Business data offline query process optimization method and device, equipment and medium

Abstract

The present application relates to the field of information technology and financial technology, and discloses a business data offline query process optimization method, device, equipment and medium, comprising: after obtaining the business data offline query business, analyzing the to-be-optimized process of the offline query business, identifying the process characteristics, determining the transformation mode and process priority, then, determining the offline configuration equipment based on the priority, detecting the real-time data of the equipment configuration data, analyzing the data interaction quality, understanding the process usage and formulating the transformation strategy, dividing the to-be-optimized process according to the strategy, determining the transformation sequence and time window, monitoring the window state to build the optimization transformation path, identifying the transformation task and key transformation point, and finally generating the process transformation scheme. The work efficiency and accuracy of the business data query are improved.

Description

Technical Field

[0001] The present invention relates to the fields of intelligent decision-making and financial technology, and in particular to a method, device, equipment and medium for optimizing an offline query process for business data. Background Art

[0002] With the continuous expansion and increasing complexity of corporate business, offline query of business data (for example, offline query of corporate sales business data) plays a vital role in corporate decision-making, operation management, etc.

[0003] Currently, the main methods for querying business data include online and offline queries. Online queries typically have relatively mature systems and processes. For example, internal enterprise management systems provide data query functions, allowing relevant staff to log in to the system to perform query operations and obtain results. However, offline queries present numerous problems. Offline queries are characterized by diverse query objects, complex query content, and widely varying requirements. Furthermore, requirements vary widely across departments and business scenarios, making it difficult to establish unified standard processes and interfaces like those for online queries. The entire offline query process often relies heavily on manual operations, which not only wastes human resources but also leads to inefficiencies and difficulties in ensuring data accuracy. Therefore, there is an urgent need for an optimization method for the offline query process of business data to improve the efficiency and accuracy of business data queries. Summary of the Invention

[0004] The present invention provides a method, device, equipment and medium for optimizing the offline query process of business data, so as to improve the working efficiency and accuracy of business data query.

[0005] In a first aspect, a method for optimizing an offline query process for business data is provided, comprising:

[0006] Obtaining offline query services from business data, analyzing the process to be optimized corresponding to the offline query services, identifying process characteristics corresponding to the process to be optimized, analyzing a transformation mode corresponding to the offline query services based on the process characteristics, and determining a process priority corresponding to the process to be optimized based on the transformation mode;

[0007] Based on the process priority, determining the offline configuration device corresponding to the offline query service, querying the device configuration data corresponding to the offline configuration device, and performing real-time detection on the device configuration data to obtain real-time device data;

[0008] Analyze the data interaction quality corresponding to the real-time data of the device, analyze the process usage of the offline query service based on the data interaction quality, and formulate a process transformation strategy corresponding to the offline query service based on the process usage;

[0009] based on the process transformation strategy, performing process blocking on the process to be optimized to obtain a transformation process block, analyzing a transformation sequence corresponding to the transformation process block, and constructing a transformation time window corresponding to the transformation process block based on the transformation sequence;

[0010] monitoring a window state corresponding to the transformation time window, constructing an optimized transformation path of the offline query service in a transformation process based on the window state, identifying a transformation task corresponding to the optimized transformation path, analyzing a key transformation point of the transformation task in task execution, and generating a process transformation scheme corresponding to the offline query service based on the key transformation point.

[0011] In a second aspect, an optimization device for a business data offline query process is provided, which includes:

[0012] a priority module configured to obtain a business data offline query service, analyze a process to be optimized corresponding to the offline query service, identify a process feature corresponding to the process to be optimized, analyze a transformation mode corresponding to the offline query service based on the process feature, and determine a process priority corresponding to the process to be optimized according to the transformation mode;

[0013] a real-time monitoring module configured to determine a offline configuration device corresponding to the offline query service based on the process priority, query device configuration data corresponding to the offline configuration device, and perform real-time detection on the device configuration data to obtain device real-time data;

[0014] a strategy formulation module configured to analyze data interaction quality corresponding to the device real-time data, analyze a process usage of the offline query service based on the data interaction quality, and formulate a process transformation strategy corresponding to the offline query service based on the process usage;

[0015] a window construction module configured to perform process blocking on the process to be optimized based on the process transformation strategy to obtain a transformation process block, analyze a transformation sequence corresponding to the transformation process block, and construct a transformation time window corresponding to the transformation process block based on the transformation sequence;

[0016] a scheme generation module configured to monitor a window state corresponding to the transformation time window, construct an optimized transformation path of the offline query service in a transformation process based on the window state, identify a transformation task corresponding to the optimized transformation path, analyze a key transformation point of the transformation task in task execution, and generate a process transformation scheme corresponding to the offline query service based on the key transformation point.

[0017] In a third aspect, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the optimization method for the offline query process of business data when executing the computer program.

[0018] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program implements the steps of the optimization method for the offline query process of business data when executed by a processor.

[0019] Compared with the problems described in the background art, first, the present application helps to comprehensively understand the actual situation of offline query business work by obtaining query services offline and analyzing the to-be-optimized process corresponding to the query services, can accurately find out the problems and deficiencies in the current process, can optimize the process in a targeted manner, improve the efficiency and accuracy of the query, reduce unnecessary time waste and error occurrence, second, the present application determines the offline configuration device corresponding to the offline query business based on the process priority, can determine the device configuration according to the process priority, can preferentially invest limited funds and device resources into the device corresponding to the process which is crucial to the offline query business work and has high priority, avoid waste and unreasonable allocation of resources, and ensure that the device can meet the changing needs of the offline query business, then, the present application can determine whether the device response time is too long, the operation is smooth, the feedback is timely and accurate, etc. by evaluating the interaction quality corresponding to the real-time data of the device, thereby optimizing in a targeted manner and making the offline query business work more efficient and convenient. Further, the present application can obtain a transformation process block by performing process blocking on the to-be-optimized process based on the process transformation strategy, can more clearly understand and grasp the structure and composition of the entire to-be-optimized process, can decompose the complex process into a plurality of relatively independent modules, so that the function and role of each module are more explicit, and in-depth analysis of the problems existing therein and determination of the specific transformation direction are facilitated. Finally, the present application can clearly understand whether each transformation process block is carried out according to the plan within the predetermined time window by monitoring the window state corresponding to the transformation time window, so as to take prompt measures to adjust when the progress is lagging behind, and ensure that the entire offline query business process transformation project is completed on time, therefore, the optimization method, device, equipment and medium for the offline query process of business data proposed by the present application can improve the work efficiency and accuracy of the business data query. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without any creative effort.

