Food processing quality tracing method, equipment and medium

Abstract

The invention discloses a food processing quality tracing method and device and a medium, and relates to the related field of food processing tracing, and the method comprises the steps: obtaining a food processing quality tracing instruction; based on the target food RFID tag, the real-time food processing node and the real-time food quality abnormal feature information, performing food processing quality breadth tracing to obtain a first food processing quality tracing result; performing food processing quality deep tracing to obtain a second food processing quality tracing result; performing data breakpoint compensation to obtain a third food processing quality tracing result; performing data source confidence optimization to obtain a fourth food processing quality tracing result; and encrypting and feeding back a fourth food processing quality tracing result. The technical problem that existing food processing quality tracing is insufficient in comprehensiveness and depth is solved, and the technical effect that comprehensive and deep tracing of the food processing quality is achieved, so that the quality problem can be found and solved in time is achieved.

Description

Technical Field

[0001] The present application relates to the field of food processing traceability, and in particular to a food processing quality traceability method, equipment and medium. Background Art

[0002] In the modern food processing industry, as consumers pay more and more attention to food safety and quality, it is particularly important to establish an efficient and accurate food processing quality traceability system. This system aims to ensure that every link from raw material procurement to finished product delivery can be accurately tracked, so that when quality problems occur, the source of the problem can be quickly located, and effective measures can be taken to prevent the problem from spreading and protect the rights and interests of consumers. However, traditional food processing quality traceability methods often rely on paper records or simple electronic data management systems. These methods have problems such as untimely information entry, easy data loss, low traceability efficiency, and poor accuracy, which are difficult to meet the high requirements of modern food processing industry for quality traceability.

[0003] Among the current relevant technologies, food processing quality traceability has technical problems such as insufficient comprehensiveness and depth of traceability. Summary of the invention

[0004] The present application provides a food processing quality traceability method, equipment and medium, which uses RFID tags to capture the processing nodes and quality abnormality feature information of the target food in real time, conducts breadth traceability, quickly locates the possible links where the problem occurs, conducts depth traceability, deeply analyzes the processing details and quality influencing factors of the specific links, compensates for data breakpoints, conducts confidence assessment and optimization of the traceability results, and feeds back the traceability results to relevant parties in an encrypted manner, thereby achieving the technical effect of realizing comprehensive and in-depth traceability of food processing quality, thereby enabling timely discovery and resolution of quality problems.

[0005] The present application provides a food processing quality tracing method, which is applied to a food processing quality tracing system. The food processing quality tracing system includes a food processing tracing interaction module and a food processing data management module. The method includes: According to the food processing traceability interaction module, a food processing quality traceability instruction is obtained, wherein the food processing quality traceability instruction includes a target food, and a target food RFID tag, a real-time food processing node and real-time food quality abnormality characteristic information corresponding to the target food; based on the target food RFID tag, the real-time food processing node and the real-time food quality abnormality characteristic information, the food processing quality breadth traceability of the target food is performed according to the food processing data management module to obtain a first food processing quality traceability result; based on the first food processing quality traceability result, the food processing quality depth traceability of the target food is performed according to the food processing data management module to obtain a second food processing quality traceability result; based on the second food processing quality traceability result, data breakpoint compensation is performed to obtain a third food processing quality traceability result; based on the third food processing quality traceability result, data source confidence optimization is performed to obtain a fourth food processing quality traceability result; according to the food processing traceability interaction module, the fourth food processing quality traceability result is encrypted and fed back.

[0006] In a possible implementation, based on the target food RFID tag, the real-time food processing node and the real-time food quality abnormality feature information, the food processing quality breadth traceability of the target food is performed according to the food processing data management module to obtain a first food processing quality traceability result, and the following processing is performed: Based on the target food RFID tag and according to the food processing data management module, the full-process food processing node information of the target food is retrieved; based on the full-process food processing node information, a first food processing quality traceability chain is constructed; based on the real-time food processing node, the first food processing quality traceability chain is segmented into foreground processing nodes to generate a second food processing quality traceability chain; based on the real-time food quality abnormality feature information, the second food processing quality traceability chain is associated and combed to generate the first food processing quality traceability result.

[0007] In a possible implementation, the second food processing quality traceability chain is associated and sorted based on the real-time food quality abnormality feature information to generate the first food processing quality traceability result, and the following processing is performed: Based on the real-time food quality abnormality feature information and the second food processing quality traceability chain, correlation analysis is performed to determine correlation coefficients of multiple abnormal feature nodes; it is judged whether the correlation coefficients of the multiple abnormal feature nodes are greater than / equal to the abnormal feature node correlation threshold, and a set of identified food processing nodes greater than / equal to the abnormal feature node correlation threshold is obtained; and the identified food processing node set is sorted and arranged to obtain the first food processing quality traceability result.

[0008] In a possible implementation, based on the first food processing quality traceability result, the food processing quality in-depth traceability of the target food is performed according to the food processing data management module to obtain a second food processing quality traceability result, and the following processing is performed: Based on the target food RFID tag, the target food processing data block in the food processing data management module is activated; the first food processing quality traceability result is associated with data indicators according to the target food processing data block to obtain a first traceability indicator distribution; the first traceability indicator distribution is associated and cleaned based on the real-time food quality abnormality feature information to determine a second traceability indicator distribution; data generation time constraint optimization is performed according to the second traceability indicator distribution to obtain a third traceability indicator distribution; data is retrieved from the target food processing data block based on the third traceability indicator distribution to generate the second food processing quality traceability result.

