Agricultural product traceability management method and system

Abstract

The invention relates to the technical field of agricultural product traceability management, and discloses an agricultural product traceability management method and system. The method is executed by a processor and comprises the steps of collecting agricultural product feature data; wherein the agricultural product characteristic data comprises breeding data, processing data, transportation data and storage data; uploading the agricultural product feature data to an agricultural product traceability platform block chain; according to the agricultural product feature data uploaded to the agricultural product traceability platform block chain, a health evaluation index is calculated through an analytic hierarchy process; according to the agricultural product traceability platform, link operation is carried out, a traceability mark is generated, and an agricultural product health evaluation index is obtained by scanning the traceability mark. According to the agricultural product traceability management method, the breeding data, the processing data, the transportation data and the storage data are comprehensively considered, and the agricultural product health evaluation can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural product traceability management, and in particular to a method and system for agricultural product traceability management. Background Art

[0002] At present, the method of agricultural product traceability management is based on blockchain and the Internet of Things (IoT). Real-time data collection is carried out in the production, processing, transportation and sales of agricultural products through IoT devices, such as soil moisture, air temperature, pesticide usage, processing temperature, transportation route and other information; these data are uploaded to the blockchain network through smart terminals, and the traceability information of each link is encrypted, stored and shared by utilizing the decentralized and tamper-proof characteristics of blockchain; consumers can access all traceability information recorded on the blockchain by scanning the QR code on the agricultural products, thereby fully understanding the entire process of the product from farmland to table.

[0003] However, the current agricultural product traceability management methods lack intuitive judgment of agricultural product health evaluation indicators. Summary of the invention

[0004] The present invention provides a method and system for agricultural product traceability management to achieve health evaluation in agricultural product traceability.

[0005] In a first aspect, in order to solve the above technical problems, the present invention provides a method for agricultural product traceability detection, comprising: Collecting data on agricultural product characteristics; The agricultural product characteristic data includes breeding data, processing data, transportation data and storage data. The breeding data includes breeding farm environment data, breeding farm epidemic prevention data and breeding farm feed health data. The processing data includes quarantine data and processing technology data. The transportation data includes logistics time data and transportation temperature data. The storage data includes storage time data and storage temperature data. Uploading the agricultural product characteristic data to an agricultural product traceability platform, wherein the agricultural product traceability platform is built based on blockchain technology; The health evaluation index is divided into the first-level index, the breeding data, the processing data, the transportation data and the storage data are divided into the second-level index, and the farm environment data, the farm epidemic prevention data, the farm feed health data, the quarantine data, the processing technology data, the logistics time data, the transportation temperature data, the storage time data and the storage temperature data are divided into the third-level index; Based on the first-level index, the second-level index and the third-level index, an importance distinction operation is performed to obtain a second-level judgment matrix and a third-level judgment matrix; Perform weight calculation according to the secondary judgment matrix and the tertiary judgment matrix to obtain a secondary evaluation index weight vector and a tertiary evaluation index weight vector; Compare the third-level indicators with the pre-acquired third-level indicator standard data to obtain the third-level indicator scores corresponding to each of the third-level indicators; Calculate the health evaluation index according to the secondary evaluation index weight vector, the tertiary evaluation index weight vector and the scores of each tertiary index; According to the agricultural product traceability platform, a link operation is performed to generate a traceability mark so that the agricultural product health evaluation index can be obtained by scanning the traceability mark.

[0006] Preferably, the collection of agricultural product characteristic data includes: conducting farm environment evaluation through farm environment image data collected by networked cameras to obtain farm environment data; obtaining farm epidemic prevention data through farm vaccination records; obtaining farm feed health data through farm feed date data and feed composition data; obtaining quarantine data through quarantine results of quarantine agencies; obtaining processing technology data through processing plant environment image data collected by networked cameras; obtaining logistics time data through logistics data collected by satellite positioning; obtaining transportation temperature data through networked temperature sensors on logistics vehicles; obtaining storage time data through processing start time and current time; obtaining storage temperature data through networked temperature sensors installed on sales counters.

[0007] Preferably, the importance distinction operation is performed based on the first-level indicator, the second-level indicator and the third-level indicator to obtain a second-level judgment matrix, including: comparing the relative importance of each two factors in the second-level indicator to obtain a first comparison result; constructing a n×n The first comparison result is filled into the matrix to obtain a secondary judgment matrix; n is the number of secondary indicators.

