Hotspot interface information optimization method and device, computer equipment and storage medium

Abstract

The invention belongs to the technical field of data analysis, and relates to a hotspot interface information optimization method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining an operation track log according to a pre-buried log burying point; performing preprocessing operation on the operation track log to obtain a preprocessed track log; according to the data size of the preprocessed track logs, the preprocessed track logs are stored on a plurality of nodes of a distributed storage system in a scattered mode; receiving a log calling request sent by a user terminal; calling a target distributed storage system corresponding to the system identification information, and extracting a stored preprocessing track log from the target distributed storage system according to a calling time interval; according to the extracted pre-processing track log, performing statistics on hotspot interface data of the seat issuing personnel; and performing optimization operation on the hotspot interface with high calling frequency according to the hotspot interface data. The response speed of the system and the user experience can be remarkably improved.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and is applicable to the financial or medical field. In particular, it relates to a method, device, computer device, and storage medium for optimizing hot interface information. Background Art

[0002] In the financial or medical field, especially in the intelligent agent scenario of online medical service consultation, various interfaces need to be frequently called to complete business operations.

[0003] However, with the growth of business volume and the improvement of system complexity, the performance problems of interface calls have become increasingly prominent. On the one hand, the seat order-issuing personnel will repeatedly call certain specific interfaces during the operation process, resulting in these interfaces becoming performance bottlenecks; on the other hand, there are significant differences in the operation habits and business proficiency levels of different seat order-issuing personnel, resulting in significant differences in the frequency and order of interface calls by different personnel during the operation process.

[0004] Therefore, how to accurately identify the hot interfaces of each seat order-issuing personnel during the operation process and optimize them accordingly has become an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of the embodiments of this application is to propose a method, device, computer device, and storage medium for optimizing hot interface information to solve the problem of how to accurately identify the hot interfaces of each seat order-issuing personnel during the operation process and optimize them accordingly.

[0006] To solve the above technical problems, the embodiments of this application provide a method for optimizing hot interface information, which adopts the following technical solutions:

[0007] Obtain operation track logs according to pre-buried log points;

[0008] Perform preprocessing operations on the operation track logs to obtain preprocessed track logs;

[0009] According to the data volume of the preprocessed track logs, disperse and store the preprocessed track logs on several nodes of a distributed storage system;

[0010] Receive a log call request sent by a user terminal, where the log call request includes target system identification information and a call time interval;

[0011] Call the target distributed storage system corresponding to the system identification information, and extract the pre-stored preprocessed track logs in the target distributed storage system according to the call time interval;

[0012] Statistically obtain the hot interface data of the seat order processing personnel based on the preprocessed trace logs extracted;

[0013] Perform optimization operations on the hot interfaces with high call counts according to the hot interface data.

[0014] Furthermore, the step of performing optimization operations on the hot interfaces with high call counts according to the hot interface data specifically includes the following steps:

[0015] Cache the hot interface data into a log-based Key-Value database;

[0016] When it is necessary to load the hot interface data, directly obtain the hot interface data from the log-based Key-Value database.

[0017] Furthermore, the step of performing preprocessing operations on the operation trace logs to obtain preprocessed trace logs specifically includes the following steps:

[0018] Perform structured processing on the collected operation trace logs through preset log parsing rules, extract key information such as interface call time, call frequency, and call order, and generate the preprocessed trace logs.

[0019] Furthermore, the step of statistically obtaining the hot interface data of the seat order processing personnel based on the preprocessed trace logs extracted specifically includes the following steps:

[0020] According to the interface call timestamps and call frequency information in the preprocessed trace log records, use the sliding window algorithm to calculate the call count of each interface within a preset time window, and identify the hot interface data with high frequency calls.

[0021] Furthermore, after the step of using the sliding window algorithm to calculate the call count of each interface within a preset time window according to the interface call timestamps and call frequency information in the preprocessed trace log records and identifying the hot interface data with high frequency calls, the following steps are also included:

[0022] Analyze the call trend of the hot interface data according to a time series-based prediction model;

[0023] If it is predicted that the future call volume will continue to increase, then confirm the hot interface data as a potential performance bottleneck;

[0024] The step of performing optimization operations on the hot interfaces with high call counts according to the hot interface data specifically includes the following steps:

[0025] Give priority to performing optimization operations on the hot interface data confirmed as potential performance bottlenecks.

