Multi-dimensional classification method and device for computing power resources

Abstract

The invention relates to the technical field of data analysis, and discloses a multi-dimensional classification method and device for computing power resources, and the method comprises the steps: obtaining the distribution information of all computing power resources within a target range; analyzing multi-dimensional information of each computing power resource according to the distribution information; according to all the multi-dimensional information, a multi-dimensional feature value of each computing power resource is calculated, and the multi-dimensional feature value is used for representing the comprehensive performance condition of the multi-dimensional feature of the computing power resource; and allocating each computing power resource to at least one classification set according to all the multi-dimensional feature values. Therefore, by implementing the method, the classification accuracy of the computing power resources can be improved from the perspective of multi-dimensional feature comprehensive analysis of the computing power resources in the level of a computing power resource management platform and before the computing power resources are applied, the coordination matching accuracy of the computing power tasks and the computing power resources can be improved, and the computing power resources can be more accurately classified. The application scientificity and application sustainability of the computing power resources are improved, and the application efficiency of the regional computing power resources is improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a multi-dimensional classification method and device for computing resources. Background Art

[0002] With the rapid development of information technology, especially the rise of emerging technologies such as big data, the Internet of Things, artificial intelligence, and blockchain, computing resources have become one of the core driving forces for the data economy and social progress.

[0003] At present, the application of computing resources is more based on the idle state of computing resources to handle the allocated data processing tasks. However, practice has found that for a certain data processing task, if it is directly handed over to any idle computing resource in the corresponding space for processing, there will be a waste of computing resources. On this basis, it is easy to further cause regional computing resource shortages, data processing task disconnections and other chain reactions, which is not conducive to the scientific application of computing resources.

[0004] It can be seen that it is particularly important to improve the classification accuracy of computing resources and make scientific application of computing resources. Summary of the invention

[0005] The present invention provides a multi-dimensional method and device for computing power resources, which can improve the classification accuracy of computing power resources and enable scientific use of computing power resources.

[0006] In order to solve the above technical problems, the first aspect of the present invention discloses a multi-dimensional classification method for computing power resources, the method comprising:

[0007] Obtain the distribution information of all computing resources within the target range;

[0008] Analyze the multi-dimensional information of each computing resource according to the distribution information;

[0009] Calculate the multi-dimensional feature value of each computing resource based on all the multi-dimensional information, where the multi-dimensional feature value is used to represent the comprehensive performance of the multi-dimensional features of the computing resource;

[0010] According to all the multi-dimensional feature values, each of the computing resources is allocated to at least one classification set.

[0011] As an optional implementation manner, in the first aspect of the present invention, analyzing the multi-dimensional information of each computing resource according to the distribution information includes:

[0012] For each of the computing power resources, according to the distribution information, analyzing the geographical location information and the network environment information to which the computing power resource belongs; according to the geographical location information and the network environment information to which it belongs, analyzing the computing power usage information of the computing power resource, the computing power usage information including at least one of computing power usage task information, computing power usage scenario information, computing power usage rate information, computing power usage delay information, computing power usage time period information, computing power usage energy consumption information, computing power usage degree information, and computing power usage object information; according to the computing power usage information, analyzing the computing power usage depreciation degree information of the computing power resource; according to the computing power usage information and the computing power usage depreciation degree information, analyzing the idle information of the computing power resource;

[0013] The multi-dimensional information of each computing power resource is analyzed based on the geographic location information, the network environment information, the computing power usage information, the computing power usage depreciation information and the idle information.

[0014] As an optional implementation manner, in the first aspect of the present invention, the multi-dimensional information includes at least two dimensional information of computing power dimensional information, computing power consumption dimensional information, application cost dimensional information, security dimensional information, usage efficiency dimensional information, and usage configuration dimensional information, and the application cost dimensional information matches the computing power task of the computing power resource, and the multi-dimensional characteristic value of each computing power resource is calculated according to all the multi-dimensional information, including:

[0015] For each type of the dimensional information, a first weight of the dimensional information is calculated according to the dimensional information of all the computing power resources; and a dimensional information performance value of the dimensional information of each computing power resource is calculated according to the first weight and the dimensional information of each computing power resource;

[0016] For each of the computing power resources, a multi-dimensional characteristic value of the computing power resource is calculated based on all the dimensional information representation values ​​of the computing power resource.

[0017] As an optional implementation manner, in the first aspect of the present invention, for each of the computing power resources, the calculating of the multi-dimensional feature value of the computing power resource according to all the dimensional information representation values ​​of the computing power resource includes:

[0018] Analyze, according to the computing power usage information of the computing power resource, a first priority value of each type of the dimensional information of the computing power resource, where the first priority value is used to indicate the degree of attention paid to each type of the dimensional information in the historical application scenario corresponding to the computing power resource;

[0019] According to the computing power usage information of all the computing power resources, a second priority value of each type of the dimensional information under all the computing power resources is analyzed, where the second priority value is used to indicate the degree of attention paid to each type of the dimensional information under the historical application scenarios corresponding to all the computing power resources;

[0020] For each type of dimension information, calculating a second weight of the type of dimension information according to the first priority value and the second priority value;

[0021] The multi-dimensional feature value of the computing power resource is calculated based on the second weight and the dimensional information performance value of all the dimensional information of the computing power resource.

[0022] As an optional implementation, in the first aspect of the present invention, allocating each computing resource to at least one classification set according to all the multi-dimensional feature values ​​includes:

[0023] For each of the computing power resources, analyzing the adaptation scenario information of the computing power resource according to the multi-dimensional feature value of the computing power resource, where the adaptation scenario corresponding to the adaptation scenario information includes a first target application scenario within the target range of the computing power resource and / or a second target application scenario outside the target range;

[0024] According to all the adaptation scenario information, each computing resource is allocated to at least one classification set, and the classification set corresponds to the adaptation scenario information.

[0025] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0026] For each of the classification sets, according to the multi-dimensional feature values ​​of each of the computing power resources in the classification set, the computing power configuration component of each of the computing power resources is matched, and the computing power configuration component is used to enable the computing power resources; according to all the computing power configuration components and all the multi-dimensional feature values, it is judged whether the classification set meets the joint computing power composition condition, and the joint computing power composition condition is used to indicate that there are at least two computing power resources in the classification set, and there are at least two computing power resources that meet the configuration condition for jointly processing computing power tasks, and the configuration condition includes that the computing power configuration components of at least two computing power resources are matched;

[0027] When it is determined that the classification set meets the joint computing power composition condition, at least one joint computing power group of the classification set is configured, and the joint computing power group is used to jointly process computing power tasks that match the joint computing power group.

