Process tracing system and method based on artificial intelligence

Abstract

The invention discloses a process tracing system and method based on artificial intelligence. The method comprises the following steps: S1, deploying a system architecture and a system environment; s2, collecting data from each link in the production process; s3, cleaning, converting and standardizing the collected original data; s4, analyzing the preprocessed data by using an artificial intelligence algorithm, and identifying abnormities and potential risks in the production process; s5, formulating a tracing strategy based on a data analysis result, and triggering a tracing operation; s6, displaying the data and the analysis result in the tracing process to the user in a chart mode; and S7, the system is optimized and iterated through user feedback, the machine learning model is updated, and the system is realized based on the method. According to the invention, by introducing the artificial intelligence technology, the data integration and sharing efficiency, the tracing precision and efficiency, the intelligence and automation level and the capability of guaranteeing the data privacy and security are improved.

Description

Technical Field

[0001] The present invention relates to the fields of process tracing and software development, and in particular to an artificial intelligence-based process tracing system and method. Background Art

[0002] Internet of Things (IoT) technology: IoT technology connects physical devices to the Internet, enabling real-time data collection and transmission. In process tracing, IoT technology can monitor every link of a product from production to sales in real time, providing data support for tracing.

[0003] However, the existing traceability system has the following problems: 1. Existing traceability system data is usually scattered in different business systems and databases with various formats, which makes data integration difficult; 2. Existing traceability methods often rely on manual input and query, which limits the traceability accuracy and efficiency; 3. Existing traceability methods often lack intelligent and automated support and cannot cope with complex supply chain environments and changing market demands; 4. During the traceability process, a large amount of sensitive data is involved, such as supplier information, customer information, etc., and data privacy and security are easily leaked.

[0004] This application proposes an artificial intelligence-based process tracing system and method to solve the above problems: Difficulties in data integration and sharing: AI builds a unified data platform and uses natural language processing and data mining technologies to achieve automatic integration and standardization of multi-source heterogeneous data. At the same time, blockchain technology is used to achieve secure data sharing and tamper-proofing, thereby improving the credibility and traceability of data. For limited traceability accuracy and efficiency: AI introduces machine learning algorithms and image recognition technology to automatically analyze data in the production process, such as temperature, humidity, pressure, etc., to improve traceability accuracy, monitor the production process in real time, automatically trigger traceability operations, and improve traceability efficiency; For the lack of intelligence and automation: AI uses deep learning algorithms to predict potential risks and problems in the supply chain, achieve early warning and prediction, automatically adjust and optimize production parameters, ensure stable product quality, and improve the level of automated quality control; Regarding data privacy and security issues: Artificial intelligence ensures the security and privacy of data during transmission, storage and use through encryption technology, access control and security auditing. Summary of the invention

[0005] The purpose of the present invention is to propose a process tracing system and method based on artificial intelligence in response to the above technical problems.

[0006] An artificial intelligence-based process tracing method includes the following sub-steps: S1: Deploy system architecture and system environment; S2: Collect data from all aspects of the production process; S3: Clean, transform and standardize the collected raw data; S4: Use artificial intelligence algorithms to analyze pre-processed data and identify anomalies and potential risks in the production process; S5: Based on the data analysis results, formulate a traceability strategy and trigger the traceability operation; S6: Display the data and analysis results of the traceability process to the user in the form of charts; S7: Optimize and iterate the system through user feedback and update the machine learning model.

[0007] Furthermore, in a process tracing method based on artificial intelligence, step S2 includes the following sub-steps: S21: Data source determination: clarify the data source that needs to be collected; The data sources include sensor data, quality inspection reports, and logistics information at the production site; S22: Data interface development: Develop data interfaces based on the characteristics of data sources to automatically collect and transmit data; S23: Data storage: storing the collected data in a database.

[0008] Furthermore, in a process tracing method based on artificial intelligence, step S3 includes the following sub-steps: S31: Data cleaning: remove duplicate data, outliers and missing values; The data sources include sensor data, quality inspection reports, and logistics information at the production site; S32: Data conversion: converting data into structured data or time series data; S33: Data standardization: Standardize data to eliminate differences between different data sources.

