Calculation power demand classification method and device

Abstract

The invention relates to the technical field of data analysis, and discloses a computing power demand classification method and device, and the method comprises the steps: obtaining first business information; analyzing first computing power demand information of the first service information according to the first service information; according to the first computing power demand information, calculating a first matching degree value of the first business information and each computing power resource expected to be covered corresponding to the first computing power demand information; according to all the first matching degree values, the first service information is allocated to at least one classification set, and computing power resources and computing power scheduling priorities corresponding to different classification sets are different. It can be seen that the computing power resources can be scientifically applied by improving the classification accuracy of the computing power requirements, the distribution accuracy of the computing power resources and the matching accuracy between different computing power resources and different computing power requirements can be improved, the application scientificity and application sustainability of the computing power resources are improved, and the computing power resources can be applied more accurately. And the application accuracy of the regional computing power resources can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a computing power demand classification method and device. Background Art

[0002] Computing power, as the core capability of data processing and result output, has become an important driving force for promoting social progress and technological innovation. With the rapid development of information technology, the demand for computing power in various industries is becoming increasingly diversified, prompting the continuous refinement and evolution of the computing power field.

[0003] At present, most of the use of computing power resources is to directly hand over computing power tasks with different computing power requirements to idle computing power resources for processing. However, practice has found that this will result in a waste of computing power resources and the computing power requirements will not be fully met. On this basis, it is easy to further encounter chain situations such as the completion of computing power tasks failing to meet expectations, regional computing power resource shortages, and computing power task chain interruptions, which is not conducive to the scientific application of computing power resources.

[0004] It can be seen that it is particularly important to improve the classification accuracy of computing power requirements so that computing power resources can be applied scientifically. Summary of the invention

[0005] The present invention provides a computing power demand classification method and device, which can improve the classification accuracy of computing power demands and enable computing power resources to be scientifically applied.

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

[0007] Obtaining first business information;

[0008] Analyzing first computing power requirement information of the first business information according to the first business information;

[0009] Calculate, according to the first computing power requirement information, a first matching value of each computing power resource that is expected to be covered by the first business information and the first computing power requirement information;

[0010] According to all the first matching degree values, the first business information is allocated to at least one classification set, and different classification sets correspond to different computing resources and computing scheduling priorities.

[0011] As an optional implementation manner, in the first aspect of the present invention, the first business information includes at least one of business type information, business-related object information, and business scenario information, and the analyzing, according to the first business information, first computing power requirement information of the first business information includes:

[0012] Determine, according to the first business information, second business information matching the first business information, where the second business information is prior business information of the first business information;

[0013] Determine at least one target item according to the second service information and the first service information, where the target item is a distinguishing item between the first service information and the second service information;

[0014] The first computing power requirement information of the first business information is analyzed according to all the target items and the second business information.

[0015] As an optional implementation manner, in the first aspect of the present invention, analyzing the first computing power requirement information of the first business information according to all the target items and the second business information includes:

[0016] For each of the target items, obtain the location information of the target item, analyze the first semantic information of the target item and the role information of the target item in the first business information according to the location information; match the first priority value of the target item according to the first semantic information and the role information; determine whether the first priority value is greater than or equal to a preset first priority threshold, and when it is determined that the first priority value is greater than or equal to the preset first priority threshold, determine that the target item is a priority item;

[0017] The first computing power requirement information of the first business information is analyzed according to all the priority items and the second business information.

[0018] As an optional implementation manner, in the first aspect of the present invention, analyzing the first computing power requirement information of the first business information according to all the priority items and the second business information includes:

[0019] analyzing the second semantic information of the first service information according to the location information of all the priority items and the second service information;

[0020] Acquire business processing information of the second business information, where the business processing information includes at least one of processing flow information, data processing accuracy information, processing network information, architecture information, computing power resource information, configuration information, and data processing efficiency information;

[0021] The first computing power requirement information of the first business information is analyzed according to the business processing information and the second semantic information.

[0022] As an optional implementation manner, in the first aspect of the present invention, the first computing power requirement information includes at least one requirement sub-information, and the calculating, according to the first computing power requirement information, a first matching value of each computing power resource that is expected to be covered by the first business information and the first computing power requirement information includes:

[0023] Analyze the expected computing power deployment range of the first business information according to the first computing power demand information;

[0024] Obtain computing power resource information of all computing power resources within the expected computing power deployment range;

[0025] For each of the computing power resources, according to the computing power resource information of the computing power resource, analyzing the current computing power demand coverage information of the computing power resource; for each of the demand sub-information, calculating the second matching value between the demand sub-information and the current computing power demand coverage information of the computing power resource;

[0026] According to all the second matching values, a first matching value of each of the computing resources that the first business information is expected to cover with the first computing power requirement information is calculated.

[0027] As an optional implementation manner, in the first aspect of the present invention, each of the demand sub-information has a corresponding second priority value, and the first matching value of each of the computing power resources that the first business information and the first computing power demand information expect to cover is calculated according to all the second matching values, including:

[0028] For each of the requirement sub-information, the second matching value of the requirement sub-information is updated according to the second priority value of the requirement sub-information; whether the updated second matching value is greater than or equal to a preset second matching threshold is determined, and when it is determined that the updated second matching value is greater than or equal to the preset second matching threshold, the requirement sub-information is determined to be the target requirement sub-information;

[0029] For each of the computing power resources, the first matching value between the first business information and the computing power resource is calculated based on the current computing power demand coverage information of the computing power resource and the updated second matching value of all the target demand sub-information corresponding to the computing power resource.

