CN115345143A - Energy consumption detection method and system based on data center - Google Patents
Energy consumption detection method and system based on data center Download PDFInfo
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- CN115345143A CN115345143A CN202210903419.2A CN202210903419A CN115345143A CN 115345143 A CN115345143 A CN 115345143A CN 202210903419 A CN202210903419 A CN 202210903419A CN 115345143 A CN115345143 A CN 115345143A
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
According to the energy consumption detection method and system based on the data center, due to the fact that the debugging thread is debugged through artificial intelligence and has the specified abnormal data of the template, and the energy consumption data can be debugged, the feature nodes are debugged according to the debugging indication and the specified abnormal data, the debugging process is more detailed and accurate, the feature nodes are obtained by being identified from the energy consumption data to be detected, finally, the debugging result obtained by debugging the feature nodes is fed back to the energy consumption data to be detected, the debugging result of the energy consumption data in the obtained energy consumption data detection result is accurate, and the accuracy of electric power energy consumption detection is improved.
Description
Technical Field
The application relates to the technical field of energy consumption detection, in particular to an energy consumption detection method and system based on a data center.
Background
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides a data center-based energy consumption detection method and system.
In a first aspect, a method for detecting energy consumption based on a data center is provided, where the method includes: obtaining energy consumption data to be detected, wherein the energy consumption data to be detected comprises energy consumption loss data; identifying a first loss result of the energy consumption loss data, and generating a first correlation condition between the first loss result and a template in an energy consumption debugging thread, wherein the first loss result is a marginalized feature node of the energy consumption loss data in the feature nodes of the energy consumption loss data; generating a debugging result of the first loss result according to a debugging instruction, the specified abnormal data of the template in the energy consumption debugging thread and the first correlation condition; determining an energy consumption detection thread of the first loss result, wherein a template in the energy consumption detection thread at least comprises a debugging result of the first loss result and a second loss result derived based on the first loss result; and updating the energy consumption detection thread in the energy consumption data to be detected by combining the energy consumption detection thread and the energy consumption data to be detected to obtain an energy consumption data detection result.
In a separately implemented embodiment, the identifying a first loss result of the energy consumption loss data comprises: identifying a characteristic node of the energy consumption loss data; obtaining a mapping result of the characteristic node in the energy consumption data to be detected; combining the energy consumption loss data in the energy consumption data to be detected and the mapping result of the characteristic node to generate a marginalized mapping result of the energy consumption loss data; and taking the characteristic node corresponding to the mapping result on the marginalization of the energy consumption loss data as the first loss result.
In an independently implemented embodiment, the generating a mapping result on marginalization of the energy consumption loss data by combining the energy consumption loss data in the energy consumption data to be detected and the mapping result of the feature node includes: configuring a plurality of requirements on a specified dimension; and determining two mapping results on marginalization of the energy consumption loss data in the mapping results of a plurality of feature nodes on each requirement.
In a separately implemented embodiment, the method further comprises: obtaining a second correlation condition of the template in the energy consumption debugging thread and the template in the energy consumption metering standard thread; the generating a first association condition between the first loss result and a 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 a third correlation condition of the first loss result and the template in the energy consumption metering standard thread; and combining the second association condition and the third association condition to generate the first association condition.
In an embodiment of an independent implementation, the obtaining a second association between the template in the energy consumption debugging thread and the template in the energy consumption metering 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 metering standard thread into the same positioning system; generating difference conditions between each template in the energy consumption debugging thread and each template in the energy consumption metering standard thread; and generating the second correlation condition by combining the difference condition between each template in the energy consumption debugging thread and each template in the energy consumption metering standard thread.
In an embodiment of an independent implementation, the combining the label of the first loss result and the label of the template in the energy consumption metering standard thread to generate a third association condition between the first loss result and the template in the energy consumption metering standard thread includes: and taking the first loss result with the same label and the template of the energy consumption measurement standard thread as template binary groups corresponding to each other to obtain the third correlation condition.
In an independently implemented embodiment, the generating a debugging result of the first loss result according to a debugging instruction, specified exception data of a template in the energy consumption debugging thread, and the first correlation condition includes: determining a debugging indication of energy consumption loss data by combining the debugging indication; obtaining first abnormal data corresponding to the debugging indication in the specified abnormal data of the template in the energy consumption debugging thread; and evaluating the first loss result corresponding to at least one template binary group in the energy consumption debugging thread by combining the first abnormal data to obtain a debugging result of the first loss result.
