CN117170273A - Intelligent electricity-saving control system based on Internet - Google Patents
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Abstract
The invention belongs to the technical field of intelligent electricity saving, and provides an intelligent electricity saving control system based on the Internet, which comprises a control system, wherein the control system comprises an online intelligent visualization platform client, an offline terminal device, a relay and a circuit, and the offline terminal device is electrically connected with the online intelligent visualization platform client and the offline terminal device; according to the invention, through the application of technologies such as various sensors, the Internet of things and the like, real-time infrared monitoring and target detection are carried out on a certain area through the STM32 series control chip, the detection data are processed and network access is carried out, the processed data are transmitted to the PLC module, and after the intelligent interconnection information I/O module processes and executes the command, the intelligent control of the power switch is realized, so that the purpose of controlling the electricity consumption of floors, buildings and target areas is achieved, the control effect of building electricity is enhanced, meanwhile, the control operation steps are simpler and more convenient, the building electricity can be quickly controlled in time, and the electricity saving control effect of the buildings is enhanced.
Description
Technical Field
The invention belongs to the technical field of intelligent power saving, and particularly relates to an intelligent power saving control system based on the Internet.
Background
The Internet refers to a huge network formed by connecting networks in series, the networks are connected by a group of general protocols to form a logically single huge international network, along with the development and progress of society, the Internet is widely applied in the aspects of life, and for buildings, the Internet is often required to be used for carrying out power saving control on a target area;
at present, building electricity has the following problems:
1. lack of stringency of management
Most electricity utilization units do not have a perfect evaluation system, even some units do not have a perfect evaluation at all on reasonable electricity utilization, and a mode of recalculating the consumed electricity is usually implemented, so that the electricity saving practicability is poor.
2. Poor device performance
Nowadays, china has a large scale for the application of electromechanical equipment, wherein a part of electromechanical equipment is in a old and lagged form and has poor performance, and the electromechanical equipment cannot be updated and modified timely, so that a very poor level is shown in the aspect of equipment performance, and further the power saving effect is poor.
Therefore, the current control effect of building electricity consumption is poor, and meanwhile, the operation steps are complicated, so that the building electricity consumption cannot be controlled rapidly in time, and the power saving control effect of the building is poor.
For this reason, those skilled in the art propose an intelligent power saving control system based on the internet to solve the problems existing in the background art.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent electricity-saving control system based on the Internet, which aims to solve the problems that in the prior art, the control effect of building electricity utilization is poor, meanwhile, the operation steps are complicated, so that the building electricity utilization cannot be controlled quickly in time, the building electricity-saving control effect is poor, and the like.
The intelligent electricity-saving control system based on the Internet comprises a control system, wherein the control system comprises an online intelligent visual platform client, an offline terminal device, a relay and a circuit, the offline terminal device is electrically connected with the online intelligent visual platform client and the offline terminal device, and the relay is connected with the circuit through control.
Preferably, the online intelligent visualization platform client comprises a computer control end, a mobile phone control end and a cloud server, wherein the computer control end and the mobile phone control end are electrically connected with the cloud server.
Preferably, the off-line terminal device comprises a human body detection module and a microcontroller, wherein the human body detection module is connected with the microcontroller, the microcontroller is connected with the cloud server through data transmission, and the microcontroller is connected with the relay;
the off-line terminal equipment is developed and produced by a team, and is matched with a relay and a contactor for use. The relay and the contactor adopt the existing small-sized intermediate relay and alternating current contactor in the market, and the circuit connection relation of the relay and the contactor can be known from the prior art, and the description is omitted here.
Preferably, the off-line terminal device further comprises an electric brake part, an infrared detection part, a camera part and a communication part;
the electric switch part adopts a mode of matching a relay and a contactor for use;
the infrared detection part is an infrared monitoring sensor part, and adopts a double-unit high-performance pyroelectric probe;
the camera part uses a sized M2DOCK as an intelligent visual hardware platform, and the platform has strong calculation performance and stronger calculation performance;
the communication part adopts excellent product bluetooth in China and wireless WiFi module, uses two kinds of wireless transmission technique comprehensively, and wireless transmission scene is extensive general, can access wireless interface product ecological chain.
