Business system optimization method and device, computer equipment and storage medium

Abstract

The invention relates to a business system optimization method and device, computer equipment and a storage medium, and the method comprises the following steps: analyzing key indexes in link operation data, and generating a resource management strategy; constructing a resource allocation mechanism according to the resource management strategy for monitoring to obtain resource state information; executing a preset hierarchical change strategy according to the resource state information to obtain change effect information; index feature extraction is carried out on the change effect information, anomaly detection is carried out based on a pre-trained anomaly detection model according to extracted index fluctuation features, and when anomaly is detected, a preset fault processing step is executed to obtain fault processing information; and generating self-healing strategy optimization information according to the fault processing information to optimize the service system. The method and the device can be applied to application scenes of a financial service system and a digital medical system, and the financial service system and the medical service system can be effectively optimized, so that the stability of the service system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, which can be applied to the fields of finance and medicine, and in particular relates to a business system optimization method and device, a computer device and a storage medium. BACKGROUND

[0002] In today's digital era, the fields of financial technology and digital medicine are facing major challenges in ensuring system stability and high availability. With the rapid development of information technology, important information systems of banks and insurance institutions and digital medical systems carry a large amount of core business functions, and their stable operation is crucial for business continuity and user experience.

[0003] In the field of financial technology, the core business systems of insurance companies' claim settlement systems and banks' online banking systems need to be stable at all times to handle massive transaction requests and customer data. For example, a bank's online banking system needs to handle millions of transfer transactions, account inquiries and other operations every day. These operations involve user financial security and privacy protection, and once the system fails or is unstable, it will not only cause transaction failure and financial loss, but also may cause a customer trust crisis, causing serious damage to the bank's reputation. At the same time, these systems usually interact with multiple external agencies (such as payment clearing organizations, credit evaluation agencies, etc.), with long chain links and numerous associated parties, increasing the complexity and management difficulty of the system. In addition, the field of digital medicine also faces similar challenges. For example, a hospital's information system, which includes patient registration, medical record management, and examination result query systems, plays a key role in the daily operation of the hospital and the patient experience. For example, a hospital's registration system needs to handle a large number of registration requests during peak hours, and if the system fails, it will prevent patients from registering and seeing a doctor in time, seriously affecting the efficiency of the hospital's medical services and patient satisfaction. At the same time, digital medical systems involve sensitive patient information (such as medical records and test results), and have very high requirements for data security and privacy protection. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a business system optimization method, device, computer device and storage medium to solve the problem of insufficient stability of the business system during operation.

[0005] In a first aspect, the embodiments of the present application provide a business system optimization method, which adopts the technical solution as follows:

[0006] Obtain link operation data of a core business link, and analyze key indicators in the link operation data to generate a resource management strategy;

[0007] construct a resource allocation mechanism according to the resource management strategy, and monitor the resource allocation mechanism to obtain resource state information;

[0008] execute a preset hierarchical change strategy according to the resource state information to obtain change effect information;

[0009] extract an index fluctuation feature from the change effect information, perform anomaly detection based on a pre-trained anomaly detection model according to the index fluctuation feature, execute a preset fault handling step when it is detected that there is an anomaly, and obtain fault handling information;

[0010] generate self-healing strategy optimization information according to the fault handling information, and optimize a business system according to the self-healing strategy optimization information.

[0011] In a second aspect, an embodiment of the present application further provides a business system optimization apparatus, which adopts the technical scheme as follows:

[0012] an index analysis module, configured to obtain link operation data of a core business link, analyze key indexes in the link operation data, and generate a resource management strategy;

[0013] a resource monitoring module, configured to construct a resource allocation mechanism according to the resource management strategy, and monitor the resource allocation mechanism to obtain resource state information;

[0014] a strategy execution module, configured to execute a preset hierarchical change strategy according to the resource state information to obtain change effect information;

[0015] a fault handling module, configured to extract an index fluctuation feature from the change effect information, perform anomaly detection based on a pre-trained anomaly detection model according to the index fluctuation feature, execute a preset fault handling step when it is detected that there is an anomaly, and obtain fault handling information;

[0016] a system optimization module, configured to generate self-healing strategy optimization information according to the fault handling information, and optimize a business system according to the self-healing strategy optimization information.

[0017] In a third aspect, an embodiment of the present application further provides a computer device, which adopts the technical scheme as follows:

[0018] A computer device includes a memory and a processor, the memory stores computer readable instructions, and the processor executes the computer readable instructions to realize the steps of the business system optimization method according to any one of the above.

[0019] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, which adopts the technical scheme as follows:

[0020] A computer readable storage medium having stored thereon computer readable instructions which, when executed by a processor, implement the steps of the business system optimization method of any one of the above.

[0021] Compared with the prior art, the embodiment of the application has the following beneficial effects: the embodiment obtains link operation data of a core service link, analyzes key indicators in the link operation data, and generates a resource management strategy; a resource allocation mechanism is constructed according to the resource management strategy, and the resource allocation mechanism is monitored to obtain resource state information; a preset hierarchical change strategy is executed according to the resource state information to obtain change effect information; index fluctuation characteristics are extracted from the change effect information to obtain the index fluctuation characteristics, and an abnormality is detected based on a pre-trained abnormality detection model according to the index fluctuation characteristics; when an abnormality is detected, a preset fault handling step is executed to obtain fault handling information; self-healing strategy optimization information is generated according to the fault handling information, and a business system is optimized according to the self-healing strategy optimization information. Thus, automatic risk detection and optimization of the business system are effectively realized to improve the real-time stability of the business system during operation. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the schemes in the application, the drawings needed in the description of the embodiments of the application will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0023] Figure 1 is an exemplary system architecture diagram to which the application can be applied;

[0024] Figure 2 is a flowchart of an embodiment of the business system optimization method according to the application;

[0025] Figure 3 is a structural schematic diagram of an embodiment of the business system optimization device according to the application;

[0026] Figure 4 is a structural schematic diagram of an embodiment of the computer device according to the application. DETAILED DESCRIPTION

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terms used in the specification are intended to describe the particular embodiments and are not intended to limit the application; the terms "include" and "have" and their any variations used in the specification and the claims and the above description of drawings are intended to cover the non-exclusive inclusion; the terms "first", "second" and the like used in the specification and the claims and the above description of drawings are intended to distinguish different objects, not to describe a particular order.

