Data processing method and device, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides a data processing method and device, electronic equipment, a storage medium and a program product. The method can be used in artificial intelligence field. According to the method, according to historical service data, the mapping relation between the parallel number and the parallel processing duration corresponding to each service type is determined, and the parallel processing duration is the duration used for parallel processing of the parallel number of service requests; according to the mapping relationship corresponding to each service type, determining a first service type belonging to priority processing in all service types and a parallel processing threshold value corresponding to the first service type; and processing the plurality of service requests received in the current time period according to the limit capability of service processing and the parallel processing thresholds corresponding to the first service types, so as to achieve the effects of improving the concurrent service processing efficiency and improving the experience.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to a data processing method, device, electronic device, storage medium and program product. Background Art

[0002] With the widespread application of the Internet in various fields, the number of users of various industry systems has increased dramatically, and the concurrent requests that the system needs to handle are also growing exponentially. How to handle high-concurrency scenarios has put forward strict requirements on the stability, scalability and performance of the system.

[0003] In the prior art, in the face of high concurrency scenarios, the number of servers can be increased or server performance can be improved to improve the system's processing capacity, or user requests can be limited to prevent too many requests from impacting the system at the same time.

[0004] However, high-concurrency scenarios usually occur less frequently. Simply adding servers is not only economically costly, but also easily leads to a waste of resources. At the same time, the existing current limiting technology cannot flexibly adjust the number of concurrently processed businesses according to the actual application scenarios, making the system inefficient in handling concurrency issues, long waiting times for users, and poor user experience. Summary of the invention

[0005] The embodiments of the present application provide a data processing method, device, electronic device, storage medium and program product to improve the efficiency of concurrent business processing and improve the user experience.

[0006] In a first aspect, an embodiment of the present application provides a data processing method, including:

[0007] Determine, based on historical business data, a mapping relationship between the number of parallel requests and the parallel processing duration corresponding to each business type, wherein the parallel processing duration is the duration used for parallel processing of the business requests of the parallel number;

[0008] Determine, according to the mapping relationship corresponding to each service type, a first type of service type that is to be processed with priority among all service types and a parallel processing threshold corresponding to the first type of service type;

[0009] According to the limit capacity of business processing and the parallel processing thresholds corresponding to each of the first business types, multiple business requests received in the current time period are processed.

[0010] In a possible implementation, determining the mapping relationship between the parallel quantity and the parallel processing duration corresponding to each service type according to the historical service data includes:

[0011] Determine, based on the historical service data, historical parallel service requests corresponding to each service type in multiple historical time periods;

[0012] For each service type, in each historical time period of the service type, determining an average processing time of each historical service request according to the historical parallel service requests in the historical time period;

[0013] For each service type, determining multiple parallel quantities according to the historical parallel service requests corresponding to multiple historical time periods respectively;

[0014] For each parallel quantity, determine the parallel processing duration corresponding to the parallel quantity according to the average processing duration corresponding to the historical parallel business requests corresponding to the parallel quantity;

[0015] According to the multiple parallel quantities and the respective parallel processing durations, a mapping relationship between the parallel quantity and the parallel processing duration of the service type is determined.

[0016] In a possible implementation, the determining, according to the mapping relationship corresponding to each service type, the first type of service type that is subject to priority processing among all service types and the parallel processing threshold corresponding to the first type of service type includes:

[0017] Obtaining a preset duration threshold, and determining a first parallel quantity corresponding to each service type according to the preset duration threshold and the mapping relationship;

[0018] For each service type, determining the service type corresponding to the service type whose first parallel quantity is less than the preset parallel quantity threshold as the first type of service type;

[0019] For each first-category service type, the parallel processing threshold is determined according to the first parallel quantity of the first-category service type and historical service data of the first-category service type in multiple historical time periods.

[0020] In a possible implementation, the processing of multiple service requests received in the current time period according to the service processing limit capacity and the parallel processing thresholds corresponding to the first service types respectively includes:

[0021] Determine the number of current parallel service requests for each of the first service types according to the service requests in the current time period;

[0022] Processing the service requests of each of the first-category service types according to the current number of parallel service requests of each of the first-category service types and the parallel processing threshold of each of the first-category service types; and / or,

[0023] The service requests of non-first-category service types in the current time period are processed according to the limit capacity and the current number of parallel service requests of each first-category service type.