[0021] Figure 1 is an application environment schematic diagram of the optimization method of the service data offline query process in an embodiment of the present application;

[0022] Figure 2 is a flow schematic diagram of the optimization method of the service data offline query process in an embodiment of the present application;

[0023] Figure 3 is a structure schematic diagram of the optimization device of the service data offline query process in an embodiment of the present application;

[0024] Figure 4 is a structure schematic diagram of the computer device in an embodiment of the present application;

[0025] Figure 5 is another structure schematic diagram of the computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without any creative effort are within the protection scope of the present application.

[0027] The optimization method of the service data offline query process provided by the embodiments of the present application can be applied in, for example, Figure 1In an application environment of the application, the client communicates with the server through a network. The server can obtain a business data offline query service through the client, analyze a to-be-optimized process corresponding to the offline query service, identify a process feature corresponding to the to-be-optimized process, analyze a transformation mode corresponding to the offline query service based on the process feature, determine a process priority corresponding to the to-be-optimized process according to the transformation mode, determine an offline configuration device corresponding to the offline query service based on the process priority, query device configuration data corresponding to the offline configuration device, perform real-time detection on the device configuration data to obtain device real-time data, analyze data interaction quality corresponding to the device real-time data, analyze a process usage of the offline query service based on the data interaction quality, formulate a process transformation strategy corresponding to the offline query service based on the process usage, perform process blocking on the to-be-optimized process based on the process transformation strategy to obtain a transformation process block, analyze a transformation sequence corresponding to the transformation process block, construct a transformation time window corresponding to the transformation process block based on the transformation sequence, monitor a window state corresponding to the transformation time window, construct an optimized transformation path of the offline query service in a transformation process based on the window state, identify a transformation task corresponding to the optimized transformation path, analyze a key transformation point of the transformation task in task execution, and generate a process transformation scheme corresponding to the offline query service based on the key transformation point.

[0028] The client can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices. The server can be implemented by an independent server or a server cluster composed of multiple servers. The application will be described in detail through specific embodiments.

[0029] Please refer to Figure 2 , Figure 2 A process schematic diagram of the optimization method of the business data offline query process provided by the embodiment of the application includes the following steps:

[0030] S1, obtaining a business data offline query service, analyzing a to-be-optimized process corresponding to the offline query service, identifying a process feature corresponding to the to-be-optimized process, analyzing a transformation mode corresponding to the offline query service based on the process feature, and determining a process priority corresponding to the to-be-optimized process according to the transformation mode.

[0031] The application can help to comprehensively understand the actual situation of the offline query service work by obtaining a business data offline query service and analyzing a to-be-optimized process corresponding to the offline query service, can accurately find problems and deficiencies in the current process, can optimize the process in a targeted manner, can improve the efficiency and accuracy of the query, and can reduce unnecessary time waste and errors.

[0032] The business data refers to various information and data related to the financial field, including but not limited to stock prices, bond yields, exchange rates, financial market transaction data, corporate financial statement data, financial institution business data, etc., which have important value for financial decision-making, investment analysis, risk management, etc. The offline query business refers to the business activities of querying business data in a non-network environment through traditional methods, such as customers going to the business outlets of financial institutions, querying the business data they need by communicating with staff or using query equipment in the business outlets, or internal staff of financial institutions using specific offline tools and channels to search and analyze business data to support business decision-making and customer service. The to-be-optimized process refers to the acquisition of business data offline query business, which can be realized by data collection tools, such as using professional data collection tools to digitize paper files, records, etc. of judicial organs to extract query business information. The analysis of the to-be-optimized process corresponding to the offline query business can be realized by data analysis tools, such as using Excel, SPSS, etc. to analyze data and find inefficient links in the process through statistical data.

[0033] Further, by identifying the process characteristics corresponding to the to-be-optimized process, the essence and internal structure of the process can be understood, the characteristics, mutual relationship and operation mode of each link in the process can be clearly grasped, the key nodes and potential problem areas in the process can be accurately located, and a solid foundation for formulating optimization strategies is provided.

[0034] The process characteristics refer to a series of unique attributes and characteristics of the to-be-optimized process of offline query business, including the complexity of the process, i.e. the number of steps and links in the process and the complexity of the mutual relationship between them; the efficiency of the process, such as the time required to complete the entire process, resource consumption and processing speed of each link; the accuracy of the process, which involves the accuracy of data processing and information transmission, whether errors or deviations are prone to occur; the flexibility of the process, i.e. the adaptability of the process to different query demands and changes in business scenarios; the controllability of the process, including the difficulty of monitoring, managing and adjusting the process; the continuity of the process, i.e. whether the connection between each link is smooth, whether there is interruption or disconnection; and the characteristics of personnel participation, data size, technology application degree, etc. The identification of the process characteristics corresponding to the to-be-optimized process can be realized by process observation, such as observing the to-be-optimized process of offline query business in the field, recording the operation mode, time consumption, personnel interaction, etc. of each link, and summarizing the process characteristics.

[0035] Further, based on the process characteristics, the application analyzes the corresponding transformation mode of the offline query business, which can make the transformation work more accurate and targeted, and different process characteristics require different transformation strategies. For example, for inefficient and cumbersome processes, it may be appropriate to use automation transformation mode to simplify the process and improve processing speed; for processes with high accuracy requirements but prone to deviation, it may be necessary to strengthen data verification and quality control link transformation to truly effectively solve the problems in the existing process and improve the overall performance and quality of offline query business.

[0036] Among them, the transformation mode refers to different types of improvement and change ways for the offline query business to be optimized process, which covers the adjustment of process structure, the update of technology application, the optimization of personnel configuration and the change of management mode, etc. It can include automation transformation mode, that is, by introducing advanced information technology and automation equipment, reducing manual intervention, improving the running efficiency and accuracy of the process, such as using intelligent query system to automatically retrieve and filter relevant information; process optimization mode, which focuses on simplifying, merging or reordering the links in the existing process to eliminate unnecessary cumbersome steps and repetitive operations and improve the overall efficiency of the process; digital transformation mode, which converts traditional paper files and manual records into electronic data to realize fast transmission and sharing of information and facilitate data analysis and management; intelligent transformation mode, which uses artificial intelligence, big data analysis and other technologies to intelligently analyze and predict the query business to provide support for decision-making and improve the accuracy and foresight of offline query; there is also a collaborative transformation mode, which emphasizes strengthening the cooperation and communication between different departments or agencies in the offline query business process, breaking down information barriers, realizing resource sharing and work cooperation, and improving overall work efficiency and quality. Optionally, the analysis of the transformation mode corresponding to the offline query business can be realized by decision matrix tool, such as: constructing decision matrix to compare and evaluate different transformation modes and process characteristics to determine the optimal transformation mode.