[0009] In a possible implementation, data breakpoint compensation is performed based on the second food processing quality traceability result to obtain a third food processing quality traceability result, and the following processing is performed: Perform breakpoint detection according to the second food processing quality traceability result to obtain first traceability breakpoint data; perform data compensation on the first traceability breakpoint data according to the food processing data backup module of the food processing quality traceability system to obtain first traceability breakpoint data compensation results; compensate the second food processing quality traceability result according to the first traceability breakpoint data compensation results to obtain second food processing quality traceability optimization results; perform breakpoint detection according to the second food processing quality traceability optimization results to obtain second traceability breakpoint data; perform trend feature association analysis on the second traceability breakpoint data according to the second food processing quality traceability optimization results to obtain breakpoint data trend feature association analysis results; perform data compensation on the second traceability breakpoint data according to the breakpoint data trend feature association analysis results to obtain second traceability breakpoint data compensation results; compensate the second food processing quality traceability optimization results according to the second traceability breakpoint data compensation results to generate the third food processing quality traceability result.

[0010] In a possible implementation, breakpoint detection is performed according to the second food processing quality traceability result to obtain first traceability breakpoint data, and the following processing is performed: Perform a change trend correlation analysis on the second food processing quality traceability result to obtain a traceability data change trend correlation analysis result; activate a pre-built breakpoint detection model; and generate the first traceability breakpoint data based on the breakpoint detection model based on the second food processing quality traceability result and the traceability data change trend correlation analysis result.

[0011] In a possible implementation, data source confidence optimization is performed according to the third food processing quality traceability result to obtain a fourth food processing quality traceability result, and the following processing is performed: According to the third food processing quality traceability result, data source is located to obtain the source of each traceability parameter; according to the source of each traceability parameter, confidence evaluation is performed to obtain the confidence evaluation result of each source; based on the confidence evaluation constraint of the traceability parameter source, the abnormal source of the third food processing quality traceability result is located according to the confidence evaluation result of each source to obtain abnormal source traceability data; according to the abnormal source traceability data, data generation process record is retrieved to obtain the abnormal source data generation process record; based on the abnormal source data generation process record and the abnormal source traceability data, confidence optimization is performed on the third food processing quality traceability result to obtain the fourth food processing quality traceability result.

[0012] The present application also provides an electronic device, including: A memory for storing executable instructions; The processor is used to implement a food processing quality traceability method when executing the executable instructions stored in the memory.

[0013] The present application also provides a computer-readable storage medium, comprising: A computer program is stored thereon, and when the program is executed by a processor, a method for tracing the quality of food processing is implemented.

[0014] A food processing quality traceability method, device and medium proposed in the present application first obtains a food processing quality traceability instruction according to a food processing traceability interaction module, wherein the food processing quality traceability instruction includes a target food, and a target food RFID tag, a real-time food processing node and real-time food quality abnormality feature information corresponding to the target food. Then, based on the target food RFID tag, the real-time food processing node and the real-time food quality abnormality feature information, the target food is traced extensively according to the food processing data management module to obtain a first food processing quality traceability result. Then, based on the first food processing quality traceability result, the target food is traced deeply according to the food processing data management module to obtain a second food processing quality traceability result. Then, based on the second food processing quality traceability result, data breakpoint compensation is performed to obtain a third food processing quality traceability result. Then, based on the third food processing quality traceability result, data source confidence optimization is performed to obtain a fourth food processing quality traceability result. Finally, according to the food processing traceability interaction module, the fourth food processing quality traceability result is encrypted and fed back, thereby achieving a comprehensive and in-depth traceability of food processing quality, thereby timely discovering and solving quality problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the method according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0016] Figure 1 A schematic flow chart of a food processing quality traceability method provided in an embodiment of the present application.

[0017] Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0018] Description of the reference numerals: input device 201 , processor 202 , memory 203 , output device 204 . DETAILED DESCRIPTION

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0020] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0021] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0022] The present application embodiment provides a food processing quality tracing method, which is applied to a food processing quality tracing system. The food processing quality tracing system includes a food processing tracing interaction module and a food processing data management module. Figure 1 As shown, the method includes: Step S100, according to the food processing traceability interaction module, obtain the food processing quality traceability instruction, wherein the food processing quality traceability instruction includes the target food, and the target food RFID tag corresponding to the target food, the real-time food processing node and the real-time food quality abnormal characteristic information. Specifically, the food processing quality traceability system is a comprehensive information management system for realizing traceability management of the whole process of food processing. The system realizes the full chain traceability of food from raw material procurement to finished product sales by integrating the food processing traceability interaction module, the food processing data management module, etc. Through this system, enterprises can monitor each link in the food processing process in real time, timely discover and deal with potential quality problems, and ensure the safety and quality stability of food. At the same time, the system also provides consumers with a convenient query channel, so that consumers can understand the source, processing process and quality information of the purchased food, and enhance consumer confidence. Among them, the food processing traceability interaction module is an interface for users to interact with the system, which is used to input traceability requests and display traceability results; the food processing data management module is used to store and manage various data generated in the food processing process, including raw material information, processing parameters, environmental indicators, quality test results, etc.

