Service management method, system and device based on business request and storage medium

Abstract

The invention discloses a service management method, system and device based on a service request and a storage medium, which are used for dynamically allocating routing resources. The service management method based on the service request comprises the following steps: receiving a service request sent by a service party through a gateway; analyzing the service request to obtain service basic information; identifying a service type according to the service basic information, and performing calculation queue and calculation resource allocation on the service request according to the service type; confirming the importance degree of the service request according to the service basic information and a preset rule; and intelligently allocating a route according to the request frequency and the importance degree of the service request, and responding to the service request based on the allocated calculation queue and the calculation resource so as to return corresponding data to the service party.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of service governance, and particularly relates to a service governance method, system and device based on a business request and a storage medium. BACKGROUND

[0002] In the current technical field of service governance, most resource allocation mechanisms adopt a static pre-configuration mode. This mode usually relies on artificial pre-set rules or simple division methods, such as allocating a resource pool equally according to business types, and pre-dividing computing, storage and network resources to different businesses. This resource allocation strategy is closely bound to the underlying hardware, and it is difficult to adjust in real time according to business needs during operation, so that the resource supply lacks necessary flexibility.

[0003] With the diversification and complexity of business development, the disadvantages of this static resource allocation mode are increasingly prominent. When new businesses are massively accessed, the pre-configured resources cannot be adjusted in time, and the new businesses may face the dilemma of insufficient resources, affecting their normal operation and service quality; and when business traffic fluctuates, the system cannot dynamically adjust the resource allocation ratio, and high-priority businesses may not be processed in time due to resource occupation, resulting in a decline in overall system performance, and even serious bottlenecks, which cannot meet the user's demand for efficient and stable services. SUMMARY

[0004] To solve the above technical problems, the present application provides a service governance method, system, device and storage medium based on a business request, which is used for dynamically allocating routing resources.

[0005] The technical solutions provided in the present application are described as follows: The first aspect of the present application provides a service governance method based on a business request, comprising: receiving a business request sent by a business party through a gateway; parsing the business request to obtain business basic information; identifying a business type according to the business basic information, and allocating a computing queue and computing resources to the business request according to the business type; confirming the importance of the business request according to the business basic information and a pre-set rule; intelligently allocating routing according to the request frequency of the business request and the importance, and responding to the business request based on the allocated computing queue and computing resources to return corresponding data to the business party.

[0006] Optionally, after identifying the business type according to the business basic information, the method further comprises: According to a preset matching rule and the service type, a priority of the service request is confirmed, the preset matching rule comprising a type list and a priority weight coefficient, the types in the type list corresponding to the priority weight coefficient one by one.

[0007] Optionally, the computing queue and computing resource allocation of the service request according to the service type comprises: When the service type is a core transaction service, the priority of the service request is confirmed as a high priority, and the service request is allocated to a core queue, the core queue using a thread exclusive mode; When the service type is a non-core management service, the priority of the service request is confirmed as a medium priority or a low priority, and the service request is allocated to a non-core queue, the non-core queue using a resource elastic preemption mode.

[0008] Optionally, the resource elastic preemption mode is: The resource utilization of each service request in the non-core queue is calculated in real time; When the resource utilization of the service request with the medium priority is higher than a preset utilization threshold, the computing resource occupied by the service request with the low priority is forcibly released; The computing resource of each service request in the non-core queue is re-allocated according to the priority weight coefficient.

[0009] Optionally, when the service request is a query request, the intelligent routing according to the request frequency and the importance of the service request comprises: According to the importance and a preset data type mapping rule, a data type to be queried by the service request is confirmed, the data type comprising hot data and cold data, the preset data type mapping rule explicitly defining the correspondence between the importance and the hot data and the cold data; The request frequency of the service request is obtained, and the request frequency is compared with a preset frequency threshold to obtain a comparison result; When the comparison result indicates that the request frequency is higher than the preset frequency threshold and the data type is hot data, the service request is routed to a real-time retrieval engine; When the comparison result indicates that the request frequency is lower than the preset frequency threshold and / or the data type is cold data, the service request is routed to an analytical database.

