Internet-based livestock and poultry meat slaughtering, cutting, packaging and tracing method and system

Abstract

The present application relates to livestock traceability technical field, specifically for livestock meat slaughtering, cutting and packaging traceability method and system based on internet, the method comprises: collecting quarantine information of livestock individual, obtaining initial traceability data; according to initial traceability data, determine the slaughter batch code, and the initial traceability data source is bound with the slaughter batch code, and the slaughter credible data package is obtained; based on the slaughter batch code, the cutting operation process information of livestock individual is tracked and filed in real time, and the cutting link data chain is obtained; based on the cutting link data chain, the multi-dimensional quality of livestock individual is judged, and the comprehensive quality index is obtained; based on the comprehensive quality index, the intelligent matching planning of meat product is carried out, and the packaging batch planning is obtained; based on the packaging batch planning, the livestock individual is coded and identified, and the product traceability code is obtained; the present application can improve the efficiency of livestock meat slaughtering, cutting and packaging traceability based on internet.

Description

Technical Field

[0001] This invention relates to the field of livestock and poultry traceability technology, and in particular to an internet-based method and system for tracing the slaughter, processing, and packaging of livestock and poultry meat. Background Technology

[0002] Existing livestock and poultry meat traceability technologies have significant shortcomings in data collection and integration. They lack a systematic capture of key information throughout the entire life cycle of individual livestock and poultry, resulting in fragmented and incomplete traceability data. Furthermore, the lack of effective encryption and binding mechanisms during data transmission and storage makes it difficult to guarantee the credibility and security of traceability information, thus hindering accurate traceability from source to end.

[0003] In traditional slaughtering, processing, and subcontracting processes, quality assessment often relies on single-dimensional data, lacking comprehensive analysis of multi-source information, resulting in insufficient accuracy and comprehensiveness in quality evaluation. Subcontracting planning lacks an intelligent matching mechanism, making it difficult to achieve efficient resource allocation based on quality indicators and order requirements, thus affecting production efficiency and supply-demand matching. Therefore, improving the accuracy of traceability in livestock and poultry slaughtering, processing, and subcontracting has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides an internet-based method and system for tracing the slaughter, processing, and packaging of livestock and poultry meat, in order to solve the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides an internet-based traceability method for slaughtering, processing, and packaging livestock and poultry meat, comprising: S1. Collect quarantine information on individual livestock and poultry to obtain initial traceability data for these individuals; S2. Based on the initial traceability data, determine the slaughter batch code of individual livestock and poultry, and bind the initial traceability data source with the slaughter batch code to obtain the trusted slaughter data packet of individual livestock and poultry. S3. Based on the slaughter batch code, the information on the cutting operation process of individual livestock and poultry is tracked and archived in real time to obtain the data chain of the cutting process of individual livestock and poultry. S4. Based on the segmented data chain, conduct multi-dimensional quality assessment of individual livestock and poultry to obtain comprehensive quality indicators for individual livestock and poultry. S5. Based on comprehensive quality indicators, intelligent matching and planning are carried out for meat products of individual livestock and poultry to obtain the batch planning of individual livestock and poultry packaging. S6. Based on the subcontracting batch planning, assign codes to individual livestock and poultry to obtain product traceability codes for individual livestock and poultry.

[0006] In a preferred embodiment, the step of collecting quarantine information from individual livestock and poultry to obtain initial traceability data for those individuals includes: Obtain unique biometric information and electronic origin quarantine certificates for individual livestock and poultry; Key fields of the electronic origin quarantine certificate are parsed to obtain standardized quarantine certificate data for individual livestock and poultry. By comparing standardized quarantine certificate data with animal health supervision information in real time, official verification characteristics of individual livestock and poultry can be obtained. The unique biometric information, standardized quarantine certificate data, and official verification characteristics are formatted and packaged to obtain the initial traceability data of individual livestock and poultry.

[0007] In a preferred embodiment, the step of determining the slaughter batch code of an individual livestock or poultry based on the initial traceability data, and binding the initial traceability data source with the slaughter batch code to obtain a reliable slaughter data packet for the individual livestock or poultry includes: Perform batch logic compilation on the initial traceability data to obtain the batch logic identifier of the initial traceability data; Based on the batch logical identifier and the preset slaughter line logical coding rules, the livestock and poultry individuals are compounded and coded to obtain a unique slaughter batch code for each livestock and poultry individual; By binding and calibrating the unique slaughter batch code with the initial traceability data, traceable data of individual livestock and poultry can be obtained; Asymmetric trusted encryption is applied to traceable data to obtain trusted slaughter data packets for individual livestock and poultry.

[0008] The method of combining batch logical identifiers with preset slaughter line logical encoding rules to perform composite encoding on individual livestock and poultry, resulting in a unique slaughter batch code for each individual livestock and poultry, includes: The identity features of the batch logical identifier are extracted in a structured manner to obtain the basic identity elements of the livestock and poultry individuals. Use the timestamp in the batch logical identifier as the encoding time sequence element for individual livestock and poultry; Based on the logical coding rules of the slaughter line, the logical position information of individual livestock and poultry is mapped to obtain the coding space elements of individual livestock and poultry. By sequentially concatenating the basic identity elements, temporal elements, and spatial elements of the coding, a unique slaughter batch code for each individual livestock or poultry is obtained.

[0009] In a preferred embodiment, the step of tracking and archiving the segmentation process information of individual livestock and poultry based on slaughter batch coding in real time to obtain a data chain of segmentation steps for individual livestock and poultry includes: Based on the slaughter batch code, the production task of each livestock and poultry individual is analyzed to obtain the basic segmentation task of the livestock and poultry individual. By mapping the basic segmentation task to product specifications, the expected output specifications of individual livestock and poultry can be obtained. Based on the basic segmentation task, visual features are extracted and real-time quantitative analysis is performed on the process node images and weight sensing data of individual livestock and poultry to obtain real-time operation records and output data of individual livestock and poultry. The real-time operation records and output data are verified against the expected output specifications to obtain the verified process data for individual livestock and poultry. The verified process data is encapsulated in a time-series chain to obtain the segmentation data chain of individual livestock and poultry.

[0010] In a preferred embodiment, the step of performing multi-dimensional quality assessment on individual livestock and poultry based on segmented data chains to obtain comprehensive quality indicators for individual livestock and poultry includes: Multi-source quality data are integrated into the segmentation data chain to obtain the original quality parameters of individual livestock and poultry. The original quality parameters are mapped to a quality standard spectrum to obtain the standardized quality spectrum of individual livestock and poultry. Weight matching is performed on the standardized mass spectrum to obtain the influence weights of the standardized mass spectrum. Based on the influence weights, the standardized quality spectrum is synthesized to obtain the comprehensive quality index of individual livestock and poultry.

