Data Center-Based Energy Consumption Detection Method and System

Abstract

For the energy consumption detection method and system based on a data center provided by this application, since the debugging thread is debugged through artificial intelligence and has specified abnormal data in the template, and the debugging of the energy consumption loss data is to debug the feature nodes according to the debugging instructions and the specified abnormal data, the debugging process is more detailed and accurate. Moreover, the feature nodes are identified from the energy consumption data to be detected, and finally, the debugging results obtained after debugging the feature nodes are fed back to the energy consumption data to be detected. Therefore, the debugging of the energy consumption loss data in the obtained energy consumption data detection results is accurate, improving the accuracy of power energy consumption detection.

Description

Technical Field

[0001] This application relates to the technical field of energy consumption detection, and more particularly, to an energy consumption detection method and system based on a data center. Background Art Summary of the Invention

[0002] To address the technical problems existing in the related art, this application provides an energy consumption detection method and system based on a data center.

[0003] In a first aspect, an energy consumption detection method based on a data center is provided. The method includes: obtaining energy consumption data to be detected, where the energy consumption data to be detected includes energy consumption loss data; identifying a first loss result of the energy consumption loss data and generating a first association between the first loss result and a template in an energy consumption debugging thread, where the first loss result is a characteristic node among the characteristic nodes of the energy consumption loss data that is marginalized in the energy consumption loss data; generating a debugging result of the first loss result according to a debugging instruction, specified abnormal data in the energy consumption debugging thread, and the first association; determining an energy consumption detection thread of the first loss result, where the template in the energy consumption detection thread at least includes the debugging result of the first loss result and a second loss result derived from the first loss result; and combining the energy consumption detection thread and the energy consumption data to be detected to update the energy consumption detection thread in the energy consumption data to be detected, obtaining an energy consumption data detection result.

[0004] In an independently implemented embodiment, the identifying the first loss result of the energy consumption loss data includes: identifying characteristic nodes of the energy consumption loss data; obtaining a mapping result of the characteristic nodes in the energy consumption data to be detected; combining the energy consumption loss data and the mapping result of the characteristic nodes in the energy consumption data to be detected to generate a mapping result on the marginalization of the energy consumption loss data; and using the characteristic node corresponding to the mapping result on the marginalization of the energy consumption loss data as the first loss result.

[0005] In an independently implemented embodiment, the combining the energy consumption loss data and the mapping result of the characteristic nodes in the energy consumption data to be detected to generate a mapping result on the marginalization of the energy consumption loss data includes: configuring a number of requirements in a specified dimension; and determining two mapping results that are marginalized in the energy consumption loss data among the mapping results of the number of characteristic nodes on each of the requirements.

[0006] In an independently implemented embodiment, it further includes: obtaining a second association situation between the templates in the energy consumption debugging thread and the templates in the energy consumption measurement standard thread; the generating of the first association situation between the first loss result and the templates in the energy consumption debugging thread includes: combining the labels of the first loss result and the labels of the templates in the energy consumption measurement standard thread to generate a third association situation between the first loss result and the templates in the energy consumption measurement standard thread; combining the second association situation and the third association situation to generate the first association situation.

[0007] In an independently implemented embodiment, the obtaining of the second association situation between the templates in the energy consumption debugging thread and the templates in the energy consumption measurement standard thread includes: converting the positioning of the templates in the energy consumption debugging thread and the positioning of the templates in the energy consumption measurement standard thread into the same positioning system; generating a difference situation between each template in the energy consumption debugging thread and each template in the energy consumption measurement standard thread; combining the difference situation between each template in the energy consumption debugging thread and each template in the energy consumption measurement standard thread to generate the second association situation.

[0008] In an independently implemented embodiment, the combining of the labels of the first loss result and the labels of the templates in the energy consumption measurement standard thread to generate the third association situation between the first loss result and the templates in the energy consumption measurement standard thread includes: taking the first loss result with the same label and the templates in the energy consumption measurement standard thread as a corresponding template binary group to obtain the third association situation.

[0009] In an independently implemented embodiment, the generating of the debugging result of the first loss result according to the debugging instruction, the specified abnormal data of the templates in the energy consumption debugging thread, and the first association situation includes: combining the debugging instruction to determine the debugging instruction of the energy consumption loss data; obtaining the first abnormal data corresponding to the debugging instruction from the specified abnormal data of the templates in the energy consumption debugging thread; combining the first abnormal data to evaluate the first loss result corresponding to at least one template binary group in the energy consumption debugging thread to obtain the debugging result of the first loss result.

