Server resource processing method and device, equipment, medium and program product

Abstract

The invention provides a server resource processing method which can be applied to the technical field of artificial intelligence. The method comprises the following steps: in response to a received server resource request related to a target project, obtaining target project data related to the target project and server resource request data; determining historical project data and historical server resource use data related to the target project from a project database according to the target project data; and according to the historical project data, the historical server resource use data, the target project data and the server resource request data, a server resource processing model is used to determine a processing result of a server resource request, and the server resource processing model is obtained by training project sample data and server resource sample data. The invention further provides a server resource processing device and equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to a server resource processing method, device, equipment, medium and program product. Background Art

[0002] With the development of the Internet, all walks of life are gradually moving towards digital management, and the realization of digitalization is inseparable from a large amount of data processing, storage and management. The server can carry complex computing tasks and high-load applications, so it can provide data processing, storage and management. However, there are problems in the relevant technology such as slow application process for server resources, low efficiency, waste of resources, repeated applications, etc. Summary of the invention

[0003] In view of the above problems, the present disclosure provides a server resource processing method, apparatus, device, medium and program product.

[0004] According to a first aspect of the present disclosure, a server resource processing method is provided, comprising:

[0005] In response to receiving a server resource request related to a target project, acquiring target project data and server resource request data related to the target project;

[0006] According to the target project data, determine historical project data and historical server resource usage data related to the target project from a project database;

[0007] Based on the above historical project data, the above historical server resource usage data, the above target project data and the above server resource request data, a server resource processing model is used to determine the processing result of the above server resource request, wherein the above server resource processing model is trained using project sample data and server resource sample data.

[0008] According to an embodiment of the present disclosure, the processing result of the server resource request is determined by using a server resource processing model based on the historical project data, the historical server resource usage data, the target project data and the server resource request data, including:

[0009] According to the historical project data, the historical server resource usage data and the target project data, using the server resource processing model to obtain a server resource prediction result for the target project;

[0010] According to the difference between the server resource prediction result and the server resource request data, the server resource processing model is used to determine the processing result of the server resource request.

[0011] According to an embodiment of the present disclosure, the server resource prediction result of the target project is obtained by using the server resource processing model based on the historical project data, the historical server resource usage data and the target project data, including:

[0012] Filtering target data corresponding to target input features of the server resource processing model according to the historical project data, the historical server resource usage data and the target project data;

[0013] According to the target data, the server resource prediction result of the target project is obtained by utilizing the server resource processing model.

[0014] According to an embodiment of the present disclosure, the method of determining the processing result of the server resource request using the server resource processing model according to the difference between the server resource prediction result and the server resource request data includes:

[0015] In the case where it is determined that the difference between the server resource prediction result and the server resource request data is less than a preset threshold, determining that the processing result of the server resource request is passed by using the server resource processing model;

[0016] When it is determined that the difference between the server resource prediction result and the server resource request data is greater than or equal to the preset threshold, the server resource processing model is used to determine that the processing result of the server resource request is failed.

[0017] According to an embodiment of the present disclosure, the historical project data and historical server resource usage data in the above-mentioned project database are obtained through vector conversion of the embedded model;

[0018] Wherein, the above-mentioned determining the historical project data and historical server resource usage data related to the above-mentioned target project from the project database according to the above-mentioned target project data includes:

[0019] Performing vector conversion on the target project data through the embedding model;

[0020] According to the converted target project data, the historical project data and the historical server resource usage data related to the target project are determined from the project database.

[0021] According to an embodiment of the present disclosure, the above method further includes:

[0022] Obtain the above-mentioned project sample data and the above-mentioned server resource sample data from the server resource ledger system and the server resource deployment system;

[0023] Determining target input features of the server resource processing model based on the project sample data and the server resource sample data;

[0024] Determine target sample data corresponding to the target input feature from the project sample data and the server resource sample data;

[0025] The initial server resource processing model is trained using the target sample data to obtain the server resource processing model.

[0026] According to an embodiment of the present disclosure, determining the target input features of the server resource processing model according to the project sample data and the server resource sample data includes:

[0027] The target input features of the server resource processing model are determined based on the project sample data and the server resource sample data by using a Pearson correlation coefficient method, a logistic regression method or a random forest method.

