A digital traceability method and system for agricultural product supply chain information

Abstract

The present invention relates to the field of artificial intelligence technology, and a digital traceability method and system for agricultural product supply chain information, including: based on weather change information data, performing abnormality identification on the picking and processing registration data based on authenticity and rationality to obtain a first abnormality identification result, and based on the regional picking and processing data set, performing abnormality analysis on the picking and processing registration data based on a clustering algorithm to obtain a second abnormality identification result; performing weighted calculation on the first abnormality identification result and the second abnormality identification result to obtain an abnormality risk label; performing growth state prediction on the crop growth environment data based on the growth state of the target agricultural product to obtain a growth state label; uploading the target agricultural product, the target agricultural region, the picking and processing registration data, the abnormality risk label and the growth state label to the blockchain to obtain an information flow chart of the agricultural product supply chain. The present invention can increase the traceability depth of the source data in the agricultural product supply chain information and further improve the reliability of the agricultural product supply chain.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a digital traceability method and system for agricultural product supply chain information. Background Art

[0002] With the continuous improvement of people's living standards, the public's requirements for the quality of agricultural products have gradually increased. The quality of agricultural products has become the primary consideration for consumers. However, consumers and purchasers cannot effectively monitor the entire process of agricultural products. Coincidentally, at this time, technologies such as digitalization, intelligence, and big data have also developed rapidly. In response to the needs of the majority of consumers, more and more agricultural products have begun to incorporate digital technologies into their own industries, and agricultural product supply chain information has thus emerged.

[0003] Today's agricultural product supply chain information is protected by science and technology and can meet the transparency and immutability of agricultural product data. However, due to the low level of scientific and technological acceptance of agricultural product growers, too many scientific and technological processes will increase the burden on growers, resulting in the initial production data in the agricultural product supply chain being only the origin identification of the crop plus some certification marks, and it is impossible to clearly understand the growth status, picking situation, drying and processing, etc. of the crops. Once there is an error at the source of agricultural products, even if the subsequent reprocessing, transportation, and sales are strictly and clearly controlled, consumers will still buy unqualified or defective products, thus causing damage to the reputation of an agricultural product brand or regional characteristics. Summary of the Invention

[0004] The present invention provides a digital traceability method for agricultural product supply chain information, and its main purpose is to increase the traceability depth of source data in agricultural product supply chain information and further improve the reliability of the agricultural product supply chain.

[0005] To achieve the above object, a digital traceability method for agricultural product supply chain information provided by the present invention includes:

[0006] Receiving the picking and processing registration data of the target agricultural product sent by a pre-constructed agricultural product user terminal, and performing an origin certification operation on the picking and processing registration data to obtain an origin certification result;

[0007] When the origin certification result is certification passed, obtaining the target agricultural area, and storing the picking and processing registration data in a pre-constructed data verification database, and obtaining the growth stage date, picking date sequence, and processing date sequence in the picking and processing registration data;

[0008] Obtain the crop growth environment data of the target agricultural area during the growth stage date from the pre - built agricultural environment monitoring database, and obtain the weather change information data of the target agricultural area within the picking date sequence and processing date sequence;

[0009] Use the data verification database to obtain all the picking and processing registration data sent by each agricultural product client in the target agricultural area, and obtain the regional picking and processing data set;

[0010] Use the pre - trained abnormality data authentication model to perform abnormality recognition based on authenticity and rationality on the picking and processing registration data according to the weather change information data, and obtain the first abnormality recognition result. Perform abnormality analysis based on the clustering algorithm on the picking and processing registration data according to the regional picking and processing data set, and obtain the second abnormality recognition result;

[0011] Perform weighted calculation on the first abnormality recognition result and the second abnormality recognition result to obtain the abnormality risk label;

[0012] Perform growth state prediction on the crop growth environment data based on the growth state of the target agricultural product to obtain the growth state label;

[0013] Construct the supply chain source data using the target agricultural product, target agricultural area, picking and processing registration data, abnormality risk label, and growth state label, and upload the supply chain source data to the pre - built agricultural product supply chain system to obtain the agricultural product supply chain information flow chart.

[0014] Optionally, the operation of performing origin authentication on the picking and processing registration data includes:

[0015] Obtain the sender information of the picking and processing registration data, and use the pre - built registration database to verify the sender information to obtain the information authentication result;

[0016] After the information authentication result is passed, obtain the registered planting area corresponding to the sender information;

[0017] Perform origin authentication operation based on quantity and region on the picking and processing registration data according to the registered planting area to obtain the origin authentication result.

[0018] Optionally, the operation of performing abnormality recognition based on authenticity and rationality on the picking and processing registration data according to the weather change information data to obtain the first abnormality recognition result includes:

[0019] Perform weather feature extraction operation on the weather change information data to obtain the set of real weather feature sequences;

[0020] Perform an operation to extract weather characteristics from the picking and processing registration data to obtain a set of registered weather characteristic sequences;

[0021] Authenticate the authenticity of the set of registered weather characteristic sequences based on the set of true weather characteristic sequences to obtain the percentage of weather anomalies;

[0022] Perform an operation to extract characteristics based on the execution actions from the picking and processing registration data to obtain a set of execution action characteristic sequences;

[0023] Judge the rationality of the set of execution action characteristic sequences according to the pre - constructed crop production management guidelines and the set of true weather characteristic sequences to obtain the percentage of action anomalies;

[0024] Perform a weighted calculation on the percentage of action anomalies and the percentage of weather anomalies to obtain a first anomaly recognition result.

[0025] Optionally, the performing an anomaly analysis based on a clustering algorithm on the picking and processing registration data according to the regional picking and processing data set to obtain a second anomaly recognition result includes:

[0026] Construct a multi - dimensional index space using a pre - constructed set of indexes;

[0027] Perform a mapping operation on the regional picking and processing data set based on the multi - dimensional index space to obtain a regional picking and processing cluster;

[0028] Identify the clustering center coordinate points of each preset index in the set of indexes in the regional picking and processing cluster to obtain a set of index center points;

[0029] Calculate the deviation of each preset index between the set of index center points and the picking and processing registration data to obtain a second anomaly recognition result.

[0030] Optionally, the performing a weighted calculation on the first anomaly recognition result and the second anomaly recognition result to obtain an anomaly risk label includes:

[0031] Calculate the mean value of the deviations of each preset index in the second anomaly recognition result to obtain an index anomaly score;

[0032] Perform a weighted calculation on the index anomaly score and the first anomaly recognition result to obtain an anomaly risk score;

[0033] Use a pre - constructed risk division interval to query the anomaly risk level corresponding to the anomaly risk score, and construct an anomaly risk label with the anomaly risk level.