[0010] In an independently implemented embodiment, before combining the first abnormal data to evaluate the first loss result corresponding to at least one template binary group in the energy consumption debugging thread to obtain the debugging result of the first loss result, it further includes: combining the debugging instruction to generate a debugging variable of the debugging instruction; combining the debugging variable to debug the first abnormal data.

[0011] In an independently implemented embodiment, the energy consumption detection thread for determining the first loss result includes: based on the association between the benchmark of the feature node and the first loss result, determining the second loss result corresponding to the first loss result as the result of the specified anomaly in the first loss result; according to the pre-configured second prediction architecture, building a second matching result between the debugging result of the first loss result and the second loss result to obtain the energy consumption detection thread.

[0012] In a second aspect, a data center-based energy consumption detection system is provided, including a processor and a memory that communicate with each other. The processor is configured to read and execute a computer program from the memory to implement the above method.

[0013] The data center-based energy consumption detection method and system provided by the embodiments of the present application obtain the energy consumption data to be detected, identify the feature nodes of the energy consumption loss data in the energy consumption data to be detected, determine the first association between the feature nodes and the template in the energy consumption debugging thread of the energy consumption loss data, and then determine the debugging result of the feature nodes according to the debugging instruction, the specified abnormal data in the energy consumption debugging thread, and the first association. Finally, according to the debugging result of the feature nodes and the energy consumption data to be detected, the energy consumption data detection result is determined. Since the debugging thread is debugged by artificial intelligence and has the specified abnormal data of the template, and the debugging of the energy consumption loss data is to debug the feature nodes according to the debugging instruction and the specified abnormal data, the debugging process is more detailed and accurate. Moreover, the feature nodes are identified from the energy consumption data to be detected, and finally the debugging result obtained by debugging the feature nodes is fed back to the energy consumption data to be detected. Therefore, the debugging of the energy consumption loss data in the obtained energy consumption data detection result is accurate, improving the accuracy of power energy consumption detection. Description of the Drawings

[0014] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1 It is a flowchart of a data center-based energy consumption detection method provided by an embodiment of the present application.

[0016] Figure 2 It is a block diagram of a data center-based energy consumption detection device provided by an embodiment of the present application.

[0017] Figure 3It is an architecture diagram of an energy consumption detection system based on a data center provided by an embodiment of the present application. Specific implementation manners

[0018] To better understand the above technical solution, the technical solution of the present application will be described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Without conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0019] Please refer to Figure 1 , which shows an energy consumption detection method based on a data center. The method may include the technical solutions described in the following steps 100-step 400.

[0020] Step 100, obtain the energy consumption data to be detected, where the energy consumption data to be detected includes energy consumption loss data.

[0021] Step 200, identify the first loss result of the energy consumption loss data, and generate the first association situation between the first loss result and the template in the energy consumption debugging thread, where the first loss result is the feature node at the margin of the feature nodes of the energy consumption loss data.

[0022] Step 300, generate the debugging result of the first loss result according to the debugging instruction, the specified abnormal data in the energy consumption debugging thread, and the first association situation; determine the energy consumption detection thread of the first loss result, where the template in the energy consumption detection thread at least includes the debugging result of the first loss result and the second loss result derived based on the first loss result.

[0023] Step 400, combine the energy consumption detection thread and the energy consumption data to be detected, and update the energy consumption detection thread in the energy consumption data to be detected to obtain the energy consumption data detection result.

[0024] It can be understood that when implementing the technical content described in the above steps 100 - 400, by obtaining the energy consumption data to be detected, identifying the characteristic nodes of the energy consumption loss data in the energy consumption data to be detected, determining the first association situation between the characteristic nodes and the template in the energy consumption debugging thread of the energy consumption loss data, then according to the debugging instruction, the specified abnormal data in the energy consumption debugging thread and the first association situation, determining the debugging result of the characteristic nodes, and finally according to the debugging result of the characteristic nodes and the energy consumption data to be detected, determining the energy consumption data detection result. Since the debugging thread is debugged by artificial intelligence and has the specified abnormal data of the template, and the debugging of the energy consumption loss data is to debug the characteristic nodes according to the debugging instruction and the specified abnormal data, the debugging process is more detailed and accurate. Moreover, the characteristic nodes are identified from the energy consumption data to be detected, and finally the debugging result obtained by debugging the characteristic nodes is fed back to the energy consumption data to be detected. Therefore, the debugging of the energy consumption loss data in the obtained energy consumption data detection result is accurate, improving the accuracy of power energy consumption detection.

