Organic fertilizer quality safety traceability management method based on multi-source data

Abstract

The invention relates to the technical field of quality traceability management, in particular to an organic fertilizer quality safety traceability management method based on multi-source data. The method comprises the following steps: collecting organic fertilizer source data in real time from links of organic fertilizer production, circulation, storage and use, performing standardization processing, and performing production batch synchronous set processing and traceability association connection file establishment to generate an organic fertilizer multi-source data traceability association connection file; and carrying out production batch tracing extraction processing on the organic fertilizer multi-source data traceability association connection file, carrying out organic fertilizer quality index evaluation analysis and quality index error limitation analysis, carrying out organic fertilizer quality safety abnormity traceability processing and abnormity decision support processing, and generating an organic fertilizer quality safety abnormity problem decision support report. Therefore, the corresponding organic fertilizer quality safety abnormity traceability problem management work is executed. The scientificity and accuracy of organic fertilizer quality safety management can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of quality traceability management, and in particular to an organic fertilizer quality safety traceability management method based on multi-source data. Background Art

[0002] As an important means to improve soil fertility, the quality and safety of organic fertilizer directly affects the effect of agricultural production and the safety of agricultural products. It can integrate information from multiple data sources such as raw material suppliers, manufacturers, transportation links, warehousing management and end users. These data include raw material sources, production processes, quality inspection results, transportation records and usage, etc., and use sensors, Internet of Things technology, etc. to collect and monitor data from various links in real time. For example, sensors installed on the production line can record various indicators in the production process in real time, and the GPS system on the transportation vehicle can track the transportation route and time in real time. At the same time, through data fusion technology, data from different sources are integrated and analyzed, and the collected data are processed using big data analysis and artificial intelligence algorithms, so as to provide a comprehensive evaluation and early warning of the quality of organic fertilizers. When quality problems are found, they can be quickly traced back to specific production batches and links, generate detailed reports, help locate the source of the problem and formulate improvement measures, so as to achieve comprehensive tracking and management of the entire process of organic fertilizer production and circulation. However, traditional organic fertilizer quality management methods usually use static data recording methods, which fail to effectively integrate data from production, transportation, storage and use. This method leads to information isolation and makes it difficult to accurately trace to specific production batches or links. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide an organic fertilizer quality and safety traceability management method based on multi-source data to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for tracing the quality and safety of organic fertilizer based on multi-source data includes the following steps:

[0005] Step S1: collecting organic fertilizer source data in real time from the organic fertilizer production link, the organic fertilizer circulation link, the organic fertilizer storage link and the organic fertilizer use link and performing standardization processing to obtain organic fertilizer production source standard data, organic fertilizer circulation source standard data, organic fertilizer storage source standard data and organic fertilizer use source standard data;

[0006] Step S2: performing production batch synchronization collection processing on the organic fertilizer production source standard data, the organic fertilizer circulation source standard data, the organic fertilizer storage source standard data and the organic fertilizer use source standard data to obtain the organic fertilizer multi-source data set under each production batch; establishing a traceability association connection file for the organic fertilizer multi-source data set under each production batch to generate an organic fertilizer multi-source data traceability association connection file;

[0007] Step S3: performing production batch tracing extraction processing on the organic fertilizer multi-source data traceability association connection archive to obtain a full life cycle tracing process diagram of organic fertilizer for each production batch; performing organic fertilizer quality index evaluation and analysis on the full life cycle tracing process diagram of organic fertilizer for each production batch to obtain full life cycle quality index data of organic fertilizer for each production batch; performing quality index error limit analysis on the full life cycle tracing process diagram of organic fertilizer for each production batch based on the full life cycle quality index data of organic fertilizer for each production batch to obtain the error limit factor between the organic fertilizer quality indicators corresponding to each production batch;

[0008] Step S4: Based on the error limiting factors between the organic fertilizer quality indicators corresponding to each production batch, the organic fertilizer full life cycle traceability process diagram of each production batch is traced for organic fertilizer quality and safety abnormalities to obtain the organic fertilizer quality and safety abnormality traceability problem points of each production batch; abnormal decision support processing is performed on the organic fertilizer quality and safety abnormality traceability problem points of each production batch, and a decision support report on organic fertilizer quality and safety abnormalities is generated to perform corresponding organic fertilizer quality and safety abnormality traceability problem management work.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: collecting organic fertilizer production source data in real time from the organic fertilizer production process, wherein the organic fertilizer production source data includes organic fertilizer raw material sources, organic fertilizer production batches, and organic fertilizer production and processing conditions;

[0011] Step S12: collecting organic fertilizer circulation source data in real time from the organic fertilizer circulation link, wherein the organic fertilizer circulation source data includes organic fertilizer logistics information and organic fertilizer sales records;

[0012] Step S13: collecting organic fertilizer storage source data in real time from the organic fertilizer storage link, wherein the organic fertilizer storage source data includes organic fertilizer storage environment conditions and organic fertilizer storage inventory information;

[0013] Step S14: collecting organic fertilizer use source data in real time from the organic fertilizer use link, wherein the organic fertilizer use source data includes organic fertilizer application records and growth status of crops using organic fertilizer;

[0014] Step S15: Preprocessing and standardizing the organic fertilizer production source data, the organic fertilizer circulation source data, the organic fertilizer storage source data, and the organic fertilizer use source data to obtain the organic fertilizer production source standard data, the organic fertilizer circulation source standard data, the organic fertilizer storage source standard data, and the organic fertilizer use source standard data.

[0015] Further, step S2 includes the following steps:

[0016] Step S21: extracting the production batches of the organic fertilizer production source standard data to obtain the organic fertilizer production batches;

[0017] Step S22: performing production identification processing on the organic fertilizer production batch to obtain the organic fertilizer production batch identification number;

[0018] Step S23: Based on the fertilizer production batch identification number, the fertilizer production source standard data, the fertilizer circulation source standard data, the fertilizer storage source standard data and the fertilizer use source standard data are processed by production batch synchronization division to obtain the fertilizer production source sub-data, the fertilizer circulation source sub-data, the fertilizer storage source sub-data and the fertilizer use source sub-data under each production batch;

[0019] Step S24: performing multi-source data set processing on the organic fertilizer production source sub-data, the organic fertilizer circulation source sub-data, the organic fertilizer storage source sub-data and the organic fertilizer use source sub-data under each production batch to obtain an organic fertilizer multi-source data set under each production batch;

[0020] Step S25: Establishing a traceability association connection file for the organic fertilizer multi-source data set under each production batch to generate an organic fertilizer multi-source data traceability association connection file.

[0021] Further, step S25 includes the following steps:

[0022] Step S251: extracting raw material input attribute data from the organic fertilizer production source sub-data in the organic fertilizer multi-source data set under each production batch, to obtain the organic fertilizer production raw material input attribute data under each production batch;

[0023] Step S252: Based on the input attribute data of the organic fertilizer production raw materials under each production batch, the organic fertilizer multi-source data set under the corresponding production batch is characterized by describing the key nodes of the data source, so as to obtain the key node characteristic description data of the organic fertilizer data source under each production batch;

[0024] Step S253: performing production batch time series correlation analysis on the key node feature description data of the organic fertilizer data source under each production batch, and obtaining the production time series correlation relationship between the key nodes of the organic fertilizer data source of each production batch;

[0025] Step S254: tracing the traceability association path of the organic fertilizer multi-source data set under each production batch based on the production time sequence association relationship between the key nodes of the organic fertilizer data source of each production batch, so as to obtain the traceability association path of the organic fertilizer data under each production batch;

[0026] Step S255: establishing a traceability association connection file for the organic fertilizer multi-source data set under each production batch according to the organic fertilizer data traceability association path under each production batch, so as to generate an organic fertilizer multi-source data traceability association connection file.

[0027] Further, step S254 includes the following steps:

[0028] Obtain the production timestamp and node information data of the key nodes of each production batch of organic fertilizer data source, and perform inter-node dynamic dependency analysis on the production timestamp and node information data of the key nodes of each production batch of organic fertilizer data source based on the production time series association relationship between the key nodes of each production batch of organic fertilizer data source, and obtain a time information dynamic dependency relationship map between the key nodes of each production batch of organic fertilizer data source;

[0029] Perform hierarchical temporal clustering analysis on the time information dynamic dependency relationship map between key nodes of each production batch of organic fertilizer data source to obtain a hierarchical temporal correlation clustering map of the production batch of organic fertilizer data source;

[0030] Based on the hierarchical time-series association clustering map of production batch organic fertilizer data sources, the traceability association path of the organic fertilizer multi-source data set under each production batch is traced to obtain the traceability association path of the organic fertilizer data under each production batch.

[0031] Further, step S3 includes the following steps:

[0032] Step S31: extracting and processing the organic fertilizer production stage nodes and stage connection attributes of the organic fertilizer multi-source data traceability association connection archive to obtain the organic fertilizer multi-source data production stage nodes and the organic fertilizer multi-source data production stage connection attributes;

[0033] Step S32: performing production batch traceability path connection processing on the organic fertilizer multi-source data traceability association connection archive based on the organic fertilizer multi-source data production stage node and the organic fertilizer multi-source data production stage connection attribute, so as to generate the organic fertilizer multi-source data full life cycle traceability path of each production batch;

[0034] Step S33: graphically processing the full life cycle traceability path of the multi-source data of each production batch of organic fertilizer to obtain a full life cycle traceability process diagram of each production batch of organic fertilizer;

[0035] Step S34: evaluating and analyzing the quality index of organic fertilizer on the whole life cycle traceability process diagram of each production batch of organic fertilizer to obtain the quality index data of the whole life cycle of organic fertilizer for each production batch;

[0036] Step S35: Based on the organic fertilizer life cycle quality index data of each production batch, a quality index error limit analysis is performed on the organic fertilizer life cycle traceability process diagram of each production batch to obtain the error limit factor between the organic fertilizer quality indicators corresponding to each production batch.

[0037] Further, step S34 includes the following steps:

[0038] Step S341: extracting and processing organic fertilizer quality-related data from the organic fertilizer life cycle traceability process diagram of each production batch to obtain a data set related to the quality of the organic fertilizer life cycle of each production batch;

[0039] Step S342: Deconstruct and classify the quality attribute-related data sets of the organic fertilizer life cycle of each production batch to obtain organic fertilizer quality-related sub-data corresponding to different quality attributes in each production batch;

[0040] Step S343: performing organic fertilizer quality index evaluation and analysis on the organic fertilizer quality-related sub-data corresponding to different quality attributes in each production batch, and obtaining the organic fertilizer full life cycle quality index data of each production batch.