[0024] In a possible implementation, the processing of service requests of each first-category service type according to the current number of parallel service requests of each first-category service type and the parallel processing threshold of each first-category service type includes:

[0025] If the number of current service requests of each first-category service type is less than or equal to the corresponding parallel processing threshold, the current service requests of each first-category service type are processed in parallel;

[0026] If the current business request of any first-category business type is greater than the parallel processing threshold of the first-category business type, select the corresponding business requests whose total number is less than or equal to the parallel processing threshold of the first-category business type from the business requests of the first-category business type; and process the selected business requests in parallel.

[0027] In a possible implementation manner, the processing of the service requests of non-first-category service types in the current time period according to the limit capacity and the current number of parallel service requests of each first-category service type includes:

[0028] Determine the service processing margin according to the number of current service requests of the plurality of first service types and the limit capacity;

[0029] The service requests of non-first-category service types in the current time period are processed according to the service processing margin.

[0030] In a possible implementation manner, the processing, according to the service processing margin, the service request of the non-first-category service type in the current time period includes:

[0031] If the number of the service requests of the non-first-category service type is less than or equal to the service processing margin, the multiple service requests of the non-first-category service type are processed in parallel;

[0032] Otherwise, multiple target non-first-category business types that are less than or equal to the business processing margin are selected from multiple business requests of non-first-category business types; and the business requests of the multiple target non-first-category business types are processed in parallel.

[0033] In a second aspect, an embodiment of the present application provides a data processing device, including:

[0034] A first determining unit is used to determine, based on historical business data, a mapping relationship between the number of parallel requests and the parallel processing duration corresponding to each business type, wherein the parallel processing duration is a duration used for parallel processing of the business requests of the parallel number;

[0035] A second determining unit is used to determine, according to the mapping relationship corresponding to each business type, a first type of business type that belongs to priority processing among all the business types and a parallel processing threshold corresponding to the first type of business type;

[0036] The processing unit is used to process multiple service requests received in the current time period according to the limit capacity of service processing and the parallel processing thresholds corresponding to each of the first types of services.

[0037] In a possible implementation manner, the first determining unit includes:

[0038] A first determination module is used to determine historical parallel business requests corresponding to each business type in multiple historical time periods according to the historical business data;

[0039] A second determination module is used to determine, for each service type and in each historical time period of the service type, an average processing time of each historical service request according to the historical parallel service requests in the historical time period;

[0040] A third determination module is used to determine, for each service type, multiple parallel quantities according to the historical parallel service requests respectively corresponding to multiple historical time periods;

[0041] A fourth determination module is used to determine, for each parallel quantity, a parallel processing duration corresponding to the parallel quantity according to an average processing duration corresponding to the historical parallel business requests corresponding to the parallel quantity;

[0042] The fifth determining module is used to determine the mapping relationship between the parallel quantity and the parallel processing duration of the service type according to the multiple parallel quantities and the respective parallel processing durations.

[0043] In a possible implementation manner, the second determining unit includes:

[0044] An acquisition module, used to acquire a preset duration threshold, and determine a first parallel quantity corresponding to each service type according to the preset duration threshold and the mapping relationship;

[0045] A sixth determination module, configured to determine, for each first-category service type, the first parallel quantity corresponding to the preset duration threshold according to the mapping relationship corresponding to the first-category service type;

[0046] A seventh determination module is used to determine, for each first-category business type, the parallel processing threshold according to the first parallel quantity of the first-category business type and historical business data of the first-category business type in multiple historical time periods.

[0047] In a possible implementation, the processing unit includes:

[0048] An eighth determination module, configured to determine the number of current parallel service requests of each of the first service types according to the service requests in the current time period;

[0049] a ninth determination module, configured to process the service requests of each of the first-category service types according to the current number of parallel service requests of each of the first-category service types and the parallel processing threshold of each of the first-category service types; and / or,

[0050] The processing module is used to process the service requests of non-first-category service types in the current time period according to the limit capacity and the current number of parallel service requests of each first-category service type.