[0023] The user first interacts with the system through the food processing traceability interactive module (which can be a web interface, mobile application or terminal device). The food processing traceability interactive module provides an interface for users to input and view traceability information. The user enters or selects the target food information to be traced (the specific food that the user wants to trace the quality), including the name, batch number, production date, etc. The system automatically or the user manually enters the RFID tag information corresponding to the target food, wherein the RFID tag is a wireless radio frequency identification technology tag that is used to uniquely identify the food and store relevant information, such as serial number, production date, production location, shelf life, etc. At the same time, the system collects processing node information related to the target food and real-time food quality abnormality feature information from various food processing nodes (such as raw material reception, cleaning, cutting, cooking, packaging, warehousing, logistics, etc.) in real time. The real-time food quality abnormality feature information is abnormal data that may affect food quality detected in real time during the processing process, such as abnormal temperature, abnormal humidity, excessive microorganisms, etc. The system integrates the target food information, RFID tag information and real-time collected data into a food processing quality traceability instruction, which contains all the key information required for traceability.

[0024] Step S200, based on the target food RFID tag, the real-time food processing node and the real-time food quality abnormality characteristic information, the food processing quality breadth traceability of the target food is performed according to the food processing data management module to obtain the first food processing quality traceability result. Specifically, according to the target food RFID tag, the system retrieves all historical data associated with the RFID tag in the food processing data management module, which includes all information of the target food from raw material procurement to the current processing node, such as raw material source, processing batch, processing time, operator, environmental parameters (such as temperature, humidity), etc. At the same time, the system will also extract data fragments directly related to the current traceability request according to the real-time food processing node and the real-time food quality abnormality characteristic information.

[0025] The target food is traced for food processing quality breadth, that is, each link of the target food in the processing process is traced comprehensively and extensively to understand the flow and quality status of the target food in the entire processing chain. This traceability method focuses on coverage and comprehensiveness. Specifically, the retrieved historical data is analyzed with real-time data to find out the associated information with the target food at each processing node, including determining the residence time, processing process, quality influencing factors, etc. of the target food at each processing node. According to the real-time food quality abnormality characteristic information, analyze whether these abnormalities are associated with the previous processing nodes and whether they are caused by the previous processing links. Based on the results of the association analysis, a complete traceability path of the target food from raw materials to the current processing node is constructed. This path shows the flow of the target food at each processing node and the key information at each node. The traceability path and the key information of each node are integrated into the first food processing quality traceability result. The first food processing quality traceability result comprehensively shows the quality status of the target food in the processing process, including normal processing links and possible abnormal points.

[0026] In a possible implementation, based on the target food RFID tag, the real-time food processing node and the real-time food quality abnormality feature information, the food processing quality breadth of the target food is traced according to the food processing data management module to obtain the first food processing quality traceability result, and step S200 further includes step S210, based on the target food RFID tag, according to the food processing data management module, the full-process food processing node information of the target food is retrieved. Specifically, the RFID tag on the target food is read by an RFID reader, and the unique identifier (such as a serial number) and other basic information stored in the tag are obtained. The unique identifier in the RFID tag is used to query all historical data associated with it in the food processing data management module, including the full-process information of the food from raw material procurement, various processing links, quality inspection, packaging, warehousing to transportation. Extract the full-process food processing node information related to the target food from the query result, including the name, timestamp, operator, processing parameters, environmental conditions, etc. of each processing node. Step S220, based on the full-process food processing node information, build a first food processing quality traceability chain. Specifically, the extracted food processing node information of the whole process is sorted in chronological order to ensure the continuity of the traceability chain. According to the sorted information, a first food processing quality traceability chain from the raw material to the current real-time food processing node is constructed. This chain shows the flow of the target food at each processing node. Step S230, based on the real-time food processing node, the first food processing quality traceability chain is segmented into foreground processing nodes to generate a second food processing quality traceability chain. Specifically, according to the real-time food processing node information, the corresponding node position is found in the first food processing quality traceability chain, starting from the real-time food processing node, and tracing forward to the raw material procurement node (or a specific starting point), and this part of the traceability chain is retained as the second food processing quality traceability chain. Step S240, the second food processing quality traceability chain is associated and combed based on the real-time food quality abnormality feature information to generate the first food processing quality traceability result. Specifically, based on the real-time food quality abnormality feature information, each processing node in the second food processing quality traceability chain is analyzed for association to determine which nodes may be directly or indirectly associated with the current quality abnormality. In the second food processing quality traceability chain, nodes that may affect food quality are marked, including normal nodes and abnormal nodes. For abnormal nodes, the specific reasons for the abnormality, the degree of impact, and other information are further marked. The sorted second food processing quality traceability chain is output as the first food processing quality traceability result. This result shows the flow of the target food at the prospect processing node and highlights the key nodes that may affect food quality.This implementation method first retrieves information from the entire process to build a complete traceability chain, and then performs targeted segmentation and sorting based on real-time processing nodes and quality abnormality information. It quickly narrows the traceability scope, reduces unnecessary data processing, and achieves the technical effect of improving the accuracy and practicality of traceability results.