[0030] As an optional implementation manner, in the first aspect of the present invention, allocating the first service information to at least one classification set according to all the first matching degree values ​​includes:

[0031] According to all the first matching degree values, determine at least one target computing power resource among all the computing power resources, the first matching degree value of the target computing power resource being greater than or equal to a preset first matching degree threshold, where the first matching degree threshold is calculated based on all the first matching degree values;

[0032] Determine resource allocation information of each of the target computing resources according to the first computing power demand information, where the resource allocation information includes at least one of computing power allocation amount information, computing power allocation timing information, and computing power link configuration information;

[0033] Determine computing power allocation link information of the first service information according to all the resource allocation information;

[0034] According to the computing power allocation link information, the first service information is allocated to at least one classification set.

[0035] A second aspect of the present invention discloses a computing power requirement classification device, the device comprising:

[0036] An acquisition module, used for acquiring first business information;

[0037] An analysis module, configured to analyze first computing power requirement information of the first business information according to the first business information;

[0038] A calculation module, configured to calculate, according to the first computing power requirement information, a first matching value of each computing power resource that is expected to be covered by the first business information and the first computing power requirement information;

[0039] A classification module is used to distribute the first business information into at least one classification set according to all the first matching degree values, and different classification sets correspond to different computing resources and computing scheduling priorities.

[0040] As an optional implementation manner, in the second aspect of the present invention, the first business information includes at least one of business type information, business-related object information, and business scenario information, and the specific manner in which the analysis module analyzes the first computing power requirement information of the first business information according to the first business information includes:

[0041] Determine, according to the first business information, second business information matching the first business information, where the second business information is prior business information of the first business information;

[0042] Determine at least one target item according to the second service information and the first service information, where the target item is a distinguishing item between the first service information and the second service information;

[0043] The first computing power requirement information of the first business information is analyzed according to all the target items and the second business information.

[0044] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the analysis module analyzes the first computing power requirement information of the first business information according to all the target items and the second business information includes:

[0045] For each of the target items, obtain the location information of the target item, analyze the first semantic information of the target item and the role information of the target item in the first business information according to the location information; match the first priority value of the target item according to the first semantic information and the role information; determine whether the first priority value is greater than or equal to a preset first priority threshold, and when it is determined that the first priority value is greater than or equal to the preset first priority threshold, determine that the target item is a priority item;

[0046] The first computing power requirement information of the first business information is analyzed according to all the priority items and the second business information.

[0047] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the analysis module analyzes the first computing power requirement information of the first business information according to all the priority items and the second business information includes:

[0048] analyzing the second semantic information of the first service information according to the location information of all the priority items and the second service information;

[0049] Acquire business processing information of the second business information, where the business processing information includes at least one of processing flow information, data processing accuracy information, processing network information, architecture information, computing power resource information, configuration information, and data processing efficiency information;

[0050] The first computing power requirement information of the first business information is analyzed according to the business processing information and the second semantic information.

[0051] As an optional implementation manner, in the second aspect of the present invention, the first computing power requirement information includes at least one requirement sub-information, and the specific manner in which the calculation module calculates the first matching degree value of each computing power resource expected to be covered by the first business information and the first computing power requirement information according to the first computing power requirement information includes:

[0052] Analyze the expected computing power deployment range of the first business information according to the first computing power demand information;

[0053] Obtain computing power resource information of all computing power resources within the expected computing power deployment range;

[0054] For each of the computing power resources, according to the computing power resource information of the computing power resource, analyzing the current computing power demand coverage information of the computing power resource; for each of the demand sub-information, calculating the second matching value between the demand sub-information and the current computing power demand coverage information of the computing power resource;

[0055] According to all the second matching values, a first matching value of each of the computing resources that the first business information is expected to cover with the first computing power requirement information is calculated.

[0056] As an optional implementation, in the second aspect of the present invention, each of the demand sub-information has a corresponding second priority value, and the specific manner in which the calculation module calculates the first matching value of each of the computing power resources that the first business information and the first computing power demand information expect to cover according to all the second matching values ​​includes:

[0057] For each of the requirement sub-information, the second matching value of the requirement sub-information is updated according to the second priority value of the requirement sub-information; whether the updated second matching value is greater than or equal to a preset second matching threshold is determined, and when it is determined that the updated second matching value is greater than or equal to the preset second matching threshold, the requirement sub-information is determined to be the target requirement sub-information;

[0058] For each of the computing power resources, the first matching value between the first business information and the computing power resource is calculated based on the current computing power demand coverage information of the computing power resource and the updated second matching value of all the target demand sub-information corresponding to the computing power resource.

[0059] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the classification module allocates the first service information to at least one classification set according to all the first matching degree values ​​includes:

[0060] According to all the first matching degree values, determine at least one target computing power resource among all the computing power resources, the first matching degree value of the target computing power resource being greater than or equal to a preset first matching degree threshold, where the first matching degree threshold is calculated based on all the first matching degree values;

[0061] Determine resource allocation information of each of the target computing resources according to the first computing power demand information, where the resource allocation information includes at least one of computing power allocation amount information, computing power allocation timing information, and computing power link configuration information;

[0062] Determine computing power allocation link information of the first service information according to all the resource allocation information;

[0063] According to the computing power allocation link information, the first service information is allocated to at least one classification set.

[0064] A third aspect of the present invention discloses another computing power requirement classification device, the device comprising:

[0065] A memory storing executable program code;

[0066] a processor coupled to the memory;

[0067] The processor calls the executable program code stored in the memory to execute the computing power requirement classification method disclosed in the first aspect of the present invention.

[0068] 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 computing power requirement classification method disclosed in the first aspect of the present invention.