[0026] Further, the step of optimizing the hot interfaces with high call counts according to the hot interface data specifically includes the following steps:

[0027] Analyze the recognition results and call paths of the hot interface data to generate an interface optimization strategy;

[0028] Perform an optimization operation on the hot interface data according to the interface optimization strategy.

[0029] To solve the above technical problems, an embodiment of the present application further provides a hot interface information optimization device, which adopts the following technical solutions:

[0030] An operation log acquisition module, configured to acquire an operation track log according to pre-buried log points;

[0031] A preprocessing module, configured to perform a preprocessing operation on the operation track log to obtain a preprocessed track log;

[0032] An operation log storage module, configured to disperse and store the preprocessed track log on several nodes of a distributed storage system according to the data volume of the preprocessed track log;

[0033] A request acquisition module, configured to receive a log call request sent by a user terminal, where the log call request includes target system identification information and a call time interval;

[0034] An operation log extraction module, configured to call a target distributed storage system corresponding to the system identification information, and extract the pre-stored preprocessed track log in the target distributed storage system according to the call time interval;

[0035] A hot interface statistics module, configured to count the hot interface data of seat order-making personnel according to the extracted preprocessed track log;

[0036] A hot interface optimization module, configured to perform an optimization operation on the hot interfaces with high call counts according to the hot interface data.

[0037] Further, the hot interface optimization module includes:

[0038] A hot interface cache sub-module, configured to cache the hot interface data into a log-type Key-Value database;

[0039] A hot interface acquisition sub-module, configured to directly acquire the hot interface data from the log-type Key-Value database when the hot interface data needs to be loaded.

[0040] To solve the above technical problems, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:

[0041] It includes a memory and a processor. Computer-readable instructions are stored in the memory. When the processor executes the computer-readable instructions, the steps of the hotspot interface information optimization method described above are implemented.

[0042] To solve the above technical problems, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:

[0043] Computer-readable instructions are stored on the computer-readable storage medium. When the computer-readable instructions are executed by a processor, the steps of the hotspot interface information optimization method described above are implemented.

[0044] The present application provides a method for optimizing hotspot interface information, including: obtaining an operation track log according to pre-buried log points; performing a preprocessing operation on the operation track log to obtain a preprocessed track log; dispersing and storing the preprocessed track log on several nodes of a distributed storage system according to the data volume of the preprocessed track log; receiving a log call request sent by a user terminal, where the log call request includes target system identification information and a call time interval; calling a target distributed storage system corresponding to the system identification information, and extracting the pre-stored preprocessed track log in the target distributed storage system according to the call time interval; statistically obtaining the hotspot interface data of the seat order-making personnel according to the extracted preprocessed track log; and performing an optimization operation on the hotspot interfaces with high call times according to the hotspot interface data. Compared with the prior art, the present application can significantly improve the system response speed and user experience by caching the hotspot interface data in a log-type Key-Value database and directly obtaining the data from the cache when needed. At the same time, reasonable caching strategies and data synchronization mechanisms are also the keys to ensuring the effectiveness and consistency of the cache system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the solutions in the present application, the following will briefly introduce the drawings required for the description of the embodiments of the present application. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is an exemplary system architecture diagram to which the present application can be applied;

[0047] Figure 2 It is a flowchart of the implementation of the hotspot interface information optimization method provided by the embodiment of the present application;

[0048] Figure 3 is a schematic structural diagram of a hotspot interface information optimization device provided by an embodiment of the present application;

[0049] Figure 4 is a schematic structural diagram of a computer device according to an embodiment of the present application. Detailed implementation manners

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of this application or the above drawings are used to distinguish different objects and not to describe a specific order.

[0051] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0052] To enable those skilled in the technical field to better understand the solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0053] As Figure 1 shown, the system architecture 100 may include a terminal device 101, a network 102, and a server 103. The terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. The network 102 is a medium for providing a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0054] Users can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications may be installed on the terminal device 101, such as a web browser application, a shopping application, a search application, an instant messaging tool, an email client, a social platform software, etc.

[0055] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop 1011, the tablet computer 1012, or the mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop portable computer, a desktop computer, and so on.