[0027] In a possible implementation, the second food processing quality traceability chain is associated and combed based on the real-time food quality abnormal feature information to generate the first food processing quality traceability result. Step S240 further includes step S241, which performs association analysis based on the real-time food quality abnormal feature information and the second food processing quality traceability chain to determine the correlation coefficients of multiple abnormal feature nodes. Specifically, specific abnormal feature items are extracted from the real-time food quality abnormal feature information, such as excessive microorganisms, abnormal additive content, appearance damage, etc. Each abnormal feature item is matched with each processing node in the second food processing quality traceability chain to find out the processing links that may cause these abnormal features. For each matched processing node, the correlation coefficient between the node and the abnormal feature is calculated based on the severity of the abnormal feature item, similar cases in historical data, process characteristics of the processing link, etc. This correlation coefficient is a quantitative indicator used to indicate the degree of association between the node and the abnormal feature. Step S242, it is determined whether the correlation coefficients of the multiple abnormal feature nodes are greater than / equal to the abnormal feature node association threshold, and a set of identified food processing nodes greater than / equal to the abnormal feature node association threshold is obtained. Specifically, according to actual needs or industry standards, an abnormal feature node association threshold is set, which is used to determine whether the association between a processing node and the abnormal feature is significant. The association coefficient of each processing node is compared with the set threshold. If the association coefficient is greater than or equal to the threshold, it is considered that there is a significant association between the node and the abnormal feature. All processing nodes greater than or equal to the threshold are collected to form an identified food processing node set, which includes all processing nodes that are significantly associated with real-time food quality abnormal features.

[0028] Step S243, sorting and arranging according to the identified food processing node set to obtain the first food processing quality traceability result. Specifically, according to actual needs or industry standards, a sorting rule is formulated, and the sorting rule can be based on factors such as the size of the correlation coefficient, the order of the processing nodes, and the severity of the abnormal characteristics. According to the formulated sorting rules, the nodes in the identified food processing node set are sorted. The purpose of sorting is to more clearly show the degree of association between each node and the abnormal characteristics and their position in the processing flow. The sorted identified food processing node set is output as the first food processing quality traceability result. This result includes processing nodes that are significantly associated with abnormal characteristics, and shows the degree of association between these nodes and abnormal characteristics and their position relationship in the processing flow. This implementation method accurately identifies the processing nodes that are significantly associated with real-time food quality abnormal characteristics through association analysis and threshold judgment. Through sorting and arranging, the position and degree of association of these nodes in the processing flow are further demonstrated, achieving the technical effect of enhancing the readability and practicality of the traceability results.

[0029] Step S300, based on the first food processing quality traceability result, the food processing quality of the target food is deeply traced according to the food processing data management module to obtain a second food processing quality traceability result. Specifically, after completing the breadth traceability, the deep traceability process is started according to the key information in the first food processing quality traceability result (such as abnormal feature nodes, key processing links, etc.). Compared with the breadth traceability, the deep traceability focuses more on specific links or factors, and reveals the root causes and potential influencing factors of quality problems through in-depth analysis.

[0030] Detailed data closely related to the key information in the first food processing quality traceability results are screened out from the food processing data management module, including but not limited to: detailed operation records of specific processing nodes, raw material batch information, environmental monitoring data, equipment operating status, etc., focusing on specific links or factors that may cause quality problems, reducing unnecessary data analysis. The screened data are deeply analyzed, and technical means such as data mining and machine learning are used to identify potential root causes of quality problems, analyze possible quality influencing factors, such as raw material quality fluctuations, abnormal processing parameters, equipment failures, personnel operation errors, etc., evaluate the contribution of each factor to quality problems, and determine the main responsible party or source of the problem. According to the results of the deep analysis, one or more deep traceability chains from raw materials to final products are constructed. These traceability chains show the entire process from the generation to the detection of quality problems. The analysis results and traceability chains of the deep traceability are organized into a report form as the second food processing quality traceability results. The second food processing quality traceability results contain a detailed description of the quality problem, root cause analysis of the problem, determination of the responsible party, and improvement suggestions.

[0031] In a possible implementation, based on the first food processing quality traceability result, the food processing quality of the target food is deeply traced according to the food processing data management module to obtain a second food processing quality traceability result, and step S300 further includes step S310, based on the target food RFID tag, activating the target food processing data block in the food processing data management module. Specifically, read the RFID tag information of the target food, use the RFID tag information as an index or query condition, and search and activate the data blocks associated with the target food in the food processing data management module. These data blocks store detailed data from raw material procurement, processing, quality testing to finished product delivery. Step S320, associate the data indicators of the first food processing quality traceability result according to the target food processing data block to obtain the first traceability indicator distribution. Specifically, extract key data indicators from the first food processing quality traceability results, such as processing temperature, humidity, time, raw material batch number, etc., match and associate these indicators with the data in the target food processing data block, and construct a data indicator distribution diagram or table of the target food in the entire processing process to obtain the first traceability indicator distribution, that is, the distribution of each data indicator at different stages or nodes. Step S330, based on the real-time food quality abnormal characteristic information, the first traceability indicator distribution is associated and cleaned to determine the second traceability indicator distribution. Specifically, the real-time food quality abnormal characteristic information is used as a filter to clean the first traceability indicator distribution, and those data indicators that do not match the abnormal characteristic information are removed or marked to reduce noise and interference. After cleaning, a more accurate and reliable second traceability indicator distribution is obtained.