[0011] The method of synthesizing indicators from the standardized quality spectrum based on influence weights to obtain comprehensive quality indicators for individual livestock and poultry includes: Multi-dimensional correlation analysis of the standardized quality spectrum was conducted to obtain the core quality dimensions of individual livestock and poultry. By adapting and assigning weights to the influence weights and core quality dimensions, a quality evaluation structure for individual livestock and poultry is obtained. Based on the quality evaluation structure, the core quality dimensions are trend converged to obtain the preliminary aggregated quality value of individual livestock and poultry. The initial aggregated quality values ​​are offset-calibrated to obtain the comprehensive quality index of individual livestock and poultry. The calculation formula for the comprehensive quality index is as follows: ; in, For comprehensive quality indicators, For the first The influence weights of the core quality dimensions For the first Standardized quality values ​​for each core quality dimension. For the first The reliability coefficient of the detection of each core quality dimension The preset detection confidence coefficient, The standardized quality value mean for the core quality dimensions. The standard deviation of the standardized quality values ​​for the core quality dimensions. For the first Consistency correction weights for each core quality dimension The total number of core quality dimensions.

[0012] In a preferred embodiment, the intelligent matching and planning of meat products from individual livestock and poultry based on comprehensive quality indicators to obtain the batch planning for subcontracting of individual livestock and poultry includes: Semantic deconstruction of customer orders for individual livestock and poultry products yields the order requirements of those products. Based on order demand and comprehensive quality indicators, a multi-objective matching assessment of meat resources of individual livestock and poultry is conducted to obtain the supply and demand matching relationship of individual livestock and poultry. Based on the supply and demand matching relationship, the meat resources of individual livestock and poultry are clustered and integrated to obtain the best candidate subcontracting batches of individual livestock and poultry. Logical conflicts are resolved among candidate subcontracting batches to obtain feasible subcontracting batches for individual livestock and poultry. The feasible subcontracting batches are sequence-coded to obtain the subcontracting batch plan for individual livestock and poultry.

[0013] In a preferred embodiment, the step of assigning codes to individual livestock and poultry based on subcontracting batch planning to obtain product traceability codes for individual livestock and poultry includes: Structured information is extracted from the subcontracting batch planning to obtain batch identifiers and key association data indexes for individual livestock and poultry; Based on batch identifiers and key associated data indexes, feature summaries are generated from the slaughter trusted data packets and the data chain of the segmentation process to obtain the traceability source data features of individual livestock and poultry. By embedding timestamps into the traceability source data features, the data blocks to be encoded for individual livestock and poultry are obtained; The data block to be encoded is encrypted and hashed to obtain a unique digital digest of each livestock and poultry individual. The unique digital digest is combined with the coded version identifier of the livestock and poultry individual and serialized to obtain the product unit traceability code of the livestock and poultry individual; By bidirectional logical anchoring of product unit traceability codes, subcontracting batch planning, and related source data of individual livestock and poultry, product traceability codes for individual livestock and poultry are obtained.

[0014] To address the aforementioned problems, this invention also provides an internet-based traceability system for livestock and poultry meat slaughtering, processing, and packaging, the system comprising: The quarantine and traceability data collection module is used to collect quarantine information from individual livestock and poultry to obtain initial traceability data for each individual. The slaughter batch code binding module is used to determine the slaughter batch code of individual livestock and poultry based on the initial traceability data, and to bind the initial traceability data source with the slaughter batch code to obtain the slaughter trusted data packet of individual livestock and poultry. The segmentation process tracking and filing module is used to track and file the segmentation process information of individual livestock and poultry based on the slaughter batch code in real time, and obtain the segmentation process data chain of individual livestock and poultry. The multi-dimensional quality assessment module is used to perform multi-dimensional quality assessment on individual livestock and poultry based on the segmented data chain, and obtain the comprehensive quality index of individual livestock and poultry. The intelligent planning module for subcontracting batches is used to intelligently match and plan the meat products of individual livestock and poultry based on comprehensive quality indicators, so as to obtain the subcontracting batch plan for individual livestock and poultry. The coding and identification module is used to assign codes to individual livestock and poultry based on the subcontracting batch planning, so as to obtain the product traceability code of the individual livestock and poultry.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention, through its livestock and poultry meat slaughtering, processing, and packaging traceability technology, significantly improves the integrity and reliability of traceability information through closed-loop data management throughout the entire process. From the collection of quarantine information to the generation of product traceability codes, data at each stage is encoded, encrypted, and encapsulated in a sequential chain, enabling the traceability data to be fully traceable and tamper-proof, greatly improving the accuracy and usability of traceability information.

[0016] 2. This invention, relying on a multi-dimensional quality assessment and intelligent subcontracting planning mechanism, effectively improves the efficiency of meat quality control and the rationality of resource allocation. By integrating data from the processing stage for standardized quality analysis, it accurately outputs comprehensive quality indicators. Simultaneously, based on order requirements and quality indicators, it achieves intelligent matching and subcontracting of meat products, ensuring meat quality stability while optimizing production flow efficiency, thus providing technical support for the high-quality development of the industry. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an internet-based traceability method for slaughtering, processing, and packaging livestock and poultry meat according to an embodiment of the present invention. Figure 2 This is a functional module diagram of an Internet-based traceability system for slaughtering, processing, and packaging livestock and poultry meat, provided in an embodiment of the present invention. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides an internet-based method for tracing the slaughter, processing, and packaging of livestock and poultry meat. The executing entity of this internet-based method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the internet-based method for tracing the slaughter, processing, and packaging of livestock and poultry meat can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cluster of cloud servers. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating an internet-based traceability method for slaughtering, processing, and packaging livestock and poultry meat according to an embodiment of the present invention. In this embodiment, the internet-based traceability method for slaughtering, processing, and packaging livestock and poultry meat includes: S1. Collect quarantine information on individual livestock and poultry to obtain initial traceability data for these individuals; In this embodiment of the invention, the step of collecting quarantine information from individual livestock and poultry to obtain initial traceability data for those individuals includes: Obtain unique biometric information and electronic origin quarantine certificates for individual livestock and poultry; Key fields of the electronic origin quarantine certificate are parsed to obtain standardized quarantine certificate data for individual livestock and poultry. By comparing standardized quarantine certificate data with animal health supervision information in real time, official verification characteristics of individual livestock and poultry can be obtained. The unique biometric information, standardized quarantine certificate data, and official verification characteristics are formatted and packaged to obtain the initial traceability data of individual livestock and poultry.

[0021] To obtain unique biometric information for individual livestock and poultry, this information must be unique and stable. This can be achieved by reading the electronic ear tags worn by the livestock and poultry to obtain the identification number, or by extracting the specific gene sequence of the livestock and poultry through gene testing technology as a unique identifier. At the same time, collect the corresponding electronic certificate of origin and quarantine certificate for the livestock and poultry. The certificate must be issued by a qualified quarantine agency and include a complete electronic signature and anti-counterfeiting mark to ensure that the document is legally sourced and the information is true and valid.

[0022] The collected electronic origin quarantine certificates are analyzed for key fields. Text recognition technology is used to extract key information such as livestock and poultry breed, breeding address, slaughter date, quarantine implementation date, quarantine personnel name and qualification number, quarantine items and results, and quarantine certificate number. The extracted information is then standardized in format, and the expression, units and precision of the information are unified according to the preset data field requirements. For example, the date is uniformly formatted as year-month-day, and the quarantine result is uniformly standardized as "qualified" or "unqualified". Finally, standardized quarantine certificate data for individual livestock and poultry is formed, ensuring that the data can be directly called and compared in subsequent processes.

