CN-121996651-A - Meat detection data management system and method

CN121996651ACN 121996651 ACN121996651 ACN 121996651ACN-121996651-A

Abstract

The invention relates to the technical field of data retrieval, in particular to a meat detection data management system and method, the system comprises a data model dynamic adjustment module, a data relation mapping module, a database architecture evolution module, a data merging and optimizing module, a history tracking and analyzing module, a data integrity recovery module and a system performance optimizing module. In the invention, the decision tree algorithm and the Bayesian network play a key role in dynamic adjustment of the data model, the optimal data model structure is effectively identified, the graph database technology and the PageRank algorithm are used for constructing and optimizing the data relationship network graph in the data relationship mapping, the effectiveness of the data storage structure is enhanced, the cluster analysis method and the K-means algorithm improve the efficiency of data processing, the data recovery algorithm and the transaction log analysis technology ensure the accuracy and the integrity of data in the data integrity recovery, and the system monitoring and optimizing strategy has reasonable resource allocation and load balance, and the overall performance and the stability of the system are remarkably improved.

Inventors

GONG PING
ZHONG LIWEI
GAO WEIMING
Chai ting
WEI PEILING
ZHANG RONGYIN
XU YANLI
WANG XIAOTAO
WU LAN
Guan Mingxuan

Assignees

新疆畜牧科学院畜牧业质量标准研究所（新疆维吾尔自治区种羊与羊毛羊绒质量安全监督检验中心）

Dates

Publication Date: 20260508
Application Date: 20240105

Claims (10)