[0010] Optionally, after the comparison between the request frequency and the preset frequency threshold and the obtaining of the comparison result, the method further comprises: The load of the real-time retrieval engine is detected in real time; When the load of the real-time retrieval engine is greater than a preset load threshold, the service request is routed to an analytical database.

[0011] Optionally, the method further comprises: Real-time collection of gateway call logs, and generation of a multi-dimensional detection index board according to the gateway call logs, data on the multi-dimensional detection index board including a call topology diagram, an interface success rate, a response time length distribution, and data delay information; According to the data on the multi-dimensional detection index board, a time series analysis algorithm is used to predict system risks, and a prediction result is obtained; When the prediction result indicates that there is a system abnormal risk, an active fault defense mechanism is triggered.

[0012] The second aspect of the application provides a service governance system based on a service request, comprising: A receiving unit configured to receive a service request sent by a service party through a gateway; An analysis unit configured to analyze the service request to obtain service basic information; An allocation unit configured to identify a service type according to the service basic information, and to perform calculation queue and calculation resource allocation on the service request according to the service type; An importance degree unit configured to confirm the importance degree of the service request according to the service basic information and a preset rule; An intelligent routing unit configured to intelligently allocate a route according to the request frequency of the service request and the importance degree, and to respond to the service request based on the allocated calculation queue and calculation resource, so as to return corresponding data to the service party.

[0013] The third aspect of the application provides a service governance device based on a service request, comprising: A processor, a memory, an input / output unit, and a bus; The processor is connected with the memory, the input / output unit, and the bus; The memory stores a program, and the processor invokes the program to execute the service governance method based on a service request of the first aspect and any optional aspect of the first aspect.

[0014] The fourth aspect of the application provides a computer readable storage medium, which stores a program, and the program executes the service governance method based on a service request of the first aspect and any optional aspect of the first aspect when executed on a computer.

[0015] From the above technical solutions, the application has the following advantages: The application provides a service management method based on a business request, receives a business request sent by a business party through a gateway; analyzes the business request to obtain business basic information; identifies a business type according to the business basic information, and performs calculation queue and calculation resource allocation on the business request according to the business type; confirms the importance of the business request according to the business basic information and a preset rule; intelligently allocates a route according to the request frequency and the importance of the business request, and responds to the business request based on the allocated calculation queue and calculation resource to return corresponding data to the business party. The method of the application identifies the business type according to the business identification, dynamically allocates the calculation queue and the resource, reasonably arranges the calculation resource obtained by different types of business requests and the response order, changes the situation that the resource supply lacks flexibility in the static pre-configuration mode, intelligently allocates the route according to the request frequency and the importance, can guide the business request with different characteristics to the most suitable processing path, improves the data processing efficiency and accuracy, and meets the needs of users for efficient and stable services. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the application, the drawings needed in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Figure 1 An embodiment flowchart of the service management method based on a business request provided by the application is shown in the figure. Figure 2 Another embodiment flowchart of the service management method based on a business request provided by the application is shown in the figure. Figure 3 Another embodiment flowchart of the service management method based on a business request provided by the application is shown in the figure. Figure 4 Another embodiment flowchart of the service management method based on a business request provided by the application is shown in the figure. Figure 5 An embodiment structure diagram of the service management system based on a business request provided by the application is shown in the figure. Figure 6 An embodiment structure diagram of the service management device based on a business request provided by the application is shown in the figure. DETAILED DESCRIPTION

[0018] The application provides a service management method, system, device and storage medium based on a business request, which is used for dynamically allocating routing resources.

[0019] It should be noted that the service request-based service management method provided by the application can be applied to a terminal and can also be applied to a system, and can also be applied to a server, for example, the terminal can be a smart phone or a computer, a tablet computer, a portable computer terminal, or a fixed terminal such as a desktop computer.