[0023] The standardized quarantine certificate data is integrated into the official animal health supervision information system. Through the data interface, the standardized quarantine certificate data is compared in real time with relevant data stored in the system, such as livestock and poultry breeding registration information, quarantine personnel qualification information, and past quarantine records. Each item in the standardized quarantine certificate data is verified to ensure complete consistency with the official records. The key checks include whether the quarantine certificate number is within the official registration range, whether the quarantine personnel have the corresponding quarantine qualifications, and whether the quarantine results are consistent with the disease monitoring data during the breeding process. Based on the comprehensive comparison results, an official verification characterization for each livestock and poultry individual is generated. If all information is consistent with the official records, the official verification characterization is "verification passed". If there are inconsistencies or missing information, the official verification characterization is "verification failed", and the specific reason for failure is clearly indicated.

[0024] The unique biometric information, standardized quarantine certificate data, and generated official verification characteristics are formatted and packaged. The data is associated according to the logical order of "unique biometric information - standardized quarantine certificate data - official verification characteristics". Corresponding field tags are added to each piece of information to ensure a clear data structure. At the same time, a unified file format is used for storage to ensure that the data will not be lost or disordered during transmission and use. The final result is complete and standardized initial traceability data for individual livestock and poultry. This data fully records the key information of the source of livestock and poultry, and provides comprehensive and reliable basic data support for subsequent traceability links such as slaughter batch coding binding and segmentation process tracking.

[0025] The beneficial effects are that by obtaining core source information of individual livestock and poultry from multiple dimensions, and through refined analysis, strict comparison with official data, and standardized packaging, the resulting initial traceability data not only has a unique identifier, but also has legality and authenticity guaranteed by official verification. Standardized processing ensures the universality and operability of the data, allowing subsequent traceability links to carry out work based on unified standard basic data, effectively avoiding problems such as chaotic and false source data, providing accurate and reliable starting point support for the whole chain traceability of livestock and poultry meat, and improving the credibility and operational efficiency of the overall traceability system.

[0026] S2. Based on the initial traceability data, determine the slaughter batch code of individual livestock and poultry, and bind the initial traceability data source with the slaughter batch code to obtain the trusted slaughter data packet of individual livestock and poultry. In this embodiment of the invention, the step of determining the slaughter batch code of an individual livestock or poultry based on initial traceability data, and binding the initial traceability data source with the slaughter batch code to obtain a reliable slaughter data packet for the individual livestock or poultry includes: Perform batch logic compilation on the initial traceability data to obtain the batch logic identifier of the initial traceability data; Based on the batch logical identifier and the preset slaughter line logical coding rules, the livestock and poultry individuals are compounded and coded to obtain a unique slaughter batch code for each livestock and poultry individual; By binding and calibrating the unique slaughter batch code with the initial traceability data, traceable data of individual livestock and poultry can be obtained; Asymmetric trusted encryption is applied to traceable data to obtain trusted slaughter data packets for individual livestock and poultry.

[0027] The method of combining batch logical identifiers with preset slaughter line logical encoding rules to perform composite encoding on individual livestock and poultry, resulting in a unique slaughter batch code for each individual livestock and poultry, includes: The identity features of the batch logical identifier are extracted in a structured manner to obtain the basic identity elements of the livestock and poultry individuals. Use the timestamp in the batch logical identifier as the encoding time sequence element for individual livestock and poultry; Based on the logical coding rules of the slaughter line, the logical position information of individual livestock and poultry is mapped to obtain the coding space elements of individual livestock and poultry. By sequentially concatenating the basic identity elements, temporal elements, and spatial elements of the coding, a unique slaughter batch code for each individual livestock or poultry is obtained.

[0028] The initial traceability data is logically compiled in batches, and key information such as place of origin, quarantine time, and livestock breed is analyzed in depth. According to the preset batch division rules, livestock individuals with the same or similar characteristics are grouped into the same batch. Through the logical integration and identification of these characteristic information, the batch logical identifier of the initial traceability data is obtained. This identifier clearly reflects the batch category and core related information of the livestock individuals.

[0029] The obtained batch logical identifiers are subjected to structured extraction of identity features. Core identity information that can clearly distinguish individual livestock and poultry in the batch is extracted from the batch logical identifiers, such as the unique biological identification information of livestock and poultry and the individual serial number in the batch. This information is organized into a structured data form as the basic identity element for encoding individual livestock and poultry, ensuring that the element can uniquely correspond to the specific individual livestock and poultry.

[0030] The timestamp information contained in the batch logical identifier is extracted. This timestamp accurately records the key time nodes when livestock and poultry complete quarantine and enter the slaughter process. This timestamp is directly used as the coding time sequence element of individual livestock and poultry, thereby clarifying the time information of livestock and poultry entering the slaughter process and providing a time dimension basis for subsequent traceability.

[0031] Based on the preset logical coding rules for slaughter lines, the rules have clearly defined the unique codes for each slaughter line in the slaughterhouse and the corresponding division of the work areas. According to the slaughter line and work area that the livestock and poultry individuals are about to enter, the actual physical work location is mapped to a unified and standardized logical location information, forming the coding spatial elements of the livestock and poultry individuals, and clearly identifying the specific production line and area of ​​livestock and poultry slaughter.

[0032] The basic identity elements, coding time sequence elements, and coding space elements are sequentially assembled in a fixed order of "basic identity elements - coding time sequence elements - coding space elements". During the assembly process, it is ensured that there is no information overlap between the elements and that the connection is natural, forming a unique character sequence containing key information. This character sequence is the unique slaughter batch code of the livestock and poultry individual, which can uniquely identify the slaughter batch and related core information of the livestock and poultry.

[0033] The generated unique slaughter batch code is bound and calibrated with the initial traceability data. First, the correspondence between the unique slaughter batch code and each piece of information in the initial traceability data is established. Then, the correlation between the code and the data is checked one by one to ensure that the individual livestock and poultry corresponding to the code is completely consistent with the individual livestock and poultry recorded in the initial traceability data. At the same time, the data is checked for omissions or errors, and any deviations found are corrected in a timely manner. Finally, traceable data of individual livestock and poultry is obtained, which realizes the accurate association between slaughter batch code and source traceability information.

[0034] Asymmetric trusted encryption is applied to traceable data. First, a public key and a private key pair are generated. The public key is used for data encryption, and the private key is used for data decryption and is kept by a designated authorized entity. The traceable data is encrypted using the public key, converting the original traceable data into encrypted ciphertext data. This ciphertext data can only be decrypted using the corresponding private key to view the original information. Ultimately, a trusted data packet for the slaughter of individual livestock and poultry is formed, ensuring the security and confidentiality of the data during transmission and storage, and preventing data from being tampered with or leaked.

