CN114723303B - Method, device, equipment and storage medium for determining energy-saving space of machine room - Google Patents
Method, device, equipment and storage medium for determining energy-saving space of machine room Download PDFInfo
- Publication number
- CN114723303B CN114723303B CN202210400132.8A CN202210400132A CN114723303B CN 114723303 B CN114723303 B CN 114723303B CN 202210400132 A CN202210400132 A CN 202210400132A CN 114723303 B CN114723303 B CN 114723303B
- Authority
- CN
- China
- Prior art keywords
- rule
- evaluation
- machine room
- index
- energy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000011156 evaluation Methods 0.000 claims abstract description 226
- 238000004364 calculation method Methods 0.000 claims abstract description 95
- 238000012545 processing Methods 0.000 claims abstract description 26
- 238000010801 machine learning Methods 0.000 claims description 24
- 238000012549 training Methods 0.000 claims description 20
- 238000001816 cooling Methods 0.000 claims description 16
- 238000012986 modification Methods 0.000 claims description 8
- 230000004048 modification Effects 0.000 claims description 8
- 230000000694 effects Effects 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims description 3
- 238000004134 energy conservation Methods 0.000 abstract description 5
- 238000004891 communication Methods 0.000 abstract description 4
- 238000000926 separation method Methods 0.000 abstract description 4
- 238000013139 quantization Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 6
- 238000005057 refrigeration Methods 0.000 description 6
- 238000011158 quantitative evaluation Methods 0.000 description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000001965 increasing effect Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000010219 correlation analysis Methods 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000011002 quantification Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The disclosure provides a method, a device, equipment and a storage medium for determining an energy-saving space of a machine room, and relates to the technical field of communication energy conservation. The method for determining the energy-saving space of the machine room comprises the following steps: acquiring an evaluation index for evaluating the energy-saving space of the machine room; receiving an evaluation calculation rule of an evaluation index; transmitting the evaluation calculation rule to a rule engine, the rule engine being configured to generate a rule file according to the received evaluation calculation rule and a predefined rule template; acquiring machine room data of a machine room to be evaluated; performing rule matching calculation on the machine room data based on a rule file provided by a rule engine to obtain an evaluation value of an evaluation index; and determining the comprehensive evaluation result of the machine room to be evaluated based on the evaluation values of the evaluation indexes. The method can simply and effectively conduct quantization processing on the energy-saving space of the machine room, realize separation of business decisions and application codes, and effectively improve convenience for developing the business decisions and flexibility and maintainability of the system.
Description
Technical Field
The disclosure relates to the technical field of communication energy conservation, in particular to a method, a device, equipment and a storage medium for determining an energy-saving space of a machine room.
Background
With the increasing demand for communication, internet data rooms (Internet Data Center, IDC for short) are becoming more and more popular. Along with implementation and promotion of energy-saving construction, energy-saving reconstruction of an IDC machine room is urgently needed. The IDC machine room has different construction years, different hardware facilities and monitoring capability, so that the energy-saving effect of the machine room is increased for accelerating the implementation progress, before the energy saving implementation, the IDC machine room reported by each province needs to be screened, and the machine room with less point compensation investment and large energy-saving space is preferentially selected as a popularization machine room. And quantitatively evaluating the reported energy-saving space of the machine room according to the conditions of the type (air cooling and water cooling) of the machine room, whether a cold and hot channel is closed, whether a terminal air conditioner is in frequency conversion, equipment loading rate, relative PUE (physical power utilization) and the like, and taking the quantitative evaluation as the basis of screening of the machine room. At present, the quantitative evaluation of the energy-saving space of the machine room generally adopts certain typical factors selected from the conditions as indexes, different scores are given to the indexes according to different conditions after weighting is added according to the influence factors, and the score of the energy-saving space of each machine room can be evaluated after comprehensive calculation.
However, the machine room environment is complex, the number of reported machine rooms is large, the factors influencing the energy-saving space of the machine room are numerous, and along with the continuous deep understanding, the quantitative evaluation mode of the energy-saving space of the machine room needs to be updated and perfected continuously. When the energy-saving space of the computer room is scored, if a manual calculation mode is adopted, the workload is huge and the accuracy of the result is difficult to ensure. If the hard coding mode is adopted, a large number of judgment conditions are written in the program, and codes are required to be modified when indexes are increased or decreased and index scores are modified, so that the flexibility of the system is poor and the maintainability is poor.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The disclosure aims to provide a method, a device, equipment and a storage medium for determining an energy-saving space of a machine room, which are used for overcoming the problem of quantitative evaluation of the energy-saving space of the machine room caused by the limitations and defects of the related art at least to a certain extent.
According to a first aspect of an embodiment of the present disclosure, there is provided a method for determining an energy-saving space of a machine room, including:
acquiring an evaluation index for evaluating the energy-saving space of the machine room;
receiving an evaluation calculation rule of the evaluation index;
transmitting the evaluation calculation rule to a rule engine configured to generate a rule file from the received evaluation calculation rule and a predefined rule template;
acquiring machine room data of a machine room to be evaluated;
performing rule matching calculation on the machine room data based on a rule file provided by the rule engine to obtain an evaluation value of the evaluation index;
and determining the comprehensive evaluation result of the machine room to be evaluated based on the evaluation values of the evaluation indexes.
In one exemplary embodiment of the present disclosure, the evaluation calculation rule is stored in a database and configured in the database as an index information table, an index parameter table, and a condition definition table; wherein,
the index information table is used for configuring an index name, an index type, an index weight and an index actual value calculation mode of the evaluation index;
the index parameter table is used for configuring the scoring condition of the evaluation index;
the condition definition table is used for configuring the scoring rules of the evaluation index under different scoring conditions.