[0020] Please refer to Figure 1 The application provides a service request-based service management method, and one embodiment of the method comprises the following steps: In step S101, a service request sent by a service party is received through a gateway. In step S102, the service request is parsed to obtain service basic information. In step S103, the service type is identified according to the service basic information, and the calculation queue and the calculation resource are allocated to the service request according to the service type. In step S104, the importance of the service request is confirmed according to the service basic information and a preset rule. In step S105, the route is intelligently allocated according to the request frequency and the importance of the service request, and the service request is responded based on the allocated calculation queue and calculation resource, so as to return corresponding data to the service party.

[0021] The service request-based service management method provided by the application first receives a service request sent by a service party through a gateway, then parses the service request to obtain service basic information, then identifies the service type according to the service basic information and allocates the calculation queue and the calculation resource to the service request according to the service type, then confirms the importance of the service request according to the service basic information and a preset rule, and finally intelligently allocates the route according to the request frequency and the importance of the service request, and responds to the service request based on the allocated calculation queue and calculation resource, so as to return corresponding data to the service party. The method provided by the application identifies the service type according to the service identification, dynamically allocates the calculation queue and the resource, reasonably arranges the calculation resource obtained by different types of service requests and the response order, changes the situation that the resource supply lacks flexibility in the static pre-configuration mode, intelligently allocates the route according to the request frequency and the importance, can guide the service request with different characteristics to the most suitable processing path, improves the data processing efficiency and accuracy, and meets the needs of users for efficient and stable services.

[0022] In step S101 of the embodiment, a service request sent by a service party is received through a gateway. When the service party initiates a request, a communication connection between the gateway and the service party is established based on an IP / TCP protocol stack, and the service request and its data packet are received through the communication connection. For establishing the communication connection based on the TCP protocol, first, the service party sends a data packet with a SYN (synchronous sequence number) flag to initiate a connection request to the gateway; after receiving the request, the gateway returns a data packet with a SYN and ACK (acknowledgement sequence number) flag to confirm the receipt of the request and agree to establish the connection; finally, the service party sends a data packet with an ACK flag to complete the establishment of the communication connection. After the successful establishment of the communication connection, the HTTP request or the TCP request can be transmitted to the gateway along the pipeline.

[0023] In step S102 of the embodiment, the service request is parsed to obtain service basic information. The received service request is structured by using a data parsing technology. For example, key information can be extracted from the service request by using a regular expression, a JSON / XML parser and the like. For the HTTP request, the request line, the request header and the request body and the like are parsed according to the HTTP protocol specification; for the TCP request, the original byte stream is parsed according to a pre-agreed data format, so that the service request content is completely obtained. The service basic information can include a service identifier, a platform identifier, signature verification information and a query condition set and the like. Thus, the process of extracting key information is as follows: the service identifier is extracted from a specific field or a marker, which is used to distinguish different services; the platform identifier is identified, which is used to explicitly indicate the request source platform; the signature verification information is decrypted and verified by using a pre-agreed encryption algorithm and key, so as to ensure the legality and integrity of the request; and the query condition set is parsed to sort out the data retrieval conditions required by the service request.

[0024] In step S103 of the embodiment, the service type is identified according to the service basic information, and the service request is calculated and allocated with computing resources according to the service type. A mapping table of service identifiers and service types can be pre-established, and the extracted service identifier is compared with the mapping table, so that the service type is quickly identified. Specifically, the mapping table is stored in the form of key-value pairs, in which the service identifier is used as a unique key corresponding to a specific service type. When the service identifier in the service basic information is obtained, the gateway quickly locates the matching service type in the mapping table by traversal or hash lookup, so as to determine the corresponding service type. Then, the service request is divided into the corresponding computing queue according to the service type, and each computing queue is provided with corresponding computing resources and response methods, so as to realize the reasonable use of resources and improve the response speed of the system.

[0025] In step S104 of the embodiment, the importance of the service request is determined according to the service basis information and preset rules. A rule library for evaluating the importance is constructed, and the rules cover multiple dimensions, such as the weight of the service type, the request frequency, the platform priority, and the like. The extracted service basis information is input into the evaluation rule library, a comprehensive score is given to each service request according to preset calculation logic and weight distribution, and the importance level is divided according to the score result, for example, three levels of high, medium and low, to provide a decision basis for subsequent routing allocation.