Preferably, the control system further comprises an online platform client design, an algorithm application design, a core circuit design and main technical indexes;
the on-line platform client design comprises a cloud service design, the cloud service design comprises a cloud application background server which is constructed on an Arian Internet of things cloud platform, the server can provide X86, ARM computing architecture, GPU heterogeneous computing and the like, elastic bare metal and super computing cluster architecture, a cloud server ECS supports multiple architectures, and the ECS can be shared by the ECS, the computing type c7, the general type g8, the general computing type, the memory type, the GPU type, the local SSD type, the big data type, the high main frequency type, the bare metal type and the like, a CPU processor comprises intel and AMD, and a user can select corresponding ECS instance specifications according to the actual use scene of the user;
the core circuit design comprises an intelligent power saving device circuit diagram and a PCB;
the main technical indexes comprise: the accuracy of human body number capture is improved, the sensitivity of the improved YOLO target detection algorithm is improved, the transmission time of the cloud server is prolonged, and the actual daily average power saving efficiency is achieved.
Preferably, the cloud service design further comprises a cloud server framework, wherein the cloud server framework comprises an application system, and the application system is composed of two parts: several local wired terminal devices and a headquarter service. The local power saving service consists of a sensor information acquisition module, a real-time monitoring and control module, an alarm module, a complex event processing module and a report module. Headquarter services need to interact with regional power management offices to report emergency events and statistics to them. All the different modules of the local offline terminal device and headquarter service are interconnected by event agents in the unified message distribution network.
Preferably, the algorithm application design comprises a YOLO target detection algorithm, prediction of hot spot areas used in a single house by adopting a GSCAN algorithm, and accurate people number prediction of people used in each room by adopting a BiLSTM neural network algorithm;
YOLO target detection algorithm: YOLO is an end-to-end object detection algorithm, has the advantages of realizing high-speed identification under the condition of keeping image detection accuracy, being simple and easy to use, and being capable of realizing multi-scene use.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, through the application of technologies such as various sensors, the Internet of things and the like, real-time infrared monitoring and target detection are carried out on a certain area through the STM32 series control chip, the detection data are processed and network access is carried out, the processed data are transmitted to the PLC module, and after the intelligent interconnection information I/O module processes and executes the command, the intelligent control of the power switch is realized, so that the purpose of controlling the electricity consumption of floors, buildings and target areas is achieved, the control effect of building electricity is enhanced, meanwhile, the control operation steps are simpler and more convenient, the building electricity can be quickly controlled in time, and the electricity saving control effect of the buildings is enhanced.
Drawings
FIG. 1 is a flow chart of the overall system framework of the present invention;
FIG. 2 is a flow chart of the operation of the product of the present invention;
FIG. 3 is a diagram of a model structure of a YOLO object detection algorithm of the present invention;
FIG. 4 is a flowchart of the improved GSCAN algorithm of the invention;
FIG. 5 is a flow chart of the house use human probability prediction based on BiLSTM model of the present invention;
FIG. 6 is a schematic circuit diagram of an intelligent power saving device of the present invention;
fig. 7 is a diagram of a PCB board of the intelligent power saving device of the present invention.
Detailed Description
Embodiments of the present invention are described in further detail below with reference to the accompanying drawings and examples. The following examples are illustrative of the invention but are not intended to limit the scope of the invention.
As shown in fig. 1 to 7:
examples: the invention provides an intelligent electricity-saving control system based on the Internet, which comprises a control system, wherein the control system comprises an online intelligent visual platform client, an offline terminal device, a relay and a circuit, the offline terminal device is electrically connected with the online intelligent visual platform client and the offline terminal device, and the relay is connected with the circuit through control.
With reference to fig. 1 and fig. 2, when the intelligent electricity-saving detection device works, an on-off power supply threshold value can be set according to the number of people, then the product uses an SSD video detection algorithm to monitor the people in real time, when the number of people reaches the threshold value, a circuit power supply is started and electricity consumption data is recorded, when the intelligent electricity-saving detection device does not reach the threshold value, then data information is transmitted to a cloud server, then data is subjected to model prediction through a BiLSTM neural network, and then the BiLSTM neural network transmits the predicted data information to an intelligent power management and control platform for real-time data visual display.
From the above, the product uses various sensors and the internet of things and other technologies, carries out real-time infrared monitoring and target detection on a certain area through an STM32 series control chip, processes detection data and accesses a network, transmits the processed data to a PLC module, and realizes intelligent control of a power switch after processing and executing a command through an intelligent interconnection information I/O module, thereby achieving the purpose of controlling electricity consumption of floors, buildings and target areas.
Preferably, the online intelligent visualization platform client comprises a computer control end, a mobile phone control end and a cloud server, wherein the computer control end and the mobile phone control end are electrically connected with the cloud server.