[0035] The beneficial effects are that by logically compiling, compounding, binding, calibrating, and encrypting the initial traceability data, a unique and secure slaughter trust data packet is generated. This not only achieves accurate identification of individual livestock and poultry slaughter batches, but also ensures the security and trustworthiness of the traceability data through asymmetric encryption. At the same time, the encoding integrates multi-dimensional information such as identity, time, and space, providing a unique and reliable core identifier for subsequent segmentation process tracking and product traceability, thereby improving the accuracy and security of traceability in the livestock and poultry meat slaughtering process.

[0036] S3. Based on the slaughter batch code, the information on the cutting operation process of individual livestock and poultry is tracked and archived in real time to obtain the data chain of the cutting process of individual livestock and poultry. In this embodiment of the invention, the step of real-time tracking and archiving of the segmentation process information of individual livestock and poultry based on slaughter batch coding to obtain a data chain of segmentation steps for individual livestock and poultry includes: Based on the slaughter batch code, the production task of each livestock and poultry individual is analyzed to obtain the basic segmentation task of the livestock and poultry individual. By mapping the basic segmentation task to product specifications, the expected output specifications of individual livestock and poultry can be obtained. Based on the basic segmentation task, visual features are extracted and real-time quantitative analysis is performed on the process node images and weight sensing data of individual livestock and poultry to obtain real-time operation records and output data of individual livestock and poultry. The real-time operation records and output data are verified against the expected output specifications to obtain the verified process data for individual livestock and poultry. The verified process data is encapsulated in a time-series chain to obtain the segmentation data chain of individual livestock and poultry.

[0037] Based on the slaughter batch code, the system retrieves the pre-stored production plan and the association information between the slaughter batch code to clarify the processing requirements that the corresponding livestock and poultry individuals need to complete, including the cutting parts, cutting methods, processing sequence and other core contents. By sorting out and breaking down these requirements, the basic cutting tasks of the livestock and poultry individuals are obtained, ensuring that the tasks are accurately matched with the slaughter batches of the livestock and poultry individuals.

[0038] For the obtained basic segmentation task, the system queries the preset product specification standard library. Based on the segmentation parts, processing accuracy and other requirements in the basic segmentation task, the corresponding product specification parameters are matched, including the size range, weight range and morphological standard of each segmentation part. These parameters are integrated to obtain the expected output specifications of individual livestock and poultry, providing a clear reference standard for subsequent processing and verification.

[0039] During the basic segmentation process of individual livestock and poultry, high-definition cameras installed at each process node collect image information of the segmentation operation in real time. At the same time, weight sensors deployed on the segmentation equipment collect the weight data of the output in real time. Visual features are extracted from the collected process node images to identify key information such as segmentation actions, tool usage, and product form. The weight sensor data is quantified and analyzed in real time and converted into specific numerical data. Combining the feature information extracted from the images and the quantified weight data, the operation time, operators, operation content, and corresponding output weight value of each process are recorded in detail to form a real-time operation record and output data of individual livestock and poultry.

[0040] The real-time operation records are compared with the output quantity data and the pre-determined expected output specifications for compliance verification. The segmentation method, processing sequence and other aspects in the real-time operation records are compared one by one to see if they meet the expected requirements. The weight values ​​in the output quantity data are within the weight range of the expected specifications. The product form meets the preset form standards. If all items meet the expectations, a verification record is directly generated. If there are non-compliance items, the differences and specific values ​​are clearly marked. Finally, all verification results are integrated to obtain the verified process data for individual livestock and poultry.

[0041] The verified process data is encapsulated in a time-series chain according to the time sequence of the segmentation operation. With time as the axis, the verified data of each process are sequentially linked to establish the sequential relationship between processes, ensuring the time sequence and continuity of the data. The verified data of each process contains the association identifier with the data of the previous process, forming a complete chain-like data structure. Finally, the data chain of the segmentation process of individual livestock and poultry is obtained, which comprehensively and orderly records the key information of the entire segmentation process.

[0042] The beneficial effects are that by accurately associating slaughter batch codes with segmentation tasks, and through specification mapping, real-time data collection and analysis, compliance verification, and time sequence encapsulation, a complete data chain for the segmentation process is formed. This not only enables real-time tracking of the entire segmentation process, ensuring that every step of the operation and output is traceable, but also ensures that the quality of the segmented products meets expectations through compliance verification. This provides comprehensive, accurate, and orderly data support for subsequent quality assessment and product traceability, and improves the completeness and reliability of the segmentation traceability process.

[0043] S4. Based on the segmented data chain, conduct multi-dimensional quality assessment of individual livestock and poultry to obtain comprehensive quality indicators for individual livestock and poultry. In this embodiment of the invention, the step of performing multi-dimensional quality assessment on individual livestock and poultry based on the segmented data chain to obtain a comprehensive quality index for the individual livestock and poultry includes: Multi-source quality data are integrated into the segmentation data chain to obtain the original quality parameters of individual livestock and poultry. The original quality parameters are mapped to a quality standard spectrum to obtain the standardized quality spectrum of individual livestock and poultry. Weight matching is performed on the standardized mass spectrum to obtain the influence weights of the standardized mass spectrum. Based on the influence weights, the standardized quality spectrum is synthesized to obtain the comprehensive quality index of individual livestock and poultry.

[0044] The method of synthesizing indicators from the standardized quality spectrum based on influence weights to obtain comprehensive quality indicators for individual livestock and poultry includes: Multi-dimensional correlation analysis of the standardized quality spectrum was conducted to obtain the core quality dimensions of individual livestock and poultry. By adapting and assigning weights to the influence weights and core quality dimensions, a quality evaluation structure for individual livestock and poultry is obtained. Based on the quality evaluation structure, the core quality dimensions are trend converged to obtain the preliminary aggregated quality value of individual livestock and poultry. The initial aggregated quality values ​​are offset-calibrated to obtain the comprehensive quality index of individual livestock and poultry. The calculation formula for the comprehensive quality index is as follows: ; in, For comprehensive quality indicators, For the first The influence weights of the core quality dimensions For the first Standardized quality values ​​for each core quality dimension. For the first The reliability coefficient of the detection of each core quality dimension The preset detection confidence coefficient, The standardized quality value mean for the core quality dimensions. The standard deviation of the standardized quality values ​​for the core quality dimensions. For the first Consistency correction weights for each core quality dimension The total number of core quality dimensions.

[0045] Multi-source quality data is integrated into the data chain of the segmentation process. Various quality-related data, such as product morphology characteristics, weight data of each part, processing accuracy parameters, and compliance status of procedures, are extracted from the data chain. These data come from multiple sources, such as image acquisition and weight sensing. All extracted quality-related data are classified and summarized, and duplicate information is removed to ensure data completeness and omission. Finally, the original quality parameters of individual livestock and poultry are obtained, which comprehensively cover the basic data reflecting product quality in the segmentation process.

