Agricultural product tracing method and system based on growth standard table and sentiment analysis

Abstract

The invention belongs to the field of agricultural traceability, and provides an agricultural product traceability method and system based on a growth standard table and sentiment analysis, and the method comprises the steps: building a crop growth standard table based on priori knowledge; according to the growth standard data in the crop growth standard table, corresponding planting environment data and production operation data in the agricultural product production process are collected, and the planting environment data and the production operation data of the same crop are merged to obtain crop production data; acquiring a scanning request of a consumer for the traceability code, analyzing and converting the traceability code to obtain crop growth process information, and generating crop growth process comparison information based on the crop growth standard table; and receiving feedback information of the consumer, performing sentiment analysis, identifying an evaluation keyword in the feedback information, and obtaining the recognition degree of the consumer on the crops. The problems that in the prior art, the traceability data utilization rate is low, data is tampered easily, and user feedback is neglected are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural traceability, and in particular relates to a method and system for tracing agricultural products based on a growth standard table and sentiment analysis. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As the most direct output of the agricultural industry, agricultural products are closely related to people's lives. In today's society, with consumers' increasing attention to food safety and quality, and the increasing complexity of the global agricultural supply chain, the transparency and traceability of the agricultural product production process have become the focus of common concern within and outside the industry.

[0004] At present, traceability technology has been used in the production process of agricultural products to a certain extent, but the current mainstream traceability technology has the following problems: The data of agricultural product growth process relies on manual recording, which has poor data accuracy and greatly increases the workload of staff. There is an urgent need for a technology that can automatically complete the growth process recording to break through the current problem, reduce the burden on staff and ensure the accuracy of data; with the advancement of agricultural modernization and the continuous development of Internet of Things technology, agricultural Internet of Things technology has gradually been widely used in the agricultural field to provide an accurate data source for agricultural product traceability. Agricultural Internet of Things technology covers multiple levels such as perception layer, network layer and application layer. Through real-time collection, transmission, processing and analysis of agricultural production data, it provides scientific basis and decision support for agricultural production. For example, in the planting industry, Internet of Things technology is widely used in precision planting, intelligent irrigation, pest and disease monitoring and other fields; in animal husbandry, Internet of Things technology is used for animal health monitoring, intelligent breeding management and other aspects; in the aquaculture industry, Internet of Things technology is used for water quality monitoring, breeding environment control and other aspects. These applications not only improve agricultural production efficiency and quality safety level, but also provide data basis and conditions for agricultural product safety traceability.

[0005] Traditional traceability technology only stores and displays the acquired data, but does not bring out the real value of the data. It plays a relatively small role in production monitoring and responsibility tracking. Traditional traceability methods are limited to the direct display of production data. Consumers without planting experience cannot understand or feel the norms and investment in crop management.

[0006] In traditional traceability technology, the protection of real data is poor, and data can be tampered with and modified manually. During the traceability process, consumers can often only view data deliberately selected by producers, which greatly reduces the significance of traceability and consumer trust.

[0007] Traditional traceability technology mainly focuses on the display of traceability data and does not pay much attention to consumer feedback. However, consumer feedback is of great significance for improving product quality and grasping market direction. At the same time, the emergence of artificial intelligence technologies such as natural language extraction and sentiment analysis also provides good conditions and foundation for consumer feedback analysis. Summary of the invention

[0008] In order to solve the above problems, the present invention proposes a method and system for tracing the origin of agricultural products based on growth standard tables and sentiment analysis. The present invention solves the problems of low utilization rate of traceability data, easy tampering of data, and neglect of user feedback in the prior art, truly realizes the full traceability of the agricultural product production process, and effectively utilizes the traceability data to improve economic benefits.

[0009] According to some embodiments, a first solution of the present invention provides an agricultural product traceability system based on a growth standard table and sentiment analysis, using the following technical solutions: An agricultural product traceability system based on growth standard table and sentiment analysis, including: A growth standard table construction module is used to establish a crop growth standard table based on prior knowledge and the time nodes of each growth stage of the crop; The data collection module is used to collect the corresponding planting environment data in the agricultural product production process according to the growth standard data in the crop growth standard table, and at the same time compare it with the crop growth standard table to determine the abnormal data and send it to the administrator for warning, obtain the production operation data in the crop growth process, and merge the planting environment data and production operation data of the same crop to obtain the crop production data; The data processing module is used to match the collected crop production data with the growth standard data of the corresponding crops in the crop growth standard table and the information on the entry and exit of agricultural materials, and generate crop growth files and crop planting files; A code generation module is used to generate a traceability code based on the planting environment data and production operation data, combined and encrypted according to the agricultural product picking batches of the crops; A traceability decoding module is used to obtain a scanning request for the traceability code from a consumer, parse and transform the scanned traceability code, obtain crop growth process information, compare the crop growth process information with the crop growth process information in combination with a crop growth standard table, and generate crop growth process comparison information; The feedback analysis module is used to receive consumers' feedback information and perform sentiment analysis, identify evaluation keywords in the feedback information, and obtain consumers' recognition of crops. Furthermore, in the growth standard table construction module, based on prior knowledge and according to the time nodes of each growth cycle of the crops, a crop growth standard table is established, specifically: Acquire planting environment data and production operation data under normal crop growth standards as prior knowledge; According to the time nodes of each growth stage of the crops, corresponding prior knowledge is obtained to construct a crop growth standard table; the crop growth standard table includes planting environment standard data and production operation standard data; Periodically obtain historical data information of crops from market feedback, and adjust and update the crop growth standard table for the corresponding crops at each growth stage of the crops.