[0032] Step S340, optimize the data generation time constraint according to the second traceability indicator distribution to obtain the third traceability indicator distribution. Specifically, analyze the data generation time in the second traceability indicator distribution, identify the existing time delays, missing or inconsistencies, apply the time constraint optimization algorithm, correct and adjust these problems to ensure the continuity and accuracy of the data in the time dimension, and after optimization, obtain a third traceability indicator distribution that is more in line with the actual situation. Step S350, retrieve data from the target food processing data block based on the third traceability indicator distribution to generate the second food processing quality traceability result. Specifically, according to the key data points and time nodes in the third traceability indicator distribution, retrieve relevant detailed data from the target food processing data block, integrate, analyze and interpret these data to form a complete second food processing quality traceability report, which contains key information such as a detailed description of the quality problem, root cause analysis of the problem, data indicator distribution diagram, timeline, etc. This implementation method activates relevant data blocks through RFID tags to quickly locate the processing data of the target food; then, through data indicator association and cleaning, irrelevant interference factors are eliminated, and time constraint optimization is used to ensure the continuity and accuracy of the data. Finally, a detailed traceability report is generated based on the optimized data, achieving the technical effect of ensuring the depth and accuracy of food processing quality traceability.

[0033] Step S400, based on the second food processing quality traceability result, data breakpoint compensation is performed to obtain a third food processing quality traceability result. Specifically, the second food processing quality traceability result is analyzed in detail to identify the data breakpoints therein, which refer to data missing or discontinuous phenomena caused by certain reasons (such as equipment failure, human error, data transmission problems, etc.) in the data sequence, which are manifested as data missing, time jump, logical discontinuity, etc., which will affect the integrity and accuracy of the traceability results. For each identified data breakpoint, the cause of its generation is further analyzed, such as checking the operation records of related equipment, querying system logs, investigating personnel operations, etc., to determine whether the breakpoint is caused by technical failure, human negligence or other reasons. According to the breakpoint cause analysis results, a corresponding data breakpoint compensation strategy is formulated, including data interpolation (a method of estimating unknown data points through known data points, such as linear interpolation, polynomial interpolation, etc.), using the average value of historical data, estimating based on expert experience, etc., to restore the integrity and continuity of the data. According to the formulated compensation strategy, the breakpoints in the second food processing quality traceability result are compensated, including inserting estimated data values ​​at the breakpoints, adjusting the values ​​of adjacent data points to be logical, etc. After the compensation is completed, the compensation results are verified to ensure that the compensated data is both logical and accurate. The verification includes comparing with known data and checking whether the data change trend is reasonable. After breakpoint compensation and verification, the third food processing quality traceability result is generated. This result not only retains the key information of the original traceability result, but also restores the integrity and continuity of the data through breakpoint compensation, and has higher reliability.

[0034] In a possible implementation, data breakpoint compensation is performed based on the second food processing quality traceability result to obtain a third food processing quality traceability result. Step S400 further includes step S410, performing breakpoint detection according to the second food processing quality traceability result to obtain first traceability breakpoint data. Specifically, the data sequence in the second food processing quality traceability result is traversed to identify discontinuous points in the data, such as time jumps, data missing, etc., which are breakpoints, and the location, timestamp, missing data type and other information of each breakpoint are recorded to form first traceability breakpoint data. The first traceability breakpoint data is a set of data breakpoints detected for the first time, and contains specific information of the breakpoints. Step S420, according to the food processing data backup module of the food processing quality traceability system, data compensation is performed on the first traceability breakpoint data to obtain the first traceability breakpoint data compensation result. Specifically, the food processing data backup module is queried. The food processing data backup module is a module in the food processing quality traceability system for storing historical data generated during food processing so as to restore or compensate when necessary. If there is historical backup data matching the first traceability breakpoint data in the food processing data backup module, the breakpoint is compensated using the historical backup data, such as direct filling, interpolation, etc., and the compensated data is recorded to form the first traceability breakpoint data compensation result. Step S430, the second food processing quality traceability result is compensated according to the first traceability breakpoint data compensation result to obtain the second food processing quality traceability optimization result. Specifically, the compensation data in the first traceability breakpoint data compensation result is inserted into the corresponding position in the second food processing quality traceability result, and the second food processing quality traceability result is updated to form the second food processing quality traceability optimization result, and the data integrity in the second food processing quality traceability optimization result is improved. Step S440, breakpoint detection is performed according to the second food processing quality traceability optimization result to obtain the second traceability breakpoint data. Specifically, the process of step S410 is repeated, but this time the breakpoint detection is performed on the second food processing quality traceability optimization result, and the newly detected breakpoint is recorded to form the second traceability breakpoint data, and the second traceability breakpoint data is the breakpoint set found in the second breakpoint detection. Step S450, according to the second food processing quality traceability optimization result, the trend feature association analysis of the second traceability breakpoint data is performed to obtain the breakpoint data trend feature association analysis result. Specifically, the data change trend before and after the second traceability breakpoint data in the second food processing quality traceability optimization result is analyzed, and the data feature association before and after the breakpoint is identified by statistical, machine learning and other methods, and the analysis results are recorded to form the breakpoint data trend feature association analysis result. Step S460, according to the breakpoint data trend feature association analysis result, the second traceability breakpoint data is compensated to obtain the second traceability breakpoint data compensation result.Specifically, the second traceability breakpoint data is compensated for data by using the trend feature association analysis results of the breakpoint data. The compensation methods include trend-based interpolation, feature matching-based prediction, etc. The compensated data is recorded to form the second traceability breakpoint data compensation results. Step S470: The second food processing quality traceability optimization result is compensated according to the second traceability breakpoint data compensation result to generate the third food processing quality traceability result. Specifically, the compensation data in the second traceability breakpoint data compensation result is inserted into the corresponding position in the second food processing quality traceability optimization result, and finally the third food processing quality traceability result is generated, which has higher data integrity and accuracy. This implementation adopts an iterative and refined method to deal with the data breakpoint problem. Through the initial breakpoint detection and compensation, most of the data integrity is quickly restored. Then, through the second breakpoint detection and trend feature association analysis, those breakpoints that were not processed for the first time or newly appeared are further identified and compensated. This step-by-step deepening and step-by-step optimization method ensures that the third food processing quality traceability result finally generated has higher data quality and reliability.

