Indication request processing method and device, electronic equipment and storage medium

Abstract

The invention provides a query request processing method and device, electronic equipment and a storage medium, and relates to the technical field of credit query, and the method comprises the steps: obtaining a latest flow index capable of representing the response condition of a query interface to interface calling, and a latest weight of the flow index when a query request is received; predicting a current load capacity quantized value of the investigation interface according to the flow index and the latest weight, determining a strategy capable of performing flow adaptive adjustment aiming at the current load capacity of the investigation interface by referring to the value, and determining the maximum request quantity of the investigation requests which can be sent by the current batch according to the strategy, and determining a target investigation request needing to be sent in the current batch by referring to the maximum request quantity. According to the invention, the weight of the flow index is updated in real time according to the dynamic change condition of the flow index and the dynamic change condition of the investigation service volume of the service end, so that the weight of the flow index is adaptively adjusted according to the change conditions, and the strain capacity to the state change of the investigation interface is improved.

Description

Technical Field

[0001] The present application relates to the field of credit inquiry technology, and in particular to a method, device, electronic device and storage medium for processing a credit inquiry request. Background Art

[0002] At present, personal credit reports are obtained through the credit data query interface provided by a specific credit center, such as the Credit Center of the People's Bank of China. All banks, financial institutions and other institutions involved in obtaining personal credit reports need to apply for approval from the credit center. Only after approval can the query interface (i.e. credit data query interface) be called. Therefore, once the query interface has interface jitter or network abnormal jitter, it will inevitably affect all connected institutions and units, and the access party (i.e. the interface caller) cannot control traffic switching and cannot switch to the backup interface, making the access party relatively passive.

[0003] Therefore, how to automatically adjust the calling speed of the query interface at the interface caller to improve the ability to respond to changes in the query interface state has become an urgent problem to be solved. Summary of the invention

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

[0005] A method for processing a query request, the method comprising: obtaining a query request to be processed, the query request being a request for querying credit data sent by a business end; obtaining the latest flow index and the latest weight of the flow index, the weight of the flow index being updated in real time according to the dynamic changes of the flow index and the dynamic changes of the query business volume of the business end, the flow index being used to characterize the response of the query interface to the interface call; predicting the load capacity of the query interface according to the latest flow index and the latest weight of the flow index to obtain a load capacity quantization value; determining the load capacity quantization value interval in which the load capacity quantization value is located from the load capacity quantization value intervals corresponding to different request volume adjustment strategies, so as to determine the load capacity quantization value interval to be executed. A request quantity adjustment strategy, wherein the request quantity adjustment strategy is used to adjust the maximum request quantity of inquiry requests that can be sent in a single batch; executing the request quantity adjustment strategy to be executed to determine the maximum request quantity of inquiry requests that can be sent in the current batch; the current batch is the batch corresponding to the current moment when the inquiry requests are sent in batches; referring to the maximum request quantity, filtering out target inquiry requests that need to be sent in the current batch from all the inquiry requests received, wherein all the inquiry requests received include the inquiry requests to be processed; sending the target inquiry request to the inquiry service end to call the inquiry interface of the inquiry service end to obtain the credit data required for the target inquiry request; sending the credit data to the business end corresponding to the target inquiry request.

[0006] In an embodiment of the present application, the traffic indicator is updated in real time based on a preset time period, and the traffic indicator includes the success rate of calling the query interface within the preset time period, the average response time of the query interface to each interface call within the preset time period, the bandwidth utilization rate of the query interface call process within the preset time period, and the number of query requests processed within the preset time period; the load capacity of the query interface is predicted based on the latest traffic indicator and the latest weight of the traffic indicator to obtain a quantified value of the load capacity, including: multiplying 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 respectively; adding all the products obtained to obtain the quantified value of the load capacity.

[0007] In an embodiment of the present application, all of the received query requests also include query requests cached in a delay queue; the query requests to be processed include query requests extracted from the delay queue and / or the latest query requests that are not cached in the delay queue, and the reference to the maximum request amount, filtering out the target query requests that need to be sent in the current batch from all of the received query requests, includes: obtaining the number of the query requests to be processed; determining the relationship between the number of the query requests to be processed and the maximum request amount; if the number of the query requests to be processed is greater than the maximum request amount, filtering out the query requests from the query requests to be processed according to the maximum request amount; The target search requests are screened out from the search requests, and the total number of the target search requests is the same as the maximum request amount; the pending search requests remaining after the screening are cached in the delay queue; if the number of the pending search requests is equal to the maximum request amount, all the pending search requests are used as the target search requests; if the number of the pending search requests is less than the maximum request amount, the cached search requests are extracted from the delay queue as candidate search requests according to the difference between the maximum request amount and the number of the pending search requests; the candidate search requests and the pending search requests are used as the target search requests.

[0008] In an embodiment of the present application, the weight of the flow index is updated in the following manner: the latest weight learning rate, the latest weight adjustment coefficient, and the latest expected value of the flow index are obtained, the weight learning rate is updated in real time according to the fluctuation of the historical data of the flow index, and the weight adjustment coefficient is updated in real time according to the fluctuation of the historical data of the queried business volume; the difference between the latest expected value and the latest flow index 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 flow index to obtain the updated weight.

[0009] In an embodiment of the present application, the weight learning rate is updated in the following manner: based on the historical data of the flow indicator, a first eigenvalue of the flow indicator is calculated, and the first eigenvalue is used to reflect the fluctuation of the historical data of the flow indicator; if the first eigenvalue is within a first preset range, the weight learning rate is increased; if the first eigenvalue is within a second preset range, 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.

[0010] In an embodiment of the present application, the weight adjustment coefficient is updated in the following manner: historical data of the business volume to be queried is obtained; based on the historical data of the business volume to be queried, a second characteristic value of the business volume to be queried is calculated, and the second characteristic value is used to reflect the fluctuation of the historical data of the business volume to be queried; 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 reduced; the minimum value of the third preset range is greater than the maximum value of the fourth preset range.