1. The meat detection data management system is characterized by comprising a data model dynamic adjustment module, a data relation mapping module, a database architecture evolution module, a data merging and optimizing module, a history tracking and analyzing module, a data integrity recovery module and a system performance optimizing module; The data model dynamic adjustment module adopts a decision tree algorithm to classify and predict through analyzing data characteristics based on real-time meat detection requirements, identifies an optimal data model structure conforming to the current data characteristics, adjusts and optimizes the data model structure by utilizing probability relation and dependency among Bayesian network analysis data, and generates a data model after dynamic adjustment; the data relation mapping module maps the data elements and the association relation thereof into a graph structure by using a graph database technology based on the dynamically adjusted data model, analyzes the importance and influence of nodes in the graph through a PageRank algorithm, optimizes the data storage structure and generates a data relation network graph; The database architecture evolution module adopts a database reconstruction algorithm to dynamically adjust a database architecture based on a data relationship network diagram, analyzes the existing architecture, identifies an area to be improved, carries out new architecture design, comprises adding a table, removing the table and adjusting an index structure, ensures smooth transition between the new architecture and the old architecture, and generates an evolved database architecture; The data merging and optimizing module classifies and analyzes meat detection data by using a clustering analysis method based on an evolved database architecture, automatically classifies and groups the data according to data characteristics by a K-means algorithm, and generates an optimized and merged data set; the history tracking and analyzing module is used for executing history data tracking based on the optimized and combined data set by utilizing a time sequence analysis technology, analyzing the change trend of data along with time by utilizing a long-period memory network, extracting key features in a time sequence by combining an autoregressive comprehensive moving average model, analyzing the data change and generating a history tracking prediction record; The data integrity recovery module is used for carrying out anomaly detection and state analysis on data by combining a transaction log analysis technology based on a history tracking prediction record and applying a data recovery algorithm, identifying positions and reasons of data damage and anomaly, recovering the correct state of the data by a log playback and data recovery technology, and generating an integrity recovered data set; The system performance optimization module applies a system monitoring and optimizing strategy based on an integrity recovered data set, analyzes the current performance condition of the system through a system resource monitoring tool, comprises the use condition of a CPU and a memory, optimizes the resource allocation and request processing flow by utilizing a load balancing technology and a resource scheduling algorithm, and generates a performance optimization scheme.
2. The meat detection data management system of claim 1, wherein the dynamically adjusted data model is specifically real-time adjustment of data fields, data types and associations among data, the data relationship network graph comprises a connection relationship graph among data elements, a data node importance score and a data storage and retrieval strategy based on node importance, the evolved database architecture comprises a new data table structure, an updated index strategy and an optimization scheme of data storage and access paths, the optimized and combined data set is specifically packet data obtained through clustering analysis, the historical tracking prediction record comprises analysis results of data change trend analysis based on time sequences, data state records of key time points and data change reasons and modes, the integrity restored data set is specifically a restored data state obtained through transaction log playback, and the performance optimization scheme comprises a system resource use condition analysis report and an optimization strategy of resource allocation and load balancing.
3. The meat detection data management system of claim 1, wherein the data model dynamic adjustment module comprises a model definition sub-module, a relationship mapping sub-module, and a compatibility analysis sub-module; The model definition submodule analyzes the detection data characteristics by adopting a decision tree algorithm based on real-time meat detection requirements, identifies a data model structure meeting the current requirements, comprises classification and prediction of the data characteristics and preliminary construction of the model structure, and generates a preliminary data model structure; The relation mapping submodule analyzes the relativity among elements in the data model by using an Apriori algorithm based on a preliminary data model structure, builds a relation mapping graph among the data, reveals the dependence and the relation among the data elements, and generates a data relation mapping graph; The compatibility analysis submodule analyzes probability relations among data elements based on the data relation mapping diagram by applying a Bayesian network, evaluates compatibility and applicability of the newly defined data model, adjusts and optimizes the data model and generates a dynamically adjusted data model.
4. The meat detection data management system of claim 1, wherein the data relationship mapping module comprises a relationship diagram construction sub-module, a structure optimization sub-module and a retrieval strategy sub-module; The relation graph construction submodule adopts graph database technology to carry out graph mapping of data elements and association relations thereof based on the data model after dynamic adjustment, converts the data elements into nodes in a graph, converts the association between the data into edges, constructs a graph structure, converts form data into a graph structure, and generates a relation graph primary structure; The structure optimization submodule is based on a relation diagram primary structure, and uses a graph theory optimization algorithm to reconstruct and optimize the graph structure, and comprises the steps of reconfiguring the connection mode of nodes and edges, adjusting the distance and connection strength between the nodes, determining the accuracy of data relation and generating an optimized relation diagram structure; The retrieval strategy submodule quantitatively evaluates the importance of nodes in the graph by using a PageRank algorithm based on the optimized relation graph structure, determines the influence and the core of the nodes based on the number of links and the link quality of the nodes, and adjusts the data retrieval strategy according to the importance of the nodes to generate a data relation network graph.
5. The meat detection data management system of claim 1, wherein the database architecture evolution module comprises an architecture reconstruction sub-module, a structure change sub-module and an index optimization sub-module; The framework reconstruction submodule is used for carrying out deep analysis and identification on a database structure by applying an entity-relation modeling technology based on a data relation network diagram, and comprises the steps of analyzing the existing data table structure, data entities and interrelationships thereof, identifying areas to be improved in the database framework, formulating a new database framework blueprint and generating a preliminarily reconstructed database framework; The structure change sub-module is based on the preliminarily reconstructed database architecture, applies a data migration technology, adjusts the table structure of the database through extraction, conversion and loading processes, and comprises the steps of adding new tables, deleting outdated tables and modifying the relationships among the tables, optimizing data organization and generating the database architecture with the structure adjusted; The index optimization submodule adopts an index optimization technology based on the database architecture with the structure adjusted, comprises a B tree index method, comprehensively evaluates and re-designs the database index, comprises analyzing the efficiency of the existing index and reconfiguring the index, optimizes the overall database performance, and generates the database architecture after evolution.