[0034] Optionally, the prediction of the crop growth environment data based on the growth state of the target agricultural product to obtain a growth state label includes:

[0035] Obtain the crop physiological knowledge data of the target agricultural product, and perform a text feature recognition operation on the crop physiological knowledge data to obtain a text word segmentation vector set;

[0036] Perform a semantic recognition operation on the text word segmentation vector set to obtain a semantic recognition result;

[0037] Perform an environmental feature extraction operation on the crop growth environment data to obtain an environmental index set and an environmental numerical feature set, where the environmental numerical feature set includes the numerical change features corresponding to each environmental index in the environmental index set;

[0038] Perform a correlation analysis on the semantic recognition result based on the environmental index set to obtain the growth correlation features of each environmental index in the environmental index set for the target agricultural product, summarize each growth correlation feature, and obtain a growth correlation feature set;

[0039] According to the environmental numerical feature set, perform a weight configuration on the growth correlation feature set to obtain a growth influence feature set, and perform a fully connected recognition operation on the growth influence feature set based on the crop growth state to obtain a growth state recognition result;

[0040] Configure the growth state recognition result as a growth state label.

[0041] Optionally, the process of uploading the supply chain source data to a pre-constructed agricultural product supply chain system includes:

[0042] Perform a sensitive word recognition operation on the supply chain source data to obtain sensitive data, and use a pre-constructed first key to encrypt the sensitive data in the supply chain source data to obtain a partially encrypted source data;

[0043] Use a pre-constructed hash algorithm to generate a unique hash value for the partially encrypted source data;

[0044] Use a pre-constructed private key to generate a digital signature for the unique hash value;

[0045] Obtain a timestamp, package the unique hash value, digital signature, and timestamp to obtain a data packet;

[0046] Use a pre-constructed public chain to upload the data packet, and use a pre-constructed consortium chain to upload the first key.

[0047] Optionally, before uploading the supply chain source data to a pre-constructed agricultural product supply chain system, the method further includes:

[0048] Determine whether the abnormal risk label is higher than or equal to a preset unqualified level;

[0049] When the abnormal risk label is higher than or equal to the unqualified level, send the supply chain source data to a pre-constructed agricultural product quality supervision database;

[0050] When the abnormal risk label is less than the unqualified level, perform the step of uploading the supply chain source data to a pre-constructed agricultural product supply chain system.

[0051] Optionally, the receiving of the picking and processing registration data of the target agricultural product sent by a pre-constructed agricultural product client includes:

[0052] Perform data cleaning operations on the initialized picking and processing registration data based on duplicates, missing values, and outliers to obtain cleaned data;

[0053] Perform data formatting processing on the cleaned data based on units, text formats, and field standardization to obtain standardized picking and processing registration data.

[0054] To achieve the above object, the present invention also provides a digital traceability system for agricultural product supply chain information, including:

[0055] An agricultural product data acquisition module, configured to receive the picking and processing registration data of the target agricultural product sent by a pre-constructed agricultural product client, perform an origin certification operation on the picking and processing registration data to obtain an origin certification result, and when the origin certification result is certification passed, obtain a target agricultural area, store the picking and processing registration data in a pre-constructed data verification database, and obtain the growth stage date, picking date sequence, and processing date sequence in the picking and processing registration data;

[0056] A certification data acquisition module, configured to obtain the crop growth environment data of the target agricultural area during the growth stage date from a pre-constructed agricultural environment monitoring database, obtain the weather change information data of the target agricultural area during the picking date sequence and the processing date sequence, and use the data verification database to obtain all the picking and processing registration data sent by each agricultural product client in the target agricultural area to obtain a regional picking and processing data set;

[0057] Anomaly recognition module, which is used to utilize a pre-trained anomaly data authentication model to perform authenticity- and rationality-based anomaly recognition on the picking and processing registration data according to the weather change information data, obtain a first anomaly recognition result, perform anomaly analysis on the picking and processing registration data based on a clustering algorithm according to the regional picking and processing data set, obtain a second anomaly recognition result, perform weighted calculation on the first anomaly recognition result and the second anomaly recognition result to obtain an anomaly risk label, and perform growth status prediction on the crop growth environment data based on the growth status of the target agricultural product to obtain a growth status label;

[0058] Source data on-chain module, which is used to construct supply chain source data by using the target agricultural product, target agricultural region, picking and processing registration data, anomaly risk label and growth status label, and upload the supply chain source data to a pre-constructed agricultural product supply chain system to obtain an information flow chart of the agricultural product supply chain.

[0059] To solve the above problems, the present invention also provides an electronic device, which includes:

[0060] A memory that stores at least one instruction;

[0061] A processor that executes the instructions stored in the memory to implement the above-mentioned digital traceability method for agricultural product supply chain information.

[0062] To solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned digital traceability method for agricultural product supply chain information.

[0063] To solve the problems described in the background art, the present invention performs abnormality analysis on the source data of the agricultural product supply chain to ensure the reliability of the supply chain from the root. First, by obtaining the picking and processing registration data of the target agricultural product sent by the agricultural product user terminal. Herein, the agricultural product user terminal can be a mobile app of farmers. Farmers click a few buttons, and the agricultural product user terminal automatically obtains the time stamp and automatically edits the picking and processing registration data, so as to obtain the agricultural product production processes such as the harvest, processing, and drying of crops to the greatest extent without increasing the burden on farmers. Further, through a pre-trained abnormality data authentication model, a comparison is made between the data itself and the real environment, and a comparison is made between the data and the collective data to perform abnormality analysis, obtaining an abnormal risk label to ensure the accuracy of the picking and processing registration data. In addition, the overall growth state of the target agricultural product in the target agricultural area can be identified according to the real environment data of the crop growth environment data, obtaining a growth state label, so as to analyze the quality of the target agricultural product from two aspects of the overall region and individual farmers, and then store it on the chain. Therefore, the present invention can increase the traceability depth of the source data in the agricultural product supply chain information and further improve the reliability of the agricultural product supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic flowchart of a digital traceability method for agricultural product supply chain information provided by an embodiment of the present invention;

[0065] Figure 2 It is a functional module diagram of a digital traceability system for agricultural product supply chain information provided by an embodiment of the present invention;

[0066] Figure 3 It is a schematic structural diagram of an electronic device for implementing the digital traceability method of the agricultural product supply chain information provided by an embodiment of the present invention.