[0056] The server 103 can be a server that provides various services, such as a background server that provides support for the pages displayed on the terminal device 101.

[0057] It should be noted that the hotspot interface information optimization method provided by the embodiments of the present application is generally executed by the server / terminal device. Correspondingly, the hotspot interface information optimization device is generally set in the server / terminal device.

[0058] It should be understood that Figure 1 the numbers of the terminal devices, networks, and servers in

[0059] Continuing to refer to Figure 2 , a flowchart of an embodiment of the hotspot interface information optimization method according to the present application is shown. The hotspot interface information optimization method includes: step S201, step S202, step S203, step S204, step S205, step S206, and step S207.

[0060] In step S201, an operation trace log is obtained according to the pre-buried log points.

[0061] In the embodiments of the present application, the system will collect the operation trace logs of each seat order-making personnel according to the previously preset log points. These log points are pre-placed at key positions in the system to capture and record various operation behaviors of users.

[0062] In the embodiments of the present application, the operation trace log refers to the operation behaviors of the seat order-making personnel in the intelligent seat in the financial or medical field. These operation behaviors include, but are not limited to, clicking buttons, filling out forms, submitting data, etc. In this way, the system can comprehensively obtain all the operation traces of the seat order-making personnel, providing data support for subsequent analysis and optimization.

[0063] In step S202, perform preprocessing operations on the operation trace logs to obtain preprocessed trace logs.

[0064] In the embodiment of the present application, after collecting the operation trace logs, the system needs to perform preprocessing operations on these logs. The main purpose of preprocessing is to clean, format, deduplicate, etc. the original logs to obtain more standardized, accurate, and easy-to-analyze preprocessed trace logs. This step is crucial for subsequent data analysis and mining because it can directly affect the accuracy and reliability of the final results.

[0065] In step S203, according to the data volume of the preprocessed trace logs, disperse and store the preprocessed trace logs on several nodes of the distributed storage system.

[0066] In the embodiment of the present application, since the data volume of the preprocessed trace logs is very large, the system needs to disperse and store these logs on several nodes of the distributed storage system. The distributed storage system is a technology that disperses and stores a large amount of data on multiple physical nodes. It can improve the storage efficiency and access speed of data, and at the same time reduce the risk of single-point failure. In this step, the system will reasonably allocate the preprocessed trace logs to each node of the distributed storage system according to the data volume size to ensure the reliability and accessibility of the data.

[0067] In step S204, receive the log call request sent by the user terminal, where the log call request includes the target system identification information and the call time interval.

[0068] In the embodiment of the present application, the system will receive the log call request from the user terminal. These requests usually include the target system identification information and the key information of the call time interval. The target system identification information is used to specify the system to which the logs that the user wants to query belong, while the call time interval is used to specify the time range of the logs that the user wants to query. The system will locate and extract the corresponding preprocessed trace logs according to this information.

[0069] In step S205, call the target distributed storage system corresponding to the system identification information, and extract the preprocessed trace logs stored in the target distributed storage system according to the call time interval.

[0070] In the embodiment of the present application, after receiving the log call request, the system will call the target distributed storage system corresponding to the target system identification information, and extract the preprocessed trace logs stored in this system according to the call time interval. This step needs to ensure that the extracted log data is accurate and meets the user's query requirements.

[0071] In step S206, based on the extracted preprocessed trace logs, the hot interface data of the agent order processing personnel is statistically obtained.

[0072] In an embodiment of the present application, after the preprocessed trace logs are extracted, the system will statistically obtain the hot interface data of the agent order processing personnel according to these log data. The hot interface data refers to the interface data that is frequently called, which usually reflects the main needs and operation habits of users. By statistically analyzing the hot interface data, the system can understand the work focus and optimization direction of the agent order processing personnel, providing data support for subsequent optimization operations.

[0073] In step S207, optimization operations are performed on the hot interfaces with high call counts according to the hot interface data.

[0074] In an embodiment of the present application, after obtaining the hot interface data, the system will perform optimization operations on the hot interfaces with high call counts. These optimization operations include optimizing the implementation logic of the interfaces, improving the response speed of the interfaces, increasing the concurrent processing ability of the interfaces, etc. By optimizing the hot interfaces, the system can further improve the work efficiency and service quality of the agent order processing personnel, thereby enhancing the performance and user experience of the entire system.