[0010] Furthermore, in the data collection module, the planting environment data includes basic planting and growth environment data of crops and environment configurable fields, wherein the environment configurable fields are added or deleted according to the planting environment standard requirements during the growth of different crops; The production operation data includes basic agricultural input usage data of crops and configurable agricultural input fields, wherein the configurable agricultural input fields are added or deleted according to the agricultural input usage standard requirements during the growth of different crops.

[0011] Furthermore, in the data processing module, the collected crop production data is matched with the growth standard data of the corresponding crops in the crop growth standard table to generate a crop growth file, specifically: Match the planting environment data in the collected crop production data with the planting environment standard data of the corresponding crops in the crop growth standard table. If they are inconsistent, they are regarded as abnormal planting data, and abnormal planting data are marked as abnormal. Combined with the agricultural product picking batches of crops, the corresponding planting environment data are summarized according to the crop growth stages to generate crop growth files.

[0012] Furthermore, in the data processing module, the collected crop production data is matched with the agricultural material storage information to generate a crop planting file, specifically: Acquire production operation data from the collected crop production data; Connect with the agricultural materials in and out warehouse information, match the agricultural materials data used in the production operation data with the agricultural materials in and out warehouse information, if they are inconsistent, treat them as abnormal usage data, and mark them as abnormal usage data; Combined with the agricultural product picking batches of crops, the corresponding production operation data are summarized according to the crop growth stages to generate crop planting files.

[0013] Furthermore, in the code generation module, based on the planting environment data and the production operation data, the agricultural product picking batches of the crops are combined and encrypted to generate a traceability code, specifically: Combine the planting environment data and production operation data of the agricultural product picking batches of the same crop; The combined data is encrypted using an encryption algorithm to generate a traceability code.

[0014] Furthermore, in the traceability decoding module, after obtaining the consumer's scanning request for the traceability code, the scanned traceability code is parsed and converted to obtain crop growth process information, and the crop growth process information is compared with the crop growth standard table to generate crop growth process comparison information, specifically: Obtaining a scanning request for the traceability code from a consumer, parsing and converting the code using a decryption algorithm, and obtaining crop growth process information, wherein the crop growth process information includes planting environment data and production operation data corresponding to different growth stages of the crop growth process; Compare the planting environment standard data and production operation standard data at different growth stages in the crop growth standard table with the planting environment data and production operation data corresponding to different growth stages in the crop growth process information; Generate comparative information of crop growth process and display it to users.

[0015] Furthermore, in the feedback analysis module, feedback information from consumers is received and sentiment analysis is performed to identify evaluation keywords in the feedback information and obtain consumers' recognition of the crops, specifically: Receive feedback from consumers; Use the pre-trained semantic recognition model to perform sentiment analysis and extract evaluation keywords; Matching the evaluation keywords with the pre-built evaluation criteria corresponding to the crop; Determine consumers' acceptance of agricultural products.

[0016] According to some embodiments, a second solution of the present invention provides a method for tracing the origin of agricultural products based on a growth standard table and sentiment analysis, using the following technical solutions: A method for tracing agricultural products based on growth standard tables and sentiment analysis, comprising: Based on prior knowledge, a standard table of crop growth is established according to the time nodes of each growth stage of crops; According to the growth standard data in the crop growth standard table, the corresponding planting environment data in the agricultural product production process is collected, and at the same time, it is compared with the crop growth standard table to determine the abnormal data and then sent to the administrator for warning, and the production operation data in the crop growth process is obtained. The planting environment data and production operation data of the same crop are combined to obtain the crop production data; Match the collected crop production data with the corresponding crop growth standard data in the crop growth standard table and the agricultural materials in and out warehouse information to generate crop growth files and crop planting files; Based on the planting environment data and production operation data, the agricultural product picking batches of the crops are combined and encrypted to generate a traceability code; After obtaining the consumer's scanning request for the traceability code, the scanned traceability code is parsed and converted to obtain crop growth process information, and the crop growth process information is compared with the crop growth process information in combination with the crop growth standard table to generate crop growth process comparison information; Receive consumer feedback and conduct sentiment analysis to identify evaluation keywords in the feedback information and derive consumers’ recognition of the crops.