In an exemplary embodiment of the present disclosure, a calculation result table is also stored in the database, and the calculation result table is used for storing an index actual value and an evaluation value of the evaluation index.
In an exemplary embodiment of the present disclosure, the index weight of the evaluation index is determined as follows:
based on a pre-trained machine learning model, acquiring a correlation coefficient between the evaluation index and the refrigerating energy-saving rate of the machine room;
receiving a weight reference value determined based on expert experience;
the index weight is determined based on the correlation coefficient and the weight reference value.
In one exemplary embodiment of the present disclosure, training a machine learning model according to a training sample set to obtain the pre-trained machine learning model; and the training sample set is constructed according to energy-saving effect data of an energy-saving implementation machine room, and the machine learning model is constructed based on a sklearn library.
In an exemplary embodiment of the present disclosure, the index type includes a discrete type index and a continuous type index; wherein the scoring condition of the discrete index is configured as a specific value, and the scoring condition of the continuous index is configured as a range value.
In one exemplary embodiment of the present disclosure, the step of transmitting the evaluation calculation rule to a rule engine configured to generate a rule file from the received evaluation calculation rule and a predefined rule template file comprises:
sending the evaluation rule to the rule engine to generate an evaluation rule condition conforming to the grammar of the rule file;
configuring a predefined rule template file in the rule engine;
and loading the evaluation rule condition into the predefined rule template file by using the rule engine to generate the rule file.
In one exemplary embodiment of the present disclosure, the predefined rule template file includes rule priority, rule type, validity time, and execution actions after matching rules.
In an exemplary embodiment of the present disclosure, when the rule engine loads the evaluation rule condition, if the loading fails, the rule engine generates error reporting information.
In an exemplary embodiment of the present disclosure, further comprising:
monitoring modification of the evaluation calculation rule;
and when the modification of the evaluation and estimation rule is monitored, the modified evaluation and estimation rule is sent to the rule engine so as to update the rule file in the rule engine.
In an exemplary embodiment of the present disclosure, the evaluation index includes a machine room cooling type, a machine room area, a number of racks, a machine room end air conditioner variable frequency property, a machine room relative energy efficiency, a cold and hot channel state, a current on-shelf rate, a machine room supply air form, a cold channel average temperature, and an air conditioner return average temperature.
In one exemplary embodiment of the present disclosure, the rule engine is configured as a Drools rule engine, an Ilog rule engine, a Jess rule engine, a JLisa rule engine, a ql express rule engine, or a flag regular rule engine.
According to a second aspect of the embodiments of the present disclosure, there is provided a machine room energy-saving spatial processing apparatus, including:
the evaluation index acquisition module is used for acquiring an evaluation index for evaluating the energy-saving space of the machine room;
the evaluation calculation rule receiving module is used for receiving the evaluation calculation rule of the evaluation index;
A rule file generation module for sending the evaluation calculation rule to a rule engine configured to generate a rule file from the received evaluation calculation rule and a predefined rule template file;
the computer room data acquisition module is used for acquiring computer room data of the computer room to be evaluated;
the matching calculation module is used for carrying out rule matching calculation on the machine room data based on the rule file provided by the rule engine to obtain an evaluation value of the evaluation index;
and the comprehensive evaluation result determining module is used for determining the comprehensive evaluation result of the machine room to be evaluated based on the evaluation values of the evaluation indexes.
According to a third aspect of embodiments of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the machine room energy saving space determination method of any one of the above via execution of the executable instructions.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a machine room energy saving space determination method as any one of the above.
The technical scheme of the present disclosure has the following beneficial effects:
according to the method for determining the energy-saving space of the machine room, the quantitative processing of the energy-saving space of the machine room is simply and effectively realized by acquiring the proper evaluation index and the evaluation calculation rule of the evaluation index. The evaluation calculation rule of the evaluation index is transmitted into the rule engine, the rule engine generates a rule file according to the predefined rule template and the evaluation calculation rule, the separation of business decision and application codes is realized, the simplification of rule configuration and the customization of requirements are facilitated, and the convenience of developing the business decision and the flexibility and maintainability of the system are effectively improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
Fig. 1 schematically illustrates a flowchart of a method for determining a room energy saving space in an exemplary embodiment of the present disclosure;
FIG. 2 schematically illustrates a schematic diagram of a database in an exemplary embodiment of the present disclosure;
FIG. 3 schematically illustrates a flowchart of an index weight determination method of an evaluation index in an exemplary embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow chart of a rule file generation method in an exemplary embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of a machine room energy saving spatial processing apparatus in an exemplary embodiment of the present disclosure;
fig. 6 schematically illustrates a block diagram of a machine room energy saving spatial processing apparatus in another exemplary embodiment of the present disclosure.
Fig. 7 schematically illustrates a block diagram of an electronic device in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. One skilled in the relevant art will recognize, however, that the aspects of the disclosure may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are only schematic illustrations of the present disclosure, in which the same reference numerals denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
The method, the device, the equipment and the storage medium for determining the energy-saving space of the machine room realize quantitative evaluation processing of the energy-saving space of the machine room so as to realize energy-saving implementation of screening a proper machine room.
The following describes example embodiments of the present disclosure in detail with reference to the accompanying drawings.