[0037] Further, according to the transformation mode, the application determines the process priority corresponding to the process to be optimized, which can clearly determine the importance of each process under different transformation modes, and can prioritize manpower, material resources and time to the processes that have the greatest impact on the overall offline query business and the most significant transformation effect, avoiding the dispersion and waste of resources. Secondly, it helps to improve the efficiency and effectiveness of transformation work.

[0038] Among them, the process priority refers to the order and importance level determined for each process to be optimized according to the comprehensive analysis of the transformation key area, priority consideration factor, correlation degree and key feature item.

[0039] As an embodiment of the present application, the determining the process priority corresponding to the to-be-optimized process according to the transformation mode comprises: analyzing a transformation focus area corresponding to the transformation mode; determining a priority consideration factor corresponding to the transformation focus area; evaluating an association degree between the to-be-optimized process and the transformation focus area based on the priority consideration factor; and extracting a key feature item in the to-be-optimized process based on the association degree, and determining the process priority corresponding to the to-be-optimized process based on the key feature item.

[0040] The transformation focus area refers to a specific process range or business field in the transformation mode of offline query business that is determined to need to be focused on for improvement and optimization. It can be a link that frequently appears problems, is low in efficiency, or has a greater impact on the overall offline query work, such as a specific type of query business processing flow, a key node of data transmission, etc. The priority consideration factor refers to a series of standards and conditions used to evaluate the importance and urgency of the to-be-optimized process after determining the transformation focus area. These factors can include the impact of the process on offline justice, the potential contribution to work efficiency improvement, the resource input-output ratio, technical feasibility, etc. The association degree refers to the close connection between the to-be-optimized process and the transformation focus area, which can be measured in multiple aspects, such as business relevance, data interaction frequency, process dependency, etc. The key feature item refers to a specific element extracted from the to-be-optimized process that can reflect the essential characteristics and important attributes of the process. These feature items can include the complexity of the process, the types of key data involved, the departments or personnel involved, time sensitivity, etc.

[0041] Further, the analysis of the transformation focus area corresponding to the transformation mode can be achieved by process disassembly analysis, such as: carefully disassembling the overall process of offline query business, analyzing the importance and improvement potential of each link under different transformation modes, and determining the transformation focus area. The determination of the priority consideration factor corresponding to the transformation focus area can be achieved by multi-factor analysis, such as: considering multiple factors such as business demand, technical feasibility, cost-effectiveness, etc., to determine the priority consideration factor of the transformation focus area. The evaluation of the association degree between the to-be-optimized process and the transformation focus area can be achieved by process mapping, such as: comparing and mapping the to-be-optimized process with the transformation focus area, analyzing the business relevance, data interaction relationship, and process dependency between the two, and evaluating the association degree. The extraction of the key feature item in the to-be-optimized process can be achieved by principal component analysis, such as: analyzing multiple features in the process and extracting the main components as key feature items. The determination of the process priority corresponding to the to-be-optimized process can be achieved by weighted scoring, such as: weighting and scoring each to-be-optimized process according to the key feature items and priority consideration factors, and determining the process priority.

[0042] S2, determine the offline configuration device corresponding to the offline query service based on the process priority, query the device configuration data corresponding to the offline configuration device, perform real-time detection on the device configuration data, and obtain device real-time data.

[0043] The application can determine device configuration according to process priority, can preferentially invest limited funds and device resources into devices corresponding to processes that are essential to offline query service work and have high priority, avoids waste and unreasonable allocation of resources, and ensures that devices can meet the changing needs of offline query service.

[0044] The offline configuration device refers to various physical hardware devices and related auxiliary facilities used in offline query service work, including but not limited to servers and storage devices for data storage, such as hard disk arrays, tape libraries, etc., to ensure that a large amount of data required in the query process can be safely and stably stored and read; query terminal devices, such as computers, dedicated query workstations, etc., for financial staff to perform information query and operation; data acquisition devices, such as scanners, card readers, etc., for converting paper files or other forms of information into electronic data for query processing; network connection devices, such as routers, switches, etc., to ensure smooth communication between offline devices and other related systems; security protection devices, such as firewalls, encryption devices, etc., to protect data security and confidentiality during offline query service; and printing devices, UPS (uninterruptible power supply) and other auxiliary devices, optionally, the determination of the offline configuration device corresponding to the offline query service can be realized through a project management tool, such as: through project management software to plan and manage offline query service projects, which includes determining the required offline configuration device.

[0045] Further, by querying the device configuration data corresponding to the offline configuration device, the performance status and operating parameters of the device can be comprehensively understood, so as to better evaluate whether it meets the needs of offline query service work, the ability of the device in processing a large number of query tasks can be accurately judged, and possible performance bottlenecks can be discovered in time, providing basis for optimizing query process and adjusting resource allocation.

[0046] The device configuration data refers to information describing various attributes and parameters of offline configured devices, including hardware configuration information of the device, such as processor model, core quantity and frequency, memory capacity and type, storage device capacity and type (such as hard disk capacity, solid state disk or mechanical hard disk, etc.), graphics card performance parameters, etc.; network configuration information, such as network interface type (such as Ethernet, wireless, etc.), IP address allocation, network bandwidth, etc.; operating system information, such as operating system version, installed software and drivers, etc.; connection mode and interface information of the device, such as USB interface quantity and version, video output interface type, etc.; security configuration information of the device, such as whether a firewall, antivirus software, encryption module, etc. are installed; and other specific attribute information of the device, such as the manufacturer of the device, the production date, the device serial number, etc. Optionally, the device configuration data corresponding to the offline configured device can be obtained by a device self-checking method, such as: the configuration data of the device can be obtained by starting a self-checking program of the device.

[0047] The device real-time data obtained by real-time detection of the device configuration data can accurately reflect the current actual operation of the device, including but not limited to specific numerical values of various device parameters, running state description of the device, possible problem warning, etc.

[0048] The device real-time data obtained by real-time detection of the device configuration data can accurately reflect the current actual operation of the device, including but not limited to specific numerical values of various device parameters, running state description of the device, possible problem warning, etc.

[0049] As an embodiment of the present application, the real-time detection of the device configuration data to obtain device real-time data includes: collecting dynamic parameter information in the device configuration data; analyzing a parameter change trend corresponding to the dynamic parameter information; quantitatively evaluating the parameter change trend to obtain a trend quantitative index; analyzing a device real-time feature corresponding to the trend quantitative index; and based on the device real-time feature, real-time detecting the device configuration data to obtain device real-time data.