[0028] Reference herein to "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive or alternative embodiments. It is expressly understood that the embodiments described herein are combinable with each other.

[0029] In order to make the person skilled in the art better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings below.

[0030] As shown in Figure 1 The system architecture 100 can include a terminal device 101, a network 102 and a server 103, and the terminal device 101 can be a notebook 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 can include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0031] The user 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 can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0032] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing, in addition to the notebook computer 1011, the tablet computer 1012 or the mobile phone 1013, the terminal device 101 can also be an electronic book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer and a desktop computer, etc.

[0033] The server 103 can be a server providing various services, for example, a background server providing support for a page displayed on the terminal device 101.

[0034] It should be noted that the business system optimization method provided in the embodiments of the present application is generally executed by a server / terminal device, and accordingly, the business system optimization apparatus is generally arranged in a server / terminal device.

[0035] It should be understood that Figure 1 The number of terminal devices, networks and servers in

[0036] With reference to Figure 2 , a flow chart of one embodiment of the method for optimizing a business system according to the present application is shown. The method for optimizing a business system includes the following steps:

[0037] In step S10, link operation data of a core business link is acquired, and key indicators in the link operation data are analyzed to generate a resource management strategy.

[0038] In the present embodiment, the core business link refers to a link composed of multiple business links in a business system, and the link operation data refers to data generated when each business link in the link performs business operation. The resource management strategy is a strategy for managing data resources of the business links in the link. In the field of financial technology, the business system can be an online banking system, and the core business link includes user login, account query, transfer transaction, etc. The link operation data includes the number of login requests at the same time, the response time of the transfer transaction, the number of account queries per second, etc. The key indicator analysis can be to find that the response time of the transfer transaction link is too long during the peak period (such as the salary payment day). According to the analysis result, the generated resource management strategy can be to increase the server resource allocation to the transfer transaction processing module, or to optimize the database query method to improve the transaction processing speed.

[0039] In the field of digital medical treatment, the business system can be a hospital information system, and the core business links include patient registration, medical record query, examination and test result uploading, etc. The link operation data includes the number of registered patients per hour of the registration system, the average response time of medical record query, etc. If it is found that the response time of medical record query is too long, the key indicator analysis can be that the medical record data storage structure is unreasonable, and the generated resource management strategy can be to re-index the medical record storage database, or to increase the memory resources of the query server to speed up the medical record query speed.

[0040] In step S20, a resource allocation mechanism is constructed according to the resource management strategy, and the resource allocation mechanism is monitored to obtain resource state information.

[0041] In this embodiment, the resource allocation mechanism can be a resource pool, and the resource state information refers to the information obtained by monitoring the resource state (such as resource content, change, etc.) in the resource pool. In the field of financial technology, the resource management strategy of the securities trading system can be to allocate computing resources according to different trading varieties (such as stocks, bonds, futures) and trading time periods (such as opening period, closing period). The constructed resource allocation mechanism dynamically adjusts the CPU, memory, etc. of the server according to the trading variety and period. In the field of digital medical treatment, the resource management strategy of the remote medical diagnosis platform can be to allocate network bandwidth and storage resources according to the emergency level of the patient's condition and the complexity of the diagnosis task. The constructed resource allocation mechanism will prioritize the diagnosis video transmission and data storage of critical patients.

[0042] In step S30, a preset hierarchical change strategy is executed according to the resource state information to obtain change effect information.

[0043] In this embodiment, in the mobile payment system in the field of financial technology, when it is monitored that the memory resources of the server are tight, the preset hierarchical change strategy is executed. For example, first limit the memory of the non-key background log recording process, and if the memory pressure is still not alleviated, further degrade some secondary payment recommendation services (reduce the complexity and quantity of recommended content). The change effect information can be obtained by observing the performance indicators of the system after the hierarchical change is executed, such as whether the memory usage rate decreases, whether the payment transaction processing delay improves, etc.

[0044] In the medical image storage system in the field of digital medical treatment, if it is monitored that the disk space of the storage server is insufficient, the hierarchical change strategy is executed. First, the image data with longer storage time and lower access frequency is stored in a compressed manner, and if the disk space is still tight, part of the image data is migrated to a low-cost archival storage device. The change effect information can be evaluated by checking whether the disk space is released enough capacity, and whether the reading speed of the image data is within an acceptable range.

[0045] In step S40, the change effect information is subjected to index feature extraction to obtain index fluctuation features, and based on a pre-trained anomaly detection model, anomaly detection is performed according to the index fluctuation features. When it is detected that there is an anomaly, a preset fault handling step is performed to obtain fault handling information.

[0046] In this embodiment, in a foreign exchange transaction system in the field of financial technology, the changed transaction processing delay, transaction success rate and other indexes are subjected to feature extraction. For example, time series data features of the transaction processing delay are extracted, including mean value, standard deviation, fluctuation frequency, etc. Then, a pre-trained anomaly detection model (such as an isolated forest algorithm model based on machine learning) is used to analyze these features. If it is detected that the transaction processing delay has an abnormal fluctuation, such as a sudden large increase, a preset fault handling step is performed, which can be restarting a service process of the transaction processing server or switching to a backup transaction processing node. The fault handling information includes the time of fault handling, the handling method, and whether the system state is restored to normal after handling, etc.

[0047] In a remote medical monitoring system in the field of digital medicine, the changed patient physiological data transmission packet loss rate, data update frequency and other indexes are subjected to feature extraction. For example, time series features of the packet loss rate are extracted, and whether it has periodic fluctuation is analyzed. A pre-trained anomaly detection model (such as a recurrent neural network model based on deep learning) is used for detection. If it is found that the packet loss rate abnormally increases, a preset fault handling step is performed, such as checking the network connection status, reconfiguring the data transmission protocol parameters, etc. The fault handling information records the network state at the time of fault occurrence, the data transmission recovery situation after handling, etc.

[0048] In step S50, self-healing strategy optimization information is generated according to the fault handling information, and the business system is optimized according to the self-healing strategy optimization information.

[0049] In this embodiment, in a cloud financial service system in the field of financial technology, self-healing strategy optimization information is generated according to multiple fault handling information (such as the number of server restarts, the handling time and recovery situation corresponding to different fault types). For example, if it is found that a certain software module frequently causes faults leading to server restarts, the self-healing strategy optimization information can suggest that the software module be subjected to code optimization or upgrade. According to this optimization information, the business system is optimized, such as repairing software vulnerabilities and improving algorithms, to improve the stability and reliability of the system.