[0008] Preferably, the importance differentiation operation is performed based on the first-level indicator, the second-level indicator and the third-level indicator to obtain a third-level judgment matrix, including: comparing the relative importance of each two factors in the third-level indicators belonging to the same second-level indicator to obtain a second comparison result; constructing n indivual m×m The second comparison result is filled into the matrix to obtain n A three-level judgment matrix; among them, m is the number of third-level indicators under a second-level indicator. n is the number of secondary indicators.

[0009] Preferably, the weight calculation is performed according to the secondary judgment matrix and the tertiary judgment matrix to obtain the secondary evaluation index weight vector, including: Multiply each row element of the secondary judgment matrix to get the product calculate of square root The calculated vector Do normalization Constructing the weight vector of secondary evaluation index ; in, Represents each element in the judgment matrix, where is the number of secondary indicators, Indicates the relative weight of each secondary indicator.

[0010] Preferably, the weight calculation is performed according to the secondary judgment matrix and the tertiary judgment matrix to obtain the tertiary evaluation index weight vector, including: Multiply each row element of each three-level judgment matrix to get the product calculate of square root The calculated vector Do normalization Constructing the weight vector of three-level evaluation indicators The number of weight vectors of the third-level evaluation indicators is the same as the number of the second-level indicators. n indivual; in, Represents each element in the judgment matrix, m is the number of third-level indicators under a second-level indicator. n is the number of secondary indicators, Indicates the relative weight of each third-level indicator.

[0011] Preferably, the comparing the third-level indicators with the pre-acquired third-level indicator standard data to obtain the third-level indicator scores corresponding to each of the third-level indicators includes: Assign full marks to the third-level indicator standard data; The farm environmental data, the farm epidemic prevention data, the farm feed health data, the quarantine data, the processing technology data, the logistics time data, the transportation temperature data, the storage time data and the storage temperature data are compared with the three-level indicator standard data, and points are assigned according to the degree of compliance to obtain the scores of each three-level indicator.

[0012] Preferably, the health evaluation index is calculated based on the secondary evaluation index weight vector, the tertiary evaluation index weight vector and each of the tertiary index scores, including: performing weight calculation based on each of the tertiary index scores and the tertiary evaluation index weight vector to obtain each secondary index score; performing weight calculation based on each of the secondary index scores and the secondary evaluation index weight vector to obtain a primary evaluation index score, and determining the primary evaluation index score as the agricultural product health evaluation index.

[0013] Preferably, the agricultural product traceability platform is used to perform a linking operation to generate a traceability mark so as to obtain the agricultural product health evaluation index by scanning the traceability mark, including: associating the agricultural product characteristic data with a unique identifier and storing it in a database; assigning the generated identifier to the product so as to obtain the agricultural product health evaluation index by scanning the traceability mark.

[0014] In a second aspect, the present invention provides a system for agricultural product traceability management, comprising: Information collection module, collecting characteristic data of agricultural products; The agricultural product characteristic data includes breeding data, processing data, transportation data and storage data. The breeding data includes breeding farm environment data, breeding farm epidemic prevention data and breeding farm feed health data. The processing data includes quarantine data and processing technology data. The transportation data includes logistics time data and transportation temperature data. The storage data includes storage time data and storage temperature data. An information uploading module uploads the characteristic data of the agricultural products to an agricultural product traceability platform, wherein the agricultural product traceability platform is built based on blockchain technology; The data preprocessing module divides the health evaluation index into a primary index, divides the breeding data, the processing data, the transportation data and the storage data into a secondary index, and divides the farm environment data, the farm epidemic prevention data, the farm feed health data, the quarantine data, the processing technology data, the logistics time data, the transportation temperature data, the storage time data and the storage temperature data into a tertiary index; The data processing module performs an importance differentiation operation based on the primary indicators, the secondary indicators and the tertiary indicators to obtain a secondary judgment matrix and a tertiary judgment matrix; performs weight calculation based on the secondary judgment matrix and the tertiary judgment matrix to obtain a secondary evaluation indicator weight vector and a tertiary evaluation indicator weight vector; compares the tertiary indicators with the pre-acquired tertiary indicator standard data to obtain a tertiary indicator score corresponding to each of the tertiary indicators; calculates the health evaluation indicator based on the secondary evaluation indicator weight vector, the tertiary evaluation indicator weight vector and each of the tertiary indicator scores; The traceability mark generation module performs a link operation according to the agricultural product traceability platform to generate a traceability mark so as to obtain the agricultural product health evaluation index by scanning the traceability mark.

[0015] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements any one of the above-mentioned methods for traceability management of agricultural products when executing the computer program.

[0016] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for agricultural product traceability management.

