The comprehensive utilization efficiency of energy is mainly reflected in safety, energy saving and economy. With the development of intelligent technology of the Internet of Things, energy monitoring and safety monitoring and management are not only oriented to energy production, storage, transmission, distribution, and application, but also need to cover the entire process of energy consumption and consumption of distributed energy nodes more extensively and deeply. Based on the monitoring of energy load objects and associated terminal equipment in different target scenarios within the user range.
In the context of the energy revolution, the energy utilization efficiency of the integrated energy system has been valued by everyone, and improving energy utilization efficiency through the collaborative design and operation of multiple energy sources is one of the important goals of the comprehensive energy system construction. First of all, at the system level, the energy utilization efficiency of the integrated energy system is the ratio between the input energy and the output energy of the system, and the energy consumption of the system is directly related to the efficiency of each energy conversion equipment in the system. Therefore, it is necessary to Establish an input and output energy conversion model for the energy conversion equipment in the system. The efficiency of energy supply equipment changes with factors such as environment and load rate in different scenarios during operation. It is necessary to consider the variable working condition characteristics of the equipment in the input and output energy conversion model of the equipment.
Blue Aosheng focuses on edge intelligence technology solutions and has launched a series of advanced and practical technology solutions in the fields of smart energy-saving electricity metering and monitoring, indoor precise positioning and tracking, low-power synchronous group control systems, and intelligent perception systems. Core products and solutions.
Combined with the current pain points of energy conservation and consumption reduction in enterprises, the explosion of developing variable switch sockets has created an opportunity.
86 intelligent switch socket double surface-mounted model, flexible combination, changeable configuration, to meet the diversified scenario needs of enterprises for wall plugs.
Power monitoring and metering socket 10A-86 type double surface-mounted-smart sensor model
Electric energy metering socket 10A - 86-type surface-mounted
Electric energy monitoring and metering socket 10A-86 type double surface-mounted-WiFi + BLE ordinary model
Electric energy metering socket 10A - 86-type surface-mounted
Using Bluetooth BLE+WiFi, flexible networking mode:
*Automatic networking, fast network configuration; supports Bluetooth broadcast, scanning, Mesh, and connection modes;
*Supports intranet and LAN modes, and can still control and measure under network disconnection and offline scenarios;
*Using Bluetooth BLE (low-power broadcast mode, efficient scanning, multi-slave) and wifi mode at close range.
And innovatively supports local control and remote control switches
*Supports background timer switch and other preset condition settings;
*Real-time power data collection: current, voltage, power, power consumption;
So how to reduce energy consumption in a real sense, what are the specific methods to reduce energy consumption, and how to intuitively reduce energy consumption through technical means. Next, we will briefly talk about the specific implementation methods and steps for comprehensive energy efficiency reduction:
Analysis environment area, target device type
Object-based scene classification: This classification method takes objects as the recognition unit and distinguishes different scenes according to the specific objects appearing in the scene. Most of the vision-based scene classification methods are based on the object as the unit, that is, through recognition Some representative objects to identify locations in nature. A typical object-based scene classification method has the following intermediate steps: feature extraction, Q-reorganization and object recognition.
After arranging and adding scenes, confirm the use environment and usage habits according to user needs, form a basic demand data table, confirm the basic mode of use of the scene, and build a more comfortable experience for the subsequent intelligent deployment. Secondly, collaborative deployment of device types completes the early stage hardware construction of comprehensive energy efficiency management. Prepare.
Form application management reports based on user needs:
1) Equipment energy conservation, environmental protection and energy utilization efficiency management;
2) Equipment usage efficiency and safety hazard reports;
3) Guidance information for improving electricity management;
4) Safety monitoring and dispatch management, etc.
Target scene monitoring/perception
The problem that IoT edge intelligence technology for target scenes needs to solve is related decision-making and services based on scene awareness. What determines the state of the target scene is a number of target objects associated with the target scene and their associated state variables. Most of the state variables are often derived from wireless sensors or other sensing monitoring devices as target object devices. These sensing monitoring devices serve as target sensing nodes. , which is also the target object device served by the edge sensing network, and directly establishes an associated binding relationship with the mobile object or location environment of the target scene served.