[0046] The integrated original quality parameters are mapped to a quality standard spectrum. The system pre-establishes a complete quality standard spectrum for livestock and poultry meat, including quality grading standards for different breeds and cuts, the acceptable ranges for various quality indicators, and the criteria for judgment. The quality standard spectrum clearly defines the specific divisions of core quality dimensions, including product quality compliance, sensory quality, processing precision, and safety and hygiene. Each core quality dimension has detailed grading standards: Grade 1 meat cut size error ≤ ±5mm, weight deviation ≤ ±2%, color uniformity ≥90%; Grade 2 meat cut size error ≤ ±8mm, weight deviation ≤ ±3%, color uniformity ≥85%; Grade 3 meat cut size error ≤... Meat of substandard grade exceeds the following limits: ±12mm, weight deviation ≤±5%, and color uniformity ≥80%. Specific indicators and data formats for the standardized quality spectrum are standardized and uniform. Weight data is accurate to 0.01kg, size data to 0.1mm, morphological regularity expressed as a percentage, microbial content in CFU / g, and drug residue in mg / kg. Each data point in the original quality parameters is matched with the corresponding standard in the quality standard spectrum. The original data is standardized and converted according to the spectrum requirements, ensuring that each data point corresponds to a specific level and numerical range in the standard spectrum. This ultimately forms a standardized quality spectrum for individual livestock and poultry, providing a unified evaluation benchmark for quality data.

[0047] The generated standardized quality spectrum is weighted and matched. Based on industry standards for livestock and poultry meat quality evaluation, market demand, and consumer concerns, the degree of influence of each quality indicator in the standardized quality spectrum on the overall quality is determined. For example, core indicators such as meat tenderness and product specification compliance rate are given higher weights, while secondary indicators are given lower weights. Through professional quality evaluation models and expert review opinions, the specific weight values ​​of each quality indicator are clarified, and the influence weight of the standardized quality spectrum is obtained to ensure that the weight allocation meets the actual quality evaluation needs.

[0048] Multi-dimensional correlation analysis was conducted on the standardized quality spectrum to identify the intrinsic relationships between various quality indicators. The correlation threshold for core quality dimensions was set at a correlation coefficient ≥ 0.7, indicating strong correlation. Dimensions with a greater impact on overall quality were retained as core quality dimensions, while the other was classified as redundant. The correlation coefficient was calculated using the Pearson correlation coefficient method, with a sample size of no less than 30% of the total number of livestock and poultry individuals in the batch. Key indicator dimensions that play a decisive role in the overall quality of livestock and poultry meat were identified, such as product quality compliance, sensory quality, and processing precision. These dimensions comprehensively cover the core elements of quality evaluation. Redundant dimensions with strong correlation and low impact were eliminated, ultimately yielding the core quality dimensions of individual livestock and poultry, making the quality evaluation more targeted and effective.

[0049] The previously determined influence weights are adapted and weighted with the core quality dimensions. According to the importance of each core quality dimension, the corresponding influence weights are accurately allocated to each dimension, clarifying the proportion of each core quality dimension in the overall quality evaluation, and forming a clear quality evaluation structure. This structure clarifies the weight allocation and evaluation criteria of each core dimension, providing a clear basis for the subsequent synthesis of quality indicators.

[0050] Based on the established quality evaluation structure, the standardized data of each core quality dimension are converged to reflect trends. The specific indicator data of each core quality dimension are summarized and weighted according to their weight ratio. The scattered indicator data are integrated into a single value that can reflect the overall level of the core dimension. Then, the integrated values ​​of all core quality dimensions are summarized to obtain the preliminary aggregated quality value of the livestock and poultry individual. This value initially reflects the overall quality level of the product.

[0051] The initial aggregated quality value is offset calibrated. Taking into account objective factors that may affect the quality evaluation results, such as environmental factors, equipment operating status, and differences in operator skills during the actual production process, a calibration coefficient library is established. According to the specific production scenario and actual influencing factors, the corresponding calibration coefficients are selected to adjust and correct the initial aggregated quality value, eliminate evaluation biases caused by various objective factors, ensure the accuracy and fairness of quality indicators, and finally obtain the comprehensive quality index of individual livestock and poultry. This index comprehensively and accurately reflects the overall quality status of livestock and poultry meat.

[0052] The process of deriving the comprehensive quality index involves first calculating the product of the influence weight of each core quality dimension, the standardized quality value, and the detection reliability coefficient. After summing all the products, the result is multiplied by the preset detection reliability coefficient. Then, the difference between the standardized quality value and the mean of each core quality dimension is calculated, divided by the standard deviation, and multiplied by the corresponding consistency correction weight. All these results are summed and divided by the total number of core quality dimensions. Finally, the two calculation results are added together. This process achieves comprehensive integration and precise quantification of the core quality dimensions of individual livestock and poultry, ultimately yielding a single indicator that comprehensively reflects the meat quality of individual livestock and poultry.

[0053] No. The influence weights of the core quality dimensions are derived from the weight matching results of the standardized quality spectrum. The standardized quality values ​​of the core quality dimensions are derived from the standardized quality spectrum formed by mapping the original quality parameters to a quality standard spectrum. The detection reliability coefficients for each core quality dimension are reliability assessment results of the detection process for each core quality dimension in the segmentation data chain. The preset detection reliability coefficients... These are fixed values ​​pre-set based on industry quality testing standards and system design requirements, ranging from 0.85 to 0.92. They are based on the average accuracy level of mainstream testing equipment in the industry and can be dynamically adjusted according to the actual accuracy of the testing equipment. For high-precision testing equipment (i.e., testing error ≤ ±1%), the value is 0.90-0.92; for conventional testing equipment (i.e., testing error ≤ ±3%), the value is 0.85-0.88. For every 1% increase in testing equipment accuracy... The value can be increased by 0.01. The mean of the standardized quality values ​​for the core quality dimensions is obtained by calculating the arithmetic mean of the standardized quality values ​​for all core quality dimensions. The standard deviation of the standardized quality values ​​for the core quality dimensions is obtained by taking the square root of the sum of the squares of the differences between the standardized quality values ​​and the mean for all core quality dimensions. The consistency correction weights for each core quality dimension are determined based on their consistency performance in multi-dimensional correlation analysis. The assignment rules are based on the reliability classification of the testing methods: instrument-based methods, such as weight sensor testing and microbial detector testing, are assigned a value of 0.25-0.3; manual sampling methods, such as on-site verification by operators, are assigned a value of 0.1-0.15; and sensory evaluation methods, such as taste and color evaluation by professional evaluators, are assigned a value of 0.15-0.2. When multiple testing methods are used for the same core quality dimension, the values ​​from each method are taken. The weighted average of the values, and the total number of core quality dimensions, are the specific number of core quality dimensions identified after multi-dimensional correlation analysis of the standardized quality spectrum.

[0054] As the weight of each core quality dimension increases, the overall quality index will also increase. Conversely, an increase in the standardized quality value of each core quality dimension will raise the overall quality index. As the reliability coefficients of the detection in each core quality dimension increase, the overall quality index shows an upward trend. An increase in the preset reliability coefficients will lead to an increase in the overall quality index. The mean of the standardized quality values ​​of the core quality dimensions is negatively correlated with the overall quality index; a decrease in the standard deviation of the standardized quality values ​​of the core quality dimensions will increase the overall quality index. When the consistency correction weight of each core quality dimension increases, the overall quality index will rise. The change in the total number of core quality dimensions is processed by affecting the mean of the calculation results in the second part. The increase in the number will make the impact of the results in the second part on the overall quality index more stable, but the overall trend is still dominated by the specific numerical changes of each core quality dimension.

