WO2011106914A1

WO2011106914A1 - Device monitoring system and method based on cloud computing

Info

Publication number: WO2011106914A1
Application number: PCT/CN2010/001362
Authority: WO
Inventors: 姜永东
Original assignee: 朗德华信（北京）自控技术有限公司
Priority date: 2010-03-05
Filing date: 2010-09-07
Publication date: 2011-09-09
Also published as: CN102193525A; CN102193525B

Abstract

A device monitoring system and method based on cloud computing. Each energy consumption device (10) is field controlled by a field controller (11). An energy parameters collector (12) collects parameters related to the energy consumption of each consumption device（10）. The cloud computing management control platform (13) makes centralized control based on the collected parameters related to the energy consumption of each consumption device （10）and the user defined parameters.

Description

TECHNICAL FIELD The present invention relates to the field of energy management control technologies, and in particular, to a cloud computing-based device monitoring system and method. BACKGROUND OF THE INVENTION With the increasing shortage of energy worldwide, energy management control systems that achieve energy efficiency are becoming more and more important.

The prior art energy management control system usually adopts traditional electrical automation technology to perform energy management control on various energy-consuming devices of a single object (such as a shopping mall, a store, a hotel, an office building industrial plant), and belongs to the field level control. The management energy-saving platforms used by different manufacturers are also different. Generally, they cannot be incompatible, and there is also a lack of communication between them. Therefore, it is impossible to form a unified platform for centralized energy management control to maximize energy conservation.

For the first time, TRIDIUM has developed a unified platform system for energy management, which is compatible with other energy management platforms and provides users with energy consumption reference data. However, the inventors have found that they still have the following problems:

1. When the system processes a large amount of historical data, it encounters problems that the processing speed is not fast and data protection cannot be realized;

2. The system does not have comprehensive energy statistics, analysis and management control from energy factors, energy policies, energy indicators, management systems, energy benchmarks, energy performance, energy statistics, energy optimization, etc. Provided to the user, let the user modify the on-site control mode according to the statistical results, so that the optimal configuration of the energy cannot be realized.

Cloud computing is a network technology developed in recent years. It distributes computing tasks on resource pools composed of a large number of computers, enabling various application systems to acquire computing power, storage space, and various software services as needed. Major IT companies have launched their own cloud computing-based cloud platform services, such as Google (G00GLE), Microsoft, Yahoo, Amazon, etc., summed up the following characteristics of cloud computing: (1) Very large scale. "Cloud" is quite large. Google Cloud Computing has more than 1 million servers. The "clouds" of Amazon, IBM, Microsoft, Yahoo, etc. all have hundreds of thousands of servers. Enterprise private clouds typically have hundreds of thousands of servers, and "clouds" give users unprecedented computing power.

(2) Virtualization. Cloud computing allows users to access application services from any location and from any location. The requested resource comes from a "cloud" rather than a fixed tangible entity. The app runs somewhere in the "cloud", but in reality the user doesn't need to know or worry about where the app is running. With just one laptop or one phone, you can do everything we need through web services, even tasks like supercomputing.

(3) High reliability. "Cloud" uses measures such as data multi-copy fault tolerance and computational node isomorphism to ensure high reliability of services. It is guilty to use cloud computing rather than using local computers.

(4) Universality. Cloud computing is not targeted at specific applications. Under the support of "cloud", it can construct ever-changing applications. The same "cloud" can support different application operations at the same time.

(5) High scalability. The size of the "cloud" can be dynamically scaled to meet the needs of application and user growth.

(6) On-demand service. "Cloud" is a huge pool of resources that you can buy on demand; clouds can be billed like tap water, electricity, and gas.

(7) Extremely cheap. Because the special fault-tolerant measures of "cloud" can use extremely cheap nodes to form a cloud, the centralized centralized management of "cloud" enables a large number of enterprises to not have to bear the increasingly high cost of data center management, and the versatility of "cloud" enables resource utilization. Compared with the traditional system, users can fully enjoy the low cost advantage of "cloud". It usually takes hundreds of dollars and several days to complete tasks that previously required tens of thousands of dollars and months. SUMMARY OF THE INVENTION In order to solve the above problems of the prior art, an object of the present invention is to provide a cloud computing-based device monitoring system and method, which can be compatible with energy-saving platforms of all different manufacturers, and a plurality of energy-consuming devices under one unified platform. Centralized monitoring, to achieve maximum energy-saving and consumption-reduction management and networked automatic control, so as to achieve optimal energy allocation, to achieve better energy-saving effects. To achieve the above objective, the present invention provides a cloud computing based device monitoring system, including:

a field controller, configured to perform on-site control on each energy-consuming device according to user-set parameters and transmit the user setting parameter to the cloud computing management control platform;

An energy consumption parameter collector, configured to collect parameters related to energy consumption of each of the energy-consuming devices and transmit the parameters to the cloud computing management control platform;

a cloud computing management control platform, configured to adjust, according to the collected parameters related to energy consumption of the respective energy-consuming devices and the user setting parameters, on-site control of the field controller by the field controller Mode

The field controller and the cloud computing management control platform, the energy consumption parameter extractor and the cloud computing management control platform communicate with each other through a communication network.

