Method and system for tracing livestock meat production information

Abstract

The invention discloses a livestock meat production information traceability method and system, and belongs to the technical field of information traceability. The method comprises the following steps: obtaining juvenile data of each livestock; uploading the preprocessed young hour data to a block chain; obtaining actual slaughtering data of each livestock; constructing a livestock growth model, and obtaining a similarity index 1 based on the livestock growth model; obtaining actual slaughter data of each livestock, constructing a livestock transportation model, and obtaining a similarity index 2 based on the livestock transportation model; and generating a traceability two-dimensional code according to the data of each livestock. According to the invention, the data of each livestock entering a farm, the data of each livestock entering a transport vehicle and the data of each livestock entering a slaughter house are obtained, the data are preprocessed, and the corresponding livestock growth model and the corresponding livestock transportation model are constructed, so that the data of each stage of the livestock are accurately analyzed and judged; therefore, precise traceability and data accuracy verification of livestock meat production information are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of information traceability, and in particular relates to a method and system for tracing livestock meat production information. Background Art

[0002] As consumers pay more and more attention to food safety and quality, the transparency and traceability of livestock meat production have become increasingly important. Especially in the context of frequent food safety issues, being able to effectively trace the entire process of livestock from breeding to slaughter and ensure product quality and safety has become the key to enhancing industry trust and market competitiveness.

[0003] Existing livestock information traceability systems record and track livestock data through Internet of Things (IoT) technology, blockchain and big data analysis. These systems mainly collect livestock growth data, transportation data and slaughter data through integrated sensors, RFID tags and other technologies, and then use blockchain technology to store and manage data, thereby ensuring the data is tamper-proof and transparent.

[0004] In the prior art, CN103903025B discloses a method and application for transmitting traceability information during the slaughter and cutting of livestock, including: associating the traceability information of the original electronic identification on the livestock carcass with the main electronic identification transmitted along with the livestock carcass; when the livestock carcass is cut, attaching a sub-electronic identification to each cut meat part, and the traceability information associated with the main electronic identification and the processing and flow information of the meat part are associated with the sub-electronic identification; based on the associated information of the sub-electronic identification attached to the meat part, producing a packaging label with all the associated information on the sub-electronic identification; and removing the sub-electronic identification on the meat part, packaging the meat part into a packaged meat product, and setting the packaging label on the packaged meat product.

[0005] However, the existing livestock information traceability technology still has some defects when it is implemented: In the existing technology, the blockchain itself cannot guarantee the accuracy of data collection. During the data entry process, there may be human errors or equipment failures, resulting in wrong data being uploaded to the blockchain, and there are problems such as inaccurate traceability information, which affects consumers' trust. Summary of the invention

[0006] In view of the above deficiencies in the prior art, the purpose of the present invention is to provide a method and system for tracing livestock meat production information, which realizes the precise traceability of livestock meat production information and data accuracy verification, and effectively solves the problems of incomplete, inaccurate and difficult to effectively supervise meat traceability information in the prior art.

[0007] To achieve the above object, the present invention provides a method for tracing livestock meat production information, comprising the following steps: Obtain the childhood data of each livestock when it enters the breeding farm as a puppy; Preprocess the childhood data and upload the preprocessed childhood data to the blockchain; Obtaining adult data of each livestock, and preprocessing the adult data, and obtaining a health index of each livestock based on the preprocessed adult data, the health index being used to judge whether the livestock is in a healthy state when it matures; If the health index is lower than the set health threshold, the livestock will not be transported; if the health index is not lower than the set health threshold, the livestock will be allowed to be transported; The data of each livestock entering the transport vehicle is obtained to obtain the actual slaughter data of each livestock, and the actual slaughter data of each livestock corresponds to the data of each livestock when it was young. The actual slaughter data is preprocessed to obtain the actual slaughter data after preprocessing; A livestock growth model is constructed, and a similarity index of one is obtained based on the livestock growth model; Obtaining data of each livestock when it enters the slaughterhouse, obtaining actual slaughter data of each livestock, and the actual slaughter data of each livestock corresponds one-to-one with the actual slaughter data of each livestock, preprocessing the actual slaughter data, and obtaining the actual slaughter data after preprocessing; A livestock transportation model is constructed, and a similarity index 2 is obtained based on the livestock transportation model; The childhood data of each livestock, the actual market data of each livestock, and the actual slaughter data of each livestock in the blockchain are recorded as the traceability data of each livestock, and the traceability data of each livestock is used to generate a traceability QR code.