[0050] In a medical Internet of Things system in the field of digital medicine, self-healing strategy optimization information is generated according to fault handling information (such as the fault frequency of a medical device data acquisition module, the type of communication fault, etc.). For example, if it is found that the data acquisition module of a certain medical device is prone to electromagnetic interference leading to data errors, the self-healing strategy optimization information can suggest improvements to the anti-interference design of the data acquisition module. According to this optimization information, the medical device data acquisition part in the business system is optimized, such as increasing shielding measures or using more anti-interference data transmission methods, thereby improving the performance of the entire system in complex medical environments.

[0051] The embodiment obtains link operation data of a core business link, analyzes key indicators in the link operation data, and generates a resource management strategy; constructs a resource allocation mechanism according to the resource management strategy, and monitors the resource allocation mechanism to obtain resource state information; executes a preset hierarchical change strategy according to the resource state information to obtain change effect information; extracts indicator characteristics from the change effect information to obtain indicator fluctuation characteristics, and performs abnormality detection based on a pre-trained abnormality detection model according to the indicator fluctuation characteristics; when an abnormality is detected, a preset fault handling step is executed to obtain fault handling information; self-healing strategy optimization information is generated according to the fault handling information, and the business system is optimized according to the self-healing strategy optimization information. Thus, automatic risk detection and optimization of the business system are effectively realized to improve the real-time stability of the business system during operation.

[0052] The method of the embodiment can be applied to business system optimization in a digital medical system. The remote medical diagnosis platform needs to support core businesses such as patient registration, online diagnosis by doctors, and remote transmission and interpretation of examination results. By collecting operation data such as patient registration request response time, smoothness of doctor diagnosis video call, and examination result transmission delay, if it is found that the doctor diagnosis video call often appears to be lagging during peak hours, after key indicator analysis, a resource management strategy is generated to allocate more network bandwidth resources to the video call module and optimize the video compression algorithm. According to the resource management strategy, a resource allocation mechanism is constructed to prioritize network bandwidth for doctor diagnosis video calls. At the same time, a monitoring system is established to monitor network bandwidth usage in real time. When the resource status information shows that the network bandwidth is tight, the preset hierarchical change strategy is executed. First, reduce the priority of non-critical businesses (such as platform advertisement push) to reduce their network resource occupation. After the change, the change effect is evaluated by monitoring indicators such as video call lag rate and doctor diagnosis efficiency, and then the fluctuation characteristics of indicators such as video call lag rate and doctor diagnosis efficiency are extracted, including their fluctuation frequency, amplitude, etc. Using a pre-trained anomaly detection model, it is found that the fluctuation characteristics of the video call lag rate are abnormal, for example, the abnormality can be due to network device failure or video server overload. Execute the preset fault handling steps, check the network device status, find that a network switch has failed, replace it in time, and perform load balancing adjustment on the video server. After fault handling, the video call returns to normal and the system runs stably. According to the self-healing strategy optimization information generated from multiple fault handling information, upgrade the network devices of the remote medical diagnosis platform, and establish a regular maintenance mechanism for the network devices. At the same time, optimize the load balancing algorithm of the video server to improve the adaptive ability of the system. Through these optimization measures, the network stability and video call quality of the remote medical diagnosis platform have been significantly improved.

[0053] The method of the embodiment can be applied to business system optimization in a financial business system. In the process of securities trading, the core business links such as order placement, matching transaction, and transaction confirmation are involved. The monitoring tool is used to obtain the response time of the order placement request, the processing delay of the matching transaction, the number of transaction confirmations per second, and other operation data. If the analysis finds that the processing delay of the matching transaction link significantly increases when the market fluctuates greatly, a resource management strategy is generated to prioritize the computing resources of the matching transaction server and optimize its database query method. According to the resource management strategy, a resource allocation mechanism is constructed to allocate more CPU and memory resources to the matching transaction server, and a real-time monitoring system is established to monitor its resource usage. When the resource status information shows that the system resources are tight, the preset hierarchical change strategy is executed. First, the non-critical market data push service is downgraded to reduce the push frequency. After the change, the order success rate, transaction processing delay, and other indicators are monitored to evaluate the change effect. The fluctuation characteristics of the transaction processing delay, order success rate, and other indicators are extracted, including their average value, standard deviation, etc. Using a pre-trained machine learning anomaly detection model, the abnormal fluctuation characteristics of the transaction processing delay are found, indicating that there may be network delay or server failure. The preset fault handling steps are executed to check the network connection status and restart the related server processes. After fault handling, the transaction processing delay returns to normal, and the system runs stably. According to the self-healing strategy optimization information generated from multiple fault handling information, the network architecture of the securities trading system is optimized, the network bandwidth is increased, and the resource allocation algorithm of the server is optimized to improve the system's adaptive ability to network fluctuations. Through these optimization measures, the overall performance of the securities trading system is significantly improved, the transaction processing delay is further reduced, and the stability and reliability of the system are greatly improved.

[0054] In some optional implementations of the embodiment, the link operation data includes link traffic data and resource usage indicators. The link operation data of the core business link is obtained, and the key indicators in the link operation data are analyzed to generate a resource management strategy, including the following steps:

[0055] The core business link is monitored in real time to obtain the link traffic data and the resource usage indicators.

[0056] In this embodiment, the core business link is monitored in real time without interruption to accurately obtain the traffic data of the link, i.e., the amount of data passing through the link per unit time, including the transmission of various business requests and data packets. At the same time, resource usage indicators are collected, covering CPU and memory usage of servers, read and write operation frequency of storage devices, and port traffic of network devices, among other multidimensional data.

[0057] The link traffic data and the resource usage indicators are analyzed to obtain the processor utilization and the network bandwidth consumption.

[0058] In this embodiment, after obtaining the link traffic data and the resource usage indicators, a data analysis algorithm is used to conduct in-depth analysis. The processor utilization, i.e., the proportion of time that the processor is used to process business tasks in a certain time period, reflects the computing load of the system. At the same time, the network bandwidth consumption, i.e., the total amount of network bandwidth occupied by the transmission of business data, reflects the intensity of the use of network resources.

