Information consultation management system based on enterprise big data processing

Abstract

The invention discloses an information consultation management system based on enterprise big data processing, and relates to the technical field of information consultation, and the information consultation management system comprises a multi-source data acquisition module, a storage framework module, a performance monitoring module and a performance trend analysis module. The system can comprehensively cover business systems in an enterprise and external data sources, breaks data islands, realizes comprehensive data acquisition, provides a wider and richer data basis for the enterprise, is helpful for mining deeper business insights, supports flexible adaptation and capture of data sources of different types and different formats, and improves the data acquisition efficiency. According to the method, all structured data, semi-structured data and unstructured data can be effectively processed, the flexibility and adaptability of data acquisition are improved, effective data and noise data can be automatically distinguished through an intelligent identification technology, and convenience is provided for subsequent data cleaning and preprocessing.

Description

Technical Field

[0001] The present invention relates to the field of information consulting technology, and in particular to an information consulting management system based on enterprise big data processing. Background Art

[0002] With the rapid development of information technology, big data has become an important resource for corporate decision-making and operations. Information consulting is a business activity based on the collection, processing, transmission, effective use and feedback of various information. The information consulting industry is a knowledge-based industry that uses various information processing technologies to collect, process, organize, analyze and transmit various types of information, and provides customers with information products such as solutions, strategies, suggestions, plans or measures to solve problems. Its service areas involve almost every aspect of society and economy, including all possible disciplines, from policy research to highly professional engineering services and technology development research. Through consulting channels, new technologies can be acquired, operating costs can be minimized, the most suitable partners can be found, powerful advertising can be carried out, the competitiveness of enterprises can be improved, and risks can be reduced;

[0003] The general information consultation management system on the market is not convenient to achieve seamless connection between the business data of various departments within the enterprise and external data sources when in use. In addition, when retrieving information, the large amount of information retrieved leads to poor retrieval efficiency. For this reason, we propose an information consultation management system based on enterprise big data processing. Summary of the invention

[0004] The purpose of the present invention is to provide an information consultation management system based on enterprise big data processing.

[0005] To solve the problems raised in the above background technology, the present invention provides the following technical solutions: an information consultation management system based on enterprise big data processing, comprising a multi-source data acquisition module, a storage framework module, a performance monitoring module and a performance trend analysis module;

[0006] The multi-source data acquisition module has the function of multi-source data acquisition, accesses all business software within the enterprise, thereby obtaining business information within the enterprise, and automatically captures external data of the enterprise. At the same time, when acquiring data, it will use intelligent recognition technology to collect relevant data;

[0007] Storage framework module: builds a distributed big data storage framework, uses the distributed big data storage framework to achieve fast data storage and efficient retrieval, and establishes a demand retrieval interface on the distributed big data storage framework to support data hierarchical storage and second-level query response;

[0008] Performance monitoring module, real-time access to the CPU, memory, disk and network usage status, to obtain the computer's runtime status information;

[0009] The performance trend analysis module obtains the computer model, analyzes the reasons for the performance degradation of the computer model, obtains the data storage information and performance data in the distributed big data storage framework, and draws a performance change trend chart of the computer.

[0010] As a further solution of the present invention: it also includes a user authority management module, an optimization module, a performance degradation prediction module and a data management module;

[0011] The user rights management module manages user rights to ensure that only authorized users can access sensitive data, and then transfers the processed information to the data management module;

[0012] The optimization module can obtain the current data storage solution, take the current data storage solution as the initial solution, generate different derivative solutions according to the effect of the initial solution, and then analyze the corresponding computer performance values ​​of the derivative solutions;

[0013] The performance degradation prediction module analyzes the time when the computer performance will degrade based on the data storage method of the current distributed big data storage framework and the performance change trend of the computer;

[0014] The data management module can organize, process and maintain data, and can encrypt data using symmetric encryption, asymmetric encryption and hash algorithms.

