CN111787123A - Intelligent heat supply network operation and maintenance management system - Google Patents
Intelligent heat supply network operation and maintenance management system Download PDFInfo
- Publication number
- CN111787123A CN111787123A CN202010732277.9A CN202010732277A CN111787123A CN 111787123 A CN111787123 A CN 111787123A CN 202010732277 A CN202010732277 A CN 202010732277A CN 111787123 A CN111787123 A CN 111787123A
- Authority
- CN
- China
- Prior art keywords
- data
- heat supply
- supply network
- real
- time
- 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
- 238000012423 maintenance Methods 0.000 title claims abstract description 14
- 238000013499 data model Methods 0.000 claims abstract description 28
- 238000007726 management method Methods 0.000 claims abstract description 16
- 230000005540 biological transmission Effects 0.000 claims abstract description 9
- 238000000034 method Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 4
- 238000012806 monitoring device Methods 0.000 claims description 24
- 238000012549 training Methods 0.000 claims description 12
- 238000010586 diagram Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 230000004913 activation Effects 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 239000013307 optical fiber Substances 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 238000007619 statistical method Methods 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims 2
- 230000008447 perception Effects 0.000 claims 1
- 238000005265 energy consumption Methods 0.000 description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
- 230000006872 improvement Effects 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 230000008439 repair process Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000009123 feedback regulation Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y10/00—Economic sectors
- G16Y10/35—Utilities, e.g. electricity, gas or water
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y30/00—IoT infrastructure
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/10—Detection; Monitoring
Abstract
The invention discloses an intelligent heat supply network operation and maintenance management system, relates to the field of heat supply network management, and solves the problem that the flow transmission and distribution of each loop are unbalanced due to hydraulic imbalance, so that the room temperature of each user is uneven due to traditional extensive centralized heat supply. The method comprises the steps that a data model is used for classifying real-time data information transmitted at the same time according to the type and the model of a data acquisition device based on softmax, and classified data are divided into a current time node data cluster and a previous time node data cluster again according to the period of the data acquisition device; the data model carries out forgetting and memory processing on data based on gate control signals of the LSTM to obtain a predicted next time node data cluster, and a real-time heat supply network decision is generated according to the predicted next time node data cluster. The invention predicts the consumption of heat supply energy and provides decision support for reasonably making an energy plan and dynamically scheduling energy.
Description
Technical Field
The invention relates to the field of energy management, in particular to an intelligent heat supply network operation and maintenance management system.
Background
At present, the problems of high energy consumption, low efficiency, high pollution and the like generally exist in urban centralized heat supply, a large amount of energy resources are wasted, and the urban environment quality is seriously influenced. The main problem of traditional extensive central heating is hydraulic imbalance, which causes unbalanced flow transmission and distribution of each loop, and causes uneven room temperature and cold of each user. Along with the continuous improvement of the resident's requirement for the comfort level of living, heat supply, accurate heat supply become new improvement direction as required.
The method has the advantages that the safety and high-efficiency operation of the heat supply network are guaranteed, the personalized heat demand of customers is considered, the data of the whole heat supply network are analyzed and simulated through mutual fusion of informatization and industrialization, gaps and reasons are searched and continuously optimized through the real-time feedback regulation and control result and the prediction model data, the optimal scheduling scheme is provided for the regulation and control strategy of the whole heat supply network on the premise of guaranteeing the heat supply safety and quality, and the self-inspection, self-balancing and self-optimization of the heat supply network are realized. Wisdom heat supply is the development needs that improve traditional inefficiency management mode, can greatly promote the happiness index of resident's life.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the invention provides an intelligent heat supply network operation and maintenance management system for solving the problems, wherein the main problem of the traditional extensive centralized heat supply is that hydraulic imbalance causes unbalanced flow transmission and distribution of each loop, so that the room temperature of each user is uneven.