Preferably, the cloud computing management control platform specifically includes:

a receiving unit, configured to receive, by the energy consumption parameter collector, a parameter related to energy consumption of each energy consuming device and the user setting parameter;

a first determining unit, configured to determine whether the collected parameters related to energy consumption of the respective energy-consuming devices and the user-set parameters match and produce a determination result;

An energy consumption model generating unit, configured to generate a corresponding energy consumption model according to parameters related to energy consumption of each energy-consuming device when the determination result of the first determining unit is a match; a historical energy consumption model database, configured to store Various historical energy consumption models;

a second determining unit, configured to determine whether the generated energy consumption model matches a corresponding historical energy consumption model in the historical energy consumption model database, and generates a determination result;

And a control mode adjusting unit, configured to adjust a field control mode of the field controller to each of the energy-consuming devices when the determination result of the first determining unit or the second determining unit is a mismatch.

Advantageously, said parameters relating to energy consumption of said respective energy consuming devices comprise real time energy consumption parameters, operating parameters and safety parameters. The real-time energy consumption parameter generally refers to the power parameter of each energy-consuming device directly collected by the electrical metering device, and the operating parameters include temperature, humidity, air volume, running time, frequency, and the like, and the parameters related to the operation of each energy-consuming device, the safety parameters include Parameters related to each energy-consuming device in the case of operating conditions, faults, alarms, etc.

Preferably, the corresponding historical energy consumption model in the historical energy consumption model database refers to a historical energy consumption model that matches the energy consumption constraint parameter with the generated energy consumption model, and the energy consumption model The constraint parameter includes one or a combination of an application environment parameter, a design parameter, an application site type parameter, and an energy supply type parameter of each of the energy consuming devices. The historical energy consumption model database contains various historical energy consumption models that conform to industry standards (design standards). These historical energy consumption models take into account the evaluation criteria of energy consumption benchmarks, efficiency benchmarks, performance benchmarks, etc. The most reasonable. The establishment of the historical energy consumption model is usually restricted by the energy consumption constraint parameters, and the energy consumption constraint parameters are different, and the corresponding historical energy consumption models are different. The application environment parameters of each energy-consuming equipment include geographic location, meteorological parameters, etc. The design parameters include design power, measurement range, design energy consumption parameters, design energy efficiency, etc., and application site type parameters include shopping malls, supermarkets, hotels, office buildings. , exhibition halls, machine rooms, industrial plants, residential buildings, national grids, etc., energy supply type parameters include coal, electricity, natural gas, petroleum, biomass, heat, renewable energy, and so on. Of course, there are other energy constraint parameters, such as control mode and so on.

Advantageously, the energy consumption parameter collector and the field controller each correspond to a network address based on the IPV4 protocol or a network address based on the IPV6 protocol.

Preferably, the user setting parameter and the collected parameters related to the energy consumption of each energy-consuming device are transmitted to the cloud computing management control platform through a communication network, and the communication network is a wireless INTERNET network or a wired INTERNET network. Any of GPRS and 3G networks.

Preferably, the field controller includes a network temperature and humidity controller; the energy consumption parameter collector includes a network temperature and humidity sensor; and the control mode adjusting unit is configured to adjust a control mode of the network temperature and humidity controller to The thermal load compensation curve dynamically sets the set temperature and humidity values.

Preferably, the field controller includes a network air volume controller; the energy consumption parameter collector includes a carbon dioxide concentration sensor; the control mode adjusting unit is configured to adjust a control mode of the network air volume controller according to the carbon dioxide The concentration of carbon dioxide collected by the concentration sensor adjusts the wind speed of the wind.

In order to achieve the above object, the present invention also provides a cloud computing-based device monitoring method, including:

S11: Perform on-site control on each energy-consuming device according to the user-set parameter and transmit the user setting parameter to the cloud computing management control platform; S12: collecting parameters related to energy consumption of each energy-consuming device and transmitting the parameters to the cloud computing management control platform;

S13: Adjust, according to the collected parameters related to energy consumption of the respective energy-consuming devices and the user setting parameters, a field control mode of the respective energy-consuming devices under the cloud computing management control platform.

Preferably, the step S13 specifically includes:

S131: determining whether the collected parameters related to the energy consumption of the respective energy-consuming devices and the user-set parameters match; if not, performing step S135, if yes, performing step S132;

S132: Generate a corresponding energy consumption model according to parameters related to energy consumption of each energy-consuming device;

S133: Determine whether the generated energy consumption model matches the corresponding historical energy consumption model in the historical energy consumption model database; if not, perform step S135, and if yes, perform step S134 to maintain the scene of each energy-consuming device. Control mode

S135: Adjust a field control mode for each of the energy-consuming devices.