[0050] The dynamic parameter information refers to parameter contents in device configuration data that change over time, for example, data information of CPU usage, memory occupancy, hard disk read / write speed, network bandwidth usage, etc. of the device changing at different times; the parameter change trend refers to a change direction and law presented by the dynamic parameter information in a period of time, for example, whether the CPU usage of a certain device gradually increases, remains stable or gradually decreases; the trend quantitative index refers to a numerical index obtained by converting the parameter change trend through a specific quantitative method, for example, the strength and characteristics of the parameter change trend can be represented by an upward slope, fluctuation amplitude, etc.; and the device real-time feature refers to a specific feature description reflecting the current running state of the device obtained by analyzing the trend quantitative index, such as a high-load running state, a low-load running state, a stable running state, etc.

[0051] Further, the collection of the dynamic parameter information in the device configuration data can be realized by a timing sampling method, for example, a fixed time interval is set, the device configuration data is collected, and the dynamic parameter information is obtained; the analysis of the parameter change trend corresponding to the dynamic parameter information can be realized by a trend prediction algorithm, for example, a linear regression, a polynomial regression, etc. algorithm can be used to predict the trend of the dynamic parameter information and analyze the future change trend of the parameter; the quantitative evaluation of the parameter change trend can be realized by a comprehensive scoring method, for example, the change trends of multiple parameters are comprehensively evaluated, and a total quantitative score is given; the analysis of the device real-time feature corresponding to the trend quantitative index can be realized by a data mining tool, for example, Weka, RapidMiner, etc. can be used to deeply analyze the trend quantitative index and extract the real-time feature of the device; and the real-time detection of the device configuration data can be realized by a real-time monitoring tool, for example, various configuration data of the device can be monitored and alarmed in real time by using the real-time monitoring tool.

[0052] S3, analyze the data interaction quality corresponding to the device real-time data, analyze the process use condition of the offline query service based on the data interaction quality, and formulate a process reform strategy corresponding to the offline query service based on the process use condition.

[0053] By analyzing the data interaction quality corresponding to the device real-time data, whether the device response time is too long, the operation is smooth, the feedback is timely and accurate, etc. can be determined, so that optimization is carried out in a targeted manner, and the offline query service work is more efficient and convenient.

[0054] The data interaction quality refers to the overall level and effect shown by the device in the interaction process with the user or other systems, which comprehensively considers the performance of each interaction dimension, including response speed, stability, ease of use, user satisfaction, etc. of the device.

[0055] As an embodiment of the present application, the analysis of the data interaction quality corresponding to the device real-time data comprises: extracting key interaction parameters in the device real-time data; analyzing interaction feature performances corresponding to the key interaction parameters; determining interaction dimensions corresponding to the device real-time data based on the interaction feature performances; querying interaction weak links in the interaction dimensions; and analyzing the data interaction quality corresponding to the device real-time data based on the interaction weak links.

[0056] The key interaction parameters are important parameters that can directly reflect the interaction between the device and the user or other systems in the device real-time data, such as response time, data transmission rate, and operation convenience parameters of the device; the interaction feature performances are specific characteristics and states presented by the key interaction parameters, such as high efficiency of interaction represented by short response time and reliability of interaction represented by stable data transmission rate; the interaction dimensions are different aspects and angles for evaluating the device interaction quality, which can include performance dimensions (such as speed and stability), usability dimensions (such as operation simplicity and clarity of error prompt), user experience dimensions (such as interface friendliness and feedback timeliness), etc.; and the interaction weak links are specific links or aspects that perform poorly in the interaction dimensions and can affect the interaction quality, such as large fluctuation of data transmission rate in the performance dimension and complex operation process in the usability dimension.

[0057] Further, the extraction of the key interaction parameters in the device real-time data can be realized by correlation analysis method, such as analyzing the correlation between each parameter in the device real-time data and the interaction behavior, and extracting parameters with higher correlation as key interaction parameters; the analysis of the interaction feature performances corresponding to the key interaction parameters can be realized by data visualization tools, such as Tableau, PowerBI, etc., which can visually display the key interaction parameters for easy analysis of the interaction feature performances; the determination of the interaction dimensions corresponding to the device real-time data can be realized by analytic hierarchy process, such as decomposing the device interaction quality problem into different levels of factors, comparing the importance of each factor to determine the interaction dimensions; the query of the interaction weak links in the interaction dimensions can be realized by gap analysis method, such as comparing the actual performance of the device in each interaction dimension with the expected target to find out the link with larger gap as the interaction weak link; and the analysis of the data interaction quality corresponding to the device real-time data can be realized by weighted scoring method, such as assigning different weights to each dimension according to the importance of each interaction dimension, scoring the performance of the device in each dimension, and comprehensively calculating the interaction quality evaluation result.

[0058] The application analyzes the process usage of the offline query service based on the data interaction quality, can accurately determine whether the interaction between the user and the query system is smooth and efficient, and thus reveals possible problems in the process, such as complicated operation and slow response, thereby providing a clear direction for optimizing the process.

[0059] The process usage refers to the actual running state and specific usage of each link in the offline query process, including the operation frequency and time length of the user to different query steps, such as the time spent by the user in inputting query conditions, browsing query results, and performing data filtering; the resource occupation of each node in the process, such as the memory usage and CPU occupancy of a specific query module; the behavior mode of the user in different process stages, such as whether the user often repeats some operations or tends to a specific query path; and possible problems and bottleneck situations in the process, such as which links are prone to error prompts and which steps have a long response time affecting the user experience. Optionally, the analysis of the process usage of the offline query service can be realized by a process tracking method, such as tracking the entire process of offline query and recording the execution and time consumption of each link.

[0060] Further, the application formulates the process transformation strategy corresponding to the offline query service based on the process usage, can accurately understand which links have problems such as low efficiency, poor user experience, or resource waste, thereby realizing the optimized allocation of resources and improving the resource utilization efficiency.

[0061] The process transformation strategy refers to a systematic and comprehensive planning and method formulated to realize the overall optimization of the offline query service process, including the overall arrangement of transformation measures for each key process node, and clear implementation steps, time nodes, and resource allocation.

[0062] As an embodiment of the application, the formulation of the process transformation strategy corresponding to the offline query service based on the process usage includes: sorting out the key process nodes in the process usage; analyzing the running efficiency indicators corresponding to the key process nodes; determining the transformation direction corresponding to the process usage based on the running efficiency indicators; generating the transformation measures corresponding to the process usage based on the transformation direction; and formulating the process transformation strategy corresponding to the offline query service based on the transformation measures.