[0026] In step S105 of the embodiment, the route is intelligently allocated according to the request frequency and the importance of the service request, and the service request is responded based on the allocated calculation queue and calculation resources to return corresponding data to the service party. The request frequency of the same service identifier within a period of time is recorded by a counter to provide rich basic data for subsequent processing. It should be noted that the request frequency is not a fixed value but a range value. Then, the routing allocation strategy is formulated according to the request frequency and the importance. For the service request with high frequency and high importance, the request is routed to the calculation resources and storage components with fast processing speed and high performance, and the service request is responded according to the order in the calculation queue and the allocated calculation resources to meet the requirement of fast response. For the complex analysis type service request with low importance or low request frequency, such as historical data reconciliation, the request is directed to the management type database with low performance and slow response speed, and the powerful data analysis capability of the management type database is utilized to respond the service request according to the order in the calculation queue and the allocated calculation resources. After the service request is processed, the processing result is encapsulated into a response data packet according to the HTTP / TCP protocol, and is returned to the service party mentioned in step S101 through the established network connection, to complete a complete service interaction.

[0027] Reference Figure 2 According to some embodiments of the application, in order to ensure the stability and efficiency of processing and responding to service requests, the intelligent gateway should also be detected throughout the process of steps S101-S105 to avoid abnormal situations in the response process and affect the workflow. The detection steps can include, but are not limited to, the following: Step S201, real-time collection of gateway call logs, and generation of multi-dimensional detection index boards according to the gateway call logs, the data on the multi-dimensional detection index boards including call topology, interface success rate, response time distribution and data delay information; Step S202, prediction of system risks by using time series analysis algorithm according to the data on the multi-dimensional detection index boards to obtain a prediction result; Step S203, triggering of the active fault defense mechanism when the prediction result indicates that there is a system abnormal risk.

[0028] In steps S201 to S203 of the embodiment, a log collection module is embedded in the gateway in advance, which records logs at key nodes of each service request processing, such as request reception, route allocation, data return, etc. The log content contains key information of the request, such as service identification, request time, processing node, response status code, etc., ensuring complete retention of the whole process data of service processing. The collected log data is transmitted in real time to the log storage center through the message queue, realizing centralized management and persistent storage of logs. Then, based on the stored gateway call logs, the data processing module generates multi-dimensional detection indicators using data aggregation and analysis techniques. For the call topology graph, the processing path of the service request is analyzed by parsing the logs, the calling relationship between each service node is identified, and a visual call topology structure is constructed using a graph database to intuitively present the flow link of the service request; when calculating the interface success rate, the ratio of the number of successful responses to the total number of requests within a certain time window is calculated; the response time distribution divides the request response time into multiple intervals, and the proportion of requests in each interval is calculated; the data delay information is obtained by comparing the difference between the request sending time and the actual processing start time, and the average value, maximum value, etc. statistical indicators are calculated. Finally, these data sets are integrated into a visual interface to generate multi-dimensional detection indicator dashboards, which facilitate real-time detection of system operation status by operation and maintenance personnel.

[0029] After generating the multi-dimensional detection indicator dashboard, an analysis algorithm suitable for time series data is selected, such as the autoregressive integrated moving average model, long short-term memory network, etc. The time series data in the multi-dimensional detection indicator dashboard is input into the algorithm, which learns the trend and pattern of historical data to establish a prediction model. For example, the autoregressive integrated moving average model analyzes the autocorrelation, difference stationarity, etc. characteristics of the data to predict the change value of the indicator in the future; the long short-term memory network has the ability to remember long sequences of data, and can mine complex patterns and potential laws in the data. In the prediction process, a reasonable prediction time step is set, such as predicting the indicator change in the next 10 minutes or 1 hour, and the prediction accuracy is optimized by continuously adjusting the model parameters. Finally, the algorithm outputs the predicted values of each detection indicator in the future, which are compared with the pre-set thresholds, such as the threshold of interface success rate, the proportion of response time exceeding the corresponding threshold, etc., to determine whether there is a system abnormal risk and form a prediction result. Once the prediction result shows that there is an abnormal risk in the system, the proactive fault defense mechanism is triggered. The proactive fault defense mechanism is a comprehensive solution based on system operation status detection and risk prediction, which intervenes in advance and takes measures to ensure stable operation of the system.