[0055] The beneficial effects are that by integrating multi-source data, standard mapping, weight matching, and multi-dimensional index synthesis in the data chain of the segmentation process, a scientific and accurate comprehensive quality index is formed. This not only comprehensively considers various quality influencing factors in the segmentation process, but also improves the pertinence and accuracy of quality evaluation through core dimension screening and offset calibration. This provides a reliable quality basis for subsequent intelligent meat matching planning and batch division of subcontracting, and helps to achieve precise and high-quality livestock and poultry meat subcontracting management.

[0056] S5. Based on comprehensive quality indicators, intelligent matching and planning are carried out for meat products of individual livestock and poultry to obtain the batch planning of individual livestock and poultry packaging. In this embodiment of the invention, the intelligent matching and planning of meat products from individual livestock and poultry based on comprehensive quality indicators to obtain the batch planning for subcontracting of individual livestock and poultry includes: Semantic deconstruction of customer orders for individual livestock and poultry products yields the order requirements of those products. Based on order demand and comprehensive quality indicators, a multi-objective matching assessment of meat resources of individual livestock and poultry is conducted to obtain the supply and demand matching relationship of individual livestock and poultry. Based on the supply and demand matching relationship, the meat resources of individual livestock and poultry are clustered and integrated to obtain the best candidate subcontracting batches of individual livestock and poultry. Logical conflicts are resolved among candidate subcontracting batches to obtain feasible subcontracting batches for individual livestock and poultry. The feasible subcontracting batches are sequence-coded to obtain the subcontracting batch plan for individual livestock and poultry.

[0057] Semantic deconstruction is performed on customer orders corresponding to individual livestock and poultry, analyzing the textual descriptions in the orders word by word to extract explicit product requirements, including key information such as the required cuts of meat, quantity, weight requirements, quality grade, and delivery time. At the same time, implicit potential requirements in the orders are identified, such as meat form preferences inferred from consumption scenarios. All extracted requirement information is classified and organized to form clear and complete order requirements for individual livestock and poultry, ensuring a comprehensive understanding of the customer's actual needs.

[0058] Based on the analyzed order demands and previously determined comprehensive quality indicators, a multi-objective matching assessment is conducted on the meat resources of individual livestock and poultry. Each indicator in the order demand is compared with the comprehensive quality indicators of the meat to evaluate whether the quality grade of the meat meets the order requirements, whether the cut is consistent with the order requirements, and whether the weight meets the order quantity standards, among other matching dimensions. At the same time, factors such as the suitability of delivery time and production cycle, and the fit between meat specifications and order requirements are considered. Based on the matching results of each dimension, the degree of fit between meat resources and order demands is determined, forming a supply and demand matching relationship for individual livestock and poultry, and clarifying which meat resources can meet which order demands.

[0059] Based on the established supply and demand matching relationship, the meat resources of individual livestock and poultry are clustered and integrated. Meat resources that are suitable for the same order demand or have similar demand characteristics are grouped into one category. At the same time, considering factors such as production efficiency, packaging specifications, and transportation convenience, the categorized meat resources are optimized and combined to ensure that each category of meat resources can form a complete and independently deliverable subcontracting unit. The optimal combination that meets the order demand and has production feasibility is selected to obtain the best candidate subcontracting batches for individual livestock and poultry. Each candidate subcontracting batch contains clear meat composition, corresponding order, and delivery information.

[0060] Logical conflict resolution is performed on the candidate subcontracting batches obtained. It is checked whether there are problems such as duplicate allocation of meat resources, overlapping order requirements, conflicting production time, and conflicting transportation routes among the candidate subcontracting batches. For the conflict problems found, conflict solutions are formulated according to the principles of order priority, maximizing production efficiency, and optimizing resource utilization. These solutions include adjusting the batch allocation of some meat resources, optimizing production scheduling, and adjusting transportation planning. Each type of logical conflict is resolved one by one to ensure that each subcontracting batch can be executed independently and smoothly, and finally, feasible subcontracting batches for individual livestock and poultry are obtained.

[0061] The identified feasible subcontracting batches are sequentially coded. According to the preset coding rules, each feasible subcontracting batch is assigned a unique code identifier. The code contains key information such as order number, batch number, production date, and delivery area. The core attributes of the subcontracting batch can be quickly identified through coding. The codes and corresponding detailed information of all feasible subcontracting batches are sorted and summarized to form a structured subcontracting batch plan for individual livestock and poultry, providing a clear basis for subsequent coding identification, production execution, and delivery management.

[0062] The beneficial effects include the formation of a scientific and reasonable batch planning for subcontracting through precise analysis of customer orders, multi-objective matching of meat resources and order requirements, clustering and integration, conflict resolution, and sequence coding. This ensures accurate matching of meat resources with customer needs, meets customers' personalized requirements, and improves production efficiency and resource utilization through optimization, integration, and conflict resolution. It also provides a clear batch basis for subsequent product coding traceability and full-process management, helping to achieve standardized and efficient management of livestock and poultry meat subcontracting.

[0063] S6. Based on the subcontracting batch planning, assign codes to individual livestock and poultry to obtain product traceability codes for individual livestock and poultry.

[0064] In this embodiment of the invention, the step of assigning codes to individual livestock and poultry based on subcontracting batch planning to obtain product traceability codes for individual livestock and poultry includes: Structured information is extracted from the subcontracting batch planning to obtain batch identifiers and key association data indexes for individual livestock and poultry; Based on batch identifiers and key associated data indexes, feature summaries are generated from the slaughter trusted data packets and the data chain of the segmentation process to obtain the traceability source data features of individual livestock and poultry. By embedding timestamps into the traceability source data features, the data blocks to be encoded for individual livestock and poultry are obtained; The data block to be encoded is encrypted and hashed to obtain a unique digital digest of each livestock and poultry individual. The unique digital digest is combined with the coded version identifier of the livestock and poultry individual and serialized to obtain the product unit traceability code of the livestock and poultry individual; By bidirectional logical anchoring of product unit traceability codes, subcontracting batch planning, and related source data of individual livestock and poultry, product traceability codes for individual livestock and poultry are obtained.

[0065] Structured information extraction is performed on the subcontracting batch planning. The core information corresponding to each subcontracting batch, such as the unique batch number, the list of meat products included in the batch, the corresponding customer order number, and the production plan number, is accurately extracted from the planning as the batch identifier for individual livestock and poultry. At the same time, key related data indexes such as slaughtering process data index, cutting process data index, and quality indicator data index associated with the subcontracting batch are sorted out to ensure that the extracted information is complete and can be accurately linked to the core data of each process.

