CN201765486U - Cloud Computing Based Equipment Monitoring System - Google Patents

Abstract

The utility model discloses a facility monitoring system based on cloud computing, which mainly conducts on-site control on each energy consumption facility by utilizing an on-site controller. An energy consumption parameter collector collects parameters relevant to the energy consumption of the energy consumption facility; and a cloud computing manage and control platform conducts centralized control on the collected parameters relevant to the energy consumption of the energy consumption facility and user set parameters (design parameters). The facility monitoring system is compatible with all energy-saving platforms of different manufacturers, so as to conduct centralized monitoring on a great deal of energy consumption facilities through one unified platform, and realize maximum energy conservation and consumption reduction management and networked automatic control. Therefore, the utility model realizes optimized configuration of energy, and achieves better energy saving effect.

Description

Cloud Computing Based Equipment Monitoring System

technical field

The utility model relates to the technical field of energy management and control, in particular to an equipment monitoring system based on cloud computing.

Background technique

As energy becomes more and more scarce all over the world, energy management and control systems capable of saving energy are becoming more and more important.

The energy management control system in the prior art usually adopts traditional electrical automation technology to manage and control the energy consumption of each energy-consuming equipment of a single object (such as shopping malls, stores, hotels, and industrial plants in office buildings), which belongs to field-level control. Different manufacturers use different management and energy-saving platforms, which are usually incompatible and lack communication with each other. Therefore, it is impossible to form a unified platform for centralized energy management and control to achieve the goal of energy conservation to the greatest extent.

TRIDIUM Corporation of the United States has developed a unified platform system for energy management for the first time, which can be compatible with other energy management platforms and provide users with energy consumption reference data. But the inventor finds that it still has the following problems:

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

2. The system does not conduct comprehensive energy statistics, analysis and management control in terms of energy factors, energy policies, energy indicators, management systems, energy consumption benchmarks, energy performance, energy statistics, energy optimization, etc. Provided to the user, allowing the user to correct the on-site control mode according to the statistical results, so that the optimal allocation of energy cannot be achieved.

Cloud computing is a network technology developed in recent years. It distributes computing tasks on a resource pool composed of a large number of computers, enabling various application systems to obtain computing power, storage space, and various software services as needed. Major IT companies have launched their own cloud computing platform services based on cloud computing, such as Google (GOOGLE), Microsoft, Yahoo, Amazon (Amazon), etc. In summary, cloud computing has the following characteristics:

(1) Very large scale. The "cloud" has a considerable scale. Google cloud computing already has more than 1 million servers, and the "clouds" of Amazon, IBM, Microsoft, Yahoo, etc. have hundreds of thousands of servers. Enterprise private cloud generally has hundreds of thousands of servers, "cloud" can give users unprecedented computing power.

(2) Virtualization. Cloud computing supports users to obtain application services at any location and using various terminals. The requested resource comes from the "cloud" rather than a fixed tangible entity. The application runs somewhere in the "cloud," but the user doesn't really need to know or worry about where the application is running. All we need is a laptop or a mobile phone, and everything we need can be realized through network services, even tasks such as supercomputing.

(3) High reliability. "Cloud" uses measures such as data multi-copy fault tolerance, isomorphic interchangeability of computing nodes and other measures to ensure high reliability of services. Using cloud computing is more reliable than using local computers.

(4) Versatility. Cloud computing is not aimed at specific applications, and a variety of applications can be constructed under the support of the "cloud", and the same "cloud" can support the operation of different applications at the same time.

(5) High scalability. The scale of the "cloud" can be dynamically expanded to meet the needs of applications and user scale growth.

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

(7) Extremely cheap. Due to the special fault-tolerant measures of the "cloud", extremely cheap nodes can be used to form the cloud, and the automated centralized management of the "cloud" makes it unnecessary for a large number of enterprises to bear the increasingly high cost of data center management. Compared with the traditional system, it has been greatly improved, so users can fully enjoy the low-cost advantages of "cloud", and often only need to spend a few hundred dollars to complete tasks that previously required tens of thousands of dollars and months to complete in a few days.