[0015] As a further solution of the present invention: after acquiring the business information of the enterprise, the multi-source data acquisition module will summarize and integrate the business information, and then obtain the business category information of the enterprise, and then establish a capture interval unit. The capture interval unit will obtain the business category obtained by analysis, and when capturing the external data of the enterprise, the information inside the capture interval unit will be used as the screening condition to filter and process the external data of the enterprise, so that the obtained external data of the enterprise is more accurate.

[0016] As a further solution of the present invention: when the enterprise external data is captured, the multi-source data acquisition module will establish a multi-structure processing unit, wherein the multi-structure processing unit stores ETL tools, programming language parsing libraries and natural language processing technology;

[0017] ETL tools can process structured data;

[0018] Programming language parsing library, capable of processing semi-structured data;

[0019] Natural language processing technology can process unstructured data;

[0020] The multi-structure processing unit can flexibly adapt and capture data in different formats.

[0021] As a further solution of the present invention: the storage framework module is capable of receiving information processed by the multi-source data acquisition module, and when storing the data, first extracting enterprise category characteristics from the acquired data, then recording the data with the same enterprise category characteristics in a folder, then compressing the folder, and then recording the folders with different data compressions on different nodes;

[0022] When retrieving data, Huffman coding and run-length coding are used to decompress the compressed data and establish a retrieval unit. When the user enters the retrieval information into the retrieval unit, the retrieval unit extracts the enterprise category characteristics of the retrieval information, and then performs a comprehensive retrieval based on the retrieval information and the enterprise category characteristics. Suppose the enterprise category characteristics in the folder are W Z =W 1 , W 2 , W 3 ,……,W X , let the enterprise type feature extracted from the retrieval information be S T =S 1 , S 2 , S 3 ,……,S Y , where X and Y are unknowns, let the same feature be T J ;

[0023] T J =W Z ∩S T =(W 1 , W 2 , W 3 ,……,W X )∩(S 1 , S 2 , S 3 ,……,S Y )

[0024] According to the above formula, the same features can be calculated. When the same features are obtained, the number of the same features can be obtained. At this time, the number of features in the analysis folder is set as T. S , let the number of features in the folder be W S , let the extraction decompression value be T J ;

[0025]

[0026] According to the above formula, the extraction decompression value can be calculated. Let the extraction threshold be T Y ;

[0027] When T J ≥T Y When TJ Extract and decompress the corresponding folder;

[0028] When T J <T Y When T J Extract and decompress the corresponding folder.

[0029] As a further solution of the present invention: the performance monitoring module can receive information processed by the storage framework module, and when monitoring the operating status of the computer in real time, Nagios and Zabbix can also be used to assist in monitoring the operating status of the computer. Nagios can be used to customize alarm rules, and alarms can be issued in time when problems occur in the computer system. Zabbix can be used to convert the monitored computer operating status into a rich graphical interface, making it more convenient and intuitive for users to view system performance data.

[0030] As a further solution of the present invention: the performance trend analysis module can obtain the information processed by the performance monitoring module, obtain the changes in computer space information and the corresponding performance changes, and at the same time establish a performance retrieval unit. The user enters the computer model in the performance retrieval unit. At this time, the performance retrieval unit will retrieve the usage status of the computer of this model in the hands of the public according to the computer model, and then it can analyze the reasons for the performance degradation of the computer of this model, and obtain the different limits of the computer performance degradation caused by different reasons, and at the same time draw a performance change trend graph according to the computer usage status and the computer performance changes.

[0031] As a further solution of the present invention: the performance degradation prediction module can obtain the information processed by the performance trend analysis module, thereby obtaining a performance change trend graph, and obtain the remaining time for the computer to maintain good performance according to the performance change trend graph. Let the remaining time for the computer to maintain good performance be L Y , where the unit of time is hours, and the sudden stop time is T X , where the sudden downtime can be adjusted according to user needs, and the predicted time is Y S , the design calculation precision cycle is J Z , where the precise calculation cycles are hours, days, weeks and months, and the monthly cycle used is connected to the Internet and can be synchronized with the daily monthly cycle;

[0032]

[0033] According to the above formula, the prediction time can be calculated, and finally the processed information is transmitted to the data management module.