Fig. 1 schematically illustrates a flowchart of a method 100 for determining a room energy saving space in an exemplary embodiment of the disclosure. Referring to fig. 1, the machine room energy saving space determining method 100 includes:
step S101, acquiring an evaluation index for evaluating the energy-saving space of a machine room;
step S102, receiving an evaluation calculation rule of the evaluation index;
Step S103, the evaluation calculation rule is sent to a rule engine, and the rule engine is configured to generate a rule file according to the received evaluation calculation rule and a predefined rule template;
step S104, acquiring machine room data of a machine room to be evaluated;
step S105, performing rule matching calculation on the machine room data based on a rule file provided by the rule engine to obtain an evaluation value of the evaluation index;
and S106, determining the comprehensive evaluation result of the machine room to be evaluated based on the evaluation values of the evaluation indexes.
According to the method for determining the energy-saving space of the machine room, the quantitative processing of the energy-saving space of the machine room is simply and effectively achieved by acquiring the proper evaluation index and the evaluation calculation rule of the evaluation index. The evaluation calculation rule of the evaluation index is transmitted into the rule engine, the rule engine generates a rule file according to the predefined rule template and the evaluation calculation rule, the separation of business decision and application codes is realized, the simplification of rule configuration and the customization of requirements are facilitated, and the convenience of developing the business decision and the flexibility and maintainability of the system are effectively improved.
Next, each step of the machine room energy saving space determination method 100 will be described in detail.
In step S101, an evaluation index for evaluating the energy saving space of the machine room is obtained. The method specifically comprises the following steps:
s1011, constructing a machine room state model based on the state data of the machine room.
Firstly, the server can acquire the state data of the machine room, and the state model of the machine room conforming to the characteristics of the data center is established by analyzing and abstracting the state data. For example, a machine room state model may be abstracted in a machine learning manner.
S1012, acquiring the evaluation index according to the machine room state model.
In exemplary embodiments of the present disclosure, the evaluation index includes, but is not limited to, a machine room cooling type, a machine room area, a number of racks, a machine room end air conditioner variable frequency property, a machine room relative energy efficiency, a cold and hot channel state, a current on-rack rate, a machine room supply air form, a cold channel average temperature, and an air conditioner return air average temperature.
In step S102, an evaluation calculation rule of the evaluation index is received.
In an exemplary embodiment of the present disclosure, the received evaluation calculation rule is stored in a database. Referring to fig. 2, in the database, evaluation calculation rules are configured as an index information table 210, an index parameter table 220, and a condition definition table 230. Wherein:
The index information table 210 is used for configuring the index name, the index type, the index weight and the index actual value calculation mode of the evaluation index.
The index name is the name of the evaluation index, such as the cooling type of the machine room, the area of the machine room, and the like.
The index type comprises a discrete index and a continuous index, and is determined according to the actual value of the index. Specifically, when the actual value of the index is a specific value, the discrete index is configured, for example, the actual value of the index of the machine room refrigeration type is only selected by air cooling and water cooling, and the discrete index is configured. When the actual value of the index is a numerical value, the continuous index is configured, for example, when the actual value of the average temperature of the cold channel is a temperature numerical value, the continuous index is configured.
The index weight is used to determine the importance of the evaluation index. Specifically, referring to fig. 3, in an exemplary embodiment of the present disclosure, the index weight of the evaluation index is determined as follows:
step S301, based on a pre-trained machine learning model, obtaining a correlation coefficient between the evaluation index and the machine room refrigeration energy saving rate.
In an exemplary embodiment of the present disclosure, the machine learning model is trained from a training sample set, resulting in the pre-trained machine learning model. The machine learning model may be constructed based on a sklearn library, for example. The training sample set is constructed according to the energy-saving effect data of the energy-saving implementation machine room.
For example, the machine room data subjected to energy saving implementation is ordered according to the refrigerating energy saving rate, firstly, a plurality of groups of data which exceed 25% (large energy saving space) and are lower than 25% (small energy saving space) are selected, and based on sklearn, the evaluation index and the correlation coefficient of the machine room refrigerating energy saving rate are calculated.
Specifically, the evaluation index is taken as an independent variable, the machine room refrigeration energy saving rate is taken as a dependent variable, and the correlation analysis is carried out to obtain the correlation coefficient. For example, training samples are: and carrying out correlation analysis on the data to obtain correlation coefficients, wherein the higher the correlation coefficient is, the larger the contribution to the result is represented.
Step S302, a weight reference value determined based on expert experience is received.
Specifically, the weight reference value of each evaluation index is determined according to expert experience, for example, the weight reference value of the machine room refrigeration type is 15% or 20% or the like.
Step S303, determining the index weight based on the correlation coefficient and the weight reference value.
And finally determining the index weight of the evaluation index by superposing expert experience on the correlation coefficient. For example, the evaluation index is the machine room refrigeration type, and the final determined index weight is 10%. For another example, the evaluation index is the average temperature in the cold aisle, and the final determined index weight is 15%.
Further, the superimposition process may be, for example, a correlation coefficient x a superimposition coefficient α+weight reference value x a superimposition coefficient β as an index weight of the evaluation index. The superposition coefficient alpha and the superposition coefficient beta are constants and are determined by the training sample size of the machine learning model. The more the training sample size, the higher the superimposition coefficient α and the lower the superimposition coefficient β.
Through the steps S301-S303, the correlation coefficient output by the machine learning model and expert experience are overlapped, so that objective and accurate index weight is obtained.
Further, after the energy saving implementation is performed on the new machine room, the machine learning model in the step S301 is continuously fed back and optimized according to the refrigerating energy saving rate of the new machine room. When the correlation coefficient and expert experience are overlapped, the overlapped coefficient alpha and the overlapped coefficient beta are adjusted according to the training times of the machine learning model.
