Method and apparatus for controlling traffic flow

Abstract

The application discloses a kind of control method and device of service traffic.It relates to the field of financial technology, and the method comprises: according to the historical business data corresponding to target business of current time target business prediction target time target business information;Target business information is converted into the alternative flow limiting parameter of target business, wherein the alternative flow limiting parameter is used to indicate the alternative maximum number of business request allowed to be processed at target time, and the absolute value of the difference between the alternative flow limiting parameter and the business request amount indicated by target business information is less than or equal to target threshold value;Detect the alternative load parameter of target business under the alternative flow limiting parameter;According to alternative load parameter and the resource information of target business, configure target flow limiting parameter at target time for target business, wherein resource information is used to indicate the resource allowed to be provided by target business at target time.By the present application, the problem of low control efficiency of service traffic in the related art is solved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and specifically, to a method and device for controlling business traffic. Background Art

[0002] With the continuous development of the financial sector, business transaction volumes continue to reach new highs. To ensure the normal operation of financial systems, transaction throttling mechanisms are often implemented. These mechanisms configure conservative throttling values ​​based on prior analysis and current production operations. While this approach protects the system, it also limits business expansion. This is because financial systems experience peak and trough periods in business volume. During peak periods, customer transactions are prone to frequent throttling, resulting in a poor customer experience. During trough periods, customer transaction volumes are low, leading to wasted resources and inefficient business flow control.

[0003] Currently, no effective solution has been proposed to address the problem of low control efficiency of business traffic in related technologies. Summary of the Invention

[0004] The main purpose of this application is to provide a method and device for controlling business traffic to solve the problem of low business traffic control efficiency in related technologies.

[0005] In order to achieve the above objectives, according to one aspect of the present application, a method for controlling service traffic is provided.

[0006] The method includes:

[0007] Predicting target service information of the target service at a target time based on historical service data corresponding to the target service at the current time, wherein the target time is a time after the current time, the historical service data is used to indicate changes in the service request volume of the target service during a reference time period before the current time, and the target service information is used to indicate the service request volume initiated for the target service at the target time;

[0008] Converting the target service information into an alternative flow limiting parameter for the target service, wherein the alternative flow limiting parameter is used to indicate an alternative maximum number of service requests allowed to be processed at the target time, and an absolute value of a difference between the alternative flow limiting parameter and the service request volume indicated by the target service information is less than or equal to a target threshold;

[0009] Detecting an alternative load parameter of the target service under the alternative current limiting parameter, wherein the alternative load parameter is used to indicate the load resources required to execute the service request that meets the alternative current limiting parameter;

[0010] A target current limiting parameter at the target time is configured for the target business based on the alternative load parameter and the resource information of the target business, wherein the resource information is used to indicate the resources that the target business is allowed to provide at the target time, the target current limiting parameter is used to indicate the maximum number of business requests allowed to be processed at the target time, and the target business is used to control the execution of the transaction request according to the target current limiting parameter at the target time.

[0011] As an optional embodiment, predicting target service information of the target service at a target moment based on historical service data corresponding to the target service at a current moment includes:

[0012] Inputting the historical business data into a target prediction model, wherein the target prediction model records the relationship between the change of business request volume over time;

[0013] The target service request value output by the target prediction model is determined as the target service information.

[0014] As an optional embodiment, the target prediction model is generated by the following method:

[0015] Inputting a target training sample into an initial prediction model, wherein the training sample is marked with a reference service request volume to be predicted;

[0016] Obtaining the candidate business request volume output by the initial prediction model;

[0017] The model parameters of the initial prediction model are modified according to the difference between the reference service request volume and the candidate service request volume to obtain the target prediction model.

[0018] As an optional embodiment, configuring a target current limiting parameter for the target service at the target time according to the alternative load parameter and the resource information of the target service includes:

[0019] Matching a target resource demand with a target resource supply of a target server, wherein the candidate load parameter includes the target resource demand, and the resource information includes the target resource supply, wherein the target resource supply is used to represent the service resources configured by the target server for handling the target service;

[0020] In a case where the target resource supply is greater than or equal to the target resource demand, the candidate current limiting parameter is determined as the target current limiting parameter of the target server.

[0021] As an optional embodiment, after matching the target resource demand with the target resource supply, the method further includes:

[0022] In a case where the target resource supply is less than the target resource demand, splitting the target resource demand into a first resource demand corresponding to the target server and a second resource demand corresponding to a reference server according to the target resource supply, wherein the reference server is a server having a binding relationship with the target server, the reference server is used to handle the target business to be handled by the target server, and the first resource demand is less than the target resource supply;

[0023] converting the candidate current limiting parameter into a first current limiting parameter and a second current limiting parameter according to a ratio between the first resource requirement and the second resource requirement;

[0024] The first current limiting parameter is determined as the target current limiting parameter of the target server, and the second current limiting parameter is determined as the reference current limiting parameter of the reference server.

[0025] As an optional embodiment, detecting the alternative load parameter of the target service under the alternative current limiting parameter includes:

[0026] Detecting a target service type of the target service, wherein the target service type is used to indicate a resource type of load resources required to be occupied when processing a service request of the target service;

[0027] Detecting a target resource type of a business resource used to handle a business of the target business type;

[0028] The candidate resource amount of the target resource type required for handling the candidate business processing amount is determined from the business processing amount and the resource amount of the target resource type that have a corresponding relationship.

[0029] As an optional embodiment, converting the target service information into an alternative current limiting parameter of the target service includes:

[0030] matching the target threshold according to a target service processing requirement of the target service, wherein the target service processing requirement is used to indicate a processing quality required to be achieved for processing a target service request of the target service;

[0031] The target request volume is converted into an alternative flow limiting parameter of the target service according to the target threshold, wherein the target service information includes the target request volume.

[0032] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a device for controlling service traffic is provided.

