CN114879542A - Machine learning-based efficient cold water machine room control system and method - Google Patents
Machine learning-based efficient cold water machine room control system and method Download PDFInfo
- Publication number
- CN114879542A CN114879542A CN202210582546.7A CN202210582546A CN114879542A CN 114879542 A CN114879542 A CN 114879542A CN 202210582546 A CN202210582546 A CN 202210582546A CN 114879542 A CN114879542 A CN 114879542A
- Authority
- CN
- China
- Prior art keywords
- module
- unit
- data
- central processing
- processing unit
- 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.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention relates to the technical field of cold water machine room control, and discloses a machine learning-based efficient cold water machine room control system which specifically comprises a central processing unit, wherein the output end of the central processing unit is electrically connected with a manual operation module, the output end of the manual operation module is electrically connected with the input end of the central processing unit, the output end of the central processing unit is electrically connected with a primary verification and correction module, and the output end of the primary verification and correction module is electrically connected with the input end of the central processing unit. Through the model self-learning module, the model sets up from the cooperation of upgrading module and operation data analysis module for the system can generate the virtual model of different environment effects according to the behavior in the operation, then form multiple data under the multiple condition, and carry out the increase from control mode after data form, thereby reach the effect from upgrading, then make the self-operation of system more stable and complete, in order to satisfy follow-up different situation, the normal operating of computer lab under the different big environment.
Description
Technical Field
The invention relates to the technical field of cold water machine room control, in particular to a machine learning-based efficient cold water machine room control system and method.
Background
The current machine learning method is rapidly developed and widely applied to the fields of image and voice recognition. In a cold water machine room, a control system needs to collect hundreds of data points such as temperature, flow, pressure, equipment state, frequency of a frequency converter, fault alarm and the like. The current control system of the cold water machine room usually adopts a PID algorithm for control, the PID algorithm only selects a certain number of thousands of data points for control according to a required control object, the algorithm only considers the optimal efficiency of the controlled object, and does not consider the influence of the running state of the controlled object on the running energy efficiency of the whole cold water machine room.
The control system adopts a water chilling unit energy consumption model for control, and has the defect that the control system only considers determined input data points in the model and cannot react on data points which are not considered by the model. The equipment in the cold water machine room is all correlated, and the change of a certain temperature or flow can bring obvious change to the operation of the cold water system. The control system adopting the energy consumption model cannot react to an unexpected situation and can not carry out self-learning to find out the influence of other data points on the energy consumption model.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a machine learning-based high-efficiency cold water machine room control system and method, which have the advantages of self-learning and upgrading, self-operation according to environmental changes and the like, and solve the problems in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention provides the following technical scheme: the high-efficiency cold water machine room control system based on machine learning comprises a central processing unit, wherein the output end of the central processing unit is electrically connected with a manual operation module, the output end of the manual operation module is electrically connected with the input end of the central processing unit, the output end of the central processing unit is electrically connected with a primary verification and correction module, the output end of the primary verification and correction module is electrically connected with the input end of the central processing unit, the output end of the central processing unit is electrically connected with a data allocation module, the output end of the data allocation module is electrically connected with the input end of the central processing unit, the output end of the central processing unit is electrically connected with an operation data analysis module, the output end of the operation data analysis module is electrically connected with the input end of the central processing unit, the output end of the central processing unit is electrically connected with a model self-learning module, and the output end of the model self-learning module is electrically connected with the input end of the central processing unit, the output end of the central processing unit is electrically connected with a model self-elevating module, and the output end of the model self-elevating module is electrically connected with the input end of the central processing unit.
Further, the output electricity of elementary verification pair module is connected with high in the clouds storage control module group, the output electricity of high in the clouds storage control module group is connected with remote control terminal module, the model is connected with from the control machine module from the output electricity of upgrading module, the output of from the control machine module is connected with cold water computer lab equipment with the equal electricity in central processing unit's output, the model is connected with the input looks electricity of high in the clouds storage control module group from the output of upgrading module.