[0009] Furthermore, in a process tracing method based on artificial intelligence, step S4 includes the following sub-steps: S41: Select machine learning algorithm: According to data characteristics and business requirements, select one of decision tree, support vector machine, or neural network as algorithm learning; S42: Model training and validation: Use historical data to train and validate the model to ensure its accuracy and generalization ability; S43: Real-time data analysis: Analyze the data collected in real time to identify anomalies and potential risks in the production process.

[0010] Furthermore, in a process tracing method based on artificial intelligence, step S5 includes the following sub-steps: S51: Develop a traceability strategy: Based on the data analysis results, develop a traceability strategy for recalling products or investigating the cause; S52: Triggering traceability operations: According to the traceability strategy, automatically or manually triggering traceability operations to solve problems in a timely manner; S53: Record traceability results: Record the traceability process and results in the system for subsequent query and analysis.

[0011] Furthermore, in a process tracing method based on artificial intelligence, step S6 includes the following sub-steps: S61: Select visualization tools: According to business needs and data characteristics, select Excel, Tableau, or PowerBI as a visualization tool; S62: Create visual charts: display the data analysis results to users in the form of charts; The charts include trend charts, bar charts, and heat maps; S63: Generate report: Generate traceability report according to user needs; The traceability report includes problem description, cause analysis, and treatment measures.

[0012] Furthermore, in a process tracing method based on artificial intelligence, step S7 includes the following sub-steps: S71: Collect user feedback: Regularly collect user feedback on the system, count the system's existing problems and improvement directions; S72: Optimize system performance: Optimize and iterate the system based on user feedback and business needs; S73: Update machine learning models: Update machine learning models regularly as business develops and data accumulates to improve model accuracy and generalization capabilities.

[0013] An artificial intelligence-based process tracing system, comprising a data acquisition module, a data preprocessing module, a data analysis module, a tracing decision module, and a data visualization module; The data acquisition module collects data from each link in the production process; The data preprocessing module cleans, converts and standardizes the collected raw data; The data analysis module uses artificial intelligence algorithms to analyze the pre-processed data and identify anomalies and potential risks in the production process; The traceability decision module formulates a traceability strategy based on the data analysis results and triggers the traceability operation; The data visualization module displays the data and analysis results of the tracing process to the user in the form of charts.

[0014] Further, in an artificial intelligence-based process tracing system, the data of the data acquisition module includes real-time sensor data, production records, and quality inspection reports; The data acquisition module supports multiple data interfaces and data formats; The preprocessing of the data preprocessing module includes data deduplication, missing value filling, and outlier detection; The analysis model of the data analysis module includes a machine learning model and a deep learning model; The triggering and tracing operation is manual triggering and automatic triggering, and the triggering and tracing operation includes recalling products and investigating reasons; The data visualization module supports a variety of visualization charts and reports, including trend charts, heat maps, and report generation.

[0015] The beneficial effects of the present invention are as follows: through an artificial intelligence-based process tracing system and method, the difficulties in data integration and sharing, limited tracing accuracy and efficiency, lack of intelligence and automation, data privacy and security issues, and technical barriers are solved; by introducing artificial intelligence technology, the efficiency of data integration and sharing, tracing accuracy and efficiency, intelligence and automation levels, and the ability to ensure data privacy and security are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flowchart of a process tracing method based on artificial intelligence.

[0017] Figure 2 It is a structural diagram of a process traceability system based on artificial intelligence. DETAILED DESCRIPTION

[0018] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described with reference to the accompanying drawings.

[0019] As attached Figure 1 As shown, an artificial intelligence-based process tracing method includes the following sub-steps: S1: Deploy system architecture and system environment; S2: Collect data from all aspects of the production process; S3: Clean, transform and standardize the collected raw data; S4: Use artificial intelligence algorithms to analyze pre-processed data and identify anomalies and potential risks in the production process; S5: Based on the data analysis results, formulate a traceability strategy and trigger the traceability operation; S6: Display the data and analysis results of the traceability process to the user in the form of charts; S7: Optimize and iterate the system through user feedback and update the machine learning model.