[0033] The device includes:

[0034] a prediction module, configured to predict target service information of a target service at a target time based on historical service data corresponding to the target service at a current time, wherein the target time is a time after the current time, the historical service data is used to indicate changes in the service request volume of the target service within a reference time period before the current time, and the target service information is used to indicate the service request volume initiated for the target service at the target time;

[0035] a conversion module, configured to convert the target service information into an alternative flow limiting parameter for the target service, wherein the alternative flow limiting parameter is used to indicate an alternative maximum number of service requests allowed to be processed at the target time, and an absolute value of a difference between the alternative flow limiting parameter and the service request amount indicated by the target service information is less than or equal to a target threshold;

[0036] A detection module, configured to detect an alternative load parameter of the target service under the alternative current limiting parameter, wherein the alternative load parameter is used to indicate the load resources required to execute a service request that meets the alternative current limiting parameter;

[0037] a configuration module, configured to configure a target current limiting parameter for the target business at the target time based on the alternative load parameter and resource information of the target business, wherein the resource information is used to indicate the resources that the target business is allowed to provide at the target time, the target current limiting parameter is used to indicate the maximum number of business requests allowed to be processed at the target time, and the target business is used to control the execution of transaction requests according to the target current limiting parameter at the target time.

[0038] According to another aspect of the embodiments of the present application, a processor is further provided, which is used to run a program, wherein the program executes the above-mentioned service traffic control method when running.

[0039] According to another aspect of an embodiment of the present application, an electronic device is also provided, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method for controlling business traffic.

[0040] Through the present application, the following steps are adopted: predicting the target business information of the target business at the target moment based on the historical business data corresponding to the target business at the current moment, wherein the target moment is the moment after the current moment, and the historical business data is used to indicate the change in the business request volume of the target business in the reference time period before the current moment, and the target business information is used to indicate the business request volume initiated on the target business at the target moment; converting the target business information into an alternative flow limiting parameter for the target business, wherein the alternative flow limiting parameter is used to indicate the alternative maximum number of business requests allowed to be processed at the target moment, and the alternative flow limiting parameter is consistent with the business request indicated by the target business information. The method comprises the following steps: detecting an alternative load parameter of the target business under the alternative flow limiting parameter, wherein the alternative load parameter is used to indicate the load resources required to execute the business request that meets the alternative flow limiting parameter; configuring a target flow limiting parameter at the target time for the target business according to the alternative load parameter and the resource information of the target business, wherein the resource information is used to indicate the resources that the target business is allowed to provide at the target time, the target flow limiting parameter is used to indicate the maximum number of business requests allowed to be processed at the target time, and the target business is used to control the execution of the transaction request according to the target flow limiting parameter at the target time. Specifically, the target business transaction request volume at the target time is predicted based on historical business data, and alternative flow control parameters are obtained based on the business request volume. Furthermore, the resources available at the target time are taken into account to comprehensively obtain the target flow control parameters to limit the execution of the target business transaction request at the target time. The target flow control parameters thus obtained not only take into account the system's resource load to ensure normal system operation, but also take into account the predicted business request volume at the target time to meet customer needs as much as possible and improve the customer experience. Therefore, the solution of this application solves the problem of low business traffic control efficiency in related technologies, thereby achieving the effect of improving business traffic control efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0042] Figure 1 This is a flow chart of a method for controlling service traffic according to an embodiment of the present application;

[0043] Figure 2 This is a diagram illustrating the flow limiting situation when flow control is performed according to the service flow control method provided in the embodiment of the present application. Figure 1 ;

[0044] Figure 3 This is a diagram illustrating the flow limiting situation when flow control is performed according to the service flow control method provided in the embodiment of the present application. Figure 2 ;

[0045] Figure 4 This is a schematic diagram of the effect of dynamic expansion according to the service flow control method provided in the embodiment of the present application. Figure 1 ;

[0046] Figure 5 This is a schematic diagram of the effect of dynamic expansion according to the service flow control method provided in the embodiment of the present application. Figure 2 ;

[0047] Figure 6 is a schematic diagram of a device for controlling service flow according to an embodiment of the present application; and

[0048] Figure 7 This is a schematic diagram of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0049] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0050] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0051] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0052] For ease of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0053] Transaction concurrency: The total number of call requests received by a server at the same time.

[0054] Throttling: An automatic service degradation method that quickly closes (e.g., discards) traffic exceeding the threshold when concurrent service resource requests reach a predetermined threshold. Transaction throttling is primarily used for system self-protection, preventing excessive transactions from overwhelming the server and causing complete service unavailability.

[0055] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display and analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set up between this system and the relevant users or institutions. Before obtaining relevant information, the user is provided with a corresponding operation entry for the user to choose to agree or reject the automated decision result; if the user chooses to reject, the expert decision process will be entered.

[0056] The present invention will be described below in conjunction with preferred implementation steps. Figure 1 Flowchart of a method for controlling service flow according to an embodiment of the present application. Figure 1 As shown, the method includes the following steps:

[0057] Step S101, predicting target service information of a target service at a target time based on historical service data corresponding to the target service at the current time, wherein the target time is a time after the current time, the historical service data is used to indicate changes in the service request volume of the target service within a reference time period before the current time, and the target service information is used to indicate the service request volume initiated for the target service at the target time;

[0058] Step S102: Convert the target service information into an alternative flow limiting parameter for the target service, wherein the alternative flow limiting parameter is used to indicate an alternative maximum number of service requests allowed to be processed at the target time, and an absolute value of a difference between the alternative flow limiting parameter and the service request volume indicated by the target service information is less than or equal to a target threshold;

[0059] Step S103: detecting an alternative load parameter of the target service under the alternative current limiting parameter, wherein the alternative load parameter is used to indicate the load resources required to execute the service request that meets the alternative current limiting parameter;

[0060] Step S104: Configure target flow limiting parameters for the target business at the target time based on the alternative load parameters and the resource information of the target business, wherein the resource information is used to indicate the resources that the target business is allowed to provide at the target time, and the target flow limiting parameters are used to indicate the maximum number of business requests allowed to be processed at the target time. The target business is used to control the execution of transaction requests according to the target flow limiting parameters at the target time.