[0035] In a possible implementation, breakpoint detection is performed according to the second food processing quality traceability result to obtain the first traceability breakpoint data, and step S410 further includes step S411, performing change trend correlation analysis according to the second food processing quality traceability result to obtain traceability data change trend correlation analysis results. Specifically, extract key quality parameter data such as temperature, humidity, timestamp, etc. from the second food processing quality traceability result, use statistical methods or machine learning algorithms to perform trend analysis on these data, identify the rising, falling, stable and other trends in the data, and further analyze the correlation between different quality parameters, such as the relationship between temperature and humidity, and how these relationships change over time. Output the analysis results in the form of charts, reports, etc. to provide a basis for subsequent breakpoint detection. Step S412, activate the pre-built breakpoint detection model. Specifically, load the pre-trained breakpoint detection model from the storage system, the breakpoint detection model is a model that can automatically identify data breakpoints (i.e., points where data suddenly changes or is missing), and uses historical data collected from various links in the food processing process, including key quality parameters such as temperature, humidity, timestamps, etc. as training data to train the machine learning algorithm. Perform necessary initialization operations on the breakpoint detection model, such as setting parameters, loading weights, etc., to ensure that the model can operate normally. Step S413, based on the second food processing quality traceability result and the traceability data change trend correlation analysis result, generate the first traceability breakpoint data according to the breakpoint detection model. Specifically, the second food processing quality traceability result and the traceability data change trend correlation analysis result are used as input and provided to the breakpoint detection model. The breakpoint detection model processes the input data, identifies the breakpoints in the data by calculating the statistical characteristics of the data, comparing the differences between the actual data and the expected pattern, etc., and determines the specific breakpoint location and properties (such as missing, abnormal, etc.) according to the output of the model. The detected breakpoint data is output in the form of a list, report, etc. as a basis for subsequent data breakpoint compensation. This implementation adopts a breakpoint detection model for breakpoint detection. The breakpoint detection model is trained based on a large amount of data and has strong generalization ability and robustness. By combining the results of the trend correlation analysis, the detection performance of the model is further improved, and the technical effect of improving the efficiency and accuracy of breakpoint detection is achieved.

[0036] Step S500, data source confidence optimization is performed according to the third food processing quality traceability result to obtain a fourth food processing quality traceability result. Specifically, all data sources in the third food processing quality traceability result are traversed, and quality assessment is performed on each data source, including data integrity, accuracy, timeliness, sensor accuracy, operator reliability, etc., to determine its reliability and accuracy. According to the assessment results, the reliability of the data source is quantified, and a confidence score is assigned to each data source. For example, a confidence scoring model is designed or selected, which can calculate the confidence score by integrating multiple factors (such as the reliability, historical performance, data type, etc. of the data source), and the confidence score of each data source is calculated using the confidence scoring model according to the result of the data source quality assessment. In the third food processing quality traceability result, there will be information from multiple data sources for the same traceability point. According to the confidence score of each data source, these information are weighted and fused, and the data source with high confidence has a greater weight in the fusion result. Through weighted fusion, more accurate and reliable traceability information is obtained. During or after the data fusion process, outlier detection is performed. Outliers may be caused by data errors, equipment failures, or human interference. The detected outliers are processed according to context information and confidence scores. For example, if the outliers are significantly deviated from other data and have low confidence, they can be directly removed; if the outliers are caused by missing data or temporary equipment failures, interpolation methods (such as linear interpolation, polynomial interpolation, etc.) can be used to fill the gaps. The data after confidence optimization processing are integrated to form the fourth food processing quality traceability results, which contain more accurate, reliable and comprehensive food processing quality information.