For example, in the early stage of machine room energy saving implementation, the number of machine rooms for which energy saving implementation is performed is limited, the superposition coefficient alpha is configured to be a lower value, and the superposition coefficient beta is configured to be a higher value, so that the index weight is determined in a mode of taking a machine learning model as an auxiliary and taking expert experience as a main. After the energy conservation is implemented by the multiple machine rooms, the optimization model is continuously fed back according to the energy conservation effect data, the superposition coefficient alpha is configured to be a higher value, and the superposition coefficient beta is configured to be a higher value, so that the method is transited to a mode of mainly using the machine learning model and secondarily using expert experience to determine the index weight. Based on the above process, the accuracy of the quantification treatment of the energy-saving space of the machine room can be continuously improved.
It will be appreciated that in other embodiments, the correlation coefficient and the weight reference value of the evaluation index may be superimposed in other manners to determine the index weight of the evaluation index.
The index actual value calculation mode is used for configuring calculation rules of the index actual value. Because the actual values of some evaluation indexes are obtained by calculating the original data, such as the average temperature in a cold channel, the average value of the return air temperature of an air conditioner and the like. The lake region of the actual value of the index is realized by configuring the actual value calculation mode of the index, and the flexibility and convenience of the scheme are improved. It can be understood that the index actual value calculation scheme can adopt, for example, sql sentences or java class sentences to support parameter input in json format.
The index parameter table 220 is used to configure the scoring condition of the evaluation index. Specifically, the score condition is valued according to the index type of the evaluation index, the score condition of the discrete index is valued as a specific value, and the score condition of the continuity index is valued as a range value. For example, the evaluation index is a machine room cooling type, and the score condition value is air cooling or water cooling. The evaluation index is the average temperature of the cold channel, and the score condition is a range value, for example, the value is 18-22 ℃.
The condition definition table 230 is used to configure the scoring rules of the evaluation index under different scoring conditions. The scoring rules of the evaluation index under different scoring conditions can be determined, for example, according to expert experience.
For example, the evaluation index is a machine room cooling type, and the conditions are defined as follows:
machine room cooling type = water cooling, 10 minutes
Machine room cooling type = air cooling, 8 minutes
For example, the evaluation index is the average temperature in the cold aisle, and the conditions are defined as follows:
18 ° <=average temperature in cold channel < =20°,10 minutes
Average temperature in 20 ° <=cold channel < =22°,8 minutes
22 ° <=average temperature in cold channel < =24°,6 minutes
Average temperature in 25 ° <=cold channel, 4 minutes
Further, in an exemplary embodiment of the present disclosure, referring to fig. 2, a calculation result table 240 is also stored in the database. The calculation result table 240 is used to store the index actual value and the evaluation value of the evaluation index. After performing rule matching calculation on the machine room data based on the rule file provided by the rule engine to obtain the evaluation value of the evaluation index, the corresponding actual index value and the evaluation value may be stored in the calculation result table 240 of the database.
In step S103, the evaluation calculation rule is sent to a rule engine configured to generate a rule file from the received evaluation calculation rule and a predefined rule template.
The rule engine is used as a component embedded in the application program, so that the business decision is separated from the application program code, the business decision is written by using a predefined semantic module, and after receiving data input, the business rule is interpreted and the business decision is made according to the business rule. In exemplary embodiments of the present disclosure, the rule engines include, but are not limited to, a Drools rule engine, an Ilog rule engine, a Jess rule engine, a JLisa rule engine, a ql express rule engine, a flag regular rule engine. Specifically, in the implementation of the present disclosure, an open-source Drools rules engine is used.
In an exemplary embodiment of the present disclosure, referring to fig. 4, step S103 specifically includes:
and S401, transmitting the evaluation rule to the rule engine to generate an evaluation rule condition conforming to the grammar of the rule file.
Specifically, the rule file grammar may include, for example, map operations, IFELSE conditional grammar, while loop grammar, and the like. In an exemplary embodiment of the present disclosure, the dynamic construction of the evaluation rule conditions is achieved by stitching the index types, operators, ranges, etc. in the evaluation calculation rule by a parser. The parser may be built into the rules engine and dynamically construct evaluation rule conditions as the rules engine reads the evaluation calculation rules in the database. Evaluation rule conditions may include fields, operators, and expressions.
Step S402, configuring a predefined rule template file in the rule engine.
Specifically, the predefined rule template file includes rule priority, rule type, validity time, and execution actions after matching rules.
Step S403, loading the evaluation rule condition into the predefined rule template file by using the rule engine, and generating the rule file.
Specifically, the rule engine loads the evaluation rule conditions and the predefined rule template file, and dynamically generates the rule file through a conversion function.
The above process realizes flexible configuration of rules through a predefined rule template file.
In an exemplary embodiment of the present disclosure, step S103 further includes:
and when the rule engine loads the evaluation rule conditions, if the loading fails, generating error reporting information.
If the loading evaluation rule condition is wrong, the rule grammar representing the evaluation rule condition is wrong. When loading fails, error reporting information is generated, so that the program debugging time can be effectively reduced.
In step S104, machine room data of the machine room to be evaluated is acquired.
Specifically, the machine room data of the machine room to be evaluated can be obtained from a big data lake of the group. And acquiring the machine room data according to the evaluation index.
The acquired machine room data are divided into static attribute data and dynamic attribute data according to whether calculation processing is needed or not. The static attribute data is directly used as an actual index value, such as a machine room area, a machine room refrigeration type and the like, without calculation processing. The dynamic attribute data needs to be subjected to calculation processing, and the result after the calculation processing is taken as an index actual value, such as the average temperature of the cold channel.