[0051] In a possible implementation manner, the twelfth determining module includes:

[0052] If the number of current service requests of each first-category service type is less than or equal to the corresponding parallel processing threshold, the current service requests of each first-category service type are processed in parallel;

[0053] If the current business request of any first-category business type is greater than the parallel processing threshold of the first-category business type, select the corresponding business requests whose total number is less than or equal to the parallel processing threshold of the first-category business type from the business requests of the first-category business type; and process the selected business requests in parallel.

[0054] In a possible implementation, the processing module includes:

[0055] Determine the service processing margin according to the number of current service requests of the plurality of first service types and the limit capacity;

[0056] The service requests of non-first-category service types in the current time period are processed according to the service processing margin.

[0057] In a possible implementation manner, the processing, according to the service processing margin, the service request of the non-first-category service type in the current time period includes:

[0058] If the number of the service requests of the non-first-category service type is less than or equal to the service processing margin, the multiple service requests of the non-first-category service type are processed in parallel;

[0059] Otherwise, multiple target non-first-category business types that are less than or equal to the business processing margin are selected from multiple business requests of non-first-category business types; and the business requests of the multiple target non-first-category business types are processed in parallel.

[0060] In a third aspect, an embodiment of the present application provides a data processing device, including: a memory, a processor;

[0061] The memory stores computer-executable instructions;

[0062] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0063] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0064] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0065] The embodiments of the present application provide a data processing method, device, electronic device, storage medium and program product. Through historical business processing data, a correspondence between concurrent data and parallel processing duration of different business types is obtained, and a parallel processing threshold is obtained according to the correspondence. Therefore, according to the maximum number of businesses that can be processed in parallel by the system and the parallel processing threshold corresponding to the first type of business type, the business of the current business processing period is processed to realize priority division of different types of business according to the number of concurrencies, and the number of parallel processing of the business is determined according to the historical scenario data corresponding to each business, so as to give priority to the business that can be processed concurrently at one time. If there is still business processing margin when processing the first type of business type, the non-priority business type is processed synchronously, thereby improving the efficiency of business processing, reducing the waiting time of users, and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0067] Figure 1 A schematic diagram of a data processing method provided for this application Figure 1 ;

[0068] Figure 2 A schematic diagram of a data processing method provided for this application Figure 2 ;

[0069] Figure 3 A schematic diagram of the structure of a data processing device provided in this application;

[0070] Figure 4 A structural schematic diagram of a data processing device provided in this application.

[0071] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0072] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0073] It should be noted that the data processing method and device of the present application can be used in the field of artificial intelligence, and can also be used in any field other than the field of artificial intelligence, such as the financial field. The application field of the data processing method and device of the present application is not limited.

[0074] With the widespread application of the Internet in various fields, the number of users of various industry systems has increased dramatically, and the concurrent requests that the system needs to handle are also growing exponentially. How to handle high-concurrency scenarios has put forward strict requirements on the stability, scalability and performance of the system.

[0075] In the existing technology, in the face of high concurrency scenarios, the number of servers can be increased or the server performance can be improved to improve the system's processing capacity, or user requests can be limited to prevent too many requests from impacting the system at the same time. Especially in the financial system, due to irregular high-concurrency access, such as transfer business and withdrawal business at the end of the month, if the high-concurrency problem cannot be effectively solved, it will cause large user complaints and even economic losses.

[0076] In one example, the number of servers may be increased, or the performance of the servers may be improved, to increase the processing capacity of the system.

[0077] However, in the above method, if the frequency of high-concurrency scenarios is low, blindly adding servers will not only have high economic costs, but also easily cause waste of resources.

[0078] In another example, user requests can be limited to prevent too many requests from impacting the system at the same time.

[0079] However, in the above method, the existing current limiting technology cannot flexibly adjust the number of concurrently processed services according to the actual application scenario, which makes the system inefficient in handling concurrency problems, the user waiting time is long, and the experience is poor.

[0080] The present application provides a data processing method, device and equipment, aiming to solve the above-mentioned technical problems.

[0081] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0082] Figure 1 A schematic diagram of a data processing method provided for this application Figure 1 ,like Figure 1 As shown, the method includes:

[0083] S101. Determine, based on historical business data, a mapping relationship between the number of parallel requests and the parallel processing duration corresponding to each business type, wherein the parallel processing duration is the duration used for parallel processing of the business requests of the parallel number.