[0011] In an embodiment of the present application, when the flow indicator is the average response time, if the second characteristic value is within the third preset range, the method further includes: determining whether the query business volume is on an upward trend; if so, reducing the expected value of the average response time to reduce the weight of the average response time; and / or; when the flow indicator is the success rate, if the second characteristic value is within the third preset range, the method further includes: determining whether the query business volume is on an upward trend; if so, increasing the expected value of the success rate to increase the weight of the success rate.

[0012] A device for processing a query request, the device comprising: a first acquisition module, used to acquire a query request to be processed, the query request being a request for querying credit data sent by a business end; a second acquisition module, used to acquire the latest flow index and the latest weight of the flow index, the weight of the flow index is updated in real time according to the dynamic changes of the flow index and the dynamic changes of the query business volume of the business end, and the flow index is used to characterize the response of the query interface to the interface call; a prediction module, used to predict the load capacity of the query interface according to the latest flow index and the latest weight of the flow index, and obtain a load capacity quantization value; a first determination module, used to determine the load capacity quantization value interval in which the load capacity quantization value is located from the load capacity quantization value intervals corresponding to different request volume adjustment strategies, so as to determine the load capacity quantization value interval to be executed. A request quantity adjustment strategy, the request quantity adjustment strategy is used to adjust the maximum request quantity of inquiry requests that can be sent in a single batch; a second determination module is used to execute the request quantity adjustment strategy to be executed, and determine the maximum request quantity of inquiry requests that can be sent in the current batch; the current batch is the batch corresponding to the current moment when the inquiry requests are sent in batches; a screening module is used to refer to the maximum request quantity to screen out the target inquiry requests that need to be sent in the current batch from all the received inquiry requests, and the received all the inquiry requests include the inquiry requests to be processed; an information acquisition module is used to send the target inquiry request to the inquiry service end to call the inquiry interface of the inquiry service end to obtain the credit data required for the target inquiry request; a sending module is used to send the credit data to the business end corresponding to the target inquiry request.

[0013] In an embodiment of the present application, the traffic indicator is updated in real time based on a preset time period, and the traffic indicator includes the success rate of calling the query interface within the preset time period, the average response time of the query interface to each interface call within the preset time period, the bandwidth utilization rate of the query interface call process within the preset time period, and the number of query 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 query requests processed within the preset time period by the corresponding latest weights respectively; add all the products obtained to obtain the load capacity quantification value.

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

[0015] In an embodiment of the present application, the device is also used to update the weight of the flow indicator in the following manner: obtain the latest weight learning rate, the latest weight adjustment coefficient, and the latest expected value of the flow indicator, the weight learning rate is updated in real time according to the fluctuation of the historical data of the flow indicator, and the weight adjustment coefficient is updated in real time according to the fluctuation of the historical data of the collected business volume; calculate the difference between the latest expected value and the latest flow indicator; calculate the product of the difference and the latest weight adjustment coefficient as the weight change; add the product between the weight change and the latest weight learning rate to the latest weight of the flow indicator to obtain the updated weight.

[0016] In an embodiment of the present application, the device is also used to update the weight learning rate in the following manner: based on the historical data of the flow indicator, calculating the first characteristic value of the flow indicator, the first characteristic value is used to reflect the fluctuation of the historical data of the flow indicator; if the first characteristic value is within a first preset range, increasing the weight learning rate; if the first characteristic value is within a second preset range, reducing the weight learning rate; the minimum value of the first preset range is greater than the maximum value of the second preset range.

[0017] In an embodiment of the present application, the device is also used to update the weight adjustment coefficient in the following manner: obtaining historical data of the business volume to be queried; calculating a second characteristic value of the business volume to be queried based on the historical data of the business volume to be queried, the second characteristic value being used to reflect the fluctuation of the historical data of the business volume to be queried; if the second characteristic value is within a third preset range, increasing the weight adjustment coefficient; if the second characteristic value is within a fourth preset range, reducing the weight adjustment coefficient; the minimum value of the third preset range is greater than the maximum value of the fourth preset range.

[0018] In an embodiment of the present application, when the flow indicator is the average response time, if the second characteristic value is within the third preset range, the device is further used to: determine whether the query business volume is on an upward trend; if so, reduce the expected value of the average response time to reduce the weight of the average response time; and / or; when the flow indicator is the success rate, if the second characteristic value is within the third preset range, the device is further used to: determine whether the query business volume is on an upward trend; if so, increase the expected value of the success rate to increase the weight of the success rate.

[0019] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for processing a search request as described in the above embodiment are implemented.

[0020] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for processing a search request as described in the above embodiment.