[0069] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0070] In an embodiment of the present invention, first business information is obtained; based on the first business information, first computing power requirement information of the first business information is analyzed; based on the first computing power requirement information, a first matching value of each computing power resource expected to be covered by the first business information and the first computing power requirement information is calculated; based on all first matching values, the first business information is allocated to at least one classification set, and different classification sets correspond to different computing power resources and computing power scheduling priorities. It can be seen that the implementation of the present invention can analyze the first computing power requirement information of the first business information according to the acquired first business information, and then calculate the first matching value of each computing power resource that the first business information and the first computing power requirement information are expected to cover based on the analyzed first computing power requirement information, and according to all the first matching values, the first business information with the first computing power requirement information is allocated to at least one classification set corresponding to different computing power resources and computing power scheduling priorities, so that the business information under different computing power requirements can be allocated to a more matching classification set, so that the corresponding business information can be processed by more matching computing power scheduling priorities and computing power resources, so as to achieve scientific application of computing power resources by improving the classification accuracy of computing power requirements, which is conducive to improving the allocation accuracy of computing power resources and the matching accuracy between different computing power resources and different computing power requirements, improving the scientificity and sustainability of the application of computing power resources, and helping to improve the application accuracy of regional computing power resources, and promote the sustainable and healthy development of the computing power field. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] 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.

[0072] Figure 1 It is a flowchart of a computing power requirement classification method disclosed in an embodiment of the present invention;

[0073] Figure 2 It is a flowchart of another computing power requirement classification method disclosed in an embodiment of the present invention;

[0074] Figure 3 It is a structural schematic diagram of a computing power demand classification device disclosed in an embodiment of the present invention;

[0075] Figure 4 It is a structural schematic diagram of another computing power requirement classification device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0076] 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.

[0077] 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.

[0078] 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.

[0079] The present invention discloses a computing power demand classification method and device, which can analyze the first computing power demand information of the first business information according to the acquired first business information, and then calculate the first matching value of each computing power resource expected to be covered by the first business information and the first computing power demand information based on the analyzed first computing power demand information, and according to all the first matching values, the first business information with the first computing power demand information is allocated to at least one classification set corresponding to different computing power resources and computing power scheduling priorities, so that the business information under different computing power requirements can be allocated to a more matching classification set, so that the corresponding business information can be processed by more matching computing power scheduling priorities and computing power resources, so as to achieve scientific application of computing power resources by improving the classification accuracy of computing power requirements, which is conducive to improving the allocation accuracy of computing power resources and the matching accuracy between different computing power resources and different computing power requirements, improving the application scientificity and application sustainability of computing power resources, and is conducive to improving the application accuracy of regional computing power resources, and helping the computing power field to continue to develop healthily. The following are detailed descriptions.

[0080] Embodiment 1

[0081] See also Figure 1 , Figure 1 : is a flow chart of a computing power requirement classification method disclosed in an embodiment of the present invention. Figure 1 The computing power demand classification method described can be applied to business management / allocation devices, and can also be applied to smart devices related to business management / allocation, including but not limited to one or more of cloud devices, edge computing devices, relay devices, base station devices, city management devices, and smart network devices, which are not limited in the embodiments of the present invention. Figure 1 As shown, the computing power requirement classification method may include the following operations:

[0082] 101. Obtain first business information;

[0083] In an embodiment of the present invention, optionally, the first service information may be obtained through a communication device, where the communication device includes but is not limited to one or more of a base station, a satellite, a signal light, a street light, an Internet of Vehicles, a mobile portable device, and a wearable device.

[0084] 102. Analyze first computing power requirement information of the first business information according to the first business information;

[0085] In the embodiment of the present invention, as an optional implementation manner, the first business information includes at least one of business type information, business-related object information, and business scenario information. The analyzing the first computing power requirement information of the first business information according to the first business information may include the following operations:

[0086] Determine, according to the first business information, second business information that matches the first business information, where the second business information is prior business information of the first business information;

[0087] Determine at least one target item according to the second business information and the first business information, where the target item is a distinguishing item between the first business information and the second business information;

[0088] The first computing power requirement information of the first business information is analyzed according to all target items and the second business information.

[0089] In an embodiment of the present invention, optionally, the second business information may represent all matching business information after the first trigger classification in the first business information, including business information that has not yet completed the actual allocation of computing power resources and matches the first business information, and may also include business information that has completed the actual allocation of computing power resources and matches the first business information, and may also include historical business information that has completed the processing of computing power tasks and matches the first business information. In an embodiment of the present invention, no specific limitation is made to this, but relatively speaking, the above-mentioned "may also include" situation may be more conducive to improving the analysis accuracy of the first computing power demand information.

[0090] It can be seen that the implementation of this optional embodiment can determine the second business information that matches and precedes the first business information based on the first business information including at least one of business type information, business-related object information, and business scenario information, and determine at least one target item used to represent the difference between the first business information and the second business information based on the second business information and the first business information. Then, based on all the target items and the second business information, the first computing power requirement information of the first business information is analyzed, which can improve the analysis accuracy, analysis efficiency, flexibility of analysis method and feasibility of analysis operation of the first computing power requirement information, which is conducive to improving the depth and efficiency of understanding of the first business information, and is conducive to improving the classification accuracy and efficiency of computing power requirements.

[0091] In this optional embodiment, as an optional implementation manner, the above-mentioned analyzing the first computing power requirement information of the first business information according to all target items and the second business information may include the following operations:

[0092] For each target item, obtain the location information of the target item, analyze the first semantic information of the target item and the role information of the target item in the first business information according to the location information; match the first priority value of the target item according to the first semantic information and the role information; determine whether the first priority value is greater than or equal to a preset first priority threshold, and when it is determined that the first priority value is greater than or equal to the preset first priority threshold, determine that the target item is a priority item;

[0093] The first computing power requirement information of the first business information is analyzed according to all the priority items and the second business information.