In an independently implemented embodiment, before the evaluating the first loss result corresponding to at least one template binary group in the energy consumption debugging thread by combining the first exception data to obtain the debugging result of the first loss result, the method further includes: generating a debugging variable of the debugging instruction by combining the debugging instruction; and debugging the first abnormal data by combining the debugging variable.
In an independently implemented embodiment, the determining the energy consumption detection thread of the first loss result includes: determining a result of a specified anomaly in the first loss result as a second loss result corresponding to the first loss result on the basis that the reference of a feature node is associated with the first loss result; and according to a second prediction framework configured in advance, 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.
In a second aspect, a data center-based energy consumption detection system is provided, which includes a processor and a memory, which are in communication with each other, and the processor is configured to read a computer program from the memory and execute the computer program to implement the method described above.
According to the energy consumption detection method and system based on the data center, the energy consumption data to be detected are obtained, the characteristic nodes capable of consuming the energy loss data in the energy consumption data to be detected are identified, the first association condition of the characteristic nodes and the template in the energy consumption debugging thread of the energy consumption data is determined, then the debugging result of the characteristic nodes is determined according to the debugging indication, the specified abnormal data of the template in the energy consumption debugging thread and the first association condition, and finally the energy consumption data detection result is determined according to the debugging result of the characteristic nodes and the energy consumption data to be detected. The debugging thread is debugged through artificial intelligence and is provided with the specified abnormal data of the template, the debugging on the energy consumption and loss data is realized by debugging the characteristic nodes according to the debugging indication and the specified abnormal data, so that the debugging process is more detailed and accurate, 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, so that the debugging on the energy consumption and loss data in the obtained energy consumption data detection result is accurate, and the accuracy of electric power energy consumption detection is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart of a method for detecting energy consumption based on a data center according to an embodiment of the present disclosure.
Fig. 2 is a block diagram of an energy consumption detection apparatus based on a data center according to an embodiment of the present application.
Fig. 3 is an architecture diagram of a data center-based energy consumption detection system according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.
Referring to fig. 1, a method for detecting energy consumption based on a data center is shown, which may include the technical solutions described in the following steps 100-400.
Step 100, energy consumption data to be detected are obtained, wherein the energy consumption data to be detected comprise energy consumption loss data.
And step 400, 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.
It can be understood that, when the technical contents described in the above steps 100 to 400 are executed, the energy consumption data to be detected is obtained, the feature node of the energy consumption data to be detected, which is capable of consuming the energy consumption data, is identified, a first association condition between the feature node and the template in the energy consumption debugging thread of the energy consumption loss data is determined, then the debugging result of the feature node is determined according to the debugging instruction, the specified abnormal data of the template in the energy consumption debugging thread, and the first association condition, and finally the energy consumption data detection result is determined according to the debugging result of the feature node and the energy consumption data to be detected. The debugging thread is debugged through artificial intelligence and is provided with the specified abnormal data of the template, the characteristic nodes are debugged according to the debugging indication and the specified abnormal data, so that the debugging process is more detailed and accurate, the characteristic nodes are obtained by identifying 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, so that the debugging of the energy consumption data in the obtained energy consumption data detection result is accurate, and the accuracy of electric power energy consumption detection is improved.
For some possible implementations, the identifying a first loss result of the energy consumption loss data comprises: identifying characteristic nodes of the energy consumption loss data; obtaining a mapping result of the characteristic node in the energy consumption data to be detected; combining the energy consumption loss data in the energy consumption data to be detected and the mapping result of the characteristic node to generate a marginalized mapping result of the energy consumption loss data; and taking the characteristic node corresponding to the mapping result on the marginalization of the energy consumption loss data as the first loss result.
For some possibly implemented embodiments, the generating a mapping result on marginalization of the energy consumption loss data by combining the energy consumption loss data in the energy consumption data to be detected and the mapping result of the feature node includes: configuring a plurality of requirements on a specified dimension; and determining two mapping results on marginalization of the energy consumption loss data in the mapping results of a plurality of feature nodes on each requirement.
For some possible implementations, further comprising: obtaining a second correlation condition of the template in the energy consumption debugging thread and the template in the energy consumption metering standard thread; the generating a first association condition between the first loss result and a template in an energy consumption debugging thread comprises: combining the label of the first loss result and the label of the template in the energy consumption metering standard thread to generate a third correlation condition of the first loss result and the template in the energy consumption metering standard thread; and combining the second association condition and the third association condition to generate the first association condition.
