Methods, devices, electronic equipment and storage media for processing search requests

Abstract

This application proposes a method, apparatus, electronic device, and storage medium for processing credit inquiry requests, relating to the field of credit inquiry technology. Upon receiving a credit inquiry request, the method obtains the latest traffic indicators and their latest weights, which characterize the response of the credit inquiry interface to interface calls. Based on the traffic indicators and the latest weights, it predicts the current load capacity quantification value of the credit inquiry interface. Referring to this value, it determines a strategy for adaptively adjusting traffic based on the current load capacity of the credit inquiry interface. Based on this strategy, it determines the maximum number of credit inquiry requests that can be sent in the current batch, and refers to this maximum number of requests to determine the target credit inquiry requests to be sent in the current batch. This application updates the weights of the traffic indicators in real time based on the dynamic changes in the traffic indicators and the dynamic changes in the volume of credit inquiry business at the service end, so as to adaptively adjust the weights of the traffic indicators in response to these changes, thereby improving the responsiveness to changes in the state of the credit inquiry interface.

Description

Technical Field

[0001] This application relates to the field of credit inquiry technology, and in particular to a method, apparatus, electronic device and storage medium for processing credit inquiry requests. Background Technology

[0002] Currently, personal credit reports are obtained through a credit data query interface provided by a specific credit reporting center, such as the Credit Reference Center of the People's Bank of China. All institutions, including banks and financial institutions, that need to obtain personal credit reports must apply for and obtain approval from this credit reporting center before they can use the query interface (i.e., the credit data query interface). Therefore, if the query interface experiences interface instability or network anomalies, it will inevitably affect all connected institutions. Furthermore, the access party (i.e., the interface caller) cannot control traffic switching or switch to a backup interface, leaving them in a relatively passive position.

[0003] Therefore, how to automatically adjust the call speed of the query interface on the interface caller to improve the ability to respond to changes in the query interface status has become an urgent problem to be solved. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, electronic device, and storage medium for processing tax collection requests to address the aforementioned technical problems, so as to automatically adjust the calling speed of the tax collection interface on the interface caller and improve the ability to respond to changes in the status of the tax collection interface.

[0005] A method for processing credit inquiry requests includes: acquiring a credit inquiry request to be processed, wherein the credit inquiry request is a request for querying credit data sent by a business terminal; acquiring the latest traffic indicator and the latest weight of the traffic indicator, wherein the weight of the traffic indicator is updated in real time based on the dynamic changes of the traffic indicator and the dynamic changes of the credit inquiry business volume of the business terminal, and the traffic indicator is used to characterize the response of the credit inquiry interface to the interface call; predicting the load capacity of the credit inquiry interface based on the latest traffic indicator and the latest weight of the traffic indicator to obtain a load capacity quantification value; and determining the load capacity quantification value range in which the load capacity quantification value is located from the load capacity quantification value ranges corresponding to different request volume adjustment strategies, so as to determine the execution to be performed. A request volume adjustment strategy is used to adjust the maximum number of credit investigation requests that can be sent in a single batch. The strategy is executed to determine the maximum number of credit investigation requests that can be sent in the current batch. The current batch refers to the batch corresponding to the current moment when sending credit investigation requests in batches. Referring to the maximum request volume, target credit investigation requests that need to be sent in the current batch are selected from all received credit investigation requests, including the pending requests. The target credit investigation requests are sent to the credit investigation server to invoke the server's credit investigation interface and obtain the credit information required for the target credit investigation request. The credit information is then sent to the business terminal corresponding to the target credit investigation request.

[0006] In this embodiment, the traffic metrics are updated in real time based on a preset time period. The traffic metrics include the success rate of calling the tax retrieval interface within the preset time period, the average response time of the tax retrieval interface for each interface call within the preset time period, the bandwidth utilization rate of the tax retrieval interface call process within the preset time period, and the number of tax retrieval requests processed within the preset time period. Predicting the load capacity of the tax retrieval interface based on the latest traffic metrics and the latest weights of the traffic metrics to obtain a quantified load capacity value includes: multiplying the success rate, the average response time, the bandwidth utilization rate, and the number of tax retrieval requests processed within the preset time period by their respective latest weights; and summing all the products to obtain the quantified load capacity value.

[0007] In this embodiment, the received all the tampering requests further include tampering requests cached in a delay queue; the tampering requests to be processed include tampering requests extracted from the delay queue, and / or the latest tampering requests not cached in the delay queue. The step of filtering target tampering requests to be sent in the current batch from all the received tampering requests, referring to the maximum request volume, includes: obtaining the number of tampering requests to be processed; determining the relationship between the number of tampering requests to be processed and the maximum request volume; if the number of tampering requests to be processed is greater than the maximum request volume, then, based on the maximum request volume, selecting target tampering requests from the received tampering requests to be sent in the current batch. The target requests are selected from the requests for evidence collection, and the total number of the target requests is the same as the maximum request volume. The remaining requests to be processed after selection are cached in the delay queue. If the number of requests to be processed is equal to the maximum request volume, all requests to be processed are used as the target requests. If the number of requests to be processed is less than the maximum request volume, the cached requests are extracted from the delay queue according to the difference between the maximum request volume and the number of requests to be processed, and used as candidate requests. The candidate requests and the requests to be processed are used as the target requests.

[0008] In this embodiment, the weight of the traffic indicator is updated in the following way: The latest weight learning rate, latest weight adjustment coefficient, and latest expected value of the traffic indicator are obtained. The weight learning rate is updated in real time based on the fluctuations of historical data of the traffic indicator, and the weight adjustment coefficient is updated in real time based on the fluctuations of historical data of the tax collection volume. The difference between the latest expected value and the latest traffic indicator is calculated. The product of the difference and the latest weight adjustment coefficient is calculated as the weight change. The product of the weight change and the latest weight learning rate is added to the latest weight of the traffic indicator to obtain the updated weight.

[0009] In this embodiment, the weight learning rate is updated in the following way: based on the historical data of the traffic indicator, a first feature value of the traffic indicator is calculated, the first feature value being used to reflect the fluctuation of the historical data of the traffic indicator; if the first feature value is within a first preset range, the weight learning rate is increased; if the first feature value is within a second preset range, the weight learning rate is decreased; the minimum value of the first preset range is greater than the maximum value of the second preset range.