[0067] Description of the figure marks:

[0068] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0069] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0071] An embodiment of the present application provides a digital traceability method for agricultural product supply chain information. The execution subject of the digital traceability method for agricultural product supply chain information includes at least one of electronic devices such as, but not limited to, a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the digital traceability method for agricultural product supply chain information can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0072] Referring to Figure 1 As shown, it is a schematic flowchart of the digital traceability method for agricultural product supply chain information provided by an embodiment of the present invention. In this embodiment, the digital traceability method for agricultural product supply chain information includes:

[0073] S1. Receive the picking and processing registration data of the target agricultural product sent by a pre-constructed agricultural product user terminal, and perform an origin certification operation on the picking and processing registration data to obtain an origin certification result.

[0074] In an embodiment of the present invention, the agricultural product user terminal can be a farmer's mobile app or a smart customer service. The farmer registers the production nodes of agricultural products such as harvesting, processing, and drying by clicking or voice, and then the mobile app automatically obtains relevant information such as timestamps for editing to obtain the picking and processing registration data.

[0075] In addition, in an embodiment of the present invention, the management platform of the digital traceability system for agricultural product supply chain information will establish a connection with the agricultural product user terminal and be responsible for supervising, managing, and uploading the picking and processing registration data of farmers to the blockchain.

[0076] Among them, the target agricultural product can be various agricultural products such as tea, tangerine peel, fruits, etc.

[0077] Specifically, in an embodiment of the present invention, a geographical indication product in the south, such as tangerine peel crops, is taken as an example of the target agricultural product for elaboration. Then the picking and processing registration data may include [Batch A tangerine peel: "In 2022: mandarin orange picking: 11.20 - 12.10 (may be continuous or discontinuous data), tangerine peel processing: 11.23 - 12.13, dried peel drying: 11.24 - 12.20"; "In 2023: drying on March 1 and September 1"; "In 2024: drying on March 1 and September 1"].

[0078] Specifically, in an embodiment of the present invention, the receiving the picking and processing registration data of the target agricultural product sent by a pre-constructed agricultural product user terminal includes:

[0079] Perform data cleaning operations on the initialized picking and processing registration data based on duplicates, missing values, and outliers to obtain cleaned data;

[0080] Perform data formatting processing on the cleaned data based on unit, text format, and field standardization to obtain standardized picking and processing registration data.

[0081] Specifically, in the embodiments of the present invention, the data cleaning refers to the operation of removing duplicate data, null values, invalid fields (such as extra characters, missing fields, etc.), and outliers from the picking and processing registration data. For example, the "harvest time" cannot be later than the "processing time", and whether the yield matches the land area.

[0082] The data formatting processing refers to unifying the format of the data. For example, the temperature unit is unified to °C, and the date is unified to "A.B.C (year.month.day)".

[0083] Specifically, in the embodiments of the present invention, data cleaning and data formatting processing operations can be performed according to tools such as Excel, Pandas library, ETL tools, and BI tools to obtain available picking and processing registration data.

[0084] In detail, in the embodiments of the present invention, the operation of performing origin certification on the picking and processing registration data includes:

[0085] Obtain the sender information of the picking and processing registration data, and use the pre-constructed registration database to verify the sender information to obtain an information certification result;

[0086] When the information certification result is passed, obtain the registered planting area corresponding to the sender information;

[0087] According to the registered planting area, perform origin certification operations on the picking and processing registration data based on quantity and region to obtain an origin certification result.

[0088] Among them, the sender information may include a specific farmer, a village collective, or a cooperative.

[0089] Among them, the registration database is a database for information management of farmer objects, and may include sensitive information such as name, gender, address, mobile phone number, and registered planting area.

[0090] In the embodiments of the present invention, when the management platform receives the picking and processing registration data, it will confirm the source of the picking and processing registration data to ensure that the picking and processing registration data is sent by a farmer registered in the management platform.

[0091] Specifically, in the embodiments of the present invention, information authentication is performed through database query operations. If it can be queried that the user is a farmer, the authentication is passed; otherwise, if the user identity information is only the agricultural product transporter, the authentication fails. Then, secondary authentication is performed to check whether the planting area in the picking and processing registration data is the registered planting area. If the secondary authentication is all passed, the origin authentication result of the target agricultural product is configured as passed.

[0092] S2. After the origin authentication result is passed, obtain the target agricultural area, store the picking and processing registration data in a pre-constructed data verification database, and obtain the growth stage date, picking date sequence, and processing date sequence in the picking and processing registration data.

[0093] Among them, the target agricultural area can be the land area contracted by a certain farmer, village, or cooperative, and the area includes units such as mu and hectare.

[0094] When the target agricultural area is registered in the management platform, the management platform can obtain weather information from the regional government and other organizations to monitor the climate level of the target agricultural area. In addition, sensors can be distributed to the target agricultural area according to the size of the target agricultural area for real-time monitoring of the temperature, humidity, soil nutrients, etc. of the target agricultural area.

[0095] Among them, the data verification database refers to the storage location before the picking and processing registration data is uploaded to the blockchain, which is used to analyze the abnormality of the picking and processing registration data, so as to avoid the situation that false or incorrect picking and processing registration data is uploaded to the blockchain, resulting in false information in the entire agricultural product supply chain.

[0096] Specifically, in the embodiments of the present invention, through a pre-constructed data interface, obtain the weather status from government agencies for [November 20 - December 10, 2022; November 23 - December 13, 2022; November 24 - December 20, 2022; March 1 and September 1, 2023; March 1 and September 1, 2024], and obtain the growth stage date [March 1, 2022 - November 20, 2022], the picking date sequence [November 20, 2022 - December 10, 2022], and the processing date sequence [November 23, 2022 - December 13, 2022; November 24, 2022 - December 20, 2022; March 1, 2023; September 1, 2023;...].

[0097] S3. Obtain the crop growth environment data of the target agricultural area during the growth stage date from a pre-constructed agricultural environment monitoring database, and obtain the weather change information data of the target agricultural area during the picking date sequence and the processing date sequence.

[0098] Among them, the agricultural environment monitoring database is used to receive the data collected by various sensors in the target agricultural area, such as temperature, humidity, soil nutrients, etc., as well as the weather data provided by relevant government departments, such as temperature, wind speed, light intensity, and precipitation.

[0099] Specifically, in the embodiments of the present invention, the crop growth environment data of the target agricultural area during the growth stage date is obtained, and the weather change information data of the target agricultural area within the picking date sequence and the processing date sequence is obtained. Among them, the crop growth environment data is the data provided by both the government and the sensors, and more detailed data is beneficial to understanding the specific growth state of the crops in the target agricultural area. And the weather change information data within the picking date sequence and the processing date sequence can be obtained through the weather information data provided by the government because it no longer depends on the data in the field.