[0028] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0029] When it is determined that the classification set does not meet the joint computing power composition conditions, for each of the computing power resources in the classification set, determine at least one target computing power resource that matches the computing power configuration component of the computing power resource in all the classification sets; based on the computing power resource and all the target computing power resources, construct at least one backup joint computing power link, and the computing power resources in the backup joint computing power link jointly process the computing power tasks that match the backup joint computing power link.

[0030] A second aspect of the present invention discloses a multi-dimensional classification device for computing resources, the device comprising:

[0031] The acquisition module is used to obtain the distribution information of all computing resources within the target range;

[0032] An analysis module, used to analyze the multi-dimensional information of each computing resource according to the distribution information;

[0033] A calculation module, used to calculate a multi-dimensional feature value of each of the computing power resources according to all the multi-dimensional information, wherein the multi-dimensional feature value is used to represent the comprehensive performance of the multi-dimensional features of the computing power resources;

[0034] A classification module is used to allocate each of the computing resources to at least one classification set according to all the multi-dimensional feature values.

[0035] As an optional implementation, in the second aspect of the present invention, the specific manner in which the analysis module analyzes the multi-dimensional information of each computing power resource according to the distribution information includes:

[0036] For each of the computing power resources, according to the distribution information, analyzing the geographical location information and the network environment information to which the computing power resource belongs; according to the geographical location information and the network environment information to which it belongs, analyzing the computing power usage information of the computing power resource, the computing power usage information including at least one of computing power usage task information, computing power usage scenario information, computing power usage rate information, computing power usage delay information, computing power usage time period information, computing power usage energy consumption information, computing power usage degree information, and computing power usage object information; according to the computing power usage information, analyzing the computing power usage depreciation degree information of the computing power resource; according to the computing power usage information and the computing power usage depreciation degree information, analyzing the idle information of the computing power resource;

[0037] The multi-dimensional information of each computing power resource is analyzed based on the geographic location information, the network environment information, the computing power usage information, the computing power usage depreciation information and the idle information.

[0038] As an optional implementation manner, in the second aspect of the present invention, the multi-dimensional information includes at least two dimensional information of computing power dimensional information, computing power consumption dimensional information, application cost dimensional information, security dimensional information, usage efficiency dimensional information, and usage configuration dimensional information, and the application cost dimensional information matches the computing power task of the computing power resource. The specific manner in which the computing module calculates the multi-dimensional characteristic value of each of the computing power resources according to all the multi-dimensional information includes:

[0039] For each type of the dimensional information, a first weight of the dimensional information is calculated according to the dimensional information of all the computing power resources; and a dimensional information performance value of the dimensional information of each computing power resource is calculated according to the first weight and the dimensional information of each computing power resource;

[0040] For each of the computing power resources, a multi-dimensional characteristic value of the computing power resource is calculated based on all the dimensional information representation values ​​of the computing power resource.

[0041] As an optional implementation, in the second aspect of the present invention, for each of the computing power resources, the specific manner in which the computing module calculates the multi-dimensional feature value of the computing power resource according to all the dimensional information representation values ​​of the computing power resource includes:

[0042] Analyze, according to the computing power usage information of the computing power resource, a first priority value of each type of the dimensional information of the computing power resource, where the first priority value is used to indicate the degree of attention paid to each type of the dimensional information in the historical application scenario corresponding to the computing power resource;

[0043] According to the computing power usage information of all the computing power resources, a second priority value of each type of the dimensional information under all the computing power resources is analyzed, where the second priority value is used to indicate the degree of attention paid to each type of the dimensional information under the historical application scenarios corresponding to all the computing power resources;

[0044] For each type of dimension information, calculating a second weight of the type of dimension information according to the first priority value and the second priority value;

[0045] The multi-dimensional feature value of the computing power resource is calculated based on the second weight and the dimensional information performance value of all the dimensional information of the computing power resource.

[0046] As an optional implementation, in the second aspect of the present invention, the classification module allocates each computing resource to at least one classification set according to all the multi-dimensional feature values ​​in a specific manner including:

[0047] For each of the computing power resources, analyzing the adaptation scenario information of the computing power resource according to the multi-dimensional feature value of the computing power resource, where the adaptation scenario corresponding to the adaptation scenario information includes a first target application scenario within the target range of the computing power resource and / or a second target application scenario outside the target range;

[0048] According to all the adaptation scenario information, each computing resource is allocated to at least one classification set, and the classification set corresponds to the adaptation scenario information.

[0049] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0050] A matching module, configured to match a computing power configuration component of each computing power resource according to the multi-dimensional feature value of each computing power resource in the classification set for each classification set, wherein the computing power configuration component is used to enable the computing power resource;

[0051] A judgment module, used to judge whether the classification set meets the joint computing power composition condition according to all the computing power configuration components and all the multi-dimensional feature values, wherein the joint computing power composition condition is used to indicate that there are at least two computing power resources in the classification set, and there are at least two computing power resources that meet the configuration condition for jointly processing computing power tasks, wherein the configuration condition includes that the computing power configuration components of at least two computing power resources match;

[0052] A configuration module is used to configure at least one joint computing power group of the classification set when the judgment module determines that the classification set meets the joint computing power composition condition, and the joint computing power group is used to jointly process computing power tasks matching the joint computing power group.

[0053] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0054] A determination module, configured to determine, for each computing power resource in the classification set, at least one target computing power resource that matches the computing power configuration component of the computing power resource in all the classification sets when the determination module determines that the classification set does not satisfy the joint computing power composition condition;

[0055] A construction module is used to construct at least one backup joint computing power link based on the computing power resource and all the target computing power resources, and the computing power resources in the backup joint computing power link jointly process the computing power tasks matching the backup joint computing power link.

[0056] A third aspect of the present invention discloses another multi-dimensional classification device for computing resources, the device comprising:

[0057] A memory storing executable program code;

[0058] a processor coupled to the memory;

[0059] The processor calls the executable program code stored in the memory to execute the multi-dimensional classification method of computing power resources disclosed in the first aspect of the present invention.

[0060] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the multi-dimensional classification method of computing power resources disclosed in the first aspect of the present invention.