[0094] In this optional embodiment, the above-mentioned analysis of the first semantic information of the target item and the role information of the target item in the first business information based on the location information of the target item can be obtained by comprehensively analyzing the location information of the target item in combination with the location information of the associated items of the target item after acquiring the location information of the target item.

[0095] It can be seen that the implementation of this optional embodiment can further determine at least one target item used to represent the difference between the first business information and the second business information. For each target item, by obtaining the position information of the target item, the first semantic information of the target item and the role information of the target item in the first business information can be further analyzed, and then according to the analyzed first semantic information and role information, the first priority value of the target item is matched, and by judging whether the first priority value is greater than or equal to the preset first priority threshold, the priority item in the target item is screened and determined, so that based on all the determined priority items and the second business information, the first computing power requirement information of the first business information is analyzed, so as to further improve the analysis accuracy, analysis efficiency and scientific analysis of the first computing power requirement information, which is conducive to improving the classification accuracy and classification efficiency of computing power requirements.

[0096] In this optional embodiment, as another optional implementation, the above-mentioned analyzing the first computing power requirement information of the first business information according to all priority items and the second business information may include the following operations:

[0097] Analyze the second semantic information of the first service information according to the position information and the second service information of all priority items;

[0098] Acquire business processing information of the second business information, where the business processing information includes at least one of processing flow information, data processing accuracy information, processing network information, architecture information, computing power resource information, configuration information, and data processing efficiency information;

[0099] The first computing power requirement information of the first business information is analyzed according to the business processing information and the second semantic information.

[0100] In this optional embodiment, optionally, the second business information may include business information that has completed actual allocation of computing resources and matches the first business information, and may also include historical business information that has completed processing of computing tasks and matches the first business information.

[0101] It can be seen that the implementation of this optional embodiment can, after further determining all priority items, further analyze the second semantic information of the first business information based on the location information and the second business information of all priority items. By obtaining the second business information including at least one of the processing flow information, data processing accuracy information, processing network information, architecture information, computing power resource information, configuration information, and data processing efficiency information, combined with the second semantic information, the first computing power requirement information of the first business information can be analyzed. This can further improve the analysis accuracy, efficiency, and scientific nature of the first computing power requirement information, which is conducive to further improving the classification accuracy and efficiency of computing power requirements.

[0102] 103. Calculate, according to the first computing power requirement information, a first matching value of each computing power resource that is expected to be covered by the first business information and the first computing power requirement information;

[0103] 104. Allocate the first business information to at least one classification set according to all first matching degree values, and different classification sets correspond to different computing resources and computing scheduling priorities.

[0104] In the embodiment of the present invention, optionally, the coverage area of ​​the computing resources corresponding to the above-mentioned classification set is not specifically limited, and cross-regional computing resources mobilization can be achieved.

[0105] It can be seen that the implementation of the embodiment of the present invention can analyze the first computing power requirement information of the first business information according to the acquired first business information, and then calculate the first matching value of each computing power resource that the first business information and the first computing power requirement information are expected to cover based on the analyzed first computing power requirement information, and according to all the first matching values, the first business information with the first computing power requirement information is allocated to at least one classification set corresponding to different computing power resources and computing power scheduling priorities, so that the business information under different computing power requirements can be allocated to a more matching classification set, so that the corresponding business information can be processed by more matching computing power scheduling priorities and computing power resources, so as to achieve scientific application of computing power resources by improving the classification accuracy of computing power requirements, which is conducive to improving the allocation accuracy of computing power resources and the matching accuracy between different computing power resources and different computing power requirements, improving the scientificity and sustainability of the application of computing power resources, and helping to improve the application accuracy of regional computing power resources, and promote the sustainable and healthy development of the computing power field.

[0106] Embodiment 2

[0107] See also Figure 2 , Figure 2 is a flow chart of another computing power requirement classification method disclosed in an embodiment of the present invention. Figure 2The computing power demand classification method described can be applied to business management / allocation devices, and can also be applied to smart devices related to business management / allocation, including but not limited to one or more of cloud devices, edge computing devices, relay devices, base station devices, city management devices, and smart network devices, which are not limited in the embodiments of the present invention. Figure 2 As shown, the computing power requirement classification method may include the following operations:

[0108] 201. Obtain first business information;

[0109] 202. Analyze first computing power requirement information of the first business information according to the first business information, where the first computing power requirement information includes at least one requirement sub-information;

[0110] In the embodiment of the present invention, for the supplementary description of step 201 and step 202, please refer to the supplementary description of step 101 and step 102 in the first embodiment, and the embodiment of the present invention will not elaborate on this.

[0111] 203. Analyze the expected computing power deployment range of the first business information according to the first computing power demand information;

[0112] 204. Obtain computing resource information of all computing resources within the expected computing deployment range;

[0113] 205. For each computing power resource, according to the computing power resource information of the computing power resource, analyze the current computing power demand coverage information of the computing power resource;

[0114] In an embodiment of the present invention, the above-mentioned current computing power demand coverage information can be understood as follows: for example, the first computing power demand information includes the number of seconds corresponding to the processing time of 30 days, and the continuous processing time of the computing power task in the current computing power demand coverage information of the computing power resource is the number of seconds corresponding to 28 days; further, it can also include computing power processing costs, such as economic costs, configuration costs, cross-regional matching costs, protocol costs, cross-regional security configuration costs, etc. One or more.