[0100] S4. Using the data verification database, all the picking and processing registration data sent by each agricultural product user terminal in the target agricultural area is obtained to obtain a regional picking and processing data set.

[0101] In the embodiments of the present invention, each agricultural area will save the picking and processing registration data in the data verification database. In order to compare the differences between the picking and processing registration data of the target agricultural area and other agricultural areas, the present invention obtains all the picking and processing registration data sent by each agricultural product user terminal to obtain a regional picking and processing data set.

[0102] S5. Using the pre-trained anomaly data authentication model, based on the weather change information data, the picking and processing registration data is subjected to anomaly identification based on authenticity and rationality to obtain a first anomaly identification result. Based on the regional picking and processing data set, the picking and processing registration data is subjected to anomaly analysis based on the clustering algorithm to obtain a second anomaly identification result.

[0103] Among them, the anomaly data authentication model is a multi-task model based on a neural network, which is used to perform anomaly identification on the picking and processing registration data from two aspects: "the authenticity of the data itself" and "the difference between the data itself and the group", and then summarize and output. Its training process is completed by machine learning on a large number of manually labeled training samples using the cross-entropy loss algorithm and the gradient descent algorithm.

[0104] Specifically, in the embodiments of the present invention, the step of performing anomaly identification based on authenticity and rationality on the picking and processing registration data according to the weather change information data to obtain a first anomaly identification result includes:

[0105] Performing a weather feature extraction operation on the weather change information data to obtain a set of real weather feature sequences;

[0106] Perform an operation to extract weather features from the picking and processing registration data to obtain a set of registered weather feature sequences;

[0107] Authenticate the authenticity of the set of registered weather feature sequences based on the set of true weather feature sequences to obtain the percentage of weather anomalies;

[0108] Perform an operation to extract features based on execution actions from the picking and processing registration data to obtain a set of execution action feature sequences;

[0109] Judge the rationality of the set of execution action feature sequences according to the pre - constructed crop production management guidelines and the set of true weather feature sequences to obtain the percentage of action anomalies;

[0110] Perform a weighted calculation on the percentage of action anomalies and the percentage of weather anomalies to obtain the first anomaly recognition result.

[0111] Among them, the weather feature extraction operation refers to an operation that uses a pre - constructed convolutional kernel layer to extract only information such as temperature, wind speed, light, and weather type in the text.

[0112] Among them, the feature extraction operation based on execution actions refers to an operation that extracts only the features related to execution actions in the text and ignores other features. In the embodiments of the present invention, the execution actions include picking, peeling, cleaning, drying, etc.

[0113] Among them, the crop production management guidelines are manuals for quality control of a certain agricultural product by regional governments or agricultural institutions, and are used to guide farmers in planting and managing crops through standardized guidelines to improve the quality of crops, thereby driving regional development or influence.

[0114] Specifically, in the embodiments of the present invention, authenticate the authenticity of the set of registered weather feature sequences according to the set of true weather feature sequences to obtain the percentage of weather anomalies. For example, when the picking and processing registration data records that a certain day is sunny, but the weather change information data provided by the government for the target planting area is rainy, this set of data can be marked as abnormal data. By sorting out the proportion of abnormal data in the picking and processing registration data, the percentage of weather anomalies can be obtained.

[0115] Specifically, in the embodiments of the present invention, the rationality judgment refers to whether some actions in the picking and processing registration data meet many management requirements indicated by the crop production management guidelines. For example, for the first long - time drying of tangerine peel, it can be carried out on sunny or cloudy days, while the subsequent annual drying requires 2 times and both are on sunny days. If the picking and processing registration data indicates that the tangerine peel is dried in the rain or has not been dried for many years, it can be judged as unreasonable.

[0116] After obtaining the action anomaly percentage and the weather anomaly percentage, weighted calculation can be performed. For example, the weight of the action anomaly percentage is 0.7, and the weight of the weather anomaly percentage is 0.3, to obtain the first anomaly recognition result.

[0117] Furthermore, an anomaly data authentication model can be used for second anomaly recognition.

[0118] Specifically, in the embodiments of the present invention, the abnormal analysis of the picking and processing registration data based on the clustering algorithm according to the regional picking and processing data set to obtain the second anomaly recognition result includes:

[0119] Using a pre-constructed index set to construct a multi-dimensional index space;

[0120] Performing a mapping operation on the regional picking and processing data set based on the multi-dimensional index space to obtain a regional picking and processing cluster;

[0121] Identifying the clustering center coordinate points of each preset index in the index set in the regional picking and processing cluster to obtain an index center point set;

[0122] Calculating the deviation of each preset index between the index center point set and the picking and processing registration data to obtain the second anomaly recognition result.

[0123] Among them, the index set includes various preset indexes such as picking, processing, and drying.

[0124] Among them, in addition to various preset indexes such as picking, processing, and drying, the multi-dimensional index space also includes a time index.

[0125] Specifically, in the embodiments of the present invention, when mapping data such as the picking date sequence corresponding to each person, such as [22.11.20~22.12.10] and the processing date sequence, such as [22.11.23~22.12.13, 22.11.24~22.12.20, 23.3.1, 23.9.1,...] into the multi-dimensional index space, a regional picking and processing cluster is obtained.

[0126] It should be noted that even with the crop production management guidelines, due to various factors such as weather conditions, labor distribution, and the newness of production machinery, the overall production process will deviate from the crop production management guidelines, and it is more referenceable.

[0127] In the embodiment of the present invention, by means of the cluster center coordinate points, a set of index center points is obtained, and then the deviation of each preset index between the set of index center points and the picking and processing registration data is calculated to obtain a second anomaly recognition result. For example, if the drying dates in the set of index center points are all [November 14 - November 16, November 18 - December 10], it may indicate that there may be windy and rainy weather on [November 17] which is not conducive to drying. If the picking and processing registration data of a certain farmer shows that drying is carried out from [November 14] to [December 10], there may be a problem that the drying management of this farmer is not strict, and the score of the second anomaly recognition result is relatively high.

[0128] S6. Perform weighted calculation on the first anomaly recognition result and the second anomaly recognition result to obtain an anomaly risk label.