[0028] A second aspect of the present disclosure provides a server resource processing device, including:

[0029] A first acquisition module, configured to, in response to receiving a server resource request related to a target project, acquire target project data and server resource request data related to the target project;

[0030] A first determination module, configured to determine, from a project database, historical project data and historical server resource usage data related to the target project according to the target project data;

[0031] The second determination module is used to determine the processing result of the above-mentioned server resource request based on the above-mentioned historical project data, the above-mentioned historical server resource usage data, the above-mentioned target project data and the above-mentioned server resource request data using the server resource processing model, wherein the above-mentioned server resource processing model is trained using the project sample data and the server resource sample data.

[0032] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0033] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

[0034] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor.

[0035] According to an embodiment of the present disclosure, in response to receiving a server resource request related to a target project, target project data and server resource request data related to the target project are obtained; based on the target project data, historical project data and historical server resource usage data related to the target project are determined from a project database; based on the historical project data, historical server resource usage data, target project data and server resource request data, a server resource processing model is used to determine a processing result of the server resource request, wherein the server resource processing model is trained using project sample data and server resource sample data, and the server resource processing model is used to process server resource requests, thereby shortening the application cycle of server resources, solving problems such as slow server resource application process, low efficiency, waste of resources, repeated applications, etc., and improving the processing efficiency of server resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0037] Figure 1 The application scenario diagram schematically shows the server resource processing method, apparatus, device, medium and program product according to the embodiment of the present disclosure;

[0038] Figure 2 A flowchart of a server resource processing method according to an embodiment of the present disclosure is schematically shown;

[0039] Figure 3 The structure diagram of the server resource processing system according to the embodiment of the present disclosure is schematically shown;

[0040] Figure 4 A flowchart schematically shows a method for processing server resources according to another embodiment of the present disclosure;

[0041] Figure 5 A schematic diagram showing a structural block diagram of a server resource processing device according to an embodiment of the present disclosure; and

[0042] Figure 6 A block diagram of an electronic device suitable for implementing a method for processing server resources according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0043] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0044] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0045] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0046] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0047] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0048] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating a person's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0049] In the related art, the main process of applying for server resources is: the developer submits a server resource application form for the target project in the system, which describes in detail the type and quantity of the required server resources. After submission, the application form is transferred to the system administrator, and the operation and maintenance personnel manually allocate it and return the relevant server information to the application form. The server application process in the related art is time-consuming, and the processing time is highly dependent on manual processing. In the case of a large number of projects, the operation and maintenance personnel have no exact control over the server resources actually required for the application project, which can easily lead to resource waste or the developer's first submission of insufficient server resource applications, resulting in repeated applications, which greatly increases communication costs.

[0050] In view of this, the present disclosure proposes a server resource processing method, including obtaining target project data and server resource request data related to the target project in response to receiving a server resource request related to a target project; determining historical project data and historical server resource usage data related to the target project from a project database based on the target project data; and determining a processing result of the server resource request using a server resource processing model based on the historical project data, the historical server resource usage data, the target project data and the server resource request data, wherein the server resource processing model is trained using project sample data and server resource sample data.

[0051] Figure 1 The application scenario diagram of the server resource processing method, apparatus, device, medium and program product according to the embodiments of the present disclosure is schematically shown.

[0052] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0053] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0054] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0055] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0056] It should be noted that the server resource processing method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the server resource processing device provided in the embodiment of the present disclosure can generally be set in the server 105. The server resource processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the server resource processing device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0057] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0058] The following will be based on Figure 1 The scene described by Figure 2~Figure 4 The server resource processing method of the disclosed embodiment is described in detail.

[0059] Figure 2 The flowchart of the server resource processing method according to the embodiment of the present disclosure is schematically shown.

[0060] like Figure 2 As shown, the server resource processing method of this embodiment includes operations S210 to S230, and the server resource processing method can be executed by a server.

[0061] In operation S210 , in response to receiving a server resource request related to a target project, target project data and server resource request data related to the target project are acquired.

[0062] In operation S220, historical project data and historical server resource usage data related to the target project are determined from a project database according to the target project data.