[0061] The method for controlling business traffic provided by the embodiment of the present application adopts the following steps: predicting target business information of the target business at a target moment based on historical business data corresponding to the target business at the current moment, wherein the target moment is a moment after the current moment, and the historical business data is used to indicate changes in the amount of business requests for the target business in a reference time period before the current moment, and the target business information is used to indicate the amount of business requests initiated on the target business at the target moment; converting the target business information into an alternative flow limiting parameter for the target business, wherein the alternative flow limiting parameter is used to indicate an alternative maximum number of business requests allowed to be processed at the target moment, and the alternative flow limiting parameter is consistent with the target business. The absolute value of the difference between the business request amounts indicated by the information is less than or equal to the target threshold; detecting the alternative load parameters of the target business under the alternative flow limiting parameters, wherein the alternative load parameters are used to indicate the load resources required to execute the business requests that meet the alternative flow limiting parameters; configuring the target flow limiting parameters for the target business at the target time according to the alternative load parameters and the resource information of the target business, wherein the resource information is used to indicate the resources allowed to be provided by the target business at the target time, and the target flow limiting parameters are used to indicate the maximum number of business requests allowed to be processed at the target time, and the target business is used to control the execution of the transaction request according to the target flow limiting parameters at the target time. Specifically, the target business transaction request volume at the target time is predicted based on historical business data, and alternative flow control parameters are obtained based on the business request volume. Furthermore, the resources available at the target time are taken into account to comprehensively obtain the target flow control parameters to limit the execution of the target business transaction request at the target time. The target flow control parameters thus obtained not only take into account the system's resource load to ensure normal system operation, but also take into account the predicted business request volume at the target time to meet customer needs as much as possible and improve the customer experience. Therefore, the solution of this application solves the problem of low business traffic control efficiency in related technologies, thereby achieving the effect of improving business traffic control efficiency.

[0062] It should be noted that the present application controls the business traffic, including but not limited to the processing of business requests of the target business by the server. On the one hand, the present application uses the historical data of the server processing the target business requests to predict the number of business requests that need to be processed at the target time. On the other hand, the application monitors the server's resource usage (such as CPU utilization, memory usage, storage usage, etc.) when processing the business requests of the target business, and comprehensively determines the flow limit value of the target business, so as to reduce the restrictions on the development of the target business while ensuring the normal operation of the server. Optionally, the target business request includes but is not limited to being processed by a single server or multiple servers.

[0063] Optionally, in the embodiment provided in step S101, the target business information of the target business at the target moment is predicted based on the historical business data corresponding to the target business at the current moment, including but not limited to collecting data on the change in the amount of business requests initiated on the target business within the reference time period before the target moment, and predicting the target business information at the target moment based on prediction algorithms such as time series analysis and regression analysis.

[0064] Optionally, in the embodiments of the present application, the target business includes, but is not limited to, businesses involving specific resource utilization characteristics, such as query services and storage services, and may also be businesses classified according to actual business classifications in the financial industry, such as insurance services and deposit services. The target business's business request volume includes, but is not limited to, a certain trend in change, and target business information of the target business at a target time can be predicted based on historical changes in business request volume.

[0065] Optionally, in the embodiment provided in step S102, the alternative flow limiting parameter is used to indicate the alternative maximum number of service requests allowed to be processed at the target time, including but not limited to directly using the service request volume initiated on the target service at the target time indicated by the target service information as the alternative flow limiting parameter; or adding the target threshold value on the basis of the service request volume, and determining the obtained value as the alternative flow limiting parameter at the target time. Through the above method, the alternative flow limiting parameter value can be appropriately larger than the predicted service request volume to cope with the sudden increase in the service request volume and reduce the occurrence of unnecessary traffic restrictions; the target threshold value can also be subtracted from the service request volume, and the obtained value can be determined as the alternative flow limiting parameter at the target time. The above method can be applied to the case where the operation of the server processing the target service request is poor, and / or when the service request volume of the target service is continuously too large. By using the above method, the number of service requests that are discarded or need to wait before they can be processed can be controlled by controlling the target threshold value, thereby achieving control of service traffic from another perspective.

[0066] Optionally, in the embodiment provided in step S103, the alternative load parameter is used to indicate the load resource conditions required to execute a business request that meets the alternative flow limiting parameters, including but not limited to selecting characteristic resources based on the occupancy of server resources when processing the target business, and determining the resource quantity of the characteristic resources required to execute the business request that meets the alternative flow limiting parameters as the alternative load parameter. The alternative load parameter can also be determined as the value obtained by weighted calculation of the resource quantities of the various resources required to execute the business request that meets the alternative flow limiting parameters based on the occupancy of server resources when processing the target business, and the various server resources required to process the target business request.

[0067] Optionally, in the embodiment provided in step S104, the resource information of the target business is used to indicate the resources that the target business is allowed to provide at the target moment, including but not limited to the resource amount of one or more resources provided. Configuring the target flow limiting parameters for the target business at the target moment based on the alternative load parameters and the resource information of the target business includes but is not limited to comparing the resources that the target business indicated by the resource information is allowed to provide at the target moment and the load resources required to execute the business request that complies with the alternative flow limiting parameters as indicated by the alternative load parameters. When the provided resources are greater than or equal to the required occupied load resources, the alternative flow limiting parameters are directly determined as the target flow limiting parameters; when the provided resources are less than the required occupied load resources, the predicted business request amount can be divided into multiple parts, and the backup server and the server that originally processes the target business request are used to jointly process the target business, or when there is no backup server, the maximum historical flow limiting parameter is queried in the historical flow limiting parameters of the target business as the target flow limiting parameter.

[0068] As an optional implementation manner, predicting target service information of the target service at a target moment based on historical service data corresponding to the target service at a current moment includes:

[0069] Inputting the historical business data into a target prediction model, wherein the target prediction model records the relationship between the change of business request volume over time;

[0070] The target service request value output by the target prediction model is determined as the target service information.