[0020] Furthermore, in a process tracing method based on artificial intelligence, step S2 includes the following sub-steps: S21: Data source determination: clarify the data source that needs to be collected; The data sources include sensor data, quality inspection reports, and logistics information at the production site; S22: Data interface development: Develop data interfaces based on the characteristics of data sources to automatically collect and transmit data; S23: Data storage: The collected data is stored in the database to ensure the security and accessibility of the data.

[0021] Furthermore, in a process tracing method based on artificial intelligence, step S3 includes the following sub-steps: S31: Data cleaning: remove duplicate data, outliers and missing values ​​to ensure data accuracy and consistency; The data sources include sensor data, quality inspection reports, and logistics information at the production site; S32: Data conversion: converting data into structured data or time series data; S33: Data standardization: Standardize data to eliminate differences between different data sources and improve data comparability.

[0022] Furthermore, in a process tracing method based on artificial intelligence, step S4 includes the following sub-steps: S41: Select machine learning algorithm: According to data characteristics and business requirements, select one of decision tree, support vector machine, or neural network as algorithm learning; S42: Model training and validation: Use historical data to train and validate the model to ensure its accuracy and generalization ability; S43: Real-time data analysis: Analyze the data collected in real time to identify anomalies and potential risks in the production process.

[0023] Furthermore, in a process tracing method based on artificial intelligence, step S5 includes the following sub-steps: S51: Develop a traceability strategy: Based on the data analysis results, develop a traceability strategy for recalling products or investigating the cause; S52: Triggering traceability operations: According to the traceability strategy, automatically or manually triggering traceability operations to solve problems in a timely manner; S53: Record traceability results: Record the traceability process and results in the system for subsequent query and analysis.

[0024] Furthermore, in a process tracing method based on artificial intelligence, step S6 includes the following sub-steps: S61: Select visualization tools: According to business needs and data characteristics, select Excel, Tableau, or PowerBI as a visualization tool; S62: Create visual charts: display the data analysis results to users in the form of charts; The charts include trend charts, bar charts, and heat maps; S63: Generate report: Generate traceability report according to user needs; The traceability report includes problem description, cause analysis, and treatment measures.

[0025] Furthermore, in a process tracing method based on artificial intelligence, step S7 includes the following sub-steps: S71: Collect user feedback: Regularly collect user feedback on the system, count the system's existing problems and improvement directions; S72: Optimize system performance: Optimize and iterate the system based on user feedback and business needs; S73: Update machine learning models: Update machine learning models regularly as business develops and data accumulates to improve model accuracy and generalization capabilities.

[0026] As attached Figure 2 As shown, a process tracing system based on artificial intelligence includes a data acquisition module, a data preprocessing module, a data analysis module, a tracing decision module, and a data visualization module; The data acquisition module collects data from each link in the production process; The data preprocessing module cleans, converts and standardizes the collected raw data; The data analysis module uses artificial intelligence algorithms to analyze the pre-processed data and identify anomalies and potential risks in the production process; The traceability decision module formulates a traceability strategy based on the data analysis results and triggers the traceability operation; The data visualization module displays the data and analysis results of the tracing process to the user in the form of charts.

[0027] Further, in an artificial intelligence-based process tracing system, the data of the data acquisition module includes real-time sensor data, production records, and quality inspection reports; The data acquisition module supports multiple data interfaces and data formats to ensure the comprehensiveness and accuracy of the data; The preprocessing of the data preprocessing module includes data deduplication, missing value filling, and outlier detection to facilitate subsequent analysis and tracing; The analysis model of the data analysis module includes a machine learning model and a deep learning model; The triggering and tracing operation is manual triggering and automatic triggering, and the triggering and tracing operation includes recalling products and investigating reasons; The data visualization module supports a variety of visualization charts and reports, including trend charts, heat maps, and report generation.

[0028] This solution solves the problems of difficulty in data integration and sharing, limited tracing accuracy and efficiency, lack of intelligence and automation, data privacy and security issues, and technical barriers through an artificial intelligence-based process tracing system and method. By introducing artificial intelligence technology, it improves the efficiency of data integration and sharing, tracing accuracy and efficiency, intelligence and automation levels, and the ability to protect data privacy and security.

