Computing power scheduling method and device of intelligent computing center

Abstract

The invention provides a computing power scheduling method and device for an intelligent computing center, and the method comprises the steps: S1, receiving a target request inputted by a target user, and enabling the target request to be used for requesting to process a computing power operation task; s2, operation behavior information of the target user is obtained, a target computing power resource is determined according to the operation behavior information, and the operation behavior information is pre-stored historical behavior information of the target user; and S3, scheduling the target computing power resource to process the computing power operation task. Therefore, the target request input by the target user is used for requesting to process the computing power operation task, the target computing power resource is determined according to the operation behavior information of the target user, and then the target computing power resource is scheduled to process the computing power operation task, that is, the computing power operation task and the target computing power resource are both adaptive to the target user. Therefore, the degree of adaptation between the computing power operation task and the target computing power resource is very high.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent computing centers, smart computing centers and computing power infrastructure, and in particular to a computing power scheduling method and device for an intelligent computing center. Background Art

[0002] With the rapid development of artificial intelligence technology, "intelligent computing centers" and "intelligent computing centers" have emerged.

[0003] An "Intelligent Computing Center" is a facility that uses large-scale heterogeneous computing resources, including general-purpose and intelligent computing power, to provide the computing power, data, and algorithms required for AI applications (such as AI deep learning model development, model training, and model inference). The Intelligent Computing Center encompasses facilities, hardware, and software, and provides a full stack of capabilities, from bottom-level computing power to top-level application enablement.

[0004] “Intelligent Computing Center” includes but is not limited to “Smart Computing Center”.

[0005] "Intelligent Computing Center" refers to an artificial intelligence computing center. It is a type of computing power infrastructure that is based on artificial intelligence theory, adopts artificial intelligence computing architecture, and provides computing power services, data services, and algorithm services required for artificial intelligence applications.

[0006] "Computing power" is the core of "intelligent computing center" and "intelligent computing center". It is the ability of computer equipment or computing / data center to process information. It is the ability of computer hardware and software to work together to perform certain computing needs. It is the computing power to achieve target result output by processing information data. It is a new type of productivity that integrates information computing power, network carrying capacity, and data storage capacity. It mainly provides services to society through computing power infrastructure.

[0007] When the current intelligent computing center receives a request for processing a computing power running task, it usually needs to process the above computing power running task according to the request. However, when processing the above computing power running task, the current intelligent computing center usually allocates fixed computing power resources to the computing power running task, and the fixed computing power resources are not necessarily compatible with the computing power running task, resulting in a very low degree of adaptability between the computing power running task and the computing power resources. It can be seen that since the emergence of the intelligent computing center, the low degree of adaptability between the computing power resources allocated to the computing power running task and the computing power running task is a problem that needs to be solved urgently. Summary of the Invention

[0008] The present invention provides a computing power scheduling method and device for an intelligent computing center, which are used to solve the problem of low adaptability between computing power operation tasks and computing power resources.

[0009] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0010] In a first aspect, the present invention provides a computing power scheduling method for an intelligent computing center, comprising:

[0011] Step S1: receiving a target request input by a target user, wherein the target request is used to request processing computing power to run a task;

[0012] Step S2: Acquire the target user's operation behavior information, and determine the target computing resources based on the operation behavior information, wherein the operation behavior information is pre-stored historical behavior information of the target user;

[0013] Step S3: Dispatching the target computing resources to process the computing operation task.

[0014] Optionally, step S2 includes:

[0015] Step S21: Acquire the target user's operation behavior information;

[0016] Step S22: determining a user profile of the target user based on the operation behavior information;

[0017] Step S23: Determine the target computing resources corresponding to the user profile.

[0018] Optionally, step S22 includes:

[0019] Step S221: Acquire multiple behavior component information included in the operation behavior information, where each behavior component information corresponds to a different dimension;

[0020] Step S222: determining the label information corresponding to each behavior component information, and obtaining a plurality of label information;

[0021] Step S223: Construct a user profile of the target user based on the multiple tag information.

[0022] Optionally, step S23 includes:

[0023] Step S231: Determine a target computing power configuration template corresponding to the user profile, where the target computing power configuration template is any one of a plurality of pre-configured computing power configuration templates;

[0024] Step S232: Determine the target computing power resources corresponding to the target computing power configuration template.