[0037] In a possible implementation, the data source confidence optimization is performed according to the third food processing quality traceability result to obtain the fourth food processing quality traceability result, and step S500 further includes step S510, which locates the data source according to the third food processing quality traceability result to obtain the source of each traceability parameter. Specifically, the third food processing quality traceability result is parsed to identify the various traceability parameters involved therein, such as temperature, humidity, raw material batch number, etc. For each traceability parameter, its source information is queried through the traceability system or database, including production equipment sensors, manual records, third-party testing agency reports, etc., and the source information of each traceability parameter is recorded to prepare for subsequent steps. Step S520, confidence evaluation is performed according to the source of each traceability parameter to obtain the confidence evaluation results of each source. Specifically, according to the reliability, accuracy, timeliness and other factors of the data source, the confidence evaluation standards and weights are formulated, and according to the evaluation standards, the source of each traceability parameter is confidence evaluated to calculate the confidence of each source. The credibility of the data source is evaluated to determine its reliability and importance in the traceability results. The confidence is a quantitative indicator representing the confidence evaluation result of the data source, such as a value between 0 and 1, and the higher the value, the higher the confidence. Step S530, based on the traceability parameter source confidence evaluation constraint, the abnormal source of the third food processing quality traceability result is located according to the confidence evaluation results of each source, and the abnormal source traceability data is obtained. Specifically, according to actual needs, a confidence threshold is set to determine whether the data source is abnormal. According to the confidence evaluation results and confidence threshold of each source, the abnormal sources below the threshold are identified, and the traceability data corresponding to these sources are located. Step S540, according to the abnormal source traceability data, the data generation process record is retrieved to obtain the abnormal source data generation process record. Specifically, for the traceability data of the abnormal source, the data generation process record is retrieved through the food processing quality traceability system or the related record system, including the records of each link such as data collection, processing, and transmission, and the retrieved process records are analyzed to find the causes or problems that lead to data abnormality. Step S550, based on the abnormal source data generation process record and the abnormal source traceability data, the third food processing quality traceability result is optimized in confidence to obtain the fourth food processing quality traceability result. Specifically, according to the analysis results of the abnormal source data generation process record, the traceability data of the abnormal source is corrected or eliminated, and after the abnormal data is corrected or eliminated, the food processing quality traceability result is recalculated to obtain a more accurate and reliable fourth food processing quality traceability result. This implementation method quantifies the reliability of data from different sources through data source positioning and confidence evaluation, and processes the abnormal data in the traceability results according to the confidence evaluation results, thereby achieving the technical effect of improving the scientific nature of data optimization.

[0038] Step S600, according to the food processing traceability interaction module, encrypt and feedback the fourth food processing quality traceability result. Specifically, an encryption algorithm (such as AES, RSA, etc.) is selected, and the fourth food processing quality traceability result is encrypted according to the encryption key of the user or system. The encrypted traceability result data is sent to the specified user or system through a secure communication protocol (such as HTTPS, TLS / SSL, etc.). After receiving the encrypted traceability result, the user or system uses the corresponding decryption key and algorithm to decrypt it, and through the food processing traceability interaction module, the encrypted traceability result is displayed to the user in a secure manner. The embodiment of the present application uses RFID tags to capture the processing nodes and quality abnormality feature information of the target food in real time, conduct breadth tracing, quickly locate the possible links where the problem occurs, conduct depth tracing, deeply analyze the processing details and quality influencing factors of the specific links, perform data breakpoint compensation, conduct confidence assessment and optimization of the traceability results, and feed back the traceability results to the relevant parties in an encrypted manner. Technical means such as achieving comprehensive and in-depth traceability of food processing quality, thereby timely discovering and solving quality problems.

[0039] Based on the foregoing embodiments, the embodiments of the present application further provide an electronic device and a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of the electronic device, it can implement the method described in any of the foregoing embodiments.

[0040] Figure 2 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing an implementation of the present invention. Figure 2 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention. The electronic device is in the form of a general computing device, and its components may include but are not limited to an input device 201, a processor 202, a memory 203, and an output device 204. Among them, the processor 202 may be one or more; the memory 203 may include a computer-readable medium and at least one program product, and the program product has a set (at least one) of program modules, which are configured to perform the functions of each embodiment of the present application.

[0041] The memory 203 shown in the embodiment of the present invention may adopt any combination of one or more computer-readable media; the computer-readable storage medium may be, but is not limited to, an infrared, semiconductor system, device or device, or any combination of the above, for storing software programs, computer executable programs and modules, such as program instructions / modules corresponding to a food processing quality traceability method in an embodiment of the present invention. The processor 202 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 203, that is, realizing the above-mentioned food processing quality traceability method.

[0042] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A food processing quality traceability method, characterized in that: The method is applied to a food processing quality traceability system, which includes a food processing traceability interaction module and a food processing data management module. The method includes: According to the food processing traceability interaction module, a food processing quality traceability instruction is obtained, wherein the food processing quality traceability instruction includes a target food, and a target food RFID tag corresponding to the target food, a real-time food processing node, and real-time food quality abnormality feature information; Based on the target food RFID tag, the real-time food processing node and the real-time food quality abnormality characteristic information, the food processing quality breadth traceability of the target food is performed according to the food processing data management module to obtain a first food processing quality traceability result; Based on the first food processing quality traceability result, the food processing quality of the target food is deeply traced according to the food processing data management module to obtain a second food processing quality traceability result; Performing data breakpoint compensation based on the second food processing quality traceability result to obtain a third food processing quality traceability result; performing data source confidence optimization according to the third food processing quality traceability result to obtain a fourth food processing quality traceability result; According to the food processing traceability interactive module, the fourth food processing quality traceability result is encrypted and fed back.