[0030] Reference Figure 3According to some embodiments of the present application, the step S103 of identifying the service type according to the service basic information and calculating the queue and allocating the computing resources according to the service type can specifically include, but is not limited to, the following: The step S301 of identifying the service type according to the service identification and confirming the priority of the service request according to the preset matching rule and the service type, wherein the preset matching rule includes a type list and a priority weight coefficient, and the types in the type list are one-to-one corresponding to the priority weight coefficients. The step S302 of confirming the priority of the service request as a high priority when the service type is a core transaction service and allocating the service request to a core queue, wherein the core queue uses a thread exclusive mode. The step S303 of confirming the priority of the service request as a medium priority or a low priority when the service type is a non-core management service and allocating the service request to a non-core queue, wherein the non-core queue uses a resource elastic preemption mode.

[0031] In the steps S301 to S303 of the embodiments, it should be noted that the resource elastic preemption mode is to calculate the resource utilization rate of each service request in the non-core queue in real time, to forcibly release the computing resources occupied by the service request with a low priority when the resource utilization rate of the service request with a medium priority is higher than a preset utilization threshold, and to re-allocate the computing resources to each service request in the non-core queue according to the priority weight coefficient. Specifically, a mapping system of the service type and the priority, i.e., the type list in the preset matching rule, is established, all service types, such as payment, order, report query, etc., and the corresponding priority weight coefficients, such as the payment service corresponding to the weight 0.8, the report query corresponding to 0.3, etc., are explicitly listed. When receiving the service request, the system compares the service identification with the type list to quickly locate the service type. Then, the priority level of the service request is determined by weighted calculation according to the priority weight coefficient corresponding to the service type and other auxiliary factors, such as the importance of the platform of the request source, the historical data of the request frequency, etc.

[0032] When the business type is identified as a core transaction business (such as payment, real-time order processing, etc.), the system immediately marks the priority of the business request as high priority. In terms of queue allocation, core business requests are directly routed to the core queue. This queue adopts a thread-exclusive mode, that is, each request is assigned a dedicated thread, and this thread will not be preempted by other tasks within the request processing period. At the same time, the core queue is allocated a higher system resource quota, such as CPU time slice, memory upper limit, to ensure that high-priority businesses can obtain stable and sufficient computing resources, achieving low latency and high reliability processing. For non-core management businesses (such as report generation, system configuration query), the system confirms the request priority as medium or low priority according to the priority weight coefficient corresponding to the business type, and allocates it to the non-core queue. In the resource elastic preemption mode, the system detects the resource utilization of each business request in the non-core queue in real time, such as CPU usage and memory occupancy. When the resource utilization of medium-priority requests is higher than the preset threshold, it indicates that there is a potential bottleneck in system resources, at which time the system will forcibly suspend and release the computing resources occupied by low-priority requests. Subsequently, the system reallocates the available resources in the non-core queue according to the priority weight coefficient of each business request, prioritizing the running efficiency of medium-priority businesses to achieve dynamic optimization of resource allocation. Steps S301-S303 achieve high-reliability protection of core businesses and elastic processing of non-core businesses through the priority-driven resource allocation strategy, effectively improving the overall system resource utilization.

[0033] Referring to Figure 4 According to some embodiments of the present application, when the business request is a query request, the intelligent allocation of the route according to the request frequency and importance of the business request in step S105, and responding to the business request based on the allocated computing queue and computing resources to return the corresponding data to the business party can specifically include, but is not limited to, the following: Step S401, according to the importance and the preset data type mapping rule, the data type to be queried by the business request is confirmed, the data type includes hot data and cold data, and the preset data type mapping rule clearly shows the correspondence between the importance and the hot data and the cold data; Step S402, the request frequency of the business request is obtained, and the request frequency is compared with the preset frequency threshold to obtain a comparison result; Step S403, when the comparison result indicates that the request frequency is higher than the preset frequency threshold, and the data type is hot data, the business request is routed to the real-time search engine; Step S404, when the comparison result indicates that the request frequency is lower than the preset frequency threshold, and / or the data type is cold data, the business request is routed to the analytical database; Step S405, the load of the real-time search engine is detected in real time; Step S406, when the load of the real-time retrieval engine is greater than the preset load threshold, the service request is routed to the analytical database.