[0029] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A process tracing method based on artificial intelligence, characterized in that: It includes the following sub-steps: S1: Deploy system architecture and system environment; S2: Collect data from all aspects of the production process; S3: Clean, transform and standardize the collected raw data; S4: Use artificial intelligence algorithms to analyze pre-processed data and identify anomalies and potential risks in the production process; S5: Based on the data analysis results, formulate a traceability strategy and trigger the traceability operation; S6: Display the data and analysis results of the traceability process to the user in the form of charts; S7: Optimize and iterate the system through user feedback and update the machine learning model.

2. The process tracing method based on artificial intelligence according to claim 1, characterized in that: The step S2 comprises the following sub-steps: S21: Data source determination: clarify the data source that needs to be collected; The data sources include sensor data, quality inspection reports, and logistics information at the production site; S22: Data interface development: Develop data interfaces based on the characteristics of data sources to automatically collect and transmit data; S23: Data storage: storing the collected data in a database.

3. The process tracing method based on artificial intelligence according to claim 1 is characterized in that: The step S3 comprises the following sub-steps: S31: Data cleaning: remove duplicate data, outliers and missing values; The data sources include sensor data, quality inspection reports, and logistics information at the production site; S32: Data conversion: converting data into structured data or time series data; S33: Data standardization: Standardize data to eliminate differences between different data sources.

4. The process tracing method based on artificial intelligence according to claim 1 is characterized in that: The step S4 comprises the following sub-steps: S41: Select machine learning algorithm: According to data characteristics and business requirements, select one of decision tree, support vector machine, or neural network as algorithm learning; S42: Model training and validation: Use historical data to train and validate the model to ensure its accuracy and generalization ability; S43: Real-time data analysis: Analyze the data collected in real time to identify anomalies and potential risks in the production process.

5. The process tracing method based on artificial intelligence according to claim 1 is characterized in that: The step S5 comprises the following sub-steps: S51: Develop a traceability strategy: Based on the data analysis results, develop a traceability strategy for recalling products or investigating the cause; S52: Triggering traceability operations: According to the traceability strategy, automatically or manually triggering traceability operations to solve problems in a timely manner; S53: Record traceability results: Record the traceability process and results in the system for subsequent query and analysis.

6. The process tracing method based on artificial intelligence according to claim 1 is characterized in that: The step S6 comprises the following sub-steps: S61: Select visualization tools: According to business needs and data characteristics, select Excel, Tableau, or PowerBI as a visualization tool; S62: Create visual charts: display the data analysis results to users in the form of charts; The charts include trend charts, bar charts, and heat maps; S63: Generate report: Generate traceability report according to user needs; The traceability report includes problem description, cause analysis, and treatment measures.

7. The process tracing method based on artificial intelligence according to claim 1 is characterized in that: The step S7 comprises the following sub-steps: S71: Collect user feedback: Regularly collect user feedback on the system, count the system's existing problems and improvement directions; S72: Optimize system performance: Optimize and iterate the system based on user feedback and business needs; S73: Update machine learning models: Update machine learning models regularly as business develops and data accumulates to improve model accuracy and generalization capabilities.

8. An artificial intelligence-based process tracing system, implemented based on an artificial intelligence-based process tracing method according to any one of claims 1 to 7, characterized in that: It includes data acquisition module, data preprocessing module, data analysis module, traceability decision module and data visualization module; The data acquisition module collects data from each link in the production process; The data preprocessing module cleans, converts and standardizes the collected raw data; The data analysis module uses artificial intelligence algorithms to analyze the pre-processed data and identify anomalies and potential risks in the production process; The traceability decision module formulates a traceability strategy based on the data analysis results and triggers the traceability operation; The data visualization module displays the data and analysis results of the tracing process to the user in the form of charts.

9. The process tracing system based on artificial intelligence according to claim 8 is characterized in that: The data of the data acquisition module includes real-time sensor data, production records, and quality inspection reports; The data acquisition module supports multiple data interfaces and data formats; The preprocessing of the data preprocessing module includes data deduplication, missing value filling, and outlier detection; The analysis model of the data analysis module includes a machine learning model and a deep learning model; The triggering and tracing operation is manual triggering and automatic triggering, and the triggering and tracing operation includes recalling products and investigating reasons; The data visualization module supports a variety of visualization charts and reports, including trend charts, heat maps, and report generation.

Images