[0061] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0062] In an embodiment of the present invention, the distribution information of all computing resources within the target range is obtained; the multi-dimensional information of each computing resource is analyzed according to the distribution information; the multi-dimensional characteristic value of each computing resource is calculated according to all the multi-dimensional information, and the multi-dimensional characteristic value is used to represent the comprehensive performance of the multi-dimensional characteristics of the computing resource; according to all the multi-dimensional characteristic values, each computing resource is allocated to at least one classification set. It can be seen that the implementation of the present invention can analyze the multi-dimensional information of each computing resource according to the distribution information of all computing resources within the target range obtained, and calculate the multi-dimensional characteristic value used to represent the comprehensive performance of the multi-dimensional characteristics of each computing resource according to all the multi-dimensional information, and then from the comprehensive situation of the multi-dimensional characteristics of each computing resource, each computing resource is allocated to at least one classification set, so that the classification accuracy of computing resources can be improved from the perspective of comprehensive analysis of the multi-dimensional characteristics of computing resources at the computing resource management platform level and before the computing resource application, which is conducive to improving the coordination and matching accuracy of computing tasks and computing resources, improving the application scientificity and application sustainability of computing resources, and improving the application efficiency of regional computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0064] Figure 1 It is a flowchart of a multi-dimensional classification method for computing power resources disclosed in an embodiment of the present invention;

[0065] Figure 2It is a flowchart of another multi-dimensional classification method of computing power resources disclosed in an embodiment of the present invention;

[0066] Figure 3 It is a structural schematic diagram of a multi-dimensional classification device for computing power resources disclosed in an embodiment of the present invention;

[0067] Figure 4 It is a structural schematic diagram of another multi-dimensional classification device for computing power resources disclosed in an embodiment of the present invention;

[0068] Figure 5 It is a structural diagram of another multi-dimensional classification device for computing power resources disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0071] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0072] The present invention discloses a multi-dimensional classification method and device for computing power resources, which can analyze the multi-dimensional information of each computing power resource according to the distribution information of all computing power resources within the target range obtained, and calculate the multi-dimensional characteristic value used to represent the comprehensive performance of the multi-dimensional characteristics of each computing power resource according to all the multi-dimensional information, and then allocate each computing power resource to at least one classification set from the comprehensive performance of the multi-dimensional characteristics of each computing power resource, so as to improve the classification accuracy of computing power resources from the perspective of comprehensive analysis of the multi-dimensional characteristics of computing power resources at the computing power resource management platform level and before the computing power resources are applied, which is conducive to improving the coordination and matching accuracy of computing power tasks and computing power resources, improving the application scientificity and application sustainability of computing power resources, and improving the application efficiency of regional computing power resources. The following are detailed descriptions.

[0073] Embodiment 1

[0074] See also Figure 1 , Figure 1 : is a flowchart of a multi-dimensional classification method for computing resources disclosed in an embodiment of the present invention. Figure 1 The described multi-dimensional classification method of computing power resources can be applied to computing power resource management and / or allocation devices, and can also be applied to intelligent devices related to computing power resource management and / or allocation devices, which include but are not limited to one or more of cloud devices, edge computing devices, relay devices, base station devices, city management devices, and intelligent networked devices, and the embodiments of the present invention are not limited thereto.

[0075] like Figure 1 As shown, the multi-dimensional classification method of computing power resources may include the following operations:

[0076] 101. Obtain the distribution information of all computing resources within the target range;

[0077] In an optional embodiment, the above distribution information may be obtained based on communication distribution information of communication devices within the acquired target range. Optionally, the above communication devices include portable / wearable devices with communication functions.

[0078] 102. Analyze the multi-dimensional information of each computing resource based on the distribution information;

[0079] In the embodiment of the present invention, as an optional implementation mode, the above-mentioned analysis of the multi-dimensional information of each computing resource according to the distribution information may include the following operations:

[0080] For each computing power resource, based on the distribution information, analyze the geographical location information and the network environment information of the computing power resource; based on the geographical location information and the network environment information, analyze the computing power usage information of the computing power resource, the computing power usage information including at least one of computing power usage task information, computing power usage scenario information, computing power usage rate information, computing power usage delay information, computing power usage time period information, computing power usage energy consumption information, computing power usage degree information, and computing power usage object information; based on the computing power usage information, analyze the computing power usage depreciation information of the computing power resource; based on the computing power usage information and the computing power usage depreciation information, analyze the idle information of the computing power resource;

[0081] Analyze the multi-dimensional information of each computing resource based on geographic location information, network environment information, computing power usage information, computing power depreciation information and idle information.

[0082] In this optional embodiment, optionally, the above-mentioned analysis of the geographical location information and the network environment information of each computing power resource based on the distribution information can be specifically based on the above-mentioned distribution information, analyzing the correlation between the various computing power resources, thereby analyzing the geographical location information and the network environment information of each computing power resource one by one.

[0083] It can be seen that the implementation of this optional embodiment can analyze the geographic location information and the network environment information of each computing power resource according to the distribution information of all computing power resources within the target range obtained, and then analyze the computing power usage information of each computing power resource including at least one of the computing power usage task information, computing power usage scenario information, computing power usage rate information, computing power usage delay information, computing power usage time period information, computing power usage energy consumption information, computing power usage degree information, and computing power usage object information. By further analyzing the computing power usage stop loss information of each computing power resource, it is possible to combine the above-mentioned computing power usage information to analyze the idle information that is beneficial to the actual healthy and sustainable operation of each computing power resource. Finally, the above-mentioned geographic location information, network environment information, computing power usage information, computing power usage depreciation degree information and idle information are combined to analyze the multi-dimensional information of each computing power resource, which can improve the analysis accuracy, analysis dimension richness and analysis method richness of the multi-dimensional information of the computing power resources, can improve the scientificity and feasibility of the analysis of the multi-dimensional information of the computing power resources, is conducive to improving the classification accuracy of the computing power resources, and is conducive to improving the application sustainability and health of the computing power resources.

[0084] 103. Calculate the multi-dimensional feature value of each computing resource based on all multi-dimensional information. The multi-dimensional feature value is used to represent the comprehensive performance of the multi-dimensional features of the computing resource.

[0085] In an embodiment of the present invention, as another optional implementation manner, the multi-dimensional information includes at least two dimensional information of computing power dimensional information, computing power consumption dimensional information, application cost dimensional information, security dimensional information, usage efficiency dimensional information, and usage configuration dimensional information, and the application cost dimensional information matches the computing power task of the computing power resource. The multi-dimensional characteristic value of each computing power resource is calculated based on all the multi-dimensional information, and the following operations may be included:

[0086] For each type of dimensional information, a first weight of the dimensional information is calculated based on the dimensional information of all computing resources; based on the first weight and the dimensional information of each computing resource, a dimensional information performance value of the dimensional information of each computing resource is calculated;

[0087] For each computing power resource, the multi-dimensional feature value of the computing power resource is calculated based on the representation value of all dimensional information of the computing power resource.