[0025] Optionally, step S3 includes:

[0026] Step S31: Obtain a scheduling policy, where the scheduling policy is a policy for scheduling the target computing power resources to process the computing power operation task;

[0027] Step S32: Scheduling the target computing resources to process the computing operation task according to the scheduling strategy.

[0028] Optionally, the method further includes:

[0029] Step S4: Obtain target parameters, which are parameters corresponding to the process of scheduling the target computing power resources to process the computing power operation task;

[0030] Step S5: Modify the scheduling strategy according to the target parameters.

[0031] In a second aspect, the present invention provides a computing power scheduling device for an intelligent computing center, comprising:

[0032] A receiving module is used to receive a target request input by a target user, wherein the target request is used to request processing computing power to run a task;

[0033] A first acquisition module is configured to acquire operation behavior information of the target user and determine target computing resources based on the operation behavior information, wherein the operation behavior information is pre-stored historical behavior information of the target user;

[0034] The scheduling module is used to schedule the target computing power resources to process the computing power operation task.

[0035] In a third aspect, the present invention provides an electronic device comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of the computing power scheduling method of the intelligent computing center as described in the first aspect above are implemented.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the computing power scheduling method of the intelligent computing center as described in the first aspect above.

[0037] In a fifth aspect, the present invention provides a computer program product comprising computer instructions, which, when executed by a processor, implement the steps of the computing power scheduling method for an intelligent computing center as described in the first aspect above.

[0038] In the present invention, a target request input by a target user is received, and the target request is used to request the processing of a computing power operation task; the operation behavior information of the target user is obtained, and the target computing power resource is determined based on the operation behavior information, and the operation behavior information is the pre-stored historical behavior information of the target user; and the target computing power resource is scheduled to process the computing power operation task. In this way, the target request input by the target user is used to request the processing of a computing power operation task, and the target computing power resource is determined based on the operation behavior information of the target user, and then the target computing power resource is scheduled to process the computing power operation task, that is, the computing power operation task and the target computing power resource are both adapted to the target user, so that the adaptability of the computing power operation task and the target computing power resource is very high, thereby improving the processing efficiency of scheduling the target computing power resource to process the computing power operation task. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0040] Figure 1 A flowchart of a computing power scheduling method for a computing center provided by the present invention;

[0041] Figure 2 A schematic diagram of the structure of a computing power scheduling device for a computing center provided by the present invention;

[0042] Figure 3 This is a schematic structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0043] The following is a clear and complete description of the technical solutions of the present invention, in conjunction with the accompanying drawings. Obviously, the description is only a portion of the present invention, not all of it. All other contents derived by persons of ordinary skill in the art based on the contents of the present invention without inventive effort are within the scope of protection of the present invention.

[0044] The "computing power" mentioned in the present invention refers to: the ability of computer equipment or computing / data centers to process information, the ability of computer hardware and software to work together to execute certain computing requirements, and the computing power to achieve target result output by processing information data. It is a new type of productivity that integrates information computing power, network carrying capacity, and data storage capacity, and mainly provides services to society through computing power infrastructure.

[0045] The "computing power" (Computational Power, CP) mentioned in the present invention refers to: the ability of a data center server to process data and output results. It is a comprehensive indicator to measure the computing power of a data center, including general computing power, super computing power and intelligent computing power. The commonly used unit of measurement is the number of floating-point operations performed per second (FLOPS, 1EFLOPS=10^18FLOPS). The larger the value, the stronger the comprehensive computing power. According to calculations, 1EFLOPS is approximately the computing power output of 5 Tianhe-2A or 500,000 mainstream server CPUs or 2 million mainstream notebooks. The calculation formula is: CP=CP 通用 +CP 智能 +CP 超级 .

[0046] The "carrying capacity" (Network Power, NP) mentioned in the present invention refers to: it is the performance of the data transmission capability of the computing power facilities, including the comprehensive capabilities of network architecture, network bandwidth, transmission latency, intelligent management and scheduling, etc. It involves network transmission within and between data centers, and is a comprehensive indicator for measuring network transmission scheduling capabilities.

