An information consulting management system based on enterprise big data processing

Abstract

The present invention discloses an information consultation management system based on enterprise big data processing. The present invention relates to the field of information consultation technology, including a multi-source data acquisition module, a storage framework module, a performance monitoring module and a performance trend analysis module. The present invention has the advantages that: by designing a multi-source data acquisition function, it can comprehensively cover various business systems within the enterprise and external data sources, break data silos, and realize comprehensive data collection, which provides a broader and richer data foundation for enterprises and helps to dig deeper business insights. The 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 intelligent recognition technology, it can automatically distinguish between valid data and noise data, providing convenience for subsequent data cleaning and preprocessing.

Description

Technical Field

[0001] The present invention relates to the technical field of information consultation, and particularly to an information consultation 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 enterprise decision-making and operation. Information consultation is a business activity based on the collection, processing, transmission, effective utilization and feedback of various information. The information consultation industry is a knowledge-based industry that collects, processes, collates, analyzes and transmits various types of information through the use of various information processing technologies, and provides customers with information products such as solutions, strategies, suggestions, plans or measures to solve problems. Its service fields cover almost every aspect of society and economy, including all possible disciplinary scopes, from policy research to highly specialized engineering services and technical development research. Through consultation, 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;

[0003] When a general information consultation management system on the market is in use, it is inconvenient to achieve seamless docking of business data of various departments within an enterprise and external data sources, and during information retrieval, the amount of retrieved information is large, resulting in 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 art, the present invention provides the following technical solution: An information consultation management system based on enterprise big data processing, including 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 establishes a multi-source data acquisition function, 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, intelligent recognition technology is used to collect relevant data;

[0007] The storage framework module constructs a distributed big data storage framework, uses the distributed big data storage framework to achieve rapid data storage and efficient retrieval, and at the same time establishes a demand retrieval interface on the distributed big data storage framework to support hierarchical data storage and second-level query response;

[0008] The performance monitoring module accesses the usage status of the CPU, memory, disk, and network in real time to obtain the status of the computer during operation;

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

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

[0011] The user permission management module can manage the permissions of users, ensure that only authorized users can access sensitive data, and then transmit 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 calculates the prediction time according to the derivative solution of the current distributed big data storage framework and the performance change trend of the computer;

[0014] The data management module can effectively 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 the multi-source data acquisition module obtains the enterprise information within the enterprise, it will summarize and integrate the enterprise information, and then obtain the business types of the enterprise, and then establish a crawling interval unit. The crawling interval unit will obtain the analyzed business types. When crawling the external data of the enterprise, it will use the information inside the crawling interval unit as the screening condition to screen and process the external data of the enterprise, making the obtained external data of the enterprise more accurate, and at the same time using the data interface provided by the external data source.

[0016] As a further solution of the present invention: when the external data of the enterprise is crawled, the multi-source data acquisition module will establish a multi-structure processing unit, and the ETL tool, programming language parsing library, and natural language processing technology are stored in the multi-structure processing unit;

[0017] The ETL tool can process structured data;

[0018] The programming language parsing library can process semi-structured data;

[0019] The natural language processing technology can process unstructured data;

[0020] Through the multi-structure processing unit, different formats of data can be flexibly adapted and crawled.

[0021] As a further solution of the present invention: the storage framework module can receive the information processed by the multi-source data acquisition module. When storing the data, first extract the enterprise type characteristics from the obtained data, then record the data with the same enterprise type characteristics in a folder, then compress the folder, and then record the compressed folders of different data on different nodes;

[0022] When retrieving data, Huffman coding and run-length coding are used to decompress the compressed data, and a retrieval unit is established. When the user enters the retrieval information into the retrieval unit, the retrieval unit will extract the enterprise type characteristics of the retrieval information, and then perform a comprehensive retrieval according to the retrieval information and the enterprise type characteristics. Let the enterprise type characteristics in the folder be W Z = W 1 、W 2 、W 3 、……、W X , let the enterprise type characteristics extracted from the retrieval information be S T = S 1 、S 2 、S 3 、……、S Y , where X and Y are unknowns, and let the same characteristic 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 characteristics can be calculated. When the same characteristics are obtained, the number of the same characteristics can be obtained. At this time, analyze the number of characteristics in the folder. Let the number of the same characteristics be T S , let the number of characteristics in the folder be W S , let the extracted decompression value be T J ;

[0025]

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

[0027] When T J ≥ T YWhen it is time, T will be J The corresponding folder will be extracted and decompressed;

[0028] When T J <T Y When it is time, T will not be J The corresponding folder will be extracted and decompressed.