[0063] In operation S230, a processing result of a server resource request is determined using a server resource processing model based on historical project data, historical server resource usage data, target project data, and server resource request data, wherein the server resource processing model is trained using project sample data and server resource sample data.

[0064] According to an embodiment of the present disclosure, the target project may be a business project, for example, a transfer business, a loan business, etc. of a financial institution.

[0065] According to an embodiment of the present disclosure, a server resource request may be initiated when a target project needs to be tested, or may be initiated when a target project needs to be put into production.

[0066] According to an embodiment of the present disclosure, a server resource request may be initiated by a developer in a server resource processing system. The server resource processing system may be a system for processing server resources, and both developers and operation and maintenance personnel may process data of related server resources through the server resource processing system.

[0067] According to an embodiment of the present disclosure, the target project data may include the target project's operating data, version, operating environment requirements, interfaces, etc. The server resource request data may include the type and quantity of the server resources to be applied for, etc. The server resources may be virtual cloud resources or physical hardware servers.

[0068] According to an embodiment of the present disclosure, the project database may include the server resource usage of different versions of multiple projects within a historical period of time, the historical project data being the project data of a project among multiple projects that matches a target project, and the historical server resource usage data being the server resource usage of a project among multiple projects that matches a target project.

[0069] According to the embodiments of the present disclosure, the server resource processing model is trained using project sample data and server resource sample data. The project sample data can represent the project data of the project, and the server resource sample data can represent the actual usage of the server resources of multiple projects. Therefore, the server resource processing model can obtain the server resource usage corresponding to the project based on the project data. Based on the historical project data, the historical server resource usage data, the target project data and the server resource request data, the server resource processing model can be used to determine whether the server resource request is reasonable, thereby obtaining the processing result of the server resource request.

[0070] According to an embodiment of the present disclosure, in response to receiving a server resource request related to a target project, target project data and server resource request data related to the target project are obtained; based on the target project data, historical project data and historical server resource usage data related to the target project are determined from a project database; based on the historical project data, historical server resource usage data, target project data and server resource request data, a server resource processing model is used to determine a processing result of the server resource request, wherein the server resource processing model is trained using project sample data and server resource sample data, and the server resource processing model is used to process server resource requests, thereby shortening the application cycle of server resources, solving problems such as slow server resource application process, low efficiency, waste of resources, repeated applications, etc., and improving the processing efficiency of server resources.

[0071] According to an embodiment of the present disclosure, a server resource processing model is used to determine a processing result of a server resource request based on historical project data, historical server resource usage data, target project data, and server resource request data, including: using a server resource processing model to obtain a server resource prediction result for a target project based on historical project data, historical server resource usage data, and target project data; and using a server resource processing model to determine a processing result of a server resource request based on a degree of difference between a server resource prediction result and a server resource request data.

[0072] According to the embodiments of the present disclosure, the historical project data may be information about the historical versions of the target project. Generally, the differences between different versions of the project are small. Therefore, the differences between the historical project data and the target project data may be compared, thereby obtaining the server resource prediction result of the target project based on the historical server resource usage data corresponding to the historical project data. For example, if the historical project data is version 1.0 of project A, which supports a maximum of 100 users, the corresponding historical server resource usage data may be 2 servers. If the target project data is version 2.0 of project A, which supports a maximum of 200 users, then the server resource prediction result may be 4 servers if other data remain unchanged.

[0073] According to an embodiment of the present disclosure, the server resource prediction result can be compared with the server resource request data, and the server resource processing model can be used to determine whether the server resource prediction result is consistent with the server resource request data, thereby determining the processing result of the server resource request. In the case where it is determined that the server resource prediction result is consistent with the server resource request data, for example, both are 4 units, the processing result of the server resource request is determined to be passed; In the case where it is determined that the server resource prediction result is inconsistent with the server resource request data, for example, the server resource prediction result is 4 units and the server resource request data is 5 units, the processing result of the server resource request can be determined to be failed.

[0074] According to the embodiments of the present disclosure, the server resource prediction results for the target project can be obtained through the server resource processing model, so that the processing results of the server resource request can be determined, which improves the processing accuracy of the server resource request and avoids the problem of multiple requests being initiated due to human misjudgment.