[0008] Preferably, obtaining the health index of each livestock based on the pre-processed adult data comprises the following steps: Obtain the body temperature stability, fur glossiness, and blood index levels of each livestock at adulthood, and perform pretreatment; The weight factors of body temperature stability, fur glossiness, and blood index levels after pretreatment on the health index were obtained through objective weighting method; The health index is obtained through the health index formula, which is expressed as: ; In the formula, JK is the health index, TWD is the body temperature stability, is the weight factor of TWD to JK, PM is the fur glossiness, is the weight factor of PM to JK, XY is the blood index level, is the weight factor of XY to JK.

[0009] Preferably, constructing a livestock growth model comprises the following steps: Obtaining historical basic data on livestock, including historical basic data on the young of different breeds of livestock and historical basic data on the slaughter of different breeds of livestock, and making one-to-one correspondence between the historical basic data on the young of different breeds of livestock and the historical basic data on the slaughter of different breeds of livestock; Preprocessing of historical livestock basic data; Based on the pre-processed historical livestock basic data, the training set and the test set are divided; The training set is input into the support vector machine algorithm model for training to obtain a preliminary livestock growth model; The test set is input into the preliminary livestock growth model for testing to obtain the livestock growth model.

[0010] Preferably, obtaining a similarity index based on a livestock growth model comprises the following steps: Based on the livestock growth model, the pre-processed childhood data are input one by one, and the ideal market data of each livestock is output accordingly; The ideal slaughter data of each livestock is compared with the corresponding actual slaughter data after preprocessing, and the similarity index 1 between the ideal slaughter data of each livestock and the corresponding actual slaughter data after preprocessing is obtained. The similarity index 1 is used to judge the data accuracy of the data of each livestock when it was young; If the similarity index is not lower than the set similarity threshold, the actual market data will be uploaded to the blockchain. If there is a similarity index that is lower than the set similarity threshold, the staff will be notified to review the livestock to determine the accuracy of the livestock's childhood data. If the data is correct, it will be uploaded to the blockchain. If the data is incorrect, the livestock will not be transported.

[0011] Preferably, the similarity index 1 acquisition step is: Obtain the weight difference, height difference, and length difference between the ideal slaughter data of each livestock and the corresponding actual slaughter data after preprocessing, and perform preprocessing; Obtain the weight factor of the pre-processed weight difference, height difference, and length difference to the similarity index 1 through the objective weighting method; Obtain similarity index 1 through similarity index formula 1; The similarity index formula 1 is: ; In the formula, is the similarity index 1, For weight difference, for right The weight factor of is the height difference, for right The weight factor of is the body length difference, for right The weight factor of .

[0012] Preferably, constructing the livestock transportation model comprises the following steps: Obtain historical basic livestock transportation data, including historical basic data on the sale of livestock of different breeds and historical basic data on the slaughter of livestock of different breeds, and match the historical basic data on the sale of livestock of different breeds with the historical basic data on the slaughter of livestock of different breeds; Preprocessing of historical livestock basic transportation data; Based on the pre-processed historical livestock basic transportation data, the training set and the test set are divided; The training set is input into the support vector machine algorithm model for training to obtain a preliminary livestock transportation model; The test set is input into the preliminary livestock transportation model for testing to obtain the livestock transportation model.

[0013] Preferably, obtaining similarity index 2 based on the livestock transportation model comprises the following steps: Based on the livestock transportation model, the actual slaughter data after preprocessing is input one by one, and the ideal slaughter data of each livestock is output accordingly; The ideal slaughter data of each livestock is compared with the corresponding actual slaughter data after preprocessing, and the similarity index 2 between the ideal slaughter data of each livestock and the corresponding actual slaughter data after preprocessing is obtained. The similarity index 2 is used to judge the data accuracy of the data of each livestock when it is young; If the similarity index 2 is not lower than the set similarity threshold 2, the actual slaughter data will be uploaded to the blockchain. If there is a similarity index 2 that is lower than the set similarity threshold 2, the staff will be notified to review the livestock to determine the accuracy of the livestock's childhood data. If the data accuracy is correct, it will be uploaded to the blockchain. If the data accuracy is incorrect, the livestock will not be slaughtered.

[0014] Preferably, the steps for obtaining the second similarity index are: Obtain the body temperature difference, blood pressure difference, and heart rate difference between the ideal slaughter data of each livestock and the corresponding actual slaughter data after preprocessing, and perform preprocessing; Obtain the weight factors of the pre-processed body temperature difference, blood pressure difference, and heart rate difference to the similarity index 2 through the objective weighting method; The similarity index 2 is obtained by similarity index formula 2.

[0015] Preferably, similarity index formula 2 is: ; In the formula, is the similarity index 2, is the temperature difference, for right The weight factor of For blood pressure difference, for right The weight factor of For heart rate difference, for right The weight factor of , e is a natural constant.