[0017] Furthermore, the collected crop production data is matched with the growth standard data of the corresponding crops in the crop growth standard table and the agricultural material in and out storage information to generate crop growth files and crop planting files, specifically: Match the planting environment data in the collected crop production data with the planting environment standard data of the corresponding crops in the crop growth standard table. If they are inconsistent, they are regarded as abnormal planting data, and abnormal planting data are marked as abnormal. Combined with the agricultural product picking batches of crops, the corresponding planting environment data is summarized according to the crop growth stage to generate crop growth files; Acquire production operation data from the collected crop production data; Connect with the agricultural materials in and out warehouse information, match the agricultural materials data used in the production operation data with the agricultural materials in and out warehouse information, if they are inconsistent, treat them as abnormal usage data, and mark them as abnormal usage data; Combined with the agricultural product picking batches of crops, the corresponding production operation data are summarized according to the crop growth stages to generate crop planting files.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention improves the traceability of the production process and clarifies responsibility tracking. Through the formulation of crop growth standards, this technology assists planting personnel in supervising and restraining the behavior of the planting process; through the dual protection mode of voluntary reporting of farming behavior and video recognition, it ensures the authenticity of farming records and the use of agricultural materials; through the comparison of various data in the traceability analysis link, it helps administrators to truly understand the planting situation, and can identify the problem points for products that do not meet quality standards, thereby realizing the traceability of the planting process.

[0019] The present invention improves the efficiency of data collection and processing, ensures authenticity, uses IoT sensors for real-time data collection, avoids errors and omissions caused by manual records, and greatly improves the accuracy and timeliness of data. The central data processing platform can automatically process and analyze the collected data, generate production process records of agricultural products, and automatically generate traceability codes, reducing manual participation and improving overall efficiency. By encrypting key data and comparison results, production data cannot be tampered with, ensuring traceability authenticity, and ensuring that administrators and consumers can truly understand the product production process.

[0020] The traceability code of the present invention is easy to interpret and understand. Combined with the authoritative crop growth standard table, it displays production data while allowing consumers to have a clear understanding of the reasonable range of the data, significantly improving consumers' recognition of the product and feeling the strict management of the product in the production process, so that the traceability information is no longer boring data or text without basis, but a genuine medium that allows consumers to feel the high-standard production.

[0021] The present invention combines AI intelligence to obtain the user's true feelings. The traditional user evaluation system mainly relies on numerical data such as scores for analysis, and does not analyze text information, which leads to a certain degree of subjectivity and deviation in the analysis results, and ignores the important text expression part; this patent combines text recognition and sentiment analysis technology to accurately remove consumers' text feedback and convert abstract text into visual digital results, thereby enhancing the objectivity and accuracy of user feedback analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0023] Figure 1 It is a flow chart of the agricultural product traceability method based on growth standard table and sentiment analysis in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0025] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0026] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0027] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0028] Embodiment 1 This embodiment provides an agricultural product traceability system based on a growth standard table and sentiment analysis, including: A growth standard table construction module is used to establish a crop growth standard table based on prior knowledge and the time nodes of each growth stage of the crop; The data collection module is used to collect the corresponding planting environment data in the agricultural product production process according to the growth standard data in the crop growth standard table, and at the same time compare it with the crop growth standard table to determine the abnormal data and send it to the administrator for warning, obtain the production operation data in the crop growth process, and merge the planting environment data and production operation data of the same crop to obtain the crop production data; The data processing module is used to match the collected crop production data with the growth standard data of the corresponding crops in the crop growth standard table and the information on the entry and exit of agricultural materials, and generate crop growth files and crop planting files; A code generation module is used to generate a traceability code based on the planting environment data and production operation data, combined and encrypted according to the agricultural product picking batches of the crops; The traceability decoding module is used to obtain the consumer's scanning request for the traceability code, parse and transform the scanned traceability code, obtain the crop growth process information, compare it with the crop growth process information in combination with the crop growth standard table, and generate crop growth process comparison information; The feedback analysis module is used to receive consumers' feedback information and perform sentiment analysis, identify evaluation keywords in the feedback information, and obtain consumers' recognition of crops. In the growth standard table construction module, based on prior knowledge and according to the time nodes of each growth cycle of the crops, a crop growth standard table is established, specifically: Acquire planting environment data and production operation data under normal crop growth standards as prior knowledge; According to the time nodes of each growth stage of the crops, corresponding prior knowledge is obtained to construct a crop growth standard table; the crop growth standard table includes planting environment standard data and production operation standard data; Periodically obtain historical data information of crops from market feedback, and adjust and update the crop growth standard table for the corresponding crops at each growth stage of the crops.