[0075] In an embodiment of the present application, a method for optimizing hot interface information is provided, including: obtaining operation trace logs according to pre-buried log points; performing preprocessing operations on the operation trace logs to obtain preprocessed trace logs; dispersedly storing the preprocessed trace logs on several nodes of a distributed storage system according to the data volume of the preprocessed trace logs; receiving a log call request sent by a user terminal, where the log call request includes target system identification information and a call time interval; calling the target distributed storage system corresponding to the system identification information, and extracting the pre-stored preprocessed trace logs in the target distributed storage system according to the call time interval; statistically obtaining the hot interface data of the agent order processing personnel according to the extracted preprocessed trace logs; performing optimization operations on the hot interfaces with high call counts according to the hot interface data. Compared with the prior art, in the present application, by caching the hot interface data in a log-type Key-Value database and directly obtaining the data from the cache when needed, the response speed of the system and the user experience can be significantly improved. At the same time, reasonable caching strategies and data synchronization mechanisms are also the keys to ensuring the effectiveness and consistency of the cache system.

[0076] In some optional implementation manners of the embodiment of the present application, the step of performing optimization operations on the hot interfaces with high call counts according to the hot interface data specifically includes the following steps:

[0077] Cache the hot interface data in a log-type Key-Value database;

[0078] When it is necessary to load the hot interface data, directly obtain the hot interface data from the log-based Key-Value database.

[0079] In the embodiments of the present application, log-based Key-Value databases (such as Redis, RocksDB, etc.) are very suitable as the cache layer for hot data due to their high-speed read and write performance, low latency, and easy extensibility.

[0080] In the embodiments of the present application, the identified hot interface data is stored in the log-based Key-Value database in a specific Key-Value format. The Key is usually the unique identifier of the interface or a combination of relevant parameters, and the Value is the data returned by the interface or the data index.

[0081] In the embodiments of the present application, the cache policy includes the LRU (Least Recently Used) cache eviction algorithm, TTL (Time-To-Live) setting, etc., to ensure the effectiveness and efficiency of the cache.

[0082] In the embodiments of the present application, in order to ensure the consistency between the cached data and the original data source, it is necessary to implement a data synchronization mechanism. This can be achieved by periodically refreshing the cache, listening for changes in the data source, and triggering cache updates, etc.

[0083] In the embodiments of the present application, when a user or system needs to load hot interface data, a data loading request will be sent to the log-based Key-Value database. The request usually contains the Key for locating the data. After receiving the request, the log-based Key-Value database will quickly retrieve the data in the cache according to the provided Key. Due to the high-performance characteristics of the Key-Value database, this process is usually very fast. Once the matching data is found, the database will return the Value part to the requester. If the Key does not exist or the data has expired, it is necessary to fallback to the original data source for loading. If the requested data hits in the cache, the cached data will be directly returned, reducing the access pressure on the original data source. If it misses, it is necessary to load the data from the original data source and consider caching it for subsequent requests. In order to ensure the stability and efficiency of the cache system, it is necessary to continuously monitor metrics such as the cache hit rate and response time, and make necessary optimizations and adjustments according to the monitoring results.

[0084] In the embodiments of the present application, by caching the hot interface data in the log-based Key-Value database and directly obtaining the data from the cache when needed, the response speed of the system and the user experience can be significantly improved. At the same time, reasonable cache policies and data synchronization mechanisms are also the keys to ensuring the effectiveness and consistency of the cache system.

[0085] In some alternative implementation manners of the embodiments of the present application, the step of preprocessing the operation trace log to obtain the preprocessed trace log specifically includes the following steps:

[0086] Perform structured processing on the collected operation trace log through a preset log parsing rule, extract key information such as interface call time, call frequency, and call order, and generate a preprocessed trace log.

[0087] In the embodiments of the present application, through structured processing and generating a preprocessed trace log, the utilization efficiency and accuracy of log data can be significantly improved. These preprocessed log data can be used in various scenarios, such as performance monitoring, fault troubleshooting, user behavior analysis, etc. At the same time, the structured log data is also convenient for subsequent data analysis and mining, providing strong data support for the optimization and improvement of the system.