[0034] As a further solution of the present invention: the optimization module can receive information processed by the performance monitoring module, so as to obtain the solution when the multi-source data acquisition module, the storage framework module and the performance monitoring module are running. The solution when the multi-source data acquisition module, the storage framework module and the performance monitoring module are running is the starting solution, and then different optimization solutions are generated according to the starting solution information, and the generated optimization solutions are displayed, and then the processed information is transmitted to the data management module.

[0035] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are:

[0036] 1. The present invention can fully cover various business systems within the enterprise and external data sources by designing a multi-source data collection function, breaking data silos and realizing comprehensive data collection, which provides enterprises with a broader and richer data foundation and helps to dig deeper business insights. This function supports flexible adaptation and capture of data sources of different types and formats, and can effectively process structured data, semi-structured data and unstructured data, thereby improving the flexibility and adaptability of data collection. Through refined user authority management, it ensures that only authorized users can access sensitive data, effectively preventing data leakage, monitoring system performance in real time, timely discovering and solving potential problems, and ensuring stable operation of the system. In addition, in the process of data collection, through intelligent recognition technology, it can automatically distinguish valid data from noise data, providing convenience for subsequent data cleaning and preprocessing;

[0037] 2. The present invention summarizes and integrates the business categories after acquiring the internal information of the enterprise through the multi-source data acquisition module, and uses this to establish a capture interval unit to screen the external data, so that the acquired external data of the enterprise is more accurate, and realizes flexible adaptation and capture of data in different formats, enriching the data sources and types. The storage framework module can quickly locate the relevant data, thereby reducing the retrieval time and resource consumption. At the same time, the extraction and decompression value and extraction threshold are set, and the corresponding folder is extracted and decompressed only when certain conditions are met, avoiding unnecessary waste of resources;

[0038] 3. The present invention can timely discover problems during computer operation through the performance monitoring module, and can convert the computer's performance data into a graphical interface, making it more convenient and intuitive for users to view computer performance. The performance trend analysis module can accurately analyze performance trends and causes of performance degradation, and the performance degradation prediction module can calculate the operating time of the computer to maintain good performance, thereby facilitating users to process the computer in advance to avoid affecting corporate business due to computer performance degradation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1Schematic diagram of the system flow in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0041] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0042] Embodiment 1:

[0043] An information consulting management system based on enterprise big data processing. With the rapid development of information technology, big data has become an important resource for enterprise decision-making and operation. Information consulting is a business activity based on the collection, processing, transmission, effective utilization and feedback of various information. The information consulting industry is a knowledge-based industry that uses various information processing technologies to collect, process, organize, analyze and transmit various types of information to provide customers with information products such as solutions, strategies, suggestions, plans or measures to solve problems. Its service areas involve almost every aspect of society and economy, including all possible disciplines, from policy research to highly professional engineering services and technology development research. Through consulting channels, new technologies can be obtained, operating costs can be minimized, the most suitable partners can be found, powerful advertising can be carried out, the competitiveness of enterprises can be improved, and risks can be reduced;

[0044] The general information consultation management system on the market is not convenient to achieve seamless connection between the business data of various departments within the enterprise and external data sources when in use, and the large amount of information retrieved during information retrieval leads to poor retrieval efficiency. For this reason, we propose an information consultation management system based on enterprise big data processing;

[0045] Therefore, in order to effectively solve the above problems, the present application proposes an information consultation management system based on enterprise big data processing, as shown in the accompanying drawings of the specification. Figure 1 As shown, it includes a multi-source data acquisition module, a storage framework module, a performance monitoring module and a performance trend analysis module;

[0046] The multi-source data acquisition module has the function of multi-source data acquisition, accesses all business software within the enterprise, thereby obtaining business information within the enterprise, and automatically captures external data of the enterprise. At the same time, when acquiring data, it will use intelligent recognition technology to collect relevant data;