[0129] Specifically, in the embodiment of the present invention, the performing weighted calculation on the first anomaly recognition result and the second anomaly recognition result to obtain an anomaly risk label includes:

[0130] Calculate the mean value of the deviations of each preset index in the second anomaly recognition result to obtain an index anomaly score;

[0131] Perform weighted calculation on the index anomaly score and the first anomaly recognition result to obtain an anomaly risk score;

[0132] Use a pre - constructed risk division interval to query the anomaly risk level corresponding to the anomaly risk score, and construct an anomaly risk label with the anomaly risk level.

[0133] Among them, the risk division interval refers to the interval divided by the management platform according to calculations and actual investigations. For example, 0 - 3 is a safe interval, 3 - 5 is a minor risk interval, and greater than 5 is a high - risk interval.

[0134] Specifically, in the embodiment of the present invention, there are multiple index anomaly recognition scores in the second anomaly recognition result. By summing up and averaging each anomaly recognition score, the overall index anomaly score can be obtained. Then perform weighted calculation with the first anomaly recognition result. For example, the weight of the first anomaly recognition result is 0.4, and the second anomaly recognition result is 0.6, to obtain an anomaly risk score.

[0135] Then, by comparing the anomaly risk score with the risk division interval, the anomaly risk level is obtained, and further an anomaly risk label is constructed. Among them, the minor risk interval of the present invention may include problems such as earlier or later harvesting, shorter or more drying time, etc., while the high - risk interval may involve situations such as improper management and data fraud.

[0136] S7. Perform a growth state prediction on the crop growth environment data based on the growth state of the target agricultural product to obtain a growth state label.

[0137] Specifically, in the embodiments of the present invention, the performing a growth state prediction on the crop growth environment data based on the growth state of the target agricultural product to obtain a growth state label includes:

[0138] Obtain the crop physiological knowledge data of the target agricultural product, and perform a text feature recognition operation on the crop physiological knowledge data to obtain a text tokenization vector set;

[0139] Perform a semantic recognition operation on the text tokenization vector set to obtain a semantic recognition result;

[0140] Perform an environmental feature extraction operation on the crop growth environment data to obtain an environmental index set and an environmental numerical feature set, where the environmental numerical feature set includes the numerical change features corresponding to each environmental index in the environmental index set;

[0141] Perform a relevance analysis on the semantic recognition result based on the environmental index set to obtain the growth correlation features of each environmental index in the environmental index set for the target agricultural product, and summarize each growth correlation feature to obtain a growth correlation feature set;

[0142] According to the environmental numerical feature set, perform a weight configuration on the growth correlation feature set to obtain a growth influence feature set, and perform a fully connected recognition operation on the growth influence feature set based on the crop growth state to obtain a growth state recognition result;

[0143] Configure the growth state recognition result as a growth state label.

[0144] Among them, the crop physiological knowledge data can be obtained from network information, investigation and research, or an agricultural platform database.

[0145] Among them, the semantic recognition operation refers to converting text characters into data that can be understood by a machine model, and can be implemented through neural network speech models such as Bert.

[0146] Since different varieties of target agricultural products may lead to different growth habits of crops, it is necessary to obtain crop physiological knowledge data before predicting the crop growth state. In the embodiments of the present invention, the crop physiological knowledge data is mostly text data. Therefore, it is necessary to perform text tokenization quantization on the crop physiological knowledge data to obtain a text tokenization vector set, and then perform semantic analysis to obtain a semantic recognition result.

[0147] In addition, since different environmental factors have different degrees of influence on crops, only some environmental factors are monitored in the crop growth environment data in the embodiments of the present invention. Therefore, only the set of environmental indicators in the crop growth environment data needs to be considered.

[0148] Specifically, in the embodiments of the present invention, through correlation analysis, the growth correlation characteristics of each environmental indicator in the set of environmental indicators with respect to the growth of the target agricultural product are obtained, and the growth correlation characteristics are summarized to obtain a set of growth correlation characteristics.

[0149] For example, a certain citrus variety requires a warm and humid climate, and temperature, light, and precipitation have a greater correlation with the growth of citrus, and the soil organic matter requirement is slightly acidic. Therefore, by identifying whether there is a gap between each value in the set of growth influence characteristics and the expected indicators in terms of temperature, light, precipitation, and soil acidity, the greater the gap, the worse the growth state.

[0150] Finally, a growth state label is constructed based on the identified growth state recognition result. Among them, the neural network for predicting the growth state can learn the knowledge of the influence of the difference between each indicator on crop growth through machine learning.

[0151] S8. Use the target agricultural product, target agricultural region, picking and processing registration data, abnormal risk label, and growth state label to construct supply chain source data, and upload the supply chain source data to a pre-constructed agricultural product supply chain system to obtain an information flow chart of the agricultural product supply chain.

[0152] Specifically, in the embodiments of the present invention, the uploading of the supply chain source data to the pre-constructed agricultural product supply chain system includes:

[0153] Perform a sensitive word recognition operation on the supply chain source data to obtain sensitive data, and use a pre-constructed first key to encrypt the sensitive data in the supply chain source data to obtain locally encrypted source data;

[0154] Use a pre-constructed hash algorithm to generate a unique hash value for the locally encrypted source data;

[0155] Use a pre-constructed private key to generate a digital signature for the unique hash value;

[0156] Obtain a time stamp, and package the unique hash value, digital signature, and time stamp to obtain a data packet;

[0157] Use a pre-constructed public chain to upload the data packet, and use a pre-constructed consortium chain to upload the first key.

[0158] Among them, the sensitive word recognition refers to identifying data that violates the privacy of farmers, such as addresses, phone numbers, etc.

[0159] Among them, the hash algorithm refers to a cryptographic algorithm, whose basic function is to perform mathematical operations on the input data (which can be of any length) to generate an output value of a fixed length (usually a short number or string), and this output value is called the hash value.

[0160] Among them, the private key is a cryptographic tool used to sign the hash value of a message or data, ensuring that only the person holding the private key can generate the corresponding digital signature.

[0161] Among them, the digital signature is an electronic authentication means based on cryptographic technology, mainly used to verify the authenticity, integrity of digital information and the identity of the sender.

[0162] Among them, the timestamp refers to a data marker used to record the precise time information of a specific event when the event occurs. The timestamp is usually represented in the form of date and time, including year, month, day, hour, minute and second.

[0163] Among them, the public blockchain refers to a blockchain network that anyone can participate in, with the characteristics of complete openness and decentralization. It allows all users to freely read, send transactions and participate in the consensus process.

[0164] Among them, the consortium blockchain is a blockchain network jointly maintained by a specific organization consortium. Participants need to be permitted, and only authorized nodes can join the network and participate in the data writing and consensus process.