Specifically, the index information table 210 stored in the database is called, and the dynamic attribute data is calculated by adopting an index actual value calculation mode stored in the table, so as to obtain an index actual value of the evaluation index.
And summarizing the calculation results of the static attribute data and the dynamic attribute data, and carrying out missing value complement processing to obtain the actual data of the machine room to be evaluated.
In step S105, rule matching calculation is performed on the machine room data based on the rule file provided by the rule engine, so as to obtain an evaluation value of the evaluation index.
Specifically, the machine room actual data obtained in step S104 is sent to a rule engine, a rule file is triggered, and after rule matching calculation is performed, an evaluation value of an evaluation index is output. And the rule engine makes a decision on each evaluation index to finally obtain the evaluation value of each evaluation index.
In step S106, a comprehensive evaluation result of the machine room to be evaluated is determined based on the evaluation values of the respective evaluation indexes.
Specifically, in this step, the evaluation values of the respective evaluation indexes are comprehensively calculated, for example, a summation calculation is performed, and the comprehensive score after the summation calculation is used as a comprehensive evaluation result of the machine room to be evaluated. After the comprehensive evaluation results of the multiple machine rooms to be evaluated are obtained, sequencing the machine rooms to be evaluated according to the magnitude of the comprehensive evaluation results. And determining a machine room for implementing energy conservation according to the sequencing result.
In an exemplary embodiment of the present disclosure, the machine room energy saving space determining method 100 of the embodiment of the present disclosure further includes:
and step S107, monitoring the modification of the evaluation calculation rule. Specifically, a listening service is configured in the database, and when it is monitored that the evaluation calculation rule in the database changes, step S108 is performed. The evaluation calculation rule changes, for example, a condition definition table in a database changes, a condition definition table is newly added or a condition definition table is deleted.
And step S108, when the modification of the evaluation calculation rule is monitored, the modified evaluation calculation rule is sent to the rule engine so as to update the rule file in the rule engine. Specifically, the rule engine loads the modified evaluation calculation rule and dynamically generates new rule conditions. A new rule file is dynamically generated based on the new rule conditions and the predefined rule template file.
Fig. 5 schematically illustrates a schematic diagram of a machine room energy saving spatial processing device 500 in an exemplary embodiment of the present disclosure. Referring to fig. 5, a machine room energy-saving space processing apparatus 500 includes:
the evaluation index acquisition module 510 is configured to acquire an evaluation index for evaluating the energy-saving space of the machine room;
an evaluation calculation rule receiving module 520, configured to receive an evaluation calculation rule of the evaluation index;
a rule file generation module 530 for sending the evaluation calculation rule to a rule engine configured to generate a rule file from the received evaluation calculation rule and a predefined rule template file;
a machine room data acquisition module 540, configured to acquire machine room data of a machine room to be evaluated;
the matching calculation module 550 is configured to perform rule matching calculation on the machine room data based on a rule file provided by the rule engine, so as to obtain an evaluation value of the evaluation index;
and the comprehensive evaluation result determining module 560 is configured to determine a comprehensive evaluation result of the machine room to be evaluated based on the evaluation values of the evaluation indexes.
The machine room energy-saving space determining device disclosed by the embodiment of the disclosure simply and effectively realizes the quantification processing of the machine room energy-saving space by acquiring the proper evaluation index and the evaluation calculation rule of the evaluation index. The evaluation calculation rule of the evaluation index is transmitted into the rule engine, the rule engine generates a rule file according to the predefined rule template and the evaluation calculation rule, the separation of business decision and application codes is realized, the simplification of rule configuration and the customization of requirements are facilitated, and the convenience of developing the business decision and the flexibility and maintainability of the system are effectively improved.
Referring to fig. 6, in an exemplary embodiment of the present disclosure, the machine room energy-saving space processing apparatus 500 further includes:
a monitoring module 610, configured to monitor modification of the evaluation calculation rule; and
and the updating module 620 is configured to send the modified evaluation rule to the rule engine when the modification of the evaluation rule is monitored, so as to update a rule file in the rule engine.
In an exemplary embodiment of the present disclosure, the machine room energy-saving spatial processing apparatus 500 may further include modules that implement other flow steps of the above-described processing method embodiments. For example, rule file generation module 530 may include an evaluation rule condition generation sub-module for sending the evaluation rule to the rule engine to generate an evaluation rule condition conforming to the rule file syntax; a rule template file configuration sub-module for configuring a predefined rule template file in the rule engine; and the loading sub-module is used for loading the evaluation rule condition into the predefined rule template file by using the rule engine to generate the rule file.
Since each function of the machine room energy-saving spatial processing device 500 is described in detail in the corresponding method embodiment, the disclosure is not repeated here.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
An electronic device 700 according to this embodiment of the invention is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 7, the electronic device 700 is embodied in the form of a general purpose computing device. Components of electronic device 700 may include, but are not limited to: the at least one processing unit 710, the at least one memory unit 720, and a bus 730 connecting the different system components, including the memory unit 720 and the processing unit 710.
Wherein the storage unit stores program code that is executable by the processing unit 710 such that the processing unit 710 performs steps according to various exemplary embodiments of the present invention described in the above-mentioned "exemplary methods" section of the present specification.
For example, in one embodiment of the present disclosure, the processing unit 710 may perform step S101 shown in fig. 1, to obtain an evaluation index for evaluating the energy saving space of the machine room; step S102, receiving an evaluation calculation rule of the evaluation index; step S103, the evaluation calculation rule is sent to a rule engine, and the rule engine is configured to generate a rule file according to the received evaluation calculation rule and a predefined rule template; step S104, acquiring machine room data of a machine room to be evaluated; step S105, performing rule matching calculation on the machine room data based on a rule file provided by the rule engine to obtain an evaluation value of the evaluation index; and S106, determining the comprehensive evaluation result of the machine room to be evaluated based on the evaluation values of the evaluation indexes.