Furthermore, the manual operation module comprises a personnel login unit for logging in a system, an external device port and a data interaction unit, the primary verification and correction module comprises a personnel information comparison unit for verifying and comparing personnel information, a cloud information feedback unit and different permission correction and giving units for giving permissions according to different personnel information, and the data allocation module comprises a personnel information storage unit for allocating and storing the personnel information and permission content, an operation log recording unit for performing backup recording on system operation data logs and a coverage data storage unit.
The model self-learning module comprises a virtual model building unit for building a virtual system running environment, a data reading and analyzing unit for performing data analysis on running data under a virtual running condition, and a simulated running error correcting unit for debugging and correcting the running data under the virtual running condition.
Furthermore, the model self-upgrading module comprises an operation data comparison unit for comparing the simulation operation data with the actual operation data, a benign data analysis unit for performing reason analysis on the data of a benign structure in the operation process of the model, and a data integration and adjustment unit for integrating, allocating and self-upgrading the actual operation data and the simulation operation data.
Furthermore, the cloud storage control module comprises a login information recording module for recording manual operation information of personnel login, a remote login receiving unit for coordinating the remote control terminal module, and a virtual model backup unit for controlling system backup after upgrading in the system.
The invention provides an operation method of a machine learning-based high-efficiency cold water machine room control system, which specifically comprises the following operations:
s1: manual login operation: the staff logs in the information of the staff through a staff logging-in unit, the staff information is transmitted to a staff information comparison unit inside the primary verification and correction module through a data interaction unit at the moment, meanwhile, the staff information comparison unit retrieves a staff information storage unit inside the data allocation module to perform information comparison according to the information, and after comparison and confirmation, the staff system control authority is given to the staff through an authority verification giving unit so as to perform system control, and the operation information is uploaded into the cloud storage control module through a cloud information feedback unit to be recorded while the authority verification giving unit gives the staff system control authority;
s2: remote login operation: the staff logs in the information of the staff through the remote control terminal module into the cloud storage control module, at the moment, the identity information of the staff is uploaded into the primary verification and correction module through the cloud storage control module, and the verification operation and the authority endowing operation in the step S1 are repeated;
s3: self-learning adjustment: the model self-learning module generates a system operation virtual environment through an internal virtual model construction unit, then extracts operation data in the operation data analysis module and operates in the virtual environment for operation, meanwhile, error correction recording is carried out on problem data in the operation process, and data analysis is carried out on the complete operation result through a data reading and analyzing unit;
s4: machine upgrading and adjusting: comparing the operation data with the actual data through an operation data comparison unit in the model self-upgrading module, analyzing the reason of benign result data in the operation result after the comparison is finished, and combining and allocating the benign result data and the actual data through a data integration and adjustment unit to form new control mode data so as to finish the upgrading of self-control;
s5: backup of the self-control system and the virtual model: the cloud storage control module performs backup storage on the self-upgraded new automatic control system through an internal virtual model backup unit, performs back-up when a serious error occurs in the system, performs corresponding backup on models with different operation results in the process of virtual model simulation operation, and can perform debugging through the model operation results in manual operation.
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.
(III) advantageous effects
Compared with the prior art, the invention provides a machine learning-based high-efficiency cold water machine room control system and method, which have the following beneficial effects:
this high-efficient cold water computer lab control system and method based on machine learning, through the model self-study module, the model sets up from upgrading module and running data analysis module's cooperation for the virtual model of system can generate different environment effects according to the operational aspect in the operation process, then form the multiple data under the multiple condition, and carry out the increase from control mode after data formation, thereby reach the effect from upgrading, then make the self-operation of system more stable and complete, in order to satisfy follow-up different circumstances, the normal operation of computer lab under the different big environment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic structural framework of the system of the present invention;
FIG. 2 is a flow chart of the system and method of the present invention.
In the figure: the system comprises a central processing unit (1), a manual operation module (2), a primary verification and correction module (3), a data allocation module (4), an operation data analysis module (5), a model self-learning module (6), a model self-upgrading module (7), a cloud storage control module (8), a remote control terminal module (9), a self-control machine module (10) and cold water machine room equipment (11).