Preferably, the off-line terminal device comprises a human body detection module and a microcontroller, wherein the human body detection module is connected with the microcontroller, the microcontroller is connected with the cloud server through data transmission, and the microcontroller is connected with the relay;
the off-line terminal equipment is developed and produced by a team, and is matched with a relay and a contactor for use. The relay and the contactor adopt the existing small-sized intermediate relay and alternating current contactor in the market, and the circuit connection relation of the relay and the contactor can be known from the prior art, and the description is omitted here.
From the above, the hardware part of the product is generally installed at the input end or the output end of the distribution box of the house. The intelligent house is installed at an input end and is used for controlling the on-off of a power system of the whole house, the number of houses for supplying normal power to each floor can be set in the APP, and house use data can be provided for a manager in real time. The installation output end is used for reading the specific number of people entering the classroom according to infrared monitoring and accurate video identification detection, and a manager can open and close the corresponding office power line according to the specific number of people entering the house, so that the situation that only 1 person enters but the house is fully opened is avoided.
Preferably, the off-line terminal device further comprises an electric brake part, an infrared detection part, a camera part and a communication part;
the electric switch part adopts a mode of matching a relay and a contactor for use;
from the above, when the control signal is transmitted to the contactor through the relay, the contactor wire can form magnetism, and the magnetism attracts the static iron core to generate electromagnetic attraction, so that the control function of on-off is realized; when the contact is powered off, the electromagnetic attraction force is weakened, and the contact is restored, so that the power-on and power-off effect is achieved, and the infrared detection part is an infrared monitoring sensor part and adopts a double-unit high-performance pyroelectric probe;
from the above, the RCWL-9196 integrates 30V pressure-resistant low pressure difference inside, can work in a wide range of 3-30V, the detection range is a maximum 14m diameter area, and when detecting that a person enters a house, the RCWL-9196 excites the video recognition detection part to enter a working state; meanwhile, the device can be additionally provided with illumination and brightness detection equipment, and the intelligent control of the lighting system in the daytime of the self-study house can be realized according to detection data.
The camera part uses a sized M2DOCK as an intelligent visual hardware platform, and the platform has strong calculation performance and stronger calculation performance;
from the above, the allwiner V831 chip can have a main frequency of 1Ghz and realize rapid deployment of the neural network model through AI acceleration with 0.2 tips computing power of hardware; the USB Type-C interface, the OTG interface, the 200W high-definition camera, the microphone and the like are provided with abundant peripherals; has the advantages of easy use, low cost and low energy consumption. YOLO is an end-to-end object detection algorithm, which can predict the types and positions of all objects in an image simultaneously in one forward propagation (forward pass), and has the advantages of high-speed identification, simplicity and easiness in use under the condition of maintaining the image detection precision, and multi-scene use. By using a YOLO algorithm on the M2DOCK, the image on the mobile terminal micro device is rapidly identified and detected, so that rapid detection of personnel quantity distribution and position location can be completed.
The communication part adopts excellent domestic products Bluetooth and wireless WiFi modules, comprehensively uses two wireless transmission technologies, has wide and universal wireless transmission scenes, and can be connected into a wireless interface product ecological chain;
from the above, the house electricity consumption (information including the number of users in the room, electricity consumption and online off-line terminal equipment) collected by the V831 camera module can be transmitted to the cloud server for data processing through the Bluetooth and WiFi modules.
Preferably, the control system further comprises an online platform client design, an algorithm application design, a core circuit design and main technical indexes;
the on-line platform client design comprises a cloud service design, the cloud service design comprises a cloud application background server which is constructed on an Arian Internet of things cloud platform, the server can provide X86, ARM computing architecture, GPU heterogeneous computing and the like, elastic bare metal and super computing cluster architecture, a cloud server ECS supports multiple architectures, and the ECS can be shared by the ECS, the computing type c7, the general type g8, the general computing type, the memory type, the GPU type, the local SSD type, the big data type, the high main frequency type, the bare metal type and the like, a CPU processor comprises intel and AMD, and a user can select corresponding ECS instance specifications according to the actual use scene of the user;
from the above, the combination of the intelligent power saving device and the cloud service is an innovative energy saving mode. By means of the huge data storage function and the powerful data operation function of the cloud server, the intelligent visual platform has more possibility.
Firstly, based on cloud service, GSCAN algorithm and BiLSTM neural network algorithm are adopted, and the electric energy quality, health condition and operation data of the power saving device are processed, optimized and managed in all directions.