[0066] Based on the extracted batch identifiers and key related data indexes, the corresponding slaughter trusted data packages and segmentation data chains are retrieved respectively. Core features such as initial traceability data of livestock and poultry, slaughter batch codes, and encrypted verification information are extracted from the slaughter trusted data packages. Key features such as verification data of each process, output quantity data, and compliance verification results are extracted from the segmentation data chains. These extracted features are summarized and organized to form traceability source data features of livestock and poultry individuals that can comprehensively reflect the key information of individual livestock and poultry from slaughter to subcontracting.

[0067] The generated traceability source data features are timestamped to record the precise time when the current batch planning for subcontracting is completed and the coding identification is about to be carried out. This time is accurate to the second. This timestamp information is associated and integrated with the traceability source data features to ensure that the timestamp and the traceability source data features correspond one-to-one and cannot be separated, forming a data block to be coded for each livestock and poultry individual containing key features of the entire process and coding time information.

[0068] The data block to be encoded is encrypted and hashed. A specific hash algorithm is used to calculate the entire data block to be encoded, and the data block to be encoded is converted into a fixed-length string. This string can uniquely correspond to the original data block to be encoded. Any slight change in the original data will cause the string to change significantly. In this way, a unique digital digest of each livestock and poultry individual is obtained, ensuring the integrity and uniqueness of the data.

[0069] The generated unique digital digest is combined with the coded version identifier of the individual livestock and poultry and serialized. The coded version identifier is used to distinguish traceability codes generated in different periods and under different rules, ensuring the compatibility and identifiability of the traceability codes. The two are concatenated in a fixed order of "unique digital digest - coded version identifier" and then processed according to a preset serialization format, so that the combined information forms a standardized, storable, and transmissible string, thus obtaining the product unit traceability code of the individual livestock and poultry.

[0070] The obtained product unit traceability code, the previously established subcontracting batch plan, and the associated source data of individual livestock and poultry are logically anchored in two directions to establish the correspondence between the product unit traceability code and various information in the subcontracting batch plan. At the same time, the associated source data of individual livestock and poultry from quarantine, slaughter, cutting to subcontracting are linked to ensure that relevant data throughout the entire process can be queried in a forward manner through the product unit traceability code, and the corresponding product unit traceability code can also be located in reverse through the data of each link, forming a complete two-way association system. Finally, the product traceability code of individual livestock and poultry is obtained, which becomes the unique traceability certificate throughout the entire process of livestock and poultry meat.

[0071] The beneficial effects are as follows: by extracting information from the subcontracting batch planning, generating full-process data feature summaries, embedding timestamps, performing encrypted hashing, combining serialization, and bidirectional logical anchoring, a unique product traceability code containing full-process traceability information is generated. This not only integrates and links data from quarantine to subcontracting of livestock and poultry meat, but also ensures the security and integrity of traceability information through encryption. Consumers, enterprises, and regulatory authorities can quickly query the full-process information of products through the traceability code, achieving full-chain traceability and improving the level of product quality and safety assurance and industry regulatory efficiency.

[0072] like Figure 2 The diagram shown is a functional block diagram of an Internet-based traceability system for slaughtering, processing, and packaging livestock and poultry meat, provided in an embodiment of the present invention.

[0073] The Internet-based livestock and poultry meat slaughtering, processing, and packaging traceability system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the Internet-based livestock and poultry meat slaughtering, processing, and packaging traceability system 100 may include a quarantine traceability data acquisition module 101, a slaughter batch coding and binding module 102, a processing tracking and filing module 103, a multi-dimensional quality assessment module 104, a packaging batch intelligent planning module 105, and a coding and identification module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0074] In this embodiment, the functions of each module / unit are as follows: The quarantine traceability data acquisition module 101 is used to collect quarantine information from individual livestock and poultry to obtain initial traceability data for individual livestock and poultry. The slaughter batch code binding module 102 is used to determine the slaughter batch code of individual livestock and poultry based on the initial traceability data, and to bind the initial traceability data source with the slaughter batch code to obtain a reliable slaughter data packet of individual livestock and poultry. The segmentation process tracking and filing module 103 is used to track and file the segmentation process information of individual livestock and poultry based on the slaughter batch code in real time, so as to obtain the segmentation process data chain of individual livestock and poultry. The multi-dimensional quality assessment module 104 is used to perform multi-dimensional quality assessment on individual livestock and poultry based on the segmented data chain to obtain the comprehensive quality index of individual livestock and poultry. The intelligent planning module 105 for subcontracting batches is used to intelligently match and plan the meat products of individual livestock and poultry based on comprehensive quality indicators, so as to obtain the subcontracting batch plan for individual livestock and poultry. The coding and identification module 106 is used to assign codes to individual livestock and poultry based on the subcontracting batch planning, so as to obtain the product traceability code of the individual livestock and poultry.

[0075] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0076] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0078] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0079] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for tracing the slaughter, processing, and packaging of livestock and poultry meat based on the internet, characterized in that: The method includes: S1. Collect quarantine information on individual livestock and poultry to obtain initial traceability data for these individuals; S2. Based on the initial traceability data, determine the slaughter batch code of individual livestock and poultry, and bind the initial traceability data source with the slaughter batch code to obtain the trusted slaughter data packet of individual livestock and poultry. S3. Based on the slaughter batch code, the information on the cutting operation process of individual livestock and poultry is tracked and archived in real time to obtain the data chain of the cutting process of individual livestock and poultry. S4. Based on the segmented data chain, conduct multi-dimensional quality assessment of individual livestock and poultry to obtain comprehensive quality indicators for individual livestock and poultry. S5. Based on comprehensive quality indicators, intelligent matching and planning are carried out for meat products of individual livestock and poultry to obtain the batch planning of individual livestock and poultry packaging. S6. Based on the subcontracting batch planning, assign codes to individual livestock and poultry to obtain product traceability codes for individual livestock and poultry.

2. The Internet-based traceability method for slaughtering, processing, and packaging livestock and poultry meat as described in claim 1, characterized in that, The collection of quarantine information for individual livestock and poultry, resulting in initial traceability data for those individuals, includes: Obtain unique biometric information and electronic origin quarantine certificates for individual livestock and poultry; Key fields of the electronic origin quarantine certificate are parsed to obtain standardized quarantine certificate data for individual livestock and poultry. By comparing standardized quarantine certificate data with animal health supervision information in real time, official verification characteristics of individual livestock and poultry can be obtained. The unique biometric information, standardized quarantine certificate data, and official verification characteristics are formatted and packaged to obtain the initial traceability data of individual livestock and poultry.

3. The Internet-based traceability method for slaughtering, processing, and packaging livestock and poultry meat as described in claim 1, characterized in that, The process involves determining the slaughter batch code of individual livestock and poultry based on initial traceability data, and binding the initial traceability data source with the slaughter batch code to obtain a trusted slaughter data packet for each individual livestock and poultry, including: Perform batch logic compilation on the initial traceability data to obtain the batch logic identifier of the initial traceability data; Based on the batch logical identifier and the preset slaughter line logical coding rules, the livestock and poultry individuals are compounded and coded to obtain a unique slaughter batch code for each livestock and poultry individual; By binding and calibrating the unique slaughter batch code with the initial traceability data, traceable data of individual livestock and poultry can be obtained; Asymmetric trusted encryption is applied to traceable data to obtain trusted slaughter data packets for individual livestock and poultry.