The invention is realized by the following technical scheme:
the intelligent heat supply network operation and maintenance management system comprises a sensing layer, a platform and facility layer, an application service layer and a comprehensive display layer;
the sensing layer collects real-time data information of a heat source, a heat exchange station and the environment and shares the real-time data information with the application service layer and the comprehensive display layer through the platform and the facility layer;
the platform and the facility layer receive real-time data information collected by the sensing layer through wireless transmission and optical fibers;
the application service layer receives real-time data information transmitted by the platform and the facility layer and establishes a data model based on an LSTM-RNN neural network, the sensing layer comprises a plurality of types of data acquisition devices, the data model is used for classifying the real-time data information transmitted at the same time according to the types and the models of the data acquisition devices based on softmax, and the classified data are divided into a current time node data cluster and a last time node data cluster again according to the period of the data acquisition devices;
the data model carries out memory processing on the forgotten data based on the gate control signal of the LSTM to obtain a predicted next time node data cluster, a real-time heat supply network decision is generated according to the predicted next time node data cluster, and the load output or input of the multi-terminal equipment or the user connected with the IOT at present is adjusted according to the real-time heat supply network decision;
and the comprehensive display layer receives the predicted next time node data cluster of the data model and sends the next time node data cluster to the monitoring center to display a statistical analysis result.
Furthermore, the multi-class data acquisition device comprises an area environment online monitoring device, a heat source output online monitoring device, a pipe network operation state sensor and a radio frequency identification device, wherein the area environment online monitoring device acquires environment real-time data information, the pipe network operation state sensor acquires real-time data information of a heat exchanger, a pump valve of the heat exchanger and a pipe network of the heat exchange station, and the heat source output online monitoring device acquires real-time data information of a boiler, a pump valve of the boiler and a pipe network of the heat source;
the radio frequency identification device provides a non-contact communication path for the regional environment on-line monitoring device, the heat source output on-line monitoring device and the pipe network running state sensor.
Further, the environment real-time data information comprises current weather conditions, outdoor temperature, highest temperature, lowest temperature, wind speed and wind direction data.
Furthermore, the heat source output online monitoring equipment and the pipe network running state sensor send real-time environment data information to the application service layer, and the application service layer analyzes all building heat load characteristics covered by the multi-type data acquisition devices on the basis of the positions of the heat source output online monitoring equipment and the pipe network running state sensor and the buildings to which the numbers of the heat source output online monitoring equipment and the pipe network running state sensor belong, and generates a building heat load regional diagram in a heat supply network region.
Further, the real-time heat supply network decision includes adjustment of operation parameters of a heat source, a heat supply network, a heat station and a user and an adjustment scheme thereof.
Further, the application service layer calculates the minimum common multiple of the multiple periods according to the periods of data transmission of the multiple types of data acquisition devices, schedules multi-node heat supply network decisions in the polling period based on the minimum common multiple of the multiple periods, and generates polling paths matched with the heat supply network areas according to all effective heat supply network decisions in the polling period.
Further, the user receives a passive control range of the user side in a real-time heat supply network decision calculated by an application service layer at the user side;
the method comprises the steps that a user actively adjusts the room temperature at a user side, the room temperature data adjusted by the user is sent to the input end of a data model of an application service layer, the data model combines the room temperature data adjusted by the user and the data cluster training of the last time node, a training value is selected, the rest of the training values are gated to be in a gating state, and the gating state records and controls the forgetting and memorizing process of the training values to obtain an output result;
the data model fits multi-user data aiming at the weight matrix of the user and combined with the output result, and the multi-user data is used for being loaded into the input end of a next time node data cluster of the data model;
generation of gating state: the user adjusts the room temperature data, the last time node data cluster is multiplied by the weight matrix of the user through the splicing vector, and the value is converted through the activation function to be used as the gating state.
The invention has the following advantages and beneficial effects:
the invention can predict the consumption of heat supply energy based on the LSTM RNN neural network algorithm, and provides decision support for reasonably making an energy plan and dynamically scheduling energy.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a diagram of the data model prediction results of the present invention.
Fig. 2 is a diagram of data records of actual operation of the system of the present invention.
FIG. 3 is a system level diagram of the present invention.
Detailed Description
Before any embodiments of the invention are explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangements of components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any inventive changes, are within the scope of the present invention.