Preferably, after performing the step S134, the method further includes the step S136, adding the generated energy consumption model to the historical energy consumption model database. The invention has the beneficial effects that the energy-saving platform of all different manufacturers can be compatible, and a plurality of energy-consuming equipments are collectively monitored under a unified platform, thereby realizing maximum energy-saving and consumption-saving management and network automatic control, thereby realizing energy. Optimized configuration for better energy savings. BRIEF DESCRIPTION OF DRAWINGS FIG. 1 is a schematic structural diagram of a cloud computing-based device monitoring system according to an embodiment of the present invention;

2 is a flow chart of a cloud computing-based device monitoring method according to an embodiment of the present invention;

3 is a flow chart of a cloud computing based device monitoring method according to another embodiment of the present invention. Specific real Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

FIG. 1 is a schematic structural diagram of a cloud computing-based device monitoring system according to an embodiment of the present invention, and the cloud computing-based device monitoring system includes:

The field controller 11 is configured to perform on-site control of each energy-consuming device 10 according to user setting parameters and transmit the user setting parameters to the cloud computing management control platform 13; the field controller 11 includes a user parameter setting unit 111, It is used for user setting parameters. For example, if the energy consumption device is an air conditioner, the user sets parameters such as temperature and air volume of the air conditioner as needed, and transmits the set parameters to the cloud computing management control platform 13. The field controller 11 commonly used in buildings includes network water valves, damper controllers, network motor controllers, network humidification controllers, network air conditioner controllers, network electromechanical device controllers, network security protection controllers, network security, Access control, alarm controller, etc.

An energy consumption parameter collector 12 is configured to collect parameters related to energy consumption of the respective energy consumption devices 10 and transmit the parameters to the cloud computing management control platform 13; parameters related to energy consumption of the respective energy consumption devices include real-time energy Consumption parameters, operating parameters and safety parameters. The real-time energy consumption parameter generally refers to the power parameter of each energy-consuming device directly collected by the electrical metering device, and the operating parameters include temperature, humidity, air volume, running time, frequency, etc., related parameters of each energy-consuming device during operation, and the safety parameters include Parameters related to each energy-consuming device in the case of operating conditions, faults, alarms, etc. The energy consumption parameter collector 12 is generally composed of various types of sensors with network transmission functions, data statistics and summary units, data analysis and uploading units, etc., to complete data collection and preliminary statistical analysis functions, and the actual number is set according to needs. As determined, there may be many energy consumption parameter collectors. The sensor can be various network temperature sensors, network humidity sensor, network air volume sensor, network energy metering sensor, network wind speed sensor, network air quality sensor, electromechanical equipment operating parameter network collector, network access control, security, alarm signal collector, Special signal network collectors (such as C0, C02, formaldehyde, water flow, etc.) and so on. The collected energy consumption parameters are transmitted to the cloud computing management control platform through the communication network 20. The communication network 20 can be a wireless Internet network, a wired Internet network, a GPRS and 3G network, or a more advanced next generation transmission network.

The current Internet is based on the IPV4 protocol, and the IPV4 protocol uses a 32-bit address length, and the limited address space is running out. Therefore, in a large-scale number of equipment monitoring systems, the field controller 11 and the energy consumption parameter collector 12 can adopt a network address based on the IPV6 protocol, and the IPV6 protocol uses a 128-bit address length. For the entire earth, its address Resources can be considered unlimited (more than 1000 network addresses per square meter), and can adapt to even global equipment monitoring systems.

The cloud computing management control platform 13 is configured to adjust the field controller 11 to the respective energy-consuming devices according to the collected parameters related to the energy consumption of the respective energy-consuming devices 10 and the user setting parameters. 10 on-site control mode. The purpose of the adjustment is to achieve an optimal configuration of the energy and reduce energy consumption. The cloud computing management control platform 13 of this embodiment includes:

The receiving unit 131 is configured to receive parameters related to energy consumption of the respective energy consuming devices 10 and the user setting parameters collected by the energy consumption parameter collector 12;

The first determining unit 132 is configured to determine whether the collected parameters related to the energy consumption of the respective energy-consuming devices 10 and the user-set parameters match and produce a determination result; the energy consumption model generating unit 133, And generating a corresponding energy consumption model according to parameters related to energy consumption of each energy-consuming device when the determination result of the first determining unit is a match; the energy consumption model includes indicators such as overall energy consumption and operating energy consumption.

The historical energy consumption model database 130 is used for storing various historical energy consumption models; the historical energy consumption model database contains various historical energy consumption models conforming to industry standards (design standards) and is agreed or recognized by relevant specifications and standards. The optimal energy consumption model, which takes into account the evaluation criteria of energy consumption benchmark, efficiency benchmark, performance benchmark, etc., is the most reasonable energy consumption.

The second determining unit 134 is configured to determine whether the generated energy consumption model matches the corresponding historical energy consumption model in the historical energy consumption model database and generate a determination result; the establishment of the historical energy consumption model is generally restricted by the energy consumption constraint parameter The energy consumption constraint parameters are different, and the corresponding historical energy consumption models are different. The energy consumption constraint parameter includes one or a combination of application environment parameters, design parameters, application site type parameters, and energy supply type parameters of the respective energy consuming devices, and a combination with other constraint parameters (eg, control modes). The application environment parameters of each energy-consuming equipment include geographical location, meteorological parameters, etc. The design parameters include design power, measurement range, design energy consumption parameters, design energy efficiency, etc. The application site type parameters include shopping malls, supermarkets, hotels, office buildings. , exhibition halls, computer rooms, industrial plants, residential buildings, national power grids, etc., energy supply type parameters include coal, electricity, natural gas, petroleum, biomass, heat, renewable energy, and so on. The user inputs the energy consumption constraint parameter of the currently generated energy consumption model through the energy consumption constraint parameter setting unit 14, and then according to these energy The constrained parameter finds the corresponding historical energy consumption model in the historical energy consumption model database 130.