Utility model content

In order to solve the above-mentioned problems in the prior art, the purpose of this utility model is to provide a device monitoring system based on cloud computing, which can be compatible with all energy-saving platforms of different manufacturers, and monitor many energy-consuming devices under a unified platform , to achieve the maximum energy saving management and network automatic control, so as to realize the optimal allocation of energy and achieve better energy saving effect.

In order to achieve the above purpose, the utility model provides a device monitoring system based on cloud computing, including:

The on-site controller is used to perform on-site control of each energy-consuming device according to the user-set parameters and transmit the user-set parameters to the cloud computing management and control platform;

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

A cloud computing management and control platform, configured to adjust the on-site control of the on-site controller to the various energy-consuming equipment according to the collected parameters related to the energy consumption of the various energy-consuming equipment and the parameters set by the user model;

The on-site controller and the cloud computing management control platform, and the energy consumption parameter collector 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 the parameters related to the energy consumption of each energy-consuming device collected by the energy-consumption parameter collector and the parameters set by the user;

A first judging unit, configured to judge whether the collected parameters related to the energy consumption of each energy-consuming device match the user-set parameters and produce a judging result;

An energy consumption model generation unit, configured to generate a corresponding energy consumption model according to parameters related to energy consumption of each energy-consuming device when the judgment result of the first judgment unit is a match;

Historical energy consumption model database, used to store various historical energy consumption models;

A second judgment unit, configured to judge whether the generated energy consumption model matches the corresponding historical energy consumption model in the historical energy consumption model database and generate a judgment result;

A control mode adjustment unit, configured to adjust the on-site control mode of the on-site controller for each energy-consuming device when the judgment result of the first judging unit or the second judging unit is mismatch.

Preferably, the parameters related to the energy consumption of each energy-consuming device include real-time energy consumption parameters, operating parameters and safety parameters. Among them, the real-time energy consumption parameters usually refer to the power parameters of each energy-consuming equipment directly collected by the electricity metering equipment. The operating parameters include temperature, humidity, air volume, running time, frequency, etc. Parameters related to each energy-consuming device under operating status, fault, alarm, etc.

Preferably, the corresponding historical energy consumption model in the historical energy consumption model database refers to a historical energy consumption model whose energy consumption constraint parameters match the generated energy consumption model, and the energy consumption constraint parameters include the One or a combination of the application environment parameters, design parameters, application site type parameters, and energy supply type parameters of the equipment. There are various historical energy consumption models that meet industry standards (design standards) in the historical energy consumption model database. most reasonable. The establishment of historical energy consumption models is usually restricted by energy consumption constraint parameters. Different energy consumption constraint parameters correspond to different historical energy consumption models. The application environment parameters of each energy-consuming equipment include geographic location, meteorological parameters, etc. Design parameters include design power, measurement range, design energy consumption parameters, design energy efficiency, etc. Application site type parameters include shopping malls, supermarkets, hotels, office buildings , exhibition hall, computer room, industrial plant, residence, national grid, etc., the energy supply type parameters include coal, electricity, natural gas, petroleum, biomass energy, thermal energy, renewable energy, etc. Of course, there are other energy consumption constraint parameters, such as control mode and so on.

Preferably, both the energy consumption parameter collector and the on-site controller correspond to a network address based on the IPV4 protocol or a network address based on the IPV6 protocol.

Preferably, the parameters set by the user and the collected parameters related to the energy consumption of each energy-consuming device are transmitted to the cloud computing management and control platform through a communication network, and the communication network is a wireless Internet network or a wired Internet network. , GPRS and 3G networks or any of the more advanced next-generation transmission networks.

Preferably, the on-site controller includes a network temperature and humidity controller; the energy consumption parameter collector includes a network temperature and humidity sensor; the control mode adjustment unit is used to adjust the control mode of the network temperature and humidity controller according to The thermal load compensation curve dynamically sets the set temperature and humidity values.

Preferably, the on-site controller includes a network air volume controller; the energy consumption parameter collector includes a carbon dioxide concentration sensor; the control mode adjustment unit is used to adjust the control mode of the network air volume controller according to the carbon dioxide The carbon dioxide concentration collected by the concentration sensor adjusts the air volume and wind speed.

The beneficial effect of the utility model is that it can be compatible with all energy-saving platforms of different manufacturers, and monitor many energy-consuming devices under a unified platform to realize maximum energy-saving and consumption-reducing management and networked automatic control, thereby realizing Optimal allocation of energy to achieve better energy-saving effects.