[0088] In some alternative implementation manners of the embodiments of the present application, the step of statistically obtaining the hot interface data of the seat order-making personnel according to the extracted preprocessed trace log specifically includes the following steps:

[0089] According to the interface call timestamp and call frequency information in the preprocessed trace log record, use the sliding window algorithm to calculate the call times of each interface within a preset time window, and identify the hot interface data with high-frequency calls.

[0090] In the embodiments of the present application, by applying the sliding window algorithm to process and analyze the interface call data in the preprocessed trace log, the hot interface data with high-frequency calls can be accurately identified, providing strong data support for the optimization and improvement of the system.

[0091] In some alternative implementation manners of the embodiments of the present application, after the step of using the sliding window algorithm to calculate the call times of each interface within a preset time window according to the interface call timestamp and call frequency information in the preprocessed trace log record and identifying the hot interface data with high-frequency calls, the following steps are further included:

[0092] Analyze the call trend of the hot interface data according to the prediction model based on time series;

[0093] If it is predicted that the future call volume will continue to increase, then confirm the hot interface data as a potential performance bottleneck;

[0094] The step of optimizing the hot interfaces with high call times according to the hot interface data specifically includes the following steps:

[0095] Give priority to optimizing the hot interface data confirmed as a potential performance bottleneck.

[0096] In the embodiments of the present application, in a complex system, the high-frequency invocation of hot interface data often has an important impact on system performance. To discover and solve potential performance problems in advance, it is necessary to use a time-series-based prediction model to analyze the invocation trend of hot interface data. By predicting future invocation volumes, those hot interfaces that may become performance bottlenecks can be identified and preferentially optimized.

[0097] In the embodiments of the present application, the time-series prediction model is a statistical method for analyzing and predicting time-series data. Time-series data refers to a series of observed values arranged in chronological order, such as the invocation volume of hot interface data. The time-series prediction model can be an ARIMA (AutoRegressive Integrated Moving Average) model, an exponential smoothing model, a machine learning algorithm (such as LSTM, GRU, etc.).

[0098] In the embodiments of the present application, collect the invocation records of historical hot interface data, including invocation timestamps and invocation counts, etc. Clean and preprocess the data, such as removing outliers and filling in missing values.

[0099] In the embodiments of the present application, select a suitable time-series prediction model according to the characteristics of the data and business requirements. Use historical data to train the model to learn the invocation trend of hot interface data.

[0100] In the embodiments of the present application, apply the trained model to predict the invocation volume of hot interface data for a period of time in the future. The prediction results are usually presented in the form of a time series, showing the changing trend of future invocation volumes.

[0101] In the embodiments of the present application, analyze the prediction results. If it is predicted that the future invocation volume continues to increase and exceeds the processing capacity of the system or a preset threshold, then the corresponding hot interface data is identified as a potential performance bottleneck. When identifying potential performance bottlenecks, other factors such as response time and error rate of interface invocations also need to be considered.

[0102] In the embodiments of the present application, perform preferential optimization operations on the hot interface data identified as potential performance bottlenecks. The optimization operations may include code optimization, database optimization, cache strategy adjustment, etc.

[0103] In the embodiments of the present application, after optimization, continuously monitor the invocation trend and performance metrics of hot interface data. According to the monitoring results, timely adjust the optimization strategy to ensure the stability and efficiency of system performance.

[0104] In the embodiments of the present application, formulate reasonable resource planning and expansion plans for future possible invocation volume growth. The resource planning may include hardware upgrades, server expansion, load balancing strategy adjustment, etc.

[0105] In the embodiments of the present application, using a prediction model based on time series to analyze the call trend of hot interface data and accordingly identify and optimize potential performance bottlenecks is an effective means to improve system performance and user experience.

[0106] In some alternative implementation manners of the embodiments of the present application, the steps of optimizing hot interfaces with high call counts according to hot interface data specifically include the following steps:

[0107] Analyze the identification results and call paths of hot interface data to generate interface optimization strategies;

[0108] Perform optimization operations on the hot interface data according to the interface optimization strategies.