[0025] For some possible embodiments, the identifying the first loss result of the energy consumption loss data includes: identifying the characteristic nodes of the energy consumption loss data; obtaining the mapping result of the characteristic nodes in the energy consumption data to be detected; combining the energy consumption loss data and the mapping result of the characteristic nodes in the energy consumption data to be detected to generate the mapping result on the marginalization of the energy consumption loss data; and using the characteristic nodes corresponding to the mapping result on the marginalization of the energy consumption loss data as the first loss result.

[0026] For some possible embodiments, the combining the energy consumption loss data and the mapping result of the characteristic nodes in the energy consumption data to be detected to generate the mapping result on the marginalization of the energy consumption loss data includes: configuring a number of requirements in a specified dimension; and determining two mapping results on the marginalization of the energy consumption loss data among the mapping results of the number of characteristic nodes on each of the requirements.

[0027] For some possible embodiments, it further includes: obtaining the second association situation between the template in the energy consumption debugging thread and the template in the energy consumption metering standard thread; the generating the first association situation between the first loss result and the template in the energy consumption debugging thread includes: combining the label of the first loss result and the label of the template in the energy consumption metering standard thread to generate the third association situation between the first loss result and the template in the energy consumption metering standard thread; and combining the second association situation and the third association situation to generate the first association situation.

[0028] For some possible embodiments, obtaining the second association situation between the templates in the energy consumption debugging thread and the templates in the energy consumption measurement standard thread includes: converting the positioning of the templates in the energy consumption debugging thread and the positioning of the templates in the energy consumption measurement standard thread into the same positioning system; generating the difference situation between each template in the energy consumption debugging thread and each template in the energy consumption measurement standard thread; and generating the second association situation by combining the difference situation between each template in the energy consumption debugging thread and each template in the energy consumption measurement standard thread.

[0029] For some possible embodiments, combining the labels of the first loss result and the labels of the templates in the energy consumption measurement standard thread to generate the third association situation between the first loss result and the templates in the energy consumption measurement standard thread includes: using the first loss result with the same label and the templates in the energy consumption measurement standard thread as corresponding template pairs to obtain the third association situation.

[0030] For some possible embodiments, generating the debugging result of the first loss result according to the debugging instruction, the specified abnormal data of the templates in the energy consumption debugging thread, and the first association situation includes: combining the debugging instruction to determine the debugging instruction of the energy consumption loss data; obtaining the first abnormal data corresponding to the debugging instruction from the specified abnormal data of the templates in the energy consumption debugging thread; and evaluating the first loss result corresponding to at least one template pair in the energy consumption debugging thread by combining the first abnormal data to obtain the debugging result of the first loss result.

[0031] For some possible embodiments, before evaluating the first loss result corresponding to at least one template pair in the energy consumption debugging thread by combining the first abnormal data to obtain the debugging result of the first loss result, it further includes: generating the debugging variable of the debugging instruction by combining the debugging instruction; and debugging the first abnormal data by combining the debugging variable.

[0032] For some possible embodiments, determining the energy consumption detection thread of the first loss result includes: on the basis that there is an association between the benchmark of the feature node and the first loss result, determining the second loss result corresponding to the first loss result as the result of the specified abnormality in the first loss result; and building a second matching result between the debugging result of the first loss result and the second loss result according to the pre-configured second prediction architecture to obtain the energy consumption detection thread.

[0033] On the above basis, please refer to Figure 2, a power consumption detection device 200 based on a data center is provided, which is applied to a power consumption detection system based on a data center. The device includes:

[0034] A data acquisition module 210, configured to acquire power consumption data to be detected, where the power consumption data to be detected includes power consumption loss data;

[0035] A result generation module 220, configured to identify a first loss result of the power consumption loss data and generate a first association condition between the first loss result and a template in a power consumption debugging thread, where the first loss result is a feature node at the edge of the feature nodes of the power consumption loss data;

[0036] A thread determination module 230, configured to generate a debugging result of the first loss result according to a debugging instruction, specified abnormal data in the template of the power consumption debugging thread, and the first association condition; determine a power consumption detection thread of the first loss result, where the template in the power consumption detection thread at least includes the debugging result of the first loss result and a second loss result derived from the first loss result;

[0037] A result detection module 240, configured to combine the power consumption detection thread and the power consumption data to be detected, and update the power consumption detection thread in the power consumption data to be detected to obtain a power consumption data detection result.

[0038] On this basis, please refer to Figure 3 , which shows a power consumption detection system 300 based on a data center, including a processor 310 and a memory 320 that communicate with each other. The processor 310 is configured to read and execute a computer program from the memory 320 to implement the above method.

[0039] On this basis, a computer-readable storage medium is further provided, on which a computer program stored thereon implements the above method when running.