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of devices consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Example 1:
referring to fig. 1, the present invention provides a technical solution: a high-efficiency cold water machine room control system based on machine learning comprises a central processing unit 1, wherein the output end of the central processing unit 1 is electrically connected with a manual operation module 2, the output end of the manual operation module 2 is electrically connected with the input end of the central processing unit 1, the output end of the central processing unit 1 is electrically connected with a primary verification and correction module 3, the output end of the primary verification and correction module 3 is electrically connected with the input end of the central processing unit 1, the output end of the central processing unit 1 is electrically connected with a data allocation module 4, the output end of the data allocation module 4 is electrically connected with the input end of the central processing unit 1, the output end of the central processing unit 1 is electrically connected with an operation data analysis module 5, the output end of the operation data analysis module 5 is electrically connected with the input end of the central processing unit 1, the output end of the central processing unit 1 is electrically connected with a model self-learning module 6, the output end of the model self-learning module 6 is electrically connected with the input end of the central processing unit 1, the output end of the central processing unit 1 is electrically connected with a model self-upgrading module 7, and the output end of the model self-upgrading module 7 is electrically connected with the input end of the central processing unit 1.
The output electricity of elementary verification proofreading module 3 is connected with high in the clouds storage control module 8, and the output electricity of high in the clouds storage control module 8 is connected with remote control terminal module 9, and the model is connected with from control machine module 10 from the output electricity of upgrading module 7, and the output of from control machine module 10 and the equal electricity in output of central processing unit 1 are connected with cold water computer lab equipment 11, and the model is connected with the input looks electricity of high in the clouds storage control module 8 from the output of upgrading module 7.
Example 2:
referring to fig. 1, the present invention provides a technical solution: a high-efficiency cold water machine room control system based on machine learning is characterized in that a manual operation module 2 comprises a personnel login unit for performing system login, an external equipment port and a data interaction unit, a primary verification and correction module 3 comprises a personnel information comparison unit for verifying and comparing personnel information, a cloud information feedback unit and permission verification and giving units for giving permissions according to different personnel information, a data allocation module 4 comprises a personnel information storage unit for allocating and storing personnel information and permission content, an operation log recording unit for performing backup recording on system operation data logs and a coverage data storage unit, a data analysis module 5 comprises a data analysis and arrangement unit, an operation data classification unit and a classification information generation unit for generating the system operation data information into logs, and a model self-learning module 6 comprises a virtual model construction unit for constructing a virtual system operation environment, a data interaction unit, a data management unit and a data management unit for managing the personnel information and the personnel information, a cloud information feedback unit and a permission verification and giving unit for giving permissions according to different personnel information, The cloud storage control module 8 comprises a login information recording module for recording manual operation information of personnel login, a remote login receiving unit for coordinating a remote control terminal module 9, and a virtual model backup unit for backing up a control system after upgrading in the system.
Example 3:
referring to fig. 2, the present invention provides the following technical solutions: an operation method of a machine learning-based efficient cold water machine room control system specifically comprises the following steps:
s1: manual login operation: the staff logs in the information of the staff through a staff logging-in unit, the staff information is transmitted to a staff information comparison unit inside the primary verification and correction module 3 through a data interaction unit at the moment, meanwhile, the staff information comparison unit calls a staff information storage unit inside the data allocation module 4 for information comparison according to the information, and after comparison and confirmation are finished, the staff system control authority is given to the staff through an authority correction giving unit so as to carry out system control, and the operation information is uploaded into the cloud storage control module 8 for recording through a cloud information feedback unit while the authority correction giving unit gives the staff system control authority;
s2: remote login operation: the staff logs in the information of the staff through the remote control terminal module 9 into the cloud storage control module 8, at the moment, the identity information of the staff is uploaded into the primary verification and correction module 3 through the cloud storage control module 8, and the checking operation and the authority giving operation in the step S1 are repeated;
s3: self-learning adjustment: the model self-learning module 6 generates a system operation virtual environment through an internal virtual model construction unit, then extracts operation data in the operation data analysis module 5 and operates in the virtual environment for operation, meanwhile, error correction recording is carried out on problem data in the operation process, and data analysis is carried out on the complete operation result through a data reading and analyzing unit;
s4: machine upgrading and adjusting: comparing the operation data with the actual data through an operation data comparison unit in the model self-upgrading module 7, analyzing the reason of benign result data in the operation result after the comparison is finished, and combining and allocating a plurality of benign result data and the actual data through a data integration adjustment unit to form new control mode data so as to finish the upgrading of self-control;
s5: backup of the self-control system and the virtual model: the cloud storage control module 8 performs backup storage on the self-upgraded new automatic control system through an internal virtual model backup unit, performs back-up when a serious error occurs in the system, performs corresponding backup on models with different operation results in the process of virtual model simulation operation, and performs debugging through the model operation results in manual operation.