4. The Internet-based traceability method for slaughtering, processing, and packaging livestock and poultry meat as described in claim 3, characterized in that, The method of using batch logical identifiers and preset slaughter line logical encoding rules to perform composite encoding on individual livestock and poultry, resulting in a unique slaughter batch code for each individual livestock and poultry, includes: The identity features of the batch logical identifier are extracted in a structured manner to obtain the basic identity elements of the livestock and poultry individuals. Use the timestamp in the batch logical identifier as the encoding time sequence element for individual livestock and poultry; Based on the logical coding rules of the slaughter line, the logical position information of individual livestock and poultry is mapped to obtain the coding space elements of individual livestock and poultry. By sequentially concatenating the basic identity elements, coding time sequence elements, and coding space elements, a unique slaughter batch code for each livestock and poultry individual is obtained.

5. The Internet-based traceability method for slaughtering, processing, and packaging livestock and poultry meat as described in claim 1, characterized in that, The process of real-time tracking and archiving of the segmentation operation information of individual livestock and poultry based on slaughter batch coding, resulting in a data chain of segmentation steps for individual livestock and poultry, includes: Based on the slaughter batch code, the production task of each livestock and poultry individual is analyzed to obtain the basic segmentation task of the livestock and poultry individual. By mapping the basic segmentation task to product specifications, the expected output specifications of individual livestock and poultry can be obtained. Based on the basic segmentation task, visual features are extracted and real-time quantitative analysis is performed on the process node images and weight sensing data of individual livestock and poultry to obtain real-time operation records and output data of individual livestock and poultry. The real-time operation records and output data are verified against the expected output specifications to obtain the verified process data for individual livestock and poultry. The verified process data is encapsulated in a time-series chain to obtain the segmentation data chain of individual livestock and poultry.

6. The Internet-based traceability method for slaughtering, processing, and packaging livestock and poultry meat as described in claim 1, characterized in that, The data chain based on segmented links is used to conduct multi-dimensional quality assessments of individual livestock and poultry, resulting in comprehensive quality indicators for each individual, including: Multi-source quality data are integrated into the segmentation data chain to obtain the original quality parameters of individual livestock and poultry. The original quality parameters are mapped to a quality standard spectrum to obtain the standardized quality spectrum of individual livestock and poultry. Weight matching is performed on the standardized mass spectrum to obtain the influence weights of the standardized mass spectrum. Based on the influence weights, the standardized quality spectrum is synthesized to obtain the comprehensive quality index of individual livestock and poultry.

7. The Internet-based traceability method for slaughtering, processing, and packaging livestock and poultry meat as described in claim 6, characterized in that, The method of synthesizing indicators from the standardized quality spectrum based on influence weights to obtain comprehensive quality indicators for individual livestock and poultry includes: Multi-dimensional correlation analysis of the standardized quality spectrum was conducted to obtain the core quality dimensions of individual livestock and poultry. By adapting and assigning weights to the influence weights and core quality dimensions, a quality evaluation structure for individual livestock and poultry is obtained. Based on the quality evaluation structure, the core quality dimensions are trend converged to obtain the preliminary aggregated quality value of individual livestock and poultry. The initial aggregated quality values ​​are offset-calibrated to obtain the comprehensive quality index of individual livestock and poultry. The calculation formula for the comprehensive quality index is as follows: ; in, For comprehensive quality indicators, For the first The influence weights of the core quality dimensions For the first Standardized quality values ​​for each core quality dimension. For the first The reliability coefficient of the detection of each core quality dimension The preset detection confidence coefficient, The standardized quality value mean of the core quality dimensions. The standard deviation of the standardized quality values ​​for the core quality dimensions. For the first Consistency correction weights for each core quality dimension The total number of core quality dimensions.

8. The Internet-based traceability method for slaughtering, processing, and packaging livestock and poultry meat as described in claim 1, characterized in that, The intelligent matching and planning of meat products from individual livestock and poultry based on comprehensive quality indicators to obtain the batch planning for subcontracting of individual livestock and poultry includes: Semantic deconstruction of customer orders for individual livestock and poultry products yields the order requirements of those products. Based on order demand and comprehensive quality indicators, a multi-objective matching assessment of meat resources of individual livestock and poultry is conducted to obtain the supply and demand matching relationship of individual livestock and poultry. Based on the supply and demand matching relationship, the meat resources of individual livestock and poultry are clustered and integrated to obtain the best candidate subcontracting batches of individual livestock and poultry. Logical conflicts are resolved among candidate subcontracting batches to obtain feasible subcontracting batches for individual livestock and poultry. The feasible subcontracting batches are sequence-coded to obtain the subcontracting batch plan for individual livestock and poultry.

9. The Internet-based traceability method for slaughtering, processing, and packaging livestock and poultry meat as described in claim 1, characterized in that, The process of assigning codes to individual livestock and poultry based on subcontracting batch planning to obtain product traceability codes for each individual livestock and poultry includes: Structured information is extracted from the subcontracting batch planning to obtain batch identifiers and key association data indexes for individual livestock and poultry; Based on batch identifiers and key associated data indexes, feature summaries are generated from the slaughter trusted data packets and the data chain of the segmentation process to obtain the traceability source data features of individual livestock and poultry. By embedding timestamps into the traceability source data features, the data blocks to be encoded for individual livestock and poultry are obtained; The data block to be encoded is encrypted and hashed to obtain a unique digital digest of each livestock and poultry individual. The unique digital digest is combined with the coded version identifier of the livestock and poultry individual and serialized to obtain the product unit traceability code of the livestock and poultry individual; By bidirectional logical anchoring of product unit traceability codes, subcontracting batch planning, and related source data of individual livestock and poultry, product traceability codes for individual livestock and poultry are obtained.

10. An internet-based traceability system for livestock and poultry meat slaughtering, processing, and packaging, characterized in that: The system for implementing the Internet-based livestock and poultry meat slaughtering, processing, and packaging traceability method of claim 1 includes: The quarantine and traceability data collection module is used to collect quarantine information from individual livestock and poultry to obtain initial traceability data for each individual. The slaughter batch code binding module is used to determine the slaughter batch code of individual livestock and poultry based on the initial traceability data, and to bind the initial traceability data source with the slaughter batch code to obtain the slaughter trusted data packet of individual livestock and poultry. The segmentation process tracking and filing module is used to track and file the segmentation process information of individual livestock and poultry based on the slaughter batch code in real time, and obtain the segmentation process data chain of individual livestock and poultry. The multi-dimensional quality assessment module is used to perform multi-dimensional quality assessment on individual livestock and poultry based on the segmented data chain, and obtain the comprehensive quality index of individual livestock and poultry. The intelligent planning module for subcontracting batches is used to intelligently match and plan the meat products of individual livestock and poultry based on comprehensive quality indicators, so as to obtain the subcontracting batch plan for individual livestock and poultry. The coding and identification module is used to assign codes to individual livestock and poultry based on the subcontracting batch planning, so as to obtain the product traceability code of the individual livestock and poultry.

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