Data Processing Method, Apparatus, Device, Medium and Program Product

Abstract

The present disclosure provides a data processing method, apparatus, device, medium and program product, which relates to the field of artificial intelligence technology, specifically to the field of big data technology. In some embodiments of the present disclosure, the basic attributes and / or operation behavior characteristics of a user are obtained; according to the basic attributes and / or operation behavior characteristics, the user potential value of the user is determined; according to the queue consumption time and the estimated consumption time of the user, the queue potential value and the estimated quota potential value of the user are determined, wherein the queue consumption time refers to the time required to match candidate push information for the user using a preset recall queue, and the estimated consumption time refers to the time required to estimate the candidate push information; according to the user potential value, queue potential value and estimated quota potential value of the user, the corresponding information push computing power is configured for the user, the push computing power is reasonably allocated, and the waste of push computing power is reduced.

Description

Technical Field

[0001] The present disclosure provides a data processing method, apparatus, device, medium and program product, which relates to the field of artificial intelligence technology, specifically to the field of big data technology. Background Art

[0002] With the vigorous development of computer technology, artificial intelligence technology has also developed rapidly.

[0003] In the process of users using application programs, in addition to recommending major news to users, it is also necessary to recommend personalized information that matches the users; for all users, the same push computing power is used for information push, and the utilization rate of the push computing power is low, resulting in a large waste of the push computing power. Summary of the Invention

[0004] The present disclosure provides a data processing method, apparatus, device, medium and program product.

[0005] In one aspect of the present disclosure, a data configuration method is provided, including:

[0006] Obtaining the basic attributes and / or operation behavior characteristics of a user;

[0007] Determining the user potential value of the user according to the basic attributes and / or the operation behavior characteristics;

[0008] Determining the queue potential value and the estimated quota potential value of the user according to the queue time consumption and the estimated time consumption of the user, where the queue time consumption refers to the time required to match candidate push information for the user using a preset recall queue, and the estimated time consumption refers to the time required to estimate the candidate push information;

[0009] Configuring information push computing power for the user according to the user potential value, the queue potential value and the estimated quota potential value.

[0010] In another aspect of the present disclosure, a data configuration apparatus is provided, including:

[0011] An obtaining module, configured to obtain the basic attributes and / or operation behavior characteristics of a user;

[0012] A first determination module, configured to determine the user potential value of the user according to the basic attributes and / or the operation behavior characteristics;

[0013] A second determination module, configured to determine the queue potential value and the estimated quota potential value of the user according to the queue time consumption and the estimated time consumption of the user, where the queue time consumption refers to the time required to match candidate push information for the user using a preset recall queue, and the estimated time consumption refers to the time required to estimate the candidate push information;

[0014] A configuration module for configuring information push computing power for the user according to the user potential value, the queue potential value, and the estimated quota potential value.

[0015] On the other hand, the present disclosure provides an electronic device, including:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the above method.

[0019] On the other hand, the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the above method.

[0020] On the other hand, the present disclosure provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the steps in the above method are implemented.

[0021] In some embodiments of the present disclosure, the basic attributes and / or operation behavior characteristics of a user are obtained; according to the basic attributes and / or operation behavior characteristics, the user potential value of the user is determined; according to the queue consumption time and the estimated consumption time of the user, the queue potential value and the estimated quota potential value of the user are determined, wherein the queue consumption time refers to the time required to match candidate push information for the user using a preset recall queue, and the estimated consumption time refers to the time required to estimate the candidate push information; according to the user potential value, the queue potential value, and the estimated quota potential value of the user, corresponding information push computing power is configured for the user, the push computing power is reasonably allocated, and the waste of the push computing power is reduced.

[0022] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used 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

[0023] The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. Among them:

[0024] Figure 1 It is a schematic flowchart of a data configuration method provided in Embodiment 1 of the present disclosure;

[0025] Figure 2Schematic flowchart of another data configuration method provided in the second embodiment of the present disclosure;

[0026] Figure 3 Schematic structural diagram of a data configuration provided in an exemplary embodiment of the present disclosure;

[0027] Figure 4 Shows a schematic block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure. Detailed implementation manners

[0028] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to assist understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, descriptions of well-known functions and structures are omitted below for clarity and conciseness.

[0029] Artificial intelligence is a discipline that studies how to make a computer simulate certain human thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). It has both hardware-level technologies and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include several major directions such as computer vision technology, speech recognition technology, natural language processing technology, and machine learning / deep learning, big data processing technology, and knowledge graph technology.