[0047] The "Storage Power" (SP) described in this invention refers to the comprehensive capabilities of a data center in terms of data storage capacity, performance, security and reliability, and environmental friendliness. It is a comprehensive indicator for measuring a data center's data storage capacity, encompassing both external storage devices such as storage arrays and internal server storage. Storage capacity is commonly measured in exabytes (EB, 1EB = 2^60 bytes), while performance is commonly measured in IOPS / TB (Input / Output Operations Per Second / TB). Disaster recovery ratio is a key indicator of security and reliability.

[0048] The "computing power infrastructure" mentioned in the present invention refers to a new type of information infrastructure that integrates information computing power, network carrying capacity, and data storage capacity, and can realize the centralized calculation, storage, transmission and application of information.

[0049] The "new information infrastructure" mentioned in the present invention refers to: mainly including network infrastructure such as 5G networks, fiber-optic broadband networks, backbone networks, international communication networks, satellite Internet, computing power infrastructure such as data centers, general computing power centers, intelligent computing centers, supercomputing centers, and new technology facilities such as artificial intelligence, blockchain, and quantum computing.

[0050] The "computing power" mentioned in the present invention includes: general computing power, intelligent computing power and super computing power.

[0051] The "general computing power" mentioned in the present invention refers to the computing power provided by servers based on CPU (Central Processing Unit) chips, which is used to support basic general computing such as cloud computing and edge computing.

[0052] The "intelligent computing power" mentioned in this invention refers to: a computing platform based on specialized chips such as GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), and ASIC (Application Specific Integrated Circuit) for various innovative artificial intelligence applications, such as natural language processing and machine vision.

[0053] The "supercomputing power" mentioned in the present invention refers to the computing power provided by high-performance computing clusters such as supercomputers. It utilizes the centralized computing resources of multiple computer systems working in parallel and uses a dedicated operating system to handle extremely complex or data-intensive problems. It is mainly used for calculations in cutting-edge scientific fields, such as planetary simulation, drug molecule design, genetic analysis, etc.

[0054] The "intelligent computing center" described in this article refers to a facility that provides the computing power, data, and algorithms required for artificial intelligence applications (such as AI deep learning model development, model training, and model inference) by utilizing large-scale heterogeneous computing resources, including general-purpose computing power (CPU) and intelligent computing power (GPU, FPGA, ASIC, etc.). The intelligent computing center encompasses facilities, hardware, and software, and can provide a full stack of capabilities, from bottom-level computing power to top-level application enablement.

[0055] The "intelligent computing center" mentioned in the present invention includes but is not limited to the "intelligent computing center".

[0056] The "intelligent computing center" mentioned in the present invention is an artificial intelligence computing center, which is a type of computing power infrastructure based on artificial intelligence theory, adopts artificial intelligence computing architecture, and provides computing power services, data services and algorithm services required for artificial intelligence applications.

[0057] The "computing power center" mentioned in the present invention refers to: a facility that is mainly composed of infrastructure such as wind, fire, water, electricity, and IT hardware and software equipment, and has computing power, transportation capacity, and storage capacity, including general data centers, intelligent computing centers, supercomputing centers, etc.

[0058] The "supercomputing center" mentioned in the present invention refers to: a supercomputing data center, which is a data center based on a supercomputer or a large-scale computing cluster, which can provide large-scale computing, storage and network services and other functions, and is widely used in application scenarios such as aerospace, national defense, oil exploration, climate modeling and genome sequencing.

[0059] The "computing resources" mentioned in the present invention refer to: technologies and facilities with information computing, transmission, storage and application capabilities required for the development of a digital society, including but not limited to computing resources such as CPUs and GPUs, network resources such as switches and routers, storage resources such as storage arrays and distributed storage, security resources such as firewalls and intrusion detection systems, and supporting and guarantee resources such as wind, fire, water and electricity.

[0060] The “model” mentioned in the present invention includes but is not limited to a “large language model” and a “multimodal large model”.

[0061] The "large language model" mentioned in the present invention refers to a large language model (LLM), which is a language model with a large parameter scale. It is designed to understand and generate human language. It is trained with a large amount of text data and can perform a wide range of tasks including text summarization, translation, sentiment analysis, etc.

[0062] The "Multimodal Large Models" mentioned in the present invention refer to models that combine multimodal information such as text, images, video, and audio for training, including but not limited to multimodal large language models.

[0063] The "computing power operation task" mentioned in the present invention refers to: a specific workload or job executed on computing power resources that requires a certain amount of computing power support, usually involving complex data processing, numerical calculations, model training or simulation scenarios.

[0064] See Figure 1 , Figure 1 This is a flow chart of a computing power scheduling method for an intelligent computing center provided by the present invention. Figure 1 As shown, the following steps are included:

[0065] Step S1: Receive a target request input by a target user, where the target request is used to request processing computing power to run a task.

[0066] Among them, the specific method of receiving the target request is not limited here. Optionally, the present invention can be applied to electronic devices, and the electronic device can be called a computing power platform of an intelligent computing center. The above-mentioned target request can be a request input by the target user on the computing power platform. It should be noted that the specific method of the target user inputting on the computing power platform is not limited here. For example: the method of the target user inputting on the computing power platform can include touch input, press input or voice input.

[0067] Optionally, the target user can also input the target request through other electronic devices, and the other electronic devices can be electrically connected to the computing power platform. In this way, the target user can input on the other electronic devices, so that the other electronic devices send the target request to the computing power platform.

[0068] For example, artificial intelligence (AI) models may be applied to other electronic devices, and target users may input target requests through the AI models, thereby improving the accuracy and intelligence of the input target requests.

[0069] For example: the target user can input the description information of the computing power running task into the AI model, and the AI model generates a target request and a computing power running task based on the above description information, and sends the above target request to the electronic device in the present invention to request the electronic device in the present invention to process the above computing power running task. In this way, the target user only needs to input the description information, and the AI model can automatically generate and send the target request based on the description information, which simplifies the operation of the target user. At the same time, it also improves the accuracy of the target request and computing power running task generation results and the intelligence of the generation method.

[0070] The computing power operation task is not specifically limited here. Optionally, the computing power operation task can be a model training task, and the type of the above-mentioned model is not limited here. For example, the above-mentioned model can be a navigation model, a computing model, a large language model, etc. Optionally, the computing power operation task can be a data processing task, for example, a task for processing navigation data or a task for processing computing data.

[0071] Step S2: Acquire the target user's operation behavior information, and determine the target computing resources based on the operation behavior information, where the operation behavior information is pre-stored historical behavior information of the target user.

[0072] Among them, the specific types of operation behavior information are not limited here. Optionally, the operation behavior information may include at least one of the following: page dwell time, click hot spots, model selection frequency, number of experimental runs, function modules with a usage frequency higher than a preset frequency, etc.

[0073] Among them, the page dwell time can be understood as the length of time the target user stays on the web page, and the dwell time on different web pages may be different; the click hot zone can be understood as the area where the target user clicks more times; the model selection frequency can be understood as the frequency with which the target user selects each model, and the selection frequency of different models may be different. The electronic device in the present invention can be integrated with multiple models, and the target user can select at least some of the multiple models for training or data processing; the number of experimental runs can be understood as the number of experimental runs of the model selected by the target user; the functional modules with a usage frequency higher than the preset frequency can be understood as the functional modules frequently selected by the target user, and the above-mentioned functional modules can be understood as modules that can realize specific functions, for example: the functional modules may include modules for realizing model training functions and modules for realizing data processing functions, etc.

[0074] It should be noted that the operation behavior information is the pre-stored historical behavior information of the target user, and the specific storage location of the operation behavior information is not limited here. Optionally, the operation behavior information can be stored on the electronic device in the present invention, or the operation behavior information can also be stored on the other electronic devices mentioned above.

[0075] Step S3: Dispatching the target computing resources to process the computing operation task.

[0076] The types of target computing resources are not specifically limited here. Optionally, the target computing resources may include computing resources corresponding to a central processing unit (CPU), a graphics processing unit (GPU), and memory.

[0077] In the present invention, through steps S1 to S3, the target request input by the target user is used to request the processing of the computing power operation task, and the target computing power resources are determined according to the operation behavior information of the target user, and then the target computing power resources are scheduled to process the computing power operation task, that is, the computing power operation task and the target computing power resources are both adapted to the target user, so that the adaptability of the computing power operation task and the target computing power resources is very high, thereby improving the processing efficiency of scheduling the target computing power resources to process the computing power operation task.