(ie, the historical energy consumption model that matches the energy consumption constraint parameter with the generated energy consumption model), and then judge whether the generated energy consumption model matches the corresponding historical energy consumption model. If the mismatch indicates that the energy consumption is unreasonable, it needs to be adjusted. . For example, the annual energy consumption per unit area of the generated energy consumption model is 20 (T300 kWh, while the annual energy consumption per unit area of the historical energy consumption model with the same energy consumption constraint parameter is about 100 kWh, which indicates that the energy consumption is unreasonable and needs to be adjusted.

The control mode adjustment unit 135 is configured to adjust the field control mode of the field device 11 to each of the energy consuming devices 10 when the determination result of the first determining unit 132 or the second determining unit 134 is not matched. . Mismatch indicates that the energy consumption does not meet the requirements, and the field control mode needs to be adjusted to reduce the energy consumption until the energy consumption is matched, thereby achieving optimal configuration of energy consumption. When the determination result of the first determining unit 132 is not matched, it indicates that the energy consumption cannot meet the requirement set by the user, and the adjustment needs to be directly performed; when the judgment result of the second determining unit 134 is not matched, the description indicates that Although the consumption can meet the user setting requirements, it is not optimal. It does not consider the evaluation criteria such as energy consumption benchmark, efficiency benchmark, performance benchmark, etc. It is necessary to adjust to further reduce energy consumption. If the judgment result of the second judging unit 134 is a match, indicating that the energy consumption model of the production is reasonably compliant, the generated energy consumption model is added to the historical energy consumption model database, enriching the history. Data, providing a reference for subsequent energy management control.

Of course, the cloud computing management control platform 13 has a variety of control modes for the field controller 11, and the above embodiment only gives one of them.

For the convenience of the user, the cloud computing-based device monitoring system of the embodiment can be made into an intuitive display interface, and the user only needs to perform management control through the display interface.

The advantages of using the cloud computing management control platform 13 for energy management control are obvious. The scale and scalability of cloud computing make it possible to achieve centralized control of ultra-large-scale energy consumption. In theory, it can realize any kind of energy in the world. Management control, including building energy management control, power transportation energy management control, etc., has a wider application scope; the virtualization characteristics of cloud computing enable individual users to perform energy management control without separately configuring an independent energy management control platform. However, it is obtained on demand in the "cloud", which greatly reduces the cost; the resource sharing characteristics of cloud computing make the historical data in the entire control platform very rich, and can match the best historical data as a reference to achieve optimal energy allocation. . The application process of the cloud computing-based device monitoring system of this embodiment is described below by taking the energy management control of a building as an example.

The building is a commercial building with a total construction area of approximately 38,000 square meters. It is located in a certain place. The structure is designed as a reinforced concrete frame, a core tube structure and a columnless structure. The energy consumption equipment is mainly divided into a cold heat source system and an air conditioning ventilation system. Water supply and drainage systems, lighting socket systems, elevator systems, large power equipment systems, etc. Some design reference standards are as follows:

1. The air conditioning cold source is an electric refrigeration system with a water supply temperature of 7 °C and a return water temperature of 12 °C. The air conditioning heat source is municipal high temperature hot water, the municipal water supply temperature is 110 °C, and the return water temperature is 70 °C. The air conditioning hot water is supplied after heat exchange. The air conditioning water supply temperature is 60 °C and the return water temperature is 50 °C.

2。 The chilled water and cooling water system working pressure is 1. 5Mpa, the experimental pressure is working pressure plus 0. 5Mpa. The working pressure of the hot water system is 1. 5Mpa, the experimental pressure is the working pressure, the force is 0. 5Mpa.

3. Set the air depreciation sensor outside, when the outdoor temperature reaches the appropriate threshold, start the fresh air system and stop the supply of hot and cold water. Or when the threshold is lower than a certain value, turn on the free cooling system and stop the air conditioner host.

4, interior design temperature: summer 25 ° C, relative humidity 55%, winter 20 ° C, relative humidity 30%; fresh air volume 50 cubic meters / person / hour;

5, outdoor parameter reference value:

Summer air conditioning outdoor calculation dry bulb temperature 33. 2 °C

Summer air conditioning outdoor calculation wet bulb temperature 26. 4

Summer ventilation outdoor calculation temperature 30 °C

Summer average outdoor wind speed 1. 9m/s

Winter air conditioning outdoor calculation dry bulb temperature -12 °C

Winter air conditioning outdoor calculation relative humidity 45%

Winter heating outdoor calculation dry bulb temperature -9 °C

Winter ventilation outdoor calculation temperature -5 °C

Average outdoor wind speed in winter 2. 8m/s

The reference standards for the energy consumption per unit area of different types of buildings are as follows:

1. Office buildings generally have low energy consumption, and the annual electricity consumption per unit area is about 100 kWh;

2. The power consumption of hotels and hotel buildings is slightly higher, and the annual electricity consumption per unit area is 10 (T200kWh; 3. There are many power-consuming equipments in shopping malls, and the number of lighting fixtures is large. The air-conditioning system has large capacity and long running time. Compared with other types of buildings, the annual power consumption per unit area of shopping malls is relatively large, basically 20 ( T300kWh _;

4. Comprehensive commercial buildings are composed of buildings of various types of buildings, and the proportion of different types of buildings is different, and the energy consumption varies. The average annual power consumption per unit area of a comprehensive commercial building is 10 (T300 kWh).