[0040] In summary, based on the above solution, by obtaining the energy consumption data to be detected, identifying the characteristic nodes of the energy consumption loss data in the energy consumption data to be detected, determining the first association between the characteristic nodes and the template in the energy consumption debugging thread of the energy consumption loss data, then according to the debugging instruction, the specified abnormal data of the template in the energy consumption debugging thread and the first association, determining the debugging result of the characteristic nodes, and finally according to the debugging result of the characteristic nodes and the energy consumption data to be detected, determining the energy consumption data detection result. Since the debugging thread is debugged by artificial intelligence and has the specified abnormal data of the template, and the debugging of the energy consumption loss data is to debug the characteristic nodes according to the debugging instruction and the specified abnormal data, the debugging process is more detailed and accurate. Moreover, the characteristic nodes are identified from the energy consumption data to be detected, and finally the debugging result obtained by debugging the characteristic nodes is fed back to the energy consumption data to be detected. Therefore, the debugging of the energy consumption loss data in the obtained energy consumption data detection result is accurate, improving the accuracy of power energy consumption detection.

[0041] It should be understood that the above-described systems and their modules can be implemented in various ways. For example, in some embodiments, the systems and their modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art can understand that the above methods and systems can be implemented using computer-executable instructions and / or included in processor control code, such as provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and their modules of the present application can be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software implemented by various types of processors, or by a combination of the above hardware circuits and software (e.g., firmware).

[0042] It should be noted that the beneficial effects that may be produced by different embodiments are different. In different embodiments, the beneficial effects that may be produced can be any one or several of the above combinations, or any other beneficial effects that may be obtained.

[0043] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this application. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are proposed in this application, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0044] Meanwhile, this application uses specific terms to describe the embodiments of this application. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0045] In addition, those skilled in the art can understand that various aspects of this application can be illustrated and described by several patentable types or situations, including any new and useful process, machine, product, or combination of substances, or any new and useful improvement to them. Accordingly, various aspects of this application can be executed entirely by hardware, can be executed entirely by software (including firmware, resident software, microcode, etc.), or can be executed by a combination of hardware and software. The above hardware or software can all be referred to as "data blocks", "modules", "engines", "units", "components", or "systems". In addition, various aspects of this application may be embodied as a computer product located in one or more computer-readable media, and the product includes computer-readable program code.

[0046] The computer storage medium may contain a propagated data signal containing computer program code, such as on a baseband or as part of a carrier wave. This propagated signal may have various forms of manifestation, including electromagnetic form, optical form, etc., or a suitable combination of forms. The computer storage medium can be any computer-readable medium other than a computer-readable storage medium, and this medium can be connected to an instruction execution system, device, or equipment to implement communication, propagation, or transmission for use of the program. The program code located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0047] The computer program code required for the operations of various parts of this application can be written in any one or more programming languages, including object-oriented programming languages such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby, and Groovy, or other programming languages. This program code can run entirely on the user's computer, or run as an independent software package on the user's computer, or partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (for example, through the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0048] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numerical and alphabetical characters, or the use of other names in this application are not used to limit the order of the processes and methods of this application. Although some currently considered useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details only serve the purpose of illustration, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on an existing server or mobile device.

[0049] Similarly, it should be noted that, in order to simplify the description of this application disclosure and thus help the understanding of one or more embodiments of the invention, in the previous description of the embodiments of this application, sometimes multiple features are merged into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than those mentioned in the claims. In fact, the features of the embodiments are less than all the features of the individual embodiments disclosed above.

[0050] In some embodiments, numbers are used to describe components and the quantity of attributes. It should be understood that such numbers used in the description of embodiments are, in some examples, modified by the modifiers "about", "approximately", or "substantially". Unless otherwise specified, "about", "approximately", or "substantially" indicate that the said numbers allow for adaptive variations. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may vary according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining general digits. Although the numerical ranges and parameters used in some embodiments of the present application to confirm the breadth of their scope are approximate values, in specific embodiments, such numerical settings are as precise as possible within the feasible range.

[0051] For each patent, patent application, patent application publication, and other materials cited in the present application, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into the present application by reference. Except for application history documents that are inconsistent with or conflict with the content of the present application, and except for documents that limit the broadest scope of the claims of the present application (currently or subsequently appended to the present application). It should be noted that if there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the supplementary materials of the present application and the content described in the present application, the descriptions, definitions, and / or uses of terms in the present application shall prevail.