[0047] Storage framework module: builds a distributed big data storage framework, uses the distributed big data storage framework to achieve fast data storage and efficient retrieval, and establishes a demand retrieval interface on the distributed big data storage framework to support data hierarchical storage and second-level query response;

[0048] Performance monitoring module, real-time access to the CPU, memory, disk and network usage status, to obtain the computer's runtime status information;

[0049] The performance trend analysis module obtains the computer model, analyzes the reasons for the performance degradation of the computer model, obtains the data storage information and performance data in the distributed big data storage framework, and draws a performance change trend chart of the computer;

[0050] It also includes a user rights management module, an optimization module, a performance degradation prediction module, and a data management module;

[0051] The user rights management module manages user rights to ensure that only authorized users can access sensitive data, and then transfers the processed information to the data management module;

[0052] The optimization module can obtain the current data storage solution, take the current data storage solution as the initial solution, generate different derivative solutions according to the effect of the initial solution, and then analyze the corresponding computer performance values ​​of the derivative solutions;

[0053] The performance degradation prediction module analyzes the time when the computer performance will degrade based on the data storage method of the current distributed big data storage framework and the performance change trend of the computer;

[0054] When a computer is in use, once the use of internal resources exceeds a certain limit, the computer's performance will drop rapidly;

[0055] Data management module, which can organize, process and maintain data, and encrypt data using symmetric encryption, asymmetric encryption and hashing algorithms;

[0056] Specific workflow: It has multi-source data collection function, accesses all business software within the enterprise, thereby obtaining business information within the enterprise, and automatically captures external data of the enterprise, builds a distributed big data storage framework, and uses the distributed big data storage framework to achieve fast data storage and efficient retrieval. At the same time, a demand retrieval interface is established on the distributed big data storage framework to obtain the status information of the computer at runtime, analyze the computer model, draw the performance change trend chart of the computer, and manage user permissions;

[0057] Obtain the current data storage plan, then generate a large number of derivative plans based on the initial plan, and calculate the computer performance values ​​of a large number of derivative plans. According to the data storage method of the current distributed big data storage framework and the performance change trend of the computer, analyze the time when the computer performance decreases, and then comprehensively manage the processed data;

[0058] In e-commerce companies, the multi-source data collection function can integrate data from their internal sales systems, inventory management systems, customer service systems, etc., while capturing external market trend data, competitor price data, user feedback data on social media, etc. Through automatic capture and intelligent identification, it can quickly screen out valuable information and provide comprehensive data support for the company's strategic decision-making;

[0059] The distributed big data storage framework can efficiently store massive amounts of transaction data, user behavior data, etc. During promotional activities, facing a large amount of order data, the storage framework can quickly store and support query responses in seconds, so as to monitor sales in real time. At the same time, according to the frequency of data access, recent popular product data is stored on high-speed storage media, while historical order data is stored on lower-cost media, achieving cost optimization and rational use of resources;

[0060] A complete management system ensures data security, encrypts and stores users' personal information and payment data, prevents improper access by internal personnel through strict permission management, monitors system performance in real time, automatically adjusts server resources during shopping peaks to ensure stable system operation, and ensures data availability in emergencies through data backup and recovery mechanisms. For example, in the event of a server failure, data can be quickly restored without affecting the normal operation of the business.

[0061] Furthermore, by designing a multi-source data collection function, it can fully cover various business systems within the enterprise and external data sources, break down data silos, and achieve comprehensive data collection, which provides enterprises with a broader and richer data foundation and helps to tap deeper business insights. This function supports flexible adaptation and capture of data sources of different types and formats. Whether it is structured data, semi-structured data or unstructured data, it can be effectively processed, which improves the flexibility and adaptability of data collection. Through refined user authority management, it ensures that only authorized users can access sensitive data, effectively prevents data leakage, monitors system performance in real time, promptly discovers and solves potential problems, ensures stable system operation, and in the data collection process, through intelligent recognition technology, can automatically distinguish between valid data and noise data, which facilitates subsequent data cleaning and preprocessing.