[0029] In the data collection module, the planting environment data includes basic planting and growth environment data of crops and environment configurable fields, wherein the environment configurable fields are added or deleted according to the planting environment standard requirements during the growth of different crops; The production operation data includes basic agricultural input usage data of crops and configurable agricultural input fields, wherein the configurable agricultural input fields are added or deleted according to the agricultural input usage standard requirements during the growth of different crops.

[0030] In the data processing module, the collected crop production data is matched with the growth standard data of the corresponding crops in the crop growth standard table to generate a crop growth file, specifically: Match the planting environment data in the collected crop production data with the planting environment standard data of the corresponding crops in the crop growth standard table. If they are inconsistent, they are regarded as abnormal planting data and abnormal planting data are marked; Combined with the agricultural product picking batches of crops, the corresponding planting environment data are summarized according to the crop growth stages to generate crop growth files.

[0031] In the data processing module, the collected crop production data is matched with the agricultural material storage information to generate crop planting files, specifically: Acquire production operation data from the collected crop production data; Connect with the information of agricultural materials in and out of the warehouse, match the agricultural materials data used in the production operation data with the information of agricultural materials in and out of the warehouse, and if there is inconsistency, treat it as abnormal usage data and mark it as abnormal usage data; Combined with the agricultural product picking batches of crops, the corresponding production operation data are summarized according to the crop growth stages to generate crop planting files.

[0032] In the code generation module, based on the planting environment data and the production operation data, the agricultural product picking batches of the crops are combined and encrypted to generate a traceability code, specifically: Combine the planting environment data and production operation data of the agricultural product picking batches of the same crop; The combined data is encrypted using an encryption algorithm to generate a traceability code.

[0033] In the traceability decoding module, after obtaining the consumer's scanning request for the traceability code, the scanned traceability code is parsed and converted to obtain the crop growth process information, and the crop growth process information is compared with the crop growth standard table to generate the crop growth process comparison information, specifically: Obtaining a scanning request for the traceability code from a consumer, parsing and converting the code using a decryption algorithm, and obtaining crop growth process information, wherein the crop growth process information includes planting environment data and production operation data corresponding to different growth stages of the crop growth process; Compare the planting environment standard data and production operation standard data at different growth stages in the crop growth standard table with the planting environment data and production operation data corresponding to different growth stages in the crop growth process information; Generate comparative information of crop growth process and display it to users.

[0034] In the feedback analysis module, the feedback information of consumers is received and sentiment analysis is performed to identify the evaluation keywords in the feedback information and obtain the consumers' recognition of the crops, specifically: Receive feedback from consumers; Use the pre-trained semantic recognition model to perform sentiment analysis and extract evaluation keywords; Matching the evaluation keywords with the pre-built evaluation criteria corresponding to the crop; Determine consumers' acceptance of agricultural products.

[0035] Embodiment 2 like Figure 1 As shown, this embodiment provides an agricultural product traceability method based on a growth standard table and sentiment analysis, including: Based on prior knowledge, a standard table of crop growth is established according to the time nodes of each growth stage of crops; According to the growth standard data in the crop growth standard table, the corresponding planting environment data in the agricultural product production process is collected, and at the same time, it is compared with the crop growth standard table to determine the abnormal data and then sent to the administrator for warning, and the production operation data in the crop growth process is obtained. The planting environment data and production operation data of the same crop are combined to obtain the crop production data; Match the collected crop production data with the corresponding crop growth standard data in the crop growth standard table and the agricultural materials in and out warehouse information to generate crop growth files and crop planting files; Based on the planting environment data and production operation data, the agricultural product picking batches of the crops are combined and encrypted to generate a traceability code; After obtaining the consumer's scanning request for the traceability code, the scanned traceability code is parsed and converted to obtain crop growth process information, and the crop growth process information is compared with the crop growth process information in combination with the crop growth standard table to generate crop growth process comparison information; Receive consumer feedback and conduct sentiment analysis to identify evaluation keywords in the feedback information and derive consumers’ recognition of the crops.

[0036] The collected crop production data is matched with the growth standard data of the corresponding crops in the crop growth standard table and the agricultural materials storage information, and abnormal data is marked to generate crop growth files and crop planting files, specifically: Match the planting environment data in the collected crop production data with the planting environment standard data of the corresponding crops in the crop growth standard table. If they are inconsistent, they are regarded as abnormal planting data, and abnormal planting data are marked as abnormal. Combined with the agricultural product picking batches of crops, the corresponding planting environment data is summarized according to the crop growth stage to generate crop growth files; Acquire production operation data from the collected crop production data; Connect with the agricultural materials in and out warehouse information, match the agricultural materials data used in the production operation data with the agricultural materials in and out warehouse information, if they are inconsistent, treat them as abnormal usage data, and mark them as abnormal usage data; Combined with the agricultural product picking batches of crops, the corresponding production operation data are summarized according to the crop growth stages to generate crop planting files.