[0010] In this embodiment, the weight adjustment coefficient is updated in the following manner: historical data of the tax collection volume is obtained; based on the historical data of the tax collection volume, a second characteristic value of the tax collection volume is calculated, the second characteristic value being used to reflect the fluctuation of the historical data of the tax collection volume; if the second characteristic value is within a third preset range, the weight adjustment coefficient is increased; if the second characteristic value is within a fourth preset range, the weight adjustment coefficient is decreased; the minimum value of the third preset range is greater than the maximum value of the fourth preset range.

[0011] In this embodiment of the application, when the traffic indicator is the average response time, if the second feature value is within the third preset range, the method further includes: determining whether the tax collection volume is on an increasing trend; if so, reducing the expected value of the average response time to decrease the weight of the average response time; and / or; when the traffic indicator is the success rate, if the second feature value is within the third preset range, the method further includes: determining whether the tax collection volume is on an increasing trend; if so, increasing the expected value of the success rate to increase the weight of the success rate.

[0012] A processing apparatus for credit investigation requests, the apparatus comprising: a first acquisition module, configured to acquire a credit investigation request to be processed, the credit investigation request being a request for querying credit data sent by a service terminal; a second acquisition module, configured to acquire the latest traffic indicator and the latest weight of the traffic indicator, the weight of the traffic indicator being updated in real time based on the dynamic changes of the traffic indicator and the dynamic changes of the credit investigation volume of the service terminal, the traffic indicator being used to characterize the response of the credit investigation interface to interface calls; a prediction module, configured to predict the load capacity of the credit investigation interface based on the latest traffic indicator and the latest weight of the traffic indicator, to obtain a load capacity quantification value; and a first determination module, configured to determine the load capacity quantification value range in which the load capacity quantification value is located from the load capacity quantification value ranges corresponding to different request volume adjustment strategies, so as to determine the execution to be performed. The system includes a request volume adjustment strategy, which adjusts the maximum number of credit investigation requests that can be sent in a single batch; a second determination module, which executes the request volume adjustment strategy to determine the maximum number of credit investigation requests that can be sent in the current batch; the current batch refers to the batch corresponding to the current time when credit investigation requests are sent in batches; a filtering module, which, with reference to the maximum request volume, filters out the target credit investigation requests that need to be sent in the current batch from all the received credit investigation requests, including the pending credit investigation requests; an information acquisition module, which sends the target credit investigation request to the credit investigation server to call the credit investigation interface of the credit investigation server and obtain the credit investigation data required for the target credit investigation request; and a sending module, which sends the credit investigation data to the business terminal corresponding to the target credit investigation request.

[0013] In this embodiment, the traffic metrics are updated in real time based on a preset time period. The traffic metrics include the success rate of calling the tax collection interface within the preset time period, the average response time of the tax collection interface for each interface call within the preset time period, the bandwidth utilization rate of the tax collection interface call process within the preset time period, and the number of tax collection requests processed within the preset time period. The prediction module is used to: multiply the success rate, the average response time, the bandwidth utilization rate, and the number of tax collection requests processed within the preset time period by the corresponding latest weights; and sum all the products to obtain the load capacity quantification value.

[0014] In this embodiment of the application, all received tamper requests also include tamper requests cached in a delay queue; the tamper requests to be processed include tamper requests extracted from the delay queue, and / or the latest tamper requests not cached in the delay queue; the filtering module is configured to: obtain the number of tamper requests to be processed; determine the relationship between the number of tamper requests to be processed and the maximum request quantity; if the number of tamper requests to be processed is greater than the maximum request quantity, then, based on the maximum request quantity, filter out the target tamper request from the tamper requests to be processed, wherein the target... The total number of target query requests is the same as the maximum request volume; the remaining query requests after filtering are cached in the delay queue; if the number of query requests to be processed is equal to the maximum request volume, then all query requests to be processed are used as target query requests; if the number of query requests to be processed is less than the maximum request volume, then the cached query requests are extracted from the delay queue according to the difference between the maximum request volume and the number of query requests to be processed, and used as candidate query requests; the candidate query requests and the query requests to be processed are used as target query requests.

[0015] In this embodiment of the application, the device is further configured to update the weight of the traffic indicator in the following manner: obtaining the latest weight learning rate, the latest weight adjustment coefficient, and the latest expected value of the traffic indicator, wherein the weight learning rate is updated in real time based on the fluctuation of historical data of the traffic indicator, and the weight adjustment coefficient is updated in real time based on the fluctuation of historical data of the tax collection volume; calculating the difference between the latest expected value and the latest traffic indicator; calculating the product of the difference and the latest weight adjustment coefficient as the weight change; and adding the product between the weight change and the latest weight learning rate to the latest weight of the traffic indicator to obtain the updated weight.

[0016] In this embodiment of the application, the device is further configured to update the weight learning rate in the following manner: calculate a first feature value of the traffic indicator based on historical data of the traffic indicator, wherein the first feature value is used to reflect the fluctuation of the historical data of the traffic indicator; if the first feature value is within a first preset range, increase the weight learning rate; if the first feature value is within a second preset range, decrease the weight learning rate; wherein the minimum value of the first preset range is greater than the maximum value of the second preset range.

[0017] In this embodiment of the application, the device is further configured to update the weight adjustment coefficient in the following manner: acquiring historical data of the tax collection volume; calculating a second characteristic value of the tax collection volume based on the historical data of the tax collection volume, the second characteristic value being used to reflect the fluctuation of the historical data of the tax collection volume; increasing the weight adjustment coefficient if the second characteristic value is within a third preset range; decreasing the weight adjustment coefficient if the second characteristic value is within a fourth preset range; the minimum value of the third preset range is greater than the maximum value of the fourth preset range.

[0018] In this embodiment of the application, when the traffic indicator is the average response time, if the second feature value is within the third preset range, the device is further configured to: determine whether the tax collection volume is on an increasing trend; if so, reduce the expected value of the average response time to decrease the weight of the average response time; and / or; when the traffic indicator is the success rate, if the second feature value is within the third preset range, the device is further configured to: determine whether the tax collection volume is on an increasing trend; if so, increase the expected value of the success rate to increase the weight of the success rate.

[0019] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for processing a query request as described in the above embodiments.

[0020] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for processing a query request as described in the above embodiments.