The energy management control process of the cloud-based equipment monitoring system is as follows: 1. Complete the detection sensor and data information through the field equipment layer to log in to the work site equipment layer: including the energy consumption parameter collector 12 (generally various types of sensors) and The field controller 11, the energy consumption parameter collector 12 mainly performs various types of signal acquisition, and the field controller 11 mainly performs on-site control of the corresponding energy consumption equipment.

All signals are directly connected to the IP network through the switch and uploaded to the cloud acquisition, storage, statistics and analysis database of the cloud-based device monitoring system via the Internet (wireless or wired).

The energy-consuming equipment and related design parameters of the building are registered through the cloud computing platform, and the information enters the equipment signal acquisition, storage, statistics, analysis and model database of the cloud computing energy management and control system.

The whole system architecture is based on Ethernet (Lan/Wan), using TCP/IP protocol. The cloud computing management control platform can communicate with the field system (field controller and energy parameter collector) through OBIX, SNMP, XML and other protocols to obtain data. . Mainly get the following data:

♦ Various detailed status, fault, operation, etc. of the control point,

♦ Alarm summary

♦ Record energy consumption data of each device through electrical metering sensors or by calculation ♦ All energy-consuming equipment and related design parameters of the building

Second, through the control and analysis layer to achieve data analysis and related control field level controller in the field according to the detection signal and the user's target setting parameters to the corresponding equipment to achieve field level control, and upload various types of signals to the cloud A database of equipment signal acquisition, storage, statistics and analysis for energy management and control systems.

Taking the temperature control of the air conditioning unit as an example, the content that the field controller can control the air conditioning unit includes:

A. Start-stop control: Complete the start-stop control according to the start-stop command signal; B. Temperature and humidity adjustment control: In winter, when the indoor or supply air temperature is higher than the set value (T=20°C), the small water valve is closed by PID control, and the indoor or air supply temperature is lower than the set value. Water valve. In summer, when the indoor or supply air temperature is higher than the set value (T=26°C), the large water valve opening degree is opened by PID control, and the small water valve is closed when the indoor or supply air temperature is lower than the set value; the humidity is also performed;

C. Control of fresh air volume: The air volume control is realized by proportional adjustment of the damper to maintain the air volume of 50 cubic meters / person / hour;

D. Record and upload signals such as cumulative measurement of unit running time, number of starts, running time, and electrical metering of the motor; the main signals are as follows:

♦ Return to fan operating status, fan airflow status, manual automatic status monitoring, start and stop control;

♦ Return to the fan inverter feedback, inverter monitoring, inverter adjustment control;

♦ Return air temperature/humidity measurement, return air C02 concentration measurement;

♦ Supply air temperature / humidity measurement;

♦ Cold and hot water coil water valve adjustment control;

♦ New, return air valve adjustment control;

♦ Humidification valve adjustment control.

E. Energy-saving control of the motor: The adjustment of the inverter is realized by the controller. When the amount of air supply required in the room changes, the motor speed is reduced as much as possible to ensure energy-saving control based on the guaranteed fresh air volume.

Third, cloud-based device monitoring

First, the cloud computing control analysis platform judges whether the collected parameters are compared with the parameters set by the user, and if so, the existing control mode is maintained, and the total energy consumption of the entire building and the energy consumption of each parameter index are calculated to generate energy consumption. Model; if it does not match, you need to adjust the control mode in time. The main parameters considered are:

■ Total energy consumption of buildings;

■ General energy consumption indicators;

■ Total energy consumption indicators for special areas;

■ HVAC system energy consumption indicators:

1) energy consumption index of air conditioning ventilation system; 2) energy consumption index of heating system;

■ Lighting system energy consumption indicators: 1) general lighting; 2) emergency lighting; 3) landscape lighting;

■ Indoor equipment energy consumption indicators;

■ Integrated service system energy consumption indicators;

■ Building water consumption total indicators; and so on.

Then, in the cloud computing operation data model platform, it is judged whether the generated energy consumption model conforms to the industry standard. If it is not met, the control mode needs to be adjusted to further reduce the energy consumption. In the cloud computing operational data model platform, there are various historical energy consumption models that conform to industry standards (design standards), and the generated energy consumption models are compared with the corresponding historical energy consumption models, if the energy consumption is higher than the historical energy consumption model. Then, the control mode needs to be adjusted. If it is lower than the historical energy consumption model, the existing control mode is kept unchanged, and the generated energy consumption model is added as the historical energy consumption model. Several common control models are given below as references:

A. Indoor temperature and humidity control model: According to different building types, different temperature and humidity control models with different control details are constructed to improve control accuracy. The main basis is the thermal load compensation curve to set the floating set point (no longer a single fixed point), which is to more effectively adjust the indoor temperature set value to save energy as much as possible within the allowable range of the building load. In this case, the field controller includes a network temperature and humidity controller; the energy consumption parameter collector includes a network temperature and humidity sensor; and the control mode adjusting unit adjusts the control mode of the network temperature and humidity controller to compensate according to the heat load. The curve is dynamically set to set the temperature and humidity values.