[0071] As an optional implementation, the target prediction model is generated by the following method:

[0072] Inputting a target training sample into an initial prediction model, wherein the training sample is marked with a reference service request volume to be predicted;

[0073] Obtaining the candidate business request volume output by the initial prediction model;

[0074] The model parameters of the initial prediction model are modified according to the difference between the reference service request volume and the candidate service request volume to obtain the target prediction model.

[0075] Optionally, through the above steps, the business request volume at the target moment is predicted based on historical business request volume data, and then the flow limiting parameters are determined based on the predicted request volume at each target moment. The flow limiting parameters obtained in this way are more adaptable to the changes in the business request volume, which can not only avoid excessive and unnecessary restrictions on the business request volume and improve customer experience, but also release idle resources in time for other businesses to use, thereby improving overall business processing efficiency.

[0076] As an optional implementation manner, configuring a target current limiting parameter for the target service at the target time according to the alternative load parameter and the resource information of the target service includes:

[0077] Matching a target resource demand with a target resource supply of a target server, wherein the candidate load parameter includes the target resource demand, and the resource information includes the target resource supply, wherein the target resource supply is used to represent the service resources configured by the target server for handling the target service;

[0078] In a case where the target resource supply is greater than or equal to the target resource demand, the candidate current limiting parameter is determined as the target current limiting parameter of the target server.

[0079] Optionally, in an embodiment of the present application, the demand for the target resource includes but is not limited to the demand for one or more resources. When it is necessary to consider the demand for multiple resources, it includes but is not limited to requiring that the supply of the target resource can meet the demand for each of the multiple demand resources.

[0080] As an optional implementation manner, after matching the target resource demand with the target resource supply, the method further includes:

[0081] In a case where the target resource supply is less than the target resource demand, splitting the target resource demand into a first resource demand corresponding to the target server and a second resource demand corresponding to a reference server according to the target resource supply, wherein the reference server is a server having a binding relationship with the target server, the reference server is used to handle the target business to be handled by the target server, and the first resource demand is less than the target resource supply;

[0082] converting the candidate current limiting parameter into a first current limiting parameter and a second current limiting parameter according to a ratio between the first resource requirement and the second resource requirement;

[0083] The first current limiting parameter is determined as the target current limiting parameter of the target server, and the second current limiting parameter is determined as the reference current limiting parameter of the reference server.

[0084] Optionally, in an embodiment of the present application, the target resource demand is split into a first resource demand corresponding to the target server and a second resource demand corresponding to the reference server according to the target resource supply, including but not limited to allocating more target resource demand to the server with better operating conditions according to the operating conditions of the target server and the reference server, or splitting the target resource demand corresponding to the resource amount that the target server can provide to the target server, and splitting the remaining part of the target resource demand to the reference server.

[0085] As an optional implementation manner, the detecting the alternative load parameter of the target service under the alternative current limiting parameter includes:

[0086] Detecting a target service type of the target service, wherein the target service type is used to indicate a resource type of load resources required to be occupied when processing a service request of the target service;

[0087] Detecting a target resource type of a business resource used to handle a business of the target business type;

[0088] The candidate resource amount of the target resource type required for handling the candidate business processing amount is determined from the business processing amount and the resource amount of the target resource type that have a corresponding relationship.

[0089] Optionally, in an embodiment of the present application, the target business type of the target business includes but is not limited to read business, and the target resource type of the business resources used to handle the business of the target business type includes but is not limited to IO overhead, etc.

[0090] Optionally, in an embodiment of the present application, including but not limited to recording the relationship between the historical processing volume of the historical target business and the resource usage of the target type of resources required to handle the target business of the historical processing volume, after determining the alternative business processing volume of the target business and the target resource type of the target business, the alternative resource amount of the target resource type required to handle the alternative business processing volume can be determined from the business processing volume with a corresponding relationship and the resource amount of the target resource type.

[0091] As an optional implementation manner, converting the target service information into an alternative current limiting parameter of the target service includes:

[0092] matching the target threshold according to a target service processing requirement of the target service, wherein the target service processing requirement is used to indicate a processing quality required to be achieved for processing a target service request of the target service;

[0093] The target request volume is converted into an alternative flow limiting parameter of the target service according to the target threshold, wherein the target service information includes the target request volume.

[0094] Optionally, in the embodiment of the present application, the target threshold includes but is not limited to matching the target business processing requirements of the target business, or may be determined based on the operating status of the server processing the target business request. The target threshold includes but is not limited to an incremental numerical threshold or a percentage threshold.

[0095] As an optional implementation method, the present application also provides an intelligent transaction flow limiting device for bank-enterprise systems, which provides the bank-enterprise system with the ability to dynamically adjust transaction flow limiting parameters and dynamically expand and shrink capacity in real time, effectively ensuring stable production operation while meeting the needs of business expansion, and significantly reducing manual operation and maintenance costs. In the embodiment of the present application, based on big data analysis and machine learning algorithms, the flow limit value is dynamically adjusted by analyzing historical flow data and system performance indicators, thereby achieving more refined flow control. Dynamic flow limiting control is achieved through the following process steps:

[0096] Step S1, data collection: Continuously collect performance data during system operation, including but not limited to the requested QPS (query rate per second), system TPS (transactions per second), response time, system resource usage (such as CPU, memory usage, IO overhead), etc.

[0097] Step S2, feature selection: Select features related to current limiting from the collected data. These features can reflect the response and processing capabilities of the system under different loads.

[0098] Step S3, data analysis: Analyze the collected data to identify the system's operating modes and performance bottlenecks under different loads.

[0099] Step S4, model training: Use the selected features and historical traffic data to train a machine learning model. This model can learn the relationship between the system's processing capacity and the optimal flow limit value (i.e., the target flow limit parameter) under different feature values.

[0100] Step S5, traffic forecasting: Use the trained model to predict future traffic trends. This can be achieved through time series analysis, regression analysis, or other forecasting algorithms.