6. The meat detection data management system of claim 1, wherein the data merge optimization module comprises a data classification sub-module, and an integration efficiency sub-module; The data classification submodule adopts a K-means algorithm to conduct feature analysis and initial classification on meat detection data based on an evolved database architecture, conducts iterative processing on samples in a data set, calculates the distance between the samples and a clustering center, conducts sample classification according to the distance, adjusts the clustering center until the clustering center is stable, and generates a primarily classified data set; The data classifying submodule refines and classifies the data based on the preliminarily classified data set by using a hierarchical clustering algorithm, and groups the data points with the similarity by calculating and comparing the similarity between the data points to generate the refined and classified data set; the integration efficiency submodule applies a data integration strategy based on the data set which is classified and thinned, the data integration strategy comprises data compression and duplication removal technology, data are combined and optimized, repeated data items are eliminated, a data storage structure is optimized, the data quality and the integrity are ensured, and an optimized combined data set is generated.
7. The meat detection data management system of claim 1, wherein the history tracking and analysis module comprises a time series analysis sub-module, a feature extraction sub-module, and a trend prediction sub-module; the time sequence analysis submodule is used for analyzing the time sequence in the data set by adopting a long-period memory network based on the optimized and combined data set, and capturing the time characteristic and the long-period trend in the meat detection data by learning the time interval and the sequence mode in the data to generate a time sequence analysis result; The feature extraction submodule applies an autoregressive comprehensive moving average model to extract time sequence features in a data set based on a time sequence analysis result, analyzes historical values and error items of the data, identifies factors influencing data change and generates a feature extraction result; The trend prediction sub-module predicts the future development trend of the meat detection data by applying a time sequence prediction technology, including an exponential smoothing method, based on the feature extraction result, analyzes the historical data features and trend modes, understands the future data change and development direction, and generates a historical tracking prediction record.
8. The meat detection data management system of claim 1, wherein the data integrity recovery module comprises a log analysis sub-module, a state reconstruction sub-module, and an exception handling sub-module; The log analysis submodule analyzes the transaction log of the meat detection data based on the history tracking prediction record by applying a transaction log analysis technology, and comprises the steps of analyzing the log record one by one, identifying key data operation and analyzing data change history, determining abnormal time and operation details and generating a log analysis result; The state reconstruction submodule performs state reconstruction on damaged and abnormal data based on a log analysis result by applying a log playback technology, rolls back the data to a consistent state, gradually applies recording operation in a log, reconstructs a data state and generates a reconstructed data state; The exception handling submodule adopts an exception detection and correction technology based on the reconstructed data state to inspect and correct the recovered data, detects and corrects potential errors in the data recovery process, maintains the data quality and the integrity, and generates an integrity recovered data set.
9. The meat detection data management system of claim 1, wherein the system performance optimization module comprises a resource monitoring sub-module, a load analysis sub-module and a tuning execution sub-module; the resource monitoring submodule monitors the real-time use condition of the CPU and the memory by adopting a system performance monitoring tool based on the data set with the recovered integrity, records the load of the CPU and the occupancy rate of the memory in real time, evaluates the current performance state and potential resource bottleneck of the system and generates a resource monitoring report; The load analysis sub-module is used for analyzing and optimizing the system load by using a load balancing technology based on the resource monitoring report and a weighted polling algorithm, evaluating the load condition of the server, dynamically adjusting task allocation according to the performance and the current load of the server, and generating an optimized load analysis report; the optimizing execution submodule applies a resource scheduling optimization strategy based on the optimized load analysis report, comprises a dynamic resource allocation algorithm, adjusts system resource allocation and processing flow, intelligently allocates resources according to the current system load and resource use condition, optimizes request processing logic and generates a performance optimization scheme.
10. A meat detection data management method, characterized in that the meat detection data management system according to any one of claims 1 to 9 is executed, comprising the steps of: Based on real-time meat detection requirements, analyzing detection data features by adopting a decision tree algorithm, identifying a data model structure meeting the current requirements, performing relevance analysis by using an Apriori algorithm, performing data element probability relation analysis by using a Bayesian network, performing graph mapping by using a graph database technology, reconstructing and optimizing the graph structure by using a graph theory optimization algorithm, evaluating the importance of nodes in the graph by adopting a PageRank algorithm, and adjusting a data retrieval strategy to generate a data relation network graph; Based on the data relation network diagram, the entity-relation modeling technology is applied to carry out deep analysis and reconstruction on the database structure, the table structure of the database is adjusted through the data migration technology, the B tree index method is used to optimize the database index, and the evolved database architecture is generated; Based on the evolved database architecture, performing feature analysis and initial classification on meat detection data by using a K-means algorithm, performing refinement classification on the data by using a hierarchical clustering algorithm, and generating an optimized combined data set by applying a data combining and optimizing strategy including data compression and de-duplication; based on the optimized and combined data set, analyzing the time sequence by adopting a long-term and short-term memory network, extracting the time sequence characteristics by adopting an autoregressive comprehensive moving average model, and carrying out trend prediction by adopting a time sequence prediction technology including an exponential smoothing method to generate a history tracking prediction record; based on the history tracking prediction record, performing log analysis by using a transaction log analysis technology, performing state reconstruction by using a log playback technology, and performing examination and correction on the recovered data by using an anomaly detection and correction technology to generate an integrity recovered data set; Based on the data set with the recovered integrity, a system performance monitoring tool is used for resource monitoring, a load balancing technology is used for load analysis, a resource scheduling optimization strategy is used for optimizing execution, and system resource allocation and a processing flow are adjusted to generate a performance optimization scheme.