[0165] Specifically, in the embodiment of the present invention, sensitive data is encrypted by a first key, and then packaged according to the unique hash value, digital signature and timestamp described above to obtain a data packet. Then consumers or other users can decrypt and view the non-sensitive data in the supply chain source data by scanning the QR code or other means, while other farmers or docking enterprises participating in the consortium blockchain can view the information in the sensitive data for convenient contact.

[0166] In detail, in the embodiment of the present invention, before uploading the supply chain source data to the pre-constructed agricultural product supply chain system, the method further includes:

[0167] Judging whether the abnormal risk label is higher than or equal to a preset unqualified level;

[0168] When the abnormal risk label is higher than or equal to the unqualified level, sending the supply chain source data to the pre-constructed agricultural product quality supervision database;

[0169] When the abnormal risk label is less than the unqualified level, execute the step of uploading the supply chain source data to the pre-constructed agricultural product supply chain system.

[0170] Among them, the unqualified level can be set as a high-risk level.

[0171] Among them, the agricultural product quality supervision database is used to store relevant data of the target agricultural area with unclear quality. Professional evaluators can conduct on-site product inspections of the target agricultural area based on the agricultural product quality supervision database. After passing the inspection, it can be supplemented and uploaded. If it fails, it may be solved by not granting a brand mark for sale or introducing insurance compensation, etc.

[0172] In the embodiment of the present invention, when the abnormal risk label is higher than or equal to the preset unqualified level, there may be product quality problems, which will hit the product brand. Therefore, the supply chain source data can be first saved to the agricultural product quality supervision database for subsequent processing.

[0173] Furthermore, in the embodiment of the present invention, the management platform is responsible for managing the data supervision and uploading part of the agricultural product end, while other agricultural product supply chain information will be summarized through other channels to the pre-constructed agricultural product supply chain. The agricultural product supply chain system in the blockchain has high automation and transparency. When the supply chain source data of the present invention is uploaded to the agricultural product supply chain system, the agricultural product supply chain system will expand the original agricultural product supply chain information, so as to obtain a more traceable agricultural product supply chain information flow chart.

[0174] To solve the problems described in the background technology, the present invention conducts abnormality analysis on the source data of the agricultural product supply chain to ensure the reliability of the supply chain from the root. First, by obtaining the picking and processing registration data of the target agricultural product sent by the agricultural product user end. Among them, the agricultural product user end can be the mobile app of farmers. Farmers click a few buttons, and the agricultural product user end automatically obtains the time stamp and automatically edits the picking and processing registration data, so as to obtain the agricultural product production processes such as the harvest, processing, and drying of crops to the greatest extent without increasing the burden on farmers. Further, through the pre-trained abnormality data authentication model, compare the data itself with the real environment, and conduct abnormality analysis by comparing the data with the collective data to obtain an abnormal risk label to ensure the accuracy of the picking and processing registration data. In addition, the overall growth state of the target agricultural product in the target agricultural area can be identified based on the real environment data of the crop growth environment data to obtain a growth state label, so as to analyze the quality of the target agricultural product from both the regional overall and individual farmer aspects, and then upload and store it. Therefore, the present invention can increase the traceability depth of the source data in the agricultural product supply chain information and further improve the reliability of the agricultural product supply chain.

[0175] As shown Figure 2 in the figure, it is a functional module diagram of a digital traceability system for agricultural product supply chain information provided by an embodiment of the present invention.

[0176] The digital traceability system 100 for agricultural product supply chain information according to the present invention can be installed in an electronic device. According to the functions achieved, the digital traceability system 100 for agricultural product supply chain information can include an agricultural product data acquisition module 101, an authentication data acquisition module 102, an anomaly identification module 103, and a source data on-chain module 104. The modules according to the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0177] The agricultural product data acquisition module 101 is configured to receive the picking and processing registration data of the target agricultural product sent by a pre-constructed agricultural product client, perform an origin authentication operation on the picking and processing registration data to obtain an origin authentication result, and when the origin authentication result is authentication passed, obtain the target agricultural area, store the picking and processing registration data in a pre-constructed data verification database, and obtain the growth stage date, picking date sequence, and processing date sequence in the picking and processing registration data;

[0178] The authentication data acquisition module 102 is configured to obtain the crop growth environment data of the target agricultural area during the growth stage date from a pre-constructed agricultural environment monitoring database, obtain the weather change information data of the target agricultural area during the picking date sequence and the processing date sequence, and use the data verification database to obtain all the picking and processing registration data sent by each agricultural product client in the target agricultural area to obtain a regional picking and processing data set;

[0179] The anomaly identification module 103 is configured to use a pre-trained anomaly data authentication model to perform anomaly identification based on authenticity and rationality on the picking and processing registration data according to the weather change information data to obtain a first anomaly identification result, perform anomaly analysis based on a clustering algorithm on the picking and processing registration data according to the regional picking and processing data set to obtain a second anomaly identification result, perform weighted calculation on the first anomaly identification result and the second anomaly identification result to obtain an anomaly risk label, and perform growth state prediction on the crop growth environment data based on the growth state of the target agricultural product to obtain a growth state label;

[0180] The source data on-chain module 104 is used to construct supply chain source data by using the target agricultural products, target agricultural regions, picking and processing registration data, abnormal risk labels, and growth status labels, and upload the supply chain source data to a pre-constructed agricultural product supply chain system to obtain an information flow diagram of the agricultural product supply chain.

[0181] Specifically, each module in the digital traceability system 100 of the agricultural product supply chain information in the embodiments of the present invention adopts the same technical means as the Figure 1 digital traceability method of the agricultural product supply chain information described above and can produce the same technical effects, which will not be elaborated here.

[0182] As Figure 3 shown, it is a schematic structural diagram of an electronic device for implementing the digital traceability method of agricultural product supply chain information provided by an embodiment of the present invention.

[0183] The electronic device 1 may include a processor 10, a memory 11, and a bus 12, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as a digital traceability method program for agricultural product supply chain information.

[0184] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as the mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 1. Further, the memory 11 also includes the internal storage unit of the electronic device 1 and the external storage device. The memory 11 can be used not only to store application software installed on the electronic device 1 and various types of data, such as the code of the digital traceability method program for agricultural product supply chain information, but also to temporarily store data that has been output or will be output.

[0185] In some embodiments, the processor 10 may be composed of an integrated circuit. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple packaged integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and circuits, and by running or executing programs or modules stored in the memory 11 (such as the program for the digital traceability method of the agricultural product supply chain information, etc.), and by calling the data stored in the memory 11, to execute various functions of the electronic device 1 and process data.