[0021] In summary, the present application proposes a method, device, electronic device and storage medium for processing query requests. After obtaining the query request to be processed, the method obtains the latest value of the traffic indicator that can characterize the response of the query interface to the interface call, that is, the latest traffic indicator; at the same time, obtains the latest weight of the traffic indicator; predicts the current load capacity of the query interface according to the traffic indicator and the latest weight of the traffic indicator to obtain a quantified value of the load capacity; selects a strategy that can adaptively adjust the traffic according to the current load capacity of the query interface from multiple request quantity adjustment strategies, that is, the request quantity adjustment strategy to be executed; executes the request quantity adjustment strategy to be executed, determines the maximum request quantity of query requests that can be sent in the current batch, and refers to the maximum request quantity to select the target query requests that need to be sent in the current batch from all the received query requests, and then sends the target query request to the query server to call the query interface of the query server, obtain the query data required for the target query request, and send these query data to the business end corresponding to the target query request. The present application updates the weight of the traffic indicator in real time according to the dynamic changes of the traffic indicator and the dynamic changes of the query business volume at the business end, so as to adaptively adjust the weight of the traffic indicator according to these changes, and pay different attention to the traffic indicator in different scenarios to improve the accuracy of the prediction of the load capacity of the query interface, and adjust the frequency of the query request call with reference to the current load capacity of the query interface to improve the response capability to the change of the query interface status. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0023] Figure 1 is a flowchart of a method for processing a search request according to an exemplary embodiment of the present application; Figure 2 is a flowchart of a method for processing a search request according to another exemplary embodiment of the present application; Figure 3 is a flowchart of a method for processing a search request according to another exemplary embodiment of the present application; Figure 4 is a flowchart of a method for processing a search request according to another exemplary embodiment of the present application; Figure 5 is a flowchart of a method for processing a search request according to another exemplary embodiment of the present application; Figure 6is a flowchart of a method for processing a search request according to another exemplary embodiment of the present application; Figure 7 is a flowchart of a method for processing a search request according to another exemplary embodiment of the present application; Figure 8 is a schematic block diagram of a device for processing a search request according to another exemplary embodiment of the present application; Fig. 9 It is a schematic block diagram of an electronic device according to an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. The embodiments described by reference to the drawings are exemplary and are intended to be used to explain the present application, and cannot be understood as limiting the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0025] The processing method of the inquiry request proposed in the embodiment of the present application can be applied to the service end of the business port such as the platform / software / webpage that has the demand for obtaining credit data, such as the service end of the banking business processing system, or the service end of the credit and financial management application software, etc. Since the personal credit report is currently obtained through the credit data query interface (referred to as the inquiry interface) provided by a specific credit center, such as the Credit Information Center of the People's Bank of China, the service end of the banking business processing system can control the inquiry flow through the processing method of the inquiry request proposed in this application when obtaining the credit data of the user, and send the determined target inquiry request to the Credit Information Center of the People's Bank of China, that is, the inquiry service end, to call the inquiry interface of the inquiry service end, so as to ensure that the inquiry interface call can be carried out efficiently and orderly.

[0026] Figure 1 is a flowchart of a method for processing a search request according to an exemplary embodiment of the present application. Figure 1 As shown, the method for processing the inquiry request may include the following steps: S101, obtaining a pending inquiry request, wherein the inquiry request is a request for inquiry of credit data sent by a business end; The query request to be processed may be a query request sent by a newly received business end, or a query request extracted from a delay queue according to a preset strategy. The preset strategy may be set as required, such as a thread pool scheduling strategy, etc., and this application does not limit it.

[0027] For example, the server can receive the query request sent by the business end in real time and process the latest query request received. When the query request sent by the business end is not received for more than a preset period of time, the cached query request can be extracted from the delay queue for processing.

[0028] Alternatively, while receiving a new query request, a certain number of query requests may be extracted from the delay queue for joint processing, thereby preventing the requests in the delay queue from waiting too long for processing.

[0029] The delay queue can be used to cache query requests that are not processed in time by the server.

[0030] S102, obtaining the latest traffic index and the latest weight of the traffic index.

[0031] After obtaining the pending query request, the server can obtain the latest traffic index and the latest weight value of the traffic index from the data center.

[0032] In some embodiments, the traffic indicators can be collected in real time and reported to the data center by setting an asynchronous collection task, so that the server can obtain the latest traffic indicators from the data center when needed.

[0033] In some embodiments, the weight of each traffic indicator may be dynamically updated in real time on the server side, and the latest weight of the traffic indicator may be stored.

[0034] In the embodiment of the present application, the weight of the flow index is continuously updated according to the dynamic changes of the flow index and the dynamic changes of the query business volume of the service end. The query business volume can be the number of query requests initiated by the service end within a period of time.

[0035] For example, when updating the weight of a traffic indicator, an observation time period may be defined in advance, and the weight of the traffic indicator may be updated according to the dynamic changes of the traffic indicator during the observation time period and the dynamic changes of the collected business volume at the business end during the observation time period.

[0036] The dynamic changes of the above-mentioned flow indicators during the observation time period can be obtained by analyzing the dynamic changes of the historical data of the flow indicators during the observation time period. The dynamic changes described here may include change trends and / or fluctuations. For example, the dynamic changes of the flow indicators can be understood as the change trends of the historical data of the flow indicators during the preset observation time period, such as an increasing trend or a decreasing trend; and / or the fluctuations of the historical data of the flow indicators during the preset observation time period, such as large fluctuations or small fluctuations.

[0037] Among them, the above-mentioned business end may include multiple business ends connected to the server end, and the queried business volume used when updating the weight of the flow indicator can be understood as the sum of the queried business volumes of all business ends connected to the server end.

[0038] The flow index is used to characterize the response of the query interface to the interface call.

[0039] S103: predicting the load capacity of the query interface according to the latest flow index and the latest weight of the flow index to obtain a quantified value of the load capacity.

[0040] The embodiment of the present application combines the flow index and the latest weight of the flow index to predict the load capacity of the query interface.

[0041] The latest weight of the traffic indicator is used to adjust the proportion of the latest traffic indicator in the prediction of the load capacity quantification value.

[0042] In an embodiment of the present application, the weight of the flow indicator changes with the change of the flow indicator. When the flow indicator exhibits different statistical characteristics (such as a growth trend and / or large fluctuations), the embodiment of the present application will adaptively update the latest weight of the flow indicator, so that when predicting the load capacity of the query interface in different scenarios, different weight values ​​are applied to the flow indicator, thereby improving the accuracy of the load capacity prediction of the query interface.

[0043] S104, determining the load capacity quantization value interval in which the load capacity quantization value is located from the load capacity quantization value intervals corresponding to different request quantity adjustment strategies, so as to determine the request quantity adjustment strategy to be executed, wherein the request quantity adjustment strategy is used to adjust the maximum request quantity of query requests that can be sent in a single batch.