[0075] According to an embodiment of the present disclosure, a server resource prediction result of a target project is obtained by using a server resource processing model based on historical project data, historical server resource usage data and target project data, including: screening target data corresponding to target input features of the server resource processing model based on historical project data, historical server resource usage data and target project data; and obtaining a server resource prediction result of the target project based on the target data by using the server resource processing model.

[0076] According to the embodiments of the present disclosure, historical project data, historical server resource usage data, and target project data may include a lot of data, from which target data that can be used by the server resource processing model needs to be screened. The target input features of the server resource processing model can be the number of applications and the number of servers in use, the number of applications for the host machine, and the actual usage of the host machine, such as memory, throughput, CPU, and the number of nodes running the project. It can also be other features, which will not be repeated here.

[0077] According to an embodiment of the present disclosure, target data is screened out from historical project data, historical server resource usage data and target project data according to target input features, and the target data is input into a server resource processing model, thereby obtaining a more accurate server resource prediction result.

[0078] According to an embodiment of the present disclosure, by screening data, the server resource processing model makes predictions using target data corresponding to target input features, which can reduce the amount of data and further increase the prediction accuracy of the server resource processing model.

[0079] According to an embodiment of the present disclosure, a server resource processing model is used to determine a processing result of a resource request based on the degree of difference between a server resource prediction result and the server resource request data, including: when it is determined that the degree of difference between the server resource prediction result and the server resource request data is less than a preset threshold, the server resource processing model is used to determine that the processing result of the server resource request is passed; when it is determined that the degree of difference between the server resource prediction result and the server resource request data is greater than or equal to the preset threshold, the server resource processing model is used to determine that the processing result of the server resource request is failed.

[0080] According to an embodiment of the present disclosure, a preset threshold value may also be set, and the difference between the server resource prediction result and the server resource request data may be compared with the preset threshold value. When the difference is greater than or equal to the preset threshold value, the processing result of the server resource request is determined to be not passed. When the difference is less than the preset threshold value, the processing result of the server resource request is determined to be passed. For example, if the preset threshold value is 2, and the server resource prediction result is 4 units and the server resource request data is 5 units, the difference is less than the preset threshold value, and the processing result of the server resource request may be determined to be passed; if the server resource prediction result is 4 units and the server resource request data is 7 units, the difference is greater than the preset threshold value, and the processing result of the server resource request may be determined to be not passed.

[0081] According to an embodiment of the present disclosure, by setting a preset threshold to determine the processing result of a server resource request, the processing flexibility of the server resource request can be increased.

[0082] According to an embodiment of the present disclosure, historical project data and historical server resource usage data in a project database are obtained through vector conversion of an embedded model.

[0083] Among them, according to the target project data, historical project data and historical server resource usage data related to the target project are determined from the project database, including: vectorizing the target project data through an embedding model; and according to the converted target project data, historical project data and historical server resource usage data related to the target project are determined from the project database.

[0084] According to an embodiment of the present disclosure, in order to facilitate data identification, the data in the project database may be vector-converted, and the data may be converted through an embedded model to obtain converted historical project data and historical server resource usage data.

[0085] According to an embodiment of the present disclosure, in order to facilitate querying data corresponding to a target project in a project database, the target project data may also be vector-converted, and historical project data and historical server resource usage data may be determined from the project database based on the converted target project data.

[0086] According to an embodiment of the present disclosure, the server resource request data may also be vector-converted, and the processing result of the server resource request may be determined using a server resource processing model based on the converted target project data, the converted server resource request data, the historical project data, and the historical server resource usage data.

[0087] According to the embodiments of the present disclosure, vectorized processing can convert data processing operations into vector operations, thereby improving data processing efficiency, thereby further improving the processing efficiency of server resources.

[0088] Figure 3 The structure diagram of the server resource processing system according to the embodiment of the present disclosure is schematically shown.

[0089] like Figure 3 As shown, the server resource processing system 300 includes a server resource usage module 310, a data processing module 320 and a request processing module 330. Among them, the server resource usage module 310 may include a server resource ledger system and a server resource deployment system. The server resource ledger system may be the application and approval records of each project in the historical time period, and the server resource deployment system may be the actual deployment of server resources of each project; the data processing module 320 may include an embedded model and a project database. The data processing module 320 may obtain the historical project data and historical server resource usage data of the project from the server resource usage module 310, and store the project database after conversion through the embedded model; the request processing module 330 may include a server resource processing model.