[0016] A livestock meat production information traceability system for implementing the above method, comprising: a data uploading module, a health detection module, a growth model building module, a transportation model building module, and a traceability code generation module; Among them, the data upload module is used to obtain the data of each livestock when it enters the breeding farm when it is young, and obtain the data of each livestock when it is young; Preprocess the childhood data and upload the preprocessed childhood data to the blockchain; The health detection module is used to obtain the adult data of each livestock and pre-process the adult data. Based on the pre-processed adult data, the health index of each livestock is obtained. The health index is used to judge whether the livestock is in a healthy state when it matures; If the health index is lower than the set health threshold, the livestock will not be transported; if the health index is not lower than the set health threshold, the livestock will be allowed to be transported; The growth model building module is used to obtain the data of each livestock when it enters the transport vehicle, obtain the actual slaughter data of each livestock, and the actual slaughter data of each livestock corresponds to the data of each livestock when it was young, pre-process the actual slaughter data, and obtain the actual slaughter data after pre-processing; A livestock growth model is constructed, and a similarity index of one is obtained based on the livestock growth model; The transportation model building module is used to obtain the data of each livestock entering the slaughterhouse, obtain the actual slaughter data of each livestock, and the actual slaughter data of each livestock corresponds to the actual slaughter data of each livestock, pre-process the actual slaughter data, and obtain the actual slaughter data after pre-processing; A livestock transportation model is constructed, and a similarity index 2 is obtained based on the livestock transportation model; The traceability code generation module is used to record the childhood data of each livestock, the actual market data of each livestock, and the actual slaughter data of each livestock in the blockchain as the traceability data of each livestock, and generate a traceability QR code from the traceability data of each livestock.

[0017] The present invention has the following beneficial effects: The present invention obtains data on each livestock when it enters the farm, the transport vehicle and the slaughterhouse when it is young, and preprocesses and constructs corresponding livestock growth models and livestock transportation models, so as to accurately analyze and judge the data of livestock at each stage, thereby realizing precise traceability of livestock meat production information and verification of data accuracy, effectively solving the problems of incomplete and inaccurate meat traceability information and difficulty in effective supervision in the prior art.

[0018] The present invention optimizes the livestock growth model and transportation model by utilizing the support vector machine algorithm, so that the data of each livestock can be compared with the actual data in real time through the similarity index, so as to promptly discover individuals that do not meet the standards, thereby ensuring that the livestock growth data and transportation process meet the preset standards and expectations, and improving the standardization of the entire production process and the traceability of the data.

[0019] The present invention uploads the data of each livestock and generates a traceability QR code through blockchain technology, ensuring that all growth, transportation, and slaughter data of each livestock are recorded in real time and can be queried at any time, thereby increasing consumers' trust in the safety of livestock meat products and effectively solving the problems of information opacity and the circulation of counterfeit and shoddy products in the current market. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 It is a schematic diagram of the structure of the system of the present invention. DETAILED DESCRIPTION

[0021] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0022] Embodiment 1: like Figure 1 As shown, a method for tracing livestock meat production information includes the following steps: obtaining the childhood data of each livestock when it enters the farm; preprocessing the childhood data, and uploading the preprocessed childhood data to the blockchain; obtaining the adult data of each livestock, and preprocessing the adult data, and obtaining the health index of each livestock based on the preprocessed adult data, and the health index is used to judge whether the livestock is in a healthy state when it grows up; if the health index is lower than the set health threshold, the livestock will not be transported, and if the health index is not lower than the set health threshold, the livestock is allowed to be transported.

[0023] The data of each livestock entering the transport vehicle is obtained to obtain the actual slaughter data of each livestock, and the actual slaughter data of each livestock corresponds one to one with the data of each livestock when it was young. The actual slaughter data is preprocessed to obtain the actual slaughter data after preprocessing; a livestock growth model is constructed, and a similarity index of one is obtained based on the livestock growth model.

[0024] The data of each livestock entering the slaughterhouse is obtained to obtain the actual slaughter data of each livestock, and the actual slaughter data of each livestock corresponds one-to-one with the actual slaughter data of each livestock. The actual slaughter data is preprocessed to obtain the preprocessed actual slaughter data; a livestock transportation model is constructed, and a similarity index 2 is obtained based on the livestock transportation model; the childhood data of each livestock, the actual slaughter data of each livestock, and the actual slaughter data of each livestock in the blockchain are recorded as the traceability data of each livestock, and a traceability QR code is generated from the traceability data of each livestock.