[0062] Embodiment 2:

[0063] Based on the first embodiment, as shown in the accompanying drawings of the specification Figure 1 As shown, after acquiring the business information of the enterprise, the multi-source data acquisition module will summarize and integrate the business information, and then obtain the business category information of the enterprise, and then establish a capture interval unit, which will obtain the business category obtained by analysis. When capturing the external data of the enterprise, the information inside the capture interval unit will be used as the screening condition to filter and process the external data of the enterprise, so as to make the obtained external data of the enterprise more accurate;

[0064] When the external data of an enterprise is captured, the multi-source data acquisition module will establish a multi-structure processing unit, in which ETL tools, programming language parsing libraries and natural language processing technology are stored;

[0065] ETL tools can process structured data;

[0066] Programming language parsing library, capable of processing semi-structured data;

[0067] Natural language processing technology can process unstructured data;

[0068] The multi-structure processing unit can flexibly adapt and capture data in different formats

[0069] The storage framework module can receive information processed by the multi-source data acquisition module. When storing data, it first extracts enterprise category characteristics from the acquired data, then records the data with the same enterprise category characteristics in a folder, then compresses the folder data, and then records the folders with different data compressions on different nodes;

[0070] When retrieving data, Huffman coding and run-length coding are used to decompress the compressed data and establish a retrieval unit. When the user enters the retrieval information into the retrieval unit, the retrieval unit extracts the enterprise category characteristics of the retrieval information, and then performs a comprehensive retrieval based on the retrieval information and the enterprise category characteristics. Suppose the enterprise category characteristics in the folder are W Z =W 1 , W 2 , W 3 ,……,W X , let the enterprise type feature extracted from the retrieval information be S T =S 1 , S 2 , S 3 ,……,S Y , where X and Y are unknowns, let the same feature be T J ;

[0071] T J =W Z ∩S T =(W 1, W 2 , W 3 ,……,W X )∩(S 1 , S 2 , S 3 ,……,S Y )

[0072] According to the above formula, the same features can be calculated. When the same features are obtained, the number of the same features can be obtained. At this time, the number of features in the analysis folder is set as T. S , let the number of features in the folder be W S , let the extraction decompression value be T J ;

[0073]

[0074] According to the above formula, the extraction decompression value can be calculated. Let the extraction threshold be T Y ;

[0075] When T J ≥T Y When T J Extract and decompress the corresponding folder;

[0076] When T J <T Y When T J Extract and decompress the corresponding folder;

[0077] Specific workflow: summarize and integrate enterprise information, obtain the enterprise's business types, establish a capture interval unit, the capture interval unit will obtain the business types obtained through analysis, and then use the information inside the capture interval unit as the screening condition to filter and process the enterprise's external data. At the same time, use the multi-structure processing unit in the multi-source data acquisition module to flexibly adapt and capture data in different formats;

[0078] Then, the enterprise category characteristics are extracted, and the data with the same enterprise category characteristics are recorded in a folder, and the folder is compressed, and then the folders with different data compression are recorded on different nodes. When retrieving data, the compressed data is decompressed, and a retrieval unit is established. When the user enters the retrieval information into the retrieval unit, the retrieval unit will extract the enterprise category characteristics of the retrieval information, and then perform a comprehensive retrieval based on the retrieval information and the enterprise category characteristics, thereby improving the accuracy of information retrieval;

[0079] In financial institutions, the multi-source data collection function can collect internal transaction data, customer information system data, risk management system data, etc., and simultaneously obtain external macroeconomic data, industry dynamic data, credit rating agency data, etc., and flexibly adapt to various data formats to ensure the comprehensiveness and accuracy of data. The distributed big data storage framework can store a large amount of financial transaction data and customer information. When conducting risk assessment, it can quickly retrieve historical transaction data and customer credit records to provide data support for risk models. Through data hierarchical storage, high-frequency transaction data is stored on high-performance storage media, while long-term historical data is stored on lower-cost media, reducing storage costs;