Real-time monitoring of power consumption status
Basic information for power consumption monitoring includes:
1) Device status: device matching binding, usage period, location area and associated information;
2) Real-time power monitoring data: real-time collection and scheduled data reporting;
3) Historical data records: including normal segment records and abnormal log records;
4) Statistical information such as power consumption, power usage status, and power usage abnormalities (early warning and protection logs).
Scenario energy efficiency evaluation algorithm
Provide multi-scenario energy efficiency assessment methods for comprehensive energy systems, including:
In step s1, a variable-working-condition energy conversion model for each energy supply device of the integrated energy system is established, and the variable-working-condition energy conversion model is used to simulate the operation of each typical scenario, and the energy efficiency evaluation index for each typical scenario is calculated.
Energy efficiency index: The management system obtains various types of difference parameters through classification difference comparison calculations, and conducts sensitive comparison assessments based on the difference indexes of the corresponding categories; the sensitive comparison assessment includes: based on the difference index D(i) and the equipment Calculate the energy efficiency index and/or hidden danger index of a certain energy consumption target object based on the certain classification assessment weight W(i) of energy efficiency and hidden dangers relative to different operating states or operating parameters, that is: energy efficiency index Ki=∑W( i)*D(i), performs weighted evaluation on different operating states; hidden danger index Kj=∑W(j)*D(j), performs weighted evaluation on different operating parameters.
Electric Energy - Energy Efficiency Monitoring/Monitoring Records
Monitoring nodes (such as electric energy meters, electric energy metering sensors, electric energy metering sockets, etc.) can support electric energy monitoring data collection; based on the perception and identification of load objects, perform electric energy signal monitoring and abnormal response processing, so as to connect and output load objects (plug (pulling out) the transient process to provide more targeted and effective protection.
In the link of load object connection and connection, the power monitoring node recognizes and perceives the connection of electric loads; it performs object matching verification on the received object identification signal to configure and adjust the monitoring mode parameters that match the current load object, so as to This solves the problem of matching security and monitoring mode flexibility for the current load object.
The power monitoring node adopts a low-power energy-saving monitoring mode for the power load under normal conditions. When the load object is not connected (no load) or running normally, the power monitoring node is in the energy-saving monitoring mode, which is conducive to saving power consumption of power monitoring and reducing data redundancy; especially in order to reduce installation costs during wireless narrowband wireless communication, through elastic data Upload to reduce wireless interference and competition for data resources.
The power monitoring node processes the target monitoring information and evaluates the status mode. For load objects in an abnormal state, it improves the real-time and security of monitoring data by upgrading the monitoring mode level; when the load object is in a potential abnormal state, it activates potential exceptions. Monitoring mode; conducive to rapid abnormal response and abnormal response processing, including recording abnormal processes, protecting data, abnormal alarms, etc.
Efficient and elastic data upload
-Reduce system resource usage and redundant data, and improve trigger response speed
– Elastic variable monitoring, multi-level abnormal condition monitoring
– Flexible floating variable analysis, efficient binary encryption upload
–Multi-path priority, matching or collaborative nodes can collect and upload
– Multi-mode automatic switching, timing/real-time/event-triggered and other modes
The Internet of Things energy consumption system uses Internet of Things technology to help enterprises build energy consumption distribution, help operators monitor various key parameters in real time, calculate energy consumption indicators for key links, and compare them with established energy consumption baselines to obtain energy consumption gap.
For manufacturing enterprises, the energy consumption of the Internet of Things can not only help enterprises realize digital and intelligent management, but also realize dynamic monitoring of warehouse inventory and energy consumption data monitoring in the process of production and operation of enterprises, thereby greatly improving manufacturing efficiency and improving Product quality, reducing product costs and resource consumption are key links for manufacturing companies to achieve smart factories, digital transformation, and "dual carbon goals."