[0101] Step S6, Policy Generation: Based on the predicted traffic trends and the system's historical performance, the machine learning algorithm can generate a dynamic throttling policy. For example, if traffic is predicted to increase, the algorithm will increase the throttling value to accommodate the upcoming high load.

[0102] Step S7, real-time monitoring: While implementing the current limiting strategy, the machine learning model will continuously monitor the actual operation of the system and collect feedback data.

[0103] Step S8, Adaptive Adjustment: Based on real-time monitoring data, the model can further adjust and optimize the current limiting strategy. This process is iterative and aims to achieve rapid response to changes in system load.

[0104] Step S9, feedback loop: the actual system response and user satisfaction are used as feedback and re-input into the model for further training and optimization to form a closed-loop control.

[0105] Through the above steps, it is possible to analyze each customer's historical transaction status through intelligent algorithms, calculate the maximum concurrent demand of each customer's each transaction at the current moment, and automatically adjust the flow limit value based on the customer and transaction dimensions. Figure 2 This is a diagram illustrating the flow limiting situation when flow control is performed according to the service flow control method provided in the embodiment of the present application. Figure 1 . Figure 3 This is a diagram illustrating the flow limiting situation when flow control is performed according to the service flow control method provided in the embodiment of the present application. Figure 2 .like Figure 2 and Figure 3 As shown, the intelligent baseline upper limit is the transaction volume upper limit and the response time upper limit of the transaction request when the optimal flow limiting value (i.e., the target flow limiting parameter) corresponding to the target business at each moment takes effect. The business flow control method provided by this application and the intelligent transaction flow limiting device for the bank-enterprise system are used to control the business flow. The function of counting the transaction response time during the operation of the device will determine the current server load situation according to the transaction response time, which can not only reduce the restrictions on business development as much as possible when setting the business flow, but also ensure that the setting of the flow limiting value will not exceed the upper limit of the server load. Figure 4 This is a schematic diagram of the effect of dynamic expansion according to the service flow control method provided in the embodiment of the present application. Figure 1 , Figure 5 This is a schematic diagram of the effect of dynamic expansion according to the service flow control method provided in the embodiment of the present application. Figure 2 .like Figure 4 and Figure 5As shown, the current limiter monitors server CPU and memory usage. When business growth rapidly pushes CPU or memory usage to critical levels, and existing servers are unable to handle the full transaction volume, dynamically adjusting the current limiter becomes inadequate. In this scenario, dynamic capacity expansion is supported, directly bringing up backup servers to handle some of the transaction traffic. Furthermore, the current limiter also provides transaction monitoring capabilities, supporting transaction statistics by day, hour, and minute.

[0106] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0107] The present application also provides a service flow control device. It should be noted that the service flow control device of the present application embodiment can be used to execute the service flow control method provided in the present application embodiment. The service flow control device provided in the present application embodiment is introduced below.

[0108] Figure 6 Schematic diagram of a device for controlling service flow according to an embodiment of the present application. Figure 6 As shown, the device includes:

[0109] a prediction module, configured to predict target service information of a target service at a target time based on historical service data corresponding to the target service at a current time, wherein the target time is a time after the current time, the historical service data is used to indicate changes in the service request volume of the target service within a reference time period before the current time, and the target service information is used to indicate the service request volume initiated for the target service at the target time;

[0110] a conversion module, configured to convert the target service information into an alternative flow limiting parameter for the target service, wherein the alternative flow limiting parameter is used to indicate an alternative maximum number of service requests allowed to be processed at the target time, and an absolute value of a difference between the alternative flow limiting parameter and the service request amount indicated by the target service information is less than or equal to a target threshold;

[0111] A detection module, configured to detect an alternative load parameter of the target service under the alternative current limiting parameter, wherein the alternative load parameter is used to indicate the load resources required to execute a service request that meets the alternative current limiting parameter;

[0112] a configuration module, configured to configure a target current limiting parameter for the target business at the target time based on the alternative load parameter and resource information of the target business, wherein the resource information is used to indicate the resources that the target business is allowed to provide at the target time, the target current limiting parameter is used to indicate the maximum number of business requests allowed to be processed at the target time, and the target business is used to control the execution of transaction requests according to the target current limiting parameter at the target time.

[0113] The service flow control device provided by the embodiment of the present application adopts the following steps: predicting the target service information of the target service at the target moment based on the historical service data corresponding to the target service at the current moment, wherein the target moment is a moment after the current moment, and the historical service data is used to indicate the change in the service request volume of the target service in the reference time period before the current moment, and the target service information is used to indicate the service request volume initiated on the target service at the target moment; converting the target service information into an alternative flow limiting parameter for the target service, wherein the alternative flow limiting parameter is used to indicate the alternative maximum number of service requests allowed to be processed at the target moment, and the alternative flow limiting parameter is consistent with the target service. The absolute value of the difference between the business request amounts indicated by the information is less than or equal to the target threshold; detecting the alternative load parameters of the target business under the alternative flow limiting parameters, wherein the alternative load parameters are used to indicate the load resources required to execute the business requests that meet the alternative flow limiting parameters; configuring the target flow limiting parameters for the target business at the target time according to the alternative load parameters and the resource information of the target business, wherein the resource information is used to indicate the resources allowed to be provided by the target business at the target time, and the target flow limiting parameters are used to indicate the maximum number of business requests allowed to be processed at the target time, and the target business is used to control the execution of the transaction request according to the target flow limiting parameters at the target time. Specifically, the target business transaction request volume at the target time is predicted based on historical business data, and alternative flow control parameters are obtained based on the business request volume. Furthermore, the resources available at the target time are taken into account to comprehensively obtain the target flow control parameters to limit the execution of the target business transaction request at the target time. The target flow control parameters thus obtained not only take into account the system's resource load to ensure normal system operation, but also take into account the predicted business request volume at the target time to meet customer needs as much as possible and improve the customer experience. Therefore, the solution of this application solves the problem of low business traffic control efficiency in related technologies, thereby achieving the effect of improving business traffic control efficiency.

