Information system integration intelligent optimization method based on big data analysis

Abstract

The invention relates to the technical field of big data analysis, and discloses an information system integration intelligent optimization method based on big data analysis, which comprises the following steps: step 1, data acquisition: acquiring original data from a plurality of heterogeneous information systems; 2, data preprocessing: cleaning, converting and normalizing the collected original data, and removing noise data; step 3, feature extraction: carrying out feature extraction on the preprocessed data by using a machine learning algorithm, and constructing a feature vector set; 4, establishing a correlation model; and step 5, intelligent optimization: optimizing information system integration by using an intelligent optimization algorithm according to the association model in combination with a preset optimization target. The method has the advantages that the relation between different system data can be found out, the system connection architecture is flexibly adjusted through an intelligent optimization algorithm, and the effects of increasing the system response speed, improving the resource use efficiency and optimizing data transmission are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and specifically refers to an intelligent optimization method for information system integration based on big data analysis. Background Art

[0002] In the trend of digital development, enterprises and institutions usually use multiple different types of information systems to support their operations. For example, in a manufacturing enterprise, there are systems for managing production resources, monitoring production lines, and handling warehouse logistics. These systems operate independently, and data cannot flow smoothly. Traditional methods of connecting systems, such as developing dedicated interfaces to dock systems, have many problems: First, it is difficult to expand. Whenever an enterprise adds a new system, new interfaces need to be developed additionally, and the cost of maintaining the interfaces will increase rapidly as the number of systems increases. There is a large multinational enterprise that, due to mergers and acquisitions, has expanded to have numerous systems, and the annual interface maintenance cost alone is very high, accounting for a large proportion of the IT budget. Second, the data processing capacity is insufficient. Facing a large amount of complex data, such as equipment operation records and customer service call recordings, which are difficult to organize, traditional data processing tools cannot parse them well, resulting in the waste of the value of a lot of data. For example, a logistics enterprise generates a large amount of transportation data every day, but due to limited processing capacity, only a small part of the data can be effectively utilized. Third, there is a lack of an intelligent adjustment mechanism. Existing system connection solutions rely on manual experience settings and cannot flexibly adapt to business changes. For example, during an e-commerce promotion period, due to the lack of optimization of the data transmission path, the order processing speed of a certain platform has dropped significantly, resulting in the loss of many customers.

[0003] Although new system integration frameworks have emerged in recent years, they are still not perfect in dealing with complex data analysis and system relationship modeling. There is an urgent need for a comprehensive optimization solution that combines big data analysis and intelligent algorithms. Summary of the Invention

[0004] To solve the above various problems, the present invention proposes an intelligent optimization method for information system integration based on big data analysis, which finds the connections between the data of different systems and then flexibly adjusts the system connection architecture using intelligent optimization algorithms to achieve the goals of accelerating the system response speed, improving resource utilization efficiency, and optimizing data transmission effects.

[0005] To solve the above technical problems, the technical solution proposed by the present invention is: an intelligent optimization method for information system integration based on big data analysis, including the following steps:

[0006] Step 1: Data collection, collecting raw data from multiple heterogeneous information systems, where the raw data includes structured data, semi-structured data, and unstructured data;

[0007] Step 2: Data preprocessing. Clean, transform, and normalize the collected raw data, remove noise data, and convert unstructured data into analyzable structured data;

[0008] Step 3: Feature extraction. Use machine learning algorithms to extract features from the preprocessed data and construct a feature vector set;

[0009] Step 4: Establish an association model. Based on the feature vector set, establish an association model between information systems through big data analysis technology;

[0010] Step 5: Intelligent optimization. According to the association model, combined with preset optimization objectives, use intelligent optimization algorithms to optimize the information system integration. The optimization objectives include system response time, resource utilization rate, and data transmission efficiency.

[0011] Preferably, in Step 1, data is collected from heterogeneous information systems using API interfaces, direct database connections, and message queues.

[0012] Preferably, in Step 2, the cleaning operation includes identifying and deleting duplicate data, filling missing values, and detecting and handling outliers.

[0013] Preferably, in Step 3, the principal component analysis PCA or linear discriminant analysis LDA algorithm is used for feature dimensionality reduction.

[0014] Preferably, in Step 4, the graph database technology is used to construct an information system association graph, with nodes representing information systems and edges representing data interaction relationships between systems.

[0015] Preferably, in Step 5, the intelligent optimization algorithm is a genetic algorithm, a particle swarm optimization algorithm, or an ant colony optimization algorithm.