[0078] Optionally, step S2 includes:

[0079] Step S21: Acquire the target user's operation behavior information;

[0080] Step S22: determining a user profile of the target user based on the operation behavior information;

[0081] Step S23: Determine the target computing resources corresponding to the user profile.

[0082] The method for obtaining the target user's operational behavior information is not limited herein. Optionally, the target user's behavior log can be obtained, and the operational behavior information can be obtained from the behavior log. For example, a user behavior tracking script can be embedded in the front end of the electronic device in the present invention to record the target user's operational behavior information.

[0083] It should be noted that, optionally, the electronic device of the present invention can determine the user portrait of the target user through a decision tree, logistic regression or deep learning algorithm.

[0084] Among them, different user profiles may correspond to different target computing resources. For example, when the user profile is used to indicate that the corresponding user likes to play games, the GPU computing resources account for a higher proportion in the computing resources corresponding to the user profile. For another example, when the user profile is used to indicate that the corresponding user processes documents frequently, the memory and CPU computing resources account for a higher proportion in the computing resources corresponding to the user profile.

[0085] It should be noted that, optionally, the above-mentioned user portrait can be a portrait created in real time based on the target user's operational behavior information. Optionally, the process of determining the above-mentioned user portrait can also be described as follows: first determine the standard behavior information in the preset database that has a high degree of match with the target user's operational behavior information, and then determine the user portrait corresponding to the standard behavior information as the user portrait of the target user. The above-mentioned standard behavior information is the labeled behavior information of other users, and other users can be understood as users of the same type as the target user.

[0086] In the present invention, since different user profiles correspond to different target computing resources, the accuracy of the determined target computing resources is improved by first determining the user profile of the target user based on the operation behavior information and then determining the target computing resources corresponding to the user profile.

[0087] Optionally, step S22 includes:

[0088] Step S221: Acquire multiple behavior component information included in the operation behavior information, where each behavior component information corresponds to a different dimension;

[0089] Step S222: determining the label information corresponding to each behavior component information, and obtaining a plurality of label information;

[0090] Step S223: Construct a user profile of the target user based on the multiple tag information.

[0091] Among them, the operation behavior information may include behavior component information of multiple dimensions, that is, the operation behavior information can be split according to different dimensions to obtain multiple behavior component information. For example, the operation behavior information can be split according to the time dimension to obtain multiple behavior component information, and the time corresponding to the above multiple behavior component information can be within the past month, the past three months, and the past year, respectively.

[0092] For another example, the operation behavior information can be split into multiple behavior component information according to the dimension of the selected model type. The multiple behavior component information can respectively correspond to the behavior component information of selecting the first model, the behavior component information of selecting the second model, etc.

[0093] It should be noted that the specific contents of the above dimensions are not specifically limited here.

[0094] The specific types of tag information are not limited here. Optionally, the tag information may include at least one of the following: proficiency, usage preference, computational density tendency, etc. The specific types of user profiles of target users constructed based on multiple tag information are also not limited here. Optionally, the user profiles may include at least one of the following: a profile that prefers automated machine learning (AutoML) models, a profile that prefers training small models, a profile that prefers rapid and frequent experiments, etc.

[0095] In the present invention, multiple behavioral component information included in the operational behavior information is obtained, each behavioral component information corresponding to a different dimension, and label information corresponding to each behavioral component information is determined to obtain multiple label information. A user profile of the target user is constructed based on the multiple label information. In this way, the accuracy of the determined user profile can be further improved, and the diversity and flexibility of the user profile determination methods are increased.

[0096] Optionally, step S23 includes:

[0097] Step S231: Determine a target computing power configuration template corresponding to the user profile, where the target computing power configuration template is any one of a plurality of pre-configured computing power configuration templates;

[0098] Step S232: Determine the target computing power resources corresponding to the target computing power configuration template.

[0099] Among them, the computing power configuration templates corresponding to different user portraits are different. For example, when the user portrait indicates that the corresponding user is a novice user, the computing power resources corresponding to the computing power configuration template corresponding to the user portrait may include: low-specification computing resources and computing power resources of the fast feedback model; for example, when the user portrait indicates that the corresponding user is an advanced user, the computing power resources corresponding to the computing power configuration template corresponding to the user portrait may include: computing power resources of high-performance GPUs and computing power resources configured with large parameters.