[0115] 206. For each demand sub-information, calculate a second matching value between the demand sub-information and the current computing power demand coverage information of the computing power resource;

[0116] 207. Calculate, according to all the second matching values, a first matching value of each computing resource that is expected to be covered by the first business information and the first computing power requirement information;

[0117] In the embodiment of the present invention, as an optional implementation manner, each of the above-mentioned demand sub-information has a corresponding second priority value, and the above-mentioned calculation of the first matching value of each computing power resource expected to be covered by the first business information and the first computing power demand information according to all the second matching values ​​may include the following operations:

[0118] For each demand sub-information, according to the second priority value of the demand sub-information, update the second matching value of the demand sub-information; determine whether the updated second matching value is greater than or equal to a preset second matching threshold, and when it is determined that the updated second matching value is greater than or equal to the preset second matching threshold, determine that the demand sub-information is the target demand sub-information;

[0119] For each computing power resource, the first matching value between the first business information and the computing power resource is calculated based on the current computing power demand coverage information of the computing power resource and the updated second matching value of all target demand sub-information corresponding to the computing power resource.

[0120] It can be seen that the implementation of this optional embodiment can further update the second matching value of each demand sub-information according to the second priority value of the demand sub-information, so that when it is judged that the updated second matching value is greater than or equal to the preset second matching threshold, the demand sub-information is determined to be the target demand sub-information, and thus for each computing power resource, the first matching value of the first business information and the computing power resource is calculated according to the current computing power demand coverage information of the computing power resource and the updated second matching value of all target demand sub-information corresponding to the computing power resource, which can further improve the accuracy of determining the target demand sub-information and the calculation accuracy of the first matching value, which is conducive to improving the classification accuracy of computing power requirements.

[0121] 208. Allocate the first business information to at least one classification set according to all first matching degree values, where different classification sets correspond to different computing resources and computing scheduling priorities.

[0122] In an embodiment of the present invention, after all first matching degree values ​​are calculated, it is equivalent to performing a pre-allocation of the expected coverage computing power resources for the first business information, and then allocating the first business information to at least one classification set, so that the corresponding computing power resources and computing power scheduling priorities in different classification sets jointly process the above-mentioned first business information.

[0123] It can be seen that the implementation of the embodiment of the present invention can analyze the expected computing power deployment range of the first business information according to the first computing power demand information after analyzing the first computing power demand information of the first business information, and analyze the current computing power demand coverage information of each computing power resource by obtaining the computing power resource information of all computing power resources under the above-mentioned expected computing power deployment range, thereby calculating the second matching value between each demand sub-information included in the first computing power demand information and the current computing power demand coverage information of each computing power resource, and calculating the first matching value of each computing power resource expected to be covered by the first computing power demand information according to all the second matching values, and then through Through all the first matching degree values, the first business information is allocated to at least one classification set, so that the above-mentioned first business information can be jointly processed by different computing power scheduling priorities and computing power resources corresponding to different classification sets, thereby further improving the classification accuracy, classification scientificity and classification feasibility of computing power requirements, which is conducive to further scientific application of computing power resources, and is conducive to further improving the allocation accuracy of computing power resources and the matching accuracy between different computing power resources and different computing power requirements, improving the application scientificity and application sustainability of computing power resources, and is conducive to improving the application accuracy of regional computing power resources, and promoting the sustainable and healthy development of the computing power field.

[0124] In the embodiment of the present invention, as another optional implementation, the above-mentioned allocating the first service information to at least one classification set according to all the first matching degree values ​​may include the following operations:

[0125] According to all the first matching values, at least one target computing resource is determined among all computing resources, the first matching value of the target computing resource is greater than or equal to a preset first matching threshold, and the first matching threshold is calculated based on all the first matching values;

[0126] Determine resource allocation information of each target computing resource according to the first computing power demand information, where the resource allocation information includes at least one of computing power allocation amount information, computing power allocation timing information, and computing power link configuration information;

[0127] Determine computing power allocation link information of the first service information according to all resource allocation information;

[0128] The first service information is allocated to at least one classification set according to the computing power allocation link information.

[0129] In this optional embodiment, the above-mentioned computing power allocation link information includes the computing power scheduling priority and computing power allocation status of the above-mentioned target computing power resources that are preferentially allocated, thereby forming a link for joint scheduling of computing power resources. In the actual computing power task processing process, the above-mentioned computing power allocation link information can be used to realize the joint scheduling processing of computing power resources, improve the joint matching application of cross-regional computing power resources, improve the application richness and adequacy of computing power resources, and make the best use of them.

[0130] It can be seen that the implementation of this optional embodiment can, after calculating the first business information and the first computing power demand information expect to cover each computing power resource, determine, based on all the first matching values, at least one target computing power resource whose first matching value is greater than or equal to the preset first matching value threshold among all the computing power resources within the expected computing power deployment range of the first business information analyzed based on the first computing power demand information, thereby determining, based on the first computing power demand information, resource allocation information of each target computing power resource including at least one of computing power allocation amount information, computing power allocation timing information, and computing power link configuration information, and determining, based on all the resource allocation information, the first computing power resource. The computing power allocation link information of a business information is obtained, and then based on the computing power allocation link information, the first business information is allocated to at least one classification set, so that the priority allocation of computing power resources is completed through the computing power allocation link information, and the computing power scheduling priority is integrated into the computing power demand classification process, which further improves the classification accuracy of computing power demand while also improving the scheduling accuracy and efficiency of computing power resources, which is conducive to further improving the matching accuracy between different computing power resources and different computing power demands, improving the application scientificity and application sustainability of computing power resources, and is conducive to improving the application accuracy of regional computing power resources, and promoting the sustainable and healthy development of the computing power field.