[0017] Compared with the prior art, the present invention has the following beneficial effects: an embodiment of the invention provides a method and system for agricultural product traceability management, the method being executed by a processor, including collecting agricultural product characteristic data; wherein the agricultural product characteristic data includes breeding data, processing data, transportation data and storage data, the breeding data includes farm environment data, farm epidemic prevention data and farm feed health data, the processing data includes quarantine data and processing technology data, the transportation data includes logistics time data and transportation temperature data, and the storage data includes storage time data and storage temperature data; the agricultural product characteristic data is uploaded to an agricultural product traceability platform, and the agricultural product traceability platform is built based on blockchain technology; the health evaluation index is divided into primary indicators, the breeding data, the processing data, the transportation data and the storage data are divided into secondary indicators, and the farm environment data, the farm epidemic prevention data, the breeding data The farm feed health data, the quarantine data, the processing technology data, the logistics time data, the transportation temperature data, the storage time data and the storage temperature data are divided into three-level indicators; based on the first-level indicators, the second-level indicators and the third-level indicators, an importance distinction operation is performed to obtain a second-level judgment matrix and a third-level judgment matrix; according to the second-level judgment matrix and the third-level judgment matrix, a weight calculation is performed to obtain a second-level evaluation indicator weight vector and a third-level evaluation indicator weight vector; the third-level indicators are compared with the pre-acquired third-level indicator standard data to obtain the third-level indicator scores corresponding to each of the third-level indicators; according to the second-level evaluation indicator weight vector, the third-level evaluation indicator weight vector and each of the third-level indicator scores, a health evaluation indicator is calculated; according to the agricultural product traceability platform, a link operation is performed to generate a traceability mark, so as to obtain the agricultural product health evaluation index by scanning the traceability mark.

[0018] The present invention comprehensively considers breeding data, processing data, transportation data and storage data, and provides an agricultural product traceability management method that can realize the health evaluation of agricultural products. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the method flow of agricultural product traceability management provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of the system structure of agricultural product traceability management provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Reference Figure 1 The first embodiment of the present invention provides a method for agricultural product traceability management, comprising the following steps: S11, collect characteristic data of agricultural products; S12, uploading the agricultural product characteristic data to an agricultural product traceability platform, wherein the agricultural product traceability platform is built based on blockchain technology; S13, dividing the health evaluation index into a primary index, dividing the breeding data, the processing data, the transportation data and the storage data into a secondary index, and dividing the farm environment data, the farm epidemic prevention data, the farm feed health data, the quarantine data, the processing technology data, the logistics time data, the transportation temperature data, the storage time data and the storage temperature data into a tertiary index; S14, performing importance differentiation operation based on the primary index, the secondary index and the tertiary index to obtain a secondary judgment matrix and a tertiary judgment matrix; S15, performing weight calculation according to the secondary judgment matrix and the tertiary judgment matrix to obtain a secondary evaluation index weight vector and a tertiary evaluation index weight vector; S16, comparing the third-level indicators with pre-acquired third-level indicator standard data to obtain third-level indicator scores corresponding to each of the third-level indicators; S17, calculating a health evaluation index according to the secondary evaluation index weight vector, the tertiary evaluation index weight vector and each of the tertiary index scores; S18, performing a link operation according to the agricultural product traceability platform to generate a traceability mark, so as to obtain the agricultural product health evaluation index by scanning the traceability mark.

[0022] In step S11, agricultural product characteristic data is collected.

[0023] It should be noted that the agricultural product characteristic data includes breeding data, processing data, transportation data and storage data. The breeding data includes farm environment data, farm epidemic prevention data and farm feed health data. The processing data includes quarantine data and processing technology data. The transportation data includes logistics time data and transportation temperature data. The storage data includes storage time data and storage temperature data.

[0024] Furthermore, the collection of agricultural product characteristic data includes: conducting farm environment evaluation through farm environment image data collected by networked cameras to obtain farm environment data; obtaining farm epidemic prevention data through farm vaccination records; obtaining farm feed health data through farm feed date data and feed composition data; obtaining quarantine data through quarantine results of quarantine agencies; obtaining processing technology data through processing plant environment image data collected by networked cameras; obtaining logistics time data through logistics data collected by satellite positioning; obtaining transportation temperature data through networked temperature sensors on logistics vehicles; obtaining storage time data through processing start time and current time; and obtaining storage temperature data through networked temperature sensors installed on sales counters.

[0025] In step S12, the agricultural product characteristic data is uploaded to an agricultural product traceability platform, and the agricultural product traceability platform is built based on blockchain technology.