[0059] According to the processor utilization and the network bandwidth consumption, resource quota information is calculated;

[0060] In this embodiment, a mathematical model is established to calculate the resource quota information according to the processor utilization and the network bandwidth consumption. By comprehensively considering the real-time demand of the business, the carrying capacity of the system, and the sensitivity of different businesses to resources, etc., the specific values of the processor time slice, the network bandwidth share, etc. that can be reasonably allocated to each business link or system component are determined, which are the resource quota information.

[0061] According to the resource quota information, the resource management strategy is generated.

[0062] In this embodiment, based on the calculated resource quota information, a corresponding resource management strategy is formulated. The strategy clearly defines the allocation rules, priority order, and dynamic adjustment mechanism of resources under different business load conditions. For example, during the peak period of business, according to the preset priority, the resource supply for critical businesses is prioritized, and non-critical businesses are moderately limited; during the trough of business, idle resources are reasonably recovered, the overall utilization of resources is improved, and the stable and efficient operation of core business links is ensured.

[0063] This embodiment obtains the link traffic data and the resource usage indicators by real-time monitoring of the core business link; obtains the processor utilization and the network bandwidth consumption by data analysis of the link traffic data and the resource usage indicators; calculates the resource quota information according to the processor utilization and the network bandwidth consumption; generates the resource management strategy according to the resource quota information. Thus, the resource management strategy corresponding to the core business link is effectively obtained to facilitate the construction of the resource allocation mechanism.

[0064] In some optional implementation manners of this embodiment, according to the resource management strategy, a resource allocation mechanism is constructed, and the resource allocation mechanism is monitored to obtain resource state information, including the following steps:

[0065] According to the resource management strategy, a resource pool is constructed;

[0066] In the embodiment, according to a predetermined resource management policy, various types of resources (such as computing resources, storage resources, network resources, etc.) are integrated to construct a resource pool. The resource pool centrally manages different types of resources according to the requirements of the resource management policy, so as to more flexibly allocate and adjust the resources.

[0067] The usage rate of the resource pool is monitored to obtain a resource usage rate.

[0068] In the embodiment, the usage of the constructed resource pool is monitored in real time to obtain the resource usage rate. The resource usage rate is used to reflect the proportion of the allocated and used resources in the total resources in the current resource pool, so as to intuitively reflect the load state of the resource pool.

[0069] It is determined whether the resource usage rate is greater than or equal to a preset usage rate threshold.

[0070] In the embodiment, the resource usage rate and the preset usage rate threshold are compared in value to determine the size relationship between the resource usage rate and the preset usage rate threshold. In the comparison, the resource usage rate and the preset usage rate threshold are in a unified numerical format, such as a percentage numerical value or a percentage numerical value.

[0071] If the resource usage rate is greater than or equal to the preset usage rate threshold, resources are dynamically allocated from a preset resource pool to the resource pool, and state data of the resource pool is used as the resource state information.

[0072] In the embodiment, when the resource usage rate is greater than or equal to the preset usage rate threshold, it indicates that the resource pool is in a resource shortage state close to an "overload" state. At this time, resources are dynamically allocated from a preset resource pool (equivalent to a backup resource reserve) to the current resource pool to alleviate the resource shortage situation, and the state data (including the resource usage rate, the allocated resource amount, etc.) of the current resource pool is used as the resource state information for subsequent monitoring and decision basis of the resource allocation mechanism.

[0073] If the resource usage rate is less than the preset usage rate threshold, the resource usage rate is obtained again after a preset time interval to determine whether the resource usage rate is greater than or equal to the preset usage rate threshold, or the monitoring of the resource pool is stopped.

[0074] In the embodiment, the preset time interval is a preset time value used as a time reference for the system to perform the resource usage rate determination step. In the embodiment, the preset time interval is initially set to 3s, which can be set and adjusted according to actual conditions.

[0075] The embodiment constructs a resource pool according to the resource management strategy; monitors the usage rate of the resource pool to obtain a resource usage rate; determines whether the resource usage rate is greater than or equal to a preset usage rate threshold; if the resource usage rate is greater than or equal to the preset usage rate threshold, dynamically allocates resources from a preset resource pool to the resource pool, and takes state data of the resource pool as the resource state information; if the resource usage rate is less than the preset usage rate threshold, again obtains the resource usage rate after a preset time interval to determine, until the resource usage rate is greater than or equal to the preset usage rate threshold, or stops monitoring the resource pool. Thus, the construction and monitoring of the resource pool are effectively realized to obtain corresponding resource state information, facilitating the subsequent execution of the preset hierarchical change strategy.

[0076] In some optional implementation manners of the embodiment, the constructing the resource pool according to the resource management strategy comprises the following steps:

[0077] Determining a resource allocation priority rule according to the resource management strategy;

[0078] In the embodiment, the resource management strategy explicitly determines the priority order between different business links, tasks or system components in resource allocation. For example, some business operations with extremely high real-time requirements (such as the payment confirmation link of online transactions) are given a higher priority, and some background statistical analysis tasks can be set to a lower priority.

[0079] Obtaining priority requirement information of the core business link, and constructing the resource pool according to the priority requirement information and the resource allocation priority rule.

[0080] In the embodiment, the priority requirement information of the core business link, i.e. how many resources are needed and how high the resource guarantee is, is obtained. Then, the priority requirement information is combined with the previously determined resource allocation priority rule to construct the resource pool. In the construction process, the resources in the resource pool are allocated according to the priority rule, the needs of high-priority businesses are preferentially met, and sufficient resources are allocated to them, and then low-priority businesses are considered in turn.

[0081] The embodiment determines a resource allocation priority rule according to the resource management strategy, obtains priority requirement information of the core business link, and constructs the resource pool according to the priority requirement information and the resource allocation priority rule. Thus, the resource pool is effectively constructed according to the priority of the core business link and the priority of resource allocation, to facilitate the subsequent execution of the preset hierarchical change strategy.

[0082] In some optional implementations of the embodiment, the obtaining the change effect information according to the preset hierarchical change strategy based on the resource state information comprises the following steps:

[0083] The performance indicators of the resource state information are obtained, and a change risk assessment is performed on the performance indicators based on a preset change control algorithm to obtain change risk information.

[0084] In the embodiment, the performance indicators include utilization rate, response time, throughput and other key data of the resource to quantify the current state of the resource. The preset change control algorithm can analyze the risks that may be caused by the change, such as resource overload and performance degradation, according to historical data and business rules, and divide the risks into high, medium and low levels according to the risk degree. In specific implementation, the preset change control algorithm can use a pre-trained risk assessment model, which can use a decision tree model trained based on historical performance indicator data or sample performance indicator data.