The intelligent heat supply network operation and maintenance management system comprises a sensing layer, a platform and facility layer, an application service layer and a comprehensive display layer;
the sensing layer collects real-time data information of a heat source, a heat exchange station and the environment and shares the real-time data information with the application service layer and the comprehensive display layer through the platform and the facility layer;
the platform and the facility layer receive real-time data information collected by the sensing layer through wireless transmission and optical fibers;
the application service layer receives real-time data information transmitted by the platform and the facility layer and establishes a data model based on an LSTM-RNN neural network, the sensing layer comprises a plurality of types of data acquisition devices, the data model is used for classifying the real-time data information transmitted at the same time according to the types and the models of the data acquisition devices based on softmax, and the classified data are divided into a current time node data cluster and a last time node data cluster again according to the period of the data acquisition devices;
the data model carries out memory processing on the forgotten data based on the gate control signal of the LSTM to obtain a predicted next time node data cluster, a real-time heat supply network decision is generated according to the predicted next time node data cluster, and the load output or input of the multi-terminal equipment or the user connected with the IOT at present is adjusted according to the real-time heat supply network decision;
and the comprehensive display layer receives the predicted next time node data cluster of the data model and sends the next time node data cluster to the monitoring center to display a statistical analysis result.
The multi-class data acquisition device comprises an on-line monitoring device for the regional environment, an on-line monitoring device for heat source output, a pipe network running state sensor and a radio frequency identification device, wherein the on-line monitoring device for the regional environment acquires real-time data information of the environment, the pipe network running state sensor acquires real-time data information of a heat exchanger, a pump valve of the heat exchanger and a pipe network of the heat exchange station, and the on-line monitoring device for the heat source output acquires real-time data information of a boiler, a pump valve of the boiler and a pipe network;
the radio frequency identification device provides a non-contact communication path for the regional environment on-line monitoring device, the heat source output on-line monitoring device and the pipe network running state sensor.
The environment real-time data information comprises current weather condition, outdoor temperature, highest temperature, lowest temperature, wind speed and wind direction data.
The heat source output online monitoring device and the pipe network running state sensor send real-time environment data information to the application service layer, and the application service layer analyzes all building heat load characteristics covered by the multi-class data acquisition device on the basis of the positions of the heat source output online monitoring device and the pipe network running state sensor and the buildings to which the numbers of the heat source output online monitoring device and the pipe network running state sensor belong, and generates a building heat load regional diagram in a heat network region.
The real-time heat supply network decision comprises adjustment of operation parameters of a heat source, a heat supply pipe network, a heat station and users and an adjustment scheme thereof.
The application service layer calculates the minimum common multiple of multiple periods according to the period of data transmission of the multiple types of data acquisition devices, schedules multi-node heat supply network decisions in the polling period based on the minimum common multiple of the multiple periods, and generates polling paths matched with heat supply network areas according to all effective heat supply network decisions in the polling period.
The user receives a passive control range of the user side in a real-time heat supply network decision calculated by an application service layer at the user side;
the method comprises the steps that a user actively adjusts the room temperature at a user side, the room temperature data adjusted by the user is sent to the input end of a data model of an application service layer, the data model combines the room temperature data adjusted by the user and the data cluster training of the last time node, a training value is selected, the rest of the training values are gated to be in a gating state, and the gating state records and controls the forgetting and memorizing process of the training values to obtain an output result;
the data model fits multi-user data aiming at the weight matrix of the user and combined with the output result, and the multi-user data is used for being loaded into the input end of a next time node data cluster of the data model;
generation of gating state: the user adjusts the room temperature data, the last time node data cluster is multiplied by the weight matrix of the user through the splicing vector, and the value is converted through the activation function to be used as the gating state.
And temperature, pressure, flow and energy consumption metering devices are arranged at outlets of each unit of the heat source and the main pipe network, energy consumption and efficiency of each unit in each link are calculated and analyzed, parameters of heat source operation and heat supply outlets are optimized, information such as flow, heat, water supply pressure, water return pressure, water supply temperature and water return temperature of each heat supply main pipe is respectively displayed, and the information is transmitted to an application service layer to monitor the parameters of the heat source and the outlet operation in real time.
When the data of the heat exchange station fluctuates obviously, the abnormal or fault condition of the heat supply pipe network in the area is indicated, the system performs early warning to arrange emergency repair personnel to perform patrol, and performs accurate positioning on the fault to perform emergency repair.