[0114] As an optional embodiment, the prediction module includes:

[0115] An input unit, configured to input the historical business data into a target prediction model, wherein the target prediction model records a relationship between changes in business request volume over time;

[0116] The first determining unit is configured to determine a target service request value output by the target prediction model as the target service information.

[0117] As an optional embodiment, the target prediction model is generated by the following method:

[0118] Inputting a target training sample into an initial prediction model, wherein the training sample is marked with a reference service request volume to be predicted;

[0119] Obtaining the candidate business request volume output by the initial prediction model;

[0120] The model parameters of the initial prediction model are modified according to the difference between the reference service request volume and the candidate service request volume to obtain the target prediction model.

[0121] As an optional embodiment, the configuration module includes:

[0122] a first matching unit, configured to match a target resource demand with a target resource supply of a target server, wherein the candidate load parameter includes the target resource demand, and the resource information includes the target resource supply, wherein the target resource supply is used to represent the service resources configured by the target server for handling the target service;

[0123] The second determining unit is configured to determine the candidate current limiting parameter as the target current limiting parameter of the target server when the target resource supply is greater than or equal to the target resource demand.

[0124] As an optional embodiment, the configuration module further includes:

[0125] a splitting unit configured to split the target resource demand into a first resource demand corresponding to the target server and a second resource demand corresponding to a reference server according to the target resource supply when the target resource supply is less than the target resource demand, wherein the reference server is a server having a binding relationship with the target server, the reference server is used to handle the target business to be handled by the target server, and the first resource demand is less than the target resource supply;

[0126] a first conversion unit, configured to convert the candidate current limiting parameter into a first current limiting parameter and a second current limiting parameter according to a ratio between the first resource requirement and the second resource requirement;

[0127] The third determining unit is configured to determine the first current limiting parameter as the target current limiting parameter of the target server, and determine the second current limiting parameter as the reference current limiting parameter of the reference server.

[0128] As an optional embodiment, the detection module includes:

[0129] A first detection unit is configured to detect a target service type of the target service, wherein the target service type is used to indicate a resource type of load resources required to be occupied when processing a service request of the target service;

[0130] A second detection unit is used to detect a target resource type of a business resource used to handle a business of the target business type;

[0131] The fourth determining unit is configured to determine the candidate resource amount of the target resource type required to handle the candidate business processing amount from the business processing amount and the resource amount of the target resource type that have a corresponding relationship.

[0132] As an optional embodiment, the conversion module includes:

[0133] a second matching unit, configured to match the target threshold according to a target service processing requirement of the target service, wherein the target service processing requirement is used to indicate a processing quality required to be achieved for processing a target service request of the target service;

[0134] The second conversion unit is configured to convert the target request volume into an alternative flow limiting parameter of the target service according to the target threshold, wherein the target service information includes the target request volume.

[0135] The service flow control device includes a processor and a memory. The above service flow control unit and the like are stored in the memory as program units, and the processor executes the above program units stored in the memory to implement corresponding functions.

[0136] The processor contains a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be configured, and efficient control of service traffic can be achieved by adjusting kernel parameters.

[0137] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0138] An embodiment of the present invention provides a computer-readable storage medium having a program stored thereon, which implements the method for controlling service traffic when the program is executed by a processor.

[0139] An embodiment of the present invention provides a processor, which is used to run a program, wherein the method for controlling business traffic is executed when the program is running.

[0140] Figure 7 Schematic diagram of an optional electronic device according to an embodiment of the present application. Figure 7 As shown, an embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented:

[0141] Predicting target service information of the target service at a target time based on historical service data corresponding to the target service at the current time, wherein the target time is a time after the current time, the historical service data is used to indicate changes in the service request volume of the target service during a reference time period before the current time, and the target service information is used to indicate the service request volume initiated for the target service at the target time;

[0142] Converting the target service information into an alternative flow limiting parameter for the target service, wherein the alternative flow limiting parameter is used to indicate an alternative maximum number of service requests allowed to be processed at the target time, and an absolute value of a difference between the alternative flow limiting parameter and the service request volume indicated by the target service information is less than or equal to a target threshold;

[0143] Detecting an alternative load parameter of the target service under the alternative current limiting parameter, wherein the alternative load parameter is used to indicate the load resources required to execute the service request that meets the alternative current limiting parameter;

[0144] A target current limiting parameter at the target time is configured for the target business based on the alternative load parameter and the resource information of the target business, wherein the resource information is used to indicate the resources that the target business is allowed to provide at the target time, the target current limiting parameter is used to indicate the maximum number of business requests allowed to be processed at the target time, and the target business is used to control the execution of the transaction request according to the target current limiting parameter at the target time.

[0145] Optionally, predicting target service information of the target service at a target moment based on historical service data corresponding to the target service at a current moment includes:

[0146] Inputting the historical business data into a target prediction model, wherein the target prediction model records the relationship between the change of business request volume over time;

[0147] The target service request value output by the target prediction model is determined as the target service information.

[0148] Optionally, the target prediction model is generated by the following method:

[0149] Inputting a target training sample into an initial prediction model, wherein the training sample is marked with a reference service request volume to be predicted;

[0150] Obtaining the candidate business request volume output by the initial prediction model;

[0151] The model parameters of the initial prediction model are modified according to the difference between the reference service request volume and the candidate service request volume to obtain the target prediction model.

[0152] Optionally, configuring a target current limiting parameter at the target time for the target service according to the alternative load parameter and the resource information of the target service includes:

[0153] Matching a target resource demand with a target resource supply of a target server, wherein the candidate load parameter includes the target resource demand, and the resource information includes the target resource supply, wherein the target resource supply is used to represent the service resources configured by the target server for handling the target service;

[0154] In a case where the target resource supply is greater than or equal to the target resource demand, the candidate current limiting parameter is determined as the target current limiting parameter of the target server.

[0155] Optionally, after matching the target resource demand with the target resource supply, the method further includes:

[0156] In a case where the target resource supply is less than the target resource demand, splitting the target resource demand into a first resource demand corresponding to the target server and a second resource demand corresponding to a reference server according to the target resource supply, wherein the reference server is a server having a binding relationship with the target server, the reference server is used to handle the target business to be handled by the target server, and the first resource demand is less than the target resource supply;

[0157] converting the candidate current limiting parameter into a first current limiting parameter and a second current limiting parameter according to a ratio between the first resource requirement and the second resource requirement;

[0158] The first current limiting parameter is determined as the target current limiting parameter of the target server, and the second current limiting parameter is determined as the reference current limiting parameter of the reference server.

[0159] Optionally, the detecting an alternative load parameter of the target service under the alternative current limiting parameter includes:

[0160] Detecting a target service type of the target service, wherein the target service type is used to indicate a resource type of load resources required to be occupied when processing a service request of the target service;

[0161] Detecting a target resource type of a business resource used to handle a business of the target business type;

[0162] The candidate resource amount of the target resource type required for handling the candidate business processing amount is determined from the business processing amount and the resource amount of the target resource type that have a corresponding relationship.

[0163] Optionally, converting the target service information into an alternative current limiting parameter of the target service includes:

[0164] matching the target threshold according to a target service processing requirement of the target service, wherein the target service processing requirement is used to indicate a processing quality required to be achieved for processing a target service request of the target service;

[0165] The target request volume is converted into an alternative flow limiting parameter of the target service according to the target threshold, wherein the target service information includes the target request volume.

[0166] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0167] The present application also provides a computer program product, which, when executed on a data processing device, is adapted to execute a program that initializes the following method steps: predicting target service information of a target service at a target time based on historical service data corresponding to the target service at a current time, wherein the target time is a time after the current time, the historical service data is used to indicate changes in the service request volume of the target service within a reference time period before the current time, and the target service information is used to indicate the service request volume initiated for the target service at the target time;

[0168] Converting the target service information into an alternative flow limiting parameter for the target service, wherein the alternative flow limiting parameter is used to indicate an alternative maximum number of service requests allowed to be processed at the target time, and an absolute value of a difference between the alternative flow limiting parameter and the service request volume indicated by the target service information is less than or equal to a target threshold;

[0169] Detecting an alternative load parameter of the target service under the alternative current limiting parameter, wherein the alternative load parameter is used to indicate the load resources required to execute the service request that meets the alternative current limiting parameter;

[0170] A target current limiting parameter at the target time is configured for the target business based on the alternative load parameter and the resource information of the target business, wherein the resource information is used to indicate the resources that the target business is allowed to provide at the target time, the target current limiting parameter is used to indicate the maximum number of business requests allowed to be processed at the target time, and the target business is used to control the execution of the transaction request according to the target current limiting parameter at the target time.

[0171] Optionally, predicting target service information of the target service at a target moment based on historical service data corresponding to the target service at a current moment includes:

[0172] Inputting the historical business data into a target prediction model, wherein the target prediction model records the relationship between the change of business request volume over time;

[0173] The target service request value output by the target prediction model is determined as the target service information.

[0174] Optionally, the target prediction model is generated by the following method:

[0175] Inputting a target training sample into an initial prediction model, wherein the training sample is marked with a reference service request volume to be predicted;

[0176] Obtaining the candidate business request volume output by the initial prediction model;

[0177] The model parameters of the initial prediction model are modified according to the difference between the reference service request volume and the candidate service request volume to obtain the target prediction model.

[0178] Optionally, configuring a target current limiting parameter at the target time for the target service according to the alternative load parameter and the resource information of the target service includes:

[0179] Matching a target resource demand with a target resource supply of a target server, wherein the candidate load parameter includes the target resource demand, and the resource information includes the target resource supply, wherein the target resource supply is used to represent the service resources configured by the target server for handling the target service;

[0180] In a case where the target resource supply is greater than or equal to the target resource demand, the candidate current limiting parameter is determined as the target current limiting parameter of the target server.

[0181] Optionally, after matching the target resource demand with the target resource supply, the method further includes:

[0182] In a case where the target resource supply is less than the target resource demand, splitting the target resource demand into a first resource demand corresponding to the target server and a second resource demand corresponding to a reference server according to the target resource supply, wherein the reference server is a server having a binding relationship with the target server, the reference server is used to handle the target business to be handled by the target server, and the first resource demand is less than the target resource supply;

[0183] converting the candidate current limiting parameter into a first current limiting parameter and a second current limiting parameter according to a ratio between the first resource requirement and the second resource requirement;

[0184] The first current limiting parameter is determined as the target current limiting parameter of the target server, and the second current limiting parameter is determined as the reference current limiting parameter of the reference server.

[0185] Optionally, the detecting an alternative load parameter of the target service under the alternative current limiting parameter includes:

[0186] Detecting a target service type of the target service, wherein the target service type is used to indicate a resource type of load resources required to be occupied when processing a service request of the target service;

[0187] Detecting a target resource type of a business resource used to handle a business of the target business type;

[0188] The candidate resource amount of the target resource type required for handling the candidate business processing amount is determined from the business processing amount and the resource amount of the target resource type that have a corresponding relationship.

[0189] Optionally, converting the target service information into an alternative current limiting parameter of the target service includes:

[0190] matching the target threshold according to a target service processing requirement of the target service, wherein the target service processing requirement is used to indicate a processing quality required to be achieved for processing a target service request of the target service;

[0191] The target request volume is converted into an alternative flow limiting parameter of the target service according to the target threshold, wherein the target service information includes the target request volume.

[0192] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0193] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0194] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0195] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0196] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0197] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0198] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0199] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0200] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0201] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for controlling business traffic, characterized in that: include: Predicting target service information of the target service at a target time based on historical service data corresponding to the target service at the current time, wherein the target time is a time after the current time, the historical service data is used to indicate changes in the service request volume of the target service during a reference time period before the current time, and the target service information is used to indicate the service request volume initiated for the target service at the target time; Converting the target service information into an alternative flow limiting parameter for the target service, wherein the alternative flow limiting parameter is used to indicate an alternative maximum number of service requests allowed to be processed at the target time, and an absolute value of a difference between the alternative flow limiting parameter and the service request volume indicated by the target service information is less than or equal to a target threshold; Detecting an alternative load parameter of the target service under the alternative current limiting parameter, wherein the alternative load parameter is used to indicate the load resources required to execute the service request that meets the alternative current limiting parameter; configuring a target rate limiting parameter at the target time for the target service based on the alternative load parameter and resource information of the target service, wherein the resource information is used to indicate resources allowed to be provided by the target service at the target time, and the target rate limiting parameter is used to indicate a maximum number of service requests allowed to be processed at the target time, and the target service is used to control the execution of transaction requests according to the target rate limiting parameter at the target time; The configuring the target current limiting parameter at the target time for the target service according to the alternative load parameter and the resource information of the target service includes: Matching a target resource demand with a target resource supply of a target server, wherein the candidate load parameter includes the target resource demand, and the resource information includes the target resource supply, wherein the target resource supply is used to represent the service resources configured by the target server for handling the target service; In a case where the target resource supply is greater than or equal to the target resource demand, determining the alternative current limiting parameter as the target current limiting parameter of the target server; When the target resource supply is less than the target resource demand, the alternative flow limiting parameters are split into the target flow limiting parameters of the target server and the reference flow limiting parameters of the reference server, wherein the reference server is a server that has a binding relationship with the target server, and the reference server is used to handle the target business to be handled by the target server.

2. The method according to claim 1, characterized in that The target service information of the target service at the target moment is predicted based on the historical service data corresponding to the target service at the current moment, including: Inputting the historical business data into a target prediction model, wherein the target prediction model records the relationship between the change of business request volume over time; The target service request value output by the target prediction model is determined as the target service information.

3. The method according to claim 2, characterized in that The target prediction model is generated by the following method: Inputting a target training sample into an initial prediction model, wherein the training sample is marked with a reference service request volume to be predicted; Obtaining the candidate business request volume output by the initial prediction model; The model parameters of the initial prediction model are modified according to the difference between the reference service request volume and the candidate service request volume to obtain the target prediction model.

4. The method according to claim 1, wherein Splitting the candidate current limiting parameters into the target current limiting parameters of the target server and the reference current limiting parameters of the reference server includes: splitting the target resource demand into a first resource demand corresponding to the target server and a second resource demand corresponding to a reference server according to the target resource supply, wherein the first resource demand is smaller than the target resource supply; converting the candidate current limiting parameter into a first current limiting parameter and a second current limiting parameter according to a ratio between the first resource requirement and the second resource requirement; The first current limiting parameter is determined as the target current limiting parameter of the target server, and the second current limiting parameter is determined as the reference current limiting parameter of the reference server.

5. The method according to claim 1, characterized in that The detecting the alternative load parameter of the target service under the alternative current limiting parameter includes: Detecting a target service type of the target service, wherein the target service type is used to indicate a resource type of load resources required to be occupied when processing a service request of the target service; Detecting a target resource type of a business resource used to handle a business of the target business type; The alternative resource amount of the target resource type required to process the service request amount at the target moment is determined from the corresponding service processing amount and the resource amount of the target resource type, wherein the alternative load parameter includes the alternative resource amount.

6. The method according to claim 1, characterized in that The converting the target service information into an alternative current limiting parameter for the target service includes: matching the target threshold according to a target service processing requirement of the target service, wherein the target service processing requirement is used to indicate a processing quality required to be achieved for processing a target service request of the target service; The target request volume is converted into an alternative flow limiting parameter of the target service according to the target threshold, wherein the target service information includes the target request volume.

7. A device for controlling business traffic, characterized in that: include: a prediction module, configured to predict target service information of a target service at a target time based on historical service data corresponding to the target service at a current time, wherein the target time is a time after the current time, the historical service data is used to indicate changes in the service request volume of the target service within a reference time period before the current time, and the target service information is used to indicate the service request volume initiated for the target service at the target time; a conversion module, configured to convert the target service information into an alternative flow limiting parameter for the target service, wherein the alternative flow limiting parameter is used to indicate an alternative maximum number of service requests allowed to be processed at the target time, and an absolute value of a difference between the alternative flow limiting parameter and the service request amount indicated by the target service information is less than or equal to a target threshold; A detection module, configured to detect an alternative load parameter of the target service under the alternative current limiting parameter, wherein the alternative load parameter is used to indicate the load resources required to execute a service request that meets the alternative current limiting parameter; a configuration module configured to configure a target current limiting parameter at the target time for the target business based on the alternative load parameter and the resource information of the target business, wherein the resource information is used to indicate the resources allowed to be provided by the target business at the target time, the target current limiting parameter is used to indicate the maximum number of business requests allowed to be processed at the target time, and the target business is used to control the execution of transaction requests according to the target current limiting parameter at the target time; configuring the target current limiting parameter at the target time for the target business based on the alternative load parameter and the resource information of the target business includes: matching a target resource demand with a target resource supply of a target server, wherein the alternative load parameter includes the target resource demand, and the resource information includes the target resource supply, wherein the target resource supply is used to represent the business resources configured by the target server for handling the target business; and determining the alternative current limiting parameter as the target current limiting parameter of the target server when the target resource supply is greater than or equal to the target resource demand; When the target resource supply is less than the target resource demand, the alternative flow limiting parameters are split into the target flow limiting parameters for the target server and the reference flow limiting parameters of the reference server, wherein the reference server is a server having a binding relationship with the target server, and the reference server is used to handle the target business to be handled by the target server.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the service flow control method according to any one of claims 1 to 6 is executed.

9. An electronic device, characterized in that: The method comprises one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

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