[0025] By performing pre-processing operations such as cleaning, sorting and standardizing the data of each livestock, the pre-processed livestock data is then sent to the blockchain network in an encrypted format through the interface of the blockchain node; the immutable and traceable characteristics of the blockchain can ensure the security and integrity of the data, and the authenticity of the traceability information, so that consumers and regulators can safely query and verify the source information of livestock meat.

[0026] By obtaining the health index of each livestock, we can judge whether each livestock is in a healthy state before transportation. If the health index is lower than the set health threshold, it means that the livestock is sick and will not be transported. This effectively prevents the spread of livestock diseases in the vehicle due to the presence of sick livestock in the transport vehicle during transportation, and ensures the health of the livestock before slaughter.

[0027] By constructing livestock growth models and livestock transportation models, the ideal livestock market data range under normal growth conditions can be predicted based on factors such as breed and initial weight, providing a scientific reference for the evaluation of livestock growth status in actual production, and analyzing the changing patterns of various indicators of livestock from market to slaughter, such as the fluctuation range of livestock physiological indicators under different transportation distances and transportation environments.

[0028] Based on the pre-processed adult data, the health index of each livestock is obtained, including the following steps: obtaining the body temperature stability, fur glossiness, and blood index level of each livestock at adulthood, and performing pre-processing; obtaining the weight factor of the pre-processed body temperature stability, fur glossiness, and blood index level on the health index through an objective weighting method; obtaining the health index through the health index formula, which is expressed as: ; In the formula, JK is the health index, TWD is the body temperature stability, is the weight factor of TWD to JK, PM is the fur glossiness, is the weight factor of PM to JK, XY is the blood index level, is the weight factor of XY to JK.

[0029] The body temperature of livestock is measured at a fixed time every day by using a thermometer, and the measurement is continued for many days to form a body temperature data series, and the body temperature stability is obtained by observing the fluctuation range of these data; the glossiness of the fur of livestock in the back, neck, buttocks and other areas is measured by a gloss meter to obtain the glossiness of the livestock's fur; based on the different types of livestock, appropriate parts are selected for blood collection using veterinary blood collection needles, the collected blood is tested, a test report is generated, and the blood index level is obtained based on the test report.

[0030] For example, when TWD=0.9, =0.4, PM=0.6, =0.3,XY=0.8, =0.3, we get JK=0.652. The higher the health index, the healthier the livestock.

[0031] Constructing a livestock growth model includes the following steps: obtaining historical livestock basic data, including historical basic data of different breeds of livestock when young and historical basic data of different breeds of livestock when slaughtered, and making one-to-one correspondence between the historical basic data of different breeds of livestock when young and the historical basic data of different breeds of livestock when slaughtered; preprocessing the historical livestock basic data; dividing the historical livestock basic data into a training set and a test set based on the preprocessed historical livestock basic data; inputting the training set into a support vector machine algorithm model for training to obtain a preliminary livestock growth model; and inputting the test set into the preliminary livestock growth model for testing to obtain a livestock growth model.

[0032] The historical basic data of different breeds of livestock when they were young include: the breed type of livestock, the corresponding weight data, height data and length data of each type of livestock when they were young; the historical basic data of different breeds of livestock when they were sold include: the breed type of livestock, the corresponding weight data, height data and length data of each type of livestock when they were sold.

[0033] The following steps are used to associate the historical basic data of different breeds of livestock when they were young with the historical basic data of different breeds of livestock when they were slaughtered: a unique identifier is set to associate the data of the livestock at different stages, and this number is used as the primary key to perform an associated query in the database, thereby making the historical basic data of different breeds of livestock when they were young with the historical basic data of livestock when they were slaughtered one by one.

[0034] Obtaining a similarity index based on a livestock growth model includes the following steps: based on the livestock growth model, inputting preprocessed childhood data one by one, and outputting the ideal slaughter data of each livestock accordingly; comparing the ideal slaughter data of each livestock with the corresponding actual slaughter data after preprocessing, and obtaining a similarity index of each livestock's ideal slaughter data and the corresponding actual slaughter data after preprocessing, wherein the similarity index is used to determine the data accuracy of each livestock's childhood data; if the similarity index is not lower than a set similarity threshold, the actual slaughter data is uploaded to the blockchain; if there is a similarity index lower than the set similarity threshold, the staff is notified to review the livestock to determine the data accuracy of the livestock's childhood data; if the data accuracy is correct, the livestock is uploaded to the blockchain; if the data accuracy is incorrect, the livestock is not transported.