[0088] In this optional embodiment, it should be noted that the application cost dimension information matches the specific computing power task. In the computing power resource classification stage, only the application cost reference under some computing power task examples is provided, which is not related to the actual computing power task situation. In the actual application process, it is necessary to recalculate the application cost in combination with the actual computing power task. This is irrelevant to the computing power resource classification provided in the embodiment of the present invention and belongs to the field of computing power resource adaptation. Optionally, the above-mentioned example can be a historical computing power task of the computing power resource. Optionally, the above-mentioned application cost dimension information can include cost information under several sub-dimensions such as economic cost, time cost, coordination cost, and security cost. It should be noted that the time cost is different from the usage efficiency dimension information. The usage efficiency dimension information focuses more on explaining the above-mentioned example. The time consumed in using the computing power resources to complete the effective work of the corresponding computing power task in the example, thus reflecting the utilization efficiency, and the time cost will include other time-consuming factors such as distance transmission time, coordination time, and adaptation time on this basis. Furthermore, the security cost is different from the security dimension information. The security dimension information is used to indicate the security status of the computing power resources themselves, such as: anti-attack configuration, etc. The security cost is used to indicate the security protection cost of using the computing power resources to complete the corresponding computing power task in the above example. On this basis, in order to improve the calculation accuracy of the security cost, reference should also be made to the degree of security protection required by the corresponding computing power task. However, this is not related to the computing power resource classification provided in the embodiment of the present invention. The above-mentioned application cost dimension information only provides reference information in an example for use in classification.

[0089] It should be noted that the dimensional information expression value is an intermediate value in the process of calculating the multi-dimensional eigenvalue, and its representation meaning also corresponds to the multi-dimensional eigenvalue, but it only represents the performance of the information under the dimension at the dimensional level.

[0090] It can be seen that the implementation of this optional embodiment can be for the multi-dimensional information of at least two dimensions of the analyzed computing power resources, including computing capacity dimension information, computing power consumption dimension information, application cost dimension information, security dimension information, usage efficiency dimension information, and usage configuration dimension information. For each dimension information, according to the specific content of the dimension information of all computing power resources, the first weight of the dimension information is adaptively calculated, so as to calculate the dimension information performance value of each computing power resource under the dimension information based on the first weight and the dimension information, and then for each computing power resource, according to all the dimension information performance values ​​of the computing power resource, the multi-dimensional characteristic value of the computing power resource is calculated, so as to improve the analysis accuracy of the comprehensive performance of the multi-dimensional characteristics of the computing power resources, that is, the calculation accuracy of the multi-dimensional characteristic values, and improve the calculation scientificity of the multi-dimensional characteristic values ​​of each computing power resource, which is beneficial to improve the classification accuracy of the computing power resources from the perspective of the comprehensive situation of the multi-dimensional characteristics of each computing power resource, and is beneficial to improve the coordination and matching accuracy of computing power tasks and computing power resources, and improve the application scientificity, sustainability and health of computing power resources.

[0091] In this optional embodiment, as an optional implementation manner, the above-mentioned calculation of the multi-dimensional feature value of each computing power resource according to all dimensional information representation values ​​of the computing power resource may include the following operations:

[0092] According to the computing power usage information of the computing power resource, a first priority value of each dimension information of the computing power resource is analyzed, where the first priority value is used to indicate the degree of attention paid to each dimension information in the historical application scenario corresponding to the computing power resource;

[0093] According to the computing power usage information of all computing power resources, the second priority value of each dimension information under all computing power resources is analyzed. The second priority value is used to indicate the degree of attention paid to each dimension information under the historical application scenarios corresponding to all computing power resources;

[0094] For each type of dimensional information, calculate a second weight of the dimensional information according to the first priority weight and the second priority weight;

[0095] The multi-dimensional feature value of the computing power resource is calculated based on the second weights of all dimensional information of the computing power resource and the dimensional information representation value.

[0096] It can be seen that the implementation of this optional embodiment can, after calculating the dimensional information performance value of each computing power resource under each dimensional information, for each computing power resource, based on the computing power usage information of the computing power resource including at least one of the computing power usage task information, computing power usage scenario information, computing power usage rate information, computing power usage delay information, computing power usage period information, computing power usage energy consumption information, computing power usage degree information, and computing power usage object information, analyze the first priority value used to represent the degree of attention of each dimensional information in the historical application scenario corresponding to the computing power resource; at the same time, based on the above-mentioned computing power usage information of all computing power resources, analyze the second priority value used to represent the degree of attention of each dimensional information in the historical application scenario corresponding to all computing power resources, and then from the dimensional information of each computing power resource At the level, according to the first priority value of the dimensional information of the computing power resource and the second priority value of the dimensional information of the computing power resource, the second weight of the dimensional information of the computing power resource is calculated, and then according to the second weight of all dimensional information of the computing power resource and the dimensional information performance value, the multi-dimensional characteristic value of the computing power resource is calculated, which can further improve the analysis accuracy of the comprehensive performance of the multi-dimensional characteristics of the computing power resources, that is, the calculation accuracy of the multi-dimensional characteristic values, and further improve the calculation scientificity of the multi-dimensional characteristic values ​​of each computing power resource, which is beneficial to further improve the classification accuracy of computing power resources from the perspective of the comprehensive situation of the multi-dimensional characteristics of each computing power resource, and is beneficial to improve the coordination and matching accuracy of computing power tasks and computing power resources, and improve the application scientificity, sustainability and health of computing power resources.

[0097] 104. Allocate each computing resource to at least one classification set according to all multi-dimensional feature values.

[0098] It can be seen that the implementation of the embodiment of the present invention can analyze the multi-dimensional information of each computing power resource according to the distribution information of all computing power resources within the target range obtained, and calculate the multi-dimensional feature value used to represent the comprehensive performance of the multi-dimensional characteristics of each computing power resource according to all the multi-dimensional information, and then allocate each computing power resource to at least one classification set based on the comprehensive performance of the multi-dimensional characteristics of each computing power resource. This can improve the classification accuracy of computing power resources from the perspective of comprehensive analysis of the multi-dimensional characteristics of computing power resources at the computing power resource management platform level and before the computing power resources are applied, which is conducive to improving the accuracy of the coordinated matching of computing power tasks and computing power resources, improving the scientificity and sustainability of the application of computing power resources, and improving the application efficiency of regional computing power resources.