Example 4:
when the self-control system in the self-control machine module 10 breaks down, a worker logs in self information through the remote control terminal module 9 or the personnel login unit, the personnel information is transmitted to the personnel information comparison unit in the primary verification and correction module 3 through the data interaction unit at the moment, the personnel information is compared and confirmed and then is given control authority to the worker through the authority verification and giving unit, at the moment, the worker is obtained through the virtual model backup unit in the cloud storage control module 8, a latest time node, the most complete self-control system and the virtual operation model are implanted again through the central processing unit 1, and then the self-control machine module 10 can be operated again to carry out self-operation control of the system.
The related modules involved in the system are all hardware system modules or functional modules combining computer software programs or protocols with hardware in the prior art, and the computer software programs or the protocols involved in the functional modules are all known in the technology of persons skilled in the art, and are not improvements of the system; the improvement of the system is the interaction relation or the connection relation among all the modules, namely the integral structure of the system is improved, so as to solve the corresponding technical problems to be solved by the system.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
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 variations, 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 of the disclosure being indicated by the following claims.
Claims (7)
1. High-efficient cold water computer lab control system based on machine learning, including central processing unit (1), its characterized in that: the output end of the central processing unit (1) is electrically connected with a manual operation module (2), the output end of the manual operation module (2) is electrically connected with the input end of the central processing unit (1), the output end of the central processing unit (1) is electrically connected with a primary verification and correction module (3), the output end of the primary verification and correction module (3) is electrically connected with the input end of the central processing unit (1), the output end of the central processing unit (1) is electrically connected with a data allocation module (4), the output end of the data allocation module (4) is electrically connected with the input end of the central processing unit (1), the output end of the central processing unit (1) is electrically connected with an operation data analysis module (5), the output end of the operation data analysis module (5) is electrically connected with the input end of the central processing unit (1), and the output end of the central processing unit (1) is electrically connected with a model self-learning module (6), the output end of the model self-learning module (6) is electrically connected with the input end of the central processing unit (1), the output end of the central processing unit (1) is electrically connected with the model self-upgrading module (7), and the output end of the model self-upgrading module (7) is electrically connected with the input end of the central processing unit (1).
2. The machine learning based high efficiency cold water machine room control system of claim 1, wherein: elementary output electricity that verifies school pair module (3) is connected with high in the clouds storage control module (8), the output electricity of high in the clouds storage control module (8) is connected with remote control terminal module (9), the output electricity of model from upgrading module (7) is connected with from control machine module (10), the output of from control machine module (10) is connected with cold water computer lab equipment (11) with the equal electricity of output of central processing unit (1), the model is connected with the input looks electricity of high in the clouds storage control module (8) from the output of upgrading module (7).
3. The machine learning based high efficiency cold water machine room control system of claim 1, wherein: the manual operation module (2) comprises a personnel login unit for performing system login, an external device port and a data interaction unit, the primary verification and correction module (3) comprises a personnel information comparison unit for verifying and comparing personnel information, a cloud information feedback unit and different permission correction and giving units for giving permissions according to different personnel information, and the data allocation module (4) comprises a personnel information storage unit for allocating and storing the personnel information and permission content, an operation log recording unit for performing backup recording on system operation data logs and a coverage data storage unit.
