Data query and processing method and device, equipment, storage medium and program

Abstract

The embodiment of the invention relates to a data query and processing method and device, equipment, a storage medium and a program product. The method comprises the steps of obtaining historical request parameters based on a timed task; based on the historical request parameters, target objects in the historical request parameters and information associated with virtual resources of the target objects are determined, and the target objects are determined based on the number of repetitions of candidate objects appearing in the historical request parameters; and storing information associated with the virtual resources of the target object in a preheating data pool, wherein the preheating data pool is configured to provide information matched with the real-time request parameters from the stored information. Therefore, the high concurrency performance of the server can be improved.

Description

Technical Field

[0001] Example embodiments of the present disclosure generally relate to the field of computer technology, and more particularly, to methods, devices, electronic devices, computer-readable storage media, and computer program products for data query and processing. Background Art

[0002] In current Internet businesses, real-time calculations based on request parameters are often required. For example, shopping platforms need to match coupons based on product information to calculate coupons that match the products currently browsed by the user. In related technologies, when a request arrives, real-time calculations are performed based on the request parameters, and the calculation results are returned to the user for display. When the logic to be calculated is complex and the concurrency is high, the system's high performance and high concurrency cannot be guaranteed. Summary of the invention

[0003] In a first aspect of the present disclosure, a method for data processing is provided. The method includes: obtaining historical request parameters based on a scheduled task; determining a target object in the historical request parameters and information associated with a virtual resource of the target object based on the historical request parameters, wherein the target object is determined based on the number of repetitions of a candidate object that appears in the historical request parameters; and storing the information associated with the virtual resource of the target object in a preheating data pool, wherein the preheating data pool is configured to provide information matching the real-time request parameters from the stored information.

[0004] In a second aspect of the present disclosure, a method for data query is provided. The method includes: obtaining real-time request parameters; parsing the real-time request parameters to obtain a user type and object information of a user; matching the user type and object information of the user from a preheated data pool; and if it is determined that the matching result is a successful match, sending information associated with a virtual resource for the target object to the corresponding user.

[0005] In a third aspect of the present disclosure, a device for data processing is provided. The device includes: a first acquisition module configured to acquire historical request parameters based on a scheduled task; a first determination module configured to determine a target object in the historical request parameters and information associated with a virtual resource of the target object based on the historical request parameters, wherein the target object is determined based on the number of repetitions of a candidate object that appears in the historical request parameters; and a storage module configured to store the information associated with the virtual resource of the target object in a preheating data pool, wherein the preheating data pool is configured to provide information matching the real-time request parameters from the stored information.

[0006] In a fourth aspect of the present disclosure, a device for data query is provided. The device includes: a second acquisition module configured to acquire real-time request parameters; a second determination module configured to determine the user type and target object of the user by parsing the real-time request parameters; a matching module configured to determine information matching the user type and the target object from a preheating data pool, the preheating data pool storing information associated with virtual resources of the object; and a reading module configured to read information matching the user type and the target object from the information stored in the preheating data pool if the matching result is a successful match, the read information being associated with the virtual resources of the target object.

[0007] In a fifth aspect of the present disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory, the at least one memory is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit. When the instructions are executed by the at least one processing unit, the electronic device executes the method of the first aspect.

[0008] In a sixth aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored on the medium, and when the computer program is executed by a processor, the method of the first aspect is implemented.

[0009] In a seventh aspect of the present disclosure, a computer program product is provided, which includes a computer program, wherein when the computer program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0010] It should be understood that the content described in this section is not intended to limit the key features or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In the following, in conjunction with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages and aspects of various implementations of the present disclosure will become more apparent. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0012] Figure 1 A schematic diagram of an example environment in which embodiments of the present disclosure can be implemented is shown;

[0013] Figure 2 A structural diagram of a data processing service according to some embodiments of the present disclosure is shown;

[0014] Figure 3 A schematic diagram showing a data processing system according to some embodiments of the present disclosure;

[0015] Figure 4A flowchart of a timing task according to some embodiments of the present disclosure is shown;

[0016] Figure 5 A flowchart showing a process for data processing according to some embodiments of the present disclosure;

[0017] Figure 6 A flowchart showing a process for data query according to some embodiments of the present disclosure is shown;

[0018] Figure 7 A schematic structural block diagram of a device for data processing according to some embodiments of the present disclosure is shown;

[0019] Figure 8 A schematic structural block diagram of a device for data query according to some embodiments of the present disclosure is shown;

[0020] Fig. 9 A block diagram of a computing device is shown in which one or more embodiments of the present disclosure may be implemented. DETAILED DESCRIPTION

[0021] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0022] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below.