For some possible embodiments, the obtaining a second association between the template in the energy consumption debugging thread and the template in the energy consumption metering 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 metering standard thread into the same positioning system; generating difference conditions between each template in the energy consumption debugging thread and each template in the energy consumption metering standard thread; and generating the second correlation condition by combining the difference condition between each template in the energy consumption debugging thread and each template in the energy consumption metering standard thread.
For some possible embodiments, the generating a third association of the first loss result and the template in the energy consumption metering standard thread by combining the label of the first loss result and the label of the template in the energy consumption metering standard thread includes: and taking the first loss result with the same label and the template of the energy consumption measurement standard thread as template binary groups corresponding to each other to obtain the third correlation condition.
For some possible embodiments, the generating a debugging result of the first loss result according to a debugging instruction, specified exception data of a template in the energy consumption debugging thread, and the first correlation condition includes: determining a debugging indication of energy consumption loss data by combining the debugging indication; acquiring first abnormal data corresponding to the debugging indication in the specified abnormal data of the template in the energy consumption debugging thread; and evaluating the first loss result corresponding to at least one template binary group in the energy consumption debugging thread by combining the first abnormal data to obtain a debugging result of the first loss result.
For some embodiments that may be implemented, before the evaluating the first loss result corresponding to at least one template binary group in the energy consumption debugging thread by combining the first exception data to obtain the debugging result of the first loss result, the method further includes: generating a debugging variable of the debugging instruction by combining the debugging instruction; and debugging the first abnormal data by combining the debugging variable.
For some possible implementations, the determining the energy consumption detection thread of the first loss result includes: determining a result of a specified anomaly in the first loss result as a second loss result corresponding to the first loss result on the basis that the reference of a feature node is associated with the first loss result; and according to a second prediction framework configured in advance, 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.
On the basis, please refer to fig. 2 in combination, there is provided a data center-based energy consumption detecting apparatus 200, applied to a data center-based energy consumption detecting system, the apparatus including:
the data obtaining module 210 is configured to obtain energy consumption data to be detected, where the energy consumption data to be detected includes energy consumption loss data;
a result generating module 220, configured to identify a first loss result of the energy consumption loss data, and generate a first association between the first loss result and a template in an energy consumption debugging thread, where the first loss result is a feature node in the feature nodes of the energy consumption loss data, where the feature node is marginalized by the energy consumption loss data;
a thread determining module 230, configured to generate a debugging result of the first loss result according to a debugging instruction, the specified abnormal data of the template in the energy consumption debugging thread, and the first association condition; determining an energy consumption detection thread of the first loss result, wherein a template in the energy consumption detection thread at least comprises a debugging result of the first loss result and a second loss result derived based on the first loss result;
and a result detection module 240, configured to 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 an energy consumption data detection result.
On the basis of the above, please refer to fig. 3 in combination, which shows a data center-based energy consumption detection system 300, which includes a processor 310 and a memory 320 that are in communication with each other, wherein the processor 310 is configured to read a computer program from the memory 320 and execute the computer program to implement the above method.
On the basis of the above, there is also provided a computer-readable storage medium on which a computer program is stored, which when executed implements the above-described method.
In summary, based on the above scheme, the energy consumption data to be detected is obtained, the feature node which consumes the energy loss data in the energy consumption data to be detected is identified, the first association condition of the feature node and the template in the energy consumption debugging thread of the energy consumption loss data is determined, then the debugging result of the feature node is determined according to the debugging instruction, the specified abnormal data of the template in the energy consumption debugging thread and the first association condition, and finally the energy consumption data detection result is determined according to the debugging result of the feature node and the energy consumption data to be detected. The debugging thread is debugged through artificial intelligence and is provided with the specified abnormal data of the template, the debugging on the energy consumption and loss data is realized by debugging the characteristic nodes according to the debugging indication and the specified abnormal data, so that the debugging process is more detailed and accurate, 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, so that the debugging on the energy consumption and loss data in the obtained energy consumption data detection result is accurate, and the accuracy of electric power energy consumption detection is improved.
It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, 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 system and its modules of the present application may 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 executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).
It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered as illustrative only and not limiting of the application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such alterations, modifications, and improvements are intended to be suggested herein and are intended to be within the spirit and scope of the exemplary embodiments of this application.