[0041] Further, step S35 includes the following steps:

[0042] Step S351: performing quality index error evaluation and analysis between each organic fertilizer quality index in the organic fertilizer full life cycle quality index data of each production batch, and obtaining the quality index error between the organic fertilizer quality indexes corresponding to each production batch;

[0043] Step S352: performing error distribution statistical analysis on the quality index errors between the quality indexes of the organic fertilizers corresponding to the various production batches, and obtaining error distribution characteristic data between the quality indexes of the organic fertilizers corresponding to the various production batches;

[0044] Step S353: Based on the error distribution characteristic data between the organic fertilizer quality indicators corresponding to each production batch, the error sensitivity evaluation and analysis is performed on the corresponding organic fertilizer production stage nodes in the organic fertilizer full life cycle traceability process diagram of each production batch, so as to obtain the sensitivity response degree of the organic fertilizer production stage nodes of each production batch to the quality indicator error;

[0045] Step S354: Based on the sensitivity response degree of the organic fertilizer production stage nodes of each production batch to the quality index error, an error limiting factor evaluation and analysis is performed between each organic fertilizer quality index in the organic fertilizer life cycle quality index data of each production batch, so as to obtain the error limiting factor between the organic fertilizer quality indicators corresponding to each production batch.

[0046] Further, step S4 includes the following steps:

[0047] Step S41: performing quantitative calculation of error coefficients on the organic fertilizer quality indicators corresponding to each production batch based on the error limiting factors between the organic fertilizer quality indicators corresponding to each production batch, so as to obtain error coefficients of the organic fertilizer quality indicators corresponding to each production batch;

[0048] Step S42: according to the preset error threshold, the error coefficient of the corresponding organic fertilizer quality index of each production batch is compared and judged, when the error coefficient of the corresponding organic fertilizer quality index of each production batch is greater than or equal to the preset error threshold, the organic fertilizer data source node corresponding to the organic fertilizer quality index under its production batch is determined as the error source node, to obtain the organic fertilizer quality error source node of each production batch; when the error coefficient of the corresponding organic fertilizer quality index of each production batch is less than the preset error threshold, no processing is performed;

[0049] Step S43: performing error mapping anomaly annotation processing on the organic fertilizer life cycle traceability process diagram of each production batch based on the organic fertilizer quality error source node of each production batch, so as to obtain an error mapping anomaly annotation diagram of the organic fertilizer life cycle of each production batch;

[0050] Step S44: performing organic fertilizer quality and safety anomaly tracing processing on the organic fertilizer full life cycle error mapping anomaly annotation diagram of each production batch to obtain the organic fertilizer quality and safety anomaly tracing problem points of each production batch;

[0051] Step S45: Perform abnormal decision support processing on the abnormal traceability problem points of organic fertilizer quality and safety of each production batch, generate an abnormal decision support report on organic fertilizer quality and safety, and perform corresponding organic fertilizer quality and safety abnormal traceability problem management work.

[0052] Further, step S44 includes the following steps:

[0053] Perform abnormal pattern recognition analysis on the abnormal labeled nodes of the organic fertilizer data source corresponding to the abnormal labeled graph of the organic fertilizer life cycle error mapping of each production batch, and obtain abnormal pattern recognition information data of the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch;

[0054] According to the abnormal pattern recognition information data of the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch, the abnormal impact factor evaluation and analysis are performed to obtain the abnormal impact factor of the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch;

[0055] Based on the abnormal influencing factors of the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch, the abnormal relationship mining analysis is performed on the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch in the abnormal labeled graph of the organic fertilizer full life cycle error mapping, and the abnormal relationship network graph between the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch is obtained;

[0056] According to the abnormal relationship network diagram between the abnormal annotation nodes of the organic fertilizer data source corresponding to each production batch, the abnormal annotation nodes of the organic fertilizer data source corresponding to the organic fertilizer full life cycle error mapping abnormal annotation diagram of each production batch are processed for organic fertilizer quality and safety abnormality traceability to obtain the organic fertilizer quality and safety abnormality traceability problem points of each production batch.

[0057] Beneficial effects of the present invention:

[0058] The organic fertilizer quality and safety traceability management method based on multi-source data proposed by the present invention, compared with the prior art, the beneficial effect of the present application is that by collecting organic fertilizer production source data in real time from the organic fertilizer production link, the raw material source, production batch and processing conditions of each batch of organic fertilizer can be accurately tracked, and this process significantly improves the transparency and traceability of the production link, helps to ensure the quality and safety of the raw materials, and prevents the use of unqualified or contaminated raw materials from entering the production process. By collecting organic fertilizer circulation source data in real time from the organic fertilizer circulation link, including logistics information and sales records, the efficiency and transparency of the circulation process can be significantly improved. The record of logistics information can track every step of the organic fertilizer in the transportation process, which is very important to ensure that the product is not damaged during transportation, does not expire, and other problems, and the real-time collection of sales records can provide real-time feedback of market demand, helping production enterprises understand market trends and consumer preferences, thereby adjusting production and marketing strategies. Real-time collection of organic fertilizer storage source data from the organic fertilizer storage process, including storage environment conditions and inventory information, is essential for ensuring the quality of organic fertilizer and managing inventory. Data collection of storage environment conditions can monitor key factors such as temperature and humidity in the storage environment in real time, which is very important for preventing organic fertilizer from deteriorating or failing during storage. Real-time collection of inventory information can provide accurate inventory data to help companies effectively manage inventory levels and avoid supply problems caused by insufficient inventory or waste of resources caused by excessive inventory. Real-time collection of organic fertilizer use source data from the organic fertilizer use process, including fertilization records and crop growth conditions, is essential for improving the application effect of organic fertilizer and crop production efficiency. Detailed collection of fertilization records can accurately track the amount, time and method of each fertilization, which helps to analyze the specific impact of different fertilization schemes on crop growth, and data collection of crop growth conditions can provide real-time information on crop health and growth, which is very helpful for timely adjustment of fertilization strategies and response to problems in crop growth, thereby providing basic data guarantee for subsequent processing. At the same time, data availability and analysis accuracy can be significantly improved by preprocessing and standardizing source data from the production, circulation, storage and use of organic fertilizers. Data preprocessing includes cleaning, deduplication and correction, which helps eliminate errors and inconsistencies in the data, ensure data quality, and provide a reliable basis for subsequent analysis. The standardization process ensures that data from different sources have consistent formats and units, which is essential for comprehensive analysis and comparison. Through data standardization, data from different links can be integrated into a unified system to provide more comprehensive and accurate insights. This data processing process also improves the compatibility and scalability of the data system, making data integration between different systems smoother.Secondly, by synchronously processing the production batches of standard data of organic fertilizer production source, standard data of organic fertilizer circulation source, standard data of organic fertilizer storage source and standard data of organic fertilizer use source, the key of this process is that it can divide the data of the whole process systematically and standardizedly according to the production batches, ensuring the consistency and integrity of the data. Through this synchronous division, the relevant data of the production batches can be integrated together to form a complete production record and traceability chain, which not only improves the efficiency of data management, but also provides strong support for subsequent data analysis and decision-making. It can also more clearly understand the production, circulation, storage and use of each batch, and conduct accurate quality control, market analysis and resource optimization, so as to effectively integrate the data of production, transportation, storage and use. In addition, by establishing the traceability association connection archive for the multi-source data sets of organic fertilizer under each production batch, the multi-source data traceability association connection archive of organic fertilizer can be generated. The key of this process is that it provides a comprehensive traceability archive that can record and track the detailed data of each production batch. The establishment of such an archive not only improves the transparency and traceability of information, but also helps enterprises to quickly provide detailed traceability information when facing quality problems, product recalls or regulatory reviews. By establishing a complete traceability archive, we can enhance market credibility and improve consumer trust. At the same time, we also provide support for the standardization and normalization of the industry, and provide data support for the subsequent organic fertilizer quality and safety traceability process, so that it can be accurately traced to specific production batches or links. Then, by connecting the production batch traceability path of the organic fertilizer multi-source data traceability association connection archive, the entire production process of each production batch can be traced in detail. This traceability path not only covers all the key nodes of production, but also includes the relationship between each stage, thereby providing a clear record of the complete life cycle of the production batch, and graphical processing can be performed to convert the traceability path into an intuitive traceability process diagram. This graphical representation makes complex traceability information easier to understand and analyze. The traceability process diagram of the production batch provides a visual tool that allows users to quickly identify the key links in the production process and their interrelationships. This method can improve the accuracy and completeness of traceability, so that when quality problems occur, the root cause of the problem can be found quickly and accurately. By evaluating and analyzing the quality indicators of organic fertilizers through the traceability process diagram of the entire life cycle of each production batch, detailed quality data can be provided for the entire life cycle of each production batch. This step makes it possible to compare the information in the traceability process diagram with the quality indicators to evaluate the quality of each production batch. This evaluation can identify the causes of quality fluctuations and provide a basis for improvement.By analyzing the quality indicator error limit of the organic fertilizer life cycle traceability process diagram of each production batch based on the quality indicator data of the organic fertilizer life cycle of each production batch, this step can identify potential errors and fluctuations in the production process, quantify the impact of these errors on quality indicators, and establish more accurate quality control standards and tolerance ranges, thereby optimizing the stability of the production process. Such analysis helps identify possible sources of variation in the production process and provides directions for improvement. The determination of error limit factors enables the formulation of more reasonable quality control strategies, reduces the risk of unqualified products, and improves overall production efficiency and product quality. Finally, by tracing the abnormal quality and safety of organic fertilizers in the organic fertilizer life cycle traceability process diagram of each production batch based on the error limit factors between the corresponding organic fertilizer quality indicators of each production batch, the quality and safety issues of each production batch can be deeply excavated and analyzed. This process can identify specific quality and safety problems through systematic analysis and processing, and trace their causes. This traceability process can not only help enterprises find the root cause of the problem, but also provide improvement suggestions to prevent similar problems from happening again. Through the traceability analysis of abnormal problems, a more effective quality and safety management system can be established, production processes can be optimized, and raw material procurement and production processes can be improved, thereby improving overall product quality and safety. In addition, abnormal decision support processing is performed on the abnormal traceability problems of organic fertilizer quality and safety in each production batch, and a corresponding decision support report is generated. The main advantage of this process is that it provides a systematic decision-making basis and helps to formulate specific improvement measures and management strategies. The decision support report can provide detailed solutions and action plans by analyzing the root causes of abnormal quality problems. This not only improves the response speed and processing efficiency when facing quality and safety issues, but also helps optimize resource allocation and improve production processes, thereby improving production efficiency and product quality. It can also provide strategic guidance for the long-term development of organic fertilizers and ensure continuous improvement and development in quality management. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0060] Figure 1 A schematic flow chart of the steps of the organic fertilizer quality safety traceability management method based on multi-source data of the present invention;

[0061] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0062] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0063] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0064] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0065] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0066] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for tracing the quality and safety of organic fertilizers based on multi-source data, the method comprising the following steps:

[0067] Step S1: collecting organic fertilizer source data in real time from the organic fertilizer production link, the organic fertilizer circulation link, the organic fertilizer storage link and the organic fertilizer use link and performing standardization processing to obtain organic fertilizer production source standard data, organic fertilizer circulation source standard data, organic fertilizer storage source standard data and organic fertilizer use source standard data;

[0068] Step S2: performing production batch synchronization collection processing on the organic fertilizer production source standard data, the organic fertilizer circulation source standard data, the organic fertilizer storage source standard data and the organic fertilizer use source standard data to obtain the organic fertilizer multi-source data set under each production batch; establishing a traceability association connection file for the organic fertilizer multi-source data set under each production batch to generate an organic fertilizer multi-source data traceability association connection file;

[0069] Step S3: performing production batch tracing extraction processing on the organic fertilizer multi-source data traceability association connection archive to obtain a full life cycle tracing process diagram of organic fertilizer for each production batch; performing organic fertilizer quality index evaluation and analysis on the full life cycle tracing process diagram of organic fertilizer for each production batch to obtain full life cycle quality index data of organic fertilizer for each production batch; performing quality index error limit analysis on the full life cycle tracing process diagram of organic fertilizer for each production batch based on the full life cycle quality index data of organic fertilizer for each production batch to obtain the error limit factor between the organic fertilizer quality indicators corresponding to each production batch;

[0070] Step S4: Based on the error limiting factors between the organic fertilizer quality indicators corresponding to each production batch, the organic fertilizer full life cycle traceability process diagram of each production batch is traced for organic fertilizer quality and safety abnormalities to obtain the organic fertilizer quality and safety abnormality traceability problem points of each production batch; abnormal decision support processing is performed on the organic fertilizer quality and safety abnormality traceability problem points of each production batch, and a decision support report on organic fertilizer quality and safety abnormalities is generated to perform corresponding organic fertilizer quality and safety abnormality traceability problem management work.