[0131] Embodiment 3

[0132] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of a computing power demand classification device disclosed in an embodiment of the present invention. The computing power demand classification device can be applied to business management / distribution equipment, and can also be applied to smart devices related to business management / distribution, including but not limited to one or more of cloud devices, edge computing devices, relay devices, base station devices, city management devices, and smart network devices, which are not limited in the embodiment of the present invention. Figure 3 As shown, the computing power requirement classification device may include:

[0133] An acquisition module 301 is used to acquire first service information;

[0134] An analysis module 302 is used to analyze first computing power requirement information of the first business information according to the first business information;

[0135] A calculation module 303 is used to calculate, according to the first computing power requirement information, a first matching value of each computing power resource that is expected to be covered by the first business information and the first computing power requirement information;

[0136] The classification module 304 is used to distribute the first business information to at least one classification set according to all the first matching degree values, and different classification sets correspond to different computing resources and computing scheduling priorities.

[0137] It can be seen that the implementation of the embodiment of the present invention can analyze the first computing power requirement information of the first business information according to the acquired first business information, and then calculate the first matching value of each computing power resource that the first business information and the first computing power requirement information are expected to cover based on the analyzed first computing power requirement information, and according to all the first matching values, the first business information with the first computing power requirement information is allocated to at least one classification set corresponding to different computing power resources and computing power scheduling priorities, so that the business information under different computing power requirements can be allocated to a more matching classification set, so that the corresponding business information can be processed by more matching computing power scheduling priorities and computing power resources, so as to achieve scientific application of computing power resources by improving the classification accuracy of computing power requirements, which is conducive to improving the allocation accuracy of computing power resources and the matching accuracy between different computing power resources and different computing power requirements, improving the scientificity and sustainability of the application of computing power resources, and helping to improve the application accuracy of regional computing power resources, and promote the sustainable and healthy development of the computing power field.

[0138] In the embodiment of the present invention, as an optional implementation manner, the first business information includes at least one of business type information, business-related object information, and business scenario information. The specific manner in which the analysis module 302 analyzes the first computing power requirement information of the first business information according to the first business information includes:

[0139] Determine, according to the first business information, second business information that matches the first business information, where the second business information is prior business information of the first business information;

[0140] Determine at least one target item according to the second business information and the first business information, where the target item is a distinguishing item between the first business information and the second business information;

[0141] The first computing power requirement information of the first business information is analyzed according to all target items and the second business information.

[0142] It can be seen that the implementation of this optional embodiment can determine the second business information that matches and precedes the first business information based on the first business information including at least one of business type information, business-related object information, and business scenario information, and determine at least one target item used to represent the difference between the first business information and the second business information based on the second business information and the first business information. Then, based on all the target items and the second business information, the first computing power requirement information of the first business information is analyzed, which can improve the analysis accuracy, analysis efficiency, flexibility of analysis method and feasibility of analysis operation of the first computing power requirement information, which is conducive to improving the depth and efficiency of understanding of the first business information, and is conducive to improving the classification accuracy and efficiency of computing power requirements.

[0143] In this optional embodiment, as an optional implementation, the specific manner in which the above-mentioned analysis module 302 analyzes the first computing power requirement information of the first business information according to all target items and the second business information includes:

[0144] For each target item, obtain the location information of the target item, analyze the first semantic information of the target item and the role information of the target item in the first business information according to the location information; match the first priority value of the target item according to the first semantic information and the role information; determine whether the first priority value is greater than or equal to a preset first priority threshold, and when it is determined that the first priority value is greater than or equal to the preset first priority threshold, determine that the target item is a priority item;

[0145] The first computing power requirement information of the first business information is analyzed according to all the priority items and the second business information.

[0146] It can be seen that the implementation of this optional embodiment can further determine at least one target item used to represent the difference between the first business information and the second business information. For each target item, by obtaining the position information of the target item, the first semantic information of the target item and the role information of the target item in the first business information can be further analyzed, and then according to the analyzed first semantic information and role information, the first priority value of the target item is matched, and by judging whether the first priority value is greater than or equal to the preset first priority threshold, the priority item in the target item is screened and determined, so that based on all the determined priority items and the second business information, the first computing power requirement information of the first business information is analyzed, so as to further improve the analysis accuracy, analysis efficiency and scientific analysis of the first computing power requirement information, which is conducive to improving the classification accuracy and classification efficiency of computing power requirements.

[0147] In this optional embodiment, as another optional implementation, the specific manner in which the above-mentioned analysis module 302 analyzes the first computing power requirement information of the first business information according to all priority items and the second business information includes:

[0148] Analyze the second semantic information of the first service information according to the position information and the second service information of all priority items;

[0149] Acquire business processing information of the second business information, where the business processing information includes at least one of processing flow information, data processing accuracy information, processing network information, architecture information, computing power resource information, configuration information, and data processing efficiency information;

[0150] The first computing power requirement information of the first business information is analyzed according to the business processing information and the second semantic information.

[0151] It can be seen that the implementation of this optional embodiment can, after further determining all priority items, further analyze the second semantic information of the first business information based on the location information and the second business information of all priority items. By obtaining the second business information including at least one of the processing flow information, data processing accuracy information, processing network information, architecture information, computing power resource information, configuration information, and data processing efficiency information, combined with the second semantic information, the first computing power requirement information of the first business information can be analyzed. This can further improve the analysis accuracy, efficiency, and scientific nature of the first computing power requirement information, which is conducive to further improving the classification accuracy and efficiency of computing power requirements.