[0080] In the manufacturing industry, the multi-source data collection function can be used to integrate the data of the internal production management system, logistics system, procurement system, etc., and collect external supplier data, market demand data, logistics and transportation data, etc. Automatic crawling and intelligent recognition technology can quickly process a large amount of supply chain data and provide enterprises with real-time supply chain insights. The distributed big data storage framework can store a large amount of production data, inventory data and logistics data of manufacturing enterprises. In production scheduling and inventory management, data can be quickly stored and retrieved to support real-time decision-making. According to the importance and access frequency of the data, key production planning data is stored on high-reliability storage media, while general logistics tracking data is stored on lower-cost media;

[0081] Furthermore, after obtaining the internal information of the enterprise, the multi-source data acquisition module summarizes and integrates the business categories, and uses this to establish a capture interval unit to screen the external data, making the acquired external data of the enterprise more accurate, and realizing flexible adaptation and capture of data in different formats, enriching the data sources and types, and using the storage framework module to quickly locate relevant data, thereby reducing retrieval time and resource consumption. At the same time, the extraction and decompression value and extraction threshold are set, and the corresponding folders are extracted and decompressed only when certain conditions are met, avoiding unnecessary waste of resources.

[0082] Embodiment three:

[0083] Based on the second embodiment, as shown in the accompanying drawings of the specification Figure 1 As shown in the figure, the performance monitoring module can receive the information processed by the storage framework module. When monitoring the running status of the computer in real time, Nagios and Zabbix can also be used to assist in monitoring the running status of the computer. Nagios can be used to customize alarm rules and issue alarms in time when problems occur in the computer system. Zabbix can be used to convert the monitored computer running status into a rich graphical interface, making it more convenient and intuitive for users to view system performance data.

[0084] The performance trend analysis module can obtain the information processed by the performance monitoring module, obtain the changes in computer space information and the corresponding performance changes, and establish a performance retrieval unit. The user enters the computer model in the performance retrieval unit. At this time, the performance retrieval unit will retrieve the usage status of the computer model in the hands of the masses according to the computer model, and then analyze the reasons for the performance degradation of the computer model, and obtain the different limits of computer performance degradation caused by different reasons. At the same time, a performance change trend chart is drawn according to the computer usage status and the computer performance changes;

[0085] The performance degradation prediction module can obtain the information processed by the performance trend analysis module, thereby obtaining a performance change trend graph, and obtain the remaining time for the computer to maintain good performance according to the performance change trend graph. Suppose the remaining time for the computer to maintain good performance is L Y , where the unit of time is hours, and the sudden stop time is T X , where the sudden downtime can be adjusted according to user needs, and the predicted time is Y S , the design calculation precision cycle is J Z , where the precise calculation cycles are hours, days, weeks and months, and the monthly cycle used is connected to the Internet and can be synchronized with the daily monthly cycle;

[0086]

[0087] According to the above formula, the prediction time can be calculated, and finally the processed information is transmitted to the data management module;

[0088] Suppose the remaining time for the computer to maintain good performance during the Pth detection is L P ;

[0089]

[0090] Where N is the total number of detections. The prediction time can be calculated according to the above formula, and finally the processed information is transmitted to the data management module;

[0091] The optimization module can receive information processed by the performance monitoring module, so as to obtain the schemes of the multi-source data acquisition module, the storage framework module and the performance monitoring module when they are running. The schemes of the multi-source data acquisition module, the storage framework module and the performance monitoring module when they are running are the starting schemes. Then, different optimization schemes are generated according to the starting scheme information, and the generated optimization schemes are displayed. Then, the processed information is transmitted to the data management module.