[0071] In the embodiment of the present invention, please refer to Figure 1 As shown, it is a schematic flow chart of the steps of the organic fertilizer quality and safety tracing management method based on multi-source data of the present invention. In this example, the organic fertilizer quality and safety tracing management method based on multi-source data comprises the following steps:

[0072] Step S1: collecting organic fertilizer source data in real time from the organic fertilizer production link, the organic fertilizer circulation link, the organic fertilizer storage link and the organic fertilizer use link and performing standardization processing to obtain organic fertilizer production source standard data, organic fertilizer circulation source standard data, organic fertilizer storage source standard data and organic fertilizer use source standard data;

[0073] In an embodiment of the present invention, by using sensor and data acquisition equipment to collect corresponding production source data in real time in the fertilizer production link, including data such as raw material source, production batch information and production and processing conditions can be uploaded to the central data unit for summary and analysis, thereby collecting and obtaining corresponding fertilizer production source data.By relying on logistics management system and sales management system to collect corresponding circulation source data in real time in the fertilizer circulation link, including logistics information, such as information such as position, transportation time, vehicle status in the record transportation process, and sales record, including sales time, place, sales quantity and customer information, all these data are transmitted to the central data unit in real time through wireless network, thereby collecting and obtaining fertilizer circulation source data.By relying on environmental monitoring system and inventory management system to collect corresponding storage source data in real time in the fertilizer storage link, including storage environment conditions, and inventory information includes storage location, quantity and storage / depot time of every kind of fertilizer, thereby collecting and obtaining fertilizer storage source data. In addition, agricultural management systems and monitoring equipment are used to collect corresponding source data in real time during the use of organic fertilizers, including fertilization records, such as fertilization time, fertilizer amount, fertilization location, etc., as well as crop growth conditions, such as recording crop growth parameters such as height, leaf color, etc. All usage data are transmitted to the central data unit via a wireless network for aggregation and analysis, thereby collecting organic fertilizer usage source data. Then, data preprocessing is performed on the organic fertilizer production source data, organic fertilizer circulation source data, organic fertilizer storage source data and organic fertilizer use source data previously collected in real time, including data cleaning, format conversion and consistency verification. The cleaning step removes duplicate values ​​and abnormal values ​​in the data, and uses data processing software to perform preliminary screening and correction on the original data. The format conversion unifies various data formats into a standard format, such as converting timestamps from different sources into a unified time format, and then performing a consistency check to ensure the consistency of information between different data sources. After these processes, the z-score standardization method is used for standardization, which can integrate data from different links into a unified system and eliminate their corresponding data dimensions, thereby providing more comprehensive and accurate insights, and finally obtaining standard data for organic fertilizer production sources, standard data for organic fertilizer circulation sources, standard data for organic fertilizer storage sources and standard data for organic fertilizer use sources.

[0074] Step S2: performing production batch synchronization collection processing on the organic fertilizer production source standard data, the organic fertilizer circulation source standard data, the organic fertilizer storage source standard data and the organic fertilizer use source standard data to obtain the organic fertilizer multi-source data set under each production batch; establishing a traceability association connection file for the organic fertilizer multi-source data set under each production batch to generate an organic fertilizer multi-source data traceability association connection file;

[0075] In an embodiment of the present invention, by extracting corresponding production batch information from organic fertilizer production source standard data, in specific implementation, using data screening and filtering tools, identifying the production batch field of each record in the organic fertilizer production source standard data, using a data processing library (such as pandas) of a programming language (such as Python), using the groupby function of the library to group by production batch, then extracting unique production batch information from each group, and encoding the extracted production batch information, in the implementation process, a hash algorithm (such as MD5, SHA-256) can be used to encrypt the production batch information to ensure that each batch has a unique and non-repeatable identification number, and the corresponding organic fertilizer production source standard data, organic fertilizer circulation source standard data, organic fertilizer storage source standard data, and organic fertilizer use source standard data are synchronously divided into production batches by using the generated organic fertilizer production batch identification number, and in specific operation, through a database management system (such as SQL According to the production batch identification number, each record is grouped in the database by the batch identification number to form a sub-dataset, thereby obtaining the organic fertilizer sub-datasets (including production source sub-data, circulation source sub-data, storage source sub-data and use source sub-data) under each production batch. Then, the organic fertilizer sub-datasets under each production batch obtained by previously dividing are processed in a collection. This step requires the sub-datasets to be integrated, and data fusion technology (such as ETL tool) is usually used to configure the data source in the tool, and the production source sub-data, circulation source sub-data, storage source sub-data and use source sub-data are merged according to the production batch. Through data integration technology, these data are unified and gathered into a comprehensive data set, thereby obtaining the organic fertilizer multi-source data set under each production batch. Finally, by utilizing the data warehouse system or archive management system, the integrated organic fertilizer multi-source data sets under each production batch are associated and processed, so as to define the association rules and logical relationships between the organic fertilizer multi-source data by setting data association connection relationships, and connect to generate detailed traceability archives, which include recording the data flow, change records and related operation logs of each production batch, thereby generating an associated connection archive containing comprehensive traceability information of each production batch, which is convenient for tracking and query, and finally generating an organic fertilizer multi-source data traceability associated connection archive.

[0076] Step S3: performing production batch tracing extraction processing on the organic fertilizer multi-source data traceability association connection archive to obtain a full life cycle tracing process diagram of organic fertilizer for each production batch; performing organic fertilizer quality index evaluation and analysis on the full life cycle tracing process diagram of organic fertilizer for each production batch to obtain full life cycle quality index data of organic fertilizer for each production batch; performing quality index error limit analysis on the full life cycle tracing process diagram of organic fertilizer for each production batch based on the full life cycle quality index data of organic fertilizer for each production batch to obtain the error limit factor between the organic fertilizer quality indicators corresponding to each production batch;

[0077] In an embodiment of the present invention, a structured analysis is performed on the organic fertilizer multi-source data traceability association connection archive obtained by the previous analysis, and a data extraction tool (such as Python's Pandas library) is used to identify key nodes of each production stage from the archive data. These nodes include but are not limited to raw material reception, mixing, fermentation, drying and packaging stages. For each production stage, the connection attributes related to the nodes are extracted by analyzing the attribute information in the archive, such as the time delay between nodes, the transfer quantity, the processing conditions, etc., to reflect the data flow in the production process and the dependency relationship between the nodes, and the corresponding organic fertilizer multi-source data traceability association connection archive is processed by combining the previously extracted production stage nodes and connection attributes to perform production batch traceability path connection processing on the corresponding organic fertilizer multi-source data traceability association connection archive, so as to screen out the corresponding production records from the organic fertilizer multi-source data traceability association connection archive according to the production batch number, and use the graph database (such as Neo4j) maps the data of each production batch into a graph, uses the production stage nodes as nodes in the graph, and uses the connection attributes as edges between nodes. When constructing the graph model, the complete production path from the raw material receiving production process to the final organic fertilizer use process is calculated through an algorithm (such as the shortest path algorithm), and the full life cycle traceability path of each production batch is generated. These path graphs show the entire process from raw materials to finished product use. At the same time, the traceability path graph is converted into a visual graph by using graphical tools (such as Gephi or D3.js), and the graphical tools are used to import the traceability path graph model generated previously, and a suitable layout algorithm (such as force-directed layout) is selected to optimize the display effects of nodes and edges, and the visual attributes of the graph, such as the size of the nodes, the thickness of the edges, color coding, etc., are adjusted to improve the readability of the graph and the efficiency of information transmission, thereby obtaining a full life cycle traceability process graph of organic fertilizer for each production batch. The quality indicators of the organic fertilizer full life cycle traceability process diagram of each production batch after graphical processing are also evaluated and analyzed, so as to collect quality data related to each production batch. These data usually include production time, source of raw materials, production process parameters, environmental conditions, final product inspection results, etc., and use data analysis tools (such as R or Matplotlib) to perform statistical analysis on these quality data to calculate the quality indicators of these quality data. These indicators include the content of effective ingredients of organic fertilizer (including nitrogen, phosphorus, potassium content, etc.), residue level, fertilizer effect durability, etc., so as to obtain the quality indicator data of the organic fertilizer full life cycle of each production batch.Then, by combining the organic fertilizer life cycle quality indicator data of each production batch obtained by previous statistical analysis, the organic fertilizer life cycle traceability process diagram of the corresponding production batch is subjected to a quality indicator error limitation analysis. First, the errors between the corresponding quality indicators in each production batch are calculated by using statistical analysis tools. For example, the difference formula (such as potassium content in the first production batch - potassium content in another production batch) is used to obtain the error between each pair of quality indicators, and statistical analysis methods (such as regression analysis or error analysis tools) are used to evaluate the error limitation factors between the quality indicators of each production batch. By setting an error threshold, the quality fluctuation range in the production process is judged, thereby generating error limitation factors for the corresponding quality indicators, and finally obtaining the error limitation factors between the quality indicators of organic fertilizers corresponding to each production batch.

[0078] Step S4: Based on the error limiting factors between the organic fertilizer quality indicators corresponding to each production batch, the organic fertilizer full life cycle traceability process diagram of each production batch is traced for organic fertilizer quality and safety abnormalities to obtain the organic fertilizer quality and safety abnormality traceability problem points of each production batch; abnormal decision support processing is performed on the organic fertilizer quality and safety abnormality traceability problem points of each production batch, and a decision support report on organic fertilizer quality and safety abnormalities is generated to perform corresponding organic fertilizer quality and safety abnormality traceability problem management work.

[0079] In embodiments of the present invention, the error limiting factor between each production batch corresponding organic fertilizer quality index obtained by combining previous evaluation analysis is carried out the quantitative calculation of error coefficient to the organic fertilizer quality index corresponding to each production batch, to determine the corresponding error coefficient by the product of error limiting factor and the specific numerical value of corresponding organic fertilizer quality index, and by using the error threshold value that is preset, the error coefficient of the organic fertilizer quality index corresponding to each production batch is compared and judged, if the error coefficient of the organic fertilizer quality index corresponding to each production batch is greater than or equal to the preset error threshold value, illustrate that there is error source in the corresponding organic fertilizer data source node, then the organic fertilizer data source node (such as production source, circulation source, storage source and use source etc.) corresponding to the organic fertilizer quality index under this production batch is marked as error source node, and for the organic fertilizer data source node corresponding to the organic fertilizer quality index under the production batch with error coefficient less than threshold value, then do not need to take any treatment measure, keep its organic fertilizer data source node to be normal state. At the same time, by combining the error source nodes of each production batch obtained by previous judgment and marking, each link of the organic fertilizer life cycle traceability process diagram of each production batch, from raw material procurement, production and processing to final product storage, transportation and use, is traced for abnormal quality and safety of organic fertilizer, so as to mark and identify the corresponding abnormal marking points from the error mapping in the organic fertilizer life cycle traceability process diagram, and also track the detailed history and related data of these abnormal marking points in the organic fertilizer life cycle production process to find out the root cause of the abnormal problem. The traceability process usually involves a detailed analysis of production records, raw material sources, production equipment status and other information to determine the specific problem points of the abnormality, thereby obtaining the abnormal traceability problem points of organic fertilizer quality and safety of each production batch. Then, by performing abnormal decision-making support processing on the identified organic fertilizer quality and safety abnormal traceability problem points in each production batch, the impact and risk level of each problem point are evaluated according to the traceability results, and an abnormal problem decision support report is generated based on the evaluated impact level and risk level response, which includes a detailed description of the problem point, risk assessment results and recommended solutions. The report can be generated using a decision support system or business intelligence tool and provided to relevant managers or technical teams to implement necessary quality management and improvement measures. These measures may include adjusting the production process, updating quality control standards or re-evaluating raw material suppliers, and finally generating an organic fertilizer quality and safety abnormal problem decision support report to perform corresponding organic fertilizer quality and safety abnormal traceability problem management work.

[0080] Further, step S1 includes the following steps:

[0081] Step S11: collecting organic fertilizer production source data in real time from the organic fertilizer production process, wherein the organic fertilizer production source data includes organic fertilizer raw material sources, organic fertilizer production batches, and organic fertilizer production and processing conditions;

[0082] Step S12: collecting organic fertilizer circulation source data in real time from the organic fertilizer circulation link, wherein the organic fertilizer circulation source data includes organic fertilizer logistics information and organic fertilizer sales records;

[0083] Step S13: collecting organic fertilizer storage source data in real time from the organic fertilizer storage link, wherein the organic fertilizer storage source data includes organic fertilizer storage environment conditions and organic fertilizer storage inventory information;

[0084] Step S14: collecting organic fertilizer use source data in real time from the organic fertilizer use link, wherein the organic fertilizer use source data includes organic fertilizer application records and growth status of crops using organic fertilizer;

[0085] Step S15: Preprocessing and standardizing the organic fertilizer production source data, the organic fertilizer circulation source data, the organic fertilizer storage source data, and the organic fertilizer use source data to obtain the organic fertilizer production source standard data, the organic fertilizer circulation source standard data, the organic fertilizer storage source standard data, and the organic fertilizer use source standard data.

[0086] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0087] Step S11: collecting organic fertilizer production source data in real time from the organic fertilizer production process, wherein the organic fertilizer production source data includes organic fertilizer raw material sources, organic fertilizer production batches, and organic fertilizer production and processing conditions;

[0088] In an embodiment of the present invention, corresponding production source data is collected in real time by using sensors and data acquisition equipment in the organic fertilizer production link. These devices are installed in key production links such as raw material processing, mixing, fermentation, and drying. The raw material source data is obtained by scanning the bar code or RFID tag on the raw material box to ensure that the supplier and purchase date of each batch of raw materials are recorded. The production batch information is automatically generated by the industrial control system on the production line. At the beginning of each production, the batch number and related production conditions, such as temperature, humidity, stirring speed, etc., are recorded. The production and processing conditions are monitored in real time by temperature sensors, pressure sensors, and flow meters installed on the equipment. The data will be uploaded to the central data unit for summary and analysis, and the organic fertilizer production source data is finally collected.

[0089] Step S12: collecting organic fertilizer circulation source data in real time from the organic fertilizer circulation link, wherein the organic fertilizer circulation source data includes organic fertilizer logistics information and organic fertilizer sales records;

[0090] In an embodiment of the present invention, corresponding circulation source data are collected in real time by relying on a logistics management system and a sales management system in the organic fertilizer circulation link, wherein the logistics information is obtained by a GPS positioning system and a barcode scanner installed on a transport vehicle, and information such as the position, transportation time, and vehicle status during the transportation process are recorded, and the sales record is updated in real time by a POS system docked with the sales system, and the electronic bill generated by each sales transaction will include sales time, location, sales quantity, and customer information, etc. All these data are transmitted to a central data unit in real time via a wireless network to ensure the accuracy and completeness of the circulation information, and finally the organic fertilizer circulation source data is collected.

[0091] Step S13: collecting organic fertilizer storage source data in real time from the organic fertilizer storage link, wherein the organic fertilizer storage source data includes organic fertilizer storage environment conditions and organic fertilizer storage inventory information;

[0092] In an embodiment of the present invention, corresponding storage source data is collected in real time by relying on an environment monitoring system and an inventory management system in the organic fertilizer storage link, wherein the storage environment conditions are monitored by installing environmental sensors in the warehouse, including temperature and humidity sensors, light sensors, etc., and the inventory information is updated at each inventory entry and exit by using a barcode scanner or RFID technology, the warehouse management system automatically records the storage location, quantity and storage / exit time of each organic fertilizer, and the environmental data and inventory data are uploaded to a central data unit in real time for monitoring storage conditions and optimizing inventory management, and finally the organic fertilizer storage source data is collected.

[0093] Step S14: collecting organic fertilizer use source data in real time from the organic fertilizer use link, wherein the organic fertilizer use source data includes organic fertilizer application records and growth status of crops using organic fertilizer;

[0094] In an embodiment of the present invention, the corresponding source data is collected in real time by using an agricultural management system and monitoring equipment in the organic fertilizer use link, wherein the fertilization record is automatically recorded by the GPS and fertilizer equipment sensors on the agricultural machinery, including the fertilization time, fertilizer amount, fertilization location, etc., and the crop growth status is monitored by agricultural sensors and image recognition systems. These sensors can record crop growth parameters such as height, leaf color, etc., and the growth status of the crop and the fertilizer effect are analyzed by regularly collected data. All usage data are transmitted to the central data unit through a wireless network for aggregation and analysis, and finally the organic fertilizer usage source data is obtained.

[0095] Step S15: Preprocessing and standardizing the organic fertilizer production source data, the organic fertilizer circulation source data, the organic fertilizer storage source data, and the organic fertilizer use source data to obtain the organic fertilizer production source standard data, the organic fertilizer circulation source standard data, the organic fertilizer storage source standard data, and the organic fertilizer use source standard data.

[0096] In an embodiment of the present invention, data preprocessing is performed on organic fertilizer production source data, organic fertilizer circulation source data, organic fertilizer storage source data, and organic fertilizer use source data previously collected in real time, including data cleaning, format conversion, and consistency verification. First, the cleaning step removes duplicate values ​​and abnormal values ​​in the data, and the original data is preliminarily screened and corrected using data processing software. The format conversion unifies various data formats into a standard format, such as converting timestamps from different sources into a unified time format, and then a consistency check is performed to ensure the consistency of information between different data sources. After these processes, the z-score standardization method is used for standardization processing, and the data from different links can be integrated into a unified system, and the corresponding data dimensions are eliminated, thereby providing more comprehensive and accurate insights, and finally obtaining organic fertilizer production source standard data, organic fertilizer circulation source standard data, organic fertilizer storage source standard data, and organic fertilizer use source standard data.

[0097] Further, step S2 includes the following steps:

[0098] Step S21: extracting the production batches of the organic fertilizer production source standard data to obtain the organic fertilizer production batches;

[0099] Step S22: performing production identification processing on the organic fertilizer production batch to obtain the organic fertilizer production batch identification number;

[0100] Step S23: Based on the fertilizer production batch identification number, the fertilizer production source standard data, the fertilizer circulation source standard data, the fertilizer storage source standard data and the fertilizer use source standard data are processed by production batch synchronization division to obtain the fertilizer production source sub-data, the fertilizer circulation source sub-data, the fertilizer storage source sub-data and the fertilizer use source sub-data under each production batch;

[0101] Step S24: performing multi-source data set processing on the organic fertilizer production source sub-data, the organic fertilizer circulation source sub-data, the organic fertilizer storage source sub-data and the organic fertilizer use source sub-data under each production batch to obtain an organic fertilizer multi-source data set under each production batch;

[0102] Step S25: Establishing a traceability association connection file for the organic fertilizer multi-source data set under each production batch to generate an organic fertilizer multi-source data traceability association connection file.

[0103] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in FIG. 1 , in this embodiment, step S2 includes the following steps:

[0104] Step S21: extracting the production batches of the organic fertilizer production source standard data to obtain the organic fertilizer production batches;

[0105] In an embodiment of the present invention, corresponding production batch information is extracted from the organic fertilizer production source standard data. In specific implementation, a data screening and filtering tool is used to identify the production batch field of each record in the organic fertilizer production source standard data, and a data processing library (e.g., pandas) of a programming language (e.g., Python) is used to group the data by production batch using the groupby function of the library. After that, a unique production batch identifier is extracted from each group to ensure that all batch information is accurately extracted, and finally the organic fertilizer production batch is obtained.

[0106] Step S22: performing production identification processing on the organic fertilizer production batch to obtain the organic fertilizer production batch identification number;

[0107] In an embodiment of the present invention, the extracted organic fertilizer production batch is subjected to identification processing to generate a unique production batch identification number, and the extracted production batch information is encoded by adopting an identifier generation tool or algorithm. During implementation, a hash algorithm (such as MD5, SHA-256) can be used to encrypt the production batch information to ensure that each batch has a unique and non-repeatable identification number. After the identification number is generated, it is attached to the original data to finally obtain the organic fertilizer production batch identification number.

[0108] Step S23: Based on the fertilizer production batch identification number, the fertilizer production source standard data, the fertilizer circulation source standard data, the fertilizer storage source standard data and the fertilizer use source standard data are processed by production batch synchronization division to obtain the fertilizer production source sub-data, the fertilizer circulation source sub-data, the fertilizer storage source sub-data and the fertilizer use source sub-data under each production batch;

[0109] In an embodiment of the present invention, the corresponding organic fertilizer production source standard data, organic fertilizer circulation source standard data, organic fertilizer storage source standard data and organic fertilizer use source standard data are synchronously divided into production batches by using the generated organic fertilizer production batch identification number. During specific operation, through a database management system (such as SQL Server, MySQL) or a data processing platform, the records in the organic fertilizer production source standard data, organic fertilizer circulation source standard data, organic fertilizer storage source standard data and organic fertilizer use source standard data are matched and divided according to the production batch identification number. Each record is grouped by the batch identification number in the database to form a sub-data set. This process needs to ensure the consistency and integrity of all relevant data at the batch level, and finally the organic fertilizer production source sub-data, organic fertilizer circulation source sub-data, organic fertilizer storage source sub-data and organic fertilizer use source sub-data under each production batch are divided.