[0016] Preferably, it further includes a monitoring and feedback step to continuously monitor the running status of the optimized information system integration, collect running data, and feedback it to the data preprocessing step for iterative optimization.

[0017] Preferably, in the monitoring and feedback step, when the running data shows that the system performance index deviates from the preset threshold, an iterative optimization process is triggered.

[0018] Preferably, the multiple heterogeneous information systems include, but are not limited to, enterprise resource planning ERP systems, customer relationship management CRM systems, and supply chain management SCM systems.

[0019] The advantages of the present invention compared with the prior art are:

[0020] Multi - technology integration improves decision - making accuracy: By integrating big data collection, pre - processing, and feature extraction technologies, the data value of heterogeneous systems can be deeply mined. For example, in manufacturing enterprises, production batch, material consumption, and equipment energy consumption data can be accurately correlated, significantly improving the accuracy of production scheduling decisions and avoiding resource waste caused by empirical judgment.

[0021] Intelligent optimization achieves a performance leap: By dynamically adjusting the system integration architecture through intelligent algorithms, in high - concurrency scenarios such as large e - commerce promotions, data routing and service call order can be automatically optimized. Compared with traditional solutions, the system response time is greatly shortened, and the order processing efficiency is significantly improved, effectively preventing customer loss due to waiting timeouts.

[0022] Full - process control reduces costs: The closed - loop process from data collection to intelligent optimization reduces interface development and maintenance costs and waste of data storage resources. Taking multinational enterprises as an example, avoiding duplicate interface development can save a large amount of IT budget; by optimizing resource scheduling, the server idle rate is reduced, achieving a double - decline in hardware costs and energy consumption costs.

[0023] Adaptive mechanism to cope with business changes: The monitoring feedback and iterative optimization mechanism endows the system with self - adaptability. When an enterprise expands new business, introduces new systems, or encounters sudden traffic peaks, it can automatically collect real - time data and re - optimize without large - scale manual configuration adjustments, ensuring that the system is always in an efficient operation state.

[0024] Strong cross - industry universality: It has been effectively verified in multiple industries such as manufacturing, healthcare, and finance. For example, in the hospital scenario, optimizing the image transmission path and storage strategy improves the patient's medical experience; in the banking field, accurately identifying cross - border payment risks ensures capital security and business compliance, demonstrating broad application potential. Brief Description of the Drawings

[0025] Figure 1 is the working principle flow chart of the present invention. Detailed Implementation Manner

[0026] The present invention will be further described in detail below with reference to the drawings.

[0027] Example 1

[0028] A certain automobile manufacturing enterprise has multiple management systems, including production resource management system, production line monitoring system, warehouse management system, and quality inspection system. Before using this method, production arrangements and material distribution did not cooperate well, and the production line often stopped.

[0029] Data collection stage: Obtain production plan data through system interfaces, collect production line equipment operation data using message - passing tools, call the warehouse system interface to obtain inventory information, and at the same time collect a large amount of image data generated by the quality inspection system.

[0030] Preprocessing stage: Remove duplicate records from the equipment operation data, predict and fill in the missing equipment status data, and identify abnormal operation data; analyze the quality inspection images to identify common defect types.

[0031] Feature engineering: Extract key features from the data of multiple systems and discover a close connection between production batches, material usage, and equipment energy consumption.

[0032] Association modeling: Construct a system relationship diagram and mark information such as the delay in data transfer between systems and the distribution efficiency.

[0033] Intelligent optimization: Use intelligent algorithms to adjust production arrangements, optimize the equipment startup sequence and the timing of material distribution, making production smoother.

[0034] Monitoring and feedback: When it is detected that the production line operation efficiency is lower than the normal level, automatically collect new data and re-optimize strategies such as material distribution.

[0035] After implementation, the production line downtime has been significantly reduced, the production cycle has been shortened, and the enterprise has saved a large amount of costs.

[0036] Example 2

[0037] A large hospital has a patient information management system, an image storage system, a laboratory system, and an auxiliary diagnosis system. Previously, patients had a slow viewing speed of examination reports and unstable image transmission.

[0038] Data collection: Synchronize patient medical record data through a standard interface, directly connect to the imaging system to obtain examination image data, and collect laboratory data using a messaging tool.

[0039] Preprocessing process: Extract key disease information from the medical record text, perform noise reduction processing on the image data, and correct abnormal data in the test results.

[0040] Feature extraction: Analyze the medical record text to find the associated features between diseases, examination items, and diagnosis results, and extract the key visual features of the images.