The change of indoor temperature and humidity is closely related to building energy conservation. According to the statistics of the National Bureau of Standards, if the set temperature is lowered by 1 °C in summer, it will increase the energy consumption by 9%. If the set temperature is raised by 1 °C in winter, it will increase the energy consumption by 12%. . Therefore, it is an effective measure to save energy in the air conditioner by controlling the indoor temperature and humidity within the set value accuracy range.

Whenever possible, the indoor temperature and humidity control accuracy can be achieved as follows: temperature is ± 1. 5 ° C, humidity is ± 5% variation range. In this way, it is possible to avoid the phenomenon of subcooling at room temperature (below the standard set value) or overheating at room temperature in winter (above the standard set value), so as to save energy and reduce consumption.

B. Outdoor climate compensation adjustment model: The cloud computing energy management and control platform changes the indoor temperature setting according to the outdoor temperature and humidity and seasonal changes, so that it can better meet people's needs and give full play to the functions of air conditioning equipment. When the outdoor temperature reaches a suitable threshold, the fresh air system is turned on to stop the supply of hot and cold water. Or when the threshold is below a certain value, open Charge refrigeration system, stop the air conditioning host.

C. Control model of fresh air volume

According to the hygiene requirements, each person in the building must ensure a certain amount of fresh air. However, too much new air volume will increase the energy consumption of new winds. 5kWh,热热12. 7kWh, in the case of the design (the outdoor temperature of the outdoor temperature of 26 ° C, the relative temperature of 60%, the temperature of the room temperature of 22 ° C, the relative humidity of 55%), the treatment of one kilogram (kg) of outdoor fresh air volume required cooling capacity of 6. 5kWh, heat 12. 7kWh Therefore, under the premise of meeting indoor hygiene requirements, the amount of fresh air is reduced, and significant energy saving effects are achieved. Implementing a new air volume control model There are several main control elements:

1) Determine the fresh air volume according to the indoor allowable carbon dioxide (C02) concentration. The C02 allowable concentration value is generally 0.1% (1000ppm) o According to the indoor or return air CO2 concentration, the fresh air volume is automatically adjusted to ensure the indoor air is fresh. The control equipment automation system with perfect control functions can meet these control requirements. Adjusting the wind speed according to the carbon dioxide concentration reflects the actual situation in the room and maximizes energy saving.

2) According to the changing rules of the personnel in the building, a statistical method is adopted to establish a new air damper control model, and the operating program is determined to control the new air damper at a corresponding time to control the fresh air volume.

3) Using the fresh air and return air ratio to adjust and influence the controlled temperature is not the main basis for adjusting the fresh air valve. The adjustment temperature is mainly completed by the table cold valve. If the adjustment of the air valve is also based on the temperature, then two devices are controlled. At the same time, influenced by one parameter and trying to stabilize the parameters at the same time, the result is that the system generates self-excitation, and it is not or difficult to achieve stability, so it can amplify the dead zone value of the fresh air regulation temperature, so that the damper is coarsely adjusted, water The valve is fine-tuned. The percentage of fresh air in the air conditioning system should not be less than 10%. Regardless of the size of each room, the amount of fresh air is greater than or equal to 30m3/h.

D. Control model for optimal start-stop of electromechanical equipment:

Through the calculation and adaptive control of the optimal start-stop time of the air-conditioning equipment, the cloud computing management control platform can shorten the unnecessary air-conditioning start-stop tolerance time and achieve energy-saving purposes while ensuring the environment is comfortable; When warming up, the new air damper is closed, which not only reduces the capacity of the equipment, but also reduces the energy consumption of cooling or heating by acquiring fresh air. For small power fans or fans with soft start, the intermittent control method of the fan can be considered. If used properly, the fan will only run for 40 to 50 minutes per hour, and the energy saving effect is obvious. After the air-conditioning equipment adopts the energy-saving running algorithm, the running time is even more It is reasonable. The data records show that the cumulative time of actual energy supply for each air conditioner in 24 hours a day is only about 2 hours.

E, lighting system control model

The time switch control is applied to the public lighting equipment, and the pre-range dimming control and the window dimming control according to the working time and outdoor light can greatly reduce the energy consumption.

F, peak-to-valley electricity price difference control model:

Taking full advantage of the peak-to-valley price policy, the cloud computing energy management and control platform system has developed a reasonable ice storage cooling control strategy, and at the peak of power consumption, choose to remove some relatively unimportant electromechanical equipment in the building to reduce the peak load, or Measures such as putting in emergency generators and releasing stored cooling capacity to achieve peak avoidance operation and reduce operating costs.

G. Control of the balance and variable flow of the air conditioning water system:

According to the heat exchange nature of the air conditioning system: the water of a certain flow is exchanged with the airflow driven by the fan through the air cooler, so the efficiency of energy exchange is not only related to the influence of the wind speed and the temperature of the air cooler on the thermal efficiency, but also the cold. Hot water supply flow is related to thermal efficiency.