The memory unit 720 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 7201 and/or cache memory 7202, and may further include Read Only Memory (ROM) 7203.
The storage unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 730 may be a bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 700 may also communicate with one or more external devices 800 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 700, and/or any device (e.g., router, modem, etc.) that enables the electronic device 700 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 750. Also, electronic device 700 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 760. As shown, network adapter 760 communicates with other modules of electronic device 700 over bus 730. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 700, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.
The program product for implementing the above-described method according to an embodiment of the present invention may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
Furthermore, the above-described drawings are only schematic illustrations of processes included in the method according to the exemplary embodiment of the present application, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (14)
1. The method for determining the energy-saving space of the machine room is characterized by comprising the following steps of:
acquiring an evaluation index for evaluating the energy-saving space of the machine room;
receiving an evaluation calculation rule of the evaluation index;
transmitting the evaluation calculation rule to a rule engine configured to generate a rule file from the received evaluation calculation rule and a predefined rule template;
Acquiring machine room data of a machine room to be evaluated;
performing rule matching calculation on the machine room data based on a rule file provided by the rule engine to obtain an evaluation value of the evaluation index;
based on the evaluation values of the evaluation indexes, determining the comprehensive evaluation result of the machine room to be evaluated;
wherein, the index weight of the evaluation index is determined according to the following steps:
based on a pre-trained machine learning model, acquiring a correlation coefficient between the evaluation index and the refrigerating energy-saving rate of the machine room;
receiving a weight reference value determined based on expert experience;
and determining the index weight based on the correlation coefficient and the weight reference value, wherein the index weight is the sum of the product of the correlation coefficient and the superposition coefficient alpha and the product of the weight reference value and the superposition coefficient beta, and the superposition coefficient alpha and the superposition coefficient beta are determined by the training sample size or the training times of a machine learning model, wherein the higher the training sample size or the training times of the machine learning model, the higher the superposition coefficient alpha and the lower the superposition coefficient beta.
2. The machine room energy saving space determination method according to claim 1, wherein the evaluation rule is stored in a database and is configured in the database as an index information table, an index parameter table, and a condition definition table; wherein,
The index information table is used for configuring an index name, an index type, an index weight and an index actual value calculation mode of the evaluation index;
the index parameter table is used for configuring the scoring condition of the evaluation index;
the condition definition table is used for configuring the scoring rules of the evaluation index under different scoring conditions.
3. The machine room energy saving space determination method according to claim 2, wherein a calculation result table is further stored in the database, and the calculation result table is used for storing an index actual value and an evaluation value of the evaluation index.
4. The machine room energy saving space determination method according to claim 1, wherein training a machine learning model according to a training sample set to obtain the pre-trained machine learning model; and the training sample set is constructed according to energy-saving effect data of an energy-saving implementation machine room, and the machine learning model is constructed based on a sklearn library.
5. The machine room energy saving space determination method according to claim 2, wherein the index type includes a discrete type index and a continuous type index; wherein the scoring condition of the discrete index is configured as a specific value, and the scoring condition of the continuous index is configured as a range value.
6. The machine room energy saving space determination method of claim 1, wherein the step of sending the evaluation calculation rule to a rule engine configured to generate a rule file from the received evaluation calculation rule and a predefined rule template file comprises:
sending the evaluation rule to the rule engine to generate an evaluation rule condition conforming to the grammar of the rule file;
configuring a predefined rule template file in the rule engine;
and loading the evaluation rule condition into the predefined rule template file by using the rule engine to generate the rule file.
7. The machine room energy saving space determination method of claim 6, wherein the predefined rule template file comprises rule priority, rule type, validity time, and execution action after matching a rule.
8. The method for determining a machine room energy saving space according to claim 6, wherein the rule engine generates error reporting information if loading fails when loading the evaluation rule condition.
9. The machine room energy saving space determination method according to claim 1, further comprising:
Monitoring modification of the evaluation calculation rule;
and when the modification of the evaluation and estimation rule is monitored, the modified evaluation and estimation rule is sent to the rule engine so as to update the rule file in the rule engine.
10. The method for determining an energy-saving space of a machine room according to claim 1, wherein the evaluation index comprises a machine room cooling type, a machine room area, a number of frames, a frequency conversion attribute of an air conditioner at the tail end of the machine room, a relative energy efficiency of the machine room, a state of a cold and hot channel, a current loading rate, an air supply form of the machine room, an average temperature of the cold channel and an average temperature of return air of the air conditioner.
11. The machine room energy saving space determination method of claim 1, wherein the rule engine is configured as a Drools rule engine, an Ilog rule engine, a Jess rule engine, a JLisa rule engine, a ql express rule engine, or a flag regular rule engine.