[0029] As a further solution of the present invention: The performance monitoring module can receive the information processed by the storage framework module. When monitoring the running state of the computer in real time, it can also use Nagios and Zabbix to assist in monitoring the running state of the computer. Among them, Nagios can customize the alarm rules and can send out alarms in time when problems occur in the computer system. Zabbix can convert the monitored running state of the computer into a rich graphical interface, making it more convenient and intuitive for users to view the 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. When the user types in the model of the computer in the performance retrieval unit, the performance retrieval unit will then 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 boundaries for the performance degradation of the computer caused by different reasons. At the same time, according to the usage status of the computer and the performance changes of the computer, a performance change trend graph is drawn.

[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, so as to obtain the performance change trend graph, and obtain the remaining running time for the computer to maintain good performance according to the performance change trend graph. Let the remaining running time for the computer to maintain good performance be L Y where the unit of time is hour, and let the sudden stop time be T X where the sudden stop time can be adjusted independently according to the user's needs, and the prediction time is Y S Let the calculation precision period be J Z where the calculation precision period is hour, day, week and month, and the monthly period used is connected to the Internet and can be synchronized with the daily monthly period;

[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 1This is the system flow schematic diagram in the embodiments of the present invention. Specific embodiments

[0040] The following further describes the specific embodiments of the present invention in conjunction with the accompanying drawings. It should be noted here that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation on 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 collects, processes, collates, analyzes and transmits various types of information through the use of various information processing technologies, and provides customers with information products such as solutions, strategies, suggestions, plans or measures to solve problems. Its service fields cover almost every aspect of society and economy, including all possible disciplinary scopes, from policy research to highly professional engineering services and research on technological development. Through consulting, 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] When a general information consulting management system on the market is in use, it is inconvenient to achieve seamless docking of business data of various departments within the enterprise and external data sources, and during information retrieval, the amount of retrieved information is large, resulting in poor retrieval efficiency. For this reason, we propose an information consulting management system based on enterprise big data processing;

[0045] Therefore, in order to effectively solve the above problems, this application proposes an information consulting management system based on enterprise big data processing, as shown in the accompanying drawings of the specification Figure 1 shown, including 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 establishes a multi-source data acquisition function, accesses all business software within the enterprise, thereby obtaining business information within the enterprise, and automatically grabs external data of the enterprise. At the same time, when obtaining data, intelligent recognition technology is used to collect relevant data;

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

[0048] Performance monitoring module, which accesses the usage status of CPU, memory, disk and network in real time to obtain the status of the computer during operation;

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

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

[0051] User permission management module, which can manage the permissions of users to ensure that only authorized users can access sensitive data, and then transmit the processed information to the data management module;

[0052] Optimization module, which can obtain the current data storage scheme, take the current data storage scheme as the initial scheme, generate different derivative schemes according to the effect of the initial scheme, and then analyze the corresponding computer performance values of the derivative schemes;

[0053] Performance degradation prediction module, which calculates the prediction time according to the derivative scheme of the current distributed big data storage framework and the performance change trend of the computer;

[0054] Data management module, which can effectively organize, process and maintain data, and can encrypt data using symmetric encryption, asymmetric encryption and hash algorithms;

[0055] Specific working process: Establish a multi-source data collection function, access all business software within the enterprise to obtain business information within the enterprise, and automatically capture data outside the enterprise, construct a distributed big data storage framework, use the distributed big data storage framework to achieve fast data storage and efficient retrieval, and at the same time establish a demand retrieval interface on the distributed big data storage framework, obtain the status of the computer during operation, analyze the computer model, draw a performance change trend graph of this computer, and manage the permissions of users;