[0035] By comparing the ideal slaughter data of each livestock with the corresponding actual slaughter data after preprocessing, a similarity index 1 is obtained. When the similarity index 1 is not lower than the set similarity threshold 1, it means that the actual slaughter data of the livestock is highly similar to the ideal slaughter data, that is, the data of the livestock when young is accurate and can be transported; When the similarity index is lower than the set similarity threshold, it means that the actual slaughter data of the livestock is less similar to the ideal slaughter data, that is, the accuracy of the livestock's childhood data is incorrect. At this time, the staff is notified to verify the livestock and determine the accuracy of the livestock's childhood data. If the accuracy meets the standard, the group of data below the similarity threshold will be uploaded to the blockchain and the livestock will be transported; if the accuracy does not meet the standard, it means that the group of data below the similarity threshold is artificially falsified, and the livestock corresponding to this group of data will not be transported.

[0036] The steps for obtaining the similarity index 1 are as follows: obtaining the weight difference, height difference, and length difference between the ideal slaughter data of each livestock and the corresponding actual slaughter data after preprocessing, and performing preprocessing; obtaining the weight factor of the preprocessed weight difference, height difference, and length difference to the similarity index 1 through the objective weighting method; obtaining the similarity index 1 through the similarity index formula 1, which is expressed as: ; In the formula, is the similarity index 1, For weight difference, for right The weight factor of is the height difference, for right The weight factor of is the body length difference, for right The weight factor of .

[0037] Cameras and weighing scales are installed at the door of the livestock house. When the livestock come out of the livestock house door, the actual slaughter weight of each livestock passing through is obtained by the weighing scale. The side image of the livestock is captured by the camera, and then the image is processed and analyzed by computer vision algorithms to identify the body outline and key parts of the livestock, and then the actual slaughter length and height data are measured. The livestock weight, length, and height data output by the livestock growth model are compared with the actual slaughter weight, length, and height data to obtain the weight difference, height difference, and length difference.

[0038] For example, when =20, =0.3, =15, =0.4, =13, =0.3, we get =3×10 -14 The larger the value of similarity index 1 is, the higher the similarity is, that is, the more accurate the data of livestock when they were young are; the smaller the value of similarity index 2 is, the lower the similarity is, that is, the data of livestock when they were young may be artificially falsified or have equipment failure problems.

[0039] Constructing a livestock transportation model includes the following steps: obtaining historical basic livestock transportation data, including historical basic data on the sale of livestock of different breeds and historical basic data on the slaughter of livestock of different breeds, and making one-to-one correspondence between the historical basic data on the sale of livestock of different breeds and the historical basic data on the slaughter of livestock of different breeds; preprocessing the historical basic livestock transportation data; dividing the historical basic livestock transportation data into a training set and a test set based on the preprocessed historical basic livestock transportation data; inputting the training set into a support vector machine algorithm model for training to obtain a preliminary livestock transportation model; and inputting the test set into the preliminary livestock transportation model for testing to obtain a livestock transportation model.

[0040] The basic data on the historical slaughter of different breeds of livestock include: the breed type of livestock, the corresponding body temperature data, blood pressure data and heart rate data of each type of livestock when it is slaughtered; the basic data on the historical slaughter of different breeds of livestock include: the breed type of livestock, the corresponding body temperature data, blood pressure data and heart rate data of each type of livestock when it is slaughtered.

[0041] Obtaining similarity index 2 based on the livestock transportation model includes the following steps: based on the livestock transportation model, inputting the actual market data after preprocessing one by one, and outputting the ideal slaughter data of each livestock accordingly; comparing the ideal slaughter data of each livestock with the corresponding actual slaughter data after preprocessing, and obtaining similarity index 2 between the ideal slaughter data of each livestock and the corresponding actual slaughter data after preprocessing, and similarity index 2 is used to judge the data accuracy of each livestock's young data; if similarity index 2 is not lower than the set similarity threshold 2, the actual slaughter data is uploaded to the blockchain, and if there is a similarity index 2 that is lower than the set similarity threshold 2, the staff is notified to review the livestock to judge the data accuracy of the livestock's young data, and if the data accuracy is correct, it is uploaded to the blockchain, and if the data accuracy is incorrect, the livestock will not be slaughtered.

[0042] By comparing the ideal slaughter data of each livestock with the corresponding actual slaughter data after preprocessing, a similarity index 2 is obtained. When the similarity index 2 is not lower than the set similarity threshold 2, it means that the actual slaughter data of the livestock is highly similar to the ideal slaughter data, that is, the data of the livestock when young is accurate and can be slaughtered; When the similarity index 2 is lower than the set similarity threshold 2, it means that the actual slaughter data of the livestock is less similar to the ideal slaughter data, that is, the accuracy of the livestock's childhood data is incorrect. At this time, the staff is notified to verify the livestock and determine the accuracy of the livestock's childhood data. If the accuracy meets the standard, the group of data below the similarity threshold 2 will be uploaded to the blockchain and the livestock will be slaughtered; if the accuracy does not meet the standard, it means that the group of data below the similarity threshold 2 is artificially falsified, and the livestock corresponding to this group of data will not be slaughtered.