[0037] Implementation steps Step 1: Create a standard table for crop growth Based on prior knowledge, a crop growth standard table is established according to the time nodes of each crop growth cycle, specifically: Acquire planting environment data and production operation data under normal crop growth standards as prior knowledge; According to the time nodes of each growth stage of the crops, corresponding prior knowledge is obtained to construct a crop growth standard table; the crop growth standard table includes planting environment standard data and production operation standard data; Periodically obtain historical data information of crops from market feedback, and adjust and update the crop growth standard table for the corresponding crops at each growth stage of the crops.

[0038] The planting environment standard data includes the basic planting and growth environment standard data of crops and the environment configurable fields, wherein the environment configurable fields are added or deleted according to the planting environment standard requirements during the growth of different crops; The production operation standard data includes basic agricultural input usage standard data of crops and agricultural input configurable fields, wherein the agricultural input configurable fields are added or deleted according to the agricultural input usage standard requirements during the growth of different crops.

[0039] It is understandable that different crops have different growth standards for the growth standard table, and they will change due to the influence of various data.

[0040] Specifically, we receive authoritative planting data, pest and disease resource libraries, and crop databases, and build targeted crop growth standard tables. We periodically obtain historical data information on crops from market feedback, and continuously adjust and optimize the growth standard tables based on historical data, yield, quality, user feedback, etc.

[0041] The platform collects various standard parameter values ​​of crops during their growth process through expert guidance, authoritative data and other data sources, including suitable soil nitrogen, phosphorus and potassium concentrations, air temperature and humidity, soil temperature and humidity, and soil pH value at each growth stage, and generates a standard crop growth table based on this. The formulation of the standard crop growth table includes but is not limited to the above values, and can add indicators such as light, accumulated temperature, or reduce useless indicators based on the actual growth factors of crops.

[0042] After the growth standard table is constructed, multiple sensor nodes are used to collect real-time planting environment data (such as temperature, humidity, light intensity, etc.), production operation data (such as fertilization, irrigation, harvest time, etc.) and logistics data in the agricultural product production process, including the following steps 2 and 3.

[0043] According to the growth standard data in the crop growth standard table, the corresponding planting environment data in the agricultural product production process is collected, and at the same time, it is compared with the crop growth standard table to determine the abnormal data and then sent to the administrator for warning, and the production operation data in the crop growth process is obtained. The planting environment data and production operation data of the same crop are combined to obtain the crop production data; The planting environment data includes basic planting and growth environment data of crops and environment configurable fields, wherein the environment configurable fields are added or deleted according to the planting environment standard requirements during the growth process of different crops; The production operation data includes basic agricultural input usage data of crops and configurable agricultural input fields, wherein the configurable agricultural input fields are added or deleted according to the agricultural input usage standard requirements during the growth process of different crops.

[0044] Step 2: Planting environment data detection Step 2.1 Sensor deployment According to the index monitoring requirements of the crop growth standard table, select the appropriate sensor type. Plan the layout of sensors in the farmland, greenhouse or production workshop to ensure coverage of the entire production area and to ensure the comprehensiveness and representativeness of the collected data.

[0045] Step 2.2 Camera deployment Cameras are placed in agricultural product production and processing sites (such as farmland, greenhouses, production workshops, etc.) to ensure coverage of the entire production area and to ensure the comprehensiveness and representativeness of the collected data.

[0046] Step 2.3 Monitoring data collection Relying on the Internet of Things system, the platform continuously collects various indicators during the growth process of crops, assigns timestamps to the data while recording the values ​​of each indicator, and stores it in the database after cleaning up invalid data. During this process, the planting environment data and the crop growth standard table will be continuously compared. For abnormal values, warnings will be sent to managers. If the abnormal state continues, all data generated during this period will be marked.

[0047] Taking tomatoes as an example, the suitable temperature for tomatoes during the fruiting period is 28℃~30℃ during the day and 16℃~13℃ at night. If the platform monitors that the planting environment temperature cannot reach the indicator value during the fruiting period, it will send a warning to the manager; if the reasonable temperature cannot be met for several consecutive days, it may affect the quality of tomatoes. The platform will mark the time period and feedback in the subsequent traceability data.

[0048] Step 3: Production operation data monitoring During the planting process, for operations involving the use of agricultural materials such as sowing, planting, spraying pesticides, and fertilizing, operators are required to use a dedicated APP to upload relevant records, including usage time, usage quantity, etc. The platform will rely on the installed cameras to automatically identify the authenticity of the operation and eliminate false reporting.

[0049] After data collection is completed, the collected planting environment data and production operation data are received and processed to generate crop production data. Timestamps and random numbers are assigned to the data, and a unique code is generated for each piece of data to ensure the authority and non-forgeability of the data, which specifically includes the following steps 4 and 5.