[0084] Exemplarily, historical business data of each business type in different historical time periods is obtained, and the historical business data includes the number of concurrent accesses to the system by this business type in this historical time period. Business types include deposit business, loan business, transfer business, international business, etc., and the processing time of each business of this business type concurrently in this time period. The number of business accesses of each business type and the processing time of a single business of each business type constitute the historical parallel business requests of this business type in the corresponding historical time period.

[0085] Each piece of historical business data corresponds to a set of data on the number of accesses and the processing time. Linear fitting is performed based on multiple sets of data on the number of accesses and the processing time corresponding to multiple historical time periods to obtain a mapping relationship between the number of parallel operations and the parallel processing time. This mapping relationship indicates that different parallel numbers require different parallel processing times.

[0086] S102: Determine, according to the mapping relationship corresponding to each service type, a first type of service type that is to be processed with priority among all the service types and a parallel processing threshold corresponding to the first type of service type.

[0087] Exemplarily, a preset processing time threshold for each business type is obtained, and based on a mapping relationship between known business types, a concurrent access value that can be processed by each business type within the preset processing time threshold is obtained. Based on whether the concurrent access value is less than the preset access value, different business types are divided into first-category business types and non-first-category business types, wherein the concurrent access value of each business type under the first-category business type is less than the preset access value, and the number of accesses and the concurrent access value in the historical business data of each business type under the first-category business type are taken as smaller to obtain a parallel processing threshold, wherein the parallel processing threshold represents the maximum number of parallel processes that can be processed simultaneously by each business type included in the first-category business type.

[0088] S103: Process multiple service requests received in the current period according to the service processing limit capacity and the parallel processing thresholds corresponding to each first-category service type.

[0089] In one example, within the limit capacity of system service processing, if the number of current service requests of each first-category service type is less than or equal to the corresponding parallel processing threshold, the current service requests of each first-category service type are processed in parallel.

[0090] In one example, if the current business request of any first-category business type is greater than the parallel processing threshold of the first-category business type, business requests whose corresponding total number is less than or equal to the parallel processing threshold of the first-category business type are selected from the business requests of the first-category business type; and the selected business requests are processed in parallel.

[0091] If the limit capacity of business processing is greater than the number of current business requests, business requests of non-first-category business types whose number is the difference between the current business request number and the limit capacity can be processed synchronously.

[0092] A data processing method provided in an embodiment of the present application uses historical business processing data to obtain a correspondence between concurrent data of different business types and parallel processing durations, and obtains a parallel processing threshold based on the correspondence. Therefore, according to the maximum number of businesses that can be processed in parallel by the system and the parallel processing threshold corresponding to the first type of business type, the business of the current business processing period is processed to achieve priority of different types of business according to the number of concurrency, and the number of parallel processing of the business is determined according to the historical scenario data corresponding to each business, so as to give priority to the business that can be processed concurrently at one time. If there is still business processing margin when processing the first type of business type, the non-priority business type is processed synchronously, thereby improving the efficiency of business processing, reducing the waiting time of users, and improving the user experience.

[0093] Figure 2 A schematic diagram of a data processing method provided for this application Figure 2 ,like Figure 2 As shown, in this embodiment Figure 2 Based on the embodiment, a data processing method is described in detail, the method comprising:

[0094] S201. Determine historical parallel service requests corresponding to each service type in multiple historical time periods according to historical service data.

[0095] Exemplarily, historical business data of each business type in different historical time periods are obtained. The historical business data of each business type includes the number of concurrent accesses to the system by the business type in the corresponding historical time period, and the processing time corresponding to each concurrent business request of the business type in the historical time period. In a historical time period, the number of accesses to each type of business and the processing time of each single business of each type of business constitute the historical parallel business requests of the business type in the corresponding historical time period, and the number of business accesses is also the number of business requests.

[0096] S202: For each service type, in each historical time period of the service type, determine an average processing time of each historical service request according to historical parallel service requests in the historical time period.

[0097] For example, when historical parallel business requests are obtained, the number of accesses to the business type corresponding to the historical parallel business requests and the processing time of each single business of the business type are obtained. The sum of the processing time of each single business is divided by the number of accesses to obtain the average processing time of each historical business request.

[0098] S203: For each service type, determine multiple parallel quantities according to historical parallel service requests corresponding to multiple historical time periods.