[0023] It should be noted that the acquisition, storage and application of user personal information involved in the technical solution of the present disclosure are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0024] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0025] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information, so that the user can independently choose whether to provide personal information to software or hardware such as electronic devices, applications, servers or storage media that execute operations of the technical solution of the present disclosure based on the prompt message.

[0026] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information is sent to the user in a manner such as a pop-up window, in which the prompt information can be presented in text form. In addition, the pop-up window can also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0027] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation to the embodiments of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the embodiments of the present disclosure.

[0028] As used herein, the term "model" can learn the association between the corresponding input and output from the training data, so that after the training is completed, the corresponding output can be generated for a given input. The generation of the model can be based on machine learning technology. Deep learning is a machine learning algorithm that processes inputs and provides corresponding outputs by using multi-layer processing units. A neural network model is an example of a model based on deep learning. In this article, "model" may also be referred to as "machine learning model", "learning model", "machine learning network" or "learning network", and these terms are used interchangeably in this article.

[0029] A "neural network" is a machine learning network based on deep learning. A neural network is capable of processing inputs and providing corresponding outputs, and typically includes an input layer and an output layer and one or more hidden layers between the input layer and the output layer. Neural networks used in deep learning applications typically include many hidden layers, thereby increasing the depth of the network. The layers of a neural network are connected in sequence so that the output of the previous layer is provided as input to the next layer, where the input layer receives the input of the neural network and the output of the output layer serves as the final output of the neural network. Each layer of a neural network includes one or more nodes (also called processing nodes or neurons), each of which processes input from the previous layer.

[0030] In some scenarios, a computing service for information associated with the virtual resources of the target object may be provided. Taking a shopping platform as an example, a request for computing product discount information may be sent to the server of the shopping platform through a checkout page. For example, a user may select a specific product and trigger a request for computing product discount information so that the server may compute the product discount information.

[0031] In such a computing service process, the client needs to generate corresponding request parameters based on the product information selected by the user and send them to the server. After receiving the request parameters, the server performs calculations based on the request parameters. Since the calculations consume the server's computing power resources, when a large number of clients send request parameters at the same time, the server's computing power is insufficient to support the huge amount of calculations, and the system's high performance and high concurrency cannot be guaranteed.

[0032] The embodiment of the present disclosure provides a data processing scheme. According to the scheme, historical request parameters can be obtained based on a scheduled task; based on the historical request parameters, a target object in the historical request parameters and information associated with a virtual resource of the target object are determined, the target object being determined based on the number of repetitions of a candidate object appearing in the historical request parameters; and the information associated with the virtual resource of the target object is stored in a preheating data pool, the preheating data pool being configured to provide information matching the real-time request parameters from the stored information.

[0033] Therefore, the embodiments of the present disclosure can store information associated with the virtual resources of the target object in a preheating data pool before receiving the real-time request parameters. After receiving the real-time request parameters, the corresponding information is first matched from the preheating data pool, thereby reducing the real-time calculation corresponding to the real-time request, thereby achieving the effect of high system concurrency with a small number of servers.

[0034] Figure 1 1 is a schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. Figure 1 As shown, example environment 100 may include terminal device 110 .

[0035] In the example environment 100, a data processing system 130 is provided on the terminal device 110. The data processing system 130 is configured to process the request parameters and return the calculation results. The data processing system 130 can support multiple types of request parameter processing, examples of which can include but are not limited to commodity price calculation request parameter processing, commodity discount calculation request parameter processing, and other suitable types of request parameter processing, and the embodiments of the present disclosure are not limited in this respect.