[0050] Step 4: Generate crop growth profile The collected crop production data is matched with the growth standard data of the corresponding crops in the crop growth standard table to generate a crop growth file, specifically: Match the planting environment data in the collected crop production data with the planting environment standard data of the corresponding crops in the crop growth standard table. If they are inconsistent, they are regarded as abnormal planting data, and abnormal planting data are marked as abnormal. Combined with the agricultural product picking batches of crops, the corresponding planting environment data are summarized according to the crop growth stages to generate crop growth files.

[0051] After the agricultural products are picked, growth files are generated for them according to the picking batches and the planting environment monitoring data. During the generation process, the data of the two will be matched according to the time nodes in the planting environment data and the growth stages of the crop growth standard table. Abnormal data in the growth process will be marked and the abnormal situations will be summarized and fed back to the manager.

[0052] Step 5: Generate crop planting files The collected crop production data is matched with the agricultural materials in and out information to generate crop planting files, specifically: Acquire production operation data from the collected crop production data; Connect with the agricultural materials in and out warehouse information, match the agricultural materials data used in the production operation data with the agricultural materials in and out warehouse information, if they are inconsistent, treat them as abnormal usage data, and mark them as abnormal usage data; Combined with the agricultural product picking batches of crops, the corresponding production operation data are summarized according to the crop growth stages to generate crop planting files.

[0053] After the agricultural products are picked, the system will compile statistics on the use of agricultural materials such as sowing, planting, spraying, and fertilizing generated in the production process according to the batches of agricultural products picked, connect them with the warehouse entry and exit data, match the usage quantities in the above data with the warehouse entry and exit of agricultural materials, generate planting files, and summarize abnormal situations and feedback them to the manager.

[0054] Step 6: Generate traceability code Based on the planting environment data and production operation data, the agricultural product picking batches of the crops are combined and encrypted to generate a traceability code, specifically: Combine the planting environment data and production operation data of the agricultural product picking batches of the same crop; The combined data is encrypted using an encryption algorithm to generate a traceability code.

[0055] The data processing of the agricultural product production process is completed. Based on the planting environment data and production operation data, the data are processed and combined according to the agricultural product picking batches to generate a traceability code. Each agricultural product corresponds to a unique traceability code.

[0056] The administrator downloads the platform and enters product-related information through the platform, including product packaging pictures, shelf life, detailed introduction, etc. The purpose of this step is to enrich the product information in the traceability information so that users can clearly understand the traced products, which can play a role in publicity and promotion to a certain extent.

[0057] Relying on the growth archive data and planting archive data, the agricultural product picking batch is used as the association to complete the binding of product information and planting process information. The encryption algorithm is used to encrypt and generate a unique traceability code, which includes the production location, time, batch number, production operator, growth process record, pesticide use record, fertilizer use record, etc. After the traceability code is generated, the system binds the traceability code with the corresponding production batch information, generates a mapping table of traceability code and production data, and stores it in the database. The system provides a traceability code query interface to facilitate operators and managers to view and manage traceability code information at any time.

[0058] It should be noted that the abnormal data marked in steps 4 and 5 will be completely retained during the traceability process and displayed in the traceability information to ensure the authenticity of the traceability data and infer that managers can strengthen crop management during the planting process. Administrators can make notes and explanations on abnormal data during the traceability code generation process, and this information will be displayed to consumers simultaneously.

[0059] Step 7: Traceability information query After obtaining the consumer's scanning request for the traceability code, the scanned traceability code is parsed and converted to obtain the crop growth process information, which is compared with the crop growth process information in combination with the crop growth standard table to generate crop growth process comparison information, specifically: Obtaining a scanning request for the traceability code from a consumer, parsing and converting the code using a decryption algorithm, and obtaining crop growth process information, wherein the crop growth process information includes planting environment data and production operation data corresponding to different growth stages of the crop growth process; Compare the planting environment standard data and production operation standard data at different growth stages in the crop growth standard table with the planting environment data and production operation data corresponding to different growth stages in the crop growth process information; Generate comparative information of crop growth process and display it to users.

[0060] Users can scan the traceability code on the packaging or label to query the production process information of agricultural products, and realize the visualization of the entire production process of agricultural products. The data will be displayed in combination with standard production data; at the same time, the system will convert abstract data such as fertilizer and pesticide usage records into total usage, usage tables, etc., which are easier for consumers to understand.

[0061] Step 7.1 Query Consumers can scan the traceability code through the consumer query system to inquire about the production process of agricultural products. The system supports multiple query methods such as mobile APP, WeChat public account, and web page.

[0062] Step 7.2 Display During the query process, the decoding module will parse and convert the traceability code. In addition to the regular product information display, it will display the growth environment data, pesticide and fertilizer data, etc. in the form of line graphs and timelines. During the display process, it will be combined with the crop growth standard table to facilitate consumers' understanding and perception; at the same time, the basis for the formulation of crop growth standards and the growth process video will be displayed to increase the persuasiveness of the information.

[0063] If there is abnormal data in the traceability information, the decoding module will fully display and mark the abnormal information, and at the same time display the administrator's comments or explanations on the information, to prevent traceability fraud and improve the authority of traceability.