Secondly, the cloud server design can achieve remote monitoring, management and optimization of the running state of the power saving device, so that the power saving efficiency and the service life of equipment are improved, and the operation and maintenance cost and risk are reduced. By adopting the cloud server, the digital twin model of the intelligent power saving device can be constructed by means of cloud computing, big data, the Internet of things, artificial intelligence and other technologies, the functions of intelligent identification, fault prediction, automatic regulation, energy consumption analysis and the like of equipment are realized, and an intelligent ecological circle of the intelligent power saving device is created. Therefore, the intelligent power saving device is combined with the cloud service, so that more efficient, more convenient and more intelligent energy saving service can be provided for the user.
The core circuit design comprises an intelligent power saving device circuit diagram and a PCB board: the product has complete independent intellectual property rights, and comprises internal circuit design and appearance manufacture, a circuit schematic diagram (shown in figure 6) and a PCB (printed circuit board) shown in figure 7;
the main technical indexes comprise: the human body number capturing accuracy, the improved YOLO target detection algorithm improves the sensitivity, the cloud server transmits time, and the actual daily average electricity saving efficiency is achieved; specific detection index data are shown in the following table:
| description of the operation | Test index |
| Measuring average power saving efficiency (teaching building as an example) | 30.28％ |
| Testing human body number capture accuracy | 99.22％ |
| Compared with the traditional detection algorithm YOLO target detection algorithm, the sensitivity is improved | 15％ |
Preferably, the cloud service design further comprises a cloud server framework, wherein the cloud server framework comprises an application system, and the application system is composed of two parts: several local wired terminal devices and a headquarter service. The local power saving service consists of a sensor information acquisition module, a real-time monitoring and control module, an alarm module, a complex event processing module and a report module. Headquarter services need to interact with regional power management offices to report emergency events and statistics to them. All the different modules of the local offline terminal device and headquarter service are interconnected by event agents in the unified message distribution network.
Preferably, the algorithm application design comprises a YOLO target detection algorithm, prediction of hot spot areas used in a single house by adopting a GSCAN algorithm, and accurate people number prediction of people used in each room by adopting a BiLSTM neural network algorithm;
YOLO target detection algorithm: YOLO is an end-to-end object detection algorithm, has the advantages of realizing high-speed identification under the condition of keeping image detection accuracy, being simple and easy to use, and being capable of realizing multi-scene use.
As can be seen from the above, in combination with fig. 3, in the processing process of the video image, the product adopts advanced image recognition technology to collect multi-frame continuous preprocessed images, then cuts the preprocessed images, extracts and recognizes the characteristics of the detection target, and classifies the images according to the characteristics; the product uses a YOLO image detection technology, and the main idea of the algorithm is to pretrain GoogLeNet on ImageNet, wherein the classification model uses the first 20 convolution layers of GoogLeNet, and after pretraining, 4 convolution layers and 2 full-connection layers are added on the 20 convolution layers of the classification model to realize feature extraction and complete neural network training; finally, inputting a picture, segmenting the picture, extracting image features by adopting CNN, and obtaining a predicted value by using a full-connection layer, wherein the whole process is fast in detection speed, and is suitable for extracting human body features under various complex conditions in an application scene of the product.
Referring to fig. 4, first, the improved GSCAN algorithm is used to excavate a hot spot area, reference values such as grid cell coordinates, the number of grid cells, a grid density threshold and the like are set, the hot spot grid cells are arranged according to multiple cell densities, and the GSCAN algorithm is used to predict the hot spot area used in a single house: ultimately predicting the use of hot spot areas within a single house.
From the above, the GSCAN algorithm can be used for predicting the hot spot electricity utilization area in a single room, processing is carried out according to the data of the hot spot utilization area in the predicted room, and more intelligent power management and control are carried out by matching with off-line terminal equipment, so that further intelligent and refined management of power energy is realized.
The two-dimensional coordinate division is carried out in the classrooms through the comparison of the hot spot areas, further intelligent refinement management is realized on circuit optimization management in a single room, the power utilization fixing areas are locked to be opened and closed by independent circuits, and the power saving efficiency and the power utilization quality are improved under the condition that the power utilization comfort of users is ensured;
with reference to fig. 5, the operation of using the BiLSTM neural network algorithm to accurately predict the number of people in each room is as follows: and using a human number probability model and using a BiLSTM neural network to conduct human number prediction, and finally obtaining house use human number prediction based on time series data.