[0109] In the embodiments of the present application, in a complex system, the high-frequency calls of hot interface data and potential performance bottlenecks have an important impact on system performance. To improve system performance, it is necessary to deeply analyze the identification results and call paths of hot interface data to generate effective interface optimization strategies and perform optimization operations on the hot interface data accordingly.

[0110] In the embodiments of the present application, according to a prediction model based on time series or other methods, identify hot interface data and its potential performance bottlenecks. Analyze key metrics such as the call frequency, response time, and error rate of hot interface data to understand its specific impact on system performance.

[0111] In the embodiments of the present application, trace the call path of hot interface data, that is, the entire process from user request to interface response. Analyze each link in the call path, including front-end requests, network transmission, back-end processing, database queries, etc., to find possible performance bottlenecks.

[0112] In the embodiments of the present application, combine the identification results and call path analysis to determine the specific location of the performance bottleneck. The bottleneck may be at the code level (such as too high algorithm complexity, too deep loop nesting, etc.), the database level (such as unoptimized query statements, missing indexes, etc.), or the network level (such as network latency, insufficient bandwidth, etc.).

[0113] In the embodiments of the present application, formulate specific interface optimization strategies for the identified performance bottlenecks. The optimization strategies may include code optimization (such as algorithm improvement, loop optimization, etc.), database optimization (such as query statement optimization, index addition, etc.), cache policy adjustment (such as adding a cache layer, optimizing cache hit rate, etc.), network optimization (such as using CDN, optimizing network transmission protocol, etc.), etc.

[0114] In the embodiments of the present application, evaluate the formulated optimization strategies, considering their feasibility, implementation difficulty, and impact on other parts of the system. According to the evaluation results, select the optimal optimization strategy for implementation.

[0115] In the embodiments of the present application, according to the selected optimization strategy, a detailed development implementation plan is formulated. The plan should clarify the specific steps, time nodes, responsible persons, etc. of the optimization operation.

[0116] In the embodiments of the present application, according to the implementation plan, code development, testing and verification are carried out. Ensure that the optimization operation does not introduce new performance problems or security risks.

[0117] In the embodiments of the present application, the optimized code is deployed to the production environment. Continuously monitor the call trends and performance metrics of hot interface data to ensure the optimization effect.

[0118] In the embodiments of the present application, according to the monitoring results, the optimization strategy and implementation plan are adjusted in a timely manner. Continuously optimize the interface performance to adapt to the changes in system requirements.

[0119] In the embodiments of the present application, analyzing the identification results and call paths of hot interface data, generating an interface optimization strategy, and performing optimization operations on the hot interface data accordingly are effective means to improve system performance. Through continuous optimization, it can be ensured that the system can operate efficiently and stably to meet user needs.

[0120] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0121] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0122] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0123] It should be understood that although the steps in the flowchart of the accompanying drawings are shown sequentially according to the indication of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless otherwise clearly stated in this document, the execution of these steps has no strict order restriction and can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0124] Further reference is made to Figure 3 , as an implementation of the method shown above Figure 2 , an embodiment of a hotspot interface information optimization device is provided in this application. This device embodiment corresponds to Figure 2 the method embodiment shown, and this device can be specifically applied to various electronic devices.

[0125] As shown in Figure 3 , the hotspot interface information optimization device 200 in the embodiment of this application includes:

[0126] An operation log acquisition module 210, configured to acquire an operation track log according to pre-buried log points;

[0127] A preprocessing module 220, configured to perform preprocessing operations on the operation track log to obtain a preprocessed track log;

[0128] An operation log storage module 230, configured to disperse and store the preprocessed track log on several nodes of a distributed storage system according to the data volume of the preprocessed track log;

[0129] A request acquisition module 240, configured to receive a log call request sent by a user terminal, where the log call request includes target system identification information and a call time interval;

[0130] An operation log extraction module 250, configured to call a target distributed storage system corresponding to the system identification information, and extract the pre-stored preprocessed track log in the target distributed storage system according to the call time interval;

[0131] A hotspot interface statistics module 260, configured to statistically obtain hotspot interface data of seat order-making personnel according to the extracted preprocessed track log;

[0132] A hotspot interface optimization module 270, configured to perform optimization operations on hotspot interfaces with high call frequencies according to the hotspot interface data.