[0026] It should be noted that the agricultural product traceability platform built on blockchain technology has several significant advantages. First, blockchain technology ensures the transparency and immutability of data, ensures that the data records in each link are authentic and reliable, and prevents tampering and falsification; second, consumers can obtain detailed information about agricultural products through the platform, including various data in the production, processing, and circulation process. This transparency greatly enhances consumers' trust in the products. Due to the decentralized nature of blockchain, data management does not rely on a single intermediary agency, reducing the risk of human intervention; in addition, the information on the blockchain is open and shared, which helps to improve the efficiency of collaboration among all parties in the supply chain and reduce the problem of information asymmetry in data transmission. At the same time, through smart contracts, automated process control can be achieved to improve operational efficiency; the traceability of blockchain technology ensures that the source of agricultural products is controllable, making the entire supply chain process more standardized and transparent, and effectively promoting the improvement of agricultural product quality and the protection of consumer rights.

[0027] In step S14, an importance distinction operation is performed based on the primary index, the secondary index, and the tertiary index to obtain a secondary judgment matrix and a tertiary judgment matrix.

[0028] The importance differentiation operation is performed based on the first-level indicator, the second-level indicator and the third-level indicator to obtain a second-level judgment matrix and a third-level judgment matrix, including: comparing the relative importance of each two factors in the second-level indicator to obtain a first comparison result; constructing a n×n The first comparison result is filled into the matrix to obtain a secondary judgment matrix; n is the number of secondary indicators.

[0029] In an optional embodiment, the importance differentiation operation is performed based on the first-level indicator, the second-level indicator and the third-level indicator to obtain a three-level judgment matrix, including: comparing the relative importance of each two factors in the third-level indicators belonging to the same second-level indicator to obtain a second comparison result; constructing n indivual m×m The second comparison result is filled into the matrix to obtain n A three-level judgment matrix; among them, m is the number of third-level indicators under a second-level indicator. n is the number of secondary indicators.

[0030] In step S15, weight calculation is performed based on the secondary judgment matrix and the tertiary judgment matrix to obtain the secondary evaluation index weight vector and the tertiary evaluation index weight vector. In an optional embodiment, performing weight calculation according to the secondary judgment matrix and the tertiary judgment matrix to obtain a secondary evaluation index weight vector includes: Multiply each row element of the secondary judgment matrix to get the product calculate of Power The calculated vector Do normalization Constructing the weight vector of secondary evaluation index ; in, Represents each element in the judgment matrix, where is the number of secondary indicators, Indicates the relative weight of each secondary indicator.

[0031] In an optional embodiment, performing weight calculation according to the secondary judgment matrix and the tertiary judgment matrix to obtain a tertiary evaluation index weight vector includes: Multiply each row element of each three-level judgment matrix to get the product calculate of square root The calculated vector Do normalization Constructing the weight vector of three-level evaluation indicators The number of weight vectors of the third-level evaluation indicators is the same as the number of the second-level indicators. n indivual; in, Represents each element in the judgment matrix, m is the number of third-level indicators under a second-level indicator. n is the number of secondary indicators, Indicates the relative weight of each third-level indicator. It should be noted that the advantage of this method is that it ensures the scientificity and rationality of weight distribution through precise mathematical calculations and matrix processing. By multiplying each row of elements in the secondary and tertiary judgment matrices to obtain the product, and through square root and normalization processing, the relative importance of each indicator can be effectively reflected. Using such a hierarchical calculation method, the weight vectors of the secondary and tertiary evaluation indicators can be gradually derived, ensuring the reasonable distribution of weights between indicators at different levels. This method is particularly suitable for complex evaluation systems. It can quantify the impact of multiple indicators and ensure that the sum of weights is 1 through normalization processing, making the evaluation process consistent and standardized. In addition, by independently processing the judgment matrix at each level, the flexibility and accuracy of the evaluation system are improved, which is suitable for application in the evaluation scenario of agricultural product health indicators.

[0032] In step S16, the third-level indicators are compared with the pre-acquired third-level indicator standard data to obtain the third-level indicator scores corresponding to each of the third-level indicators.

[0033] In an optional embodiment, full marks are assigned to the third-level indicator standard data; the farm environmental data, the farm epidemic prevention data, the farm feed health data, the quarantine data, the processing technology data, the logistics time data, the transportation temperature data, the storage time data and the storage temperature data are compared with the third-level indicator standard data, and points are assigned according to the degree of compliance to obtain scores for each third-level indicator.

[0034] It should be noted that the relative importance comparison is scored using a 1-9 scale, where 1 represents that two factors are equally important, 3 represents that one factor is slightly more important than the other, 5 represents that one factor is obviously more important than the other, 7 represents that one factor is strongly more important than the other, 9 represents that one factor is absolutely more important than the other, and 2, 4, 6, and 8 are intermediate values ​​between each judgment.