[0044] In practice, the server can process multiple inquiry requests at the same time, that is, send inquiry requests to the inquiry server to call the inquiry interface to obtain credit data.

[0045] The number of search requests sent simultaneously each time can be understood as the request quantity sent in a single batch in the embodiment of the present application, and the request quantity refers to the number of search requests.

[0046] The embodiment of the present application pre-sets different request quantity adjustment strategies, and the request quantity adjustment strategy is used to adjust the maximum request quantity that can be sent in a single batch. The maximum request quantity can be understood as the maximum number of query requests that can be sent in a single batch.

[0047] To improve the accuracy of request volume adjustment, when the load capacity of the query interface is in different states / stages, different request volume adjustment strategies can be used to specifically adjust the maximum number of requests that the server can send in a single batch.

[0048] In some embodiments, for example, request quantity adjustment strategy A is to increase the maximum request quantity that can be sent in a single batch, and request quantity adjustment strategy A corresponds to load capacity quantification interval a; request quantity adjustment strategy B is to reduce the maximum request quantity that can be sent in a single batch, and request quantity adjustment strategy B corresponds to load capacity quantification interval b; request quantity adjustment strategy C is to maintain the current maximum request quantity that can be sent in a single batch, and request quantity adjustment strategy C corresponds to load capacity quantification interval c.

[0049] It should be noted that the division of load capacity quantization intervals corresponding to different request volume adjustment strategies can be achieved through statistical processing of a large amount of sample data or deep learning model processing, and this application does not impose any restrictions.

[0050] S105, executing the request quantity adjustment strategy to be executed, and determining the maximum request quantity of the inquiry requests that can be sent in the current batch; the current batch is the batch corresponding to the current moment when the inquiry requests are sent in batches.

[0051] The maximum number of requests that can be sent in a single batch, determined according to the request quantity adjustment strategy to be executed, is used as the maximum number of requests that can be sent in the current batch.

[0052] S106, referring to the maximum request amount, screening out target query requests that need to be sent in the current batch from all the received query requests, wherein all the received query requests include the query requests to be processed.

[0053] S107, sending the target inquiry request to the inquiry server to call the inquiry interface of the inquiry server to obtain the credit data required by the target inquiry request.

[0054] Send the target inquiry request to the inquiry server. The inquiry server can parse the target inquiry request, obtain inquiry input parameters, input the inquiry input parameters into the inquiry interface, and obtain the credit data required by the target inquiry request. The inquiry server returns the credit data to the above-mentioned server (i.e., the server corresponding to the business end).

[0055] The above-mentioned “received query request” may include the latest query request received from the service end and the query request received by the service end from the service end at a historical moment.

[0056] The maximum request amount determined in the above step S105 is used to constrain the number of target query requests determined in step S106, and the total number of target query requests does not exceed the maximum request amount determined in the above step S105.

[0057] S108, sending the credit investigation data to the business end corresponding to the target credit investigation request.

[0058] The server returns the credit data obtained from the inquiry interface to the business end that sent the target inquiry request.

[0059] In summary, the processing method of the inquiry request proposed in the present application obtains the latest value of the traffic indicator that can characterize the response of the inquiry interface to the interface call after obtaining the inquiry request to be processed, that is, the latest traffic indicator; at the same time, obtains the latest weight of the traffic indicator; predicts the current load capacity of the inquiry interface according to the traffic indicator and the latest weight of the traffic indicator, and obtains a quantified value of the load capacity; selects from multiple request volume adjustment strategies a strategy that can adaptively adjust the traffic according to the current load capacity of the inquiry interface, that is, the request volume adjustment strategy to be executed; executes the request volume adjustment strategy to be executed, determines the maximum request volume of inquiry requests that can be sent in the current batch, and with reference to the maximum request volume, selects the target inquiry requests that need to be sent in the current batch from the received inquiry requests, and then sends the target inquiry request to the inquiry server to call the inquiry interface of the inquiry server, obtains the credit data required for the target inquiry request, and sends these inquiry data to the business end corresponding to the target inquiry request. This application updates the weight of the flow index in real time according to the dynamic changes of the flow index and the dynamic changes of the business volume of the business end, so as to adaptively adjust the weight of the index with reference to the changes of each index, and pay different attention to different indicators in different scenarios to improve the accuracy of the prediction of the load capacity of the query interface, and adjust the frequency of the query interface call with reference to the current load capacity of the query interface to improve the response capacity to the change of the query interface state. Avoid increasing the processing pressure of the query interface and improve the query efficiency as a whole.

[0060] Based on the above embodiments, Figure 2 As shown, the flow index is updated in real time based on a preset time period, and the flow index includes the success rate of calling the query interface within the preset time period, the average response time of the query interface to each interface call within the preset time period, the bandwidth usage rate of the query interface call process within the preset time period, and the number of query requests processed within the preset time period; It is not difficult to understand that bandwidth utilization is an indicator to measure network performance and network resource utilization efficiency. It refers to the percentage of actual network bandwidth usage to the total network bandwidth capacity in a specific period of time. Network bandwidth is the capacity or rate of data transmission in network communication, usually measured in bits per second (bps).

[0061] The bandwidth utilization rate can reflect the degree of network congestion, the density of query interface calls, and the efficiency of network services. At lower bandwidth utilization rates, the network can usually handle data transmission more smoothly, and the user experience will be better. However, when the bandwidth utilization rate approaches or reaches 100%, the network may be congested, resulting in a decrease in data transmission speed, an increase in latency, and may even affect the normal operation of network services.

[0062] In some embodiments, the success rate of calling the query interface within a preset time period may be determined based on the total number of target query requests sent to the query server within the preset time period and the total number of target query requests successfully responded to by the query server; The average response time of the inquiry interface to each interface call within the preset time period can be determined according to the response time required by the server from sending the target inquiry request to receiving the credit information data required by the target inquiry request within the preset time period; The number of inquiry requests processed within the preset time period can be determined according to the number of target inquiry requests for credit investigation data that can be successfully received by the server within the preset time period.