[0034] In steps S401-S406 of the embodiment, first, the data type required for query is determined based on the importance of the service request evaluated by step S104, in combination with the preset data type mapping rule. For example, for real-time transaction type services with high importance, the system determines the associated data type as hot data, such as current order status, real-time inventory, etc. For low importance historical analysis type services, such as quarterly financial statement query, the corresponding data type is classified as cold data, such as historical transaction records, archived statements, etc. This mapping relationship can be pre-defined through a configuration file or a rule engine to ensure the accuracy of data type determination.

[0035] At the same time, similar to the request frequency of the service request obtained in step S105, the occurrence frequency of the service request is also real-time counted, i.e. the number of requests for the same service identifier within a period of time is recorded by a counter. Then it is compared with the preset frequency threshold. The frequency threshold can be dynamically adjusted according to system performance indicators and service characteristics to adapt to the routing decision needs in different scenarios. During the comparison process, the average request frequency is continuously calculated to ensure that the statistical result can reflect the real business pressure.

[0036] When the request frequency is higher than the preset threshold and the data type is hot data, the system will preferentially route the service request to the real-time retrieval engine, such as Elasticsearch. This kind of engine is optimized for high-concurrency and low-latency real-time query scenarios and can quickly handle high-frequency hot data access requests. During the routing process, the system will select the real-time retrieval engine node with the lowest current load through the load balancer to ensure that the request can be responded as quickly as possible. At the same time, the system will mark this routing decision in the request log for subsequent performance analysis and strategy optimization.

[0037] If the request frequency is lower than the threshold or the data type is cold data, the system will route the service request to the analytical database, such as SelectDB. Analytical databases are good at handling complex queries and large-scale data aggregation and are suitable for the deep analysis needs of cold data. For such requests, the system will use batch processing or asynchronous query mode to reduce the requirement for real-time performance, while fully utilizing the parallel computing capability of the analytical database to improve query efficiency. In addition, the system will manage the requests routed to the analytical database in a queue to avoid excessive resource occupation affecting other services.

[0038] Further, through the built-in detection component, the key performance indicators of the real-time retrieval engine are continuously collected, such as CPU usage, memory occupation, query response time, etc. These indicators are aggregated into a unified detection platform and stored and analyzed through a time series database. The detection frequency can be configured to be second-level, ensuring that the changing trend of engine load can be captured in time. At the same time, the system sets a health threshold for each performance indicator for subsequent load judgment. When the load of the real-time retrieval engine is monitored to exceed the preset threshold, for example, CPU usage ≥ 80%, the system triggers the routing protection mechanism. At this time, the hot data requests that should be routed to the real-time retrieval engine are temporarily directed to the analytical database. In order to avoid affecting the business, the system uses a gradual switching strategy: first, switch part of the low-priority hot data requests to the analytical database, while closely observing the change of engine load; if the load does not decrease, gradually expand the switching range until the engine load returns to a safe level. During the switching process, the system sends a warning notice to the operation and maintenance personnel and records the switching reason and time for subsequent capacity planning and performance tuning. Steps S401-S406 realize intelligent shunting of data access through multi-dimensional judgment and dynamic adjustment, which not only guarantees the real-time demand of high-frequency hot data, but also fully utilizes the powerful computing power of the analytical database, while enhancing the fault tolerance of the system in the case of overload.