[0030] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information and other processes all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0031] With the vigorous development of computer technology, artificial intelligence technology has also developed rapidly.

[0032] In the process of a user using an application program, in addition to recommending major news to the user, it is also necessary to recommend personalized information that matches the user. The information recommendation process is roughly as follows:

[0033] 1. For a certain user, by triggering a large number of recall queues, pre-screened information that the user may be interested in is screened out from the information database;

[0034] 2. According to preset screening rules, candidate information is screened out from the pre-screened information; the candidate information is scored using an estimation scoring module to obtain the score of each piece of candidate information;

[0035] 3. Target information with scores meeting a preset score threshold is screened out from the candidate information and pushed to the user.

[0036] Currently, for all users, the same push computing power is used for information push, and the utilization rate of the push computing power is low, resulting in a large waste of the push computing power.

[0037] In view of the above technical problems, in some disclosed embodiments, the basic attributes and / or operation behavior characteristics of the user are obtained; according to the basic attributes and / or operation behavior characteristics, the user potential value of the user is determined; according to the queue time consumption and the estimated time consumption of the user, the queue potential value and the estimated quota potential value of the user are determined, where the queue time consumption refers to the time required to match candidate push information for the user using a preset recall queue, and the estimated time consumption refers to the time required to estimate the candidate push information; according to the user potential value, the queue potential value and the estimated quota potential value of the user, the corresponding information push computing power is configured for the user, the push computing power is reasonably allocated, and the waste of the push computing power is reduced.

[0038] The following will describe in detail the technical solutions provided by the embodiments of the present disclosure with reference to the accompanying drawings.

[0039] Figure 1 It is a schematic flowchart of a data configuration method provided in Embodiment 1 of the present disclosure. As Figure 1 shown, the method includes:

[0040] S101: Obtain the basic attributes and / or operation behavior characteristics of the user;

[0041] S102: Determine the user potential value of the user according to the basic attributes and / or operation behavior characteristics;

[0042] S103: Determine the queue potential value and the estimated quota potential value of the user according to the queue time consumption and the estimated time consumption of the user, where the queue time consumption refers to the time required to match candidate push information for the user using a preset recall queue, and the estimated time consumption refers to the time required to estimate the candidate push information;

[0043] S104: Configure the information push computing power for the user according to the user potential value, the queue potential value and the estimated quota potential value.

[0044] In this embodiment, the execution subject of the above method may be a terminal device or a server.

[0045] When the execution subject is a terminal device, the specific implementation form of the terminal device is not limited. The terminal device includes but is not limited to any one of the following: a personal computer, a tablet computer, a smart phone, and a smart wearable device.

[0046] When the execution entity is a server, for example, the server can be a conventional server, a cloud server, a cloud host, a virtual center, or other server devices. Among them, the composition of the server mainly includes a processor, a hard disk, a memory, a system bus, etc., and a general computer architecture type.

[0047] Computing power refers to the data processing ability, that is, the computing ability of hardware such as CPUs and GPUs used for data operations. The level of computing power depends on the performance of hardware such as CPUs and GPUs on the one hand, and on the software logic computing ability on the other hand. This disclosure mainly focuses on optimizing the software logic computing ability, reasonably allocating information push computing power, and reducing the waste of push computing power.

[0048] The push computing power of this disclosure includes recall computing power and pre-estimation computing power. During the information push process, first, a large number of recall queues need to be triggered to screen out pre-screened information that the user may be interested in from the information database. This step requires recall computing power for data processing; second, a pre-estimation scoring module is used to score the candidate information to obtain the score of each candidate information. This step requires pre-estimation computing power for data processing.

[0049] Before pushing information to the user, this disclosure reasonably allocates recall computing power and pre-estimation computing power to reduce the waste of push computing power. For the specific process of allocating computing power, please refer to the description part of the following embodiments.

[0050] In this embodiment, the basic attributes and / or operation behavior characteristics of the user are obtained; according to the basic attributes and / or operation behavior characteristics, the user potential value of the user is determined; according to the queue consumption time and pre-estimation consumption time of the user, the queue potential value and pre-estimation quota potential value of the user are determined, where the queue consumption time refers to the time required to match candidate push information for the user using a preset recall queue, and the pre-estimation consumption time refers to the time required to pre-estimate the candidate push information; according to the user potential value, queue potential value, and pre-estimation quota potential value of the user, the corresponding information push computing power is configured for the user, and the push computing power is reasonably allocated to reduce the waste of push computing power.