[0063] In the above step S103, "predicting the load capacity of the query interface according to the latest flow index and the latest weight of the flow index to obtain a load capacity quantification value" includes the following steps: S201, multiplying the success rate, the average response time, the bandwidth usage rate, and the number of query requests processed within the preset time period by the corresponding latest weights respectively; S202: Add all the obtained products to obtain the load capacity quantization value.

[0064] For example: Set the following normalized latest weights and the corresponding latest traffic indicators: The latest weight corresponding to the success rate is wSR=0.35; The latest weight corresponding to the average time consumption is wRT=0.25; The latest weight corresponding to bandwidth usage is wBW=0.25; The latest weight corresponding to the number of search requests is wRV=0.15; The latest traffic indicators: The latest success rate SR=0.85; The latest average time: RT=0.5; The latest bandwidth usage: BW=0.7; The number of the latest search requests: RV=100.

[0065] The quantified value of load capacity = 0.35×0.85+0.25×0.5+0.25×0.7+0.15×100=15.5975.

[0066] The embodiment of the present application comprehensively considers multi-dimensional traffic indicators. By combining the success rate, average time consumption, bandwidth utilization rate and the number of query requests processed within a preset time period, it can more comprehensively and accurately reflect the operating status of the query server, or the load capacity of the query interface. By referring to the operating status of the query server, more precise traffic control can be achieved on the server, that is, the control of the query interface call frequency.

[0067] Based on the above embodiments, Figure 3 As shown, the above step S106 of "referring to the maximum request amount, filtering out the target query requests that need to be sent in the current batch from all the received query requests" may include the following steps: S301, obtaining the number of the pending inquiry requests; S302, determining the relationship between the number of the pending inquiry requests and the maximum request amount; S303, if the number of the pending query requests is greater than the maximum request amount, then according to the maximum request amount, the target query requests are screened out from the pending query requests, and the total number of the target query requests is the same as the maximum request amount; S304, caching the remaining pending query requests after screening into the delay queue; S305, if the number of the pending query requests is equal to the maximum request amount, all the pending query requests are used as the target query requests; S306, if the number of the query requests to be processed is less than the maximum request amount, extracting cached query requests from the delay queue as candidate query requests according to the difference between the maximum request amount and the number of the query requests to be processed; S307: Use the candidate query request and the query request to be processed as the target query request.

[0068] For example: when the server receives a query request sent by the business end, it obtains the latest traffic indicators in real time statistics from the data center through the traffic control center; then it calculates and determines the maximum number of requests that can be sent in the current batch through an algorithm; and takes the determined maximum number of requests as the upper limit, preferentially filters out the target query requests from the most recently received query requests, and then filters out the remaining number of query requests from the delay queue.

[0069] In some embodiments, it can be determined at each moment whether a query request sent by the service end is received based on the refresh rate. If no new query request is received, the cached query request is extracted from the delay queue as the "pending query request" in step S101.

[0070] In the embodiment of the present application, a delay queue is used to cache the query requests that the server fails to process in time. Then, when starting to send each batch of requests, the maximum request amount is determined to determine whether it is necessary to extract the query requests from the delay queue for sending, thereby forming a complete request processing flow that can flexibly respond to the status and load capacity of the query interface, and timely adjust the interface call frequency on the server side, thereby improving the overall processing efficiency of the query requests.

[0071] In some embodiments, the processing priority can be divided according to the identity information such as the user identification in the query request or the identification of the device sending the query request. For all the query requests received, after determining the maximum request amount, the query requests with higher priority can be preferentially screened out from the query requests to be processed and / or the query requests cached in the delay queue as the target query requests, so as to speed up the processing of the requests with higher priority.

[0072] Based on the above embodiments, Figure 4 As shown, the weight of any of the above traffic indicators can be updated in the following ways: S401, 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 according to the fluctuation of the historical data of the traffic indicator, and the weight adjustment coefficient is updated in real time according to the fluctuation of the historical data of the queried business volume; S402, calculating the difference between the latest expected value and the latest flow index; S403, calculating the product of the difference and the latest weight adjustment coefficient as the weight change; S404: Add the product of the weight change and the latest weight learning rate to the latest weight of the traffic indicator to obtain an updated weight.

[0073] In the embodiment of the present application, the weight update formula is: .

[0074] in, is the weight of the i-th flow index at time t, is the weight learning rate (which can be understood as the step size of each adjustment), is the weight change.

[0075] Weight change It can be calculated based on the difference between the latest traffic index and the latest expected value. For example, the weight change of the success rate: ; in, is the weight adjustment coefficient, is the latest expected value of the success rate, is the latest value of the success rate.

[0076] In some embodiments, to ensure that the sum of the weights is 1, the adjusted weights need to be normalized.

[0077] In the embodiment of the present application, the historical data may be data within a recent period of time (such as the observation period mentioned in the above embodiment). The weight adjustment coefficient is updated according to the fluctuation of the historical data of the business volume, for example, the update frequency of the weight adjustment coefficient is changed with reference to the fluctuation, or the update direction of the weight adjustment coefficient is changed (such as increasing the weight adjustment coefficient or reducing the weight adjustment coefficient), or the update amplitude of the weight adjustment coefficient is changed (such as increasing the difference before and after the weight adjustment coefficient is updated or reducing the difference before and after the weight adjustment coefficient is updated).

[0078] Correspondingly, the method of updating the weight learning rate according to the fluctuation of the historical data of the traffic index can refer to the above-mentioned method of updating the weight adjustment coefficient according to the fluctuation of the historical data of the collected business volume, which will not be repeated here.