Based on the LSTM RNN neural network algorithm, the consumption of heat supply energy can be predicted, and decision support is provided for reasonably making an energy plan and dynamically scheduling energy. The comparison result between the energy consumption prediction data model and the real data is shown in fig. 1-2.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. The intelligent heat supply network operation and maintenance management system is characterized by comprising a perception layer, a platform and facility layer, an application service layer and a comprehensive display layer;
the sensing layer collects real-time data information of a heat source, a heat exchange station and the environment and shares the real-time data information with the application service layer and the comprehensive display layer through the platform and the facility layer;
the platform and the facility layer receive real-time data information collected by the sensing layer through wireless transmission and optical fibers;
the application service layer receives real-time data information transmitted by the platform and the facility layer and establishes a data model based on an LSTM-RNN neural network, the sensing layer comprises a plurality of types of data acquisition devices, the data model is used for classifying the real-time data information transmitted at the same time according to the types and the models of the data acquisition devices based on softmax, and the classified data are divided into a current time node data cluster and a last time node data cluster again according to the period of the data acquisition devices;
the data model carries out memory processing on the forgotten data based on the gate control signal of the LSTM to obtain a predicted next time node data cluster, a real-time heat supply network decision is generated according to the predicted next time node data cluster, and the load output or input of the multi-terminal equipment or the user connected with the IOT at present is adjusted according to the real-time heat supply network decision;
and the comprehensive display layer receives the predicted next time node data cluster of the data model and sends the next time node data cluster to the monitoring center to display a statistical analysis result.
2. The intelligent heat supply network operation and maintenance management system of claim 1, wherein the multiple types of data acquisition devices comprise an on-line area environment monitoring device, an on-line heat source output monitoring device, a pipe network operation state sensor and a radio frequency identification device, the on-line area environment monitoring device acquires real-time environment data information, the pipe network operation state sensor acquires real-time environment data information of a heat exchanger, a pump valve of the heat exchanger and a pipe network of the heat exchange station, and the on-line heat source output monitoring device acquires real-time environment data information of a boiler, a pump valve of the boiler, a pipe network of the heat exchanger and a heat source;
the radio frequency identification device provides a non-contact communication path for the regional environment on-line monitoring device, the heat source output on-line monitoring device and the pipe network running state sensor.
3. The intelligent heat supply network operation and maintenance management system of claim 2, wherein the real-time environmental data information comprises current weather conditions, outdoor temperature, maximum temperature, minimum temperature, wind speed, and wind direction data.
4. The intelligent heat supply network operation and maintenance management system according to claim 2, wherein the heat source output online monitoring device and the pipe network operation state sensor send real-time environmental data information to the application service layer, and the application service layer analyzes all building heat load characteristics covered by the multiple types of data acquisition devices based on the positions of the heat source output online monitoring device and the pipe network operation state sensor and the buildings to which the numbers of the heat source output online monitoring device and the pipe network operation state sensor belong, and generates a building heat load area diagram in a heat supply network area.
5. The intelligent heat supply network operation and maintenance management system of claim 4, wherein the real-time heat supply network decision comprises adjustment of operation parameters of heat sources, heat supply network, heat power stations and users and adjustment schemes thereof.
6. The intelligent heat supply network operation and maintenance management system of claim 5, wherein the application service layer calculates the least common multiple of the multiple periods according to the periods of data transmission of the multiple types of data acquisition devices, schedules the multi-node heat supply network decisions in the polling period for the polling period based on the least common multiple of the multiple periods, and generates the polling paths matching the heat supply network areas according to all valid heat supply network decisions in the polling period.