[0186] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable connection communication between the memory 11 and at least one processor 10, etc.

[0187] Figure 3 Only the electronic device with components is shown. Those skilled in the art can understand that Figure 3 the shown structure does not constitute a limitation on the electronic device 1, and it may include fewer or more components than shown, or combine certain components, or have a different component layout.

[0188] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so as to implement functions such as charge management, discharge management, and power consumption management through the power management device. The power source may also include any components such as one or more DC or AC power sources, a recharge device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0189] Furthermore, the electronic device 1 may further include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0190] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visual user interface.

[0191] The digital traceability method program of the agricultural product supply chain information stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0192] Receive the picking and processing registration data of the target agricultural product sent by a pre-built agricultural product user terminal, and perform an origin certification operation on the picking and processing registration data to obtain an origin certification result;

[0193] When the origin certification result is certified to be passed, obtain the target agricultural area, store the picking and processing registration data in a pre-built data verification database, and obtain the growth stage date, the picking date sequence, and the processing date sequence in the picking and processing registration data;

[0194] Obtain the crop growth environment data of the target agricultural area during the growth stage date from a pre-built agricultural environment monitoring database, and obtain the weather change information data of the target agricultural area during the picking date sequence and the processing date sequence;

[0195] Use the data verification database to obtain all the picking and processing registration data sent by each agricultural product user terminal in the target agricultural area to obtain a regional picking and processing data set;

[0196] Use a pre-trained anomaly data authentication model to perform anomaly recognition based on authenticity and rationality on the picking and processing registration data according to the weather change information data to obtain a first anomaly recognition result, and perform anomaly analysis based on a clustering algorithm on the picking and processing registration data according to the regional picking and processing data set to obtain a second anomaly recognition result;

[0197] Perform a weighted calculation on the first anomaly recognition result and the second anomaly recognition result to obtain an anomaly risk label;

[0198] Perform a growth state prediction on the crop growth environment data based on the growth state of the target agricultural product to obtain a growth state label;

[0199] Construct supply chain source data by using the target agricultural products, target agricultural regions, picking and processing registration data, abnormal risk labels, and growth status labels, and upload the supply chain source data to a pre-constructed agricultural product supply chain system to obtain an information flow chart of the agricultural product supply chain.

[0200] Specifically, for the specific implementation method of the above instructions by the processor 10, reference can be made to Figures 1 to 3 the description of the relevant steps in the corresponding embodiments, which will not be elaborated here.

[0201] Furthermore, if the modules / units integrated in the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory).

[0202] The present invention also provides a computer-readable storage medium, where the readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, it can implement:

[0203] Receive the picking and processing registration data of the target agricultural products sent by a pre-constructed agricultural product user terminal, and perform an origin certification operation on the picking and processing registration data to obtain an origin certification result;

[0204] When the origin certification result is passed, obtain the target agricultural region, store the picking and processing registration data in a pre-constructed data verification database, and obtain the growth stage date, picking date sequence, and processing date sequence in the picking and processing registration data;

[0205] Obtain the crop growth environment data of the target agricultural region during the growth stage date from a pre-constructed agricultural environment monitoring database, and obtain the weather change information data of the target agricultural region during the picking date sequence and processing date sequence;

[0206] Use the data verification database to obtain all the picking and processing registration data sent by each agricultural product user terminal in the target agricultural region to obtain a regional picking and processing data set;

[0207] Using a pre-trained anomaly data authentication model, based on the weather change information data, perform anomaly identification based on authenticity and rationality on the picking and processing registration data to obtain a first anomaly identification result. Based on the regional picking and processing data set, perform anomaly analysis on the picking and processing registration data using a clustering algorithm to obtain a second anomaly identification result;

[0208] Perform weighted calculation on the first anomaly identification result and the second anomaly identification result to obtain an anomaly risk label;

[0209] Perform growth state prediction on the crop growth environment data based on the growth state of the target agricultural product to obtain a growth state label;

[0210] Use the target agricultural product, target agricultural region, picking and processing registration data, anomaly risk label, and growth state label to construct supply chain source data, and upload the supply chain source data to a pre-constructed agricultural product supply chain system to obtain an information flow diagram of the agricultural product supply chain.

[0211] In several embodiments provided by the present invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and there can be other division methods in actual implementation.

[0212] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0213] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware, or in the form of a combination of hardware and software functional modules.

[0214] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0215] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A digital traceability method for agricultural product supply chain information, characterized in that: The method comprises: Receiving the picking and processing registration data of the target agricultural product sent by the pre-built agricultural product user terminal, and performing an origin authentication operation on the picking and processing registration data to obtain an origin authentication result; When the origin authentication result is authentication passed, the target agricultural area is obtained, and the picking and processing registration data is stored in a pre-built data verification database, and the growth stage date, picking date sequence and processing date sequence in the picking and processing registration data are obtained; Acquire the crop growth environment data of the target agricultural area within the growth stage date, and acquire the weather change information data of the target agricultural area within the picking date sequence and the processing date sequence from a pre-constructed agricultural environment monitoring database; Using the data verification database, all the picking and processing registration data sent by each agricultural product user terminal in the target agricultural area are obtained to obtain a regional picking and processing data set; Using the pre-trained abnormal data authentication model, according to the weather change information data, the picking and processing registration data is subjected to abnormality identification based on authenticity and rationality to obtain a first abnormal identification result; according to the regional picking and processing data set, the picking and processing registration data is subjected to abnormality analysis based on a clustering algorithm to obtain a second abnormal identification result; Performing weighted calculation on the first anomaly recognition result and the second anomaly recognition result to obtain an anomaly risk label; Predicting the growth state of the target agricultural product based on the crop growth environment data to obtain a growth state label; The target agricultural products, target agricultural areas, picking and processing registration data, abnormal risk labels and growth status labels are used to construct supply chain source data, and the supply chain source data is uploaded to a pre-built agricultural product supply chain system to obtain an agricultural product supply chain information flow chart.

2. The digital traceability method for agricultural product supply chain information according to claim 1, characterized in that: The performing of origin-based authentication operation on the picking and processing registration data includes: Obtaining the sender information of the picking and processing registration data, and using a pre-built registration database to verify the sender information to obtain an information authentication result; When the information authentication result is passed, obtaining the registered planting area corresponding to the sender information; According to the registered planting area, the picking and processing registration data is subjected to a quantity- and region-based origin authentication operation to obtain an origin authentication result.