[0133] In an embodiment of the present application, an optimization device 200 for hot interface information is provided, including: an operation log acquisition module 210, configured to acquire an operation trace log according to pre-buried log points; a preprocessing module 220, configured to perform preprocessing operations on the operation trace log to obtain a preprocessed trace log; an operation log storage module 230, configured to disperse and store the preprocessed trace log on several nodes of a distributed storage system according to the data volume of the preprocessed trace log; a request acquisition module 240, configured to receive a log call request sent by a user terminal, where the log call request includes target system identification information and a call time interval; an operation log extraction module 250, configured to call a target distributed storage system corresponding to the system identification information and extract the pre-stored preprocessed trace log in the target distributed storage system according to the call time interval; a hot interface statistics module 260, configured to statistically obtain hot interface data of seat order-making personnel according to the extracted preprocessed trace log; a hot interface optimization module 270, configured to perform optimization operations on hot interfaces with high call times according to the hot interface data. Compared with the prior art, in the present application, by caching the hot interface data in a log-based Key-Value database and directly obtaining the data from the cache when needed, the response speed of the system and the user experience can be significantly improved. At the same time, a reasonable cache policy and data synchronization mechanism are also the keys to ensuring the effectiveness and consistency of the cache system.

[0134] In some optional implementation manners of the embodiment of the present application, the above-mentioned hot interface optimization module includes:

[0135] A hot interface caching sub-module, configured to cache the hot interface data in a log-based Key-Value database;

[0136] A hot interface acquisition sub-module, configured to directly obtain the hot interface data from the log-based Key-Value database when it is necessary to load the hot interface data.

[0137] To solve the above technical problems, an embodiment of the present application also provides a computer device. For details, please refer to Figure 4 , Figure 4 which is the basic structural block diagram of the computer device in the embodiment of the present application.

[0138] The computer device 300 includes a memory 310, a processor 320, and a network interface 330 that are communicatively connected to each other via a system bus. It should be noted that only the computer device 300 with components 310-330 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Among them, those skilled in the art of the present technology can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0139] The computer device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touchpad, a voice control device, etc.

[0140] The memory 310 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 310 may be an internal storage unit of the computer device 300, such as the hard disk or memory of the computer device 300. In other embodiments, the memory 310 may also be an external storage device of the computer device 300, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 300. Of course, the memory 310 may also include both the internal storage unit of the computer device 300 and its external storage device. In the embodiments of the present application, the memory 310 is generally used to store the operating system and various application software installed on the computer device 300, such as computer-readable instructions for the hotspot interface information optimization method. In addition, the memory 310 can also be used to temporarily store various types of data that have been output or will be output.

[0141] In some embodiments, the processor 320 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 320 is generally used to control the overall operation of the computer device 300. In the embodiments of the present application, the processor 320 is used to run the computer-readable instructions stored in the memory 310 or process data, such as running the computer-readable instructions of the hotspot interface information optimization method.

[0142] The network interface 330 may include a wireless network interface or a wired network interface, and this network interface 330 is generally used to establish a communication connection between the computer device 300 and other electronic devices.

[0143] For the computer device provided by the present application, by caching the hotspot interface data into the log-based Key-Value database and directly obtaining the data from the cache when needed, the response speed of the system and the user experience can be significantly improved. At the same time, a reasonable caching strategy and data synchronization mechanism are also the keys to ensuring the effectiveness and consistency of the cache system.

[0144] The present application also provides another implementation manner, that is, to provide a computer-readable storage medium storing computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to execute the steps of the hotspot interface information optimization method as described above.

[0145] For the computer-readable storage medium provided by the present application, by caching the hotspot interface data into the log-based Key-Value database and directly obtaining the data from the cache when needed, the response speed of the system and the user experience can be significantly improved. At the same time, a reasonable caching strategy and data synchronization mechanism are also the keys to ensuring the effectiveness and consistency of the cache system.

[0146] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation manner. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions to enable a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in the various embodiments of the present application.