[0085] A preset change strategy table is obtained, and the corresponding preset hierarchical change strategy is found in the preset change strategy table according to the change risk information.

[0086] In the embodiment, the preset change strategy table is a set of rules defined in advance, which specifies the change strategies to be taken for different risk levels. According to the change risk information obtained in the previous step, the corresponding preset hierarchical change strategy is found in the preset change strategy table. For example, if the evaluation result shows high risk, the strategy table can recommend a more conservative change strategy, such as only making small-scale changes to non-critical services; if the risk is low, more extensive changes can be implemented.

[0087] The test is performed based on the blue-green deployment according to the preset hierarchical change strategy to obtain the change effect information.

[0088] In the embodiment, the blue-green deployment is a software deployment strategy that creates two identical environments (blue environment and green environment), applies changes to one of the environments (such as the green environment) for testing, and the other environment (the blue environment) continues to provide stable services. According to the preset hierarchical change strategy, the change is performed in the test environment, and the change effect information is collected.

[0089] The embodiment obtains the performance indicators of the resource state information, performs change risk assessment on the performance indicators based on a preset change control algorithm, obtains change risk information, obtains a preset change strategy table, finds the corresponding preset hierarchical change strategy in the preset change strategy table according to the change risk information, and performs test based on blue-green deployment according to the preset hierarchical change strategy to obtain the change effect information. Thus, the identification and risk assessment of the resource state change condition are effectively realized, and the appropriate preset hierarchical change strategy is selected based on the identification and assessment results to obtain the corresponding change effect information, facilitating subsequent abnormality detection processing.

[0090] In some optional implementation manners of the embodiment, the change effect information is subjected to index feature extraction to obtain index fluctuation features, and an abnormality detection model is pre-trained to perform abnormality detection on the index fluctuation features. When it is detected that there is an abnormality, a preset fault processing step is performed to obtain fault processing information, including the following steps:

[0091] The business index change information in the change effect information is obtained, and the business index change information is subjected to feature extraction to obtain the index fluctuation features.

[0092] In the embodiment, after the business system is changed, the business index change information in the change effect information is collected. The business index can include key performance indicators such as transaction success rate, response time, and user activity. The business index change information is subjected to feature extraction to obtain index fluctuation features. The feature extraction process can be performed by calculating statistical features such as mean, variance, fluctuation frequency, and trend of the index to quantify the change mode of the index.

[0093] The index fluctuation features are input into the abnormality detection model to perform historical baseline deviation comparison to obtain an abnormality detection result.

[0094] In the embodiment, the extracted index fluctuation features are input into the abnormality detection model. The model compares the current index fluctuation with historical baseline data to determine whether the current index fluctuation is abnormal. The historical baseline is a reference standard established based on the business index data of the system in a normal operation state. The model evaluates the deviation between the current index fluctuation features and the historical baseline. If the deviation exceeds a preset threshold, it is considered that there may be an abnormality.

[0095] It is determined whether the abnormality detection result is an abnormality.

[0096] In the embodiment, it is determined whether there is an abnormality according to the output of the abnormality detection model. If the model determines that the current index fluctuation is within a normal range, it is considered that there is no abnormality. Otherwise, if the model detects that the index fluctuation deviates from the historical baseline seriously, it is determined that there is an abnormality.

[0097] If the abnormality detection result is that there is an abnormality, abnormality positioning analysis information is generated, and the preset fault handling step is executed according to the abnormality positioning analysis information to obtain the fault handling information.

[0098] In this embodiment, if an abnormality is detected, abnormality positioning analysis information is generated. This information is used to locate the root cause of the abnormality, which can include the link where the abnormality occurs, the related resource state, the affected business scope, and the like. According to the abnormality positioning analysis information, a preset fault handling step is executed. The fault handling step can include operations such as restarting related services, rolling back changes, adjusting resource configurations, and the like, to solve the abnormality problem, and recording fault handling information, which includes handling measures, handling time, handling effect, and the like.

[0099] If the abnormality detection result is that there is no abnormality, the abnormality detection on the obtained index fluctuation feature is continued until the abnormality detection result is that there is an abnormality, or the abnormality detection operation is stopped.

[0100] In this embodiment, if no abnormality is detected, the abnormality detection on the obtained index fluctuation feature is continued. This process continues until an abnormality is detected or the abnormality detection operation is stopped according to business requirements. The purpose of continuous monitoring is to timely discover subsequent abnormal problems that can occur, and to ensure stable operation of the system.

[0101] In this embodiment, the business index change information in the change effect information is obtained, and feature extraction is performed on the business index change information to obtain the index fluctuation feature. The index fluctuation feature is input into the abnormality detection model for historical baseline deviation comparison to obtain an abnormality detection result. It is judged whether the abnormality detection result is that there is an abnormality. If the abnormality detection result is that there is an abnormality, abnormality positioning analysis information is generated, and the preset fault handling step is executed according to the abnormality positioning analysis information to obtain the fault handling information. If the abnormality detection result is that there is no abnormality, the abnormality detection on the obtained index fluctuation feature is continued until the abnormality detection result is that there is an abnormality, or the abnormality detection operation is stopped. Thus, the abnormality detection and fault handling on the change effect information are realized to obtain corresponding fault handling information, thereby providing reliable fault handling experience basis for subsequent generation of self-healing strategy optimization information.

[0102] In some optional implementation manners of this embodiment, the generation of self-healing strategy optimization information according to the fault handling information and the business system optimization according to the self-healing strategy optimization information include the following steps:

[0103] The processing record data of the fault handling information is obtained, and a preset self-healing strategy is updated according to the processing record data to obtain an updated self-healing strategy.

[0104] In this embodiment, the processing record data is the record data of fault processing in the fault processing information. The processing record data records key information such as the time, location, cause, processing measures and processing effect of the fault occurrence. The preset self-healing strategy is updated based on the processing record data to form an updated self-healing strategy. For example, if it is found that a certain type of fault often occurs under certain conditions, and the existing self-healing strategy is not effective in this case, then when updating the self-healing strategy, processing rules for this case can be added or existing rules can be optimized to improve the pertinence and effectiveness of the self-healing strategy.

[0105] According to the updated self-healing strategy, feedback learning optimization is performed on the anomaly detection model to obtain self-healing strategy optimization information.