4. The machine learning based high efficiency cold water machine room control system of claim 1, wherein: the data analysis module (5) comprises a data analysis and arrangement unit, an operation data classification unit and a classification information log generation unit for generating system operation data information into logs, and the model self-learning module (6) comprises a virtual model construction unit for constructing a virtual system operation environment, a data reading and analyzing unit for performing data analysis on operation data under a virtual operation condition and a simulation operation error correction unit for debugging and correcting the operation data under the virtual operation condition.
5. The machine learning-based efficient cold water machine room control system and method according to claim 1, characterized in that: the model self-upgrading module (7) comprises an operation data comparison unit for comparing simulation operation data with actual operation data, a benign data analysis unit for performing reason analysis on data of a benign structure in the operation process of the model, and a data integration and adjustment unit for integrating, allocating and self-upgrading the actual operation data and the simulation operation data.
6. The machine learning based high efficiency cold water machine room control system of claim 2, wherein: the cloud storage control module (8) comprises a login information recording module for recording manual operation information of personnel login, a remote login receiving unit for coordinating the remote control terminal module (9), and a virtual model backup unit for backing up the control system after the control system is upgraded in the system.
7. An operation method for the machine learning-based high-efficiency cold water machine room control system of any one of claims 1-6 is characterized by specifically operating as follows:
s1: manual login operation: the staff logs in the information of the staff through a staff logging-in unit, the staff information is transmitted to a staff information comparison unit inside the primary verification and correction module (3) through a data interaction unit at the moment, meanwhile, the staff information comparison unit calls a staff information storage unit inside the data allocation module (4) to perform information comparison according to the information, and after the comparison is completed and confirmed, the staff system control authority is given to the staff through an authority correction and giving unit so as to perform system control, and the operation information is uploaded into a cloud storage control module (8) through a cloud information feedback unit to be recorded while the authority correction and giving unit gives the staff system control authority;
s2: remote login operation: the staff logs in the information of the staff into the cloud storage control module (8) through the remote control terminal module (9), at the moment, the identity information of the staff is uploaded into the primary verification and proofreading module (3) through the cloud storage control module (8), and the checking operation and the permission giving operation in the step S1 are repeated;
s3: self-learning adjustment: the model self-learning module (6) generates a system operation virtual environment through an internal virtual model construction unit, then extracts operation data in the operation data analysis module (5) and operates in the virtual environment for operation, meanwhile, error correction recording is carried out on problem data in the operation process, and data analysis is carried out on the complete operation result through a data reading and analyzing unit;
s4: machine upgrading and adjusting: the operation data and the actual data are compared through an operation data comparison unit in the model self-upgrading module (7), the reason of benign result data in the operation result is analyzed after the comparison is finished, and then the benign result data and the actual data are combined and allocated through a data integration adjustment unit to form new control mode data, so that the self-control upgrading is finished;
s5: backup of the self-control system and the virtual model: the cloud storage control module (8) performs backup storage on a self-upgraded new automatic control system through an internal virtual model backup unit, performs file returning when a serious error occurs in the system, performs corresponding backup on models with different operation results in the virtual model simulation operation process, and performs debugging through the model operation results in manual operation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210582546.7A CN114879542A (en) | 2022-05-25 | 2022-05-25 | Machine learning-based efficient cold water machine room control system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210582546.7A CN114879542A (en) | 2022-05-25 | 2022-05-25 | Machine learning-based efficient cold water machine room control system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114879542A true CN114879542A (en) | 2022-08-09 |