[0147] Obviously, the embodiments described above are only a part of the embodiments of this application, rather than all embodiments. The preferred embodiments of this application are shown in the drawings, but they do not limit the patent scope of this application. This application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of this application more thorough and comprehensive. Although this application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure that makes use of the content of the specification and drawings of this application, directly or indirectly applied in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for optimizing hotspot interface information, characterized in that: The steps include: Obtain operation trace logs based on pre-set log points; Performing a preprocessing operation on the operation trace log to obtain a preprocessing trace log; According to the data volume of the preprocessing trace log, the preprocessing trace log is dispersedly stored on several nodes of the distributed storage system; Receiving a log calling request sent by a user terminal, wherein the log calling request includes target system identification information and a calling time interval; Calling a target distributed storage system corresponding to the system identification information, and extracting a stored preprocessing trace log in the target distributed storage system according to the calling time interval; According to the extracted pre-processing trajectory log, the hotspot interface data of the seat order personnel are counted; The hotspot interfaces with high call times are optimized according to the hotspot interface data.

2. The hotspot interface information optimization method according to claim 1, characterized in that: The step of optimizing the hotspot interfaces with high call times according to the hotspot interface data specifically includes the following steps: Cache the hotspot interface data in a log-type Key-Value database; When the hotspot interface data needs to be loaded, the hotspot interface data is directly obtained from the log-type Key-Value database.

3. The hotspot interface information optimization method according to claim 1, characterized in that: The step of performing a preprocessing operation on the operation trace log to obtain a preprocessed trace log specifically includes the following steps: The collected operation trace log is structured and processed by using preset log parsing rules to extract key information such as interface call time, call frequency, and call sequence, and generate the preprocessing trace log.

4. The hotspot interface information optimization method according to claim 1, characterized in that: The step of calculating the hotspot interface data of the agent order-issuing personnel according to the extracted pre-processing trajectory log specifically includes the following steps: According to the interface call timestamp and call frequency information in the preprocessing trace log record, a sliding window algorithm is used to calculate the number of calls of each interface within a preset time window, and the hot spot interface data with high frequency calls is identified.

5. The hotspot interface information optimization method according to claim 4, characterized in that: After the step of calculating the number of calls of each interface within a preset time window using a sliding window algorithm according to the interface call timestamp and call frequency information in the preprocessing trace log record, and identifying the hotspot interface data with high frequency calls, the following step is also included: Analyzing the calling trend of the hotspot interface data according to a prediction model based on a time series; If it is predicted that the call volume will continue to increase in the future, the hotspot interface data is confirmed as a potential performance bottleneck; The step of optimizing the hotspot interfaces with high call times according to the hotspot interface data specifically includes the following steps: Prioritize optimization of hotspot interface data identified as potential performance bottlenecks.

6. The hotspot interface information optimization method according to claim 1, characterized in that: The step of optimizing the hotspot interfaces with high call times according to the hotspot interface data specifically includes the following steps: Analyze the identification results and call paths of the hotspot interface data to generate an interface optimization strategy; The hotspot interface data is optimized according to the interface optimization strategy.

7. A hotspot interface information optimization device, characterized in that: include: The operation log acquisition module is used to obtain the operation track log according to the pre-set log embedding points; A preprocessing module, used for performing a preprocessing operation on the operation trace log to obtain a preprocessing trace log; An operation log storage module, used for dispersively storing the pre-processing trace log on a plurality of nodes of a distributed storage system according to the data volume of the pre-processing trace log; A request acquisition module, used to receive a log call request sent by a user terminal, wherein the log call request includes target system identification information and a call time interval; An operation log extraction module, used to call a target distributed storage system corresponding to the system identification information, and extract the stored pre-processing trace log in the target distributed storage system according to the calling time interval; Hotspot interface statistics module, used to count the hotspot interface data of the seat order issuer based on the extracted pre-processing trajectory log; The hotspot interface optimization module is used to optimize the hotspot interface with a high number of calls according to the hotspot interface data.

8. The hotspot interface information optimization device according to claim 7, characterized in that: The hotspot interface optimization module includes: The hotspot interface cache submodule is used to cache the hotspot interface data into a log-type Key-Value database; The hotspot interface acquisition submodule is used to directly acquire the hotspot interface data from the log-type Key-Value database when the hotspot interface data needs to be loaded.

9. A computer device comprising a memory and a processor, characterized in that: The memory stores computer-readable instructions, and the processor implements the steps of the hotspot interface information optimization method according to any one of claims 1 to 6 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the hotspot interface information optimization method according to any one of claims 1 to 6 are implemented.

Images