[0021] In summary, this application proposes a method, apparatus, electronic device, and storage medium for processing tax collection requests. The method, after receiving a tax collection request to be processed, obtains the latest value of a traffic indicator that characterizes the response of the tax collection interface to interface calls, i.e., the latest traffic indicator; simultaneously obtains the latest weight of the traffic indicator; predicts the current load capacity of the tax collection interface based on the traffic indicator and its latest weight, obtaining a quantified load capacity value; selects a strategy from multiple request volume adjustment strategies that can adaptively adjust the traffic for the current load capacity of the tax collection interface, i.e., a request volume adjustment strategy to be executed; executes the request volume adjustment strategy to determine the maximum number of tax collection requests that can be sent in the current batch; and, referring to the maximum request volume, selects the target tax collection request to be sent in the current batch from all received tax collection requests; then sends the target tax collection request to the tax collection server to call the tax collection interface of the tax collection server, obtains the tax collection data required for the target tax collection request, and sends this tax collection data to the business terminal corresponding to the target tax collection request. This application updates the weight of traffic metrics in real time based on the dynamic changes in traffic metrics and the dynamic changes in tax collection and inspection volume on the business side. This allows for adaptive adjustment of the weight of traffic metrics in response to these changes, applying different levels of attention to traffic metrics in different scenarios. This improves the accuracy of predicting the load capacity of the tax collection and inspection interface and adjusts the frequency of tax collection and inspection request calls based on the current load capacity of the tax collection and inspection interface to enhance the ability to respond to changes in the state of the tax collection and inspection interface. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a method for processing a search request according to an exemplary embodiment of this application;

[0024] Figure 2 This is a flowchart illustrating a method for processing a search request according to another exemplary embodiment of this application;

[0025] Figure 3 This is a flowchart illustrating a method for processing a search request according to another exemplary embodiment of this application;

[0026] Figure 4 This is a flowchart illustrating a method for processing a search request according to another exemplary embodiment of this application;

[0027] Figure 5This is a flowchart illustrating a method for processing a search request according to another exemplary embodiment of this application;

[0028] Figure 6 This is a flowchart illustrating a method for processing a search request according to another exemplary embodiment of this application;

[0029] Figure 7 This is a flowchart illustrating a method for processing a search request according to another exemplary embodiment of this application;

[0030] Figure 8 This is a schematic block diagram illustrating a processing apparatus for a search request according to another exemplary embodiment of this application;

[0031] Figure 9 This is a schematic block diagram of an electronic device according to an exemplary embodiment of this application. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The embodiments described with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0033] The credit inquiry request processing method proposed in this application can be applied to the server side of business ports such as platforms / software / web pages that have a need to obtain credit data, such as the server side of banking business processing systems, or the server side of credit and wealth management application software. Since personal credit reports are currently obtained through a specific credit reference center, such as the Credit Reference Center of the People's Bank of China, which provides a credit data query interface (hereinafter referred to as the inquiry interface), the server side of the banking business processing system can control the inquiry traffic when obtaining user credit data using the credit inquiry request processing method proposed in this application. This method sends the determined target inquiry requests to the Credit Reference Center of the People's Bank of China, i.e., the inquiry server side, to call the inquiry interface of the inquiry server side, ensuring that the inquiry interface call can be performed efficiently and orderly.

[0034] Figure 1 This is a flowchart illustrating a method for processing a search request according to an exemplary embodiment of this application, such as... Figure 1 As shown, the method for processing this inquiry request may include the following steps:

[0035] S101, Obtain the pending credit inquiry request, which is a request sent by the business terminal to query credit data;

[0036] The pending query requests can be newly received queries from the business terminal, or query requests extracted from the delay queue according to a preset strategy. This preset strategy can be configured as needed, such as a thread pool scheduling strategy, and this application does not impose any restrictions.

[0037] For example, the server can receive tax collection requests sent by the business side in real time, process the latest received tax collection requests, and if no tax collection request is received from the business side after a preset time, the server can retrieve the cached tax collection requests from the delay queue for processing.

[0038] Alternatively, when receiving new query requests, a certain number of query requests can be extracted from the delayed queue for processing together, thereby avoiding excessively long waiting times for requests in the delayed queue.

[0039] Delayed queues can be used to cache query requests that the server has not processed in a timely manner.

[0040] S102, obtain the latest traffic metrics and the latest weights of the traffic metrics.

[0041] After receiving a query request to be processed, the server can obtain the latest traffic metrics and the latest weight values ​​of the traffic metrics from the data center.

[0042] In some embodiments, traffic metrics can be collected in real time and reported to the data center by setting up asynchronous collection tasks, so that the server can obtain the latest traffic metrics from the data center when needed.

[0043] In some embodiments, the weights of each traffic metric can be dynamically updated in real time on the server side, and the latest weights of the traffic metrics can be stored.

[0044] In this embodiment, the weight of the traffic indicator is continuously updated based on the dynamic changes in the traffic indicator and the dynamic changes in the tax collection volume of the business terminal. The tax collection volume can be the number of tax collection requests initiated by the business terminal within a certain period.

[0045] For example, when updating the weight of traffic indicators, an observation period can be predefined, and the weight of traffic indicators can be updated based on the dynamic changes of traffic indicators and the dynamic changes of tax collection volume at the business end during the observation period.

[0046] The dynamic changes of the aforementioned flow indicators during the observation period can be obtained by analyzing the dynamic changes of historical data of the flow indicators during the observation period. The dynamic changes mentioned here may include the trend and / or fluctuation. For example, the dynamic changes of the flow indicators can be understood as the trend of historical data of the flow indicators during the preset observation period, such as an increasing trend or a decreasing trend; and / or the fluctuation of historical data of the flow indicators during the preset observation period, such as large fluctuations or small fluctuations.

[0047] The aforementioned business terminals may include multiple business terminals connected to the server. When updating the weight of traffic indicators, the amount of tax collection business used can be understood as the sum of the tax collection business volumes of all business terminals connected to the server.

[0048] The traffic metrics are used to characterize the response of the query interface to interface calls.

[0049] S103, based on the latest traffic indicators and the latest weights of the traffic indicators, predict the load capacity of the query interface and obtain a quantified value of the load capacity.

[0050] This application embodiment combines traffic metrics and the latest weight of those traffic metrics to predict the load capacity of the query interface.