2. A food processing quality tracing method as claimed in claim 1, characterized in that: Based on the target food RFID tag, the real-time food processing node and the real-time food quality abnormality characteristic information, the food processing quality breadth tracing of the target food is performed according to the food processing data management module to obtain a first food processing quality tracing result, including: Based on the target food RFID tag, according to the food processing data management module, retrieve the full-process food processing node information of the target food; Constructing a first food processing quality traceability chain based on the food processing node information of the entire process; Based on the real-time food processing nodes, the first food processing quality traceability chain is segmented into foreground processing nodes to generate a second food processing quality traceability chain; Based on the real-time food quality abnormality characteristic information, the second food processing quality traceability chain is associated and sorted to generate the first food processing quality traceability result.

3. A food processing quality tracing method as claimed in claim 2, characterized in that: Based on the real-time food quality abnormality feature information, the second food processing quality traceability chain is associated and sorted to generate the first food processing quality traceability result, including: Performing association analysis based on the real-time food quality abnormality feature information and the second food processing quality traceability chain to determine association coefficients of multiple abnormal feature nodes; Determine whether the correlation coefficients of the plurality of abnormal feature nodes are greater than / equal to an abnormal feature node correlation threshold, and obtain a set of identified food processing nodes greater than / equal to the abnormal feature node correlation threshold; The identified food processing node set is sorted and arranged to obtain the first food processing quality traceability result.

4. A food processing quality tracing method as claimed in claim 1, characterized in that: Based on the first food processing quality traceability result, the food processing quality of the target food is deeply traced according to the food processing data management module to obtain a second food processing quality traceability result, including: Based on the target food RFID tag, activating the target food processing data block in the food processing data management module; Associating the first food processing quality traceability result with data indicators according to the target food processing data block to obtain a first traceability indicator distribution; Based on the real-time food quality abnormality characteristic information, the first traceability indicator distribution is associated and cleaned to determine a second traceability indicator distribution; Performing data generation time constraint optimization according to the second traceability indicator distribution to obtain a third traceability indicator distribution; The target food processing data block is retrieved based on the third traceability indicator distribution to generate the second food processing quality traceability result.

5. A food processing quality tracing method as claimed in claim 1, characterized in that: Performing data breakpoint compensation based on the second food processing quality traceability result to obtain a third food processing quality traceability result includes: Perform breakpoint detection according to the second food processing quality traceability result to obtain first traceability breakpoint data; According to the food processing data backup module of the food processing quality traceability system, data compensation is performed on the first traceability breakpoint data to obtain a first traceability breakpoint data compensation result; Compensating the second food processing quality traceability result according to the first traceability breakpoint data compensation result to obtain a second food processing quality traceability optimization result; Perform breakpoint detection according to the second food processing quality traceability optimization result to obtain second traceability breakpoint data; Performing trend feature correlation analysis on the second traceability breakpoint data according to the second food processing quality traceability optimization result to obtain a breakpoint data trend feature correlation analysis result; Performing data compensation on the second tracing breakpoint data according to the result of the breakpoint data trend feature association analysis to obtain a second tracing breakpoint data compensation result; The second food processing quality traceability optimization result is compensated according to the second traceability breakpoint data compensation result to generate the third food processing quality traceability result.

6. A food processing quality tracing method as claimed in claim 5, characterized in that: Performing breakpoint detection according to the second food processing quality traceability result to obtain first traceability breakpoint data includes: Performing a correlation analysis of the change trend according to the second food processing quality traceability result to obtain a correlation analysis result of the traceability data change trend; Activate pre-built breakpoint detection models; Based on the second food processing quality traceability result and the traceability data change trend correlation analysis result, the first traceability breakpoint data is generated according to the breakpoint detection model.

7. A food processing quality tracing method as claimed in claim 1, characterized in that: According to the third food processing quality traceability result, data source confidence optimization is performed to obtain a fourth food processing quality traceability result, including: Locate the data source according to the third food processing quality traceability result to obtain the source of each traceability parameter; Perform confidence evaluation according to the sources of each traceability parameter to obtain confidence evaluation results of each source; Based on the traceability parameter source confidence evaluation constraint, the abnormal source of the third food processing quality traceability result is located according to the confidence evaluation results of each source, and the abnormal source traceability data is obtained; Retrieve the data generation process record according to the abnormal source tracing data to obtain the abnormal source data generation process record; The third food processing quality traceability result is confidence optimized based on the abnormal source data generation process record and the abnormal source traceability data to obtain the fourth food processing quality traceability result.

8. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable instructions; A processor, used to implement a food processing quality traceability method as described in any one of claims 1 to 7 when executing executable instructions stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a food processing quality traceability method as described in any one of claims 1 to 7 is implemented.

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