[0064] Taking tomatoes as an example, the suitable temperature for tomatoes during the fruiting period is 28℃~30℃ during the day and 16℃~13℃ at night. Since consumers do not know at what temperature tomatoes should grow, directly displaying the actual temperature is of little significance. Therefore, the standard temperature range curve and the actual temperature curve will be displayed on the traceability display page to show that the tomatoes are grown strictly in accordance with the planting standards, which is convenient for consumers to understand. At the same time, a link to the construction basis of the planting standards is provided to meet consumers' in-depth understanding needs.

[0065] Step 8 Complaints and Feedback Receive consumer feedback and conduct sentiment analysis to identify evaluation keywords in the feedback information and obtain consumers’ recognition of crops, specifically: Receive feedback from consumers; Use the pre-trained semantic recognition model to perform sentiment analysis and extract evaluation keywords; Matching the evaluation keywords with the pre-built evaluation criteria corresponding to the crop; Determine consumers' acceptance of agricultural products.

[0066] Users can provide feedback on their consumption experience through the traceability code entrance. This module will collect user feedback, transform it into intuitive results through artificial intelligence technologies such as natural language extraction and sentiment recognition, and then feed it back to the product operator.

[0067] The traceability code provides user feedback function. At the same time, the platform establishes various crop evaluation system models and enters the evaluation items of various crops. The platform will automatically extract core vocabulary from the user feedback language and match it with the crop evaluation standards in combination with AI text recognition technology. It uses sentiment analysis technology to accurately identify the degree of match between keywords in user feedback and crop evaluation standards. Combined with the overall context, it automatically identifies feedback and generates statistical reports to send to managers. It conducts in-depth analysis of user feedback from multiple dimensions to provide managers with more comprehensive user portraits and decision support.

[0068] Taking tomatoes as an example, different consumers have different perceptions of the sourness and size of tomatoes. If a scoring system is used, the feedback results will be subjective. By establishing tomato evaluation standards in advance, entering evaluation items such as "sour", "sweet", "delicious", and "full appearance", and using language recognition to identify key words such as "a bit sour" and "delicious", and combining the overall context, the actual sourness and sweetness of tomato products and consumer recognition can be determined, and converted into intuitive data to be reported to managers, which can better feedback customer evaluations.

[0069] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. The agricultural product traceability system based on growth standard table and sentiment analysis is characterized by: include: A growth standard table construction module is used to establish a crop growth standard table based on prior knowledge and the time nodes of each growth stage of the crop; The data collection module is used to collect the corresponding planting environment data in the agricultural product production process according to the growth standard data in the crop growth standard table, and at the same time compare it with the crop growth standard table to determine the abnormal data and send it to the administrator for warning, obtain the production operation data in the crop growth process, and merge the planting environment data and production operation data of the same crop to obtain the crop production data; The data processing module is used to match the collected crop production data with the growth standard data of the corresponding crops in the crop growth standard table and the information on the entry and exit of agricultural materials, and generate crop growth files and crop planting files; A code generation module is used to generate a traceability code based on the planting environment data and production operation data, combined and encrypted according to the agricultural product picking batches of the crops; A traceability decoding module is used to obtain a scanning request for the traceability code from a consumer, parse and transform the scanned traceability code, obtain crop growth process information, compare the crop growth process information with the crop growth process information in combination with a crop growth standard table, and generate crop growth process comparison information; The feedback analysis module is used to receive consumers' feedback information and perform sentiment analysis, identify evaluation keywords in the feedback information, and obtain consumers' recognition of crops.

2. The agricultural product traceability system based on growth standard table and sentiment analysis as claimed in claim 1, characterized in that: In the growth standard table construction module, based on prior knowledge and according to the time nodes of each growth cycle of the crops, a crop growth standard table is established, specifically: Acquire planting environment data and production operation data under normal crop growth standards as prior knowledge; According to the time nodes of each growth stage of the crops, corresponding prior knowledge is obtained to construct a crop growth standard table; the crop growth standard table includes planting environment standard data and production operation standard data; Periodically obtain historical data information of crops from market feedback, and adjust and update the crop growth standard table for the corresponding crops at each growth stage of the crops.

3. The agricultural product traceability system based on growth standard table and sentiment analysis as claimed in claim 1, characterized in that: In the data collection module, the planting environment data includes basic planting and growth environment data of crops and environment configurable fields, wherein the environment configurable fields are added or deleted according to the planting environment standard requirements during the growth of different crops; The production operation data includes basic agricultural input usage data of crops and configurable agricultural input fields, wherein the configurable agricultural input fields are added or deleted according to the agricultural input usage standard requirements during the growth of different crops.