Compared with other deep learning models, the BiLSTM deep learning model can learn the past time characteristics and the future time characteristics at the same time, and better utilizes the time sequence characteristics of the people flow, so that the prediction effect is obviously better than that of the other deep learning models, the people flow at the future time can be accurately predicted, and the BiLSTM deep learning model can be used as a means for predicting the short-term people flow.
The downslide data processing part mainly adopts a BiLSTM neural network to process and analyze data, and under the condition that the MSE loss value of output data is smaller than 0.031, the prediction of the number of users based on time series data is carried out on each house, so that a manager can conveniently formulate a more reasonable power consumption scheme.
While embodiments of the present invention have been shown and described above for purposes of illustration and description, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (6)
1. An intelligent electricity-saving control system based on the Internet is characterized in that: the intelligent visual platform comprises a control system, wherein the control system comprises an intelligent visual platform client on line, an off-line terminal device, a relay and a circuit, the off-line terminal device is electrically connected with the intelligent visual platform client on line and the off-line terminal device, and the relay is connected with the circuit through control.
2. The intelligent power saving control system based on the internet of claim 1, wherein: the online intelligent visual platform client comprises a computer control end, a mobile phone control end and a cloud server, wherein the computer control end and the mobile phone control end are electrically connected with the cloud server.
3. The intelligent power saving control system based on the internet of claim 1, wherein: the off-line terminal equipment comprises a human body detection module and a microcontroller, wherein the human body detection module is connected with the microcontroller, the microcontroller is connected with the cloud server through data transmission, and the microcontroller is connected with the relay.
4. The intelligent power saving control system based on the internet of claim 1, wherein: the off-line terminal device further comprises an electric switch part, an infrared detection part, a camera part and a communication part.
5. The intelligent power saving control system based on the internet of claim 1, wherein: the control system also comprises an online platform client design, an algorithm application design, a core circuit design and main technical indexes.
6. The intelligent power saving control system based on the internet as claimed in claim 5, wherein: the algorithm application design comprises a YOLO target detection algorithm, prediction of hot spot areas used in a single house by adopting a GSCAN algorithm, and accurate people number prediction of people used in each house by adopting a BiLSTM neural network algorithm.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109407525A (en) * | 2018-07-26 | 2019-03-01 | 昆山智汇无界信息技术有限公司 | Building energy saving automatic control system and method based on artificial intelligence |
| CN109673092A (en) * | 2019-01-03 | 2019-04-23 | 山东师范大学 | A kind of classroom intelligent lighting management system and method |
| CN112565401A (en) * | 2020-12-03 | 2021-03-26 | 华东师范大学 | Closed environment personnel number detection visualization method and system based on Internet of things cloud platform |
| CN114742209A (en) * | 2022-04-24 | 2022-07-12 | 北京建筑大学 | Short-term traffic flow prediction method and system |
| CN114897241A (en) * | 2022-05-10 | 2022-08-12 | 南京英诺森软件科技有限公司 | Intelligent building energy efficiency supervision and prediction method based on digital twins |
| CN218332347U (en) * | 2022-10-11 | 2023-01-17 | 桂林电子科技大学 | Intelligent power saving device based on Internet of things technology |
-
2023
- 2023-08-07 CN CN202310984786.4A patent/CN117170273A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109407525A (en) * | 2018-07-26 | 2019-03-01 | 昆山智汇无界信息技术有限公司 | Building energy saving automatic control system and method based on artificial intelligence |
| CN109673092A (en) * | 2019-01-03 | 2019-04-23 | 山东师范大学 | A kind of classroom intelligent lighting management system and method |
| CN112565401A (en) * | 2020-12-03 | 2021-03-26 | 华东师范大学 | Closed environment personnel number detection visualization method and system based on Internet of things cloud platform |
| CN114742209A (en) * | 2022-04-24 | 2022-07-12 | 北京建筑大学 | Short-term traffic flow prediction method and system |
| CN114897241A (en) * | 2022-05-10 | 2022-08-12 | 南京英诺森软件科技有限公司 | Intelligent building energy efficiency supervision and prediction method based on digital twins |
| CN218332347U (en) * | 2022-10-11 | 2023-01-17 | 桂林电子科技大学 | Intelligent power saving device based on Internet of things technology |
Non-Patent Citations (2)
| Title |
|---|
| 李晖晖等: "《深度学习与计算机视觉》", 31 October 2021, 西北工业大学出版社, pages: 4 * |
| 郑林江等: "基于出租车轨迹数据的城市热点出行区域挖掘", 《计算机应用与软件》, vol. 35, no. 1, 31 January 2018 (2018-01-31) * |
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