Family
ID=82677878
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210582546.7A Pending CN114879542A (en) | 2022-05-25 | 2022-05-25 | Machine learning-based efficient cold water machine room control system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114879542A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020069369A1 (en) * | 2000-07-05 | 2002-06-06 | Tremain Geoffrey Donald | Method and apparatus for providing computer services |
| CN104142629A (en) * | 2013-05-09 | 2014-11-12 | 洛克威尔自动控制技术股份有限公司 | Using cloud-based data for virtualization of an industrial machine environment |
| CN105988367A (en) * | 2015-03-16 | 2016-10-05 | 洛克威尔自动控制技术股份有限公司 | Modeling of industrial automation environment the cloud |
| CN109298635A (en) * | 2018-10-29 | 2019-02-01 | 花静霞 | A kind of cold water computer room control system based on deep learning |
| CN109539457A (en) * | 2018-10-29 | 2019-03-29 | 花静霞 | A kind of cold water computer room control method based on deep learning |
-
2022
- 2022-05-25 CN CN202210582546.7A patent/CN114879542A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020069369A1 (en) * | 2000-07-05 | 2002-06-06 | Tremain Geoffrey Donald | Method and apparatus for providing computer services |
| CN104142629A (en) * | 2013-05-09 | 2014-11-12 | 洛克威尔自动控制技术股份有限公司 | Using cloud-based data for virtualization of an industrial machine environment |
| CN105988367A (en) * | 2015-03-16 | 2016-10-05 | 洛克威尔自动控制技术股份有限公司 | Modeling of industrial automation environment the cloud |
| CN109298635A (en) * | 2018-10-29 | 2019-02-01 | 花静霞 | A kind of cold water computer room control system based on deep learning |
| CN109539457A (en) * | 2018-10-29 | 2019-03-29 | 花静霞 | A kind of cold water computer room control method based on deep learning |
Non-Patent Citations (1)
| Title |
|---|
| 杨冰 等: "面向云计算中虚拟化技术的等级保护要求研究", 《信息安全与通信保密》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111262346B (en) | Integrated automatic acceptance system and method for transformer substation | |
| CN102005818B (en) | Method for detecting consistency of SCD (System Configuration Document) and IED (Intelligent Electronic Device) model on line | |
| CN112737813A (en) | Power business management method and system based on 5G network slice | |
| Hoogsteen et al. | Demkit: a decentralized energy management simulation and demonstration toolkit | |
| CN103176084A (en) | Intelligent substation simulation system in panoramic replace mode and integration testing method for intelligent substation simulation system | |
| CN109933521A (en) | Automated testing method, device, computer equipment and storage medium based on BDD | |
| CN107911251A (en) | A kind of method for configuring network equipment, device and medium | |
| CN110008005B (en) | Cloud platform-based power grid communication resource virtual machine migration system and method | |
| CN114879542A (en) | Machine learning-based efficient cold water machine room control system and method | |
| CN112015813A (en) | Data sharing method, system and device of power distribution network and computer equipment | |
| Tugarinov et al. | Digital twin of distributed energy devices | |
| CN116360294A (en) | Simulation method and device based on active power distribution network | |
| Bondy et al. | Procedure for validation of aggregators providing demand response | |
| CN115657493A (en) | Digital twin simulation test system of power grid dispatching platform | |
| KR101759893B1 (en) | Virtual device management apparatus based on scenario for distributed energy resources | |
| CN110889515A (en) | Data processing method, device and system | |
| Florea et al. | From bridge to control hub—The power Smart Grid evolution | |
| Evins et al. | Using simple predictive models to improve control of complex building systems | |
| Peng et al. | Digital Simulation and Automatic Test of Source Network Load Storage Cooperative Control | |
| WO2022267866A1 (en) | Method for acquiring power supply information, electronic device, and computer-readable storage medium | |
| CN107332346B (en) | Transformer substation secondary system big data analysis system oriented to time-space distribution characteristic analysis and implementation method | |
| CN116365518B (en) | Reconfigurable method and system for power distribution network based on intelligent switch | |
| CN111030862B (en) | Equipment off-line management system based on acquisition terminal | |
| Murphy et al. | Applying the industrial internet reference architecture to a smart grid testbed | |
| CN117113708A (en) | Redundant system design method based on SysML and Modelica |
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 |