[0090] According to an embodiment of the present disclosure, the project database can be combined with the server resource processing model through enhanced retrieval (RAG), which is a technology that uses information from private or proprietary data sources to assist text generation. The server resource processing model can be implemented based on a large language model.

[0091] According to the embodiments of the present disclosure, the establishment of a server resource processing system can collect information on various links of a project and can also increase close cooperation and communication between developers and operation and maintenance personnel.

[0092] According to an embodiment of the present disclosure, the server resource processing method may further include a training method for a server resource processing model, and the training method for the server resource processing model includes: obtaining project sample data and server resource sample data from a server resource ledger system and a server resource deployment system; determining target input features of the server resource processing model based on the project sample data and the server resource sample data; determining target sample data corresponding to the target input features from the project sample data and the server resource sample data; and using the target sample data to train an initial server resource processing model to obtain a server resource processing model.

[0093] According to an embodiment of the present disclosure, project sample data and server resource sample data can be obtained from a server resource ledger system and a server resource deployment system through a system embedded script, and the obtained project sample data and server resource sample data can be converted into vectors.

[0094] According to an embodiment of the present disclosure, positive and negative samples are selected from the project sample data and the server resource sample data, and the number of positive and negative samples is consistent.

[0095] According to an embodiment of the present disclosure, the data in the project sample data and the server resource sample data are multidimensional data, and the dimensions that contribute more to the server resource processing model can be determined based on the project sample data and the server resource sample data, and the target input features of the server resource processing model can be determined. Based on the project sample data and the server resource sample data, the target input features of the server resource processing model can be determined by the Pearson correlation coefficient method, the logistic regression method, or the random forest method.

[0096] According to the embodiments of the present disclosure, target sample data that meets the target input characteristics can be selected from the project sample data and the server resource sample data, and 70% of the target sample data can be used as a training set and 30% can be used as a test set. The model is trained with the training set, and each data is input into the initial server resource processing model; the accuracy, precision, and recall of the initial server resource processing model obtained after training with the above different methods are calculated with the test set to obtain the server resource processing model.

[0097] According to an embodiment of the present disclosure, a server resource processing model is obtained by training with data of server resources in a server resource ledger system and a server resource deployment system, which can increase the accuracy of the server resource processing model.

[0098] Figure 4 The flowchart of a server resource processing method according to another embodiment of the present disclosure is schematically shown.

[0099] like Figure 4 As shown, the server resource processing method may include operations S410 to S460.

[0100] In operation S410, in response to receiving a server resource request related to a target project, target project data and server resource request data related to the target project are acquired.

[0101] In operation S420, historical project data and historical server resource usage data related to the target project are determined from a project database according to the target project data.

[0102] In operation S430, a server resource prediction result of the target project is obtained using a server resource processing model according to the historical project data, the historical server resource usage data, the target project data, and the server resource request data.

[0103] In operation S440, determine whether the difference between the server resource prediction result and the server resource request data is less than a preset threshold. If it is determined that the difference between the server resource prediction result and the server resource request data is less than the preset threshold, perform operation S450. If it is determined that the difference between the server resource prediction result and the server resource request data is not less than the preset threshold, perform operation S460.

[0104] In operation S450, it is determined by using the server resource processing model that the processing result of the server resource request is passed.

[0105] In operation S460, it is determined by using the server resource processing model that the processing result of the server resource request is not passed.

[0106] According to an embodiment of the present disclosure, the server resource request may be initiated in the server resource processing system, and the processing result of the server resource request may be returned to the requesting user through the server resource processing system. Operation S410 to Operation S460 may refer to the description of other embodiments of the present disclosure, and will not be repeated here.

[0107] According to the embodiments of the present disclosure, the server resource processing method of the present disclosure solves the problems of slow server resource application process, insufficient efficiency, waste of resources, repeated applications, etc. in the related technology, and uses the server resource processing model to reasonably estimate and approve the server resource application, which greatly shortens the application cycle of server resources, provides more sufficient time for other links of software engineering, effectively promotes various links of software engineering, and is conducive to rapid product iteration.