12. An energy-saving space treatment device for a machine room, which is characterized by comprising:
the evaluation index acquisition module is used for acquiring an evaluation index for evaluating the energy-saving space of the machine room;
the evaluation calculation rule receiving module is used for receiving the evaluation calculation rule of the evaluation index;
A rule file generation module for sending the evaluation calculation rule to a rule engine configured to generate a rule file from the received evaluation calculation rule and a predefined rule template file;
the computer room data acquisition module is used for acquiring computer room data of the computer room to be evaluated;
the matching calculation module is used for carrying out rule matching calculation on the machine room data based on the rule file provided by the rule engine to obtain an evaluation value of the evaluation index;
the comprehensive evaluation result determining module is used for determining the comprehensive evaluation result of the machine room to be evaluated based on the evaluation values of the evaluation indexes;
wherein, the processing device is further used for determining the index weight of the evaluation index:
based on a pre-trained machine learning model, acquiring a correlation coefficient between the evaluation index and the refrigerating energy-saving rate of the machine room;
receiving a weight reference value determined based on expert experience;
and determining the index weight based on the correlation coefficient and the weight reference value, wherein the index weight of the evaluation index is the sum of the product of the correlation coefficient and the superposition coefficient alpha and the product of the weight reference value and the superposition coefficient beta, and the superposition coefficient alpha and the superposition coefficient beta are determined by the training sample size or the training times of a machine learning model, wherein the higher the training sample size or the training times of the machine learning model are, the higher the superposition coefficient alpha is, and the lower the superposition coefficient beta is.
13. An electronic device, comprising: a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the machine room energy saving space determination method of any one of claims 1 to 11 via execution of the executable instructions.
14. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the machine room energy saving space determination method of any one of claims 1 to 11.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210400132.8A CN114723303B (en) | 2022-04-15 | 2022-04-15 | Method, device, equipment and storage medium for determining energy-saving space of machine room |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210400132.8A CN114723303B (en) | 2022-04-15 | 2022-04-15 | Method, device, equipment and storage medium for determining energy-saving space of machine room |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114723303A CN114723303A (en) | 2022-07-08 |
| CN114723303B true CN114723303B (en) | 2023-10-31 |
Family
ID=82242852
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210400132.8A Active CN114723303B (en) | 2022-04-15 | 2022-04-15 | Method, device, equipment and storage medium for determining energy-saving space of machine room |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114723303B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115955398B (en) * | 2022-10-13 | 2023-09-15 | 浙江瑞盈通信技术股份有限公司 | Intelligent design system and method for communication machine room |
Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102609808A (en) * | 2012-01-17 | 2012-07-25 | 北京百度网讯科技有限公司 | Method and device for performing energy consumption management on data center |
| CN103049814A (en) * | 2012-12-14 | 2013-04-17 | 新疆希望电子有限公司 | Energy-saving emission-reducing checking and evaluating system |
| CN103673200A (en) * | 2012-09-06 | 2014-03-26 | 中国建筑科学研究院 | Energy consumption control system and method for data center |
| CN103839108A (en) * | 2013-04-08 | 2014-06-04 | 江苏理工学院 | Energy saving evaluation system and method for power supply and distribution networks of industrial enterprises |
| CN104732450A (en) * | 2015-03-06 | 2015-06-24 | 国家电网公司 | Power supply quality evaluation system based on rule engine |
| CN107103421A (en) * | 2017-04-21 | 2017-08-29 | 江苏省特种设备安全监督检验研究院南通分院 | The overall evaluation system and its construction method of industry boiler energy-conservation scheme |
| CN107291189A (en) * | 2016-04-02 | 2017-10-24 | 贺州学院 | A kind of thermal management method based on green data center |
| CN108256711A (en) * | 2016-12-29 | 2018-07-06 | 李明皓 | Centralized heating boiler plant energy-saving index appraisement system is established |
| CN109064020A (en) * | 2018-08-02 | 2018-12-21 | 中邮建技术有限公司 | A kind of Energy efficiency evaluation method and apparatus |
| CN109597606A (en) * | 2018-10-24 | 2019-04-09 | 中国平安人寿保险股份有限公司 | Method, equipment and the storage medium of operational decision making are carried out using regulation engine |
| CN110188932A (en) * | 2019-05-20 | 2019-08-30 | 国核电力规划设计研究院有限公司 | Consumption of data center prediction technique based on evaluation optimization |
| CN110704037A (en) * | 2019-08-28 | 2020-01-17 | 贵州医渡云技术有限公司 | Rule engine implementation method and device |
| CN110852498A (en) * | 2019-10-31 | 2020-02-28 | 西安交通大学 | Method for predicting data center energy consumption efficiency PUE based on GRU neural network |
| CN111539652A (en) * | 2020-05-14 | 2020-08-14 | 泛海物业管理有限公司 | Building energy management system and method based on Internet of things |
| CN111612318A (en) * | 2020-05-10 | 2020-09-01 | 紫光云引擎科技(苏州)有限公司 | Industrial development index comprehensive evaluation system and construction method |
| CN111724238A (en) * | 2020-06-23 | 2020-09-29 | 中国平安人寿保险股份有限公司 | Method, device and equipment for evaluating product recommendation accuracy and storage medium |
| CN112070353A (en) * | 2020-08-04 | 2020-12-11 | 中国科学院信息工程研究所 | Method and system for accurately detecting energy efficiency of data center |
| CN112184008A (en) * | 2020-09-27 | 2021-01-05 | 科大国创云网科技有限公司 | Base station intelligent energy-saving model evaluation method and system based on analytic hierarchy process |