[0063] The key process node refers to a link that plays a key role in the offline query business process, has an important influence on the smooth operation and efficiency of the whole process, for example, a query condition input link, a data retrieval link, a result display link, etc. The operation efficiency indicator refers to a specific parameter for measuring the operation efficiency of the key process node, such as query response time, data retrieval accuracy, result display speed, etc. The direction to be reformed refers to the direction that needs to be improved and optimized according to the operation efficiency indicator of the key process node, for example, improving query response speed, improving data retrieval accuracy, optimizing result display mode, etc. The reform measure refers to a specific action plan proposed for the direction to be reformed, such as optimizing the query algorithm to shorten the query response time, updating the data retrieval engine to improve the accuracy, using more intuitive charts to display the results, etc.

[0064] Further, the key process node in the process usage can be obtained by process analysis, for example, analyzing the whole process of offline query business in detail, finding out the links that have greater influence on the process result, frequent user interaction or are prone to problems as key process nodes. The operation efficiency indicator corresponding to the key process node can be obtained by time measurement, for example, measuring the execution time of the key process node, such as query response time, result generation time, etc. as the operation efficiency indicator. The direction to be reformed corresponding to the process usage can be obtained by gap analysis, for example, comparing the actual operation efficiency indicator of the key process node with the expected target, finding out the aspects with greater gap as the direction to be reformed. The reform measure corresponding to the process usage can be obtained by a measure generation tool, for example, Ideaflip, Miro, etc. The process reform strategy corresponding to the offline query business can be obtained by overall planning, for example, starting from the overall goal of offline query business, formulating a comprehensive process reform strategy.

[0065] S4, based on the process reform strategy, process blocking is performed on the process to be optimized to obtain a reform process block, an improvement sequence corresponding to the reform process block is analyzed, and a reform time window corresponding to the reform process block is constructed based on the improvement sequence.

[0066] Based on the process reform strategy, the process to be optimized is blocked to obtain a reform process block, so that the structure and composition of the whole process to be optimized can be more clearly understood and grasped, the complex process can be decomposed into a plurality of relatively independent modules, the function and role of each module are more clear, and the problems existing therein can be analyzed in depth and the specific reform direction can be determined.

[0067] The transformation process block refers to a relatively independent process unit obtained by dividing the offline query business process to be optimized based on a process transformation strategy.

[0068] Further, the application can analyze the transformation sequence corresponding to the transformation process block, and can process those transformation process blocks that have a greater impact on the entire offline query business process and have obvious bottlenecks first, so as to reasonably arrange resources such as manpower, material resources and time, ensure sufficient resource investment at key links, and improve resource utilization efficiency.

[0069] The transformation sequence refers to the order followed when the transformation process blocks obtained after dividing the offline query business process to be optimized are transformed, which takes into account the importance, mutual dependency and transformation difficulty of each transformation process block in the entire process. The transformation sequence can be from a key process block to an auxiliary process block, or can be arranged from large to small according to the degree of influence on user experience, or can be determined according to the urgency of transformation and the availability of resources. Optionally, the analysis of the transformation sequence corresponding to the transformation process block can be realized by the importance evaluation method, such as evaluating the importance of each transformation process block to the overall offline query business process, and the transformation with high importance is given priority.

[0070] Based on the transformation sequence, the application constructs a transformation time window corresponding to the transformation process block, which can reasonably arrange the sequence of transformation tasks, avoid the conflict and interference of transformation work of different process blocks, ensure the transformation of the entire offline query business process to proceed in an orderly manner, better plan the allocation of manpower, material resources and technical resources, and ensure that resources are concentrated in the corresponding transformation process block at a specific time node, avoiding the idling and waste of resources.

[0071] The transformation time window refers to a specific transformation time period determined for each transformation process block, which takes into account factors such as preliminary time planning, resource availability, and the influence of other related work.

[0072] As an embodiment of the present invention, constructing a transformation time window corresponding to the transformation process block based on the transformation sequence includes: analyzing the key transformation nodes in the transformation sequence; determining the priority process block corresponding to the transformation process block based on the key transformation nodes; evaluating the transformation coefficient corresponding to the priority process block; formulating a preliminary time plan corresponding to the transformation process block based on the transformation coefficient; and constructing a transformation time window corresponding to the transformation process block based on the preliminary time plan.

[0073] Among them, the key transformation nodes refer to the key links or steps that have a significant impact on the transformation sequence and determine the direction and progress of the transformation, such as those that play a key role in improving the performance of the entire offline query business process, involve core function adjustments or have complex dependencies; the priority process blocks refer to the transformation process blocks that need to be given priority during the transformation process based on the key transformation nodes; the transformation coefficient refers to a comprehensive indicator used to evaluate the difficulty, resource requirements, risk level, etc. of the transformation of priority process blocks, which may include considerations of multiple dimensions such as technical difficulty coefficient, time consumption coefficient, and manpower requirement coefficient; the preliminary time plan refers to a preliminary time arrangement plan for the transformation process blocks based on the transformation coefficient, including the expected start time, duration and end time of each process block.

[0074] Furthermore, the analysis of key transformation nodes in the transformation sequence can be achieved through an influencing factor analysis method, such as analyzing the influencing factors of each transformation process block on the performance, efficiency, user experience, etc. of the offline query business process, and determining the node with the greater impact as the key transformation node; the determination of the priority process block corresponding to the transformation process block can be achieved through a multi-criteria decision-making algorithm, such as establishing multiple evaluation criteria, such as urgency, benefit, risk, etc., and comprehensively evaluating the transformation process block through a multi-criteria decision-making algorithm to determine the priority process block; the evaluation of the transformation coefficient corresponding to the priority process block can be achieved through a risk assessment tool, such as RiskMatrix, FMEA, etc.; the formulation of a preliminary time plan corresponding to the transformation process block can be achieved through a workload estimation method, such as estimating the workload of each process block based on the transformation tasks and resource requirements of the priority process block, and then formulating a preliminary time plan based on available resources and time constraints; the construction of the transformation time window corresponding to the transformation process block can be achieved through a time management tool, such as Todoist, Toggl, etc., which can help manage and allocate time resources and construct a transformation time window.

[0075] S5, monitor the window state corresponding to the reconstruction time window, based on the window state, construct an optimized reconstruction path of the offline query service in the reconstruction process, identify the reconstruction task corresponding to the optimized reconstruction path, analyze the key reconstruction points of the reconstruction task in task execution, and generate a process reconstruction scheme corresponding to the offline query service based on the key reconstruction points.

[0076] The application can clearly understand whether each reconstruction process block is carried out as planned within the predetermined time window by monitoring the window state corresponding to the reconstruction time window, so that measures can be taken to adjust when progress is delayed, and ensure that the entire offline query service process reconstruction project is completed on time.

[0077] The window state refers to the actual situation and performance of the reconstruction time window in a specific time period, including the progress of the reconstruction process block in the time window, such as the amount of work completed, the amount of work remaining, and whether it is carried out as planned; the use of resources, such as the degree of consumption and remaining amount of manpower, material resources and time resources; possible risks and problems, such as technical difficulties, external interference, etc. affecting the reconstruction progress; and the coordination with other related process blocks or projects, etc. Optionally, the monitoring of the window state corresponding to the reconstruction time window can be realized by a project management tool, such as Trello, Asana, etc., which can be used to track the progress of the reconstruction process block, record the resource usage, and visually display the window state through the board, task list, etc.

[0078] Further, the application constructs the optimized reconstruction path of the offline query service in the reconstruction process based on the window state, which can accurately grasp the progress and problems of each reconstruction process block, so as to adjust the reconstruction strategy, select the optimal path to promote the reconstruction work, and clearly understand the direction and focus of the reconstruction, better coordinate the work, and improve the overall work efficiency.

[0079] The optimized reconstruction path refers to the specific action plan and steps formulated in the offline query reconstruction process in order to achieve efficient reconstruction, including selecting which process block to reconstruct first, adopting which technical means, how to reasonably allocate resources, etc. to ensure that the reconstruction work is carried out efficiently and orderly.

[0080] As an embodiment of the present application, the optimization transformation path of the offline query service in the transformation process is constructed based on the window state, including: querying the transformation progress corresponding to the window state; determining the state level of the offline query service in the current transformation process based on the transformation progress; calculating the feasibility index corresponding to the offline query service based on the state level; analyzing the transformation optimization conditions corresponding to the feasibility index; and constructing the optimization transformation path of the offline query service in the transformation process based on the transformation optimization conditions.

[0081] The transformation progress refers to the completion degree and time progress of each transformation process block in the transformation process of the offline query service, such as the number of completed process blocks, the completion ratio of the process block being performed, and the estimated remaining time, etc. The state level refers to the classification level of the state of the current transformation process of the offline query service according to the transformation progress, which can be divided into different levels such as not started, initial, middle, late, and close to completion, reflecting the overall stage and urgency of the transformation. The feasibility index refers to a quantitative index for measuring the possibility and feasibility of efficient transformation of the offline query service, which comprehensively considers factors such as resource availability, technical difficulty, and time limit, and the higher the value, the greater the feasibility of efficient transformation. The transformation optimization condition refers to specific requirements and conditions that need to be met to achieve efficient transformation of the offline query service, such as specific technical solutions, resource allocation requirements, and time node limits, etc.

[0082] Further, the querying of the transformation progress corresponding to the window state can be realized by a progress prediction algorithm, such as using an algorithm to predict the future transformation progress based on historical data and current progress. The determination of the state level of the offline query service in the current transformation process can be realized by a decision tree algorithm, such as constructing a decision tree model to determine the state level according to key indicators and stage targets. The calculation of the feasibility index corresponding to the offline query service can be realized by an analytic hierarchy process algorithm, such as determining the weight of each factor by the analytic hierarchy process algorithm, and then scoring each indicator to calculate the feasibility index. The analysis of the transformation optimization conditions corresponding to the feasibility index can be realized by a gap analysis tool, such as using the gap analysis tool to compare the current state with the target state to find the place of the transformation optimization condition. The construction of the optimization transformation path of the offline query service in the transformation process can be realized by a critical path optimization method, such as determining the key process blocks and key tasks in the transformation process, and constructing an efficient transformation path by optimizing the task execution order and time arrangement on the critical path.

[0083] The application can more targetedly allocate resources and arrange time by identifying the reconstruction task corresponding to the optimized reconstruction path and analyzing the key reconstruction points of the reconstruction task in task execution, avoiding waste of resources and time delay, and ensuring that the reconstruction work is done well at the core link, thereby improving the quality of the entire offline query business reconstruction.

[0084] The reconstruction task refers to the specific work content that needs to be performed in the offline query business reconstruction process according to the efficient reconstruction path, such as optimization of a specific database, redesign of a user interface, improvement of a query algorithm, and other specific action items. The key reconstruction point refers to an important link or factor that plays a decisive role in the reconstruction effect and affects the overall reconstruction quality and progress, such as the establishment method of an index and the adjustment of a data storage structure in the database optimization task. In the user interface redesign task, the key reconstruction point may be the convenience of user operation and the clarity of information display. Optionally, the identification of the reconstruction task corresponding to the optimized reconstruction path can be achieved by a process analysis method, such as detailed analysis of the existing process of offline query to find bottlenecks and optimization points in the process and determine specific reconstruction tasks. The analysis of the key reconstruction points of the reconstruction task in task execution can be achieved by a risk analysis method, such as analyzing the risks that may be faced in the execution process of the specific reconstruction task and determining the links with higher risks as key reconstruction points.

[0085] Further, based on the key reconstruction points, the application generates a process reconstruction scheme corresponding to the offline query business, which can concentrate resources and efforts to prioritize processing these key links, thereby speeding up the entire reconstruction process, enabling the offline query business to enjoy the benefits of reconstruction faster, improving the quality of reconstruction, and fundamentally solving the bottleneck problem in the business process, thereby improving the overall business quality.

[0086] The process reconstruction scheme refers to a series of specific improvement measures and action plans formulated based on the identified key reconstruction points for offline query business, which includes adjustment, optimization or redesign of each link in the existing business process to improve the efficiency, accuracy and user experience of offline query business. The process reconstruction scheme involves technical improvements such as database optimization and algorithm upgrade. It may also include management adjustments such as personnel configuration optimization and workflow specification. At the same time, the process reconstruction scheme also specifies the implementation steps, time nodes, responsible persons and required resources of each reconstruction measure. Optionally, the generation of the process reconstruction scheme corresponding to the offline query business can be achieved by a scheme generation tool, such as Lucidchart, Miro, etc.

[0087] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0088] In an embodiment, an optimization device for a business data offline query process is provided, which corresponds to the optimization method for the business data offline query process in the above embodiments. As shown in the figure, the optimization device for the business data offline query process includes a priority module 201, a real-time monitoring module 202, a strategy making module 203, a window construction module 204, and a scheme generation module 205. The detailed description of each functional module is as follows: Figure 3

[0089] The priority module 201 is configured to obtain a business data offline query service, analyze a to-be-optimized process corresponding to the offline query service, identify a process feature corresponding to the to-be-optimized process, analyze a transformation mode corresponding to the offline query service based on the process feature, and determine a process priority corresponding to the to-be-optimized process according to the transformation mode.

[0090] The real-time monitoring module 202 is configured to determine an offline configuration device corresponding to the offline query service based on the process priority, query device configuration data corresponding to the offline configuration device, and perform real-time detection on the device configuration data to obtain device real-time data.

[0091] The strategy making module 203 is configured to analyze data interaction quality corresponding to the device real-time data, analyze a process usage of the offline query service based on the data interaction quality, and make a process transformation strategy corresponding to the offline query service based on the process usage.

[0092] The window construction module 204 is configured to perform process blocking on the to-be-optimized process based on the process transformation strategy to obtain a transformation process block, analyze a transformation order corresponding to the transformation process block, and construct a transformation time window corresponding to the transformation process block based on the transformation order.

[0093] The scheme generation module 205 is configured to monitor a window state corresponding to the transformation time window, construct an optimization transformation path of the offline query service in a transformation process based on the window state, identify a transformation task corresponding to the optimization transformation path, analyze a key transformation point of the transformation task in task execution, and generate a process transformation scheme corresponding to the offline query service based on the key transformation point.

[0094] ​In one embodiment, the priority module 201 determines the process priority corresponding to the process to be optimized according to the transformation mode, including: analyzing the transformation focus area corresponding to the transformation mode; determining the priority consideration factor corresponding to the transformation focus area; based on the priority consideration factor, evaluating the correlation degree between the process to be optimized and the transformation focus area; based on the correlation degree, extracting the key feature item in the process to be optimized; based on the key feature item, determining the process priority corresponding to the process to be optimized.

[0095] In one embodiment, the real-time monitoring module 202 performs real-time detection on the device configuration data to obtain device real-time data, including: collecting dynamic parameter information in the device configuration data; analyzing the parameter change trend corresponding to the dynamic parameter information; quantitatively evaluating the parameter change trend to obtain a trend quantitative index; analyzing the device real-time feature corresponding to the trend quantitative index; based on the device real-time feature, performing real-time detection on the device configuration data to obtain device real-time data.

[0096] In one embodiment, the strategy making module 203 analyzes the data interaction quality corresponding to the device real-time data, including: extracting a key interaction parameter in the device real-time data; analyzing the interaction feature performance corresponding to the key interaction parameter; based on the interaction feature performance, determining the interaction dimension corresponding to the device real-time data; querying the interaction weak link in the interaction dimension; based on the interaction weak link, analyzing the data interaction quality corresponding to the device real-time data.

[0097] In one embodiment, the strategy making module 203 formulates the process transformation strategy corresponding to the offline query business based on the process usage, including: combing a key process node in the process usage; analyzing the running efficiency index corresponding to the key process node; based on the running efficiency index, determining the direction to be transformed corresponding to the process usage; based on the direction to be transformed, generating the transformation measure corresponding to the process usage; based on the transformation measure, formulating the process transformation strategy corresponding to the offline query business.

[0098] In one embodiment, the window construction module 204 constructs the transformation time window corresponding to the transformation process block based on the transformation sequence, including: analyzing a key transformation node in the transformation sequence; based on the key transformation node, determining the priority process block corresponding to the transformation process block; evaluating the transformation coefficient corresponding to the priority process block; according to the transformation coefficient, formulating the preliminary time planning corresponding to the transformation process block; based on the preliminary time planning, constructing the transformation time window corresponding to the transformation process block.

[0099] In one embodiment, the scheme generation module 205 executes, based on the window state, constructing an optimized transformation path of the offline query service in the transformation process, including: querying the transformation progress corresponding to the window state; determining the state level of the offline query service in the current transformation process based on the transformation progress; calculating the feasibility index corresponding to the offline query service based on the state level; analyzing the transformation optimization condition corresponding to the feasibility index; and constructing the optimized transformation path of the offline query service in the transformation process based on the transformation optimization condition.

[0100] The specific limitations of the business data offline query process optimization device can refer to the limitations of the business data offline query process optimization method described above, which will not be repeated here. Each module in the business data offline query process optimization device described above can be realized by software, hardware, and their combinations in whole or in part. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0101] In one embodiment, a computer device is provided, which can be a server, and its internal structure diagram can be as shown in Figure 4 The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with the external client through the network connection. The computer program is executed by the processor to implement the functions or steps of the business data offline query process optimization method server side.

[0102] In one embodiment, a computer device is provided, which can be a client, and its internal structure diagram can be as shown in Figure 5 The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with the external server through the network connection. The computer program is executed by the processor to implement the functions or steps of the business data offline query process optimization method client side.

[0103] In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:

[0104] Obtaining business data offline query business, analyzing a to-be-optimized process corresponding to the offline query business, identifying a process feature corresponding to the to-be-optimized process, analyzing a transformation mode corresponding to the offline query business based on the process feature, and determining a process priority corresponding to the to-be-optimized process according to the transformation mode;

[0105] Determining an offline configuration device corresponding to the offline query business based on the process priority, querying device configuration data corresponding to the offline configuration device, and performing real-time detection on the device configuration data to obtain device real-time data;

[0106] Analyzing data interaction quality corresponding to the device real-time data, analyzing a process usage of the offline query business based on the data interaction quality, and formulating a process transformation strategy corresponding to the offline query business based on the process usage;

[0107] Based on the process transformation strategy, performing process blocking on the to-be-optimized process to obtain a transformation process block, analyzing a transformation order corresponding to the transformation process block, and constructing a transformation time window corresponding to the transformation process block based on the transformation order;

[0108] Monitoring a window state corresponding to the transformation time window, constructing an optimized transformation path of the offline query business in a transformation process based on the window state, identifying a transformation task corresponding to the optimized transformation path, analyzing a key transformation point of the transformation task in task execution, and generating a process transformation scheme corresponding to the offline query business based on the key transformation point.

[0109] In one embodiment, a computer readable storage medium is provided, having a computer program stored thereon, the computer program being executed by a processor to implement the following steps:

[0110] Obtaining business data offline query business, analyzing a to-be-optimized process corresponding to the offline query business, identifying a process feature corresponding to the to-be-optimized process, analyzing a transformation mode corresponding to the offline query business based on the process feature, and determining a process priority corresponding to the to-be-optimized process according to the transformation mode;

[0111] Determining an offline configuration device corresponding to the offline query business based on the process priority, querying device configuration data corresponding to the offline configuration device, and performing real-time detection on the device configuration data to obtain device real-time data;

[0112] analyze data interaction quality corresponding to the device real-time data, analyze process usage of the offline query service based on the data interaction quality, and formulate a process reform strategy corresponding to the offline query service based on the process usage;

[0113] perform process blocking on the to-be-optimized process based on the process reform strategy to obtain a reform process block, analyze a reform sequence corresponding to the reform process block, and construct a reform time window corresponding to the reform process block based on the reform sequence;

[0114] monitor a window state corresponding to the reform time window, construct an optimized reform path of the offline query service in a reform process based on the window state, identify a reform task corresponding to the optimized reform path, analyze a key reform point of the reform task in task execution, and generate a process reform scheme corresponding to the offline query service based on the key reform point.