[0110] Step S24: performing multi-source data set processing on the organic fertilizer production source sub-data, the organic fertilizer circulation source sub-data, the organic fertilizer storage source sub-data and the organic fertilizer use source sub-data under each production batch to obtain an organic fertilizer multi-source data set under each production batch;

[0111] In an embodiment of the present invention, the organic fertilizer production source sub-data, organic fertilizer circulation source sub-data, organic fertilizer storage source sub-data and organic fertilizer use source sub-data under each production batch obtained by previously dividing are collectively processed, and this step requires that the sub-data sets be integrated, and data fusion technology (such as ETL tool) is usually used, and the data source is configured in the tool, and the production source sub-data, circulation source sub-data, storage source sub-data and use source sub-data are merged according to the production batch, and these data are unified and gathered into a comprehensive data set through data integration technology to ensure the comprehensiveness and consistency of the data, and finally obtain the organic fertilizer multi-source data set under each production batch.

[0112] Step S25: Establishing a traceability association connection file for the organic fertilizer multi-source data set under each production batch to generate an organic fertilizer multi-source data traceability association connection file.

[0113] In an embodiment of the present invention, by utilizing a data warehouse system or an archive management system, the integrated organic fertilizer multi-source data sets under each production batch are subjected to association processing, so as to define the association rules and logical relationships between the organic fertilizer multi-source data by setting the data association connection relationship, and connect and generate a detailed traceability file, which includes recording the data flow, change records and related operation logs of each production batch, thereby generating an association connection file containing comprehensive traceability information of each production batch, facilitating tracking and querying, and finally generating an organic fertilizer multi-source data traceability association connection file.

[0114] Further, step S25 includes the following steps:

[0115] Step S251: extracting raw material input attribute data from the organic fertilizer production source sub-data in the organic fertilizer multi-source data set under each production batch, to obtain the organic fertilizer production raw material input attribute data under each production batch;

[0116] In an embodiment of the present invention, the raw material input attributes of the organic fertilizer production source sub-data in the organic fertilizer multi-source data set under each production batch are extracted and processed, so that the raw material input attributes of each production batch in the organic fertilizer production source sub-data are extracted by using a data parsing tool, and the extracted raw material input attributes include information such as raw material type, input amount, source, etc. This process can use data extraction software (such as the pandas library in Python) to read data files (such as CSV or Excel), and filter out fields related to raw material input, through data cleaning and screening, ensure that the extracted attribute data is accurate and complete, form a raw material input attribute data set for each production batch, and finally obtain the organic fertilizer production raw material input attribute data under each production batch.

[0117] Step S252: Based on the input attribute data of the organic fertilizer production raw materials under each production batch, the organic fertilizer multi-source data set under the corresponding production batch is characterized by describing the key nodes of the data source, so as to obtain the key node characteristic description data of the organic fertilizer data source under each production batch;

[0118] In an embodiment of the present invention, by combining the organic fertilizer production raw material input attribute data under each production batch extracted previously, the organic fertilizer multi-source data set under the corresponding production batch is characterized by describing the key nodes, and this step involves using a data analysis tool (such as Scikit-learn in Python or a data processing package in R language) to perform statistical analysis and feature extraction on the raw material input attributes, and by analyzing the raw material input attributes of each production batch, generating feature description data of each key node, these feature descriptions include attribute values, distribution, correlation and other information of the key nodes, and finally obtaining the key node feature description data of the organic fertilizer data source under each production batch.

[0119] Step S253: performing production batch time series correlation analysis on the key node feature description data of the organic fertilizer data source under each production batch, and obtaining the production time series correlation relationship between the key nodes of the organic fertilizer data source of each production batch;

[0120] In an embodiment of the present invention, a time series association analysis is performed on the feature description data of the key nodes of the data source under each production batch obtained by the previous analysis. Specifically, a time series analysis method (such as time series analysis, correlation analysis) is adopted to identify and describe the production time series association relationship between key nodes, or by using a data analysis tool (such as the statsmodels library in Python) to model and analyze the time series data of the key nodes, determine the association order and time series relationship of each node in the production process, and finally obtain the production time series association relationship between the key nodes of the organic fertilizer data source of each production batch.

[0121] Step S254: tracing the traceability association path of the organic fertilizer multi-source data set under each production batch based on the production time sequence association relationship between the key nodes of the organic fertilizer data source of each production batch, so as to obtain the traceability association path of the organic fertilizer data under each production batch;

[0122] In an embodiment of the present invention, the organic fertilizer multi-source data set under the corresponding production batch is traced by combining the production time sequence association relationship between the key nodes of the organic fertilizer data source of each production batch obtained by the previous association analysis, so as to use the path tracing algorithm (such as Dijkstra algorithm or A* algorithm) to perform traceability analysis on the key nodes of the data source of each production batch, and by tracing the various node paths from the raw material input to the final product, the traceability association path between each node is identified, and a visualized traceability path is generated, and the tracing results are displayed using a data visualization tool (such as Gephi or Matplotlib), so as to facilitate a comprehensive understanding and review of the organic fertilizer production process, and finally obtain the organic fertilizer data traceability association path under each production batch.

[0123] Step S255: establishing a traceability association connection file for the organic fertilizer multi-source data set under each production batch according to the organic fertilizer data traceability association path under each production batch, so as to generate an organic fertilizer multi-source data traceability association connection file.

[0124] In an embodiment of the present invention, the organic fertilizer data traceability association path under each production batch obtained by previous tracking is combined to perform traceability association connection processing on the organic fertilizer multi-source data set under the corresponding production batch, so as to utilize a document management tool (such as a database management system or a data visualization tool) according to the organic fertilizer data traceability association path under each production batch. The corresponding organic fertilizer multi-source data (including data on key nodes such as organic fertilizer production source, circulation source, storage source, and use source) is integrated into an archive file, and the archive file contains the traceability path detailed information of each production batch, such as the production time, input amount, source, etc. of each key node. After the archive file is generated, the data is archived and indexed so that it can be easily retrieved and queried, thereby realizing comprehensive recording and systematic management of the organic fertilizer multi-source data, and finally connecting to generate the organic fertilizer multi-source data traceability association connection archive.

[0125] Further, step S254 includes the following steps:

[0126] Obtain the production timestamp and node information data of the key nodes of each production batch of organic fertilizer data source, and perform inter-node dynamic dependency analysis on the production timestamp and node information data of the key nodes of each production batch of organic fertilizer data source based on the production time series association relationship between the key nodes of each production batch of organic fertilizer data source, and obtain a time information dynamic dependency relationship map between the key nodes of each production batch of organic fertilizer data source;

[0127] In an embodiment of the present invention, by collecting the production timestamps and node information data of the key nodes of the data source of each production batch of organic fertilizer, these data can be extracted from the production equipment through an automated data acquisition system, and the time series analysis tool is used to integrate the timestamp data with the node information data to establish a basic information data set containing the time series data of each key node, and by calculating the time difference between the nodes and based on the production time series association relationship between the key nodes of the data source of each production batch of organic fertilizer, a graph algorithm is applied to construct a time information dynamic dependency relationship map, and these algorithms may include a shortest path algorithm and a graph traversal algorithm to determine the time dependency relationship between the key nodes of the data source of each production batch of organic fertilizer, to form a map, the map shows the dynamic dependency relationship between the production timestamp and the node information of each node, and finally obtains the time information dynamic dependency relationship map between the key nodes of the data source of each production batch of organic fertilizer.

[0128] Preferably, a hierarchical temporal cluster analysis is performed on the time information dynamic dependency relationship map between the key nodes of each production batch of organic fertilizer data source to obtain a hierarchical temporal correlation cluster map of the production batch of organic fertilizer data source;

[0129] In an embodiment of the present invention, after obtaining the time information dynamic dependency graph between the key nodes of each production batch of organic fertilizer data source, a hierarchical time series clustering analysis is performed, so as to analyze the time information dynamic dependency graph by using a hierarchical clustering algorithm (such as an agglomerative hierarchical clustering algorithm), which calculates the similarity according to the time dependency between the nodes, and forms a hierarchical structure by merging similar nodes, and divides the nodes into multiple levels using a similarity matrix and a dendrogram (such as a tree clustering diagram), which help identify the main association patterns in the time series data and are visualized as a hierarchical graph, and finally obtain a hierarchical time series association clustering graph of the production batch organic fertilizer data source.

[0130] Preferably, based on the hierarchical temporal association clustering map of the production batch organic fertilizer data source, the organic fertilizer multi-source data set under each production batch is traced for the traceability association path to obtain the organic fertilizer data traceability association path under each production batch.

[0131] In an embodiment of the present invention, the traceability association path of the organic fertilizer multi-source data set under each production batch is tracked by combining the hierarchical time-series association clustering map of the production batch organic fertilizer data source generated by previous clustering, so as to select a data set of a production batch, and compare its key node information with the hierarchical time-series association clustering map, and identify the path from the final product node back to the original production stage by using a path tracing algorithm (such as depth-first search or breadth-first search). By analyzing these paths, the traceability path of the organic fertilizer data source of each production batch can be traced, and by combining the production timestamp with the node information, the data flow and source can be clarified, and finally the traceability association path of the organic fertilizer data under each production batch is obtained.

[0132] Further, step S3 includes the following steps:

[0133] Step S31: extracting and processing the organic fertilizer production stage nodes and stage connection attributes of the organic fertilizer multi-source data traceability association connection archive to obtain the organic fertilizer multi-source data production stage nodes and the organic fertilizer multi-source data production stage connection attributes;

[0134] In an embodiment of the present invention, a structured analysis is performed on the traceability association connection archive of the organic fertilizer multi-source data obtained by the previous analysis, and a data extraction tool (such as Python's Pandas library) is used to identify the key nodes of each production stage from the archive data. These nodes include but are not limited to the stages of raw material reception, mixing, fermentation, drying and packaging. For each production stage, the connection attributes related to the nodes are extracted by analyzing the attribute information in the archive, such as the time delay between nodes, the transfer quantity, the processing conditions, etc. These connection attributes can reflect the data flow in the production process and the dependency between the nodes, thereby generating a detailed list of the production stage nodes and connection attributes of the organic fertilizer multi-source data. The data extraction technology used in this process ensures the accurate capture and recording of each stage, and finally obtains the production stage nodes of the organic fertilizer multi-source data and the connection attributes of the organic fertilizer multi-source data production stage.