[0079] In the embodiment of the present application, the weight adjustment coefficient is updated with reference to the dynamic change of the queried business volume, so that the weight change amount at each weight update can adapt to the dynamic change of the queried business volume; the weight learning rate is updated with reference to the dynamic change of the flow index, and the actual change of the flow index can be considered when the weight is updated to adjust the influence of the weight change amount on the current weight. In this way, when updating the weight of the flow index, the actual queried demand represented by the queried business volume and the response state of the queried interface represented by the flow index are comprehensively considered to determine the weight that is suitable for the current queried scenario, and the weight is used to apply the attention that is suitable for the current queried scenario to the flow index.

[0080] Based on the above embodiments, Figure 5 As shown, the above weight learning rate is updated in the following way: S501, calculating a first characteristic value of the flow indicator according to the historical data of the flow indicator, where the first characteristic value is used to reflect the fluctuation of the historical data of the flow indicator; S502, if the first eigenvalue is within a first preset range, increasing the weight learning rate; S503: If the first eigenvalue is within a second preset range, reduce the weight learning rate.

[0081] The minimum value of the first preset range is greater than the maximum value of the second preset range.

[0082] In some embodiments, the stability and volatility of the traffic index can be determined by analyzing the mean, variance, extreme value and other characteristic values ​​in the historical data. Alternatively, a time series prediction model (such as ARIMA, LSTM, etc.) can be established based on the historical data to predict the dynamic changes of future traffic indicators and future dynamic changes of the collected business volume, and the weights can be updated according to the future dynamic changes.

[0083] When the characteristic value of the traffic indicator fluctuates greatly, the weight learning rate is reduced to reduce the impact of over-adjusting the entire query process on the server side.

[0084] When the characteristic value of the traffic indicator fluctuates slightly, increase the weight learning rate to speed up the server's response to the change.

[0085] Based on the above embodiments, Figure 6 As shown, the above weight adjustment coefficient is updated in the following way: S601, obtaining the historical data of the tax-collected business volume; S602, calculating a second characteristic value of the levied business volume according to the historical data of the levied business volume, wherein the second characteristic value is used to reflect the fluctuation of the historical data of the levied business volume; S603, if the second eigenvalue is within a third preset range, increasing the weight adjustment coefficient; S604: If the second eigenvalue is within a fourth preset range, reduce the weight adjustment coefficient.

[0086] The minimum value of the third preset range is greater than the maximum value of the fourth preset range.

[0087] In the embodiment of the present application, when the traffic volume fluctuates greatly, the weight adjustment coefficient is increased to make the entire query process of the server more sensitive to traffic changes. When the traffic volume fluctuates slightly, the weight adjustment coefficient is reduced to reduce unnecessary adjustments.

[0088] It should be noted that the above-mentioned first preset range, second preset range, third preset range and fourth preset range can be set as needed, and this application does not limit them.

[0089] Based on the above embodiments, Figure 7 As shown, if the second characteristic value is within the third preset range, the method further includes the following steps: S701, determining whether the tax inquiry business volume is on an increasing trend; S702: If yes, then lower 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.

[0090] The expected value of the average response time is reduced to below the latest value of the average response time, that is, the expected value of the average response time is adjusted to below the latest actual value. The weight change of the average response time calculated in this way will be a negative value, and the updated weight will be smaller than the weight before the update, thereby achieving the purpose of reducing the weight of the average response time.

[0091] In an embodiment of the present application, when the business volume surges, the weight of the response time is reduced and the weight of the success rate is increased to ensure priority processing of key requests.

[0092] When the business volume decreases or tends to be stable, no further adjustment is required to the expected values ​​of various traffic indicators.

[0093] The method for processing the inquiry request proposed in the present application comprehensively considers multi-dimensional indicators. By combining the success rate, average time consumption, bandwidth utilization rate and the number of inquiry requests processed within a preset time period, it can more comprehensively and accurately reflect the operating status of the entire inquiry system of the business end-server-inquiry interface, and achieve more accurate flow control. The maximum number of requests that can be processed in a single batch can be dynamically adjusted according to real-time data, thereby improving the responsiveness and stability of the entire inquiry request processing process. The embodiment of the present application introduces an adaptive weight adjustment mechanism, which enables the entire inquiry request processing process to automatically optimize the flow control of the server according to different business scenarios and operating states, and has high adaptability and flexibility.

[0094] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean 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 the present application.

[0095] Figure 8 is a block diagram of a processing device for a search request according to an exemplary embodiment of the present application. 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 screening module 806, an information acquisition module 807 and a sending module 808.

[0096] The first acquisition module 801 is used to acquire a pending inquiry request, where the inquiry request is a request for inquiry of credit data sent by a business end; The second acquisition module 802 is used to acquire the latest flow index and the latest weight of the flow index. The weight of the flow index is updated in real time according to the dynamic change of the flow index and the dynamic change of the query business volume of the business end. The flow index is used to characterize the response of the query interface to the interface call; A prediction module 803, configured to predict the load capacity of the query interface according to the latest flow index and the latest weight of the flow index, and obtain a quantified value of the load capacity; A first determination module 804 is used to determine the load capacity quantization value interval in which the load capacity quantization value is located from the load capacity quantization value intervals corresponding to different request quantity adjustment strategies, so as to determine the request quantity adjustment strategy to be executed, wherein the request quantity adjustment strategy is used to adjust the maximum request quantity of the query requests that can be sent in a single batch; The second determination module 805 is used to execute the request quantity adjustment strategy to be executed, and determine the maximum request quantity of the inquiry requests that can be sent in the current batch; the current batch is the batch corresponding to the current moment when the inquiry requests are sent in batches; A screening module 806 is used to screen target search requests that need to be sent in the current batch from all the received search requests, with reference to the maximum request amount, wherein all the received search requests include the search requests to be processed; The information acquisition module 807 is used to send the target inquiry request to the inquiry server to call the inquiry interface of the inquiry server to obtain the credit data required by the target inquiry request; The sending module 808 is used to send the credit investigation data to the business end corresponding to the target credit investigation request.