[0043] The steps for obtaining the similarity index 2 are as follows: obtaining the body temperature difference, blood pressure difference, and heart rate difference between the ideal slaughter data of each livestock and the corresponding actual slaughter data after preprocessing, and performing preprocessing; obtaining the weight factor of the preprocessed body temperature difference, blood pressure difference, and heart rate difference for the similarity index 2 through the objective weighting method; and obtaining the similarity index 2 through the similarity index formula 2.

[0044] Temperature sensors, remote blood pressure measuring devices and non-contact heart rate monitoring sensors are installed at the cargo door of the transport vehicle. When the livestock get off the vehicle from the cargo door, the actual slaughter body temperature, blood pressure and heart rate of each livestock passing through are obtained through the temperature sensors, remote blood pressure measuring devices and non-contact heart rate monitoring sensors, and the actual slaughter body temperature, blood pressure and heart rate are then measured. The livestock body temperature, blood pressure and heart rate data output by the livestock transport model are compared with the actual slaughter body temperature, blood pressure and heart rate data to obtain the body temperature difference, blood pressure difference and heart rate difference.

[0045] Similarity index formula 2 is: ; In the formula, is the similarity index 2, is the temperature difference, for right The weight factor of For blood pressure difference, for right The weight factor of For heart rate difference, for right The weight factor of , e is a natural constant.

[0046] For example, when =4, =0.4, =8, =0.3, =10, =0.3, we get =9×10 -4 The larger the value of similarity index 2 is, the higher the similarity is, that is, the more accurate the data of livestock when they were young are; the smaller the value of similarity index 2 is, the lower the similarity is, that is, the data of livestock when they were young may be artificially falsified or have equipment failure problems.

[0047] Example 2 like Figure 2 As shown, a livestock meat production information traceability system is used to implement the method in Example 1, including: a data uploading module, a health detection module, a growth model building module, a transportation model building module, and a traceability code generation module; wherein the data uploading module is used to obtain the data of each livestock when it enters the breeding farm when it is young, and obtain the young data of each livestock; pre-process the young data, and upload the pre-processed young data to the blockchain; With the help of the blockchain's unalterable nature, the pre-processed childhood data is uploaded to the blockchain, effectively preventing the data from being maliciously tampered with in subsequent processes. Whether consumers, regulatory authorities or other relevant parties inquire about traceability information, the authenticity and reliability of the information obtained can be ensured, thereby enhancing trust in the entire meat production process.

[0048] The health detection module is used to obtain the adult data of each livestock and pre-process the adult data. Based on the pre-processed adult data, the health index of each livestock is obtained. The health index is used to judge whether the livestock is in a healthy state when it grows up. If the health index is lower than the set health threshold, the livestock will not be transported. If the health index is not lower than the set health threshold, the livestock will be allowed to be transported. By calculating the health index of livestock, we can ensure that the livestock entering the transport vehicle are in good health, and prevent sick livestock from mixing into the transport vehicle, causing other healthy livestock in the transport vehicle to be infected with the disease.

[0049] The growth model building module is used to obtain the data of each livestock when it enters the transport vehicle, obtain the actual slaughter data of each livestock, and the actual slaughter data of each livestock corresponds to the data of each livestock when it was young, pre-process the actual slaughter data, and obtain the actual slaughter data after pre-processing; build a livestock growth model, and obtain a similarity index of one based on the livestock growth model; Based on the actual data of livestock entering the transport vehicle and the corresponding data when they were young, a livestock growth model is constructed and a similarity index of one is obtained. This can scientifically compare the ideal and actual growth conditions, accurately determine whether the growth of each livestock in the breeding stage meets expectations, and promptly discover possible breeding management problems.

[0050] The transportation model building module is used to obtain the data of each livestock entering the slaughterhouse, obtain the actual slaughter data of each livestock, and the actual slaughter data of each livestock corresponds to the actual slaughter data of each livestock, pre-process the actual slaughter data, and obtain the actual slaughter data after pre-processing; build a livestock transportation model, and obtain the similarity index 2 based on the livestock transportation model; By obtaining the actual slaughter data of livestock when they enter the slaughterhouse, combining it with the previous actual output data to build a livestock transportation model and obtain the similarity index 2, it is possible to analyze the changes in the status of livestock during transportation, accurately evaluate the impact of transportation conditions (such as transportation time, ambient temperature, transportation density, etc.) on livestock physiological indicators, and promptly discover stress reactions, health damage and other problems that may occur in the transportation process.