[0035] It should be noted that the advantage of this method is that it effectively ensures the objectivity and meticulousness of the evaluation results by scoring each three-level indicator through scientific full score assignment and 1-9 scale method. First, the full score assignment of the three-level indicator standard data provides a clear reference standard, so that the scoring of actual data can be based on a unified benchmark, avoiding subjectivity and arbitrariness. Secondly, by comparing the data of farm environment, epidemic prevention, feed health, quarantine, processing technology, logistics time, transportation temperature, storage time and storage temperature with the standard data, and assigning points according to the degree of compliance, the scientificity and accuracy of the scoring are guaranteed. The use of the 1-9 scale method allows the relative importance of each factor to be clearly quantitatively described, and the refinement of the scale makes the scoring more flexible and can accurately distinguish the differences between different factors. In addition, the standardization of the scale method provides consistency in the evaluation process, allowing different evaluators to make judgments based on the same scale, reducing subjective bias in the scoring. In summary, this method not only realizes multi-dimensional comprehensive evaluation, but also improves the fairness and transparency of the evaluation of agricultural product health indicators.

[0036] In step S17, the health evaluation index is calculated based on the secondary evaluation index weight vector, the tertiary evaluation index weight vector and the scores of each tertiary index. In an optional embodiment, the health evaluation index is calculated based on the secondary evaluation index weight vector, the tertiary evaluation index weight vector and each of the tertiary index scores, including: performing weight calculation based on each of the tertiary index scores and the tertiary evaluation index weight vector to obtain each secondary index score; performing weight calculation based on each of the secondary index scores and the secondary evaluation index weight vector to obtain a primary evaluation index score, and determining the primary evaluation index score as the agricultural product health evaluation index.

[0037] Furthermore, the specific steps of the calculation method include: Calculate the secondary indicator score and perform weight calculation based on the scores of each tertiary indicator and the corresponding tertiary evaluation indicator weight vector. The specific steps are to multiply the score of each tertiary indicator by its corresponding weight value, and then add up the weighted scores of all tertiary indicators to get the corresponding secondary indicator score. The formula is: Calculate the first-level evaluation index score, and perform weight calculation based on the scores of each second-level index and the corresponding second-level evaluation index weight vector. The specific steps are to multiply the score of each second-level index by its corresponding weight value, and then add up the weighted scores of all second-level indicators to get the final first-level evaluation index score. The formula is: in, mis the number of third-level indicators under a second-level indicator. n is the number of secondary indicators, is the third-level indicator score, is the weight of the third-level indicator, is the score of each secondary indicator, is the secondary indicator weight, It is the first-level evaluation index score.

[0038] Determine the health evaluation index, and determine the final calculated first-level evaluation index score as the health evaluation index of agricultural products. This index combines the weighted scores of all third-level, second-level and first-level indicators, and can comprehensively and comprehensively reflect the health status of agricultural products.

[0039] In step S18, a link operation is performed according to the agricultural product traceability platform to generate a traceability mark, so that the agricultural product health evaluation index can be obtained by scanning the traceability mark.

[0040] In an optional embodiment, the agricultural product traceability platform is used to perform a linking operation to generate a traceability mark so that the agricultural product health evaluation index can be obtained by scanning the traceability mark, including: associating the agricultural product characteristic data with a unique identifier and storing it in a database; assigning the generated identifier to the product so as to obtain the agricultural product health evaluation index by scanning the traceability mark.

[0041] It should be noted that in the process of generating traceability marks based on the agricultural product traceability platform, multiple traceability mark types can be selected, including: QR code is a widely used type of traceability mark, which has the characteristics of large data capacity and can accommodate a variety of information. Consumers and related parties can quickly obtain health evaluation indicators, production information and logistics data of agricultural products by scanning QR codes with smartphones. QR codes are simple to generate, easy to promote, and support a large amount of information storage.

[0042] Barcodes are usually used to identify basic information of products, such as product numbers or batch numbers. Although barcodes store a small amount of information, they are widely used in retail systems and can be quickly read by scanners, making them suitable for use in large quantities of goods.

[0043] NFC tags, NFC (near field communication) tags can be embedded in agricultural product packaging or labels. Consumers can obtain the health evaluation indicators of products by scanning them at close range using NFC-enabled devices (such as smartphones). NFC tags have anti-counterfeiting functions that can prevent forgery or tampering, and are suitable for high-value agricultural product traceability.