[0106] In this embodiment, feedback learning is a machine learning method, and the model can automatically update its parameters and rules according to new data or strategies. In this process, the anomaly detection model will relearn and adjust the judgment criteria, detection methods and corresponding processing procedures for anomalies according to the new knowledge provided by the updated self-healing strategy. For example, if the updated self-healing strategy adds processing rules for a new type of fault, the anomaly detection model will learn how to identify early signs of this new fault type and adjust its detection algorithm to more accurately detect this type of fault. After feedback learning optimization, self-healing strategy optimization information is obtained, which includes the update content of model parameters, the adjustment details of detection rules, etc., reflecting the specific optimization of the self-healing strategy at the model level.

[0107] According to the self-healing strategy optimization information, the parameters of the business system are adjusted for optimization.

[0108] In this embodiment, according to the guidance provided in the self-healing strategy optimization information, the relevant parameters of the business system are adjusted to achieve optimization of the business system. Business system parameters can involve resource allocation, task scheduling, threshold setting, etc. For example, if the self-healing strategy optimization information indicates that increasing the allocation of a certain type of resource can effectively prevent the occurrence of faults in a certain business scenario, then the resource allocation parameters of the business system can be adjusted accordingly to allocate more resources for this business scenario. In this way, the achievements of self-healing strategy optimization are transformed into actual business system configuration adjustments, thereby improving the overall performance and stability of the business system.

[0109] The embodiment obtains the processing record data of the fault processing information, updates a preset self-healing strategy according to the processing record data, obtains an updated self-healing strategy, performs feedback learning optimization on the abnormality detection model according to the updated self-healing strategy, and obtains self-healing strategy optimization information, and adjusts the business system parameters for optimization according to the self-healing strategy optimization information. Thus, the business system is effectively optimized to improve the stability and reliability of the business system.

[0110] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by computer readable instructions instructing related hardware, and the computer readable instructions can be stored in a computer readable storage medium. When the program is executed, the processes of the above-mentioned embodiments can be included. The 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).

[0111] It should be understood that although each step in the flowchart of the accompanying drawings is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other sequences. Moreover, at least part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence is not necessarily sequential, but can be alternately executed with other steps or sub-steps or stages of other steps.

[0112] Further referring to Figure 3 , as an implementation of the method shown in Figure 1 , the present application provides an embodiment of a business system optimization device, which corresponds to the method embodiment shown in Figure 1 , and the device can be applied to various electronic devices.

[0113] As shown in Figure 3 , the business system optimization device 600 of the embodiment includes an index analysis module 601, a resource monitoring module 602, a policy execution module 603, a fault processing module 604, and a system optimization module 605. Among them:

[0114] The index analysis module 601 is configured to obtain link operation data of a core business link, analyze key indicators in the link operation data, and generate a resource management strategy.

[0115] The resource monitoring module 602 is configured to construct a resource allocation mechanism according to the resource management policy, and monitor the resource allocation mechanism to obtain resource state information.

[0116] The policy execution module 603 is configured to execute a preset hierarchical change policy according to the resource state information to obtain change effect information.

[0117] The fault processing module 604 is configured to perform index feature extraction on the change effect information to obtain index fluctuation features, and perform abnormality detection based on a pre-trained abnormality detection model according to the index fluctuation features. When it is detected that there is an abnormality, a preset fault processing step is executed to obtain fault processing information.

[0118] The system optimization module 605 is configured to generate self-healing policy optimization information according to the fault processing information, and perform business system optimization according to the self-healing policy optimization information.

[0119] According to the business system optimization device, link operation data of a core business link can be obtained, key indexes in the link operation data can be analyzed, and a resource management policy can be generated. A resource allocation mechanism can be constructed according to the resource management policy, and the resource allocation mechanism can be monitored to obtain resource state information. A preset hierarchical change policy can be executed according to the resource state information to obtain change effect information. Index feature extraction can be performed on the change effect information to obtain index fluctuation features, and abnormality detection can be performed based on a pre-trained abnormality detection model according to the index fluctuation features. When it is detected that there is an abnormality, a preset fault processing step is executed to obtain fault processing information. Self-healing policy optimization information can be generated according to the fault processing information, and business system optimization can be performed according to the self-healing policy optimization information. Therefore, automatic risk detection and optimization of a business system can be effectively realized to improve real-time stability of the business system during operation.

[0120] In some optional implementation manners of the embodiment, the index analysis module 601 includes a link monitoring unit, a data analysis unit, a resource calculation unit, and a policy generation unit.

[0121] In some optional implementation manners of the embodiment, the index analysis module 601 includes a link monitoring unit, a data analysis unit, a resource calculation unit, and a policy generation unit.

[0122] The link monitoring unit is configured to monitor the core business link in real time to obtain the link traffic data and the resource usage index.

[0123] The data analysis unit is configured to perform data analysis on the link traffic data and the resource usage index to obtain processor utilization and network bandwidth consumption.

[0124] The resource calculation unit is configured to calculate resource quota information according to the processor utilization and the network bandwidth consumption.

[0125] The policy generation unit is configured to generate the resource management policy according to the resource quota information.

[0126] The index analysis module 601 is configured to effectively implement the acquisition of the resource management policy corresponding to the core service link, so as to facilitate the construction of the resource allocation mechanism.

[0127] In some optional implementation manners of the embodiment, the resource monitoring module 602 includes a resource pool construction unit, a resource pool monitoring unit, a usage rate judgment unit, a first resource processing unit, and a second resource processing unit. In this case:

[0128] The resource pool construction unit is configured to construct a resource pool according to the resource management policy.

[0129] The resource pool monitoring unit is configured to monitor the usage rate of the resource pool to obtain a resource usage rate.

[0130] The usage rate judgment unit is configured to judge whether the resource usage rate is greater than or equal to a preset usage rate threshold.

[0131] The first resource processing unit is configured to, if the resource usage rate is greater than or equal to the preset usage rate threshold, dynamically allocate resources from a preset resource pool to the resource pool, and take the state data of the resource pool as the resource state information.

[0132] The second resource processing unit is configured to, if the resource usage rate is less than the preset usage rate threshold, reacquire the resource usage rate for judgment after a preset time interval, until the resource usage rate is greater than or equal to the preset usage rate threshold, or stop monitoring the resource pool.

[0133] The resource monitoring module 602 includes a rule extraction unit and a task information extraction unit, so as to effectively implement the construction and monitoring of the resource pool to obtain the corresponding resource state information, and facilitate the execution of the preset hierarchical change policy.