[0051] It should be noted that the basic attributes of the user, such as gender, age, nickname, permanent city, education level, and permanent city level. The operation behavior characteristics of the user refer to the operation behavior characteristics of the user on the platform in the past time period. For example, the number of active days, the number of likes, the number of comments, the number of followed users, and the resource allocation quota within a set historical period. The set historical period can be the past 7 days, 14 days, 28 days, three months, or six months.

[0052] In the above embodiments, the user potential value of the user is determined according to the basic attributes and / or operation behavior characteristics. One feasible way is to input the basic attributes and / or operation behavior characteristics into a trained user potential value model to obtain the user potential value of the user. Among them, the present disclosure uses the user potential value model to evaluate the user potential value of the user, and the accuracy of the user potential value is higher. It should be noted that the method for determining the user potential value of the user in the present disclosure is not limited to the model, and other determination algorithms can also be used.

[0053] In the above embodiments, the queue potential value and the estimated quota potential value of the user are determined according to the queue consumption time and the estimated consumption time of the user. One feasible way is to input the queue consumption time and the estimated consumption time of the user into a trained queue and estimated quota potential value model to obtain the queue potential value and the estimated quota potential value of the user. Among them, the queue consumption time refers to the time required to match candidate push information for the user using a preset recall queue, and the estimated consumption time refers to the time required to estimate the candidate push information. Both the queue consumption time and the estimated consumption time are historical consumption times. Among them, the present disclosure uses the estimated quota potential value model to estimate the queue potential value and the estimated quota potential value of the user, and the accuracy of the queue potential value and the estimated quota potential value of the user is higher. It should be noted that the method for determining the queue potential value and the estimated quota potential value of the user in the present disclosure is not limited to the model, and other determination algorithms can also be used.

[0054] In the above embodiments, information push computing power is configured for the user according to the user potential value, the queue potential value, and the estimated quota potential value. In one embodiment, recall computing power is configured for the user according to the user potential value and the queue potential value; and pre-estimation computing power is configured for the user according to the user potential value and the estimated quota potential value.

[0055] In some embodiments of the present disclosure, recall computing power is configured for the user according to the user potential value and the queue potential value. One feasible way is to select a target recall queue whose queue potential value is located at a set order from a preset recall queue as the recall computing power configured for the user; wherein, the user potential value of the user is negatively correlated with the number of recall queues configured for the user. It should be noted that the present application does not limit the set order, and it can be adjusted according to the actual situation; the set order, for example, the first, the second, and the third, etc. When performing queue recall on the user, the recall queues that the user needs to trigger are trimmed according to the user potential value and the queue trimming configuration. The lower the potential value of the user, the less recall computing power is allocated, and the more queues with relatively low queue potential values are trimmed.

[0056] For example, the user potential values of User 1, User 2, and User 3 are 30, 50, and 90 respectively. The queue potential values of Recall Queue 1, Recall Queue 2, Recall Queue 3, Recall Queue 4, and Recall Queue 5 are 90, 88, 85, 60, and 20 respectively. Among them, User 1 selects Recall Queue 1 and Recall Queue 2 with the top two queue potential values as the recall computing power configured for the user. User 2 selects Recall Queue 1, Recall Queue 2, and Recall Queue 3 in the top three as the recall computing power configured for the user. User 1 selects Recall Queue 1, Recall Queue 2, Recall Queue 3, Recall Queue 4, and Recall Queue 5 as the recall computing power configured for the user.

[0057] In some other embodiments of the present disclosure, according to the user potential value and the estimated quota potential value, pre-estimated computing power is configured for the user. One feasible way is to determine the estimated quota configured for the user according to the user potential value and the estimated quota potential value; and determine the pre-estimated computing power configured for the user according to the estimated quota. Before estimating the user, the estimated quota of the user is calculated according to the user potential value and the estimated quota potential value. The lower the potential value of the user, the less pre-estimated computing power is allocated, and the smaller the estimated quota.

[0058] Optionally, the estimated quota configured for the user is determined according to the user potential value and the estimated quota potential value. One feasible way is to calculate the user potential value percentage of the user according to the rank of the user potential value among all users and the number of all users; and calculate the estimated quota configured for the user according to the user potential value percentage and the estimated quota potential value.

[0059] For example, the number of users is 10, and the ranking of the user potential value of the current user among the user potential values of 10 users is 4. Then the user potential value percentage of the user is 4 / 10 = 0.4. According to the user potential value percentage; according to the user potential value percentage and the estimated quota potential value, the estimated quota configured for the user is calculated to be 0.5. According to the estimated quota of 0.5, 50% of the pre-estimated computing power is determined to be configured for the user.