[0036] exist Figure 1In the environment 100 , the remote device 120 can send a request parameter to the terminal device 110 , and the terminal device 110 obtains a calculation result in response to receiving the request parameter and sends it to the remote device 120 .

[0037] In some embodiments, the remote device 120 communicates with the terminal device 110 to enable the use of the services of the data processing system 130. The remote device 120 can be any type of mobile terminal, fixed terminal or portable terminal, including a mobile phone, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a media computer, a multimedia tablet, a handheld computer, a portable game terminal, a VR / AR device, a personal communication system (Personal Communication System, PCS) device, a personal navigation device, a personal digital assistant (Personal Digital Assistant, PDA), an audio / video player, a digital camera / camcorder, a positioning device, a television receiver, a radio broadcast receiver, an e-book device, a game device or any combination of the foregoing, including accessories and peripherals of these devices or any combination thereof. In some embodiments, the terminal device 110 can also support any type of interface for the user (such as a "wearable" circuit, etc.).

[0038] The terminal device 110 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks, and big data and artificial intelligence platforms. The terminal device 110 may include, for example, a computing system / server, such as a mainframe, an edge computing node, a computing device in a cloud environment, and the like.

[0039] A communication connection may be established between the remote device 120 and the terminal device 110. The communication connection may be established in a wired manner or a wireless manner. The communication connection may include, but is not limited to, a Bluetooth connection, a mobile network connection, a Universal Serial Bus (USB) connection, a Wireless Fidelity (WiFi) connection, etc., and the embodiments of the present disclosure are not limited in this respect. In the embodiments of the present disclosure, the remote device 120 and the terminal device 110 may implement signaling interaction through the communication connection between the two.

[0040] It should be understood that the structure and function of the various elements in the environment 100 are described for exemplary purposes only and do not imply any limitation on the scope of the present disclosure.

[0041] Some example embodiments of the present disclosure will be described below with continued reference to the accompanying drawings.

[0042] For example, the term "target object" used in the embodiments of the present disclosure may relate to a commodity, and the term "information associated with the virtual resources of the target object" may relate to preferential information for the price of the commodity. In the following, some embodiments of the present disclosure will be described by taking the "target object" as a commodity as an example. It should be understood that the "target object" may relate to other objects and the "information associated with the virtual resources of the target object" may also relate to other available information. The scope of the present disclosure is not limited in this respect.

[0043] Figure 2 2 shows data processing logic 200 according to some embodiments of the present disclosure. Figure 2 As shown, the data processing logic 200 includes a business interface layer 210, a data analysis layer 220 and a data calculation layer 230. The data processing logic 200 is used to implement the functions of the data processing service 130.

[0044] In some embodiments, the business interface layer 210 is configured as an interface for providing query services to the outside. The business interface layer 210 is used to receive the request parameters sent by the remote device 120, then obtain the calculation results of the data calculation layer 230, and finally send the calculation results to the remote device 120. Taking the commodity discount calculation scenario as an example, the business interface layer 210 receives a discount calculation request for a certain commodity sent by the remote device 120, and sends the discount calculation request to the data analysis layer 220. Furthermore, the business interface layer 210 receives the calculation results of the commodity discount by the data calculation layer 230, and sends the calculation results to the remote device 120.

[0045] In some embodiments, the data analysis layer 220 is configured to parse the request parameters in response to receiving the request parameters and obtain the target object. Taking the commodity discount calculation scenario as an example, after receiving the request parameters, the data analysis layer 220 parses the request parameters and obtains, for example, the commodity identification (ID) in the request parameters, and then the data analysis layer 220 sorts the commodity identification (ID) according to the number of repetitions, and screens and marks the N commodities with the most repetitions as hot commodities (i.e., target objects).

[0046] In some embodiments, the data computing layer 230 is configured to calculate information associated with the virtual resources of the target object (e.g., preferential information of the target object) according to the parsed request parameters. Taking the commodity preferential calculation scenario as an example, the data computing layer 230 receives the parsed request parameters, retrieves the corresponding commodity price information, discount information, etc. according to the commodity ID, and calculates the preferential information of the commodity.