[0100] In the present invention, each user portrait can be pre-configured with a corresponding computing power configuration template, and the computing power configuration template can be pre-configured with computing power resources. In this way, when the user portrait of the target user is determined, the target computing power configuration template corresponding to the user portrait of the target user and the target computing power resources corresponding to the target computing power configuration template can be quickly determined, thereby improving the efficiency of determining the target computing power resources and further improving the accuracy of the determined target computing power resources.

[0101] Optionally, step S3 includes:

[0102] Step S31: Obtain a scheduling policy, where the scheduling policy is a policy for scheduling the target computing power resources to process the computing power operation task;

[0103] Step S32: Scheduling the target computing resources to process the computing operation task according to the scheduling strategy.

[0104] Among them, the scheduling strategy can be understood as including at least one of the following: the scheduling priority of different types of computing power resources in the target computing power resources, the scheduling time of different types of computing power resources in the target computing power resources, the scheduling method of different types of computing power resources in the target computing power resources, the scheduling system of different types of computing power resources in the target computing power resources, etc.

[0105] Optionally, the above-mentioned scheduling system can be referred to as the underlying computing power scheduling system, and the scheduling system can include at least one of the following: a Kubernetes (K8s) system and a Yet Another Resource Negotiator (YARN) system.

[0106] In the present invention, the target computing power resources are scheduled to process computing power operation tasks according to the scheduling strategy. In this way, the scheduling of the target computing power resources can be made more orderly and accurate, thereby improving the processing efficiency of the computing power operation tasks.

[0107] Optionally, the method further includes:

[0108] Step S4: Obtain target parameters, which are parameters corresponding to the process of scheduling the target computing power resources to process the computing power operation task;

[0109] Step S5: Modify the scheduling strategy according to the target parameters.

[0110] Among them, the specific types of target parameters are not limited here. Optionally, the target parameters may include at least one of the following: the running time of the computing power running task, the utilization rate of the target computing power resources, the model performance and user behavior result information.

[0111] It should be noted that, optionally, the above scheduling strategy can be applied to a scheduling model, and the modified scheduling strategy can also be referred to as a modified scheduling model.

[0112] In the present invention, the scheduling strategy is modified according to the target parameters, thereby making the scheduling strategy more accurate, thereby making the scheduling of target computing resources more orderly and accurate, and processing computing tasks more efficient. At the same time, the overall performance of the electronic device in the present invention can also be improved.

[0113] See also Figure 2 , Figure 2 A schematic diagram of the structure of a computing power scheduling device for an intelligent computing center provided by the present invention is shown as follows: Figure 2 As shown, the computing power scheduling device 200 of the intelligent computing center includes:

[0114] The receiving module 201 is used to receive a target request input by a target user, wherein the target request is used to request processing of a computing power execution task;

[0115] A first acquisition module 202 is configured to acquire operation behavior information of the target user and determine target computing resources based on the operation behavior information, wherein the operation behavior information is pre-stored historical behavior information of the target user;

[0116] The scheduling module 203 is used to schedule the target computing power resources to process the computing power operation task.

[0117] Optionally, the first acquisition module 202 includes:

[0118] A first acquisition submodule is used to acquire the operation behavior information of the target user;

[0119] A first determining submodule is configured to determine a user profile of the target user based on the operation behavior information;

[0120] The second determination submodule is used to determine the target computing resources corresponding to the user portrait.

[0121] Optionally, the first determining submodule includes:

[0122] A first acquiring unit is configured to acquire a plurality of behavior component information included in the operation behavior information, wherein each behavior component information corresponds to a different dimension;

[0123] A first determining unit is configured to determine label information corresponding to each behavior component information to obtain a plurality of label information;

[0124] A construction unit is used to construct a user profile of the target user based on the multiple tag information.

[0125] Optionally, the second determining submodule includes:

[0126] A second determining unit is configured to determine a target computing power configuration template corresponding to the user profile, where the target computing power configuration template is any one of a plurality of pre-configured computing power configuration templates;

[0127] The third determining unit is used to determine the target computing power resources corresponding to the target computing power configuration template.