[0135] Step S32: performing production batch traceability path connection processing on the organic fertilizer multi-source data traceability association connection archive based on the organic fertilizer multi-source data production stage node and the organic fertilizer multi-source data production stage connection attribute, so as to generate the organic fertilizer multi-source data full life cycle traceability path of each production batch;

[0136] In an embodiment of the present invention, the production batch traceability path connection processing is performed on the corresponding organic fertilizer multi-source data traceability associated connection archive by combining the previously extracted organic fertilizer multi-source data production stage nodes and connection attributes, so as to screen out the corresponding production records from the organic fertilizer multi-source data traceability associated connection archive according to the production batch number, and use a graph database (such as Neo4j) to map the data of each production batch into a graph, use the production stage nodes as nodes in the graph, and use the connection attributes as edges between the nodes. When constructing a graph model, an algorithm (such as the shortest path algorithm) is used to calculate the complete production path from the raw material receiving production process to the final organic fertilizer use process, and generate a full life cycle traceability path for each production batch. These path graphs show the entire process from raw materials to finished product use, help trace the full life cycle trajectory of each production batch, ensure the integrity and consistency of the data, and finally connect and generate the full life cycle traceability path of the organic fertilizer multi-source data of each production batch.

[0137] Step S33: graphically processing the full life cycle traceability path of the multi-source data of each production batch of organic fertilizer to obtain a full life cycle traceability process diagram of each production batch of organic fertilizer;

[0138] In an embodiment of the present invention, the full life cycle traceability path diagram generated for each production batch is graphically processed, so that the traceability path diagram is converted into a visual graph by using a graphical tool (such as Gephi or D3.js), and the traceability path diagram model generated previously is imported using the graphical tool, and a suitable layout algorithm (such as force-directed layout) is selected to optimize the display effect of nodes and edges, and adjust the visual properties of the graph, such as the size of the node, the thickness of the edge, color coding, etc., to improve the readability and information communication efficiency of the graph, so as to display each node of the production batch and its connection relationship, and finally obtain the full life cycle traceability process diagram of the organic fertilizer of each production batch.

[0139] Step S34: evaluating and analyzing the quality index of organic fertilizer on the whole life cycle traceability process diagram of each production batch of organic fertilizer to obtain the quality index data of the whole life cycle of organic fertilizer for each production batch;

[0140] In an embodiment of the present invention, the quality indicators of the organic fertilizer life cycle traceability process diagram of each production batch after graphical processing are evaluated and analyzed, and the quality data related to each production batch are collected, and these data generally include production time, source of raw materials, production process parameters, environmental conditions, final product inspection results, etc., and these quality data are statistically analyzed using data analysis tools (such as R or Matplotlib) to calculate the quality indicators of these quality data, which include the effective ingredient content of organic fertilizer (including nitrogen, phosphorus, potassium content, etc.), residue level, fertilizer effect persistence, etc., and finally the quality indicator data of the organic fertilizer life cycle of each production batch is obtained.

[0141] Step S35: Based on the organic fertilizer life cycle quality index data of each production batch, a quality index error limit analysis is performed on the organic fertilizer life cycle traceability process diagram of each production batch to obtain the error limit factor between the organic fertilizer quality indicators corresponding to each production batch.

[0142] In an embodiment of the present invention, a quality indicator error limitation analysis is performed on the organic fertilizer life cycle traceability process diagram of the corresponding production batch by combining the organic fertilizer life cycle quality indicator data of each production batch obtained by previous statistical analysis. First, the errors between the corresponding quality indicators in each production batch are calculated by using a statistical analysis tool. For example, a difference formula (for example, potassium content in the first production batch - potassium content in another production batch) is used to obtain the error between each pair of quality indicators, and a statistical analysis method (such as regression analysis or an error analysis tool) is used to evaluate the error limitation factor between the quality indicators of each production batch, so as to judge the quality fluctuation range in the production process by setting an error threshold, thereby generating an error limitation factor for the corresponding quality indicator, and finally obtaining the error limitation factor between the quality indicators of organic fertilizers corresponding to each production batch.

[0143] Further, step S34 includes the following steps:

[0144] Step S341: extracting and processing organic fertilizer quality-related data from the organic fertilizer life cycle traceability process diagram of each production batch to obtain a data set related to the quality of the organic fertilizer life cycle of each production batch;

[0145] In an embodiment of the present invention, by using a data mining tool, such as data extraction software, all relevant quality data are extracted from the whole life cycle traceability process diagram of each production batch of organic fertilizer obtained after previous graphical processing. These data generally include production time, raw material source, production process parameters, environmental conditions, final product inspection results, etc., and are summarized into a structured data set. The life cycle quality data set of each production batch should include data records of the entire process from raw material input to product output, and finally a data set related to the whole life cycle quality of each production batch of organic fertilizer is obtained.

[0146] Step S342: Deconstruct and classify the quality attribute-related data sets of the organic fertilizer life cycle of each production batch to obtain organic fertilizer quality-related sub-data corresponding to different quality attributes in each production batch;

[0147] In an embodiment of the present invention, when deconstructing and classifying the quality attributes of the organic fertilizer life cycle quality-related data sets of each previously extracted production batch, the quality-related data need to be systematically divided according to the set quality attribute standards. This process can use a data classification algorithm, such as K-means clustering or a decision tree algorithm, to classify the quality indicators (such as fertilizer composition, water content, nutrient release rate, etc.) in the data set. The basis for classification is the set quality attribute standards, which can be based on industry specifications or internal quality requirements of the enterprise. In the data classification process, the data of each production batch must first be labeled according to the preset quality attributes, and then, through the data classification tool, the labeled data is classified according to the quality attributes, and sub-data sets under different quality attributes are generated. Each sub-data set should focus on a specific quality attribute, and finally obtain the organic fertilizer quality-related sub-data corresponding to the different quality attributes in each production batch.

[0148] Step S343: performing organic fertilizer quality index evaluation and analysis on the organic fertilizer quality-related sub-data corresponding to different quality attributes in each production batch, and obtaining the organic fertilizer full life cycle quality index data of each production batch.

[0149] In an embodiment of the present invention, quality indicators of organic fertilizer quality-related sub-data corresponding to different quality attributes in each production batch are evaluated and analyzed, and quality indicators of each sub-data set are evaluated by using statistical analysis software, such as SPSS or R language, and applying descriptive statistical analysis methods (such as mean, standard deviation, variance analysis). In the analysis process, the quality indicators in each sub-data set are calculated, and these indicators include the effective ingredient content of organic fertilizer (including nitrogen, phosphorus, potassium content, etc.), residue level, fertilizer effect persistence, etc., and finally the quality indicator data of the organic fertilizer full life cycle of each production batch is obtained.

[0150] Further, step S35 includes the following steps:

[0151] Step S351: performing quality index error evaluation and analysis between each organic fertilizer quality index in the organic fertilizer full life cycle quality index data of each production batch, and obtaining the quality index error between the organic fertilizer quality indexes corresponding to each production batch;

[0152] In an embodiment of the present invention, the error of the quality index is evaluated and calculated between each organic fertilizer quality index (including the content of active ingredients such as nitrogen, phosphorus and potassium content, residue level, fertilizer effect persistence, etc.) in the organic fertilizer life cycle quality index data of each production batch previously collected, so as to calculate the error between the corresponding quality indicators in each production batch by using a statistical analysis tool, for example, using a difference formula (such as potassium content in the first production batch - potassium content in another production batch) to obtain the error between each pair of quality indicators, these errors need to be systematically recorded, and quality index error data are generated to ensure that the deviations between the corresponding quality indicators of each production batch are accurately reflected, and finally the quality index errors between the corresponding organic fertilizer quality indicators of each production batch are obtained.

[0153] Step S352: performing error distribution statistical analysis on the quality index errors between the quality indexes of the organic fertilizers corresponding to the various production batches, and obtaining error distribution characteristic data between the quality indexes of the organic fertilizers corresponding to the various production batches;

[0154] In an embodiment of the present invention, by performing a statistical analysis of error distribution on the quality index errors between the quality indexes of organic fertilizers corresponding to each production batch obtained by previous quantitative calculation, the error data of each production batch is collected, and a statistical analysis method, such as a frequency distribution table and an error histogram, is used to describe the distribution of the errors, and the statistical characteristics such as the mean value, standard deviation, skewness and kurtosis of the errors are calculated, and the overall distribution characteristics of the errors are clarified. This analysis process helps identify the central tendency and variation range of the errors, and finally obtains the error distribution characteristic data between the quality indexes of organic fertilizers corresponding to each production batch.

[0155] Step S353: Based on the error distribution characteristic data between the organic fertilizer quality indicators corresponding to each production batch, the error sensitivity evaluation and analysis is performed on the corresponding organic fertilizer production stage nodes in the organic fertilizer full life cycle traceability process diagram of each production batch, so as to obtain the sensitivity response degree of the organic fertilizer production stage nodes of each production batch to the quality indicator error;

[0156] In an embodiment of the present invention, the error sensitivity of the corresponding organic fertilizer production stage nodes in the organic fertilizer life cycle traceability process diagram of each production batch is evaluated and analyzed by combining the error distribution characteristic data between the organic fertilizer quality indicators of each production batch obtained by previous statistical analysis. The specific steps include using the error distribution characteristic data to analyze the sensitivity response of each production stage to the quality indicator error. For example, by establishing a mathematical model or using a sensitivity analysis tool, the influence of each production stage node on the quality indicator error is evaluated. These evaluation results can determine which production stages are more sensitive to errors, thereby identifying key production links that need to be paid attention to, and finally obtaining the sensitivity response degree of each production batch of organic fertilizer production stage nodes to the quality indicator error.

[0157] Step S354: Based on the sensitivity response degree of the organic fertilizer production stage nodes of each production batch to the quality index error, an error limiting factor evaluation and analysis is performed between each organic fertilizer quality index in the organic fertilizer life cycle quality index data of each production batch, so as to obtain the error limiting factor between the organic fertilizer quality indicators corresponding to each production batch.

[0158] In an embodiment of the present invention, an error limiting factor is evaluated and calculated between each organic fertilizer quality indicator in the organic fertilizer life cycle quality indicator data of each production batch by combining the sensitivity response degree of each production batch of organic fertilizer production stage node to the quality indicator error obtained by previous analysis, so as to calculate the error limiting factor for the quality indicator of each production batch in combination with the sensitivity degree of the production stage to the quality indicator error. These error limiting factors are also determined by combining the definition of the error time decay rate and the allowable deviation range to limit and control the error degree of the quality indicator. For example, the following error limiting factor formula can be used: To calculate the corresponding error limiting factor, where ε ij is the error limiting factor between the i-th production batch and the j-th organic fertilizer quality index, i is the item measurement parameter of the production batch, and its range is 1,…,n, j is the item measurement parameter of the organic fertilizer quality index, and its range is 1,…,m, t is the integral time range parameter, τ is the time variable parameter, α ij (τ) is the error term between the i-th production batch and the j-th organic fertilizer quality index at time τ, δ is the sensitivity response degree, λ is the error time decay rate, UCL j LCL is the upper limit of the allowable deviation range of the jth organic fertilizer quality index. j is the lower limit of the allowable deviation range of the jth organic fertilizer quality index, and finally the error limiting factor between the organic fertilizer quality indicators corresponding to each production batch is obtained.