[0060] After configuring the information push computing power for the user using the above method, the computing power is used to push information to the user. One can determine the target push information of the user according to the information push computing power; and push the target push information to the user. Optionally, after determining the target push information of the user, the target push information is stored.

[0061] Combined with the descriptions of the above embodiments, Figure 2 is a schematic flowchart of another data configuration method provided in Embodiment 2 of the present disclosure. As Figure 2 shown, the method includes:

[0062] S201: Obtain the basic attributes and / or operation behavior characteristics of the user;

[0063] S202: Input the basic attributes and / or operation behavior characteristics into the trained user potential value model to obtain the user's potential value.

[0064] S203: Input the user's queue consumption time and estimated consumption time into the trained queue and estimated quota potential value model to obtain the user's queue potential value and estimated quota potential value; wherein, the queue consumption time refers to the time required to match candidate push information for the user using the preset recall queue, and the estimated consumption time refers to the time required to estimate the candidate push information.

[0065] S204: Configure information push computing power for the user according to the user's potential value, queue potential value, and estimated quota potential value.

[0066] In this embodiment, the execution subject of the above method may be a terminal device or a server.

[0067] When the execution subject is a terminal device, the specific implementation form of the terminal device is not limited. The terminal device includes but is not limited to any one of the following: personal computer, tablet computer, smart phone, and smart wearable device.

[0068] When the execution subject is a server, for example, the server may be a conventional server, cloud server, cloud host, virtual center, or other server devices. Among them, the server mainly includes a processor, hard disk, memory, system bus, etc., and a general computer architecture type.

[0069] The implementation manners of the steps in this embodiment can be referred to the descriptions of the foregoing embodiments, and will not be elaborated in this embodiment. At the same time, this embodiment can achieve the beneficial effects of the corresponding parts of the foregoing embodiments.

[0070] In the above method embodiment of the present disclosure, obtain the basic attributes and / or operation behavior characteristics of the user; determine the user's potential value according to the basic attributes and / or operation behavior characteristics; determine the user's queue potential value and estimated quota potential value according to the user's queue consumption time and estimated consumption time, wherein the queue consumption time refers to the time required to match candidate push information for the user using the preset recall queue, and the estimated consumption time refers to the time required to estimate the candidate push information; configure the corresponding information push computing power for the user according to the user's potential value, queue potential value, and estimated quota potential value, reasonably allocate the push computing power, and reduce the waste of the push computing power.

[0071] Figure 3 FIG. 30 is a schematic structural diagram of a data configuration device 30 provided for an exemplary embodiment of the present disclosure. The data configuration device 30 includes an acquisition module 31, a first determination module 32, a second determination module 33, and a configuration module 34.

[0072] Among them, an obtaining module 31 is configured to obtain the basic attributes and / or operation behavior characteristics of a user;

[0073] A first determination module 32 is configured to determine the user potential value of the user according to the basic attributes and / or operation behavior characteristics;

[0074] A second determination module 33 is configured to determine the queue potential value and the estimated quota potential value of the user according to the queue time consumption and the estimated time consumption of the user, where the queue time consumption refers to the time required to match candidate push information for the user using a preset recall queue, and the estimated time consumption refers to the time required to estimate the candidate push information;

[0075] A configuration module 34 is configured to configure information push computing power for the user according to the user potential value, the queue potential value, and the estimated quota potential value.

[0076] Optionally, when determining the user potential value of the user according to the basic attributes and / or operation behavior characteristics, the first determination module 32 is configured to:

[0077] Input the basic attributes and / or operation behavior characteristics into a trained user potential value model to obtain the user potential value of the user.

[0078] Optionally, when determining the queue potential value and the estimated quota potential value of the user according to the queue time consumption and the estimated time consumption of the user, the second determination module 33 is configured to:

[0079] Input the queue time consumption and the estimated time consumption of the user into a trained queue and estimated quota potential value model to obtain the queue potential value and the estimated quota potential value of the user.

[0080] Optionally, the information push computing power includes recall computing power and pre-estimation computing power. When configuring the information push computing power for the user according to the user potential value, the queue potential value, and the estimated quota potential value, the configuration module 34 is configured to:

[0081] Configure recall computing power for the user according to the user potential value and the queue potential value; and

[0082] Configure pre-estimation computing power for the user according to the user potential value and the estimated quota potential value.