[0056] Obtain the current data storage scheme, then generate a large number of derivative schemes according to the initial scheme, calculate the computer performance values of a large number of derivative schemes, calculate the prediction time according to the derivative scheme of the current distributed big data storage framework and the performance change trend of the computer, and then comprehensively manage the processed data;

[0057] In e-commerce enterprises, the multi-source data collection function can integrate data from its internal sales systems, inventory management systems, customer service systems, etc., and simultaneously capture external market trend data, competitor price data, user feedback data on social media, etc. Through automatic capture and intelligent recognition, valuable information can be quickly screened out to provide comprehensive data support for the enterprise's strategic decision-making;

[0058] Using a distributed big data storage framework, it is capable of efficiently storing massive amounts of transaction data, user behavior data, etc. During promotional activities, in the face of a large number of order data, the storage framework can quickly store and support second-level query responses to monitor sales in real time. At the same time, according to the access frequency of the data, the data of recently popular products is stored on high-speed storage media, while historical order data is stored on media with lower costs, achieving cost optimization and rational utilization of resources;

[0059] A perfect 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 the stable operation of the system, and through data backup and recovery mechanisms, guarantees the availability of data in case of emergencies, such as being able to quickly recover data in case of server failures without affecting the normal conduct of business;

[0060] Furthermore, by designing the multi-source data collection function, it can comprehensively cover various business systems within the enterprise and external data sources, break data silos, and achieve comprehensive data collection. This provides a wider and richer data foundation for the enterprise, helps to dig deeper business insights. This function supports flexible adaptation and capture of different types and formats of data sources. Whether it is structured data, semi-structured data, or unstructured data, it can be effectively processed, improving the flexibility and adaptability of data collection. Through refined user permission management, it ensures that only authorized users can access sensitive data, effectively preventing data leakage, monitors system performance in real time, discovers and solves potential problems in a timely manner to ensure the stable operation of the system, and during the data collection process, through intelligent recognition technology, it can automatically distinguish valid data from noise data, facilitating subsequent data cleaning and preprocessing.

[0061] Example 2:

[0062] Based on Example 1, as shown in the accompanying drawings of the specification Figure 1As shown, after the multi-source data acquisition module obtains the enterprise information within the enterprise, it will summarize and integrate the enterprise information, and then obtain the business types of the enterprise. Then, it will establish a crawling range unit. The crawling range unit will obtain the analyzed business types. When crawling the external data of the enterprise, it will use the information within the crawling range unit as the screening condition to screen and process the external data of the enterprise, making the obtained external data of the enterprise more accurate. At the same time, it will use the data interface provided by the external data source;

[0063] When the external data of the enterprise is being crawled, the multi-source data acquisition module will establish a multi-structure processing unit, in which the ETL tool, programming language parsing library, and natural language processing technology are stored;

[0064] The ETL tool can process structured data;

[0065] The programming language parsing library can process semi-structured data;

[0066] The natural language processing technology can process unstructured data;

[0067] Through the multi-structure processing unit, different formats of data can be flexibly adapted and crawled

[0068] The storage framework module can receive the information processed by the multi-source data acquisition module. When storing the data, it first extracts the enterprise type characteristics from the obtained data, then records the data with the same enterprise type characteristics in a folder, then compresses the folder, and then records the folders compressed with different data on different nodes;

[0069] When retrieving data, it will use Huffman coding and run-length coding to decompress the compressed data and establish a retrieval unit. When the user enters the retrieval information into the retrieval unit, the retrieval unit will extract the enterprise type characteristics of the retrieval information, and then conduct a comprehensive retrieval based on the retrieval information and the enterprise type characteristics. Let the enterprise type characteristics in the folder be W Z =W 1 、W 2 、W 3 、……、W X Let the enterprise type characteristics extracted from the retrieval information be S T =S 1 、S 2 、S 3 、……、S Y where X and Y are unknowns, and let the same characteristics be T J ;

[0070] T J =W Z ∩S T =(W1 , W 2 , W 3 , ……, W X ) ∩ (S 1 , S 2 , S 3 , ……, S Y )

[0071] According to the above formula, the same features can be calculated. After obtaining the same features, the number of the same features can be obtained. At this time, analyze the number of features in the folder. Let the number of the same features be T S , let the number of features in the folder be W S , let the extraction and decompression value be T J ;