[0039] The above embodiments illustrate the method provided by the application. The following describes a service management system based on service requests, an apparatus, and a storage medium provided by the application: Please refer to Figure 5 The application provides a service management system based on service requests, which comprises: A receiving unit 501 is configured to receive a service request sent by a service party through a gateway. An analysis unit 502 is configured to analyze the service request to obtain service basic information. An allocation unit 503 is configured to identify a service type according to the service basic information, and to allocate a calculation queue and calculation resources to the service request according to the service type. An importance degree unit 504 is configured to determine the importance degree of the service request according to the service basic information and a preset rule. An intelligent routing unit 505 is configured to intelligently allocate a route according to the request frequency and the importance degree of the service request, and to respond to the service request based on the allocated calculation queue and calculation resources, so as to return corresponding data to the service party.

[0040] Optionally, the service management system based on service requests further comprises: The collection unit 506 is configured to collect gateway call logs in real time, and generate a multi-dimensional detection index board according to the gateway call logs, wherein data on the multi-dimensional detection index board includes a call topology diagram, an interface success rate, a response time length distribution, and data delay information. The prediction unit 507 is configured to predict system risks by using a time series analysis algorithm according to the data on the multi-dimensional detection index board, and obtain a prediction result. The triggering unit 508 is configured to trigger an active fault defense mechanism when the prediction result indicates that there is a system abnormal risk.

[0041] Optionally, after the distribution unit 503, the following further includes: The priority unit 509 is configured to determine the priority of the service request according to a preset matching rule and a service type, wherein the preset matching rule includes a type list and a priority weight coefficient, and the types in the type list are in one-to-one correspondence with the priority weight coefficients.

[0042] Optionally, the distribution unit 503 is specifically configured to: When the service type is a core transaction service, the priority of the service request is determined as a high priority, and the service request is distributed to a core queue, and the core queue uses a thread exclusive mode; When the service type is a non-core management service, the priority of the service request is determined as a medium priority or a low priority, and the service request is distributed to a non-core queue, and the non-core queue uses a resource elastic preemption mode.

[0043] Optionally, the resource elastic preemption mode is: The resource utilization rate of each service request in the non-core queue is calculated in real time; When the resource utilization rate of the service request with the medium priority is higher than a preset utilization rate threshold, the computing resource occupied by the service request with the low priority is forcibly released; The computing resource is re-distributed to each service request in the non-core queue according to the priority weight coefficient.

[0044] Optionally, when the service request is a query request, the intelligent routing unit 505 is specifically configured to: According to the importance degree and a preset data type mapping rule, the data type to be queried by the service request is determined, wherein the data type includes hot data and cold data, and the preset data type mapping rule clearly defines the correspondence between the importance degree and the hot data and the cold data; The request frequency of the service request is obtained, and the request frequency is compared with a preset frequency threshold to obtain a comparison result; When the comparison result indicates that the request frequency is higher than the preset frequency threshold and the data type is hot data, the service request is routed to a real-time retrieval engine; When the comparison result represents that the request frequency is lower than the preset frequency threshold value, and / or the data type is cold data, the service request is routed to the analytical database.

[0045] Optionally, the intelligent routing unit 505 is specifically configured to: detect the load of the real-time retrieval engine in real time; when the load of the real-time retrieval engine is greater than the preset load threshold value, route the service request to the analytical database.

[0046] The application further provides a service governance device based on a service request, please refer to Figure 6 The service governance device based on the service request comprises: a processor 601, a memory 602, an input output unit 603, and a bus 604; The processor 601 is connected with the memory 602, the input output unit 603, and the bus 604; The memory 602 stores a program, and the processor 601 calls the program to execute the method as shown in Figure 1 、 Figure 2 、 Figure 3 or Figure 4 the embodiment.

[0047] The application further relates to a computer readable storage medium, and the computer readable storage medium stores a program, when the program runs on a computer, so that the computer executes the method as shown in Figure 1 、 Figure 2 、 Figure 3 or Figure 4 the embodiment.

[0048] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0049] In the several embodiments provided in the application, it should be understood that the disclosed system, device and method can be implemented by other manners. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0050] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0051] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0052] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical scheme of the present application essentially or the part that contributes to the prior art or the whole or part of the technical scheme can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, read-only memory), a random access memory (RAM, random access memory), a magnetic disk or an optical disk, and various program code storage media.