[0108] Based on the above server resource processing method, the present disclosure also provides a server resource processing device. Figure 5 The device is described in detail.

[0109] Figure 5 The structure block diagram of the server resource processing device according to the embodiment of the present disclosure is schematically shown.

[0110] like Figure 5 As shown, the server resource processing device 500 of this embodiment includes a first acquisition module 510 , a first determination module 520 and a second determination module 530 .

[0111] The first acquisition module 510 is used to obtain target project data and server resource request data related to the target project in response to receiving a server resource request related to the target project. In one embodiment, the first acquisition module 510 can be used to perform the operation S210 described above, which will not be repeated here.

[0112] The first determination module 520 is used to determine the historical project data and historical server resource usage data related to the target project from the project database according to the target project data. In one embodiment, the first determination module 520 can be used to perform the operation S220 described above, which will not be described in detail here.

[0113] The second determination module 530 is used to determine the processing result of the server resource request using the server resource processing model according to the historical project data, the historical server resource usage data, the target project data and the server resource request data, wherein the server resource processing model is trained using the project sample data and the server resource sample data. In one embodiment, the second determination module 530 can be used to perform the operation S230 described above, which will not be described in detail here.

[0114] According to an embodiment of the present disclosure, the second determination module 530 for determining the processing result of the server resource request using the server resource processing model according to the historical project data, the historical server resource usage data, the target project data and the server resource request data includes:

[0115] A first determination unit is used to obtain a server resource prediction result of a target project by using a server resource processing model according to historical project data, historical server resource usage data and target project data;

[0116] The second determining unit is used to determine the processing result of the server resource request by using the server resource processing model according to the difference between the server resource prediction result and the server resource request data.

[0117] According to an embodiment of the present disclosure, a first determining unit for obtaining a server resource prediction result of a target project by using a server resource processing model according to historical project data, historical server resource usage data and target project data includes:

[0118] A first determination subunit is used to screen target data corresponding to target input features of the server resource processing model based on historical project data, historical server resource usage data and target project data;

[0119] The second determination subunit is used to obtain the server resource prediction result of the target project according to the target data by using the server resource processing model.

[0120] According to an embodiment of the present disclosure, a second determining unit for determining a processing result of a server resource request using a server resource processing model according to a difference between a server resource prediction result and server resource request data includes:

[0121] A third determining subunit is used to determine that the processing result of the server resource request is passed by using the server resource processing model when it is determined that the difference between the server resource prediction result and the server resource request data is less than a preset threshold;

[0122] The fourth determining subunit is used to determine that the processing result of the server resource request is not passed by using the server resource processing model when it is determined that the difference between the server resource prediction result and the server resource request data is greater than or equal to a preset threshold.

[0123] According to an embodiment of the present disclosure, historical project data and historical server resource usage data in the project database are obtained through vector conversion of the embedded model;

[0124] The first determining module for determining the historical project data and the historical server resource usage data related to the target project from the project database according to the target project data includes:

[0125] A third determination unit is used to perform vector conversion on the target project data through the embedding model;

[0126] The fourth determining unit is used to determine the historical project data and historical server resource usage data related to the target project from the project database according to the converted target project data.

[0127] According to an embodiment of the present disclosure, the server resource processing device further includes:

[0128] The second acquisition module is used to acquire project sample data and server resource sample data from the server resource ledger system and the server resource deployment system;

[0129] A third determination module is used to determine target input features of the server resource processing model based on the project sample data and the server resource sample data;

[0130] A fourth determination module, used to determine target sample data corresponding to the target input feature from the project sample data and the server resource sample data;

[0131] The module is used to train the initial server resource processing model using the target sample data to obtain the server resource processing model.

[0132] According to an embodiment of the present disclosure, the third determination module for determining the target input feature of the server resource processing model according to the project sample data and the server resource sample data includes:

[0133] The fifth determination unit is used to determine the target input features of the server resource processing model through the Pearson correlation coefficient method, the logistic regression method or the random forest method according to the project sample data and the server resource sample data.