7. The intelligent heat supply network operation and maintenance management system of claim 6, wherein the user receives the passive control range of the user terminal in the real-time heat supply network decision calculated by the application service layer at the user terminal;
the method comprises the steps that a user actively adjusts the room temperature at a user side, the room temperature data adjusted by the user is sent to the input end of a data model of an application service layer, the data model combines the room temperature data adjusted by the user and the data cluster training of the last time node, a training value is selected, the rest of the training values are gated to be in a gating state, and the gating state records and controls the forgetting and memorizing process of the training values to obtain an output result;
the data model fits multi-user data aiming at the weight matrix of the user and combined with the output result, and the multi-user data is used for being loaded into the input end of a next time node data cluster of the data model;
generation of gating state: the user adjusts the room temperature data, the last time node data cluster is multiplied by the weight matrix of the user through the splicing vector, and the value is converted through the activation function to be used as the gating state.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010732277.9A CN111787123A (en) | 2020-07-27 | 2020-07-27 | Intelligent heat supply network operation and maintenance management system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010732277.9A CN111787123A (en) | 2020-07-27 | 2020-07-27 | Intelligent heat supply network operation and maintenance management system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111787123A true CN111787123A (en) | 2020-10-16 |
Family
ID=72764913
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010732277.9A Pending CN111787123A (en) | 2020-07-27 | 2020-07-27 | Intelligent heat supply network operation and maintenance management system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111787123A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112268312A (en) * | 2020-10-23 | 2021-01-26 | 哈尔滨派立仪器仪表有限公司 | Intelligent heat supply management system based on deep learning |
| CN112461296A (en) * | 2020-11-23 | 2021-03-09 | 中国华能集团清洁能源技术研究院有限公司 | Heat exchange unit with optical fiber monitoring function and monitoring method thereof |
| CN112944450A (en) * | 2021-02-03 | 2021-06-11 | 大唐吉林发电有限公司热力分公司 | Monitoring method and system for remotely and autonomously controlling heat exchange station equipment |
| CN113418229A (en) * | 2021-06-11 | 2021-09-21 | 沈阳工业大学 | Heating management and control valve intelligent regulation and control system based on convolution-self-coding neural network |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014063518A1 (en) * | 2012-10-24 | 2014-05-01 | 中兴通讯股份有限公司 | Remote home healthcare system |
| US20140278709A1 (en) * | 2013-03-14 | 2014-09-18 | Combined Energies LLC | Intelligent CCHP System |
| CN105003958A (en) * | 2015-08-14 | 2015-10-28 | 大连海事大学微电脑开发公司 | Internet-of-Things-based urban region centralized heat supply monitoring system and heat supply system |
| CN107942960A (en) * | 2017-10-23 | 2018-04-20 | 中国科学院地理科学与资源研究所 | A kind of intelligentized information processing system |
| CN108197734A (en) * | 2017-12-28 | 2018-06-22 | 北京首钢自动化信息技术有限公司 | A kind of energy source dispensing optimization system |
| CN108684106A (en) * | 2018-06-27 | 2018-10-19 | 江苏集萃有机光电技术研究所有限公司 | Lighting system and Internet of Things network control system |
| CN109253494A (en) * | 2018-09-05 | 2019-01-22 | 东北大学 | A kind of electric heat-storage device control system and method based on heat load prediction |
| CN109270842A (en) * | 2018-10-25 | 2019-01-25 | 浙江大学 | A kind of district heating model predictive control system and method based on Bayesian network |
| CN109373438A (en) * | 2018-09-11 | 2019-02-22 | 太原理工大学 | Heating energy-saving control method and system based on transfer learning algorithm |
| CN109373441A (en) * | 2018-12-20 | 2019-02-22 | 普瑞森能源科技（北京）股份有限公司 | Heat supply network energy management system and its processing method |
| CN110701672A (en) * | 2019-10-28 | 2020-01-17 | 沈阳瑞科斯节能科技有限公司 | Heat supply management system |
-
2020
- 2020-07-27 CN CN202010732277.9A patent/CN111787123A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014063518A1 (en) * | 2012-10-24 | 2014-05-01 | 中兴通讯股份有限公司 | Remote home healthcare system |
| US20140278709A1 (en) * | 2013-03-14 | 2014-09-18 | Combined Energies LLC | Intelligent CCHP System |
| CN105003958A (en) * | 2015-08-14 | 2015-10-28 | 大连海事大学微电脑开发公司 | Internet-of-Things-based urban region centralized heat supply monitoring system and heat supply system |
| CN107942960A (en) * | 2017-10-23 | 2018-04-20 | 中国科学院地理科学与资源研究所 | A kind of intelligentized information processing system |
| CN108197734A (en) * | 2017-12-28 | 2018-06-22 | 北京首钢自动化信息技术有限公司 | A kind of energy source dispensing optimization system |