[0159] Further, step S4 includes the following steps:

[0160] Step S41: performing quantitative calculation of error coefficients on the organic fertilizer quality indicators corresponding to each production batch based on the error limiting factors between the organic fertilizer quality indicators corresponding to each production batch, so as to obtain error coefficients of the organic fertilizer quality indicators corresponding to each production batch;

[0161] In an embodiment of the present invention, the error limiting factor between each production batch corresponding to the fertilizer quality index obtained by previous evaluation and analysis is used to carry out quantitative calculation of error coefficient to the fertilizer quality index corresponding to each production batch, so as to determine the corresponding error coefficient by multiplying the error limiting factor and the specific numerical value of the corresponding fertilizer quality index, and finally obtain the error coefficient of the corresponding fertilizer quality index of each production batch.

[0162] Step S42: according to the preset error threshold, the error coefficient of the corresponding organic fertilizer quality index of each production batch is compared and judged, when the error coefficient of the corresponding organic fertilizer quality index of each production batch is greater than or equal to the preset error threshold, the organic fertilizer data source node corresponding to the organic fertilizer quality index under its production batch is determined as the error source node, to obtain the organic fertilizer quality error source node of each production batch; when the error coefficient of the corresponding organic fertilizer quality index of each production batch is less than the preset error threshold, no processing is performed;

[0163] In an embodiment of the present invention, by using a preset error threshold, the error coefficient of the corresponding organic fertilizer quality index of each production batch is compared and judged, if the error coefficient of the corresponding organic fertilizer quality index of each production batch is greater than or equal to the preset error threshold, it is explained that there is an error source in the corresponding organic fertilizer data source node, then the organic fertilizer data source node (such as production source, circulation source, storage source and use source etc.) corresponding to the organic fertilizer quality index under this production batch is marked as an error source node, data comparison tool or user-defined program can be utilized to perform this judgment process, and the error source node identified is marked, so as to further analyze, and for the organic fertilizer data source node corresponding to the organic fertilizer quality index under the production batch with an error coefficient less than the threshold, then it is not necessary to take any treatment measure, and keeping its organic fertilizer data source node is a normal state.

[0164] Step S43: performing error mapping anomaly annotation processing on the organic fertilizer life cycle traceability process diagram of each production batch based on the organic fertilizer quality error source node of each production batch, so as to obtain an error mapping anomaly annotation diagram of the organic fertilizer life cycle of each production batch;

[0165] In an embodiment of the present invention, the organic fertilizer quality error source nodes of each production batch obtained by combining the previously judged mark are annotated with the corresponding organic fertilizer data source nodes in the organic fertilizer full life cycle traceability process diagram of each production batch. The organic fertilizer full life cycle traceability process diagram includes each link from raw material procurement, production and processing to final product storage, transportation, and use. The error source node is marked through each node in the traceability process diagram to form an error mapping anomaly marking diagram. This process involves using a graphics processing tool or software to highlight or annotate the identified error source nodes in the traceability diagram so as to visually clearly indicate the link where the quality problem is located, and finally obtain the error mapping anomaly marking diagram of the organic fertilizer full life cycle of each production batch.

[0166] Step S44: performing organic fertilizer quality and safety anomaly tracing processing on the organic fertilizer full life cycle error mapping anomaly annotation diagram of each production batch to obtain the organic fertilizer quality and safety anomaly tracing problem points of each production batch;

[0167] In an embodiment of the present invention, an abnormality traceability process for the quality and safety of organic fertilizer is performed on an abnormality annotation map of the whole life cycle error mapping of each production batch previously obtained through abnormal annotation, so as to identify corresponding abnormality annotation points from the abnormality annotation map of the whole life cycle error mapping of organic fertilizer, and track the detailed history and relevant data of these abnormality annotation points in the whole life cycle production process of organic fertilizer to find out the root cause of the abnormal problem. The tracing process usually involves a detailed analysis of production records, sources of raw materials, production equipment status and other information to determine the specific problem points of the abnormality, and finally obtain the abnormality traceability problem points of the quality and safety of organic fertilizer of each production batch.

[0168] Step S45: Perform abnormal decision support processing on the abnormal traceability problem points of organic fertilizer quality and safety of each production batch, generate an abnormal decision support report on organic fertilizer quality and safety, and perform corresponding organic fertilizer quality and safety abnormal traceability problem management work.

[0169] In an embodiment of the present invention, abnormal decision-making support processing is performed on the identified organic fertilizer quality and safety abnormal traceability problem points in each production batch, so as to evaluate the impact and risk level of each problem point according to the traceability results, and generate an abnormal problem decision support report according to the evaluated impact level and risk level response, which includes a detailed description of the problem point, risk assessment results and suggested solutions. The report can be generated using a decision support system or a business intelligence tool and provided to relevant managers or technical teams to implement necessary quality management and improvement measures, which may include adjusting the production process, updating quality control standards or re-evaluating raw material suppliers, and finally generating an organic fertilizer quality and safety abnormal problem decision support report to perform corresponding organic fertilizer quality and safety abnormal traceability problem management work.

[0170] Further, step S44 includes the following steps:

[0171] Perform abnormal pattern recognition analysis on the abnormal labeled nodes of the organic fertilizer data source corresponding to the abnormal labeled graph of the organic fertilizer life cycle error mapping of each production batch, and obtain abnormal pattern recognition information data of the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch;

[0172] In an embodiment of the present invention, the abnormal annotation nodes of the organic fertilizer data source in the abnormal annotation graph of the organic fertilizer life cycle error mapping of each production batch are extracted, and these abnormal annotation nodes refer to specific data abnormal points previously obtained by error mapping in the production process, and these abnormal nodes are analyzed by applying pattern recognition technology, so as to utilize machine learning models, such as support vector machines (SVM), decision trees or neural networks, for training and testing, so as to identify the patterns of these abnormal nodes, and the specific operations include inputting the abnormal node data into the pattern recognition algorithm for training, establishing a model of the abnormal pattern, and matching the abnormal node data of each production batch with the model, generating abnormal pattern recognition information data of each batch, and these recognition information data will include detailed information such as the type of the abnormal node, the severity and the position of the abnormal node in the data source, and finally obtaining the abnormal pattern recognition information data of the abnormal annotation nodes of the organic fertilizer data source corresponding to each production batch.

[0173] Preferably, an abnormal impact factor evaluation and analysis is performed based on the abnormal pattern recognition information data of the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch, to obtain the abnormal impact factor of the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch;

[0174] In an embodiment of the present invention, an evaluation and analysis of abnormal influencing factors is performed by performing abnormal pattern recognition information data of abnormal labeled nodes of an organic fertilizer data source corresponding to each production batch obtained according to previous identification and analysis. This step involves an in-depth analysis of the abnormal behavior patterns of the abnormal labeled nodes of each production batch to evaluate the influencing factors of these abnormal behavior patterns. The specific operation includes inputting the pattern recognition information data of each abnormal node into an abnormal influencing factor evaluation model, which can be a regression analysis model, a factor analysis model or a multivariate statistical analysis model. The purpose is to determine the impact of the abnormality on the production process and product quality, and by analyzing the distribution of abnormal nodes in the data source, the frequency and type of abnormal patterns, evaluate the potential impact on production quality and safety, and finally obtain the abnormal influencing factors of the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch.

[0175] Preferably, based on the abnormal influencing factors of the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch, the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch in the abnormal labeled graph of the organic fertilizer life cycle error mapping are subjected to abnormal relationship mining analysis to obtain an abnormal relationship network graph between the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch;

[0176] In an embodiment of the present invention, by utilizing the abnormal influencing factors obtained from the previous evaluation and analysis, the abnormal relationship mining analysis is performed on the abnormal labeled nodes corresponding to the organic fertilizer data source in the abnormal labeled graph of the organic fertilizer life cycle error mapping of each production batch. This step is intended to construct an abnormal relationship network graph in order to understand the mutual influence relationship between the abnormal nodes. The specific operation includes inputting the abnormal labeled nodes and their abnormal influencing factor data of each production batch into a relationship mining and analysis tool. The tool can be a graph analysis software or a complex network analysis tool, and applying graph theory techniques, such as clustering algorithms, association rule mining or network analysis algorithms, to identify the relationship between abnormal nodes. The generated abnormal relationship network graph will display the connection between abnormal nodes, the influence path and its intensity, and other information, thereby identifying the potential dependency and propagation path between the abnormal nodes, and finally obtaining the abnormal relationship network graph between the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch.

[0177] Preferably, according to the abnormal relationship network diagram between the abnormal annotation nodes of the organic fertilizer data source corresponding to each production batch, the abnormal annotation nodes of the organic fertilizer data source corresponding to the organic fertilizer life cycle error mapping abnormal annotation diagram of each production batch are subjected to organic fertilizer quality and safety abnormality traceability processing to obtain the organic fertilizer quality and safety abnormality traceability problem points of each production batch.

[0178] In an embodiment of the present invention, by utilizing the abnormal relationship network diagram between the abnormal annotation nodes of the organic fertilizer data source corresponding to each production batch previously generated, the abnormal annotation nodes corresponding to the organic fertilizer data source in the abnormal annotation diagram of the organic fertilizer life cycle error mapping of each production batch are subjected to abnormal traceability processing. The goal of this process is to determine the root cause of the abnormality in the production batch, and to determine the corresponding abnormal problem points therefrom. The specific operation steps include comparing and analyzing the abnormal relationship network diagram with the quality data of the abnormal annotation nodes of each production batch, and determining the source and propagation path of these nodes by tracking the key nodes in the abnormal relationship network, and at the same time, combining the production process records, raw material information and operation logs to conduct a comprehensive investigation of the abnormal points, confirm the specific problem points, possible causes and improvement suggestions of the abnormalities, and finally obtain the abnormal traceability problem points of the organic fertilizer quality and safety of each production batch.

[0179] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0180] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for tracing the quality and safety of organic fertilizers based on multi-source data, characterized in that: The following steps are involved: Step S1: collecting organic fertilizer source data in real time from the organic fertilizer production link, the organic fertilizer circulation link, the organic fertilizer storage link and the organic fertilizer use link and performing standardization processing to obtain organic fertilizer production source standard data, organic fertilizer circulation source standard data, organic fertilizer storage source standard data and organic fertilizer use source standard data; Step S2: performing production batch synchronization collection processing on the organic fertilizer production source standard data, the organic fertilizer circulation source standard data, the organic fertilizer storage source standard data and the organic fertilizer use source standard data to obtain the organic fertilizer multi-source data set under each production batch; establishing a traceability association connection file for the organic fertilizer multi-source data set under each production batch to generate an organic fertilizer multi-source data traceability association connection file; Step S3: performing production batch tracing extraction processing on the organic fertilizer multi-source data traceability association connection archive to obtain a full life cycle tracing process diagram of organic fertilizer for each production batch; performing organic fertilizer quality index evaluation and analysis on the full life cycle tracing process diagram of organic fertilizer for each production batch to obtain full life cycle quality index data of organic fertilizer for each production batch; performing quality index error limit analysis on the full life cycle tracing process diagram of organic fertilizer for each production batch based on the full life cycle quality index data of organic fertilizer for each production batch to obtain the error limit factor between the organic fertilizer quality indicators corresponding to each production batch; Step S4: Based on the error limiting factors between the organic fertilizer quality indicators corresponding to each production batch, the organic fertilizer full life cycle traceability process diagram of each production batch is traced for organic fertilizer quality and safety abnormalities to obtain the organic fertilizer quality and safety abnormality traceability problem points of each production batch; abnormal decision support processing is performed on the organic fertilizer quality and safety abnormality traceability problem points of each production batch, and a decision support report on organic fertilizer quality and safety abnormalities is generated to perform corresponding organic fertilizer quality and safety abnormality traceability problem management work.