[0044] RFID tags, RFID (Radio Frequency Identification) tags are another advanced traceability tagging technology that uses radio frequency signals for identification and reading. RFID tags can store more agricultural product information, and do not need to be directly aligned with the reading device. They have a fast reading speed and are suitable for automated management and tracking of bulk commodities. RFID is widely used in supply chain and logistics management.

[0045] Digital watermarking is a technology that hides information in images or packaging designs. Users can decode health evaluation indicators and traceability data by scanning the appearance image of the product through a special application. This mark is not easy to detect and has anti-counterfeiting and anti-tampering functions, suitable for high-end or special products.

[0046] Digital fingerprint is a unique identifier generated based on physical or digital features, usually used in combination with machine learning or artificial intelligence technology. By encoding the physical characteristics of the agricultural product itself, such as shape, texture, etc., a unique traceability mark is generated. Consumers can obtain the corresponding health evaluation information by scanning the feature. Digital fingerprints are highly personalized and anti-counterfeiting, and are suitable for high-precision traceability needs.

[0047] It should be noted that the type of traceability mark should be selected based on factors such as product characteristics, application scenarios, data security requirements, consumer usage habits, and technical support capabilities. Overall, QR codes and NFC tags are commonly used traceability mark types and are suitable for most scenarios, while for high-end or special agricultural products, digital watermarks and RFID tags can provide stronger anti-counterfeiting and traceability capabilities.

[0048] By associating the characteristic data of agricultural products with unique identifiers and storing them in a secure database, the uniqueness and security of the data are ensured, and data loss or tampering is prevented. At the same time, combined with blockchain technology, the immutability of the data is further improved, and the credibility and transparency of the data are enhanced. The generated unique identifier is assigned to a specific product, and users can quickly obtain the health evaluation indicators of agricultural products by scanning the traceability mark, which greatly simplifies the information acquisition process, improves the user experience, and helps consumers fully understand the health status of the purchased agricultural products, thereby enhancing their trust in product quality. In addition, the binding of digital management and unique identifiers also improves the operational efficiency of the supply chain, realizes the automation and efficiency of data tracking, product certification and quality monitoring, and promotes the transparency and standardization of the entire agricultural product supply chain.

[0049] The embodiment of the present invention provides a method and system for agricultural product traceability management, the method is executed by a processor, and includes collecting agricultural product characteristic data; wherein the agricultural product characteristic data includes breeding data, processing data, transportation data and storage data, the breeding data includes farm environment data, farm epidemic prevention data and farm feed health data, the processing data includes quarantine data and processing technology data, the transportation data includes logistics time data and transportation temperature data, and the storage data includes storage time data and storage temperature data; uploading the agricultural product characteristic data to an agricultural product traceability platform, the agricultural product traceability platform is built based on blockchain technology; dividing health evaluation indicators into primary indicators, dividing the breeding data, the processing data, the transportation data and the storage data into secondary indicators, and dividing the farm environment data, the farm epidemic prevention data, the farm feed health data, the The quarantine data, the processing technology data, the logistics time data, the transportation temperature data, the storage time data and the storage temperature data are divided into three-level indicators; based on the first-level indicators, the second-level indicators and the third-level indicators, an importance distinction operation is performed to obtain a second-level judgment matrix and a third-level judgment matrix; according to the second-level judgment matrix and the third-level judgment matrix, a weight calculation is performed to obtain a second-level evaluation indicator weight vector and a third-level evaluation indicator weight vector; the third-level indicators are compared with the pre-acquired third-level indicator standard data to obtain the third-level indicator scores corresponding to each of the third-level indicators; according to the second-level evaluation indicator weight vector, the third-level evaluation indicator weight vector and each of the third-level indicator scores, a health evaluation indicator is calculated; according to the agricultural product traceability platform, a link operation is performed to generate a traceability mark, so as to obtain the agricultural product health evaluation index by scanning the traceability mark.

[0050] The present invention comprehensively considers breeding data, processing data, transportation data and storage data, and provides an agricultural product traceability management method that can realize the health evaluation of agricultural products.