3. The digital traceability method for agricultural product supply chain information according to claim 2, characterized in that: According to the weather change information data, the harvesting and processing registration data is subjected to abnormality identification based on authenticity and rationality to obtain a first abnormality identification result, including: Performing a weather feature extraction operation on the weather change information data to obtain a real weather feature sequence set; Performing a weather feature extraction operation on the harvesting and processing registration data to obtain a set of registered weather feature sequences; According to the real weather feature sequence set, the registered weather feature sequence set is authenticated to obtain a weather anomaly percentage; Performing a feature extraction operation based on the execution action on the picking and processing registration data to obtain an execution action feature sequence set; According to the pre-built crop production management guide and the real weather feature sequence set, the rationality of the execution action feature sequence set is judged to obtain the action abnormality percentage; The action abnormality percentage and the weather abnormality percentage are weightedly calculated to obtain a first abnormality recognition result.

4. The digital traceability method for agricultural product supply chain information according to claim 3, characterized in that: According to the regional picking and processing data set, performing abnormality analysis on the picking and processing registration data based on a clustering algorithm to obtain a second abnormality identification result includes: Use pre-built indicator sets to build a multi-dimensional indicator space; Performing a mapping operation based on the multidimensional index space on the regional picking and processing data set to obtain a regional picking and processing cluster; Identify the cluster center coordinate points of each preset indicator in the indicator set in the picking and processing cluster in the area to obtain an indicator center point set; The deviations of each preset indicator between the indicator center point set and the picking and processing registration data are calculated to obtain a second abnormality recognition result.

5. The digital traceability method for agricultural product supply chain information according to claim 4, characterized in that: The step of performing weighted calculation on the first abnormality recognition result and the second abnormality recognition result to obtain an abnormality risk label includes: Calculating the mean of the deviations of the preset indicators in the second abnormality identification result to obtain an indicator abnormality score; Performing weighted calculation on the indicator abnormality score and the first abnormality identification result to obtain an abnormality risk score; The pre-constructed risk classification interval is used to query the abnormal risk level corresponding to the abnormal risk score, and the abnormal risk level is used to construct an abnormal risk label.

6. The digital traceability method for agricultural product supply chain information according to claim 5, characterized in that: The step of predicting the growth state of the target agricultural product based on the crop growth environment data to obtain a growth state label includes: Acquiring crop physiological knowledge data of the target agricultural product, and performing a text feature recognition operation on the crop physiological knowledge data to obtain a text segmentation vector set; Performing a semantic recognition operation on the text word segmentation vector set to obtain a semantic recognition result; Performing an environmental feature extraction operation on the crop growth environment data to obtain an environmental indicator set and an environmental numerical feature set, wherein the environmental numerical feature set includes numerical change features corresponding to each environmental indicator in the environmental indicator set; Performing a correlation analysis on the semantic recognition result based on the environmental indicator set to obtain growth correlation features of each environmental indicator in the environmental indicator set for the target agricultural product, and summarizing each growth correlation feature to obtain a growth correlation feature set; According to the environmental numerical feature set, weight configuration is performed on the growth-related feature set to obtain a growth-influencing feature set, and a full-connection recognition operation based on the growth state of the crop is performed on the growth-influencing feature set to obtain a growth state recognition result; The growth status identification result is configured as a growth status label.

7. The digital traceability method for agricultural product supply chain information according to claim 6, characterized in that: The step of uploading the supply chain source data to a pre-built agricultural product supply chain system includes: Performing a sensitive word recognition operation on the supply chain source data to obtain sensitive data, and encrypting the sensitive data in the supply chain source data using a pre-built first key to obtain partially encrypted source data; Using a pre-built hash algorithm, generating a unique hash value for the locally encrypted source data; Generate a digital signature for the unique hash value using a pre-built private key; Obtain a timestamp, and package the unique hash value, digital signature, and timestamp to obtain a data packet; The data packet is chained using a pre-built public chain, and the first key is chained using a pre-built consortium chain.

8. The digital traceability method for agricultural product supply chain information according to claim 7, characterized in that: Before uploading the supply chain source data to the pre-built agricultural product supply chain system, the method further includes: Determining whether the abnormal risk label is higher than or equal to a preset unqualified level; When the abnormal risk tag is higher than or equal to the unqualified level, sending the supply chain source data to a pre-built agricultural product quality supervision database; When the abnormal risk label is less than the unqualified level, the step of uploading the supply chain source data to the pre-built agricultural product supply chain system is performed.

9. The digital traceability method for agricultural product supply chain information according to claim 8, characterized in that: The receiving of the picking and processing registration data of the target agricultural product sent by the pre-built agricultural product user terminal includes: Perform data cleaning operations based on duplication, missing values ​​and abnormal values ​​on the initialized picking and processing registration data to obtain cleaned data; The cleaning data is formatted based on unit, text format and field standardization to obtain standardized picking and processing registration data.

10. A digital traceability system for agricultural product supply chain information, characterized in that: The system comprises: The agricultural product data acquisition module is used to receive the picking and processing registration data of the target agricultural product sent by the pre-built agricultural product user terminal, and perform an origin authentication operation on the picking and processing registration data to obtain an origin authentication result, and when the origin authentication result is authentication passed, obtain the target agricultural area, and store the picking and processing registration data in a pre-built data verification database, and obtain the growth stage date, picking date sequence and processing date sequence in the picking and processing registration data; The authentication data acquisition module is used to obtain the crop growth environment data of the target agricultural area within the growth stage date from the pre-constructed agricultural environment monitoring database, and obtain the weather change information data of the target agricultural area within the picking date sequence and the processing date sequence, and use the data verification database to obtain all the picking and processing registration data sent by each agricultural product user terminal in the target agricultural area to obtain a regional picking and processing data set; An abnormality identification module is used to use a pre-trained abnormality data authentication model to perform abnormality identification based on authenticity and rationality on the picking and processing registration data according to the weather change information data to obtain a first abnormality identification result, perform abnormality analysis based on a clustering algorithm on the picking and processing registration data according to the regional picking and processing data set to obtain a second abnormality identification result, perform weighted calculation on the first abnormality identification result and the second abnormality identification result to obtain an abnormal risk label, and perform a growth state prediction on the crop growth environment data based on the growth state of the target agricultural product to obtain a growth state label; The source data chain module is used to construct the supply chain source data using the target agricultural products, target agricultural areas, picking and processing registration data, abnormal risk labels and growth status labels, and chain the supply chain source data to the pre-built agricultural product supply chain system to obtain the agricultural product supply chain information flow chart.

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