[0083] Optionally, when configuring recall computing power for the user according to the user potential value and the queue potential value, the configuration module 34 is configured to:

[0084] Select a target recall queue whose queue potential value is located at a set order from a preset recall queue as the recall computing power configured for the user;

[0085] Among them, the user potential value of the user is negatively correlated with the number of recall queues configured for the user.

[0086] Optionally, when configuring the pre-estimated computing power for the user according to the user potential value and the estimated quota potential value, the configuration module 34 is used for:

[0087] Determine the estimated quota configured for the user according to the user potential value and the estimated quota potential value;

[0088] Determine the pre-estimated computing power configured for the user according to the estimated quota.

[0089] Optionally, when determining the estimated quota configured for the user according to the user potential value and the estimated quota potential value, the configuration module 34 is used for:

[0090] Calculate the user potential value percentage of the user according to the rank of the user potential value among all users and the number of all users;

[0091] Calculate the estimated quota configured for the user according to the user potential value percentage and the estimated quota potential value.

[0092] Optionally, after configuring the information push computing power for the user according to the user potential value, the queue potential value and the estimated quota potential value, the configuration module 34 can also be used for:

[0093] Determine the target push information of the user according to the information push computing power;

[0094] Push the target push information to the user.

[0095] Optionally, after determining the target push information of the user according to the information push computing power, the configuration module 34 can also be used for:

[0096] Store the target push information of the user.

[0097] Regarding the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here. At the same time, the text processing device of the present disclosure can also achieve the corresponding beneficial effects of the above text processing method.

[0098] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0099] Figure 4FIG. 0 shows a schematic block diagram of an exemplary electronic device 400 that may be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as, for example, personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0100] As Figure 4 shown, the device 400 includes a computing unit 401 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0101] A plurality of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0102] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 executes the various methods and processes described above, such as the text processing method. For example, in some embodiments, the text processing method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the text processing method described above can be executed. Alternatively, in other embodiments, the computing unit 401 can be configured to execute the text processing method in any other suitable manner (e.g., by means of firmware).

[0103] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0104] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0105] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0106] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0107] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0108] A computer system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system or a server combined with a blockchain.

[0109] It should be understood that various forms of processes shown above can be used, steps can be reordered, added or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitations are imposed herein.

[0110] In the above device, equipment, storage medium, and computer program product embodiments of the present disclosure, obtain the basic attributes and / or operation behavior characteristics of the user; determine the user potential value of the user according to the basic attributes and / or operation behavior characteristics; determine the queue potential value and the estimated quota potential value of the user according to the queue time consumption and the estimated time consumption of the user, where the queue time consumption refers to the time required to match candidate push information for the user using a preset recall queue, and the estimated time consumption refers to the time required to estimate the candidate push information; configure the corresponding information push computing power for the user according to the user potential value, queue potential value, and estimated quota potential value of the user, reasonably allocate the push computing power, and reduce the waste of the push computing power.

[0111] The above specific implementation manners do not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A data configuration method, comprising: Obtaining the basic attributes and / or operation behavior characteristics of a user; Determining the user potential value of the user according to the basic attributes and / or the operation behavior characteristics; Determining the queue potential value and the estimated quota potential value of the user according to the queue time consumption and the estimated time consumption of the user, wherein the queue time consumption refers to the time required to match candidate push information for the user using a preset recall queue, and the estimated time consumption refers to the time required to estimate the candidate push information; Configuring information push computing power for the user according to the user potential value, the queue potential value and the estimated quota potential value.

2. The method according to claim 1, wherein The determining the user potential value of the user according to the basic attributes and / or the operation behavior characteristics includes: Inputting the basic attributes and / or the operation behavior characteristics into a trained user potential value model to obtain the user potential value of the user.

3. The method according to claim 1, wherein, The determining the queue potential value and the estimated quota potential value of the user according to the queue time consumption and the estimated time consumption of the user includes: Inputting the queue time consumption and the estimated time consumption of the user into a trained queue and estimated quota potential value model to obtain the queue potential value and the estimated quota potential value of the user.

4. The method according to claim 1, wherein The information push computing power includes recall computing power and pre-estimation computing power. Configuring information push computing power for the user according to the user potential value, the queue potential value and the estimated quota potential value includes: Configuring the recall computing power for the user according to the user potential value and the queue potential value; and Configuring the pre-estimation computing power for the user according to the user potential value and the estimated quota potential value.