[0097] In an embodiment of the present application, the traffic indicator is updated in real time based on a preset time period, and the traffic indicator includes the success rate of calling the query interface within the preset time period, the average response time of the query interface to each interface call within the preset time period, the bandwidth utilization rate of the query interface call process within the preset time period, and the number of query 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 query requests processed within the preset time period by the corresponding latest weights respectively; add all the products obtained to obtain the load capacity quantification value.

[0098] In an embodiment of the present application, all of the received query requests also include query requests cached in a delay queue; the query requests to be processed include query requests extracted from the delay queue and / or the latest query requests that are not cached in the delay queue; the screening module is used to: obtain the number of query requests to be processed; determine the relationship between the number of query requests to be processed and the maximum request amount; if the number of query requests to be processed is greater than the maximum request amount, then based on the maximum request amount, screen out the target query request from the query requests to be processed, and the target query request The total number of target search requests is the same as the maximum request amount; the query requests to be processed remaining after screening are cached in the delay queue; if the number of query requests to be processed is equal to the maximum request amount, all the query requests to be processed are used as the target search requests; if the number of query requests to be processed is less than the maximum request amount, the cached query requests are extracted from the delay queue as candidate query requests according to the difference between the maximum request amount and the number of query requests to be processed; the candidate query requests and the query requests to be processed are used as the target query requests.

[0099] In an embodiment of the present application, the device is also used to update the weight of the flow indicator in the following manner: obtain the latest weight learning rate, the latest weight adjustment coefficient, and the latest expected value of the flow indicator, the weight learning rate is updated in real time according to the fluctuation of the historical data of the flow indicator, and the weight adjustment coefficient is updated in real time according to the fluctuation of the historical data of the collected business volume; calculate the difference between the latest expected value and the latest flow indicator; calculate the product of the difference and the latest weight adjustment coefficient as the weight change; add the product between the weight change and the latest weight learning rate to the latest weight of the flow indicator to obtain the updated weight.

[0100] In an embodiment of the present application, the device is also used to update the weight learning rate in the following manner: based on the historical data of the flow indicator, calculating the first characteristic value of the flow indicator, the first characteristic value is used to reflect the fluctuation of the historical data of the flow indicator; if the first characteristic value is within a first preset range, increasing the weight learning rate; if the first characteristic value is within a second preset range, reducing the weight learning rate; the minimum value of the first preset range is greater than the maximum value of the second preset range.

[0101] In an embodiment of the present application, the device is also used to update the weight adjustment coefficient in the following manner: obtaining historical data of the business volume to be queried; calculating a second characteristic value of the business volume to be queried based on the historical data of the business volume to be queried, the second characteristic value being used to reflect the fluctuation of the historical data of the business volume to be queried; if the second characteristic value is within a third preset range, increasing the weight adjustment coefficient; if the second characteristic value is within a fourth preset range, reducing the weight adjustment coefficient; the minimum value of the third preset range is greater than the maximum value of the fourth preset range.

[0102] In an embodiment of the present application, when the flow indicator is the average response time, if the second characteristic value is within the third preset range, the device is further used to: determine whether the query business volume is on an upward trend; if so, reduce the expected value of the average response time to reduce the weight of the average response time; and / or; when the flow indicator is the success rate, if the second characteristic value is within the third preset range, the device is further used to: determine whether the query business volume is on an upward trend; if so, increase the expected value of the success rate to increase the weight of the success rate.

[0103] In summary, after obtaining the inquiry request to be processed, the device obtains the latest value of the flow indicator that can characterize the response of the inquiry interface to the interface call, that is, the latest flow indicator; at the same time, obtains the latest weight of the flow indicator; predicts the current load capacity of the inquiry interface according to the flow indicator and the latest weight of the flow indicator, and obtains a quantified value of the load capacity; selects a strategy that can adaptively adjust the flow according to the current load capacity of the inquiry interface from multiple request volume adjustment strategies, that is, the request volume adjustment strategy to be executed; executes the request volume adjustment strategy to be executed, determines the maximum request volume that can be sent in the current batch, and refers to the maximum request volume to select the target inquiry request that needs to be sent in the current batch from the received inquiry requests, and then sends the target inquiry request to the inquiry server to call the inquiry interface of the inquiry server, obtain the credit data required for the target inquiry request, and send these inquiry data to the business end corresponding to the target inquiry request. This application updates the weight of the flow index in real time according to the dynamic changes of the flow index and the dynamic changes of the query business volume of the business end, so as to adaptively adjust the weight of the index with reference to the changes of each index, and pay different attention to different indicators in different scenarios to improve the accuracy of the prediction of the load capacity of the query interface, and adjust the frequency of the query request call with reference to the current load capacity of the query interface to improve the response capacity to the change of the query interface state. Avoid increasing the processing pressure of the query interface and improve the query efficiency as a whole.

[0104] To implement the above embodiment, the embodiment of the present 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, the steps of the method for processing a search request as described in the above embodiment are implemented.

[0105] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may 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 many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0106] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by 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.