[0099] Embodiment 2

[0100] See also Figure 2 , Figure 2 1 is a flow chart of another multi-dimensional classification method for computing resources disclosed in an embodiment of the present invention. Figure 2 The multi-dimensional classification method of computing power resources described can be applied to computing power resource management and / or allocation devices, and can also be applied to intelligent devices related to computing power resource management and / or allocation devices, such intelligent devices including but not limited to cloud devices, edge computing devices, relay devices, base station devices, city management devices, and intelligent network devices, which are not limited in the embodiments of the present invention. Figure 2 As shown, the multi-dimensional classification method of computing power resources may include the following operations:

[0101] 201. Obtain the distribution information of all computing resources within the target range;

[0102] 202. Analyze the multi-dimensional information of each computing resource based on the distribution information;

[0103] 203. Calculate the multi-dimensional feature value of each computing resource based on all multi-dimensional information, where the multi-dimensional feature value is used to represent the comprehensive performance of the multi-dimensional features of the computing resource;

[0104] In the embodiment of the present invention, for the supplementary explanation of step 201 to step 203, please refer to the supplementary explanation of step 101 to step 103 in the first embodiment, and the embodiment of the present invention will not elaborate on this.

[0105] 204. For each computing power resource, analyze the adaptation scenario information of the computing power resource according to the multi-dimensional feature value of the computing power resource, where the adaptation scenario corresponding to the adaptation scenario information includes a first target application scenario within a target range of the adaptive computing power resource and / or a second target application scenario outside the target range;

[0106] 205. According to all the adaptation scenario information, each computing resource is allocated to at least one classification set, and the classification set corresponds to the adaptation scenario information.

[0107] In an embodiment of the present invention, optionally, after obtaining the adaptation scenario information of each computing power resource mentioned above, from the perspective of the platform, the configuration information of each computing power resource corresponding to the adaptation scenario can be further supplemented to improve the application convenience and efficiency of the adaptation scenario of the computing power resource.

[0108] It can be seen that the implementation of the embodiment of the present invention can, after calculating the multi-dimensional feature value used to represent the comprehensive performance of the multi-dimensional features of the computing power resources, analyze the adaptation scenario information of each computing power resource, including the first target application scenario within the target range of the adaptive computing power resource and / or the second target application scenario outside the target range, according to the multi-dimensional feature value of each computing power resource, thereby jumping out of the target range, improving the comprehensiveness of the application scenario analysis of the computing power resources and improving the accuracy of the cross-regional application scenario analysis of the computing power resources, and then according to all the adaptation scenario information, each computing power resource is allocated to at least one classification set, and the classification set corresponds to the adaptation scenario information, further improving the scientificity of the classification of the computing power resources, the accuracy of the analysis and the comprehensiveness of the classification perspective, which is conducive to the realization of the cross-regional joint application of computing power resources, and further conducive to improving the accuracy of the cross-regional coordinated matching of computing power tasks and computing power resources, improving the application scientificity, application scenario richness and application sustainability of computing power resources, and improving the application efficiency of regional computing power resources in a larger range, which is conducive to making the best use of resources.

[0109] In the embodiment of the present invention, as an optional implementation manner, the method may further include the following operations:

[0110] For each classification set, according to the multi-dimensional feature value of each computing power resource in the classification set, the computing power configuration component of each computing power resource is matched, and the computing power configuration component is used to enable the computing power resource; according to all computing power configuration components and all multi-dimensional feature values, it is judged whether the classification set meets the joint computing power composition condition, and the joint computing power composition condition is used to indicate that there are at least two computing power resources in the classification set, and there are at least two computing power resources that meet the configuration condition for jointly processing computing power tasks, and the configuration condition includes that the computing power configuration components of at least two computing power resources are matched;

[0111] When it is determined that the classification set meets the joint computing power composition conditions, at least one joint computing power group of the classification set is configured, and the joint computing power group is used to jointly process computing power tasks that match the joint computing power group.

[0112] It can be seen that the implementation of this optional embodiment can, after allocating each computing power resource to at least one classification set according to all adaptation scenario information, for each classification set, match the computing power configuration component used to enable the computing power resource according to the multi-dimensional feature value of each computing power resource in the classification set, and then determine whether the classification set satisfies the preset joint computing power composition condition for indicating that there are at least two computing power resources in the classification set and that the computing power configuration components of at least two computing power resources match, based on all computing power configuration components and all multi-dimensional feature values. When it is determined that the classification set satisfies the joint computing power composition condition, at least one joint computing power group of the classification set is configured, and the joint computing power group is used to jointly process computing power tasks that match the joint computing power group. On the basis of improving the accuracy and scientificity of computing power resource classification, it can also further improve the richness of application scenarios of computing power resources and the application flexibility of computing power resources by configuring joint computing power groups, which is conducive to further improving the application scientificity, richness of application scenarios and application sustainability of computing power resources, improving the application efficiency of regional computing power resources in a larger range, and making the best use of resources.

[0113] In this optional embodiment, as another optional implementation, the method may further include the following operations:

[0114] When it is determined that the classification set does not meet the joint computing power composition conditions, for each computing power resource in the classification set, at least one target computing power resource that matches the computing power configuration component of the computing power resource is determined in all classification sets; based on the computing power resource and all target computing power resources, at least one backup joint computing power link is constructed, and the computing power resources in the backup joint computing power link jointly process the computing power tasks that match the backup joint computing power link.

[0115] It can be seen that the implementation of this optional embodiment can, after determining that the classification set does not meet the above-mentioned joint computing power composition conditions, also determine at least one target computing power resource that matches the computing power configuration component of at least one computing power resource in the classification set in all classification sets, and build at least one backup joint computing power link based on the computing power resource and all target computing power resources. The computing power resources in the backup joint computing power link jointly process the computing power tasks that match the backup joint computing power link, thereby further improving the cross-scenario application richness of the joint computing power application, which is conducive to further improving the application scenario richness of computing power resources and improving the application flexibility of computing power resources, and is conducive to further improving the application scientificity, application scenario richness and application sustainability of computing power resources, improving the application efficiency of regional computing power resources in a larger range, and is conducive to making the best use of resources.

[0116] Embodiment 3

[0117] See also Figure 3, Figure 3 It is a structural diagram of a multi-dimensional classification device for computing power resources disclosed in an embodiment of the present invention. The multi-dimensional classification device for computing power resources can be applied to computing power resource management and / or allocation equipment, and can also be applied to intelligent devices related to computing power resource management and / or allocation equipment, and the intelligent devices include but are not limited to one or more of cloud devices, edge computing devices, relay devices, base station devices, city management devices, and intelligent network devices, which are not limited in the embodiments of the present invention. Figure 3 As shown, the multi-dimensional classification device of computing power resources may include:

[0118] The acquisition module 301 is used to obtain the distribution information of all computing resources within the target range;

[0119] An analysis module 302 is used to analyze multi-dimensional information of each computing resource according to the distribution information;

[0120] A calculation module 303 is used to calculate the multi-dimensional feature value of each computing resource based on all the multi-dimensional information, and the multi-dimensional feature value is used to represent the comprehensive performance of the multi-dimensional features of the computing resource;

[0121] The classification module 304 is used to allocate each computing resource to at least one classification set according to all multi-dimensional feature values.