[0047] The following will further combine Figure 3The functional implementation of the data processing system 130 is described in detail. Figure 3 1 shows a data processing system 130 according to some embodiments of the present disclosure. Figure 3 As shown, the data processing system 130 includes but is not limited to the following functional modules: a real-time request acquisition module 310 , a calculation module 315 , an information matching module 320 and a scheduled task module 335 .

[0048] like Figure 3 As shown, the real-time request acquisition module 310 can receive the real-time request parameters 330 sent by the remote device 120 in real time. The scheduled task module 335 can obtain the historical request parameters from the request data pool 325. Taking the commodity discount calculation scenario as an example, the real-time request acquisition module 310 obtains the discount calculation request for the commodity from the request data pool 325 through the scheduled task module 335, or the real-time request acquisition module 310 receives the discount calculation request for a certain commodity sent by the remote device 120 in real time. Among them, the request data pool is used to store the historical request parameters sent by the remote device 120.

[0049] Additionally, the data processing system 130 also filters the historical request parameters before storing them in the request data pool 325. For example, after the data processing system 130 obtains the discount calculation request for product A from the remote device 120, it matches it from the request data pool 325. If there is already a discount calculation request for product A from the remote device 120, the current discount calculation request for product A from the remote device 120 is ignored; if there is no matching result, the request parameters are stored in the request data pool 325.

[0050] like Figure 3 As shown, the scheduled task module 335 can also obtain the target object set 340 according to the historical request parameters. The target object is determined after the candidate target meets the preset screening conditions, and the target object set 340 is a collection of target objects. For example, in the commodity discount calculation scenario, the candidate target is the commodity identification (ID) of various commodities included in the request parameters. The scheduled task module 335 selects the N commodities with the most repetitions from the commodities according to the heap sorting algorithm and marks these commodities as popular commodities (i.e., target objects), and finally stores the target objects in the target object set 340.

[0051] Additionally, the target object may be screened from the candidate objects by using a heap sort algorithm on the candidate object set, or by storing the target object in a preset statistical table and using the statistical table to complete the screening of the target object.

[0052] In some embodiments, the timing task module 335 is configured to obtain historical request parameters received within 10 minutes from the request data pool every 5 minutes. Then the timing task module 335 parses the historical request parameters received within 10 minutes, obtains the commodity ID in the historical request parameters, and stores the commodity identification (ID) obtained by parsing in the commodity set (i.e., the candidate object set). Then the timing task module 335 selects N commodities with the most repetitions from the commodity set and marks these commodities as hot commodities (i.e., target objects), and finally stores the hot commodities in the hot commodity set (i.e., the target object set). For example, at 1:10, the data in the candidate object set obtained by the timing task module 335 according to the historical request parameters from 1 to 1:10 is: {111, 222, 333, 444, 111, 222, 555, 666, 777, 123, 444}. The timing task module 335 obtains these data and calculates the TOP3 data when analyzing, and the TOP3 data is obtained by calculation as {111, 222, 444}.

[0053] like Figure 3 As shown, the calculation module 315 can calculate the information associated with the virtual resources of the target object according to the request parameters. Figure 3 The calculation methods of the two calculation modules 315 in the same way are only that the interfaces of the input and output ends of the two calculation modules 315 are connected to different components. The calculation module 315 retrieves the first available information of the virtual resource for the target object according to the target object in the target object pool (or the specific object in the real-time request parameter). In one example, the virtual resource can be the discount information of the target commodity, or it can be the coupon that can be used for the target commodity. The first available information can indicate the specific discount value or coupon of the target commodity under a certain activity. Further, the calculation module 315 determines the second available information of the virtual resource of the target object according to the user type of the user in the historical request parameter. In one example, the second available information can be the discount information or coupon determined according to the user type such as the user's membership level and membership duration. The calculation module 315 determines the information associated with the virtual resource of the target object for the current user type according to the first available information and the second available information. In one example, the information associated with the virtual resource of the target object can include the final preferential information of the target commodity to the target user.

[0054] In some embodiments, the calculation module 315 can, for example, obtain popular products in a popular product set, and retrieve the first preferential information (i.e., first available information) of the preferential treatment (i.e., virtual resources) of the popular products. The calculation module 315 also retrieves the second preferential information (i.e., second available information) of the preferential treatment related to the user type of the user according to the user type of the user in the historical request parameters. In one example, the second available information can be a member level discount or a member-exclusive coupon, etc. Finally, the calculation module 315 calculates the final preferential information (i.e., information associated with the virtual resource) of the preferential information of the popular product according to the first preferential information and the second preferential information. The calculation result obtained can be "Available coupon for an instant reduction of 10 yuan" or, for example, "A 10% discount coupon can be used for this product" or "A coupon for an instant reduction of 10 yuan can be used for this product on the basis of the member price".

[0055] like Figure 3 As shown, the calculation module 315 can store the calculation results obtained according to the historical request parameters in the preheating data pool 345. The preheating data pool 345 is used to store information associated with the virtual resources of the target object during the scheduled task cycle. The data structure in the preheating data pool 345 can be a KV structure. For example, the key can be a product ID and a membership level, and the value can be the corresponding discount information, {key: 111, A, value: a 10 yuan discount coupon}.

[0056] like Figure 3 As shown, the information matching module 320 can respond to receiving the real-time request parameters and perform information matching from the preheating data pool 345 according to the real-time request parameters. The information matching module 320 can parse the real-time request parameters to obtain the target object and the user type of the user. Then the information matching module 320 performs matching in the preheating data pool according to the target object and the user type. If the match is successful, it means that the virtual resources of the target object are already in the preheating data pool, and there is no need to calculate again. The information associated with the virtual resources of the target object can be directly read from the preheating data pool. If the match fails, it means that the virtual resources of the target object are not in the preheating data pool, and real-time calculation is required, so the real-time request parameters are sent to the calculation module 315 to obtain the real-time calculation results of the calculation module 315.

[0057] In some embodiments, after receiving the commodity discount calculation request sent by the user, the information matching module 320 extracts the user's membership level and commodity ID in the commodity discount calculation request. Then the information matching module 320 matches the membership level and commodity ID in the preheating data pool. If the same matching result is obtained, the corresponding commodity discount information is directly obtained according to the commodity ID. If no matching result is obtained, the information matching module 320 sends the commodity discount calculation request to the calculation module 315 for real-time calculation to obtain commodity discount information.

[0058] Therefore, the embodiments of the present disclosure can store information associated with the virtual resources of the target object in a preheating data pool before receiving the real-time request parameters. After receiving the real-time request parameters, the corresponding information is first matched from the preheating data pool, thereby reducing the real-time calculation corresponding to the real-time request, thereby achieving the effect of high system concurrency with a small number of servers.

[0059] Figure 4 A schematic diagram of a scheduled task process 400 in the scheduled task module 335 is shown.

[0060] In block 410 , the terminal device 110 loads historical request parameters within a preset time from a request data pool in response to a preset start time.

[0061] In block 420 , the terminal device 110 then parses the historical request parameters, extracts candidate objects within the preset fields, and obtains a candidate object set.

[0062] In block 430 , the terminal device 110 determines a target object from the candidate object set based on a preset screening condition, and obtains a target object set.

[0063] Figure 5 FIG. 5 is a schematic diagram of a process 500 for data processing according to some embodiments of the present disclosure. The process 500 may be implemented in the terminal device 110 .

[0064] In block 510 , the terminal device 110 obtains historical request parameters based on the scheduled task.

[0065] In block 520, the terminal device 110 determines the target object in the historical request parameters and the information associated with the virtual resource of the target object based on the historical request parameters, wherein the target object is determined based on the number of repetitions of the candidate object appearing in the historical request parameters.

[0066] In block 530 , the terminal device 110 stores information associated with the virtual resources of the target object in a preheated data pool, and the preheated data pool is configured to provide information matching the real-time request parameters from the stored information.

[0067] In some embodiments, obtaining historical request parameters based on a scheduled task includes: the terminal device 110 obtains historical request parameters within a preset time from a request data pool.

[0068] In some embodiments, determining the target object in the historical request parameters includes: the terminal device 110 determines the number of repetitions of the candidate object by parsing the historical request parameters; and in response to determining that the number of repetitions meets a preset screening condition, determining the candidate object as the target object.

[0069] In some embodiments, determining information associated with the virtual resources of the target object in the historical request parameters includes: determining first available information of the virtual resources available for the target object. Determining second available information of the virtual resources available for the user type according to the user type of the user in the historical request parameters. Based on the first available information and the second available information, determining information associated with the virtual resources of the target object.

[0070] In some embodiments, process 500 further includes storing the target object, the user type, and information associated with the virtual resources of the target object in a pre-warming data pool.

[0071] In some embodiments, the process 500 further includes obtaining a request parameter of the specific request object for the specific object, and ignoring the request parameter in response to determining that the historical request parameter in the request data pool already includes the request of the specific request object for the specific object.

[0072] Figure 6 FIG. 6 is a schematic diagram of a process 600 for data storage according to some embodiments of the present disclosure. The process 600 may be implemented in the terminal device 110 .

[0073] like Figure 6 As shown, in block 610 , the terminal device 110 obtains real-time request parameters.

[0074] In block 620 , the terminal device 110 determines the user type and object information of the user by parsing the real-time request parameters.

[0075] In block 630 , the terminal device 110 determines information matching the user type and the target object of the user from the preheated data pool.

[0076] In block 640 , the terminal device 110 determines that if the matching result is a successful match, information matching the user type and the target object is read from the information stored in the preheating data pool, where the information is related to the virtual resources of the target object.

[0077] In some embodiments, process 600 further includes, if the matching result is a matching failure, determining information associated with the virtual resource for the target object according to the user type and the first available information of the target object.

[0078] The embodiments of the present disclosure also provide corresponding devices for implementing the above methods or processes. Figure 7 A schematic structural block diagram of an apparatus 700 for data query according to some embodiments of the present disclosure is shown. The apparatus 700 may be implemented as or included in a terminal device 110. Each module / component in the apparatus 700 may be implemented by hardware, software, firmware, or any combination thereof.

[0079] like Figure 7 As shown, the apparatus 700 includes:

[0080] The first acquisition module 710 is configured to acquire historical request parameters based on a scheduled task; the first determination module 720 is configured to determine, based on the historical request parameters, a target object in the historical request parameters and information associated with the virtual resources of the target object, wherein the target object is determined based on the number of repetitions of the candidate object appearing in the historical request parameters; and the storage module 730 is configured to store the information associated with the virtual resources of the target object in a preheating data pool, wherein the preheating data pool is configured to provide information matching the real-time request parameters from the stored information.

[0081] In some embodiments, the apparatus 700 is further configured to obtain historical request parameters within a preset time from a request data pool.

[0082] In some embodiments, the apparatus 700 is further configured to determine the number of repetitions of the candidate object by parsing historical request parameters; and in response to determining that the number of repetitions satisfies a preset screening condition, determine the candidate object as a target object.

[0083] In some embodiments, the device 700 is further configured to determine first available information of virtual resources available for the target object, determine second available information of virtual resources available for the user type according to the user type of the user in the historical request parameter, and determine information associated with the virtual resources of the target object based on the first available information and the second available information.

[0084] In some embodiments, the apparatus 700 is further configured to store the target object, the user type, and the information associated with the virtual resources of the target object in an association in the preheating data pool.

[0085] In some embodiments, the apparatus 700 is further configured to obtain a request parameter of a specific request object for a specific object, and in response to determining that the historical request parameter in the request data pool already includes a request of the specific request object for the specific object, ignore the request parameter.

[0086] The units and / or modules included in the device 700 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units and / or modules can be implemented using software and / or firmware, such as machine executable instructions stored on a storage medium. In addition to or as an alternative to machine executable instructions, some or all of the units and / or modules in the device 700 can be implemented at least in part by one or more hardware logic components. As an example and not limitation, exemplary types of hardware logic components that can be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0087] It should be understood that one or more steps in the above method can be performed by a suitable electronic device or a combination of electronic devices. Such an electronic device or a combination of electronic devices may include, for example, Figure 1 The terminal device 110 in.

[0088] Figure 8 A schematic structural block diagram of an apparatus 800 for data processing according to some embodiments of the present disclosure is shown. The apparatus 800 may be implemented as or included in a terminal device 110. Each module / component in the apparatus 800 may be implemented by hardware, software, firmware, or any combination thereof.

[0089] like Figure 8 As shown, the device 800 includes: a second acquisition module 810, configured to acquire real-time request parameters; a second determination module 820, configured to obtain the user type and target object of the user by parsing the real-time request parameters. A matching module 830, configured to determine information matching the user type and the target object in the preheating data pool. A reading module 840, configured to read information matching the user type and the target object from the information stored in the preheating data pool if the matching result is a successful match, and the read information is associated with the virtual resources of the target object.

[0090] In some embodiments, the apparatus 800 is further configured to: if the matching result is a matching failure, determine information associated with the virtual resource of the target object according to the user type and the first available information of the target object.

[0091] The units and / or modules included in the device 800 can be implemented in various ways, including software, hardware, firmware or any combination thereof. In some embodiments, one or more units and / or modules can be implemented using software and / or firmware, such as machine executable instructions stored on a storage medium. In addition to or as an alternative to machine executable instructions, some or all of the units and / or modules in the device 800 can be implemented at least in part by one or more hardware logic components. As an example and not limitation, exemplary types of hardware logic components that can be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0092] It should be understood that one or more steps in the above method can be performed by a suitable electronic device or a combination of electronic devices. Such an electronic device or a combination of electronic devices may include, for example, Figure 1 The terminal device 110 in.

[0093] Fig. 9 900 is a block diagram of an electronic device in which one or more embodiments of the present disclosure may be implemented. It should be understood that Fig. 9 The electronic device 900 shown is merely exemplary and should not constitute any limitation on the functionality and scope of the embodiments described herein. Fig. 9 The electronic device 900 shown can be used to implement Figure 1 The terminal device 110 or Figure 7 The device 700 or Figure 8 The device 800 in FIG.

[0094] like Fig. 9 As shown, the electronic device 900 is in the form of a general electronic device. The components of the electronic device 900 may include, but are not limited to, one or more processors or processing units 910, a memory 920, a storage device 930, one or more communication units 940, one or more input devices 950, and one or more output devices 960. The processing unit 910 may be an actual or virtual processor and is capable of performing various processes according to a program stored in the memory 920. In a multi-processor system, multiple processing units execute computer executable instructions in parallel to improve the parallel processing capability of the electronic device 900.

[0095] The electronic device 900 typically includes a plurality of computer storage media. Such media can be any available media accessible to the electronic device 900, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 920 can be a volatile memory (e.g., register, cache, random access memory (RAM)), a non-volatile memory (e.g., a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), flash memory) or some combination thereof. The storage device 730 can be a removable or non-removable medium, and can include a machine-readable medium, such as a flash drive, a disk, or any other medium, which can be used to store information and / or data and can be accessed within the electronic device 900.

[0096] The electronic device 900 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 7 As shown in , a disk drive for reading or writing from a removable, non-volatile disk (e.g., a "floppy disk") and an optical drive for reading or writing from a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to the bus (not shown) by one or more data media interfaces. Memory 920 may include a computer program product 925 having one or more program modules that are configured to perform various methods or actions of various embodiments of the present disclosure.

[0097] The communication unit 940 implements communication with other electronic devices through a communication medium. Additionally, the functions of the components of the electronic device 900 can be implemented with a single computing cluster or multiple computing machines that can communicate through a communication connection. Therefore, the electronic device 900 can operate in a networked environment using a logical connection with one or more other servers, a network personal computer (PC), or another network node.

[0098] The input device 950 may be one or more input devices, such as a mouse, a keyboard, a tracking ball, etc. The output device 960 may be one or more output devices, such as a display, a speaker, a printer, etc. The electronic device 900 may also communicate with one or more external devices (not shown) through the communication unit 940 as needed, such as a storage device, a display device, etc., communicate with one or more devices that allow a user to interact with the electronic device 900, or communicate with any device that allows the electronic device 900 to communicate with one or more other electronic devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface (not shown).