[0041] Association modeling: Construct a medical system relationship diagram and mark the sensitivity level and transmission priority of the data. For example, emergency images are given priority for transmission.

[0042] Intelligent optimization: Aiming at the target of report viewing speed, use an algorithm to adjust the image storage strategy and pre-store commonly used images closer to the usage end.

[0043] Monitoring and iteration: When the waiting time for image viewing is too long, collect new usage data and re-optimize the storage plan.

[0044] After implementation, the report viewing time has been significantly shortened, image transmission is almost error-free, and patient satisfaction has been significantly improved.

[0045] Example 3

[0046] An international bank has a core business system, a risk control system, a payment system, and a customer behavior analysis system. Previously, the cross-border payment processing time was long, and there were many cases of incorrect risk judgments.

[0047] Data collection: Obtain cross-border transaction data through specific interfaces, connect to the database to collect risk records, and use messaging tools to receive customer mobile device operation data.

[0048] Preprocessing operations: Check whether the transaction data is duplicate, supplement missing transaction information, and organize customer operation behavior data into an ordered sequence.

[0049] Feature engineering: Analyze transaction data to find abnormal transaction pattern features, convert customer operation behavior data into feature vectors, and identify high-risk operation features.

[0050] Association modeling: Construct a financial system relationship diagram, and mark the system security level and the degree of association between transactions and risk control rules.

[0051] Intelligent optimization: For the payment efficiency and risk control objectives, use algorithms to optimize the payment channel selection and the risk control rule trigger order, and select a better payment processing path.

[0052] Monitoring and feedback: When the cross-border payment time is too long or there are too many incorrect risk judgments, start re-optimization and adjust the payment strategy according to new data.

[0053] After implementation, the payment processing time was significantly shortened, the incorrect risk judgment rate was significantly reduced, and the bank saved a large amount of compliance costs.

[0054] The above embodiments show that through the big data-driven intelligent optimization mechanism of the present invention, significant performance improvements have been achieved in different industry scenarios, and it has broad application value and technological leadership.

[0055] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. An intelligent optimization method for information system integration based on big data analysis, characterized in that, It includes the following steps: Step 1: Data collection, collecting raw data from multiple heterogeneous information systems, where the raw data includes structured data, semi-structured data, and unstructured data; Step 2: Data preprocessing, cleaning, transforming, and normalizing the collected raw data, removing noise data, and converting unstructured data into analyzable structured data; Step 3: Feature extraction, using machine learning algorithms to extract features from the preprocessed data and constructing a set of feature vectors; Step 4: Establishing an association model, based on the set of feature vectors, establishing an association model between information systems through big data analysis techniques; Step 5: Intelligent optimization, according to the association model, combined with preset optimization goals, using intelligent optimization algorithms to optimize the integration of information systems, where the optimization goals include system response time, resource utilization rate, and data transmission efficiency.

2. The intelligent optimization method for information system integration based on big data analysis according to claim 1, wherein: In Step 1, data is collected from heterogeneous information systems by using API interfaces, direct database connections, and message queues.

3. An intelligent optimization method for information system integration based on big data analysis according to claim 1, characterized in that: In Step 2, the cleaning operation includes identifying and deleting duplicate data, filling in missing values, and detecting and handling outliers.

4. An intelligent optimization method for information system integration based on big data analysis according to claim 1, characterized in that: In Step 3, the principal component analysis PCA or linear discriminant analysis LDA algorithm is used for feature dimension reduction.

5. An intelligent optimization method for information system integration based on big data analysis according to claim 1, characterized in that: In Step 4, the graph database technology is used to construct an information system association graph, with nodes representing information systems and edges representing data interaction relationships between systems.

6. An intelligent optimization method for information system integration based on big data analysis according to claim 1, characterized in that: In Step 5, the intelligent optimization algorithm is a genetic algorithm, a particle swarm optimization algorithm, or an ant colony optimization algorithm.

7. An intelligent optimization method for information system integration based on big data analysis according to claim 1, characterized in that: It also includes a monitoring and feedback step, which monitors the running status of the optimized information system integration in real time, collects running data and feeds it back to the data preprocessing step for iterative optimization.

8. An intelligent optimization method for information system integration based on big data analysis according to claim 7, characterized in that: In the monitoring and feedback step, when the running data shows that the system performance indicators deviate from the preset thresholds, an iterative optimization process is triggered.

9. An intelligent optimization method for information system integration based on big data analysis according to claim 1, characterized in that: The multiple heterogeneous information systems include, but are not limited to, enterprise resource planning ERP systems, customer relationship management CRM systems, and supply chain management SCM systems.

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