[0134] In some optional implementation manners of the embodiment, the resource pool construction unit includes a rule determination subunit and a resource pool construction subunit. In this case:

[0135] The rule determination subunit is configured to determine a resource allocation priority rule according to the resource management policy.

[0136] The resource pool construction subunit is configured to acquire priority requirement information of the core service link, and construct the resource pool according to the priority requirement information and the resource allocation priority rule.

[0137] The resource pool construction unit is configured to include an information association subunit and a technology tree construction subunit, so that a reasonable resource pool is constructed according to the priority of the core service link and the priority of resource allocation, thereby facilitating subsequent execution of the preset hierarchical change strategy.

[0138] In some optional implementation manners of the embodiment, the strategy execution module 603 includes a risk assessment subunit, a strategy acquisition subunit, and a strategy test subunit. In this case:

[0139] The risk assessment subunit is configured to acquire a performance index of the resource state information, and perform change risk assessment on the performance index based on a preset change control algorithm to obtain change risk information.

[0140] The strategy acquisition subunit is configured to acquire a preset change strategy table, and find the corresponding preset hierarchical change strategy in the preset change strategy table according to the change risk information.

[0141] The strategy test subunit is configured to perform execution test based on blue-green deployment according to the preset hierarchical change strategy to obtain the change effect information.

[0142] The strategy execution module 603 is configured to include a risk assessment subunit, a strategy acquisition subunit, and a strategy test subunit, so that the resource state change condition is effectively identified and risk is assessed, and a suitable preset hierarchical change strategy is selected based on the identification and assessment results to obtain corresponding change effect information, thereby facilitating subsequent abnormality detection processing.

[0143] In some optional implementation manners of the embodiment, the fault processing module 604 includes a map information extraction subunit, a model processing subunit, a result judgment subunit, a first abnormality processing subunit, and a second abnormality processing subunit. In this case:

[0144] The map information extraction subunit is configured to acquire service index change information in the change effect information, and perform feature extraction on the service index change information to obtain index fluctuation features.

[0145] The model processing subunit is configured to input the index fluctuation features into the abnormality detection model to perform historical baseline deviation comparison, thereby obtaining an abnormality detection result.

[0146] The result judgment subunit is configured to judge whether the abnormality detection result is abnormal.

[0147] The first anomaly processing unit is configured to generate anomaly location analysis information if the anomaly detection result indicates the presence of an anomaly, and execute the preset fault processing steps based on the anomaly location analysis information to obtain the fault processing information;

[0148] The second anomaly handling unit is used to continuously perform anomaly detection on the acquired index fluctuation characteristics if the anomaly detection result is that there is no anomaly, until the anomaly detection result is that there is an anomaly, or to stop the anomaly detection operation.

[0149] This embodiment sets up a fault handling module 604, which includes a map information extraction unit, a model processing unit, a result judgment unit, a first anomaly handling unit, and a second anomaly handling unit, to detect and handle anomalies in change effect information, so as to obtain corresponding fault handling information and provide reliable fault handling experience for the generation of subsequent self-healing strategy optimization information.

[0150] In some optional implementations of this embodiment, the system optimization module 605 includes: a policy update unit, a policy optimization unit, and a system optimization unit. Wherein:

[0151] The strategy update unit is used to acquire the processing record data of the fault handling information, and update the preset self-healing strategy according to the processing record data to obtain the updated self-healing strategy.

[0152] The strategy optimization unit is used to perform feedback learning optimization on the anomaly detection model according to the updated self-healing strategy to obtain the self-healing strategy optimization information.

[0153] The system optimization unit is used to adjust the parameters of the business system according to the self-healing strategy optimization information for optimization.

[0154] This embodiment achieves effective optimization of the business system by setting up a system optimization module 605, which includes a region determination unit, a path rule generation unit, a path rule parsing unit, and a navigation operation execution unit, thereby improving the stability and reliability of the business system.

[0155] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0156] The computer device 7 comprises a memory 71, a processor 72, and a network interface 73 which are communicatively connected through a system bus. It should be noted that only the computer device 7 with components 71-73 is shown in the figure, but it should be understood that not all of the shown components are required to be implemented, and more or fewer components can be alternatively implemented. Among them, those skilled in the art can understand that the computer device herein is a device capable of automatically performing numerical calculation 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.

[0157] The computer device can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The computer device can interact with the user through a keyboard, a mouse, a remote controller, a touchpad, a voice control device, and the like.

[0158] The memory 71 comprises at least one type of readable storage medium, including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., 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, and the like. In some embodiments, the memory 71 can be an internal storage unit of the computer device 7, such as a hard disk or a memory of the computer device 7. In other embodiments, the memory 71 can also be an external storage device of the computer device 7, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like. Of course, the memory 71 can also include both the internal storage unit and the external storage device of the computer device 7. In the present embodiment, the memory 71 is generally used to store an operating system and various application software installed in the computer device 7, such as computer readable instructions of the business system optimization method, and the like. In addition, the memory 71 can also be used to temporarily store various data that have been output or will be output.

[0159] The processor 72 may, in some embodiments, be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 72 is generally used to control the overall operation of the computer device 7. In the present embodiment, the processor 72 is configured to execute computer-readable instructions stored in the memory 71 or to process data, such as computer-readable instructions for implementing the business system optimization method.

[0160] The network interface 73 may include a wireless network interface or a wired network interface, and is generally used to establish a communication connection between the computer device 7 and other electronic devices.

[0161] By using the above computer device, the present embodiment can obtain link operation data of a core business link, analyze key indicators in the link operation data, and generate a resource management strategy. A resource allocation mechanism is constructed according to the resource management strategy, and the resource allocation mechanism is monitored to obtain resource state information. A preset hierarchical change strategy is executed according to the resource state information to obtain change effect information. Index feature extraction is performed on the change effect information to obtain index fluctuation features, and an abnormality detection model is pre-trained to detect abnormalities based on the index fluctuation features. When an abnormality is detected, a preset fault handling step is executed to obtain fault handling information. Self-healing strategy optimization information is generated according to the fault handling information, and the business system is optimized according to the self-healing strategy optimization information. Thus, automatic risk detection and optimization of the business system are effectively realized to improve the real-time stability of the business system during operation.

[0162] The present application also provides another embodiment, i.e., a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the business system optimization method as described above.