[0152] In an optional embodiment, the first computing power requirement information includes at least one requirement sub-information, and the specific manner in which the calculation module 303 calculates the first matching value of each computing power resource expected to be covered by the first business information and the first computing power requirement information according to the first computing power requirement information includes:

[0153] Analyze the expected computing power deployment range of the first business information according to the first computing power demand information;

[0154] Obtain computing resource information of all computing resources within the expected computing deployment range;

[0155] For each computing power resource, according to the computing power resource information of the computing power resource, analyze the current computing power demand coverage information of the computing power resource; for each demand sub-information, calculate the second matching value between the demand sub-information and the current computing power demand coverage information of the computing power resource;

[0156] According to all the second matching values, the first matching value of each computing resource that the first business information and the first computing power requirement information expect to cover is calculated.

[0157] It can be seen that the implementation of the embodiment of the present invention can analyze the expected computing power deployment range of the first business information according to the first computing power demand information after analyzing the first computing power demand information of the first business information, and analyze the current computing power demand coverage information of each computing power resource by obtaining the computing power resource information of all computing power resources under the above-mentioned expected computing power deployment range, thereby calculating the second matching value between each demand sub-information included in the first computing power demand information and the current computing power demand coverage information of each computing power resource, and calculating the first matching value of each computing power resource expected to be covered by the first computing power demand information according to all the second matching values, and then through Through all the first matching degree values, the first business information is allocated to at least one classification set, so that the above-mentioned first business information can be jointly processed by different computing power scheduling priorities and computing power resources corresponding to different classification sets, thereby further improving the classification accuracy, classification scientificity and classification feasibility of computing power requirements, which is conducive to further scientific application of computing power resources, and is conducive to further improving the allocation accuracy of computing power resources and the matching accuracy between different computing power resources and different computing power requirements, improving the application scientificity and application sustainability of computing power resources, and is conducive to improving the application accuracy of regional computing power resources, and promoting the sustainable and healthy development of the computing power field.

[0158] In this optional embodiment, as an optional implementation, each of the above-mentioned demand sub-information has a corresponding second priority value, and the above-mentioned calculation module 303 calculates the first matching value of each computing power resource expected to be covered by the first business information and the first computing power demand information according to all the second matching values. The specific method includes:

[0159] For each demand sub-information, according to the second priority value of the demand sub-information, update the second matching value of the demand sub-information; determine whether the updated second matching value is greater than or equal to a preset second matching threshold, and when it is determined that the updated second matching value is greater than or equal to the preset second matching threshold, determine that the demand sub-information is the target demand sub-information;

[0160] For each computing power resource, the first matching value between the first business information and the computing power resource is calculated based on the current computing power demand coverage information of the computing power resource and the updated second matching value of all target demand sub-information corresponding to the computing power resource.

[0161] It can be seen that the implementation of this optional embodiment can further update the second matching value of each demand sub-information according to the second priority value of the demand sub-information, so that when it is judged that the updated second matching value is greater than or equal to the preset second matching threshold, the demand sub-information is determined to be the target demand sub-information, and thus for each computing power resource, the first matching value of the first business information and the computing power resource is calculated according to the current computing power demand coverage information of the computing power resource and the updated second matching value of all target demand sub-information corresponding to the computing power resource, which can further improve the accuracy of determining the target demand sub-information and the calculation accuracy of the first matching value, which is conducive to improving the classification accuracy of computing power requirements.

[0162] In this optional embodiment, as another optional implementation, the specific manner in which the classification module 304 allocates the first service information to at least one classification set according to all the first matching degree values ​​includes:

[0163] According to all the first matching values, at least one target computing resource is determined among all computing resources, the first matching value of the target computing resource is greater than or equal to a preset first matching threshold, and the first matching threshold is calculated based on all the first matching values;

[0164] Determine resource allocation information of each target computing resource according to the first computing power demand information, where the resource allocation information includes at least one of computing power allocation amount information, computing power allocation timing information, and computing power link configuration information;

[0165] Determine computing power allocation link information of the first service information according to all resource allocation information;

[0166] The first service information is allocated to at least one classification set according to the computing power allocation link information.

[0167] It can be seen that the implementation of this optional embodiment can, after calculating the first business information and the first computing power demand information expect to cover each computing power resource, determine, based on all the first matching values, at least one target computing power resource whose first matching value is greater than or equal to the preset first matching value threshold among all the computing power resources within the expected computing power deployment range of the first business information analyzed based on the first computing power demand information, thereby determining, based on the first computing power demand information, resource allocation information of each target computing power resource including at least one of computing power allocation amount information, computing power allocation timing information, and computing power link configuration information, and determining, based on all the resource allocation information, the first computing power resource. The computing power allocation link information of a business information is obtained, and then based on the computing power allocation link information, the first business information is allocated to at least one classification set, so that the priority allocation of computing power resources is completed through the computing power allocation link information, and the computing power scheduling priority is integrated into the computing power demand classification process, which further improves the classification accuracy of computing power demand while also improving the scheduling accuracy and efficiency of computing power resources, which is conducive to further improving the matching accuracy between different computing power resources and different computing power demands, improving the application scientificity and application sustainability of computing power resources, and is conducive to improving the application accuracy of regional computing power resources, and promoting the sustainable and healthy development of the computing power field.