[0051] The traceability code generation module is used to record the childhood data of each livestock, the actual market data of each livestock, and the actual slaughter data of each livestock in the blockchain as the traceability data of each livestock, and generate a traceability QR code from the traceability data of each livestock.

[0052] The data of livestock at various stages from hatching to actual market release to actual slaughter are integrated to generate a traceability QR code, which is convenient for all parties (consumers, retailers, regulatory agencies, etc.) to quickly and comprehensively obtain detailed production information of the livestock at any stage of the meat product circulation by simply scanning the QR code, thereby achieving convenient and efficient information traceability and enhancing consumers' confidence in the quality and safety of meat products.

[0053] The algorithms and systems involved in Example 1 and Example 2 can be executed by an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the above algorithms and systems are implemented by executing software through the processor.

Claims

1. A method for tracing livestock meat production information, characterized in that: The following steps are involved: Obtain the childhood data of each livestock when it enters the breeding farm as a puppy; Preprocess the childhood data and upload the preprocessed childhood data to the blockchain; Obtaining adult data of each livestock, and preprocessing the adult data, and obtaining a health index of each livestock based on the preprocessed adult data, the health index is used to judge whether the livestock is in a healthy state when it matures; If the health index is lower than the set health threshold, the livestock will not be transported; if the health index is not lower than the set health threshold, the livestock will be allowed to be transported; The data of each livestock entering the transport vehicle is obtained to obtain the actual slaughter data of each livestock, and the actual slaughter data of each livestock corresponds to the data of each livestock when it was young. The actual slaughter data is preprocessed to obtain the actual slaughter data after preprocessing; A livestock growth model is constructed, and a similarity index of one is obtained based on the livestock growth model; Obtaining data of each livestock when it enters the slaughterhouse, obtaining actual slaughter data of each livestock, and the actual slaughter data of each livestock corresponds one-to-one with the actual slaughter data of each livestock, pre-processing the actual slaughter data, and obtaining the actual slaughter data after pre-processing; A livestock transportation model is constructed, and a similarity index 2 is obtained based on the livestock transportation model; The childhood data of each livestock, the actual market data of each livestock, and the actual slaughter data of each livestock in the blockchain are recorded as the traceability data of each livestock, and the traceability data of each livestock is used to generate a traceability QR code.

2. A method for tracing livestock meat production information according to claim 1, characterized in that: Based on the pre-processed adult data, the health index of each livestock is obtained, including the following steps: Obtain the body temperature stability, fur glossiness, and blood index levels of each livestock at adulthood, and perform pretreatment; The weight factors of body temperature stability, fur glossiness, and blood index levels after pretreatment on the health index were obtained through objective weighting method; The health index is obtained through the health index formula, which is expressed as: ； In the formula, JK is the health index, TWD is the body temperature stability, is the weight factor of TWD to JK, PM is the fur glossiness, is the weight factor of PM to JK, XY is the blood index level, is the weight factor of XY to JK.

3. A method for tracing livestock meat production information according to claim 1, characterized in that: The construction of livestock growth model includes the following steps: Obtaining historical basic data on livestock, including historical basic data on the young of different breeds of livestock and historical basic data on the slaughter of different breeds of livestock, and making one-to-one correspondence between the historical basic data on the young of different breeds of livestock and the historical basic data on the slaughter of different breeds of livestock; Preprocessing of historical livestock basic data; Based on the pre-processed historical livestock basic data, the training set and the test set are divided; The training set is input into the support vector machine algorithm model for training to obtain a preliminary livestock growth model; The test set is input into the preliminary livestock growth model for testing to obtain the livestock growth model.

4. A method for tracing livestock meat production information as claimed in claim 3, characterized in that: Determining the similarity index based on the livestock growth model includes the following steps: Based on the livestock growth model, the pre-processed childhood data are input one by one, and the ideal market data of each livestock is output accordingly; The ideal slaughter data of each livestock is compared with the corresponding actual slaughter data after preprocessing, and the similarity index 1 between the ideal slaughter data of each livestock and the corresponding actual slaughter data after preprocessing is obtained. The similarity index 1 is used to judge the data accuracy of the data of each livestock when it was young; If the similarity index is not lower than the set similarity threshold, the actual market data will be uploaded to the blockchain. If there is a similarity index that is lower than the set similarity threshold, the staff will be notified to review the livestock to determine the accuracy of the livestock's childhood data. If the data is correct, it will be uploaded to the blockchain. If the data is incorrect, the livestock will not be transported.