Also, the present application uses specific words to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, certain features, structures, or characteristics may be combined as suitable in one or more embodiments of the application.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C + +, C #, VB.NET, python, and the like, a conventional programming language such as C, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, unless explicitly recited in the claims, the order of processing elements and sequences, use of numbers and letters, or use of other designations in this application is not intended to limit the order of the processes and methods in this application. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features are required than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit-preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application history document is inconsistent or conflicting with the present application as to the extent of the present claims, which are now or later appended to this application. It is to be understood that the descriptions, definitions and/or uses of terms in the attached materials of this application shall control if they are inconsistent or inconsistent with the statements and/or uses of this application.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art to which the present application pertains. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.
Claims (10)
1. A method for detecting energy consumption based on a data center is characterized by comprising the following steps:
obtaining energy consumption data to be detected, wherein the energy consumption data to be detected comprises energy consumption loss data;
identifying a first loss result of the energy consumption loss data, and generating a first correlation condition between the first loss result and a template in an energy consumption debugging thread, wherein the first loss result is a marginalized feature node of the energy consumption loss data in the feature nodes of the energy consumption loss data;
generating a debugging result of the first loss result according to a debugging instruction, the specified abnormal data of the template in the energy consumption debugging thread and the first correlation condition; determining an energy consumption detection thread of the first loss result, wherein a template in the energy consumption detection thread at least comprises a debugging result of the first loss result and a second loss result derived based on the first loss result;
and updating the energy consumption detection thread in the energy consumption data to be detected by combining the energy consumption detection thread and the energy consumption data to be detected to obtain an energy consumption data detection result.
2. The data center-based energy consumption detection method of claim 1, wherein the identifying a first loss result of the energy consumption loss data comprises:
identifying a characteristic node of the energy consumption loss data; obtaining a mapping result of the characteristic node in the energy consumption data to be detected;
combining the energy consumption loss data in the energy consumption data to be detected and the mapping result of the characteristic node to generate a marginalized mapping result of the energy consumption loss data;
and 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 data center-based energy consumption detection method according to claim 2, wherein the generating a mapping result on marginalization of the energy consumption loss data by combining the energy consumption loss data in the energy consumption data to be detected and the mapping result of the feature node comprises:
configuring a plurality of requirements on a specified dimension;
and determining two mapping results on marginalization of the energy consumption loss data in the mapping results of a plurality of feature nodes on each requirement.
4. The data center-based energy consumption detection method according to claim 1, further comprising: obtaining a second correlation condition of the template in the energy consumption debugging thread and the template in the energy consumption metering standard thread; the generating a first association condition between the first loss result and a 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 a third correlation condition of the first loss result and the template in the energy consumption metering standard thread;
and combining the second association condition and the third association condition to generate the first association condition.
5. The method according to claim 4, wherein the obtaining a second correlation between the template in the energy consumption debugging thread and the template in the energy consumption metering standard thread comprises:
converting the positioning of the template in the energy consumption debugging thread and the positioning of the template in the energy consumption metering standard thread into the same positioning system; generating difference conditions between each template in the energy consumption debugging thread and each template in the energy consumption metering standard thread;
and generating the second correlation condition by combining the difference condition between each template in the energy consumption debugging thread and each template in the energy consumption metering standard thread.
6. The data center-based energy consumption detection method according to claim 4, wherein the generating a third correlation between the first loss result and the template in the energy consumption metering standard thread by combining the label of the first loss result and the label of the template in the energy consumption metering standard thread comprises: and taking the first loss result with the same label and the template of the energy consumption metering standard thread as template binary groups corresponding to each other to obtain the third correlation condition.
7. The data center-based energy consumption detection method according to claim 6, wherein the 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 correlation condition includes:
determining a debugging indication of energy consumption loss data by combining the debugging indication; obtaining first abnormal data corresponding to the debugging indication in the specified abnormal data of the template in the energy consumption debugging thread;
and evaluating the first loss result corresponding to at least one template binary group in the energy consumption debugging thread by combining the first abnormal data to obtain a debugging result of the first loss result.
8. The method according to claim 7, wherein before the 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 the debugging result of the first loss result, the method further comprises: generating a debugging variable of the debugging instruction by combining the debugging instruction; and debugging the first abnormal data by combining the debugging variable.
9. The data center-based energy consumption detection method according to claim 2, wherein the determining the energy consumption detection thread of the first loss result comprises:
determining a result of a specified anomaly in the first loss result as a second loss result corresponding to the first loss result on the basis that the reference of a feature node is associated with the first loss result;
and according to a second prediction framework configured in advance, 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.
10. A data center based energy consumption detection system, comprising a processor and a memory communicating with each other, the processor being configured to read a computer program from the memory and execute the computer program to implement the method of any one of claims 1 to 9.
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