[0051] Reference Figure 2 The second embodiment of the present invention provides a system for tracing the source of agricultural products, including: Information collection module, collecting characteristic data of agricultural products; The agricultural product characteristic data includes breeding data, processing data, transportation data and storage data. The breeding data includes breeding farm environment data, breeding farm epidemic prevention data and breeding farm feed health data. The processing data includes quarantine data and processing technology data. The transportation data includes logistics time data and transportation temperature data. The storage data includes storage time data and storage temperature data. An information uploading module uploads the agricultural product characteristic data to an agricultural product traceability platform, wherein the agricultural product traceability platform is built based on blockchain technology; The data preprocessing module divides the health evaluation index into a primary index, divides the breeding data, the processing data, the transportation data and the storage data into a secondary index, and divides the farm environment data, the farm epidemic prevention data, the farm feed health data, the quarantine data, the processing technology data, the logistics time data, the transportation temperature data, the storage time data and the storage temperature data into a tertiary index; The data processing module performs an importance differentiation operation based on the primary indicators, the secondary indicators and the tertiary indicators to obtain a secondary judgment matrix and a tertiary judgment matrix; performs weight calculation based on the secondary judgment matrix and the tertiary judgment matrix to obtain a secondary evaluation indicator weight vector and a tertiary evaluation indicator weight vector; compares the tertiary indicators with the pre-acquired tertiary indicator standard data to obtain a tertiary indicator score corresponding to each of the tertiary indicators; calculates the health evaluation indicator based on the secondary evaluation indicator weight vector, the tertiary evaluation indicator weight vector and each of the tertiary indicator scores; The traceability mark generation module performs a link operation according to the agricultural product traceability platform to generate a traceability mark so as to obtain the agricultural product health evaluation index by scanning the traceability mark.

[0052] It should be noted that the agricultural product traceability management system provided in the embodiment of the present invention is used to execute all the process steps of the agricultural product traceability management method of the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be described in detail.

[0053] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data acquisition program. When the processor executes the computer program, the steps in the above-mentioned agricultural product traceability management method embodiments are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0054] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0055] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0056] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0057] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0058] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0059] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0060] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for agricultural product traceability management, characterized in that: Executed by the processor, including: Collecting data on agricultural product characteristics; The agricultural product characteristic data includes breeding data, processing data, transportation data and storage data. The breeding data includes breeding farm environment data, breeding farm epidemic prevention data and breeding farm feed health data. The processing data includes quarantine data and processing technology data. The transportation data includes logistics time data and transportation temperature data. The storage data includes storage time data and storage temperature data. Uploading the agricultural product characteristic data to an agricultural product traceability platform, wherein the agricultural product traceability platform is built based on blockchain technology; The health evaluation index is divided into the first-level index, the breeding data, the processing data, the transportation data and the storage data are divided into the second-level index, and the farm environment data, the farm epidemic prevention data, the farm feed health data, the quarantine data, the processing technology data, the logistics time data, the transportation temperature data, the storage time data and the storage temperature data are divided into the third-level index; Based on the first-level index, the second-level index and the third-level index, an importance distinction operation is performed to obtain a second-level judgment matrix and a third-level judgment matrix; Perform weight calculation according to the secondary judgment matrix and the tertiary judgment matrix to obtain a secondary evaluation index weight vector and a tertiary evaluation index weight vector; Compare the third-level indicators with the pre-acquired third-level indicator standard data to obtain the third-level indicator scores corresponding to each of the third-level indicators; Calculate the health evaluation index according to the secondary evaluation index weight vector, the tertiary evaluation index weight vector and the scores of each tertiary index; According to the agricultural product traceability platform, a link operation is performed to generate a traceability mark so that the agricultural product health evaluation index can be obtained by scanning the traceability mark.

2. The method for agricultural product traceability management according to claim 1, characterized in that: The collecting of agricultural product characteristic data includes: The farm environment image data collected by the networked camera is used to evaluate the farm environment and obtain the farm environment data; Obtain farm epidemic prevention data through farm vaccination records; Obtain farm feed health data through farm feed date data and feed composition data; Obtain quarantine data through quarantine results from quarantine agencies; Collect image data of the processing environment through networked cameras to obtain processing data; The logistics data collected through satellite positioning can be used to obtain logistics time data; The transportation temperature data is obtained through the networked temperature sensor on the logistics vehicle; Obtain storage time data through processing start time and current time; The storage temperature data is obtained through the networked temperature sensor installed on the sales cabinet.

3. The method for agricultural product traceability management according to claim 1, characterized in that: The importance differentiation operation is performed based on the first-level indicator, the second-level indicator and the third-level indicator to obtain a second-level judgment matrix, including: Compare the relative importance of each two factors in the secondary indicators to obtain the first comparison result; Build a n×n Matrix, fill the first comparison result into the matrix to obtain a secondary judgment matrix; in n is the number of secondary indicators.

4. The method for agricultural product traceability management according to claim 1, characterized in that: The importance differentiation operation is performed based on the first-level indicator, the second-level indicator and the third-level indicator to obtain a three-level judgment matrix, including: Compare the relative importance of every two factors in the third-level indicators belonging to the same second-level indicator to obtain the second comparison result; Build n indivual m×m The second comparison result is filled into the matrix to obtain n A three-level judgment matrix; in, m is the number of third-level indicators under a second-level indicator. n is the number of secondary indicators.