[0163] The embodiment can obtain link operation data of a core service link, analyze key indicators in the link operation data, and generate a resource management strategy; construct a resource allocation mechanism according to the resource management strategy, monitor the resource allocation mechanism, and obtain resource state information; execute a preset hierarchical change strategy according to the resource state information, obtain change effect information; extract indicator characteristics from the change effect information, obtain indicator fluctuation characteristics, perform abnormality detection based on a pre-trained abnormality detection model according to the indicator fluctuation characteristics, execute a preset fault processing step when an abnormality is detected, and obtain fault processing information; generate self-healing strategy optimization information according to the fault processing information, and optimize a business system according to the self-healing strategy optimization information. Thus, automatic risk detection and optimization of the business system are effectively realized, so as to improve real-time stability of the business system during operation.

[0164] Through the description of the above-mentioned embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and a general hardware platform, of course, they can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, an optical disk), and includes a plurality of instructions for causing a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the methods described in the various embodiments of the present application.

[0165] Obviously, the above-described embodiments are only some of the embodiments of the present application, not all the embodiments, and the preferred embodiments of the present application are given in the drawings, but do not limit the patent scope of the present application. The present application can be implemented in many different forms, and conversely, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing specific embodiments, or make equivalent replacements to some technical features. Any equivalent structure made by using the contents of the specification and drawings, directly or indirectly applied to other related technical fields, is also within the scope of the patent protection of the present application.

[0166] The non-company software tools or components appearing in the embodiments of the present application are only examples for introduction, and do not represent actual use.

Claims

1. A business system optimization method characterized by, The method comprises the following steps: obtaining link operation data of a core service link, analyzing key indicators in the link operation data, and generating a resource management strategy; constructing a resource allocation mechanism according to the resource management strategy and monitoring the resource allocation mechanism to obtain resource state information; executing a preset hierarchical change strategy according to the resource state information to obtain change effect information; extracting index characteristics from the change effect information to obtain index fluctuation characteristics, performing abnormality detection based on a pre-trained abnormality detection model according to the index fluctuation characteristics, executing a preset fault handling step when an abnormality is detected, and obtaining fault handling information; generating self-healing strategy optimization information according to the fault handling information, and optimizing a business system according to the self-healing strategy optimization information.

2. The business system optimization method of claim 1, wherein, The link operation data includes link traffic data and resource usage indicators. The step of obtaining link operation data of a core service link and analyzing key indicators in the link operation data to generate a resource management strategy specifically comprises: real-time monitoring of the core service link to obtain the link traffic data and the resource usage indicators; data analysis of the link traffic data and the resource usage indicators to obtain processor utilization and network bandwidth consumption; calculating resource quota information according to the processor utilization and the network bandwidth consumption; generating the resource management strategy according to the resource quota information.

3. The business system optimization method of claim 1, wherein, The step of constructing a resource allocation mechanism according to the resource management strategy and monitoring the resource allocation mechanism to obtain resource state information specifically comprises: constructing a resource pool according to the resource management strategy; monitoring the usage rate of the resource pool to obtain resource usage rate; determining whether the resource usage rate is greater than or equal to a preset usage rate threshold; if the resource usage rate is greater than or equal to the preset usage rate threshold, dynamically allocating resources from a preset resource pool to the resource pool, and taking state data of the resource pool as the resource state information.

4. The business system optimization method of claim 3, wherein, The step of constructing a resource pool according to the resource management strategy specifically comprises: determining resource allocation priority rules according to the resource management strategy; obtaining priority requirement information of the core service link, and constructing the resource pool according to the priority requirement information and the resource allocation priority rules.

5. The business system optimization method of claim 1, wherein, The step of executing a preset hierarchical change strategy according to the resource state information to obtain change effect information specifically comprises: obtaining performance indicators of the resource state information, and performing change risk assessment on the performance indicators based on a preset change control algorithm to obtain change risk information; obtaining a preset change strategy table, and finding the corresponding preset hierarchical change strategy in the preset change strategy table according to the change risk information; performing execution testing based on blue-green deployment according to the preset hierarchical change strategy to obtain the change effect information.

6. The business system optimization method of claim 1, wherein, The step of performing index feature extraction on the change effect information to obtain index fluctuation features, and performing abnormality detection based on a pre-trained abnormality detection model according to the index fluctuation features, when detecting that there is an abnormality, performing a preset fault handling step to obtain fault handling information, specifically includes: Obtain service index change information in the change effect information, and perform feature extraction on the service index change information to obtain the index fluctuation features; Input the index fluctuation features into the abnormality detection model to perform historical baseline deviation comparison to obtain an abnormality detection result; Determine whether the abnormality detection result is abnormal; If the abnormality detection result is abnormal, generate abnormality positioning analysis information, and perform the preset fault handling step according to the abnormality positioning analysis information to obtain the fault handling information.

7. The business system optimization method of claim 1, wherein, The step of generating self-healing strategy optimization information according to the fault handling information, and optimizing the business system according to the self-healing strategy optimization information, specifically includes: Obtain processing record data of the fault handling information, and update a preset self-healing strategy according to the processing record data to obtain an updated self-healing strategy; According to the updated self-healing strategy, perform feedback learning optimization on the abnormality detection model to obtain the self-healing strategy optimization information; According to the self-healing strategy optimization information, adjust business system parameters for optimization.

8. A business system optimization apparatus, characterized by, Comprise: An index analysis module configured to obtain link operation data of a core business link, and analyze key indicators in the link operation data to generate a resource management strategy; A resource monitoring module configured to construct a resource allocation mechanism according to the resource management strategy, and monitor the resource allocation mechanism to obtain resource state information; A strategy execution module configured to execute a preset hierarchical change strategy according to the resource state information to obtain change effect information; A fault handling module configured to perform index feature extraction on the change effect information to obtain index fluctuation features, and perform abnormality detection based on a pre-trained abnormality detection model according to the index fluctuation features, when detecting that there is an abnormality, performing a preset fault handling step to obtain fault handling information; A system optimization module configured to generate self-healing strategy optimization information according to the fault handling information, and optimize the business system according to the self-healing strategy optimization information.

9. A computer device, comprising: Comprise a memory and a processor, the memory stores computer readable instructions, the processor executes the computer readable instructions to realize the steps of the business system optimization method in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer readable instructions, and the computer readable instructions are executed by the processor to realize the steps of the business system optimization method in any one of claims 1 to 7.

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