[0134] According to an embodiment of the present disclosure, any multiple modules of the first acquisition module 510, the first determination module 520, and the second determination module 530 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first acquisition module 510, the first determination module 520, and the second determination module 530 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the first acquisition module 510, the first determination module 520, and the second determination module 530 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0135] Figure 6 A block diagram of an electronic device suitable for implementing a method for processing server resources according to an embodiment of the present disclosure is schematically shown.

[0136] like Figure 6 As shown, the electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 to a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0137] In RAM 603, various programs and data required for the operation of electronic device 600 are stored. Processor 601, ROM 602 and RAM 603 are connected to each other via bus 604. Processor 601 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 602 and / or RAM 603. It should be noted that the program can also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0138] According to an embodiment of the present disclosure, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the input / output (I / O) interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 608 as needed.

[0139] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0140] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: 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), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0141] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the server resource processing method provided by the embodiment of the present disclosure.

[0142] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 601. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0143] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0144] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, apparatus, module, unit, etc. described above can be implemented by a computer program module.

[0145] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0146] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0147] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0148] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A server resource processing method, characterized in that: The method comprises: In response to receiving a server resource request associated with a target project, acquiring target project data and server resource request data associated with the target project; According to the target project data, determining historical project data and historical server resource usage data related to the target project from a project database; Based on the historical project data, the historical server resource usage data, the target project data and the server resource request data, a server resource processing model is used to determine a processing result of the server resource request, wherein the server resource processing model is trained using project sample data and server resource sample data.

2. The method according to claim 1, characterized in that Determining the processing result of the server resource request by using a server resource processing model according to the historical project data, the historical server resource usage data, the target project data and the server resource request data includes: Obtaining a server resource prediction result for the target project using the server resource processing model according to the historical project data, the historical server resource usage data and the target project data; The processing result of the server resource request is determined using the server resource processing model according to the difference between the server resource prediction result and the server resource request data.

3. The method according to claim 2, characterized in that The step of obtaining a server resource prediction result of the target project by using the server resource processing model according to the historical project data, the historical server resource usage data and the target project data includes: filtering target data corresponding to target input features of the server resource processing model based on the historical project data, the historical server resource usage data and the target project data; The server resource prediction result of the target project is obtained according to the target data using the server resource processing model.

4. The method according to claim 2, characterized in that: Determining the processing result of the server resource request by using the server resource processing model according to the difference between the server resource prediction result and the server resource request data includes: In the case where it is determined that the difference between the server resource prediction result and the server resource request data is less than a preset threshold, determining, using the server resource processing model, that the processing result of the server resource request is passed; When it is determined that the difference between the server resource prediction result and the server resource request data is greater than or equal to the preset threshold, the server resource processing model is used to determine that the processing result of the server resource request is not passed.

5. The method according to any one of claims 1 to 4, characterized in that The historical project data and historical server resource usage data in the project database are obtained by vector conversion through the embedding model; Wherein, determining the historical project data and historical server resource usage data related to the target project from the project database according to the target project data includes: Performing vector conversion on the target project data through the embedding model; According to the converted target project data, the historical project data and the historical server resource usage data related to the target project are determined from the project database.

6. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Acquire the project sample data and the server resource sample data from the server resource ledger system and the server resource deployment system; Determining target input features of the server resource processing model based on the project sample data and the server resource sample data; Determining target sample data corresponding to the target input feature from the project sample data and the server resource sample data; The target sample data is used to train an initial server resource processing model to obtain the server resource processing model.

7. The method according to claim 6, characterized in that The step of determining target input features of the server resource processing model according to the project sample data and the server resource sample data includes: The target input features of the server resource processing model are determined based on the project sample data and the server resource sample data by using a Pearson correlation coefficient method, a logistic regression method or a random forest method.

8. A server resource processing device, characterized in that: The device comprises: A first acquisition module, configured to acquire target project data and server resource request data related to the target project in response to receiving a server resource request related to the target project; A first determination module, configured to determine, from a project database, historical project data and historical server resource usage data related to the target project according to the target project data; The second determination module is used to determine the processing result of the server resource request using a server resource processing model based on the historical project data, the historical server resource usage data, the target project data and the server resource request data, wherein the server resource processing model is trained using project sample data and server resource sample data.

9. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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