[0168] Embodiment 4

[0169] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of another computing power demand classification device disclosed in an embodiment of the present invention. The computing power demand classification device can be applied to business management / distribution equipment, and can also be applied to smart devices related to business management / distribution, including but not limited to one or more of cloud devices, edge computing devices, relay devices, base station devices, city management devices, and smart network devices, which are not limited in the embodiment of the present invention. Figure 4 As shown, the computing power requirement classification device may include:

[0170] The memory 401 stores executable program codes.

[0171] A processor 402 is coupled to the memory 401 .

[0172] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the computing power requirement classification method described in the first embodiment of the present invention or the second embodiment of the present invention.

[0173] Embodiment 5

[0174] 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 computing power requirement classification method described in Embodiment 1 or Embodiment 2 of the present invention.

[0175] Embodiment 6

[0176] 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 computing power requirement classification method described in Example 1 or Example 2.

[0177] 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.

[0178] 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.

[0179] Finally, it should be noted that the computing power requirement classification method and device 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 solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A computing power requirement classification method, characterized in that: The method comprises: Obtaining first business information; Analyzing first computing power requirement information of the first business information according to the first business information; Calculate, according to the first computing power requirement information, a first matching value of each computing power resource that is expected to be covered by the first business information and the first computing power requirement information; According to all the first matching degree values, the first business information is allocated to at least one classification set, and different classification sets correspond to different computing resources and computing scheduling priorities.

2. The computing power requirement classification method according to claim 1, characterized in that: The first business information includes at least one of business type information, business-related object information, and business scenario information. The analyzing, according to the first business information, first computing power requirement information of the first business information includes: Determine, according to the first business information, second business information matching the first business information, where the second business information is prior business information of the first business information; Determine at least one target item according to the second service information and the first service information, where the target item is a distinguishing item between the first service information and the second service information; The first computing power requirement information of the first business information is analyzed according to all the target items and the second business information.

3. The computing power requirement classification method according to claim 2, characterized in that: The analyzing the first computing power requirement information of the first business information according to all the target items and the second business information includes: For each of the target items, obtain the location information of the target item, analyze the first semantic information of the target item and the role information of the target item in the first business information according to the location information; match the first priority value of the target item according to the first semantic information and the role information; determine whether the first priority value is greater than or equal to a preset first priority threshold, and when it is determined that the first priority value is greater than or equal to the preset first priority threshold, determine that the target item is a priority item; The first computing power requirement information of the first business information is analyzed according to all the priority items and the second business information.

4. The computing power requirement classification method according to claim 3, characterized in that: The step of analyzing the first computing power requirement information of the first business information according to all the priority items and the second business information includes: analyzing the second semantic information of the first service information according to the location information of all the priority items and the second service information; Acquire business processing information of the second business information, where the business processing information includes at least one of processing flow information, data processing accuracy information, processing network information, architecture information, computing power resource information, configuration information, and data processing efficiency information; The first computing power requirement information of the first business information is analyzed according to the business processing information and the second semantic information.

5. The computing power requirement classification method according to any one of claims 1 to 4, characterized in that: The first computing power requirement information includes at least one requirement sub-information, and the calculating, according to the first computing power requirement information, a first matching value of each computing power resource that is expected to be covered by the first business information and the first computing power requirement information includes: Analyze the expected computing power deployment range of the first business information according to the first computing power demand information; Obtain computing power resource information of all computing power resources within the expected computing power deployment range; For each of the computing power resources, analyzing the current computing power demand coverage information of the computing power resource according to the computing power resource information of the computing power resource; for each of the demand sub-information, calculating the second matching value between the demand sub-information and the current computing power demand coverage information of the computing power resource; According to all the second matching values, a first matching value of each of the computing resources that the first business information is expected to cover with the first computing power requirement information is calculated.

6. The computing power requirement classification method according to claim 5, characterized in that: Each of the demand sub-information has a corresponding second priority value, and calculating, according to all the second matching values, the first matching value of each of the computing resources that the first business information expects to be covered by the first computing power demand information includes: For each of the requirement sub-information, the second matching value of the requirement sub-information is updated according to the second priority value of the requirement sub-information; whether the updated second matching value is greater than or equal to a preset second matching threshold is determined, and when it is determined that the updated second matching value is greater than or equal to the preset second matching threshold, the requirement sub-information is determined to be the target requirement sub-information; For each of the computing power resources, the first matching value between the first business information and the computing power resource is calculated based on the current computing power demand coverage information of the computing power resource and the updated second matching value of all the target demand sub-information corresponding to the computing power resource.

7. The computing power requirement classification method according to claim 5, characterized in that: The allocating the first service information to at least one classification set according to all the first matching degree values ​​includes: According to all the first matching degree values, determine at least one target computing power resource among all the computing power resources, the first matching degree value of the target computing power resource being greater than or equal to a preset first matching degree threshold, where the first matching degree threshold is calculated based on all the first matching degree values; Determine resource allocation information of each of the target computing resources according to the first computing power demand information, where the resource allocation information includes at least one of computing power allocation amount information, computing power allocation timing information, and computing power link configuration information; Determine computing power allocation link information of the first service information according to all the resource allocation information; According to the computing power allocation link information, the first service information is allocated to at least one classification set.

8. A computing power demand classification device, characterized in that: The device comprises: An acquisition module, used for acquiring first business information; An analysis module, configured to analyze first computing power requirement information of the first business information according to the first business information; A calculation module, configured to calculate, according to the first computing power requirement information, a first matching value of each computing power resource that is expected to be covered by the first business information and the first computing power requirement information; A classification module is used to distribute the first business information into at least one classification set according to all the first matching degree values, and different classification sets correspond to different computing resources and computing scheduling priorities.

9. A computing power demand classification device, 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 computing power requirement classification method as described in any one of claims 1-7.

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

Images