[0099] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which computer-executable instructions are stored, wherein the computer-executable instructions are executed by a processor to implement the method described above. According to an exemplary implementation of the present disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method described above.

[0100] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices, equipment, and computer program products implemented according to the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0101] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0102] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0103] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple implementations of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of a module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some implementations as replacements, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be realized by a special hardware-based system that performs the function or action of the specification, or can be realized by a combination of special hardware and computer instructions.

[0104] The above descriptions of various implementations of the present disclosure are exemplary, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The selection of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the various implementations disclosed herein.

Claims

1. A method for data processing, comprising: Get historical request parameters based on scheduled tasks; Based on the historical request parameters, determining a target object in the historical request parameters and information associated with a virtual resource of the target object, wherein the target object is determined based on a number of repetitions of a candidate object that appears in the historical request parameters; as well as The information associated with the virtual resource of the target object is stored in a pre-warmed data pool, and the pre-warmed data pool is configured to provide information matching the real-time request parameters from the stored information.

2. The method according to claim 1, wherein obtaining the historical request parameters based on the scheduled task comprises: The historical request parameters within a preset time are obtained from the request data pool.

3. The method according to claim 1, wherein determining the target object in the historical request parameters comprises: Determine the number of repetitions of the candidate object by parsing the historical request parameters; as well as In response to determining that the number of repetitions satisfies a preset screening condition, the candidate object is determined as the target object.

4. The method according to claim 1, wherein determining the information associated with the virtual resource of the target object in the historical request parameters comprises: Determining first available information of virtual resources available to the target object; Determining second available information of virtual resources available to the user type according to the user type in the historical request parameters; as well as The information associated with the virtual resource of the target object is determined based on the first available information and the second available information.

5. The method according to claim 4, further comprising: The target object, the user type, and the information associated with the virtual resource of the target object are stored in association in the preheating data pool.

6. The method according to claim 2, further comprising: Get the request parameters of a specific request object for a specific object; as well as In response to determining that the historical request parameters in the request data pool already include the request of the specific request object for the specific object, ignoring the request parameters.

7. A data query method, comprising: Get real-time request parameters; Determine the user type and target object of the user by parsing the real-time request parameters; Determining information matching the user type and the target object from the preheating data pool, wherein the preheating data pool is constructed according to any one of the methods of claims 1 to 6, and the preheating data pool stores information associated with virtual resources of an object; as well as If the result of the matching is a successful match, information matching the user type and the target object is read from the information stored in the preheating data pool, and the read information is associated with the virtual resources of the target object.

8. The method according to claim 7, further comprising: If the result of the matching is a matching failure, information associated with the virtual resource of the target object is determined according to the user type and the first available information of the target object.

9. A device for data processing, comprising: A first acquisition module is configured to acquire historical request parameters based on a scheduled task; A first determination module is configured to determine, based on the historical request parameters, a target object in the historical request parameters and information associated with a virtual resource of the target object, wherein the target object is determined based on a number of repetitions of a candidate object that appears in the historical request parameters; as well as The storage module is configured to store the information associated with the virtual resource of the target object in a preheating data pool, and the preheating data pool is configured to provide information matching the real-time request parameters from the stored information.

10. A device for data query, comprising: A second acquisition module is configured to acquire real-time request parameters; A second determination module is configured to determine a user type and a target object of the user by parsing the real-time request parameters; a matching module configured to determine information matching the user type and the target object from the preheating data pool, wherein the preheating data pool is constructed according to any one of the methods of claims 1 to 6, and the preheating data pool stores information associated with virtual resources of an object; as well as The reading module is configured to read information matching the user type and the target object from the information stored in the preheating data pool if the matching result is a successful match, and the read information is associated with the virtual resources of the target object.

11. An electronic device, comprising: at least one processing unit; as well as At least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 8 when executed by the at least one processing unit.

12. A computer-readable storage medium having a computer program stored thereon, wherein the computer program can be executed by a processor to implement the method according to any one of claims 1 to 8.

13. A computer program product tangibly stored in a computer storage medium and comprising computer executable instructions which, when executed by a device, cause the device to perform the method according to any one of claims 1 to 8.

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