[0122] It can be seen that the implementation of the embodiment of the present invention can analyze the multi-dimensional information of each computing power resource according to the distribution information of all computing power resources within the target range obtained, and calculate the multi-dimensional feature value used to represent the comprehensive performance of the multi-dimensional characteristics of each computing power resource according to all the multi-dimensional information, and then allocate each computing power resource to at least one classification set based on the comprehensive performance of the multi-dimensional characteristics of each computing power resource. This can improve the classification accuracy of computing power resources from the perspective of comprehensive analysis of the multi-dimensional characteristics of computing power resources at the computing power resource management platform level and before the computing power resources are applied, which is conducive to improving the accuracy of the coordinated matching of computing power tasks and computing power resources, improving the scientificity and sustainability of the application of computing power resources, and improving the application efficiency of regional computing power resources.

[0123] In the embodiment of the present invention, as an optional implementation, the specific manner in which the above-mentioned analysis module 302 analyzes the multi-dimensional information of each computing power resource according to the distribution information includes:

[0124] For each computing power resource, based on the distribution information, analyze the geographical location information and the network environment information of the computing power resource; based on the geographical location information and the network environment information, analyze the computing power usage information of the computing power resource, the computing power usage information including at least one of computing power usage task information, computing power usage scenario information, computing power usage rate information, computing power usage delay information, computing power usage time period information, computing power usage energy consumption information, computing power usage degree information, and computing power usage object information; based on the computing power usage information, analyze the computing power usage depreciation information of the computing power resource; based on the computing power usage information and the computing power usage depreciation information, analyze the idle information of the computing power resource;

[0125] Analyze the multi-dimensional information of each computing resource based on geographic location information, network environment information, computing power usage information, computing power depreciation information and idle information.

[0126] It can be seen that the implementation of this optional embodiment can analyze the geographic location information and the network environment information of each computing power resource according to the distribution information of all computing power resources within the target range obtained, and then analyze the computing power usage information of each computing power resource including at least one of the computing power usage task information, computing power usage scenario information, computing power usage rate information, computing power usage delay information, computing power usage time period information, computing power usage energy consumption information, computing power usage degree information, and computing power usage object information. By further analyzing the computing power usage stop loss information of each computing power resource, it is possible to combine the above-mentioned computing power usage information to analyze the idle information that is beneficial to the actual healthy and sustainable operation of each computing power resource. Finally, the above-mentioned geographic location information, network environment information, computing power usage information, computing power usage depreciation degree information and idle information are combined to analyze the multi-dimensional information of each computing power resource, which can improve the analysis accuracy, analysis dimension richness and analysis method richness of the multi-dimensional information of the computing power resources, can improve the scientificity and feasibility of the analysis of the multi-dimensional information of the computing power resources, is conducive to improving the classification accuracy of the computing power resources, and is conducive to improving the application sustainability and health of the computing power resources.

[0127] In the embodiment of the present invention, as another optional implementation manner, the multi-dimensional information includes at least two dimensional information of computing power dimensional information, computing power consumption dimensional information, application cost dimensional information, security dimensional information, usage efficiency dimensional information, and usage configuration dimensional information, and the application cost dimensional information matches the computing power task of the computing power resource. The specific manner in which the above-mentioned calculation module 303 calculates the multi-dimensional characteristic value of each computing power resource according to all the multi-dimensional information includes:

[0128] For each type of dimensional information, a first weight of the dimensional information is calculated based on the dimensional information of all computing resources; based on the first weight and the dimensional information of each computing resource, a dimensional information performance value of the dimensional information of each computing resource is calculated;

[0129] For each computing power resource, the multi-dimensional feature value of the computing power resource is calculated based on the representation value of all dimensional information of the computing power resource.

[0130] It can be seen that the implementation of this optional embodiment can be for the multi-dimensional information of at least two dimensions of the analyzed computing power resources, including computing capacity dimension information, computing power consumption dimension information, application cost dimension information, security dimension information, usage efficiency dimension information, and usage configuration dimension information. For each dimension information, according to the specific content of the dimension information of all computing power resources, the first weight of the dimension information is adaptively calculated, so as to calculate the dimension information performance value of each computing power resource under the dimension information based on the first weight and the dimension information, and then for each computing power resource, according to all the dimension information performance values ​​of the computing power resource, the multi-dimensional characteristic value of the computing power resource is calculated, so as to improve the analysis accuracy of the comprehensive performance of the multi-dimensional characteristics of the computing power resources, that is, the calculation accuracy of the multi-dimensional characteristic values, and improve the calculation scientificity of the multi-dimensional characteristic values ​​of each computing power resource, which is beneficial to improve the classification accuracy of the computing power resources from the perspective of the comprehensive situation of the multi-dimensional characteristics of each computing power resource, and is beneficial to improve the coordination and matching accuracy of computing power tasks and computing power resources, and improve the application scientificity, sustainability and health of computing power resources.

[0131] In this optional embodiment, as an optional implementation, for each computing resource, the calculation module 303 calculates the multi-dimensional feature value of the computing resource according to all dimensional information representation values ​​of the computing resource in a specific manner including:

[0132] According to the computing power usage information of the computing power resource, a first priority value of each dimension information of the computing power resource is analyzed, where the first priority value is used to indicate the degree of attention paid to each dimension information in the historical application scenario corresponding to the computing power resource;

[0133] According to the computing power usage information of all computing power resources, the second priority value of each dimension information under all computing power resources is analyzed. The second priority value is used to indicate the degree of attention paid to each dimension information under the historical application scenarios corresponding to all computing power resources;

[0134] For each type of dimensional information, calculate a second weight of the dimensional information according to the first priority weight and the second priority weight;

[0135] The multi-dimensional feature value of the computing power resource is calculated based on the second weights of all dimensional information of the computing power resource and the dimensional information representation value.