| US10964224B1 (en) * | 2016-03-15 | 2021-03-30 | Educational Testing Service | Generating scores and feedback for writing assessment and instruction using electronic process logs |
| CN113827980A (en) * | 2021-08-17 | 2021-12-24 | 杭州电魂网络科技股份有限公司 | Loss user prediction method and device and computer readable storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11152810B2 (en) * | 2018-05-21 | 2021-10-19 | Sling Media Pvt. Ltd. | Rule based smart charging |
-
2022
- 2022-04-15 CN CN202210400132.8A patent/CN114723303B/en active Active
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102609808A (en) * | 2012-01-17 | 2012-07-25 | 北京百度网讯科技有限公司 | Method and device for performing energy consumption management on data center |
| CN103673200A (en) * | 2012-09-06 | 2014-03-26 | 中国建筑科学研究院 | Energy consumption control system and method for data center |
| CN103049814A (en) * | 2012-12-14 | 2013-04-17 | 新疆希望电子有限公司 | Energy-saving emission-reducing checking and evaluating system |
| CN103839108A (en) * | 2013-04-08 | 2014-06-04 | 江苏理工学院 | Energy saving evaluation system and method for power supply and distribution networks of industrial enterprises |
| CN104732450A (en) * | 2015-03-06 | 2015-06-24 | 国家电网公司 | Power supply quality evaluation system based on rule engine |
| US10964224B1 (en) * | 2016-03-15 | 2021-03-30 | Educational Testing Service | Generating scores and feedback for writing assessment and instruction using electronic process logs |
| CN107291189A (en) * | 2016-04-02 | 2017-10-24 | 贺州学院 | A kind of thermal management method based on green data center |
| CN108256711A (en) * | 2016-12-29 | 2018-07-06 | 李明皓 | Centralized heating boiler plant energy-saving index appraisement system is established |
| CN107103421A (en) * | 2017-04-21 | 2017-08-29 | 江苏省特种设备安全监督检验研究院南通分院 | The overall evaluation system and its construction method of industry boiler energy-conservation scheme |
| CN109064020A (en) * | 2018-08-02 | 2018-12-21 | 中邮建技术有限公司 | A kind of Energy efficiency evaluation method and apparatus |
| CN109597606A (en) * | 2018-10-24 | 2019-04-09 | 中国平安人寿保险股份有限公司 | Method, equipment and the storage medium of operational decision making are carried out using regulation engine |
| CN110188932A (en) * | 2019-05-20 | 2019-08-30 | 国核电力规划设计研究院有限公司 | Consumption of data center prediction technique based on evaluation optimization |
| CN110704037A (en) * | 2019-08-28 | 2020-01-17 | 贵州医渡云技术有限公司 | Rule engine implementation method and device |
| CN110852498A (en) * | 2019-10-31 | 2020-02-28 | 西安交通大学 | Method for predicting data center energy consumption efficiency PUE based on GRU neural network |
| CN111612318A (en) * | 2020-05-10 | 2020-09-01 | 紫光云引擎科技(苏州)有限公司 | Industrial development index comprehensive evaluation system and construction method |
| CN111539652A (en) * | 2020-05-14 | 2020-08-14 | 泛海物业管理有限公司 | Building energy management system and method based on Internet of things |
| CN111724238A (en) * | 2020-06-23 | 2020-09-29 | 中国平安人寿保险股份有限公司 | Method, device and equipment for evaluating product recommendation accuracy and storage medium |
| CN112070353A (en) * | 2020-08-04 | 2020-12-11 | 中国科学院信息工程研究所 | Method and system for accurately detecting energy efficiency of data center |
| CN112184008A (en) * | 2020-09-27 | 2021-01-05 | 科大国创云网科技有限公司 | Base station intelligent energy-saving model evaluation method and system based on analytic hierarchy process |
| CN113827980A (en) * | 2021-08-17 | 2021-12-24 | 杭州电魂网络科技股份有限公司 | Loss user prediction method and device and computer readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| "通信基站节能减排评估指标及评估方法研究";张高记等;《电信科学》(2011年第3期);第101页左栏第1段-第105页右栏第1段 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114723303A (en) | 2022-07-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110807515B (en) | Model generation method and device | |
| CN109360012B (en) | Advertisement delivery channel selection method and device, storage medium and electronic equipment | |
| CN110852438A (en) | Model generation method and device | |
| CN112711581B (en) | Medical data checking method and device, electronic equipment and storage medium | |
| CN110727437B (en) | Code optimization item acquisition method and device, storage medium and electronic equipment | |
| CN111508604A (en) | Acute kidney injury patient mortality prediction method, server and storage medium | |
| CN107911251B (en) | Network equipment configuration method, device and medium | |
| CN114723303B (en) | Method, device, equipment and storage medium for determining energy-saving space of machine room | |
| CN112036515A (en) | Oversampling method and device based on SMOTE algorithm and electronic equipment | |
| CN112650841A (en) | Information processing method and device and electronic equipment | |
| US11645540B2 (en) | Deep graph de-noise by differentiable ranking | |
| CN111353601A (en) | Method and apparatus for predicting delay of model structure | |
| CN116684330A (en) | Traffic prediction method, device, equipment and storage medium based on artificial intelligence | |
| CN117236805B (en) | Power equipment control method, device, electronic equipment and computer readable medium | |
| CN111582649B (en) | Risk assessment method and device based on user APP single-heat coding and electronic equipment | |
| CN112347316A (en) | GraphSAGE-based bad preference behavior detection method and device and electronic equipment | |
| CN114385396B (en) | Log analysis method, device, equipment and medium | |
| CN114297453B (en) | Alarm prediction method and device, electronic equipment and storage medium | |
| CN116028811A (en) | Data backtracking method, medium, device and computing equipment | |
| CN109977011A (en) | Automatic generation method, device, storage medium and the electronic equipment of test script | |
| CN111274480B (en) | Feature combination method and device for content recommendation | |
| CN111883126A (en) | Data processing mode selection method and device and electronic equipment | |
| CN114650190B (en) | Energy-saving method, system, terminal equipment and storage medium for data center network | |
| CN115238805B (en) | Training method of abnormal data recognition model and related equipment | |
| CN117726147B (en) | Adaptation-based server management method, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |