and sideline products at the next process flow node is predicted, so that when the matching degree between the ideal state information and the recorded stage state information is lower than the preset matching degree, an authenticity alarm of the process environmental condition parameter is generated and recorded in the blockchain system.

[0138] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0139] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0140] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0141] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0143] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0144] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A product full-process supervision method based on blockchain, characterized in that: include: Obtaining initial state information of agricultural and sideline products after they are produced, the initial state information including growth data, initial appearance data, and initial weight data, so as to record the initial state information in the blockchain system; Obtaining the environmental condition parameters of the entire process of storage, transportation and sales, as well as the stage status information of the agricultural and sideline products at each process flow node, wherein the stage status information includes stage appearance data and stage weight data, so as to record the environmental condition parameters of the entire process and the stage status information in the blockchain system; Based on the initial state information or stage state information and each process environmental condition parameter, the ideal state information of the agricultural and sideline products at the next process flow node is predicted, so that when the matching degree between the ideal state information and the recorded stage state information is lower than the preset matching degree, an authenticity alarm of the process environmental condition parameter is generated and recorded in the blockchain system.

2. The method according to claim 1, characterized in that Also includes: The shelf life information of the agricultural and sideline products is updated based on the acquired stage status information, wherein the shelf life information includes a shelf life and associated storage conditions.

3. The method according to claim 1, characterized in that Also includes: When the agricultural and sideline products are transferred to the sales stage, obtaining environmental condition parameters of the sales stage; The agricultural and sideline products are divided into quality change time intervals and theoretical quality change states corresponding to each interval based on the environmental condition parameters.

4. The method according to claim 3, characterized in that Also includes: The recommended selling price of the agricultural and sideline products is adjusted at the start time of the quality change time interval based on the theoretical quality change state corresponding to each interval.

5. The method according to claim 3, characterized in that: Also includes: Before the end of the previous quality change time interval corresponding to the theoretical quality change state that does not meet the selling state, a removal prompt message is sent to the smart terminal device associated with the sales stage of the agricultural and sideline products.

6. The method according to claim 3, characterized in that Also includes: Based on the environmental condition parameters, the agricultural and sideline products are divided into quality change time intervals and taste change states corresponding to each interval; When a traceability query request based on the traceability identifier is received from the terminal, the current theoretical taste status of the agricultural and sideline products is displayed.

7. The method according to claim 6, characterized in that Also includes: When receiving a traceability query request from a terminal based on a traceability identifier, predicting the current theoretical quality status of the agricultural and sideline products based on the initial status information of the agricultural and sideline products and the environmental condition parameters of each process; The current theoretical appearance image of the agricultural and sideline products is generated based on the theoretical quality state and the initial state information, so that personnel can judge whether the environmental condition parameters are appropriate and make adjustments at different stages.

8. A product full-process supervision system based on blockchain, characterized in that: The system comprises: A first acquisition unit is used to acquire initial state information of agricultural and sideline products after they are produced, wherein the initial state information includes growth data, initial appearance data, and initial weight data, so as to record the initial state information in a blockchain system; The second acquisition unit is used to acquire the environmental condition parameters of the entire process of storage, transportation and sales, and the stage status information of the agricultural and sideline products at each process flow node, wherein the stage status information includes stage appearance data and stage weight data, so as to record the environmental condition parameters of the entire process and the stage status information in the blockchain system; The analysis unit is used to predict the ideal state information of agricultural and sideline products at the next process flow node based on the initial state information or stage state information and each process environment condition parameter, so as to generate a process environment condition parameter authenticity alarm and record it to the blockchain system when the matching degree between the ideal state information and the recorded stage state information is lower than a preset matching degree.

9. An electronic device, characterized in that: The electronic device includes at least one processor and at least one memory connected to the processor, wherein the processor is used to call program instructions in the memory to execute the blockchain-based full-process product supervision method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium includes a stored program, wherein, when the program is running, the device where the storage medium is located is controlled to execute the blockchain-based full-process product supervision method as described in any one of claims 1 to 7.

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