[0072]

[0073] According to the above formula, the extraction and decompression value can be calculated. Let the extraction threshold be T Y ;

[0074] When T J ≥ T Y , the corresponding folder of T J will be extracted and decompressed;

[0075] When T J < T Y , the corresponding folder of T J will not be extracted and decompressed;

[0076] Specific working process: Summarize and integrate the enterprise information to obtain the business types of the enterprise, establish a crawling interval unit. The crawling interval unit will obtain the analyzed business types, and then use the information inside the crawling interval unit as the screening condition to screen and process the external data of the enterprise. At the same time, use the multi-structure processing unit in the multi-source data acquisition module to flexibly adapt to and crawl data in different formats;

[0077] Then extract the enterprise type features, record the data with the same enterprise type features in a folder, compress the folder, and then record the folders with different data compressions on different nodes. When retrieving data, decompress the compressed data and establish a retrieval unit. When the user enters the retrieval information into the retrieval unit, the retrieval unit will extract the enterprise type features of the retrieval information, and then conduct a comprehensive retrieval based on the retrieval information and the enterprise type features, so as to improve the accuracy of information retrieval;

[0078] In financial institutions, the multi-source data collection function can collect internal transaction data, customer information system data, risk management system data, etc., and at the same time obtain external macroeconomic data, industry dynamics data, credit rating agency data, etc. It can 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 the risk model. Through data hierarchical storage, high-frequency transaction data is stored on high-performance storage media, while long-term historical data is stored on media with lower costs to reduce storage costs;

[0079] In the manufacturing industry, the multi-source data collection function can be used to integrate data from internal production management systems, logistics systems, procurement systems, etc., and at the same time collect external supplier data, market demand data, logistics transportation data, etc. Automatic capture and intelligent recognition technologies can quickly process a large amount of supply chain data to 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, it can quickly store and retrieve data to support real-time decision-making. According to the importance and access frequency of data, key production plan data is stored on highly reliable storage media, while general logistics tracking data is stored on media with lower costs;

[0080] Furthermore, after obtaining enterprise internal information through the multi-source data collection module, the business types are summarized and integrated, and based on this, a capture interval unit is established to screen external data, making the obtained enterprise external data more accurate. It also realizes the flexible adaptation and capture of different format data, enriching the data sources and types. The storage framework module can quickly locate relevant data, thereby reducing retrieval time and resource consumption. At the same time, extraction decompression values and extraction thresholds are set, and the corresponding folder is extracted and decompressed only when certain conditions are met, avoiding unnecessary resource waste.

[0081] Example Three:

[0082] Based on Example Two, as shown in the accompanying drawings of the specification Figure 1 The performance monitoring module can receive the information processed by the storage framework module. When real-time monitoring the running state of the computer, it can also use Nagios and Zabbix to assist in monitoring the running state of the computer. Among them, Nagios can customize alarm rules and can send out alarms in a timely manner when problems occur in the computer system. Zabbix can convert the monitored computer running state into a rich graphical interface, making it more convenient and intuitive for users to view system performance data;

[0083] 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. When the user enters the model of the computer in the performance retrieval unit, the performance retrieval unit will then retrieve the usage status of the computer of this model in the hands of the public according to the computer model, and then it will be able to analyze the reasons for the performance degradation of the computer of this model, and obtain the different thresholds for the performance degradation of the computer caused by different reasons. At the same time, according to the usage status of the computer and the performance changes of the computer, a performance change trend graph is drawn;

[0084] The performance degradation prediction module can obtain the information processed by the performance trend analysis module, so as to obtain the performance change trend graph, and obtain the remaining running time for the computer to maintain good performance according to the performance change trend graph. Let the remaining running time for the computer to maintain good performance be L Y , where the unit of time is hour, and let the sudden stop time be T X , where the sudden stop time can be adjusted independently according to the user's needs, and the prediction time is Y S , let the calculation precision period be J Z , where the calculation precision period is hour, day, week and month, and the monthly period used is connected to the Internet and can be synchronized with the daily monthly period;

[0085]