5. The method according to claim 4, wherein, The configuring the recall computing power for the user according to the user potential value and the queue potential value includes: Selecting a target recall queue whose queue potential value is located at a set order from the preset recall queue as the recall computing power configured for the user; Wherein, the user potential value of the user is negatively correlated with the number of recall queues configured for the user.

6. The method according to claim 4, wherein The configuring the pre-estimation computing power for the user according to the user potential value and the estimated quota potential value includes: Determining the estimated quota configured for the user according to the user potential value and the estimated quota potential value; Determining the pre-estimation computing power configured for the user according to the estimated quota.

7. The method according to claim 6, wherein, The determining the estimated quota configured for the user according to the user potential value and the estimated quota potential value includes: Calculating the user potential value percentage of the user according to the order of the user potential value among all users and the number of all users; Calculating the estimated quota configured for the user according to the user potential value percentage and the estimated quota potential value.

8. The method according to claim 1, wherein, After configuring the information push computing power for the user according to the user potential value, the queue potential value and the estimated quota potential value, the method further includes: Determining the target push information of the user according to the information push computing power; Pushing the target push information to the user.

9. According to the method described in claim 8, after pushing computing power according to the information and determining the target push information of the user, the method further includes: Storing the target push information of the user.

10. A data configuration device, comprising: An acquisition module, configured to acquire the basic attributes and / or operation behavior characteristics of a user; A first determination module, configured to determine the user potential value of the user according to the basic attributes and / or the operation behavior characteristics; A second determination module, configured to determine the queue potential value and the estimated quota potential value of the user according to the queue consumption time and the estimated consumption time of the user, where the queue consumption time refers to the time required to match candidate push information for the user using a preset recall queue, and the estimated consumption time refers to the time required to estimate the candidate push information; A configuration module, configured to configure information push computing power for the user according to the user potential value, the queue potential value, and the estimated quota potential value.

11. The apparatus according to claim 10, wherein, When determining the user potential value of the user according to the basic attributes and / or the operation behavior characteristics, the first determination module is configured to: Input the basic attributes and / or the operation behavior characteristics into a trained user potential value model to obtain the user potential value of the user.

12. The apparatus according to claim 10, wherein, When determining the queue potential value and the estimated quota potential value of the user according to the queue consumption time and the estimated consumption time of the user, the second determination module is configured to: Input the queue consumption time and the estimated consumption time of the user into a trained queue and estimated quota potential value model to obtain the queue potential value and the estimated quota potential value of the user.

13. The apparatus according to claim 10, wherein, The information push computing power includes recall computing power and pre-estimation computing power. When configuring the information push computing power for the user according to the user potential value, the queue potential value, and the estimated quota potential value, the configuration module is configured to: Configure the recall computing power for the user according to the user potential value and the queue potential value; and Configure the pre-estimation computing power for the user according to the user potential value and the estimated quota potential value.

14. The apparatus according to claim 13, wherein, When configuring the recall computing power for the user according to the user potential value and the queue potential value, the configuration module is configured to: Select a target recall queue whose queue potential value is located at a set order from the preset recall queue as the recall computing power configured for the user; Wherein, the user potential value of the user is negatively correlated with the number of recall queues configured for the user.

15. The device according to claim 13, wherein, When configuring the pre-estimation computing power for the user according to the user potential value and the estimated quota potential value, the configuration module is configured to: Determine the estimated quota configured for the user according to the user potential value and the estimated quota potential value; Determine the pre-estimation computing power configured for the user according to the estimated quota.

16. The apparatus according to claim 15, wherein, When determining the estimated quota configured for the user according to the user potential value and the estimated quota potential value, the configuration module is configured to: Calculate the user potential value percentage of the user according to the rank of the user potential value of the user among all users and the number of all users; Based on the user potential value percentage and the estimated quota potential value, an estimated quota configured for the user is calculated.

17. The apparatus according to claim 10, wherein, After the configuration module configures the information push computing power for the user according to the user potential value, the queue potential value, and the estimated quota potential value, it can also be used for: Determine the target push information of the user according to the information push computing power; Push the target push information to the user.

18. The device according to claim 17, wherein, After the configuration module determines the target push information of the user according to the information push computing power, it can also be used for: Store the target push information of the user.

19. An electronic device, comprising: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1-9.

20. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to execute the method according to any one of claims 1-9.

21. A computer program product, comprising computer programs / instructions, and when the computer programs / instructions are executed by a processor, the steps in the method according to any one of claims 1-9 are implemented.

Images