[0128] Optionally, the scheduling module 203 includes:

[0129] A second acquisition submodule is used to acquire a scheduling policy, where the scheduling policy is a policy for scheduling the target computing power resources to process the computing power operation task;

[0130] The scheduling submodule is used to schedule the target computing power resources to process the computing power operation task according to the scheduling strategy.

[0131] Optionally, the computing power scheduling device 200 of the intelligent computing center further includes:

[0132] A second acquisition module is used to obtain target parameters, where the target parameters are corresponding parameters in the process of scheduling the target computing power resources to process the computing power operation task;

[0133] A correction module is used to correct the scheduling strategy according to the target parameters.

[0134] The computing power scheduling device 200 of the intelligent computing center provided by the present invention can execute each step in the computing power scheduling method of the above-mentioned intelligent computing center, and thus has the same beneficial technical effects as the computing power scheduling method of the above-mentioned intelligent computing center, and will not be described in detail here.

[0135] Please refer to Figure 3 The present invention also provides an electronic device 30, including a processor 31, a memory 32, and a computer program stored in the memory 32 and executable on the processor 31. When the computer program is executed by the processor 31, the various processes shown in the computing power scheduling method of the above-mentioned intelligent computing center are implemented, and the same technical effects can be achieved. To avoid repetition, they will not be described here.

[0136] The present invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the computer program implements each process of the computing power scheduling method of the intelligent computing center described above and can achieve the same technical effect. To avoid repetition, the details are not described here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0137] The present invention also provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the above Figure 1 The various processes of the computing power scheduling method of the intelligent computing center shown in the figure can achieve the same technical effect. To avoid repetition, they will not be repeated here.

[0138] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0139] Through the description of the above embodiments, those skilled in the art can clearly understand that the method provided by the above invention can be implemented by means of software plus the necessary general hardware platform, or of course by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the various methods provided by the present invention.

[0140] The present invention is described above with reference to the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A computing power scheduling method for an intelligent computing center, characterized in that: include: Step S1: receiving a target request input by a target user, wherein the target request is used to request processing computing power to run a task; Step S2: Acquire the target user's operation behavior information, and determine the target computing resources based on the operation behavior information, wherein the operation behavior information is pre-stored historical behavior information of the target user; Step S3: Dispatching the target computing resources to process the computing operation task.

2. The method according to claim 1, characterized in that The step S2 comprises: Step S21: Acquire the target user's operation behavior information; Step S22: determining a user profile of the target user based on the operation behavior information; Step S23: Determine the target computing resources corresponding to the user profile.

3. The method according to claim 2, characterized in that The step S22 includes: Step S221: Acquire multiple behavior component information included in the operation behavior information, where each behavior component information corresponds to a different dimension; Step S222: determining the label information corresponding to each behavior component information, and obtaining a plurality of label information; Step S223: Construct a user profile of the target user based on the multiple tag information.

4. The method according to claim 2, characterized in that The step S23 includes: Step S231: Determine a target computing power configuration template corresponding to the user profile, where the target computing power configuration template is any one of a plurality of pre-configured computing power configuration templates; Step S232: Determine the target computing power resources corresponding to the target computing power configuration template.

5. The method according to any one of claims 1 to 4, characterized in that The step S3 comprises: Step S31: Obtain a scheduling policy, where the scheduling policy is a policy for scheduling the target computing power resources to process the computing power operation task; Step S32: Scheduling the target computing resources to process the computing operation task according to the scheduling strategy.

6. The method according to claim 5, characterized in that After step S32, the method further includes: Step S4: Obtain target parameters, which are parameters corresponding to the process of scheduling the target computing power resources to process the computing power operation task; Step S5: Modify the scheduling strategy according to the target parameters.

7. A computing power scheduling device for an intelligent computing center, characterized in that: include: A receiving module is used to receive a target request input by a target user, wherein the target request is used to request processing computing power to run a task; A first acquisition module is configured to acquire operation behavior information of the target user and determine target computing resources based on the operation behavior information, wherein the operation behavior information is pre-stored historical behavior information of the target user; The scheduling module is used to schedule the target computing power resources to process the computing power operation task.

8. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of the computing power scheduling method for an intelligent computing center as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the computing power scheduling method for an intelligent computing center according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the computing power scheduling method of the intelligent computing center as described in any one of claims 1 to 6.

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