5. The method for agricultural product traceability management according to claim 1, characterized in that: The weight calculation is performed according to the secondary judgment matrix and the tertiary judgment matrix to obtain the secondary evaluation index weight vector, including: Multiply each row element of the secondary judgment matrix to get the product calculate of square root The calculated vector Do normalization Constructing the weight vector of secondary evaluation index ; in, Represents each element in the judgment matrix, where is the number of secondary indicators, Indicates the relative weight of each secondary indicator.

6. The method for agricultural product traceability management according to claim 1, characterized in that: The weight calculation is performed according to the secondary judgment matrix and the tertiary judgment matrix to obtain the tertiary evaluation index weight vector, including: Multiply each row element of each three-level judgment matrix to get the product calculate of square root The calculated vector Do normalization Constructing the weight vector of three-level evaluation indicators The number of weight vectors of the third-level evaluation indicators is the same as the number of the second-level indicators. n indivual; in, Represents each element in the judgment matrix, m is the number of third-level indicators under a second-level indicator. n is the number of secondary indicators, Indicates the relative weight of each third-level indicator.

7. The method for agricultural product traceability management according to claim 1, characterized in that: The step of comparing the third-level indicators with the pre-acquired third-level indicator standard data to obtain the third-level indicator scores corresponding to each of the third-level indicators includes: Assign full marks to the third-level indicator standard data; The farm environmental data, the farm epidemic prevention data, the farm feed health data, the quarantine data, the processing technology data, the logistics time data, the transportation temperature data, the storage time data and the storage temperature data are compared with the three-level indicator standard data, and points are assigned according to the degree of compliance to obtain the scores of each three-level indicator.

8. The method for agricultural product traceability management according to claim 1, characterized in that: The health evaluation index is calculated based on the secondary evaluation index weight vector, the tertiary evaluation index weight vector and each of the tertiary index scores, including: Perform weight calculation based on the scores of each of the three-level indicators and the weight vectors of the three-level evaluation indicators to obtain scores of each of the two-level indicators; A weight calculation is performed based on the scores of each secondary indicator and the weight vector of the secondary evaluation indicator to obtain a primary evaluation indicator score, and the primary evaluation indicator score is determined as the agricultural product health evaluation indicator.

9. The method for agricultural product traceability management according to claim 1, characterized in that: The linking operation is performed according to the agricultural product traceability platform to generate a traceability mark, so as to obtain the agricultural product health evaluation index by scanning the traceability mark, including: Associating the agricultural product characteristic data with a unique identifier and storing the data in a database; The generated identifiers are assigned to products to obtain the health evaluation indicators of agricultural products by scanning the traceability marks.

10. A system for tracing the origin of agricultural products, characterized in that: include: Information collection module, collecting characteristic data of agricultural products; The agricultural product characteristic data includes breeding data, processing data, transportation data and storage data. The breeding data includes breeding farm environment data, breeding farm epidemic prevention data and breeding farm feed health data. The processing data includes quarantine data and processing technology data. The transportation data includes logistics time data and transportation temperature data. The storage data includes storage time data and storage temperature data. An information uploading module uploads the agricultural product characteristic data to an agricultural product traceability platform, wherein the agricultural product traceability platform is built based on blockchain technology; The data preprocessing module divides the health evaluation index into a primary index, divides the breeding data, the processing data, the transportation data and the storage data into a secondary index, and divides the farm environment data, the farm epidemic prevention data, the farm feed health data, the quarantine data, the processing technology data, the logistics time data, the transportation temperature data, the storage time data and the storage temperature data into a tertiary index; The data processing module performs an importance differentiation operation based on the primary indicators, the secondary indicators and the tertiary indicators to obtain a secondary judgment matrix and a tertiary judgment matrix; performs weight calculation based on the secondary judgment matrix and the tertiary judgment matrix to obtain a secondary evaluation indicator weight vector and a tertiary evaluation indicator weight vector; compares the tertiary indicators with the pre-acquired tertiary indicator standard data to obtain a tertiary indicator score corresponding to each of the tertiary indicators; calculates the health evaluation indicator based on the secondary evaluation indicator weight vector, the tertiary evaluation indicator weight vector and each of the tertiary indicator scores; The traceability mark generation module performs a link operation according to the agricultural product traceability platform to generate a traceability mark so as to obtain the agricultural product health evaluation index by scanning the traceability mark.

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