[0107] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for processing a search request, characterized in that: The method comprises: Obtaining a pending inquiry request, wherein the inquiry request is a request sent by a business end to inquire about credit investigation data; Obtain the latest flow index and the latest weight of the flow index. The weight of the flow index is updated in real time according to the dynamic changes of the flow index and the dynamic changes of the query business volume of the business end. The flow index is used to characterize the response of the query interface to the interface call; Predicting the load capacity of the query interface according to the latest flow index and the latest weight of the flow index to obtain a quantified value of the load capacity; Determine the load capacity quantization value interval in which the load capacity quantization value is located from the load capacity quantization value intervals corresponding to different request quantity adjustment strategies, so as to determine the request quantity adjustment strategy to be executed, wherein the request quantity adjustment strategy is used to adjust the maximum request quantity of the query requests that can be sent in a single batch; Execute the request quantity adjustment strategy to be executed to determine the maximum request quantity of the inquiry requests that can be sent in the current batch; the current batch is the batch corresponding to the current moment when the inquiry requests are sent in batches; Referring to the maximum request amount, filtering out target query requests that need to be sent in the current batch from all the received query requests, wherein all the received query requests include the query requests to be processed; Sending the target inquiry request to the inquiry service end to call the inquiry interface of the inquiry service end to obtain the credit data required by the target inquiry request; The credit investigation data is sent to the business end corresponding to the target inquiry request.

2. The method according to claim 1, characterized in that The flow index is updated in real time based on a preset time period, and includes the success rate of calling the query interface within the preset time period, the average response time of the query interface to each interface call within the preset time period, the bandwidth usage rate of the query interface call process within the preset time period, and the number of query requests processed within the preset time period; The step of predicting the load capacity of the query interface according to the latest flow index and the latest weight of the flow index to obtain a load capacity quantification value includes: Multiplying the success rate, the average response time, the bandwidth usage rate, and the number of query requests processed within the preset time period by the corresponding latest weights respectively; All the obtained products are added together to obtain the load capacity quantification value.

3. The method according to claim 1, characterized in that All the received query requests also include query requests cached in the delay queue; the query requests to be processed include query requests extracted from the delay queue and / or the latest query request not cached in the delay queue; The referring to the maximum request amount, filtering out the target query requests that need to be sent in the current batch from all the received query requests, includes: Obtaining the number of pending inquiry requests; Determining the relationship between the number of the pending inquiry requests and the maximum request amount; If the number of the pending query requests is greater than the maximum request amount, then according to the maximum request amount, the target query requests are screened out from the pending query requests, and the total number of the target query requests is the same as the maximum request amount; Cache the remaining pending query requests after screening into the delay queue; If the number of the pending query requests is equal to the maximum request amount, all the pending query requests are used as the target query requests; If the number of the pending query requests is less than the maximum request amount, extracting cached query requests from the delay queue as candidate query requests according to the difference between the maximum request amount and the number of the pending query requests; The candidate query request and the query request to be processed are used as the target query request.

4. The method according to claim 2 or 3, characterized in that The weight of the traffic indicator is updated in the following way: Obtain 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 according to the fluctuation of the historical data of the traffic indicator, and the weight adjustment coefficient is updated in real time according to the fluctuation of the historical data of the queried business volume; Calculating the difference between the latest expected value and the latest flow index; Calculate the product of the difference and the latest weight adjustment coefficient as the weight change; The product of the weight change and the latest weight learning rate is added to the latest weight of the flow indicator to obtain an updated weight.

5. The method according to claim 4, characterized in that The weight learning rate is updated by: Calculating a first characteristic value of the flow index according to the historical data of the flow index, where the first characteristic value is used to reflect the fluctuation of the historical data of the flow index; If the first eigenvalue is within a first preset range, increasing the weight learning rate; If the first eigenvalue is within a second preset range, reducing the weight learning rate; The minimum value of the first preset range is greater than the maximum value of the second preset range.

6. The method according to claim 4, characterized in that The weight adjustment coefficient is updated by: Obtaining historical data of the tax-collected business volume; Calculating a second characteristic value of the levied business volume according to the historical data of the levied business volume, wherein the second characteristic value is used to reflect the fluctuation of the historical data of the levied business volume; If the second eigenvalue is within a third preset range, increasing the weight adjustment coefficient; If the second eigenvalue is within a fourth preset range, reducing the weight adjustment coefficient; The minimum value of the third preset range is greater than the maximum value of the fourth preset range.

7. The method according to claim 6, characterized in that In the case where the flow indicator is the average response time, if the second characteristic value is within the third preset range, the method further includes: Determining whether the volume of tax collection business is on an increasing trend; If yes, then lower the expected value of the average response time to reduce the weight of the average response time; and / or; In the case where the flow indicator is the success rate, if the second characteristic value is within the third preset range, the method further includes: Determining whether the volume of tax collection business is on an increasing trend; If so, the expected value of the success rate is increased to increase the weight of the success rate.

8. A device for processing a search request, characterized in that: The device comprises: A first acquisition module is used to acquire a pending inquiry request, wherein the inquiry request is a request for inquiry of credit data sent by a business end; The second acquisition module is used to obtain the latest flow index and the latest weight of the flow index. The weight of the flow index is updated in real time according to the dynamic change of the flow index and the dynamic change of the query business volume of the business end. The flow index is used to characterize the response of the query interface to the interface call; A prediction module, used to predict the load capacity of the query interface according to the latest flow index and the latest weight of the flow index, and obtain a quantified value of the load capacity; A first determination module is used to determine the load capacity quantization value interval in which the load capacity quantization value is located from the load capacity quantization value intervals corresponding to different request quantity adjustment strategies, so as to determine the request quantity adjustment strategy to be executed, wherein the request quantity adjustment strategy is used to adjust the maximum request quantity of the inquiry requests that can be sent in a single batch; A second determination module is used to execute the request quantity adjustment strategy to be executed, and determine the maximum request quantity of the inquiry requests that can be sent in the current batch; the current batch is the batch corresponding to the current moment when the inquiry requests are sent in batches; A screening module, for screening target search requests that need to be sent in a current batch from all the received search requests, with reference to the maximum request amount, wherein all the received search requests include the search requests to be processed; An information acquisition module, used to send the target inquiry request to the inquiry service end, so as to call the inquiry interface of the inquiry service end and obtain the credit information data required by the target inquiry request; The sending module is used to send the credit investigation data to the business end corresponding to the target credit investigation request.

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

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

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