[0136] It can be seen that the implementation of this optional embodiment can, after calculating the dimensional information performance value of each computing power resource under each dimensional information, for each computing power resource, based on the computing power usage information of the computing power resource including at least one of the computing power usage task information, computing power usage scenario information, computing power usage rate information, computing power usage delay information, computing power usage period information, computing power usage energy consumption information, computing power usage degree information, and computing power usage object information, analyze the first priority value used to represent the degree of attention of each dimensional information in the historical application scenario corresponding to the computing power resource; at the same time, based on the above-mentioned computing power usage information of all computing power resources, analyze the second priority value used to represent the degree of attention of each dimensional information in the historical application scenario corresponding to all computing power resources, and then from the dimensional information of each computing power resource At the level, according to the first priority value of the dimensional information of the computing power resource and the second priority value of the dimensional information of the computing power resource, the second weight of the dimensional information of the computing power resource is calculated, and then according to the second weight of all dimensional information of the computing power resource and the dimensional information performance value, the multi-dimensional characteristic value of the computing power resource is calculated, which can further improve the analysis accuracy of the comprehensive performance of the multi-dimensional characteristics of the computing power resources, that is, the calculation accuracy of the multi-dimensional characteristic values, and further improve the calculation scientificity of the multi-dimensional characteristic values ​​of each computing power resource, which is beneficial to further improve the classification accuracy of computing power resources from the perspective of the comprehensive situation of the multi-dimensional characteristics of each computing power resource, and is beneficial to improve the coordination and matching accuracy of computing power tasks and computing power resources, and improve the application scientificity, sustainability and health of computing power resources.

[0137] In an optional embodiment, the classification module 304 allocates each computing resource to at least one classification set according to all multi-dimensional feature values ​​in a specific manner including:

[0138] For each computing power resource, according to the multi-dimensional characteristic values ​​of the computing power resource, the adaptation scenario information of the computing power resource is analyzed, and the adaptation scenario corresponding to the adaptation scenario information includes a first target application scenario within the target range of the adaptive computing power resource and / or a second target application scenario outside the target range;

[0139] According to all the adaptation scenario information, each computing resource is allocated to at least one classification set, and the classification set corresponds to the adaptation scenario information.

[0140] It can be seen that the implementation of the embodiment of the present invention can, after calculating the multi-dimensional feature value used to represent the comprehensive performance of the multi-dimensional features of the computing power resources, analyze the adaptation scenario information of each computing power resource, including the first target application scenario within the target range of the adaptive computing power resource and / or the second target application scenario outside the target range, according to the multi-dimensional feature value of each computing power resource, thereby jumping out of the target range, improving the comprehensiveness of the application scenario analysis of the computing power resources and improving the accuracy of the cross-regional application scenario analysis of the computing power resources, and then according to all the adaptation scenario information, each computing power resource is allocated to at least one classification set, and the classification set corresponds to the adaptation scenario information, further improving the scientificity of the classification of the computing power resources, the accuracy of the analysis and the comprehensiveness of the classification perspective, which is conducive to the realization of the cross-regional joint application of computing power resources, and further conducive to improving the accuracy of the cross-regional coordinated matching of computing power tasks and computing power resources, improving the application scientificity, application scenario richness and application sustainability of computing power resources, and improving the application efficiency of regional computing power resources in a larger range, which is conducive to making the best use of resources.

[0141] In another optional embodiment, Figure 4 As shown, the device may also include:

[0142] A matching module 305 is used to match a computing power configuration component of each computing power resource for each classification set according to the multi-dimensional feature value of each computing power resource in the classification set, where the computing power configuration component is used to enable the computing power resource;

[0143] The judgment module 306 is used to judge whether the classification set meets the joint computing power composition condition according to all computing power configuration components and all multi-dimensional feature values. The joint computing power composition condition is used to indicate that there are at least two computing power resources in the classification set, and there are at least two computing power resources that meet the configuration condition for jointly processing the computing power task. The configuration condition includes that the computing power configuration components of at least two computing power resources match;

[0144] The configuration module 307 is used to configure at least one joint computing power group of the classification set when the judgment module 306 determines that the classification set meets the joint computing power composition conditions. The joint computing power group is used to jointly process computing power tasks that match the joint computing power group.

[0145] It can be seen that the implementation of this optional embodiment can, after allocating each computing power resource to at least one classification set according to all adaptation scenario information, for each classification set, match the computing power configuration component used to enable the computing power resource according to the multi-dimensional feature value of each computing power resource in the classification set, and then determine whether the classification set satisfies the preset joint computing power composition condition for indicating that there are at least two computing power resources in the classification set and that the computing power configuration components of at least two computing power resources match, based on all computing power configuration components and all multi-dimensional feature values. When it is determined that the classification set satisfies the joint computing power composition condition, at least one joint computing power group of the classification set is configured, and the joint computing power group is used to jointly process computing power tasks that match the joint computing power group. On the basis of improving the accuracy and scientificity of computing power resource classification, it can also further improve the richness of application scenarios of computing power resources and the application flexibility of computing power resources by configuring joint computing power groups, which is conducive to further improving the application scientificity, richness of application scenarios and application sustainability of computing power resources, improving the application efficiency of regional computing power resources in a larger range, and making the best use of resources.

[0146] In this optional embodiment, as an optional implementation, Figure 4 As shown, the device may also include:

[0147] The determination module 308 is configured to determine, for each computing resource in the classification set, at least one target computing resource that matches the computing configuration component of the computing resource in all classification sets when the determination module 306 determines that the classification set does not satisfy the joint computing power composition condition;

[0148] The construction module 309 is used to construct at least one backup joint computing power link based on the computing power resources and all target computing power resources, and the computing power resources in the backup joint computing power link jointly process the computing power tasks matching the backup joint computing power link.

[0149] It can be seen that the implementation of this optional embodiment can, after determining that the classification set does not meet the above-mentioned joint computing power composition conditions, also determine at least one target computing power resource that matches the computing power configuration component of at least one computing power resource in the classification set in all classification sets, and build at least one backup joint computing power link based on the computing power resource and all target computing power resources. The computing power resources in the backup joint computing power link jointly process the computing power tasks that match the backup joint computing power link, thereby further improving the cross-scenario application richness of the joint computing power application, which is conducive to further improving the application scenario richness of computing power resources and improving the application flexibility of computing power resources, and is conducive to further improving the application scientificity, application scenario richness and application sustainability of computing power resources, improving the application efficiency of regional computing power resources in a larger range, and is conducive to making the best use of resources.

[0150] Embodiment 4

[0151] See also Figure 5 , Figure 5 It is a structural diagram of another multi-dimensional classification device for computing power resources disclosed in an embodiment of the present invention. The multi-dimensional classification device for computing power resources can be applied to computing power resource management and / or allocation equipment, and can also be applied to intelligent devices related to computing power resource management and / or allocation equipment, and the intelligent devices include but are not limited to one or more of cloud devices, edge computing devices, relay devices, base station devices, city management devices, and intelligent network devices, which are not limited in the embodiments of the present invention. Figure 5 As shown, the multi-dimensional classification device of computing power resources may include:

[0152] The memory 401 stores executable program codes.