[0115] It should be noted that the functions or steps described above with respect to the computer-readable storage medium or the computer device can correspond to the related descriptions of the server side and the client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0116] Those skilled in the art can understand that all or part of the processes in the foregoing method embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the foregoing method embodiments. In each embodiment provided in the present application, any reference to a memory, storage, database or other medium can include a non-volatile and / or volatile memory. The non-volatile memory can include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM) or a flash memory. The volatile memory can include a random access memory (RAM) or an external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0117] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0118] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the foregoing embodiments of the present application have been described in detail, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. It should be noted that if a software tool or component of a company other than the company appears in the embodiments of the present application, it is only used for illustration, and does not represent actual use.

Claims

1. A method for optimizing the offline query process of business data, characterized in that: include: Obtaining offline query services from business data, analyzing the process to be optimized corresponding to the offline query services, identifying process characteristics corresponding to the process to be optimized, analyzing a transformation mode corresponding to the offline query services based on the process characteristics, and determining a process priority corresponding to the process to be optimized based on the transformation mode; Based on the process priority, determining the offline configuration device corresponding to the offline query service, querying the device configuration data corresponding to the offline configuration device, and performing real-time detection on the device configuration data to obtain real-time device data; Analyze the data interaction quality corresponding to the real-time data of the device, analyze the process usage of the offline query service based on the data interaction quality, and formulate a process transformation strategy corresponding to the offline query service based on the process usage; Based on the process transformation strategy, the process to be optimized is divided into process blocks to obtain transformation process blocks, the transformation sequence corresponding to the transformation process blocks is analyzed, and based on the transformation sequence, the transformation time window corresponding to the transformation process block is constructed; Monitor the window status corresponding to the transformation time window, and based on the window status, construct an optimized transformation path for the offline query business during the transformation process, identify the transformation tasks corresponding to the optimized transformation path, analyze the key transformation points of the transformation tasks during task execution, and generate a process transformation plan corresponding to the offline query business based on the key transformation points.

2. The method for optimizing the offline query process of business data according to claim 1, characterized in that: Determining the process priority corresponding to the process to be optimized according to the transformation mode includes: Analyze the key transformation areas corresponding to the transformation model; Determine the priority factors corresponding to the key areas of transformation; Based on the priority considerations, evaluate the degree of correlation between the process to be optimized and the key areas of transformation; Extracting key feature items in the process to be optimized based on the degree of association; Based on the key feature items, the process priority corresponding to the process to be optimized is determined.

3. The method for optimizing the offline query process of business data according to claim 1, characterized in that: The real-time detection of the device configuration data to obtain the real-time data of the device includes: Collecting dynamic parameter information in the device configuration data; Analyzing parameter change trends corresponding to the dynamic parameter information; Quantitatively evaluating the parameter change trend to obtain a trend quantitative index; Analyze the real-time characteristics of the device corresponding to the trend quantitative indicators; Based on the real-time characteristics of the device, the device configuration data is detected in real time to obtain real-time data of the device.

4. The method for optimizing the offline query process of business data according to claim 1, characterized in that: The analyzing the data interaction quality corresponding to the real-time data of the device includes: Extracting key interaction parameters from the real-time data of the device; Analyzing the interaction feature performance corresponding to the key interaction parameters; Determining an interaction dimension corresponding to the real-time data of the device based on the interaction feature performance; Querying the weak interaction links in the interaction dimension; Based on the weak links in the interaction, the data interaction quality corresponding to the real-time data of the device is analyzed.

5. The method for optimizing the offline query process of business data according to claim 1, characterized in that: Formulating a process transformation strategy corresponding to the offline query business based on the process usage includes: Sorting out the key process nodes in the usage of the process; Analyze the operating efficiency indicators corresponding to the key process nodes; Based on the operational efficiency indicators, determine the direction to be modified corresponding to the process usage; Based on the direction to be transformed, generating transformation measures corresponding to the process usage; Based on the transformation measures, a process transformation strategy corresponding to the offline query business is formulated.

6. The method for optimizing the offline query process of business data according to claim 1, characterized in that: The step of constructing a transformation time window corresponding to the transformation process block based on the transformation sequence includes: Analyze key transformation nodes in the transformation sequence; Based on the key transformation node, determining a priority process block corresponding to the transformation process block; Evaluating the transformation coefficient corresponding to the priority process block; Formulate a preliminary time plan corresponding to the transformation process block based on the transformation coefficient; Based on the preliminary time plan, a transformation time window corresponding to the transformation process block is constructed.

7. The method for optimizing the offline query process of business data according to claim 1, characterized in that: The step of constructing an optimized transformation path for the offline query service during the transformation process based on the window status includes: Query the transformation progress corresponding to the window status; Based on the transformation progress, determining a status level of the offline query service in the current transformation process; Based on the status level, calculating a feasibility index corresponding to the offline query service; Analyze the transformation optimization conditions corresponding to the feasibility index; Based on the transformation optimization conditions, an optimization transformation path for the offline query service during the transformation process is constructed.

8. A device for optimizing the offline query process of business data, characterized in that: include: A priority module is used to obtain offline query services of business data, analyze the process to be optimized corresponding to the offline query services, identify the process characteristics corresponding to the process to be optimized, analyze the transformation mode corresponding to the offline query services based on the process characteristics, and determine the process priority corresponding to the process to be optimized according to the transformation mode; A real-time monitoring module is used to determine the offline configuration device corresponding to the offline query service based on the process priority, query the device configuration data corresponding to the offline configuration device, perform real-time detection on the device configuration data, and obtain real-time device data; a strategy formulation module, configured to analyze the data interaction quality corresponding to the real-time data of the device, analyze the process usage of the offline query service based on the data interaction quality, and formulate a process transformation strategy corresponding to the offline query service based on the process usage; A window construction module is used to divide the process to be optimized into blocks based on the process transformation strategy to obtain transformation process blocks, analyze the transformation sequence corresponding to the transformation process blocks, and construct a transformation time window corresponding to the transformation process blocks based on the transformation sequence; A solution generation module is used to monitor the window status corresponding to the transformation time window, construct an optimized transformation path for the offline query business during the transformation process based on the window status, identify the transformation tasks corresponding to the optimized transformation path, analyze the key transformation points of the transformation tasks during task execution, and generate a process transformation plan corresponding to the offline query business based on the key transformation points.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for optimizing the offline query process of business data as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing the offline query process of business data as described in any one of claims 1 to 7 are implemented.

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