[0052] Finally, it should be understood that the embodiments described in the present application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, by way of example and not limitation, alternative configurations of the embodiments of the present application may be considered consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

[0053] The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. An energy consumption detection method based on a data center, characterized in that, the method includes: obtaining energy consumption data to be detected, wherein the energy consumption data to be detected includes energy consumption loss data; identifying a first loss result of the energy consumption loss data and generating a first association situation between the first loss result and a template in an energy consumption debugging thread, wherein the first loss result is a characteristic node in the characteristic nodes of the energy consumption loss data that is marginalized in the energy consumption loss data; generating a debugging result of the first loss result according to a debugging instruction, specified abnormal data in the template in the energy consumption debugging thread, and the first association situation; determining an energy consumption detection thread of the first loss result, wherein the template in the energy consumption detection thread at least includes the debugging result of the first loss result and a second loss result derived based on the first loss result; combining the energy consumption detection thread and the energy consumption data to be detected, and updating the energy consumption detection thread in the energy consumption data to be detected to obtain an energy consumption data detection result; further including: obtaining a second association situation between a template in the energy consumption debugging thread and a template in an energy consumption measurement standard thread; the generating of the first association situation between the first loss result and the template in the energy consumption debugging thread includes: combining a label of the first loss result and a label of the template in the energy consumption measurement standard thread to generate a third association situation between the first loss result and the template in the energy consumption measurement standard thread; combining the second association situation and the third association situation to generate the first association situation; the obtaining of the second association situation between the template in the energy consumption debugging thread and the template in the energy consumption measurement standard thread includes: converting the positioning of the template in the energy consumption debugging thread and the positioning of the template in the energy consumption measurement standard thread into the same positioning system; generating a difference situation between each template in the energy consumption debugging thread and each template in the energy consumption measurement standard thread; combining the difference situation between each template in the energy consumption debugging thread and each template in the energy consumption measurement standard thread to generate the second association situation.

2. The energy consumption detection method based on a data center according to claim 1, characterized in that, the identifying of the first loss result of the energy consumption loss data includes: identifying the characteristic nodes of the energy consumption loss data; obtaining a mapping result of the characteristic nodes in the energy consumption data to be detected; combining the energy consumption loss data and the mapping result of the characteristic nodes in the energy consumption data to be detected to generate a mapping result on the marginalization of the energy consumption loss data; taking the characteristic node corresponding to the mapping result on the marginalization of the energy consumption loss data as the first loss result.

3. The energy consumption detection method based on a data center according to claim 2, characterized in that, the combining of the energy consumption loss data and the mapping result of the characteristic nodes in the energy consumption data to be detected to generate a mapping result on the marginalization of the energy consumption loss data includes: configuring a number of requirements in a specified dimension; Among the mapping results of several of the above-mentioned feature nodes for each of the above requirements, determine two mapping results that are on the margin of the energy consumption loss data.

4. The energy consumption detection method based on a data center according to claim 1, wherein, combining the label of the first loss result and the label of the template in the energy consumption measurement standard thread to generate a third association situation between the first loss result and the template in the energy consumption measurement standard thread, including: taking the first loss result with the same label and the template of the energy consumption measurement standard thread as a corresponding template binary group to obtain the third association situation.

5. The energy consumption detection method based on a data center according to claim 4, wherein, generating a debugging result of the first loss result according to the debugging instruction, the specified abnormal data of the template in the energy consumption debugging thread, and the first association situation, including: determining a debugging instruction for the energy consumption loss data in combination with the debugging instruction; obtaining first abnormal data corresponding to the debugging instruction from the specified abnormal data of the template in the energy consumption debugging thread; evaluating the first loss result corresponding to at least one template binary group in the energy consumption debugging thread in combination with the first abnormal data to obtain a debugging result of the first loss result.

6. The energy consumption detection method based on a data center according to claim 5, wherein, before evaluating the first loss result corresponding to at least one template binary group in the energy consumption debugging thread in combination with the first abnormal data to obtain a debugging result of the first loss result, it further includes: generating a debugging variable of the debugging instruction in combination with the debugging instruction; debugging the first abnormal data in combination with the debugging variable.

7. The energy consumption detection method based on a data center according to claim 2, wherein, determining the energy consumption detection thread of the first loss result includes: on the basis that there is an association between the benchmark of the feature node and the first loss result, determining a second loss result corresponding to the first loss result with the specified abnormal result in the first loss result; building a second matching result between the debugging result of the first loss result and the second loss result according to a pre-configured second prediction architecture to obtain the energy consumption detection thread.

8. An energy consumption detection system based on a data center, wherein, it includes a processor and a memory that communicate with each other, and the processor is used to read and execute a computer program from the memory to implement the method according to any one of claims 1-7.

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