2. The organic fertilizer quality safety tracing management method based on multi-source data according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: collecting organic fertilizer production source data in real time from the organic fertilizer production process, wherein the organic fertilizer production source data includes organic fertilizer raw material sources, organic fertilizer production batches, and organic fertilizer production and processing conditions; Step S12: collecting organic fertilizer circulation source data in real time from the organic fertilizer circulation link, wherein the organic fertilizer circulation source data includes organic fertilizer logistics information and organic fertilizer sales records; Step S13: collecting organic fertilizer storage source data in real time from the organic fertilizer storage link, wherein the organic fertilizer storage source data includes organic fertilizer storage environment conditions and organic fertilizer storage inventory information; Step S14: collecting organic fertilizer use source data in real time from the organic fertilizer use link, wherein the organic fertilizer use source data includes organic fertilizer application records and growth status of crops using organic fertilizer; Step S15: Preprocessing and standardizing the organic fertilizer production source data, the organic fertilizer circulation source data, the organic fertilizer storage source data, and the organic fertilizer use source data to obtain the organic fertilizer production source standard data, the organic fertilizer circulation source standard data, the organic fertilizer storage source standard data, and the organic fertilizer use source standard data.

3. The organic fertilizer quality safety tracing management method based on multi-source data according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: extracting the production batches of the organic fertilizer production source standard data to obtain the organic fertilizer production batches; Step S22: performing production identification processing on the organic fertilizer production batch to obtain the organic fertilizer production batch identification number; Step S23: Based on the fertilizer production batch identification number, the fertilizer production source standard data, the fertilizer circulation source standard data, the fertilizer storage source standard data and the fertilizer use source standard data are processed by production batch synchronization division to obtain the fertilizer production source sub-data, the fertilizer circulation source sub-data, the fertilizer storage source sub-data and the fertilizer use source sub-data under each production batch; Step S24: performing multi-source data set processing on the organic fertilizer production source sub-data, the organic fertilizer circulation source sub-data, the organic fertilizer storage source sub-data and the organic fertilizer use source sub-data under each production batch to obtain an organic fertilizer multi-source data set under each production batch; Step S25: Establishing a traceability association connection file for the organic fertilizer multi-source data set under each production batch to generate an organic fertilizer multi-source data traceability association connection file.

4. The organic fertilizer quality safety tracing management method based on multi-source data according to claim 3, characterized in that, Step S25 includes the following steps: Step S251: extracting raw material input attribute data from the organic fertilizer production source sub-data in the organic fertilizer multi-source data set under each production batch, to obtain the organic fertilizer production raw material input attribute data under each production batch; Step S252: Based on the input attribute data of the organic fertilizer production raw materials under each production batch, the organic fertilizer multi-source data set under the corresponding production batch is characterized by describing the key nodes of the data source, so as to obtain the key node characteristic description data of the organic fertilizer data source under each production batch; Step S253: performing production batch time series correlation analysis on the key node feature description data of the organic fertilizer data source under each production batch, and obtaining the production time series correlation relationship between the key nodes of the organic fertilizer data source of each production batch; Step S254: tracing the traceability association path of the organic fertilizer multi-source data set under each production batch based on the production time series association relationship between the key nodes of the organic fertilizer data source of each production batch, so as to obtain the traceability association path of the organic fertilizer data under each production batch; Step S255: establishing a traceability association connection file for the organic fertilizer multi-source data set under each production batch according to the organic fertilizer data traceability association path under each production batch, so as to generate an organic fertilizer multi-source data traceability association connection file.

5. The organic fertilizer quality safety tracing management method based on multi-source data according to claim 4, characterized in that, Step S254 includes the following steps: Obtain the production timestamp and node information data of the key nodes of each production batch of organic fertilizer data source, and perform inter-node dynamic dependency analysis on the production timestamp and node information data of the key nodes of each production batch of organic fertilizer data source based on the production time series association relationship between the key nodes of each production batch of organic fertilizer data source, and obtain a time information dynamic dependency relationship map between the key nodes of each production batch of organic fertilizer data source; Perform hierarchical temporal clustering analysis on the time information dynamic dependency relationship map between key nodes of each production batch of organic fertilizer data source to obtain a hierarchical temporal correlation clustering map of the production batch of organic fertilizer data source; Based on the hierarchical time-series association clustering map of production batch organic fertilizer data sources, the traceability association path of the organic fertilizer multi-source data set under each production batch is traced to obtain the traceability association path of the organic fertilizer data under each production batch.

6. The organic fertilizer quality safety tracing management method based on multi-source data according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: extracting and processing the organic fertilizer production stage nodes and stage connection attributes of the organic fertilizer multi-source data traceability association connection archive to obtain the organic fertilizer multi-source data production stage nodes and the organic fertilizer multi-source data production stage connection attributes; Step S32: performing production batch traceability path connection processing on the organic fertilizer multi-source data traceability association connection archive based on the organic fertilizer multi-source data production stage node and the organic fertilizer multi-source data production stage connection attribute, so as to generate the organic fertilizer multi-source data full life cycle traceability path of each production batch; Step S33: graphically processing the full life cycle traceability path of the multi-source data of each production batch of organic fertilizer to obtain a full life cycle traceability process diagram of each production batch of organic fertilizer; Step S34: evaluating and analyzing the quality index of organic fertilizer on the whole life cycle traceability process diagram of each production batch of organic fertilizer to obtain the quality index data of the whole life cycle of organic fertilizer for each production batch; Step S35: Based on the organic fertilizer life cycle quality index data of each production batch, a quality index error limit analysis is performed on the organic fertilizer life cycle traceability process diagram of each production batch to obtain the error limit factor between the organic fertilizer quality indicators corresponding to each production batch.

7. The organic fertilizer quality safety tracing management method based on multi-source data according to claim 6, characterized in that, Step S34 includes the following steps: Step S341: extracting and processing organic fertilizer quality-related data from the organic fertilizer life cycle traceability process diagram of each production batch to obtain a data set related to the quality of the organic fertilizer life cycle of each production batch; Step S342: Deconstruct and classify the quality attribute-related data sets of the organic fertilizer life cycle of each production batch to obtain organic fertilizer quality-related sub-data corresponding to different quality attributes in each production batch; Step S343: performing organic fertilizer quality index evaluation and analysis on the organic fertilizer quality-related sub-data corresponding to different quality attributes in each production batch, and obtaining the organic fertilizer full life cycle quality index data of each production batch.

8. The organic fertilizer quality safety tracing management method based on multi-source data according to claim 6, characterized in that: Step S35 includes the following steps: Step S351: performing quality index error evaluation and analysis between each organic fertilizer quality index in the organic fertilizer full life cycle quality index data of each production batch, and obtaining the quality index error between the organic fertilizer quality indexes corresponding to each production batch; Step S352: performing error distribution statistical analysis on the quality index errors between the quality indexes of the organic fertilizers corresponding to the various production batches, and obtaining error distribution characteristic data between the quality indexes of the organic fertilizers corresponding to the various production batches; Step S353: Based on the error distribution characteristic data between the organic fertilizer quality indicators corresponding to each production batch, the error sensitivity evaluation and analysis is performed on the corresponding organic fertilizer production stage nodes in the organic fertilizer full life cycle traceability process diagram of each production batch, so as to obtain the sensitivity response degree of the organic fertilizer production stage nodes of each production batch to the quality indicator error; Step S354: Based on the sensitivity response degree of the organic fertilizer production stage nodes of each production batch to the quality index error, an error limiting factor evaluation and analysis is performed between each organic fertilizer quality index in the organic fertilizer life cycle quality index data of each production batch, so as to obtain the error limiting factor between the organic fertilizer quality indicators corresponding to each production batch.

9. The organic fertilizer quality safety tracing management method based on multi-source data according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: performing quantitative calculation of error coefficients on the organic fertilizer quality indicators corresponding to each production batch based on the error limiting factors between the organic fertilizer quality indicators corresponding to each production batch, so as to obtain error coefficients of the organic fertilizer quality indicators corresponding to each production batch; Step S42: according to the preset error threshold, the error coefficient of the corresponding organic fertilizer quality index of each production batch is compared and judged, when the error coefficient of the corresponding organic fertilizer quality index of each production batch is greater than or equal to the preset error threshold, the organic fertilizer data source node corresponding to the organic fertilizer quality index under its production batch is determined as the error source node, to obtain the organic fertilizer quality error source node of each production batch; when the error coefficient of the corresponding organic fertilizer quality index of each production batch is less than the preset error threshold, no processing is performed; Step S43: performing error mapping anomaly annotation processing on the organic fertilizer life cycle traceability process diagram of each production batch based on the organic fertilizer quality error source node of each production batch, so as to obtain an error mapping anomaly annotation diagram of the organic fertilizer life cycle of each production batch; Step S44: performing organic fertilizer quality and safety anomaly tracing processing on the organic fertilizer full life cycle error mapping anomaly annotation diagram of each production batch to obtain the organic fertilizer quality and safety anomaly tracing problem points of each production batch; Step S45: Perform abnormal decision support processing on the abnormal traceability problem points of organic fertilizer quality and safety of each production batch, generate an abnormal decision support report on organic fertilizer quality and safety, and perform corresponding organic fertilizer quality and safety abnormal traceability problem management work.

10. The organic fertilizer quality safety tracing management method based on multi-source data according to claim 9 is characterized in that: Step S44 includes the following steps: Perform abnormal pattern recognition analysis on the abnormal labeled nodes of the organic fertilizer data source corresponding to the abnormal labeled graph of the organic fertilizer life cycle error mapping of each production batch, and obtain abnormal pattern recognition information data of the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch; According to the abnormal pattern recognition information data of the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch, the abnormal impact factor evaluation and analysis are performed to obtain the abnormal impact factor of the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch; Based on the abnormal influencing factors of the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch, the abnormal relationship mining analysis is performed on the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch in the abnormal labeled graph of the organic fertilizer full life cycle error mapping, and the abnormal relationship network graph between the abnormal labeled nodes of the organic fertilizer data source corresponding to each production batch is obtained; According to the abnormal relationship network diagram between the abnormal annotation nodes of the organic fertilizer data source corresponding to each production batch, the abnormal annotation nodes of the organic fertilizer data source corresponding to the organic fertilizer full life cycle error mapping abnormal annotation diagram of each production batch are processed for organic fertilizer quality and safety abnormality traceability to obtain the organic fertilizer quality and safety abnormality traceability problem points of each production batch.

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