Description

Meat detection data management system and method Technical Field The invention relates to the technical field of data retrieval, in particular to a meat detection data management system and method. Background The technical field of data retrieval is focused on effectively searching and retrieving specific information from a large amount of data, and relates to a database management system, a search algorithm, a data index and a data mining technology, aiming at improving the efficiency of searching information and ensuring the accuracy and the relativity of the data, which is widely applied to various industries including medical treatment, finance, education, scientific research and the like. The key to data retrieval technology is how to organize, store, and process data so that users can quickly and accurately access the desired information. With the development of big data and artificial intelligence technology, the field of data retrieval is becoming more intelligent and efficient. The meat detection data management system is an information system specially designed for processing and managing data generated in the meat detection process, and mainly aims to improve the processing efficiency and accuracy of meat detection data and ensure that food safety and quality control standards are complied with. Through centralized management of detection data, the detection mechanism can be helped to rapidly analyze the quality of meat, identify potential health risks, and ensure that all products meet regulatory requirements. The system typically enables automatic collection, classification, and analysis of data to support decision-making and risk management. The lack of flexible data model adjustment mechanisms in conventional systems results in processing inefficiencies in dealing with diverse data features. Traditional data relationship mapping methods fail to fully utilize graph database techniques, limiting the optimization potential of data storage structures. In the aspect of database architecture, the dynamic adjustment capability is lacking, so that the database is difficult to adapt to the rapidly-changing data requirement, and the expansibility and maintainability of the system are affected. In terms of data merging and optimization, the conventional method lacks an efficient automatic classification grouping mechanism, so that the data processing efficiency is low. In the aspect of historical data tracking and analysis, the traditional system cannot effectively utilize an advanced time sequence analysis technology, so that the data change trend is difficult to accurately capture, and the accuracy of decision making is affected. Disclosure of Invention The invention aims to solve the defects in the prior art, and provides a meat detection data management system and method. In order to achieve the aim, the meat detection data management system adopts the following technical scheme that the meat detection data management system comprises a data model dynamic adjustment module, a data relation mapping module, a database architecture evolution module, a data merging and optimizing module, a history tracking and analyzing module, a data integrity recovery module and a system performance optimizing module; The data model dynamic adjustment module adopts a decision tree algorithm to classify and predict through analyzing data characteristics based on real-time meat detection requirements, identifies an optimal data model structure conforming to the current data characteristics, adjusts and optimizes the data model structure by utilizing probability relation and dependency among Bayesian network analysis data, and generates a data model after dynamic adjustment; the data relation mapping module maps the data elements and the association relation thereof into a graph structure by using a graph database technology based on the dynamically adjusted data model, analyzes the importance and influence of nodes in the graph through a PageRank algorithm, optimizes the data storage structure and generates a data relation network graph; The database architecture evolution module adopts a database reconstruction algorithm to dynamically adjust a database architecture based on a data relationship network diagram, analyzes the existing architecture, identifies an area to be improved, carries out new architecture design, comprises adding a table, removing the table and adjusting an index structure, ensures smooth transition between the new architecture and the old architecture, and generates an evolved database architecture; The data merging and optimizing module classifies and analyzes meat detection data by using a clustering analysis method based on an evolved database architecture, automatically classifies and groups the data according to data characteristics by a K-means algorithm, and generates an optimized and merged data set; the history tracking and analyzing module is used for executing history data tracking base