The cloud computing management control platform measures the flow and control effects of the air conditioners at the farthest and most proximal ends of the air conditioning system (relative to the air conditioning system for returning moisture and sump) in different energizing states and different operating states. The analysis of the parameters shows that the air conditioning system has obvious dynamic characteristics. In the operating state, the cloud computing energy management and control system dynamically adjusts the regulating valves of each air conditioner according to the actual needs of the heat exchange, and the control flow changes accordingly, so the total supply The return water flow value is also constantly changing. In response to this change, the supply and return water pressure difference must be adjusted to achieve a new balance. A mathematical model (algorithm) of variable flow control is established through experiments and historical data to change the air conditioning supply and return system from an open loop system to a closed loop system.

The measured data indicates that when the flow rate of the air handler reaches the rated flow condition, the pressure at both ends of the regulating valve is only 0.66 kg/cm2-lkg/cm2. Dynamically adjust the number of water supply pumps to be put into operation according to the actual number of operating units of the air handler and the operating flow conditions, and assist the fine adjustment of the bypass valve to achieve the variable flow control mode, which can avoid leakage, improve control accuracy, and reduce unnecessary flow. Loss and power redundancy result in significant energy savings. According to actual data calculation, the energy saving effect is above 25%. And the dynamic parameters of the water supply and return flow are used as feedback quantities to adjust the operating conditions of the chiller to achieve significant energy saving and consumption reduction effects.

Energy-saving control mode of cloud computing management control platform scientifically using intelligent buildings And algorithms that dynamically adjust equipment operation to effectively overcome energy waste due to equipment capacity and power redundancy caused by HVAC design. According to statistics, in the regulation of the heating system,

The 48-hour daily average temperature forecast determines the water supply and return water temperature of the boiler house. Compared with the experience of heating, it can save about 3% of energy while ensuring that the room temperature is not lower than 18 °C. Only adopting the climate compensation method can save 3% to 5% of energy, and the heating part of the system can automatically detect the outdoor temperature and the indoor temperature, which is an important basis for the heating load, and the energy in the heating season is not Less than 5%.

H, spring transition mode, autumn transition mode control model:

1) Historical outdoor calculation (dry ball) temperature record in the region, 2) Whether the outdoor daily average temperature reaches 10C°. When the above two conditions are met, the spring transition season mode is entered. At this time, the system will automatically adjust the air volume of the air conditioning unit according to the schedule to ensure the indoor comfort.

When the outdoor maximum temperature exceeds 26C. At the time, the system will adopt the control mode of the autumn transition season, using the nighttime purge method, making full use of the outdoor cool air to clean the room and take the rest of the room. The purge time can be adjusted according to the climate change. The night sweep system is mainly based on the heat load curve, not the main time program.

1) The historical outdoor (dry ball) temperature record of the area, 2) Whether the outdoor daily average temperature reaches 8C°. When the above two conditions are met, the system enters the autumn transition season mode. At this time, the system will automatically adjust the fresh air volume of the air conditioning unit according to the running heat and humidity load curve and schedule. However, if the outdoor maximum temperature is lower than 15C. At the time, the system will adopt the control mode of the spring transition season and cancel the nighttime purge.

I. Control model using equivalent temperature and region

The human body is sensitive to temperature, but the response to relative humidity is much slower. The relative humidity is between 35% and 65%. The human body's response is slow, but after 65% or less than 35%, the body's humidity is The reaction is very intense and so on. In the energy management control process, without using a single temperature as a control index, instead of using the control comfort index, i.e., using the equivalent control target temperature (T = 25 ° C, Φ = 50%) ο except that equivalent As a control index, temperature should also adopt the method of regional control, that is, the human body feels comfortable to the outside environment in a certain area, so it is not necessary to control the equivalent temperature at one point, but to control it to a certain range. This can make the system more stable and stable, and it can save energy very effectively. With this technology alone, energy saving can be achieved in the year. Save another 10% on the basis of the general strategy.

There are many types of model algorithms in the cloud computing management control platform, which are mainly divided into periodic algorithms and event triggering algorithms. The periodic algorithms include: algebraic calculation, total value calculation, device running time, Boolean Boolean operation, data integration, piecewise linear function. , maximum and minimum records, etc., event triggering algorithms include: report tasks and display events, site group control, regional or group alarms, alarms of combined structures, and so on. When using, select an algorithm according to specific needs and establish a control model.

FIG. 2 is a flowchart of a cloud computing-based device monitoring method according to an embodiment of the present invention, the method comprising:

S11: Perform on-site control on each energy-consuming device according to the user-set parameter and transmit the user setting parameter to the cloud computing management control platform;

S12: collecting parameters related to energy consumption of each energy-consuming device and transmitting the parameters to the cloud computing management control platform; the parameters related to energy consumption of each energy-consuming device include real-time energy consumption parameters, operating parameters, and Safety parameters. The real-time energy consumption parameter generally refers to the power parameter of each energy-consuming device directly collected by the electrical metering device, and the operating parameters include temperature, humidity, air volume, running time, frequency, etc., related parameters of each energy-consuming device during operation, and the safety parameters include Parameters related to each energy-consuming device in the case of operating conditions, faults, alarms, etc. The parameters related to the energy consumption of each energy-consuming device are transmitted to the cloud computing management control platform through any one of a wireless internet network, a wired internet network, a GPRS, and a 3G network.