[0092] Specific workflow: monitor the computer's operating status in real time, and use Nagios and Zabbix to assist in monitoring the computer's operating status. When a problem occurs in the computer system, an alarm can be issued in a timely manner. At the same time, the computer's operating status will be converted into a rich graphical interface. According to the changes in computer space information and corresponding performance changes, a large amount of usage information of the same computer model is retrieved, and the reasons for the decline in computer performance of this model are analyzed. The different limits of computer performance decline caused by different reasons are obtained, and then the performance change trend chart of the computer model is drawn. According to the performance change trend chart, the remaining time for the computer to maintain good performance is obtained, and then the starting plan of the multi-source data acquisition module, storage framework module and performance monitoring module when running is obtained, and a large number of optimization plans are generated according to the starting plan;

[0093] In financial institutions, a sound management system ensures the security and privacy of financial data. Strict authority management ensures that only authorized personnel can access sensitive customer financial information and transaction data. Data is encrypted for transmission and storage to prevent data leakage. Real-time monitoring of system performance ensures that the system can operate stably in the case of rapid changes in the financial market. Through data management functions, the integrity and consistency of data are guaranteed, providing a reliable data basis for risk assessment and decision-making.

[0094] In the manufacturing industry, a complete management system ensures the security of the supply chain data of manufacturing enterprises. Through permission management, access to confidential production data and supplier information is restricted, data is encrypted to prevent data from being stolen during transmission, and system performance is monitored in real time to ensure stable operation of the system during peak production periods. Through data backup and recovery mechanisms, data availability is guaranteed in the event of natural disasters or equipment failures, ensuring the continuity of the supply chain.

[0095] Furthermore, the performance monitoring module can promptly detect problems encountered during computer operation and convert the computer's performance data into a graphical interface, making it more convenient and intuitive for users to view computer performance. The performance trend analysis module can accurately analyze performance trends and causes of performance degradation, and the performance degradation prediction module can calculate the operating time for the computer to maintain good performance, thereby facilitating users to process the computer in advance and avoid affecting corporate business due to computer performance degradation.

[0096] The above shows and describes the basic principles, main features and 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. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An information consultation management system based on enterprise big data processing, characterized by: It includes multi-source data acquisition module, storage framework module, performance monitoring module and performance trend analysis module; The multi-source data acquisition module has the function of multi-source data acquisition, accesses all business software within the enterprise, thereby obtaining business information within the enterprise, and automatically captures external data of the enterprise. At the same time, when acquiring data, it will use intelligent recognition technology to collect relevant data; Storage framework module: builds a distributed big data storage framework, uses the distributed big data storage framework to achieve fast data storage and efficient retrieval, and establishes a demand retrieval interface on the distributed big data storage framework to support data hierarchical storage and second-level query response; Performance monitoring module, real-time access to the CPU, memory, disk and network usage status, to obtain the computer's runtime status information; The performance trend analysis module obtains the computer model, analyzes the reasons for the performance degradation of the computer model, obtains the data storage information and performance data in the distributed big data storage framework, and draws a performance change trend chart of the computer.

2. The information consultation management system based on enterprise big data processing according to claim 1 is characterized by: It also includes a user rights management module, an optimization module, a performance degradation prediction module, and a data management module; The user rights management module manages user rights to ensure that only authorized users can access sensitive data, and then transfers the processed information to the data management module; The optimization module can obtain the current data storage solution, take the current data storage solution as the initial solution, generate different derivative solutions according to the effect of the initial solution, and then analyze the corresponding computer performance values ​​of the derivative solutions; The performance degradation prediction module analyzes the time when the computer performance will degrade based on the data storage method of the current distributed big data storage framework and the performance change trend of the computer; The data management module can organize, process and maintain data, and can encrypt data using symmetric encryption, asymmetric encryption and hash algorithms.

3. The information consultation management system based on enterprise big data processing according to claim 2 is characterized by: After acquiring the business information of the enterprise, the multi-source data acquisition module will summarize and integrate the business information to obtain the business category information of the enterprise, and then establish a capture interval unit. The capture interval unit will obtain the business categories obtained through analysis. When capturing the external data of the enterprise, the information inside the capture interval unit will be used as the screening condition to filter and process the external data of the enterprise, so as to make the obtained external data of the enterprise more accurate.

4. The information consultation management system based on enterprise big data processing according to claim 3 is characterized by: When the enterprise external data is captured, the multi-source data acquisition module will establish a multi-structure processing unit, wherein the multi-structure processing unit stores ETL tools, programming language parsing libraries and natural language processing technology; ETL tools can process structured data; Programming language parsing library, capable of processing semi-structured data; Natural language processing technology can process unstructured data; The multi-structure processing unit can flexibly adapt and capture data in different formats.