[0099] For example, in order to make the historical business data selected by each business type universal and eliminate the randomness of data selection, multiple copies of historical business data are usually selected for data collection, because each copy of historical business data corresponds to a non-overlapping historical time period, and the number of concurrent access systems of the business type corresponding to the historical business data in each historical time period is the historical parallel number of the corresponding business type in the historical time period. Multiple parallel numbers can be determined according to the historical parallel numbers corresponding to different historical time periods.

[0100] S204: For each parallel quantity, determine the parallel processing duration corresponding to the parallel quantity according to the average processing duration corresponding to the historical parallel business requests corresponding to the parallel quantity.

[0101] Exemplarily, the parallel processing duration corresponding to the parallel number is the average processing duration of the number of services corresponding to the parallel number.

[0102] S205: Determine a mapping relationship between the parallel quantity and the parallel processing duration of the service type according to the multiple parallel quantities and the respective parallel processing durations.

[0103] Exemplarily, taking a parallel number and a corresponding parallel processing duration as a group, linear fitting is performed on multiple groups of data consisting of multiple parallel numbers and their respective parallel processing durations to obtain a mapping relationship between the parallel number and the parallel processing duration of the business type. The mapping relationship represents the number of parallel businesses of the corresponding business type that the system can simultaneously process within a certain parallel processing duration.

[0104] S206: Obtain a preset duration threshold, and determine the first parallel quantity corresponding to each service type according to the preset duration threshold and the mapping relationship.

[0105] Exemplarily, a preset time threshold is obtained, and the preset time threshold represents the critical value of the parallel time for processing the corresponding business type. If the preset time threshold is lower than the critical value of the parallel time, the system processes a low amount of parallel tasks, and the user's waiting time cost is high. If the preset time threshold is higher than the critical value of the parallel time, the system has spare time for processing parallel tasks, which affects subsequent business processing. Substitute the preset time threshold of each business type into the mapping relationship to obtain the first parallel quantity of each type.

[0106] In one example, the preset duration threshold can be determined based on the following steps: obtaining multiple historical service request data, determining the waiting time in each historical service request data, wherein the waiting time is the interval between the gateway sending the corresponding service request and receiving the corresponding reply information; determining the probability density function of the waiting time based on the waiting times corresponding to the multiple historical service request data; determining the processing time threshold based on the probability density function of the waiting time and a preset first probability, wherein the integral of the probability density function of the waiting time from the processing time threshold to positive infinity is less than or equal to the first probability.

[0107] At the same time, after determining the waiting time in each historical business request data, calculate the mean and variance of all waiting time samples, calculate the product of the variance and the number of waiting time samples, determine the quotient of the product and the probability value, and the square root of the quotient, wherein the probability value is used to characterize the probability that an acceptable deviation from the mean exceeds the expected value; correct the preset time threshold to the sum of the processing time threshold and the square root.

[0108] S207: For each service type, determine the service type whose corresponding first parallel quantity is less than a preset parallel quantity threshold as a first category service type.

[0109] Exemplarily, a preset parallel quantity threshold is obtained for each business type. If there is a business type whose first parallel quantity is less than the preset parallel quantity threshold, then the business type is a first-category business type. The remaining business types that do not meet the condition that the first parallel quantity is less than the preset parallel quantity threshold are all non-first-category business types. The first-category business type represents the business type that the system prioritizes.

[0110] S208: For each first-category service type, determine a parallel processing threshold according to a first parallel quantity of the first-category service type and historical service data of the first-category service type in multiple historical time periods.

[0111] Exemplarily, when it is determined that the business type belongs to the first category of business type, the potential parallel number of this business type in the system is obtained based on the number of concurrent accesses to the system by this business type in the historical business data of each business type. Because each first category of business type selects multiple historical business data, there are multiple potential parallel numbers in different historical time periods. The smaller of the multiple potential parallel numbers of each first category of business type and its first concurrent number is taken to obtain the parallel processing threshold.

[0112] In an example, the potential parallel quantity of the first type of business may also be predicted based on the business request data of each first type of business in a previous historical period before the current period of the system.

[0113] Using the service request data of each first-category service type of the system in the previous historical period before the current period as the pre-service request data of each first-category service type corresponding to the current period; obtaining multiple historical periods; for each historical period, using the service request data of each first-category service type of the system in the previous historical period before the historical period as the pre-service request data of each first-category service type corresponding to the historical period;

[0114] Determine similar historical time periods corresponding to the current time period for each first-category service type based on the pre-service request data corresponding to the current time period for each first-category service type and the pre-service request data corresponding to each historical time period;

[0115] The potential parallel quantity of each service type is predicted based on the service request data of each first-category service type in a similar historical period corresponding to the current period.

[0116] S209: Determine the number of current parallel service requests of each first service type according to the service requests in the current time period.

[0117] Exemplarily, the current system service access request is obtained, and the corresponding service request quantities are determined according to the first-category service type and the non-first-category service type.

[0118] S210: Process the service requests of each first-category service type according to the current number of parallel service requests of each first-category service type and the parallel processing threshold of each first-category service type.

[0119] In a possible implementation, step S210 includes the following process:

[0120] The first step of step S210: if the number of current service requests of each first-category service type is less than or equal to the corresponding parallel processing threshold, the current service requests of each first-category service type are processed in parallel.

[0121] The second step of step S210: if the current business request of any first-category business type is greater than the parallel processing threshold of the first-category business type, select the corresponding business requests whose total number is less than or equal to the parallel processing threshold of the first-category business type from the business requests of the first-category business type; and process the selected business requests in parallel.

[0122] S211 . Processing service requests of non-first-category service types in the current time period according to the limit capacity and the current number of parallel service requests of each first-category service type.

[0123] In a possible implementation, step S211 includes the following process:

[0124] The first step of step S211: determining the service processing margin according to the number of current service requests of the plurality of first service types and the limit capacity;

[0125] The second step of step S211: Processing the service requests of non-first-category service types in the current time period according to the service processing margin.

[0126] In a possible implementation manner, the second step of step S211 further includes the following process:

[0127] If the number of the service requests of the non-first-category service types is less than or equal to the service processing margin, the service requests of the non-first-category service types are processed in parallel;

[0128] Otherwise, multiple target non-first-class business types that are less than or equal to the business processing margin are selected from the multiple business requests of the non-first-class business types; and the business requests of the multiple target non-first-class business types are processed in parallel.

[0129] A data processing method provided by an embodiment of the present application determines, based on historical business data, historical parallel business requests corresponding to each business type in multiple historical time periods; for each business type, in each historical time period of the business type, the average processing time of each historical business request is determined based on the historical parallel business requests in the historical time period; for each business type, multiple parallel quantities are determined based on the historical parallel business requests corresponding to multiple historical time periods; for each parallel quantity, the parallel processing time corresponding to the parallel quantity is determined based on the average processing time corresponding to the historical parallel business requests corresponding to the parallel quantity; based on the multiple parallel quantities and their respective parallel processing times, a mapping relationship between the parallel quantity and the parallel processing time of the business type is determined; a preset time threshold is obtained; based on the preset time threshold and the mapping relationship, a first parallel quantity corresponding to each business type is determined; for each business type, a business type whose corresponding first parallel quantity in the business type is less than the preset parallel quantity threshold is determined as a first type of business. For each first-category business type, a parallel processing threshold is determined according to the first parallel number of the first-category business type and the historical business data of the first-category business type in multiple historical time periods; the current parallel business request number of each first-category business type is determined according to the business requests in the current time period; the business requests of each first-category business type are processed according to the current parallel business request number of each first-category business type and the parallel processing threshold of each first-category business type; the business requests of non-first-category business types in the current time period are processed according to the limit capacity and the current parallel business request number of each first-category business type, so as to realize the priority division of different types of businesses according to the number of concurrency, and determine the parallel processing number of the business according to the historical scenario data corresponding to each business, so as to give priority to the businesses that can be processed concurrently at one time; if there is still business processing margin when processing the first-category business type, the non-priority business types are processed synchronously, which improves the efficiency of business processing, reduces the waiting time of users, and improves the user experience.

[0130] Figure 3 A structural diagram of a data processing device provided in this application, such as Figure 3 As shown, a data processing device 30 provided in this embodiment includes:

[0131] The first determining unit 301 is used to determine the mapping relationship between the parallel quantity and the parallel processing duration corresponding to each business type according to the historical business data, wherein the parallel processing duration is the duration used for parallel processing of the business requests of the parallel quantity;

[0132] The second determining unit 302 is used to determine, according to the mapping relationship corresponding to each service type, the first type of service type that is to be processed first among all the service types and the parallel processing threshold corresponding to the first type of service type;

[0133] The processing unit 303 is used to process multiple service requests received in the current time period according to the service processing limit capacity and the parallel processing thresholds corresponding to each first type of service.

[0134] In a possible implementation manner, the first determining unit 301 includes:

[0135] A first determination module 3011 is used to determine historical parallel business requests corresponding to each business type in multiple historical time periods according to historical business data;

[0136] The second determination module 3012 is used to determine, for each service type and in each historical time period of the service type, an average processing time of each historical service request according to the historical parallel service requests in the historical time period;

[0137] The third determination module 3013 is used to determine multiple parallel quantities for each service type according to the parallel service requests corresponding to multiple historical time periods;

[0138] The fourth determination module 3014 is used to determine, for each parallel quantity, the parallel processing duration corresponding to the parallel quantity according to the average processing duration corresponding to the historical parallel business requests corresponding to the parallel quantity;

[0139] The fifth determining module 3015 is used to determine the mapping relationship between the parallel quantity and the parallel processing duration of the service type according to the multiple parallel quantities and the respective parallel processing durations.

[0140] In a possible implementation manner, the second determining unit 302 includes:

[0141] An acquisition module 3021 is used to acquire a preset duration threshold, and determine a first parallel quantity corresponding to each service type according to the preset duration threshold and a mapping relationship;

[0142] A sixth determination module 3022 is used to determine, for each first-category service type, a first parallel quantity corresponding to a preset duration threshold according to a mapping relationship corresponding to the first-category service type;

[0143] The seventh determination module 3023 is used to determine, for each first-category service type, a parallel processing threshold according to a first parallel quantity of the first-category service type and historical service data of the first-category service type in multiple historical time periods.

[0144] In a possible implementation, the processing unit 303 includes:

[0145] A ninth determination module 3031, configured to determine the number of current parallel service requests of each first service type according to the service requests in the current period;

[0146] a tenth determining module 3032, configured to process the service requests of each first-category service type according to the current number of parallel service requests of each first-category service type and the parallel processing threshold of each first-category service type; and / or,

[0147] The processing module 3033 is used to process the service requests of non-first-category service types in the current period according to the limit capacity and the current number of parallel service requests of each first-category service type.

[0148] In a possible implementation, the twelfth determining module 3032 includes:

[0149] If the number of current service requests of each first-category service type is less than or equal to the corresponding parallel processing threshold, the current service requests of each first-category service type are processed in parallel;

[0150] If the current business request of any first-category business type is greater than the parallel processing threshold of the first-category business type, select the corresponding business requests whose total number is less than or equal to the parallel processing threshold of the first-category business type from the business requests of the first-category business type; and process the selected business requests in parallel.

[0151] In a possible implementation, the processing module 3033 includes:

[0152] Determine the service processing margin according to the number of current service requests and the limit capacity of the plurality of first-category service types;

[0153] According to the service processing margin, service requests of non-first-category service types in the current period are processed.

[0154] In a possible implementation, processing a service request of a non-first-category service type in the current time period according to the service processing margin includes:

[0155] If the number of the service requests of the non-first-category service types is less than or equal to the service processing margin, the service requests of the non-first-category service types are processed in parallel;

[0156] Otherwise, multiple target non-first-class business types that are less than or equal to the business processing margin are selected from the multiple business requests of the non-first-class business types; and the business requests of the multiple target non-first-class business types are processed in parallel.

[0157] The (device subject) provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be described in detail here.

[0158] Figure 4 This is a schematic diagram of the structure of a data processing device provided by this application. Figure 4 As shown, the electronic device 40 provided in this embodiment includes: at least one processor 401 and a memory 402. Optionally, the device 40 also includes a communication component 403. The processor 401, the memory 402 and the communication component 403 are connected via a bus 404.

[0159] In a specific implementation process, at least one processor 401 executes the computer-executable instructions stored in the memory 402, so that at least one processor 401 executes the above method.

[0160] The specific implementation process of the processor 401 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0161] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0162] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0163] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0164] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0165] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0166] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0167] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0168] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0169] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0170] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0171] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0172] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0173] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A data processing method, characterized in that: include: Determine, based on historical business data, a mapping relationship between the number of parallel requests and the parallel processing duration corresponding to each business type, wherein the parallel processing duration is the duration used for parallel processing of the business requests of the parallel number; Determine, according to the mapping relationship corresponding to each service type, a first type of service type that is prioritized for processing among all service types and a parallel processing threshold corresponding to the first type of service type; According to the limit capacity of business processing and the parallel processing thresholds corresponding to each of the first business types, multiple business requests received in the current time period are processed.

2. The method according to claim 1, characterized in that Determining the mapping relationship between the number of parallel operations and the parallel processing duration corresponding to each business type according to the historical business data includes: Determine, based on the historical service data, historical parallel service requests corresponding to each service type in multiple historical time periods; For each service type, in each historical time period of the service type, determining an average processing time of each historical service request according to the historical parallel service requests in the historical time period; For each service type, determining multiple parallel quantities according to the historical parallel service requests corresponding to multiple historical time periods respectively; For each parallel quantity, determine the parallel processing duration corresponding to the parallel quantity according to the average processing duration corresponding to the historical parallel business requests corresponding to the parallel quantity; According to the multiple parallel quantities and the respective parallel processing durations, a mapping relationship between the parallel quantity and the parallel processing duration of the service type is determined.

3. The method according to claim 1, characterized in that The determining, according to the mapping relationship corresponding to each service type, a first type of service type that is prioritized among all service types and a parallel processing threshold corresponding to the first type of service type, comprises: Obtaining a preset duration threshold, and determining a first parallel quantity corresponding to each service type according to the preset duration threshold and the mapping relationship; For each service type, determining the service type corresponding to the service type whose first parallel quantity is less than the preset parallel quantity threshold as the first type of service type; For each first-category service type, the parallel processing threshold is determined according to the first parallel quantity of the first-category service type and historical service data of the first-category service type in multiple historical time periods.

4. The method according to claim 1, characterized in that The processing of multiple service requests received in the current time period according to the service processing limit capacity and the parallel processing thresholds respectively corresponding to the first service types includes: Determine the number of current parallel service requests for each of the first service types according to the service requests in the current time period; Processing the service requests of each of the first-category service types according to the current number of parallel service requests of each of the first-category service types and the parallel processing threshold of each of the first-category service types; and / or, The service requests of non-first-category service types in the current time period are processed according to the limit capacity and the current number of parallel service requests of each first-category service type.

5. The method according to claim 4, characterized in that The current number of parallel service requests of each of the first service types, the parallel processing threshold of each of the first service types, and processing the service requests of each of the first service types, including: If the number of current service requests of each first-category service type is less than or equal to the corresponding parallel processing threshold, the current service requests of each first-category service type are processed in parallel; If the current business request of any first-category business type is greater than the parallel processing threshold of the first-category business type, select the corresponding business requests whose total number is less than or equal to the parallel processing threshold of the first-category business type from the business requests of the first-category business type; and process the selected business requests in parallel.

6. The method according to claim 4, characterized in that The processing of the service requests of non-first-category service types in the current time period according to the limit capacity and the current number of parallel service requests of each first-category service type includes: Determine the service processing margin according to the number of current service requests of the plurality of first service types and the limit capacity; The service requests of non-first-category service types in the current time period are processed according to the service processing margin.

7. The method according to claim 6, characterized in that The processing of the service request of the non-first-category service type in the current time period according to the service processing margin includes: If the number of the service requests of the non-first-category service type is less than or equal to the service processing margin, the multiple service requests of the non-first-category service type are processed in parallel; Otherwise, multiple target non-first-category business types that are less than or equal to the business processing margin are selected from multiple business requests of non-first-category business types; and the business requests of the multiple target non-first-category business types are processed in parallel.

8. A data processing device, characterized in that: include: A first determining unit is used to determine, based on historical business data, a mapping relationship between the number of parallel requests and the parallel processing duration corresponding to each business type, wherein the parallel processing duration is a duration used for parallel processing of the business requests of the parallel number; A second determining unit is used to determine, according to the mapping relationship corresponding to each business type, a first type of business type that is subject to priority processing among all business types and a parallel processing threshold corresponding to the first type of business type; The processing unit is used to process multiple service requests received in the current time period according to the limit capacity of service processing and the parallel processing thresholds corresponding to each of the first types of services.

9. A data processing device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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