[0153] A processor 402 is coupled to the memory 401 .

[0154] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the multi-dimensional classification method of computing resources described in the first embodiment of the present invention or the second embodiment of the present invention.

[0155] Embodiment 5

[0156] An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the multi-dimensional classification method of computing power resources described in Embodiment 1 or Embodiment 2 of the present invention.

[0157] Embodiment 6

[0158] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the multi-dimensional classification method for computing power resources described in Example 1 or Example 2.

[0159] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0160] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0161] Finally, it should be noted that the multi-dimensional classification method and device for computing resources disclosed in the embodiments of the present invention disclose only the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical schemes described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the embodiments of the present invention.

Claims

1. A multi-dimensional classification method for computing resources, characterized in that: The method comprises: Obtain the distribution information of all computing resources within the target range; Analyze the multi-dimensional information of each computing resource according to the distribution information; Calculate the multi-dimensional feature value of each computing resource based on all the multi-dimensional information, where the multi-dimensional feature value is used to represent the comprehensive performance of the multi-dimensional features of the computing resource; According to all the multi-dimensional feature values, each of the computing resources is allocated to at least one classification set.

2. The multi-dimensional classification method of computing resources according to claim 1 is characterized in that: Analyzing the multi-dimensional information of each computing resource according to the distribution information includes: For each of the computing power resources, according to the distribution information, analyzing the geographical location information and the network environment information to which the computing power resource belongs; according to the geographical location information and the network environment information to which it belongs, analyzing the computing power usage information of the computing power resource, the computing power usage information including at least one of computing power usage task information, computing power usage scenario information, computing power usage rate information, computing power usage delay information, computing power usage time period information, computing power usage energy consumption information, computing power usage degree information, and computing power usage object information; according to the computing power usage information, analyzing the computing power usage depreciation degree information of the computing power resource; according to the computing power usage information and the computing power usage depreciation degree information, analyzing the idle information of the computing power resource; The multi-dimensional information of each computing power resource is analyzed based on the geographic location information, the network environment information, the computing power usage information, the computing power usage depreciation information and the idle information.

3. The multi-dimensional classification method of computing resources according to claim 2 is characterized in that: The multi-dimensional information includes at least two dimensional information of computing power dimensional information, computing power consumption dimensional information, application cost dimensional information, security dimensional information, usage efficiency dimensional information, and usage configuration dimensional information. The application cost dimensional information matches the computing power task of the computing power resource. The multi-dimensional characteristic value of each computing power resource is calculated according to all the multi-dimensional information, including: For each type of the dimensional information, a first weight of the dimensional information is calculated according to the dimensional information of all the computing power resources; and a dimensional information performance value of the dimensional information of each computing power resource is calculated according to the first weight and the dimensional information of each computing power resource; For each of the computing power resources, a multi-dimensional characteristic value of the computing power resource is calculated based on all the dimensional information representation values ​​of the computing power resource.

4. The multi-dimensional classification method for computing resources according to claim 3 is characterized in that: For each of the computing power resources, calculating the multi-dimensional feature value of the computing power resource according to all the dimensional information representation values ​​of the computing power resource includes: Analyze, according to the computing power usage information of the computing power resource, a first priority value of each type of the dimensional information of the computing power resource, where the first priority value is used to indicate the degree of attention paid to each type of the dimensional information in the historical application scenario corresponding to the computing power resource; According to the computing power usage information of all the computing power resources, a second priority value of each type of the dimensional information under all the computing power resources is analyzed, where the second priority value is used to indicate the degree of attention paid to each type of the dimensional information under the historical application scenarios corresponding to all the computing power resources; For each type of dimension information, calculating a second weight of the type of dimension information according to the first priority value and the second priority value; The multi-dimensional feature value of the computing power resource is calculated based on the second weight and the dimensional information performance value of all the dimensional information of the computing power resource.

5. The multi-dimensional classification method for computing resources according to any one of claims 1 to 4, characterized in that: The allocating each of the computing resources to at least one classification set according to all of the multi-dimensional feature values ​​includes: For each of the computing power resources, analyzing the adaptation scenario information of the computing power resource according to the multi-dimensional characteristic value of the computing power resource, where the adaptation scenario corresponding to the adaptation scenario information includes a first target application scenario within the target range of the computing power resource and / or a second target application scenario outside the target range; According to all the adaptation scenario information, each computing resource is allocated to at least one classification set, and the classification set corresponds to the adaptation scenario information.

6. The multi-dimensional classification method of computing resources according to claim 5 is characterized in that: The method further comprises: For each of the classification sets, according to the multi-dimensional feature values ​​of each of the computing power resources in the classification set, the computing power configuration component of each of the computing power resources is matched, and the computing power configuration component is used to enable the computing power resources; according to all the computing power configuration components and all the multi-dimensional feature values, it is judged whether the classification set meets the joint computing power composition condition, and the joint computing power composition condition is used to indicate that there are at least two computing power resources in the classification set, and there are at least two computing power resources that meet the configuration condition for jointly processing computing power tasks, and the configuration condition includes that the computing power configuration components of at least two computing power resources are matched; When it is determined that the classification set meets the joint computing power composition condition, at least one joint computing power group of the classification set is configured, and the joint computing power group is used to jointly process computing power tasks that match the joint computing power group.

7. The multi-dimensional classification method of computing resources according to claim 6 is characterized in that: The method further comprises: When it is determined that the classification set does not meet the joint computing power composition conditions, for each of the computing power resources in the classification set, determine at least one target computing power resource that matches the computing power configuration component of the computing power resource in all the classification sets; based on the computing power resource and all the target computing power resources, construct at least one backup joint computing power link, and the computing power resources in the backup joint computing power link jointly process the computing power tasks that match the backup joint computing power link.

8. A multi-dimensional classification device for computing resources, characterized in that: The device comprises: The acquisition module is used to obtain the distribution information of all computing resources within the target range; An analysis module, used to analyze the multi-dimensional information of each computing resource according to the distribution information; A calculation module, used to calculate a multi-dimensional feature value of each of the computing power resources according to all the multi-dimensional information, wherein the multi-dimensional feature value is used to represent the comprehensive performance of the multi-dimensional features of the computing power resources; A classification module is used to allocate each of the computing resources to at least one classification set according to all the multi-dimensional feature values.

9. A multi-dimensional classification device for computing resources, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the multi-dimensional classification method of computing power resources as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, which, when called, are used to execute the multi-dimensional classification method for computing power resources as described in any one of claims 1-7.

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