5. A method for tracing livestock meat production information as claimed in claim 4, characterized in that: The steps for obtaining the similarity index are: Obtain the weight difference, height difference, and length difference between the ideal slaughter data of each livestock and the corresponding actual slaughter data after preprocessing, and perform preprocessing; Obtain the weight factor of the pre-processed weight difference, height difference, and length difference to the similarity index 1 through the objective weighting method; Obtain similarity index 1 through similarity index formula 1; The similarity index formula 1 is: ； In the formula, is the similarity index 1, For weight difference, for right The weight factor of is the height difference, for right The weight factor of is the body length difference, for right The weight factor of .

6. A method for tracing livestock meat production information according to claim 1, characterized in that: Building a livestock transport model involves the following steps: Obtain historical basic livestock transportation data, including historical basic data on the sale of livestock of different breeds and historical basic data on the slaughter of livestock of different breeds, and match the historical basic data on the sale of livestock of different breeds with the historical basic data on the slaughter of livestock of different breeds; Preprocessing of historical livestock basic transportation data; Based on the pre-processed historical livestock basic transportation data, the training set and the test set are divided; The training set is input into the support vector machine algorithm model for training to obtain a preliminary livestock transportation model; The test set is input into the preliminary livestock transportation model for testing to obtain the livestock transportation model.

7. A method for tracing livestock meat production information according to claim 6, characterized in that: Determining similarity index 2 based on the livestock transportation model includes the following steps: Based on the livestock transportation model, the actual slaughter data after preprocessing is input one by one, and the ideal slaughter data of each livestock is output accordingly; The ideal slaughter data of each livestock is compared with the corresponding actual slaughter data after preprocessing, and the similarity index 2 between the ideal slaughter data of each livestock and the corresponding actual slaughter data after preprocessing is obtained. The similarity index 2 is used to judge the data accuracy of the data of each livestock when it is young; If the similarity index 2 is not lower than the set similarity threshold 2, the actual slaughter data will be uploaded to the blockchain. If there is a similarity index 2 that is lower than the set similarity threshold 2, the staff will be notified to review the livestock to determine the accuracy of the livestock's childhood data. If the data accuracy is correct, it will be uploaded to the blockchain. If the data accuracy is incorrect, the livestock will not be slaughtered.

8. A method for tracing livestock meat production information as claimed in claim 7, characterized in that: The steps to obtain the similarity index 2 are: Obtain the body temperature difference, blood pressure difference, and heart rate difference between the ideal slaughter data of each livestock and the corresponding actual slaughter data after preprocessing, and perform preprocessing; Obtain the weight factors of the pre-processed body temperature difference, blood pressure difference, and heart rate difference to the similarity index 2 through the objective weighting method; The similarity index 2 is obtained by similarity index formula 2.

9. A method for tracing livestock meat production information as claimed in claim 8, characterized in that: Similarity index formula 2 is: ； In the formula, is the similarity index 2, is the temperature difference, for right The weight factor of For blood pressure difference, for right The weight factor of For heart rate difference, for right The weight factor of , e is a natural constant.

10. A livestock meat production information traceability system for implementing the method according to any one of claims 1 to 9, characterized in that: include: Data upload module, health detection module, growth model construction module, transportation model construction module, traceability code generation module; Among them, the data upload module is used to obtain the data of each livestock when it enters the breeding farm when it is young, and obtain the data of each livestock when it is young; Preprocess the childhood data and upload the preprocessed childhood data to the blockchain; The health detection module is used to obtain the adult data of each livestock and pre-process the adult data. Based on the pre-processed adult data, the health index of each livestock is obtained. The health index is used to judge whether the livestock is in a healthy state when it matures; If the health index is lower than the set health threshold, the livestock will not be transported; if the health index is not lower than the set health threshold, the livestock will be allowed to be transported; The growth model building module is used to obtain the data of each livestock when it enters the transport vehicle, obtain the actual slaughter data of each livestock, and the actual slaughter data of each livestock corresponds to the data of each livestock when it was young, pre-process the actual slaughter data, and obtain the actual slaughter data after pre-processing; A livestock growth model is constructed, and a similarity index of one is obtained based on the livestock growth model; The transportation model building module is used to obtain the data of each livestock entering the slaughterhouse, obtain the actual slaughter data of each livestock, and the actual slaughter data of each livestock corresponds to the actual slaughter data of each livestock, pre-process the actual slaughter data, and obtain the actual slaughter data after pre-processing; A livestock transportation model is constructed, and a similarity index 2 is obtained based on the livestock transportation model; The traceability code generation module is used to record the childhood data of each livestock, the actual market data of each livestock, and the actual slaughter data of each livestock in the blockchain as the traceability data of each livestock, and generate a traceability QR code from the traceability data of each livestock.

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