4. The agricultural product traceability system based on growth standard table and sentiment analysis according to claim 1, characterized in that: In the data processing module, the collected crop production data is matched with the growth standard data of the corresponding crops in the crop growth standard table to generate a crop growth file, specifically: Match the planting environment data in the collected crop production data with the planting environment standard data of the corresponding crops in the crop growth standard table. If they are inconsistent, they are regarded as abnormal planting data and abnormal planting data are marked; Combined with the agricultural product picking batches of crops, the corresponding planting environment data are summarized according to the crop growth stages to generate crop growth files.

5. The agricultural product traceability system based on growth standard table and sentiment analysis as claimed in claim 1, characterized in that: In the data processing module, the collected crop production data is matched with the agricultural material storage information to generate crop planting files, specifically: Acquire production operation data from the collected crop production data; Connect with the information of agricultural materials in and out of the warehouse, match the agricultural materials data used in the production operation data with the information of agricultural materials in and out of the warehouse, and if there is inconsistency, treat it as abnormal usage data and mark it as abnormal usage data; Combined with the agricultural product picking batches of crops, the corresponding production operation data are summarized according to the crop growth stages to generate crop planting files.

6. The agricultural product traceability system based on growth standard table and sentiment analysis according to claim 1, characterized in that: In the code generation module, based on the planting environment data and the production operation data, the agricultural product picking batches of the crops are combined and encrypted to generate a traceability code, specifically: Combine the planting environment data and production operation data of the agricultural product picking batches of the same crop; The combined data is encrypted using an encryption algorithm to generate a traceability code.

7. The agricultural product traceability system based on growth standard table and sentiment analysis according to claim 1, characterized in that: In the traceability decoding module, after obtaining the consumer's scanning request for the traceability code, the scanned traceability code is parsed and converted to obtain the crop growth process information, and the crop growth process information is compared with the crop growth standard table to generate the crop growth process comparison information, specifically: Obtaining a scanning request for the traceability code from a consumer, parsing and converting the code using a decryption algorithm, and obtaining crop growth process information, wherein the crop growth process information includes planting environment data and production operation data corresponding to different growth stages of the crop growth process; Compare the planting environment standard data and production operation standard data at different growth stages in the crop growth standard table with the planting environment data and production operation data corresponding to different growth stages in the crop growth process information; Generate comparative information of crop growth process and display it to users.

8. The agricultural product traceability system based on growth standard table and sentiment analysis as claimed in claim 1, characterized in that: In the feedback analysis module, the feedback information of consumers is received and sentiment analysis is performed to identify the evaluation keywords in the feedback information and obtain the consumers' recognition of the crops, specifically: Receive feedback from consumers; Use the pre-trained semantic recognition model to perform sentiment analysis and extract evaluation keywords; Matching the evaluation keywords with the pre-built evaluation criteria corresponding to the crop; Determine consumers' acceptance of agricultural products.

9. The agricultural product traceability method based on growth standard table and sentiment analysis is characterized by: include: Based on prior knowledge, a standard table of crop growth is established according to the time nodes of each growth stage of crops; According to the growth standard data in the crop growth standard table, the corresponding planting environment data in the agricultural product production process is collected, and at the same time, it is compared with the crop growth standard table to determine the abnormal data and then sent to the administrator for warning, and the production operation data in the crop growth process is obtained. The planting environment data and production operation data of the same crop are combined to obtain the crop production data; Match the collected crop production data with the corresponding crop growth standard data in the crop growth standard table and the agricultural materials in and out warehouse information to generate crop growth files and crop planting files; Based on the planting environment data and production operation data, the agricultural product picking batches of the crops are combined and encrypted to generate a traceability code; After obtaining the consumer's scanning request for the traceability code, the scanned traceability code is parsed and converted to obtain crop growth process information, and the crop growth process information is compared with the crop growth process information in combination with the crop growth standard table to generate crop growth process comparison information; Receive consumer feedback and conduct sentiment analysis to identify evaluation keywords in the feedback information and derive consumers’ recognition of the crops.

10. The agricultural product traceability method based on growth standard table and sentiment analysis according to claim 9, characterized in that: The collected crop production data is matched with the growth standard data of the corresponding crops in the crop growth standard table and the agricultural materials in and out warehouse information to generate crop growth files and crop planting files, specifically: Match the planting environment data in the collected crop production data with the planting environment standard data of the corresponding crops in the crop growth standard table. If they are inconsistent, they are regarded as abnormal planting data and abnormal planting data are marked; Combined with the agricultural product picking batches of crops, the corresponding planting environment data is summarized according to the crop growth stage to generate crop growth files; Acquire production operation data from the collected crop production data; Connect with the information of agricultural materials in and out of the warehouse, match the agricultural materials data used in the production operation data with the information of agricultural materials in and out of the warehouse, and if there is inconsistency, treat it as abnormal usage data and mark it as abnormal usage data; Combined with the agricultural product picking batches of crops, the corresponding production operation data are summarized according to the crop growth stages to generate crop planting files.

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