[0086] According to the above formula, the prediction time can be calculated, and finally the processed information is transmitted to the data management module;

[0087] Let the remaining running time for the computer to maintain good performance in the Pth detection be L P ;

[0088]

[0089] where N is the total number of detections. According to the above formula, the prediction time can be calculated, and finally the processed information is transmitted to the data management module;

[0090] The optimization module can receive the information processed by the performance monitoring module, so as to obtain the operation plans of the multi-source data acquisition module, the storage framework module and the performance monitoring module when they are running. The operation plans of the multi-source data acquisition module, the storage framework module and the performance monitoring module when they are running are the starting plans. Then, different optimization plans are generated according to the starting plan information and the generated optimization plans are displayed, and then the processed information is transmitted to the data management module;

[0091] Specific workflow: Monitor the running status of the computer in real time. Meanwhile, use Nagios and Zabbix to assist in monitoring the running status of the computer. When problems occur in the computer system, alarms can be given in a timely manner. At the same time, the running status of the computer will be converted into a rich graphical interface, including changes in computer space information and corresponding performance changes. Retrieve the usage information of a large number of computers of the same model, analyze the reasons for the performance degradation of this model of computer, and obtain different thresholds for performance degradation caused by different reasons. Then draw the performance change trend graph of this model of computer, obtain the remaining running time for the computer to maintain good performance according to the performance change trend graph, and then obtain the initial plans for the operation of the multi-source data acquisition module, storage framework module, and performance monitoring module, and generate a large number of optimized plans according to the initial plans;

[0092] In financial institutions, a perfect management system safeguards the security and privacy of financial data. Strict permission management ensures that only authorized personnel can access sensitive customer financial information and transaction data. Encrypt data during transmission and storage to prevent data leakage. Monitor system performance in real time to ensure the stable operation of the system in the face of rapid changes in the financial market. Through data management functions, ensure the integrity and consistency of data, providing a reliable data basis for risk assessment and decision-making;

[0093] In the manufacturing industry, a perfect management system ensures the security of the supply chain data of manufacturing enterprises. Through permission management, restrict access to production confidential data and supplier information, encrypt data to prevent data from being stolen during transmission, monitor system performance in real time to ensure the stable operation of the system during peak production periods, and ensure the availability of data in the event of natural disasters or equipment failures through data backup and recovery mechanisms, ensuring the continuity of the supply chain;

[0094] Furthermore, through the performance monitoring module, problems during the operation of the computer can be detected in a timely manner, and the performance data of the computer can be converted into a graphical interface, making it more convenient and intuitive for users to view the computer performance. And the performance trend analysis module can accurately analyze the performance trend and the reasons for performance degradation. Through the performance degradation prediction module, the running time for the computer to maintain good performance can be calculated, thus facilitating users to process the computer in advance and avoiding the impact on enterprise operations due to computer performance degradation.

[0095] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended 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; Multi-source data collection module: Establish multi-source data collection function, access all business software within the enterprise, thereby obtaining business information within the enterprise, and automatically capture data outside the enterprise. At the same time, when acquiring data, it will use intelligent recognition technology to collect relevant data; After acquiring the enterprise information within the enterprise, the multi-source data acquisition module will summarize and integrate the enterprise information, and then derive the business types of the enterprise, and then establish a capture interval unit, which will obtain the business types obtained through analysis. When capturing the external data of the enterprise, the information within the capture interval unit will be used as the screening condition to filter and process the external data of the enterprise, so that the acquired external data of the enterprise is more accurate, and at the same time, the external data source is used to provide a data interface; 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; 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; Performance monitoring module, real-time access to the CPU, memory, disk and network usage status, to obtain the status of the computer at runtime; 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; 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 calculates the prediction time based on the derivative scheme of the current distributed big data storage framework and the performance change trend of the computer; 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; The prediction time is calculated according to the above formula, and finally the processed information is transmitted to the data management module.

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 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 data management module can effectively 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 1 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.

4. The information consultation management system based on enterprise big data processing according to claim 2 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.

5. The information consultation management system based on enterprise big data processing according to claim 4 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.

6. The information consultation management system based on enterprise big data processing according to claim 4 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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