5. The information consultation management system based on enterprise big data processing according to claim 3 is characterized by: The storage framework module can receive information processed by the multi-source data acquisition module, and when storing the data, first extract the enterprise category characteristics from the acquired data, then record the data with the same enterprise category characteristics in a folder, then compress the folder, and then record the folders with different data compression on different nodes; When retrieving data, Huffman coding and run-length coding are used to decompress the compressed data and establish a retrieval unit. When the user enters the retrieval information into the retrieval unit, the retrieval unit extracts the enterprise category characteristics of the retrieval information, and then performs a comprehensive retrieval based on the retrieval information and the enterprise category characteristics. Suppose the enterprise category characteristics in the folder are W Z =W 1 , W 2 , W 3 ,……,W X , let the enterprise type feature extracted from the retrieval information be S T =S 1 , S 2 , S 3 ,……,S Y , where X and Y are unknowns, let the same feature be T J ; T J =W Z ∩S T =(W 1 、W 2 、W 3 、……、W X )∩(S 1 、S 2 、S 3 、……、S Y ) According to the above formula, the same features can be calculated. When the same features are obtained, the number of the same features can be obtained. At this time, the number of features in the analysis folder is set as T. S , let the number of features in the folder be W S , let the extraction decompression value be T J ; According to the above formula, the extraction decompression value can be calculated. Let the extraction threshold be T Y ; When T J ≥T Y When T J Extract and decompress the corresponding folder; When T J <T Y When T J Extract and decompress the corresponding folder.

6. The information consultation management system based on enterprise big data processing according to claim 5 is characterized by: The performance monitoring module can receive information processed by the storage framework module. When monitoring the operating status of the computer in real time, Nagios and Zabbix can also be used to assist in monitoring the operating status of the computer. Nagios can be used to customize alarm rules and issue alarms in time when problems occur in the computer system. Zabbix can be used to convert the monitored computer operating status into a rich graphical interface, making it more convenient and intuitive for users to view system performance data.

7. The information consultation management system based on enterprise big data processing according to claim 6 is characterized by: The performance trend analysis module can obtain the information processed by the performance monitoring module, obtain the changes in computer space information and the corresponding performance changes, and establish a performance retrieval unit. The user enters the computer model in the performance retrieval unit. At this time, the performance retrieval unit will retrieve the usage status of the computer of this model in the hands of the public according to the computer model, and then it can analyze the reasons for the performance decline of the computer of this model, and obtain the different limits of the computer performance decline caused by different reasons. At the same time, a performance change trend graph is drawn according to the computer usage status and the computer performance changes.

8. The information consultation management system based on enterprise big data processing according to claim 7 is characterized by: The performance degradation prediction module can obtain the information processed by the performance trend analysis module, thereby obtaining a performance change trend graph, and obtain the remaining time for the computer to maintain good performance according to the performance change trend graph. Suppose the remaining time for the computer to maintain good performance is L Y , where the unit of time is hours, and the sudden stop time is T X , where the sudden downtime can be adjusted according to user needs, and the predicted time is Y S , the design calculation precision cycle is J Z , where the precise calculation cycles are hours, days, weeks and months, and the monthly cycle used is connected to the Internet and can be synchronized with the daily monthly cycle; According to the above formula, the prediction time can be calculated, and finally the processed information is transmitted to the data management module.

9. The information consultation management system based on enterprise big data processing according to claim 6 is characterized by: The optimization module can receive information processed by the performance monitoring module, so as to obtain the scheme when the multi-source data acquisition module, the storage framework module and the performance monitoring module are running. The scheme when the multi-source data acquisition module, the storage framework module and the performance monitoring module are running is the starting scheme, and then different optimization schemes are generated according to the starting scheme information, and the generated optimization schemes are displayed, and then the processed information is transmitted to the data management module.

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