| CN108684106A (en) * | 2018-06-27 | 2018-10-19 | 江苏集萃有机光电技术研究所有限公司 | Lighting system and Internet of Things network control system |
| CN109253494A (en) * | 2018-09-05 | 2019-01-22 | 东北大学 | A kind of electric heat-storage device control system and method based on heat load prediction |
| CN109373438A (en) * | 2018-09-11 | 2019-02-22 | 太原理工大学 | Heating energy-saving control method and system based on transfer learning algorithm |
| CN109270842A (en) * | 2018-10-25 | 2019-01-25 | 浙江大学 | A kind of district heating model predictive control system and method based on Bayesian network |
| CN109373441A (en) * | 2018-12-20 | 2019-02-22 | 普瑞森能源科技（北京）股份有限公司 | Heat supply network energy management system and its processing method |
| CN110701672A (en) * | 2019-10-28 | 2020-01-17 | 沈阳瑞科斯节能科技有限公司 | Heat supply management system |
Non-Patent Citations (3)
| Title |
|---|
| A.A. ZAPOROZHETS等: "Development of an Intelligent System for Diagnosing the Technical Condition of the Heat Power Equipment", 《2018 IEEE 13TH INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE ON COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES (CSIT)》 * |
| 李剑波等, 华中科技大学出版社 * |
| 赵琼: "智慧城市供热系统建模仿真与运行优化控制", 《中国优秀博硕士学位论文全文数据库(博士)工程科技Ⅱ辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112268312A (en) * | 2020-10-23 | 2021-01-26 | 哈尔滨派立仪器仪表有限公司 | Intelligent heat supply management system based on deep learning |
| CN112461296A (en) * | 2020-11-23 | 2021-03-09 | 中国华能集团清洁能源技术研究院有限公司 | Heat exchange unit with optical fiber monitoring function and monitoring method thereof |
| CN112944450A (en) * | 2021-02-03 | 2021-06-11 | 大唐吉林发电有限公司热力分公司 | Monitoring method and system for remotely and autonomously controlling heat exchange station equipment |
| CN112944450B (en) * | 2021-02-03 | 2022-06-10 | 大唐吉林发电有限公司热力分公司 | Monitoring method and system for remotely and autonomously controlling heat exchange station equipment |
| CN113418229A (en) * | 2021-06-11 | 2021-09-21 | 沈阳工业大学 | Heating management and control valve intelligent regulation and control system based on convolution-self-coding neural network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111787123A (en) | Intelligent heat supply network operation and maintenance management system | |
| CN104571068A (en) | Optimized operation control method and system of distributed energy system | |
| CN111722540A (en) | Energy Internet digital twin simulation system and method | |
| CN104698843A (en) | Model prediction control based energy saving control method of data center | |
| CN102777974A (en) | Automatic adjustment control system for centralized heating | |
| CN112268312A (en) | Intelligent heat supply management system based on deep learning | |
| CN105868487A (en) | Energy saving expert system based on energy consumption device models | |
| KR20150026230A (en) | Self running building energy management system using bim data | |
| Wei et al. | Hierarchical and distributed demand response control strategy for thermostatically controlled appliances in smart grid | |
| CN113778215A (en) | Method for realizing data center PUE prediction and consumption reduction strategy based on big data | |
| CN113513783A (en) | Heat exchange station online monitoring system and heat exchange station control method | |
| CN113655762A (en) | Operation optimization control method and system for gas energy supply system | |
| CN116109104A (en) | Intelligent gas repair management method, internet of things system, device and medium | |
| CN116436033A (en) | Temperature control load frequency response control method based on user satisfaction and reinforcement learning | |
| CN113110056B (en) | Heat supply intelligent decision-making method and intelligent decision-making machine based on artificial intelligence | |
| CN113065249B (en) | Method and device for predicting supply and return water temperature of heating system | |
| CN116633016B (en) | Power management system and method based on high-frequency data acquisition technology | |
| CN211526662U (en) | Subway station ventilation air conditioner economizer system based on load prediction | |
| CN112950001A (en) | Intelligent energy management and control system and method based on cloud edge closed-loop architecture | |
| Zhang et al. | Flow rate control in smart district heating systems using deep reinforcement learning | |
| CN116070795A (en) | Intelligent energy management and control method and system based on Internet of things | |
| CN216481239U (en) | Two-way control's wisdom heating device | |
| CN113283649B (en) | Method, device, equipment and medium for controlling energy efficiency of supply and demand cooperative operation | |
| US20230119984A1 (en) | System and Method for Development of an AI Computational Intelligence Platform for Energy Resilience in Buildings | |
| CN117249537B (en) | Virtual power plant scheduling and control system and method based on central air conditioner |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201016 |
|
| RJ01 | Rejection of invention patent application after publication |