[0051] Adjust the weight of the latest traffic metrics in the prediction of load capacity quantification using the latest weights of the traffic metrics.

[0052] In this embodiment, the weight of the traffic indicator changes with the traffic indicator. When the traffic indicator exhibits different statistical characteristics (such as showing an increasing trend and / or showing large fluctuations), this embodiment will adaptively update the latest weight of the traffic indicator so as to apply different weight values ​​to the traffic indicator when predicting the load capacity of the tax query interface in different scenarios, thereby improving the accuracy of predicting the load capacity of the tax query interface.

[0053] S104, determine the load capacity quantification value range where the load capacity quantification value is located from the load capacity quantification value range corresponding to different request volume adjustment strategies, so as to determine the request volume adjustment strategy to be executed. The request volume adjustment strategy is used to adjust the maximum request volume of the investigation request that can be sent in a single batch.

[0054] In practice, the server can handle multiple credit investigation requests simultaneously, that is, send credit investigation requests to the credit investigation server to call the credit investigation interface to obtain credit data.

[0055] The number of investigation requests sent simultaneously each time can be understood as the number of requests sent in a single batch in this application embodiment, where the number of requests refers to the number of investigation requests.

[0056] In this application embodiment, different request volume adjustment strategies are pre-set. The request volume adjustment strategy is used to adjust the maximum request volume that can be sent in a single batch. The maximum request volume can be understood as the maximum number of investigation requests that can be sent in a single batch.

[0057] To improve the accuracy of request volume adjustment, different request volume adjustment strategies can be used to adjust the maximum number of requests that the server can send in a single batch when the load capacity of the query interface is in different states / stages.

[0058] In some embodiments, for example, request volume adjustment strategy A increases the maximum request volume that can be sent in a single batch, and request volume adjustment strategy A corresponds to the load capacity quantization interval a; request volume adjustment strategy B decreases the maximum request volume that can be sent in a single batch, and request volume adjustment strategy B corresponds to the load capacity quantization interval b; request volume adjustment strategy C maintains the current maximum request volume that can be sent in a single batch, and request volume adjustment strategy C corresponds to the load capacity quantization interval c.

[0059] It should be noted that the division of load capacity quantification intervals corresponding to different request volume adjustment strategies can be achieved through statistical processing of a large amount of sample data or processing by a deep learning model, and this application does not impose any restrictions.

[0060] S105, execute the pending request volume adjustment strategy to determine the maximum number of investigation requests that can be sent in the current batch; the current batch is the batch corresponding to the current moment when investigation requests are sent in batches.

[0061] The maximum number of requests that can be sent in a single batch, determined by the adjustment strategy based on the number of requests to be executed, will be used as the maximum number of requests that can be sent in the current batch.

[0062] S106, referring to the maximum request volume, select the target investigation requests that need to be sent in the current batch from all the received investigation requests, wherein all the received investigation requests include the investigation requests to be processed.

[0063] S107, the target credit investigation request is sent to the credit investigation server to call the credit investigation interface of the credit investigation server and obtain the credit investigation data required for the target credit investigation request.

[0064] The target credit investigation request is sent to the credit investigation server. The credit investigation server can parse the target credit investigation request, obtain the credit investigation input parameters from it, input the credit investigation input parameters into the credit investigation interface, obtain the credit investigation data required for the target credit investigation request, and return the credit investigation data to the aforementioned server (i.e. the server corresponding to the business end).

[0065] The aforementioned "received tax inquiry request" may include the latest tax inquiry request received from the business terminal and the tax inquiry request received by the server from the business terminal at a historical time.

[0066] The maximum request volume determined in step S105 is used to constrain the number of target investigation requests determined in step S106, ensuring that the total number of target investigation requests does not exceed the maximum request volume determined in step S105.

[0067] S108, the credit data is sent to the business terminal corresponding to the target credit inquiry request.

[0068] The server will return the credit data obtained from the credit investigation interface to the business unit that sent the credit investigation request.

[0069] In summary, the method for processing credit investigation requests proposed in this application, after obtaining the credit investigation request to be processed, obtains the latest value of the traffic indicator that can characterize the response of the credit investigation interface to the interface call, i.e., the latest traffic indicator; at the same time, it obtains the latest weight of the traffic indicator; predicts the current load capacity of the credit investigation interface based on the traffic indicator and the latest weight of the traffic indicator, and obtains a quantitative value of the load capacity; selects a strategy that can adaptively adjust the traffic for the current load capacity of the credit investigation interface from multiple request volume adjustment strategies, i.e., the request volume adjustment strategy to be executed; executes the request volume adjustment strategy to be executed, determines the maximum number of credit investigation requests that can be sent in the current batch, and with reference to the maximum number of requests, selects the target credit investigation request that needs to be sent in the current batch from the received credit investigation requests, and then sends the target credit investigation request to the credit investigation server to call the credit investigation interface of the credit investigation server to obtain the credit investigation data required by the target credit investigation request, and sends this credit investigation data to the business terminal corresponding to the target credit investigation request. This application updates the weights of traffic metrics in real time based on dynamic changes in traffic indicators and the volume of tax collection and verification services on the business side. This allows for adaptive adjustments to the weights of various metrics based on their changes, assigning different levels of attention to different metrics in different scenarios. This improves the accuracy of predicting the load capacity of the tax collection interface and adjusts the frequency of tax collection interface calls based on its current load capacity, thereby enhancing its responsiveness to changes in the interface's status. This avoids increasing the processing pressure on the tax collection interface and improves overall tax collection efficiency.

[0070] Based on the above embodiments, such as Figure 2 As shown, the traffic metrics are updated in real time based on a preset time period. The traffic metrics include the success rate of calling the tax collection interface within the preset time period, the average response time of the tax collection interface to each interface call within the preset time period, the bandwidth utilization rate of the tax collection interface call process within the preset time period, and the number of tax collection requests processed within the preset time period.

[0071] As is easily understood, bandwidth utilization is an indicator that measures network performance and the efficiency of network resource utilization. It refers to the percentage of network bandwidth actually used within a specific time period relative to the total network bandwidth capacity. Network bandwidth is the data transmission capacity or rate in network communication, usually measured in bits per second (bps).

[0072] Bandwidth utilization reflects network congestion, the intensity of API calls, and the efficiency of network services. At lower bandwidth utilization, networks typically process data transmission more smoothly, resulting in a better user experience. However, when bandwidth utilization approaches or reaches 100%, network congestion may occur, leading to slower data transmission speeds, increased latency, and potentially impacting the normal operation of network services.

[0073] In some embodiments, the success rate of calling the tax collection interface within a preset time period can be determined based on the total number of target tax collection requests sent to the tax collection server within the preset time period and the total number of target tax collection requests successfully responded to by the tax collection server.

[0074] The average response time of each API call within a preset time period can be determined based on the response time required by the server from sending the target credit inquiry request to receiving the credit data required for the target credit inquiry request within the preset time period.

[0075] The number of credit inquiry requests processed within a preset time period can be determined based on the target number of credit inquiry requests that the server can successfully receive within the preset time period.

[0076] The step S103 above, "predicting the load capacity of the query interface based on the latest traffic indicators and the latest weights of the traffic indicators, and obtaining a quantified value of the load capacity," includes the following steps:

[0077] S201, multiply the success rate, the average response time, the bandwidth utilization rate, and the number of query requests processed within the preset time period by the corresponding latest weight;

[0078] S202, add up all the obtained products to obtain the quantified value of the load capacity.

[0079] For example: Set the following normalized latest weights and corresponding latest traffic metrics:

[0080] The latest weight for the success rate is wSR = 0.35;

[0081] The latest weight wRT corresponding to the average time consumed is 0.25;

[0082] The latest weight for bandwidth utilization is wBW = 0.25;

[0083] The latest weight wRV corresponding to the number of claims is 0.15;

[0084] The latest traffic metrics:

[0085] The latest success rate SR is 0.85.

[0086] Latest average execution time: RT=0.5;

[0087] Latest bandwidth utilization: BW=0.7;

[0088] The latest number of claims requests: RV=100.

[0089] The quantified value of load capacity = 0.35×0.85+0.25×0.5+0.25×0.7+0.15×100=15.5975.

[0090] This application embodiment comprehensively considers multiple traffic indicators. By combining success rate, average time consumption, bandwidth utilization, and the number of tax collection requests processed within a preset time period, it can more comprehensively and accurately reflect the operating status of the tax collection server, or the load capacity of the tax collection interface. By referring to the operating status of the tax collection server, more precise traffic control can be achieved on the server side, that is, control of the frequency of tax collection interface calls.

[0091] Based on the above embodiments, such as Figure 3 As shown, step S106 above, "referring to the maximum request volume, selecting the target investigation requests to be sent in the current batch from all received investigation requests," may include the following steps:

[0092] S301, obtain the number of pending investigation requests;

[0093] S302, determine the relationship between the number of pending retrieval requests and the maximum request quantity;

[0094] S303, if the number of pending investigation requests is greater than the maximum number of requests, then the target investigation requests are selected from the pending investigation requests according to the maximum number of requests, and the total number of the target investigation requests is the same as the maximum number of requests;

[0095] S304, the remaining pending investigation requests after filtering are cached in the delay queue;

[0096] S305, if the number of pending search requests is equal to the maximum number of requests, then all pending search requests are taken as the target search request;

[0097] S306, if the number of pending trace requests is less than the maximum request amount, then according to the difference between the maximum request amount and the number of pending trace requests, cached trace requests are extracted from the delay queue as candidate trace requests.

[0098] S307, the candidate search request and the search request to be processed are taken as the target search request.

[0099] For example, when the server receives a query request from the business side, it obtains the latest real-time traffic metrics from the data center through the traffic control center; then it calculates the maximum number of requests that can be sent in the current batch using an algorithm; with the determined maximum number of requests as the upper limit, it prioritizes filtering the target query requests from the latest received query requests, and then filters the remaining number of query requests from the delayed queue.

[0100] In some embodiments, it can be determined at each time step whether a tax collection request sent by the business side has been received based on the refresh rate. If no new tax collection request is received, the cached tax collection request is extracted from the delay queue and used as the "pending tax collection request" in step S101.

[0101] In this embodiment, a delayed queue is used to cache the investigation requests that the server cannot process in time. Then, when each batch of requests is sent, the maximum number of requests is determined to determine whether it is necessary to extract investigation requests from the delayed queue for sending. This forms a complete request processing flow, which can flexibly respond to the status and load capacity of the investigation interface, and adjust the interface call frequency on the server in a timely manner, thereby improving the overall processing efficiency of investigation requests.

[0102] In some embodiments, processing priorities can be assigned based on identity information such as the user identifier or the identifier of the device sending the query request in the query request. For all received query requests, after determining the maximum request volume, higher-priority query requests can be selected from the pending query requests and / or cached query requests in the delay queue as target query requests, thereby accelerating the processing of higher-priority requests.

[0103] Based on the above embodiments, such as Figure 4 As shown, the weight of any of the above traffic metrics can be updated in the following ways:

[0104] S401, obtain the latest weight learning rate, latest weight adjustment coefficient, and latest expected value of the traffic indicator. The weight learning rate is updated in real time based on the fluctuation of the historical data of the traffic indicator, and the weight adjustment coefficient is updated in real time based on the fluctuation of the historical data of the tax collection volume.

[0105] S402, calculate the difference between the latest expected value and the latest flow rate indicator;

[0106] S403, calculate the product of the difference and the latest weight adjustment coefficient as the weight change amount;

[0107] S404, the product of the change in weight and the latest weight learning rate is added to the latest weight of the traffic indicator to obtain the updated weight.

[0108] In this embodiment of the application, the weight update formula is as follows: .

[0109] in, It is the weight of the i-th traffic indicator at time t. It is the weight learning rate (which can be understood as the step size of each adjustment). It is the change in weight.

[0110] Weight change This can be calculated based on the difference between the latest traffic metrics and the latest expected values. For example, the change in the weight of the success rate: ;

[0111] in, It is the weighting adjustment factor. This is the latest expected value of the success rate. This is the latest success rate value.

[0112] In some embodiments, to ensure that the total weights are equal to 1, the adjusted weights need to be normalized.

[0113] In this embodiment, historical data can be data from a recent period (such as the observation period mentioned in the above embodiments). The weight adjustment coefficient is updated based on the fluctuation of historical data on tax collection volume. For example, the update frequency of the weight adjustment coefficient may be changed with reference to the fluctuation, or the update direction of the weight adjustment coefficient may be changed (such as increasing or decreasing the weight adjustment coefficient), or the update magnitude of the weight adjustment coefficient may be changed (such as increasing or decreasing the difference before and after the weight adjustment coefficient update).

[0114] Correspondingly, the method of updating the weight learning rate based on the fluctuation of historical data of traffic indicators can refer to the method of updating the weight adjustment coefficient based on the fluctuation of historical data of tax collection business volume, which will not be repeated here.

[0115] In this embodiment, the weight adjustment coefficient is updated with reference to the dynamic changes in tax collection volume, so that the weight change at each update is adapted to the dynamic changes in tax collection volume. The weight learning rate is updated with reference to the dynamic changes in traffic indicators, allowing the impact of actual changes in traffic indicators on the current weight to be considered during weight updates. Thus, when updating the weight of traffic indicators, the actual tax collection demand represented by tax collection volume and the response status of the tax collection interface represented by traffic indicators are comprehensively considered to determine a weight adapted to the current tax collection scenario. This weight is then used to apply a level of attention to the traffic indicators appropriate to the current tax collection scenario.

[0116] Based on the above embodiments, such as Figure 5 As shown, the learning rate for the above weights is updated in the following way:

[0117] S501, Calculate a first characteristic value of the flow index based on the historical data of the flow index. The first characteristic value is used to reflect the fluctuation of the historical data of the flow index.

[0118] S502, if the first feature value is within a first preset range, then increase the weight learning rate;

[0119] S503, if the first feature value is within the second preset range, then reduce the weight learning rate.

[0120] The minimum value of the first preset range is greater than the maximum value of the second preset range.

[0121] In some embodiments, the stability and volatility of traffic flow indicators can be determined by analyzing characteristic values ​​such as mean, variance, and extreme values ​​in historical data. Alternatively, time series forecasting models (such as ARIMA and LSTM) can be built based on historical data to predict future dynamic changes in traffic flow indicators and future tax collection volume, and the weights can be updated according to future dynamic changes.

[0122] When the characteristic values ​​of traffic metrics fluctuate significantly, reduce the weight learning rate to minimize the impact of over-adjusting the entire data collection process on the server side.

[0123] When the characteristic values ​​of traffic metrics fluctuate little, increase the weight learning rate to speed up the server's response to changes.

[0124] Based on the above embodiments, such as Figure 6 As shown, the above weight adjustment coefficients are updated in the following way:

[0125] S601, Obtain historical data on the tax collection volume;

[0126] S602, calculate a second characteristic value of the tax collection volume based on the historical data of the tax collection volume, the second characteristic value being used to reflect the fluctuation of the historical data of the tax collection volume;

[0127] S603, if the second feature value is within a third preset range, then increase the weight adjustment coefficient;

[0128] S604, if the second feature value is within the fourth preset range, then reduce the weight adjustment coefficient.

[0129] The minimum value of the third preset range is greater than the maximum value of the fourth preset range.

[0130] In this embodiment, when business volume fluctuates significantly, the weight adjustment coefficient is increased to make the entire tax collection process on the server side more sensitive to traffic changes. When business volume fluctuates less, the weight adjustment coefficient is decreased to reduce unnecessary adjustments.

[0131] It should be noted that the first, second, third, and fourth preset ranges mentioned above can be set as needed, and this application does not impose any limitations on them.

[0132] Based on the above embodiments, such as Figure 7 As shown, if the second feature value is within the third preset range, the method further includes the following steps:

[0133] S701, determine whether the tax collection volume is on an increasing trend;

[0134] S702, if so, then reduce the expected value of the average response time and increase the expected value of the success rate, so as to reduce the weight of the average response time and increase the weight of the success rate.

[0135] By reducing the expected value of the average response time to below the latest value of the average response time, that is, adjusting the expected value of the average response time to below the latest actual value, the weight change of the average response time calculated in this way will be negative, and the updated weight will be less than the weight before the update, thereby achieving the purpose of reducing the weight of the average response time.

[0136] In this embodiment of the application, when the workload surges, the weight of response time is reduced and the weight of success rate is increased to ensure that critical requests are processed first.

[0137] When business volume decreases or stabilizes, there is no need to further adjust the expected values ​​of various traffic indicators.

[0138] The method for processing tax collection requests proposed in this application comprehensively considers multiple dimensions of indicators. By combining success rate, average processing time, bandwidth utilization, and the number of tax collection requests processed within a preset time period, it can more comprehensively and accurately reflect the operational status of the entire tax collection system—from the business end to the server end and the tax collection interface—and achieve more precise traffic control. It can dynamically adjust the maximum number of requests that can be processed in a single batch based on real-time data, improving the responsiveness and stability of the entire tax collection request processing process. The embodiments of this application introduce an adaptive weight adjustment mechanism, enabling the entire tax collection request processing process to automatically optimize server-side traffic control according to different business scenarios and operational statuses, exhibiting high adaptability and flexibility.

[0139] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0140] Figure 8 This is a block diagram of a processing apparatus for a search request according to an exemplary embodiment of this application, such as... Figure 8 As shown, the device 800 includes: a first acquisition module 801, a second acquisition module 802, a prediction module 803, a first determination module 804, a second determination module 805, a filtering module 806, an information acquisition module 807, and a sending module 808.

[0141] The first acquisition module 801 is used to acquire a credit inquiry request to be processed, wherein the credit inquiry request is a request to query credit data sent by the business terminal;

[0142] The second acquisition module 802 is used to acquire the latest traffic indicators and the latest weights of the traffic indicators. The weights of the traffic indicators are updated in real time based on the dynamic changes of the traffic indicators and the dynamic changes of the tax collection business volume of the business end. The traffic indicators are used to characterize the response of the tax collection interface to the interface call.

[0143] The prediction module 803 is used to predict the load capacity of the query interface based on the latest traffic index and the latest weight of the traffic index, and obtain a quantitative value of the load capacity.

[0144] The first determining module 804 is used to determine the load capacity quantification value range in which the load capacity quantification value is located from the load capacity quantification value range corresponding to different request volume adjustment strategies, so as to determine the request volume adjustment strategy to be executed. The request volume adjustment strategy is used to adjust the maximum request volume of the investigation request that can be sent in a single batch.

[0145] The second determining module 805 is used to execute the request volume adjustment strategy to be executed and determine the maximum request volume of the current batch that can be sent; the current batch is the batch corresponding to the current moment when sending the request in batches.

[0146] The filtering module 806 is used to filter out the target query requests that need to be sent in the current batch from all the received query requests, with reference to the maximum request volume, wherein all the received query requests include the query requests to be processed.

[0147] The information acquisition module 807 is used to send the target credit investigation request to the credit investigation server to call the credit investigation interface of the credit investigation server and obtain the credit investigation data required by the target credit investigation request;

[0148] The sending module 808 is used to send the credit data to the business terminal corresponding to the target credit inquiry request.

[0149] In this embodiment, the traffic metrics are updated in real time based on a preset time period. The traffic metrics include the success rate of calling the tax collection interface within the preset time period, the average response time of the tax collection interface for each interface call within the preset time period, the bandwidth utilization rate of the tax collection interface call process within the preset time period, and the number of tax collection requests processed within the preset time period. The prediction module is used to: multiply the success rate, the average response time, the bandwidth utilization rate, and the number of tax collection requests processed within the preset time period by the corresponding latest weights; and sum all the products to obtain the load capacity quantification value.

[0150] In this embodiment of the application, all received tamper requests also include tamper requests cached in a delay queue; the tamper requests to be processed include tamper requests extracted from the delay queue, and / or the latest tamper requests not cached in the delay queue; the filtering module is configured to: obtain the number of tamper requests to be processed; determine the relationship between the number of tamper requests to be processed and the maximum request quantity; if the number of tamper requests to be processed is greater than the maximum request quantity, then, based on the maximum request quantity, filter out the target tamper request from the tamper requests to be processed, wherein the target... The total number of target query requests is the same as the maximum request volume; the remaining query requests after filtering are cached in the delay queue; if the number of query requests to be processed is equal to the maximum request volume, then all query requests to be processed are used as target query requests; if the number of query requests to be processed is less than the maximum request volume, then the cached query requests are extracted from the delay queue according to the difference between the maximum request volume and the number of query requests to be processed, and used as candidate query requests; the candidate query requests and the query requests to be processed are used as target query requests.

[0151] In this embodiment of the application, the device is further configured to update the weight of the traffic indicator in the following manner: obtaining the latest weight learning rate, the latest weight adjustment coefficient, and the latest expected value of the traffic indicator, wherein the weight learning rate is updated in real time based on the fluctuation of historical data of the traffic indicator, and the weight adjustment coefficient is updated in real time based on the fluctuation of historical data of the tax collection volume; calculating the difference between the latest expected value and the latest traffic indicator; calculating the product of the difference and the latest weight adjustment coefficient as the weight change; and adding the product between the weight change and the latest weight learning rate to the latest weight of the traffic indicator to obtain the updated weight.

[0152] In this embodiment of the application, the device is further configured to update the weight learning rate in the following manner: calculate a first feature value of the traffic indicator based on historical data of the traffic indicator, wherein the first feature value is used to reflect the fluctuation of the historical data of the traffic indicator; if the first feature value is within a first preset range, increase the weight learning rate; if the first feature value is within a second preset range, decrease the weight learning rate; wherein the minimum value of the first preset range is greater than the maximum value of the second preset range.

[0153] In this embodiment of the application, the device is further configured to update the weight adjustment coefficient in the following manner: acquiring historical data of the tax collection volume; calculating a second characteristic value of the tax collection volume based on the historical data of the tax collection volume, the second characteristic value being used to reflect the fluctuation of the historical data of the tax collection volume; increasing the weight adjustment coefficient if the second characteristic value is within a third preset range; decreasing the weight adjustment coefficient if the second characteristic value is within a fourth preset range; the minimum value of the third preset range is greater than the maximum value of the fourth preset range.

[0154] In this embodiment of the application, when the traffic indicator is the average response time, if the second feature value is within the third preset range, the device is further configured to: determine whether the tax collection volume is on an increasing trend; if so, reduce the expected value of the average response time to decrease the weight of the average response time; and / or; when the traffic indicator is the success rate, if the second feature value is within the third preset range, the device is further configured to: determine whether the tax collection volume is on an increasing trend; if so, increase the expected value of the success rate to increase the weight of the success rate.

[0155] In summary, after receiving a credit investigation request to be processed, the device obtains the latest value of a traffic indicator that characterizes the response of the credit investigation interface to the interface call, i.e., the latest traffic indicator; it also obtains the latest weight of the traffic indicator; based on the traffic indicator and its latest weight, it predicts the current load capacity of the credit investigation interface and obtains a quantified load capacity value; it selects a strategy from multiple request volume adjustment strategies that can adaptively adjust the traffic for the current load capacity of the credit investigation interface, i.e., the request volume adjustment strategy to be executed; it executes the request volume adjustment strategy to determine the maximum number of requests that can be sent in the current batch, and with reference to the maximum number of requests, it selects the target credit investigation request to be sent in the current batch from the received credit investigation requests, and then sends the target credit investigation request to the credit investigation server to call the credit investigation interface of the credit investigation server, obtain the credit investigation data required by the target credit investigation request, and send this credit investigation data to the business terminal corresponding to the target credit investigation request. This application updates the weights of traffic metrics in real time based on dynamic changes in traffic indicators and the volume of tax collection and verification services on the business side. This allows for adaptive adjustments to the weights of various metrics based on their changes, assigning different levels of attention to different metrics in different scenarios. This improves the accuracy of predicting the load capacity of the tax collection interface and adjusts the frequency of tax collection request calls based on the current load capacity of the interface, thereby enhancing its responsiveness to changes in the interface's status. This avoids increasing the processing pressure on the tax collection interface and improves overall tax collection efficiency.

[0156] To implement the above embodiments, this application also proposes an electronic device 900, including a memory 901, a processor 902, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the query request processing method as described in the above embodiments.

[0157] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0158] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0159] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for processing a query request, characterized in that, The method includes: Obtain pending credit inquiry requests, which are requests sent by the business terminal to query credit data; Obtain the latest traffic metrics and the latest weights of the traffic metrics. The latest weights of the traffic metrics are updated in real time based on the dynamic changes of the traffic metrics and the dynamic changes of the tax collection business volume of the business end. The traffic metrics are used to characterize the response of the tax collection interface to the interface call. Based on the latest traffic metrics and the latest weights of the traffic metrics, the load capacity of the query interface is predicted to obtain a quantitative value of the load capacity. From the load capacity quantization value range corresponding to different request volume adjustment strategies, determine the load capacity quantization value range in which the load capacity quantization value is located, so as to determine the request volume adjustment strategy to be executed. The request volume adjustment strategy is used to adjust the maximum request volume of the investigation request that can be sent in a single batch. The pending request volume adjustment strategy is executed to determine the maximum number of investigation requests that can be sent in the current batch; the current batch is the batch corresponding to the current moment when investigation requests are sent in batches. Referring to the maximum request volume, target investigation requests that need to be sent in the current batch are selected from all the received investigation requests, including the investigation requests to be processed; Send the target credit investigation request to the credit investigation server to call the credit investigation interface of the credit investigation server and obtain the credit investigation data required for the target credit investigation request; The credit data is sent to the business terminal corresponding to the target credit inquiry request.

2. The method as described in claim 1, characterized in that, The traffic metrics are updated in real time based on a preset time period. The traffic metrics include the success rate of calling the tax collection interface within the preset time period, the average response time of the tax collection interface to each interface call within the preset time period, the bandwidth utilization rate of the tax collection interface call process within the preset time period, and the number of tax collection requests processed within the preset time period. The step of predicting the load capacity of the query interface based on the latest traffic metrics and the latest weights of the traffic metrics, and obtaining a quantified value of the load capacity, includes: The success rate, the average response time, the bandwidth utilization rate, and the number of query requests processed within the preset time period are each multiplied by the corresponding latest weight. Add all the products together to obtain the quantified value of the load capacity.

3. The method as described in claim 1, characterized in that, The received collection requests also include collection requests cached in a delay queue; the pending collection requests include collection requests extracted from the delay queue, and / or the latest collection requests not cached in the delay queue. Referring to the maximum request volume, the process of filtering out target query requests that need to be sent in the current batch from all received query requests includes: Obtain the number of pending investigation requests; Determine the relationship between the number of pending investigation requests and the maximum request volume; If the number of pending investigation requests is greater than the maximum number of requests, then the target investigation requests are selected from the pending investigation requests based on the maximum number of requests, and the total number of the target investigation requests is the same as the maximum number of requests. The remaining pending investigation requests after filtering are cached in the delay queue; If the number of pending retrieval requests is equal to the maximum request amount, then all pending retrieval requests will be used as the target retrieval request. If the number of pending trace requests is less than the maximum number of requests, then the cached trace requests are extracted from the delay queue as candidate trace requests based on the difference between the maximum number of requests and the number of pending trace requests. The candidate search request and the search request to be processed are used as the target search request.

4. The method as described in claim 2, characterized in that, Update the weights of the traffic metrics in the following ways: The latest weight learning rate, latest weight adjustment coefficient, and latest expected value of the traffic indicator are obtained. The weight learning rate is updated in real time based on the fluctuation of the historical data of the traffic indicator, and the weight adjustment coefficient is updated in real time based on the fluctuation of the historical data of the tax collection volume. Calculate the difference between the latest expected value and the latest flow rate indicator; Calculate the product of the difference and the latest weight adjustment coefficient as the weight change; The updated weights are obtained by adding the product of the weight change and the latest weight learning rate to the latest weight of the traffic indicator.

5. The method as described in claim 4, characterized in that, The weight learning rate is updated in the following way: Based on the historical data of the flow index, a first characteristic value of the flow index is calculated, and the first characteristic value is used to reflect the fluctuation of the historical data of the flow index. If the first feature value is within a first preset range, then the weight learning rate is increased; If the first feature value is within the second preset range, then the weight learning rate is reduced; The minimum value of the first preset range is greater than the maximum value of the second preset range.

6. The method as described in claim 4, characterized in that, The weight adjustment coefficients are updated in the following manner: Obtain historical data on the tax collection volume; Based on the historical data of the tax collection volume, a second characteristic value of the tax collection volume is calculated. The second characteristic value is used to reflect the fluctuation of the historical data of the tax collection volume. If the second feature value is within a third preset range, then the weight adjustment coefficient is increased; If the second feature value is within the fourth preset range, then the weight adjustment coefficient is reduced; The minimum value of the third preset range is greater than the maximum value of the fourth preset range.

7. The method as described in claim 6, characterized in that, If the flow rate indicator is the average response time, and the second feature value is within the third preset range, the method further includes: Determine whether the volume of tax collection business is on an upward trend; If so, reduce the expected value of the average response time to decrease the weight of the average response time; and / or; If the success rate is the specified traffic metric, and the second feature value falls within the third preset range, the method further includes: Determine whether the volume of tax collection business is on an upward trend; If so, the expected value of the success rate is increased to increase the weight of the success rate.

8. A processing device for a claim request, characterized in that, The device includes: The first acquisition module is used to acquire pending credit inquiry requests, which are requests sent by the business terminal to query credit data; The second acquisition module is used to acquire the latest traffic indicators and the latest weight of the traffic indicators. The latest weight of the traffic indicators is updated in real time according to the dynamic changes of the traffic indicators and the dynamic changes of the tax collection business volume of the business end. The traffic indicators are used to characterize the response of the tax collection interface to the interface call. The prediction module is used to predict the load capacity of the query interface based on the latest traffic indicators and the latest weights of the traffic indicators, and obtain a quantitative value of the load capacity. The first determining module is used to determine the load capacity quantification value range in which the load capacity quantification value is located from the load capacity quantification value range corresponding to different request volume adjustment strategies, so as to determine the request volume adjustment strategy to be executed. The request volume adjustment strategy is used to adjust the maximum request volume of the investigation request that can be sent in a single batch. The second determining module is used to execute the request volume adjustment strategy to be executed and determine the maximum number of investigation requests that can be sent in the current batch; the current batch is the batch corresponding to the current moment when the investigation requests are sent in batches. The filtering module is used to filter out target query requests that need to be sent in the current batch from all the received query requests, with reference to the maximum request volume. The received query requests include the query requests to be processed. The information acquisition module is used to send the target credit investigation request to the credit investigation server to call the credit investigation interface of the credit investigation server and obtain the credit data required for the target credit investigation request. The sending module is used to send the credit data to the business terminal corresponding to the target credit inquiry request.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for processing a search request as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the steps of the method for processing a search request as described in any one of claims 1-7.

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