Claims

1. A service governance method based on business requests, characterized in that: The method comprises: Receive business requests sent by business parties through the gateway; Parsing the business request to obtain basic business information; Identify the business type according to the business basic information, and perform computing queue and computing resource allocation on the business request according to the business type; Determine the importance of the business request based on the business basic information and preset rules; Intelligently allocate routes according to the request frequency and importance of the business request, and respond to the business request based on the allocated computing queue and computing resources to return corresponding data to the business party.

2. The service governance method according to claim 1, characterized in that: After identifying the service type according to the basic service information, the method further includes: The priority of the service request is determined according to a preset matching rule and the service type, wherein the preset matching rule includes a type list and a priority weight coefficient, and the types in the type list correspond to the priority weight coefficients in a one-to-one manner.

3. The service governance method according to claim 2, characterized in that: The performing of computing queue and computing resource allocation for the service request according to the service type includes: When the business type is a core transaction business, confirming that the priority of the business request is high priority, and allocating the business request to a core queue, wherein the core queue uses a thread exclusive mode; When the service type is a non-core management service, it is determined that the priority of the service request is a medium priority or a low priority, and the service request is allocated to a non-core queue, and the non-core queue uses a resource elastic preemption mode.

4. The service governance method according to claim 3, characterized in that: The resource elastic preemption mode is: Calculating the resource utilization rate of each service request in the non-core queue in real time; When the resource utilization of medium-priority service requests exceeds the preset utilization threshold, the computing resources occupied by low-priority service requests are forcibly released; Computing resources are reallocated to each service request in the non-core queue according to the priority weight coefficient.

5. The service governance method according to claim 1, characterized in that: When the service request is a query request, the intelligently allocating routes according to the request frequency and the importance of the service request includes: According to the importance and a preset data type mapping rule, the data type to be queried by the business request is determined, where the data type includes hot data and cold data, and the preset data type mapping rule clarifies the correspondence between the importance and the hot data and the cold data; Obtaining a request frequency of the service request, and comparing the request frequency with a preset frequency threshold to obtain a comparison result; When the comparison result indicates that the request frequency is higher than the preset frequency threshold and the data type is hot data, routing the business request to a real-time search engine; When the comparison result indicates that the request frequency is lower than the preset frequency threshold, and / or the data type is cold data, the business request is routed to the analytical database.

6. The service governance method according to claim 5, characterized in that: After comparing the request frequency with the preset frequency threshold and obtaining a comparison result, the method further includes: detecting the load of the real-time search engine in real time; When the load of the real-time search engine is greater than a preset load threshold, the business request is routed to the analytical database.

7. The service governance method according to any one of claims 1 to 6, characterized in that: The method further comprises: Collect gateway call logs in real time and generate a multi-dimensional detection indicator dashboard based on the gateway call logs. The data on the multi-dimensional detection indicator dashboard includes call topology, interface success rate, response time distribution and data delay information; Based on the data on the multi-dimensional detection indicator dashboard, a time series analysis algorithm is used to predict system risks and obtain a prediction result; When the prediction result indicates that there is a risk of system abnormality, an active fault prevention mechanism is triggered.

8. A service management system based on business requests, characterized in that: The system comprises: A receiving unit, configured to receive a service request sent by a service party through a gateway; A parsing unit, configured to parse the service request and obtain basic service information; an allocation unit, configured to identify a service type according to the service basic information, and allocate computing queues and computing resources to the service request according to the service type; An importance unit, configured to determine the importance of the service request based on the basic service information and preset rules; An intelligent routing unit is used to intelligently allocate routes according to the request frequency and importance of the business request, and respond to the business request based on the allocated computing queue and computing resources to return corresponding data to the business party.

9. A service management device based on business request, characterized in that: The device comprises: processor, memory, input and output units, and buses; The processor is connected to the memory, the input and output unit, and the bus; The memory stores a program, and the processor calls the program to execute the service governance method based on business requests according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, and when the program is executed on a computer, the service governance method based on business requests according to any one of claims 1 to 7 is executed.

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