Due to the use of cloud computing management control platform for energy management control, the scale and scalability of cloud computing make it possible to achieve centralized control of ultra-large-scale energy consumption. In theory, it can realize any kind of energy management control worldwide. Including building energy management control, power transportation energy management control, etc., the application scope is wider; the virtualization characteristics of cloud computing enable each user to perform energy management control without separately configuring an independent energy management control platform, but Obtained on demand in the "cloud" greatly reduces the cost; the characteristics of cloud computing resource sharing make the historical data in the entire control platform very rich, and can match the best historical data as a reference to achieve optimal energy allocation. FIG. 3 is a flowchart of a cloud computing-based device monitoring method according to another embodiment of the present invention. The method is based on the cloud computing-based device monitoring method shown in FIG.

S133: Determine whether the generated energy consumption model matches the corresponding historical energy consumption model in the historical energy consumption model database; if not, perform step S135, and if yes, perform step S134 to maintain the scene of each energy-consuming device. a control mode; the historical energy consumption model in the historical energy consumption model database refers to a historical energy consumption model that matches the energy consumption constraint parameter with the generated energy consumption model, and the energy consumption constraint parameter includes the respective energy consumption One or a combination of application environment parameters, design parameters, application site type parameters, and energy supply type parameters of the device.

S135: Adjust a field control mode for each of the energy-consuming devices.

After the step S134 is performed, the method further includes the step S136, adding the generated energy consumption model to the historical energy consumption model database, enriching historical data, and providing reference for subsequent energy consumption management control.

For a more detailed introduction, please refer to the description in the above embodiment of the cloud computing-based device monitoring system.

The method of the embodiment is based on the cloud computing-based device monitoring method shown in FIG. 2, and specifically provides a method for adjusting the control mode of the field controller under the cloud computing management control platform, which is sufficient Utilizing the rich historical features of the cloud computing management control platform, the energy consumption model is further optimized and energy consumption is reduced. The above embodiments are merely exemplary embodiments of the invention, and are not intended to limit the invention, the scope of the invention is defined by the appended claims. A person skilled in the art can make various modifications or equivalents to the invention within the spirit and scope of the invention, and such modifications or equivalents are also considered to fall within the scope of the invention.

Claims

Rights request

A cloud computing-based device monitoring system, comprising: a field controller, configured to perform on-site control on each energy-consuming device according to a user-set parameter, and transmit the user setting parameter to the cloud computing management Control platform

An energy consumption parameter collector is configured to collect parameters related to energy consumption of the respective energy consumption devices and transmit the parameters to the cloud computing management control platform;

2. The cloud computing device monitoring system according to claim 1, wherein the cloud computing management control platform specifically comprises:

The cloud computing-based device monitoring system according to claim 1 or 2, wherein the parameters related to the energy consumption of the respective energy-consuming devices include real-time energy consumption parameters, operating parameters, and security parameters. .

The cloud computing-based device monitoring system according to claim 1 or 2, wherein the energy consumption parameter collector and the field controller both correspond to a network address based on the IPV4 protocol or a network based on the IPV6 protocol. address.

The cloud computing-based device monitoring system according to claim 2, wherein the corresponding historical energy consumption model in the historical energy consumption model database refers to the energy consumption constraint parameter matching the generated energy consumption model The historical energy consumption model includes one or a combination of application environment parameters, design parameters, application site type parameters, and energy supply type parameters of the respective energy consuming devices.

The cloud computing-based device monitoring system according to claim 2, wherein the field controller comprises a network temperature and humidity controller; the energy consumption parameter collector comprises a network temperature and humidity sensor; and the control mode The adjusting unit is configured to adjust the control mode of the network temperature and humidity controller to dynamically set the set temperature and humidity value according to the heat load compensation curve.

The cloud computing-based device monitoring system according to claim 1 or 2, wherein the field controller comprises a network air volume controller; the energy consumption parameter collector comprises a carbon dioxide concentration sensor; and the control mode The adjusting unit is configured to adjust a control mode of the network air volume controller to adjust the air volume wind speed according to the carbon dioxide concentration collected by the carbon dioxide concentration sensor.

8. A cloud computing-based device monitoring method, comprising:

S12: collecting parameters related to energy consumption of each energy-consuming device and transmitting the parameters to the cloud computing management control platform;

The cloud computing-based device monitoring method according to claim 8, wherein the step S13 specifically includes:

S131: determining whether the collected parameters related to the energy consumption of the respective energy-consuming devices and the user-set parameters match; if not, performing step S135, if yes, performing step S132; S132: Generate a corresponding energy consumption model according to parameters related to energy consumption of each energy-consuming device;

S135: Adjust a field control mode for each of the energy-consuming devices.

The cloud computing-based device monitoring method according to claim 9, wherein after performing the step S134, the method further includes the step S136, adding the generated energy consumption model to the historical energy consumption model database. in.