Description of drawings

Fig. 1 is a schematic structural diagram of a cloud computing-based equipment monitoring system according to an embodiment of the present invention.

Detailed ways

Embodiments of the utility model will be described in detail below in conjunction with the accompanying drawings.

As shown in Figure 1, the structural representation of the cloud computing-based equipment monitoring system of the utility model embodiment, the cloud computing-based equipment monitoring system includes:

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

The energy consumption parameter collector 12 is used to collect parameters related to the energy consumption of the various energy-consuming devices 10 and transmit them to the cloud computing management control platform 13; the parameters related to the energy consumption of the various energy-consuming devices include real-time energy consumption parameters, operating parameters and safety parameters. Among them, the real-time energy consumption parameters usually refer to the power parameters of each energy-consuming equipment directly collected by the electricity metering equipment. The operating parameters include temperature, humidity, air volume, running time, frequency, etc. Parameters related to each energy-consuming device under operating status, fault, alarm, etc. The energy consumption parameter collector 12 is generally composed of various sensors with network transmission functions, data statistics and summary units, data analysis and upload units, etc., to complete data collection and preliminary statistical analysis functions, and the actual number is set according to needs Yes, there may be many energy consumption parameter collectors. Sensors can be various network temperature sensors, network humidity sensors, network air volume sensors, network electricity metering sensors, network wind speed sensors, network air quality sensors, network collectors for operating parameters of electromechanical equipment, network access control, security, alarm signal collectors, Special signal network collector (such as CO, CO2, formaldehyde, water flow, etc.) and so on. It transmits the collected energy consumption parameters to the cloud computing management control platform 13 through the communication network 20. The communication network 20 can be a wireless Internet network, a wired Internet network, GPRS, a 3G network or a more advanced next-generation transmission network and the like.

The current Internet is based on the IPV4 protocol. The IPV4 protocol uses a 32-bit address length, and the limited address space is about to be exhausted. Therefore, in a large-scale equipment monitoring system, the on-site controller 11 and the energy consumption parameter collector 12 can use network addresses based on the IPV6 protocol, and the IPV6 protocol uses a 128-bit address length. For the entire earth, its address resources can be regarded as It is unlimited (more than 1,000 network addresses can be allocated per square meter), and can adapt to equipment monitoring systems even on a global scale. The cloud computing management control platform 13 is used to adjust the on-site controller 11 to adjust the energy consumption of each energy-consuming equipment according to the collected parameters related to the energy consumption of each energy-consuming equipment 10 and the user-set parameters. 10 live control modes. The purpose of the adjustment is to achieve the optimal allocation of energy and reduce energy consumption. The cloud computing management control platform 13 of the present embodiment specifically includes:

The receiving unit 131 is configured to receive the parameters related to the energy consumption of each energy-consuming device 10 collected by the energy-consumption parameter collector 12 and the parameters set by the user;

The first judging unit 132 is configured to judge whether the collected parameters related to the energy consumption of each energy-consuming device 10 match the parameters set by the user and produce a judging result;

An energy consumption model generating unit 133, configured to generate a corresponding energy consumption model according to parameters related to energy consumption of each energy consumption device when the judgment result of the first judgment unit is a match; the energy consumption model includes the overall energy consumption and Operating energy consumption and other indicators.

The historical energy consumption model database 130 is used to store various historical energy consumption models; the historical energy consumption model database stores various historical energy consumption models that meet industry standards (design standards) and are agreed or recognized by relevant norms, standards and other documents These historical energy consumption models take into account evaluation criteria such as energy consumption benchmarks, efficiency benchmarks, and performance benchmarks, and the energy consumption is relatively the most reasonable.

The second judging unit 134 is used to judge whether the generated energy consumption model matches the corresponding historical energy consumption model in the historical energy consumption model database and generate a judgment result; the establishment of the historical energy consumption model is usually restricted by energy consumption constraint parameters , the energy consumption constraint parameters are different, and the corresponding historical energy consumption models are different. The energy consumption constraint parameters include one or a combination of application environment parameters, design parameters, application site type parameters, and energy supply type parameters of each energy-consuming device, as well as a combination with other constraint parameters (such as control modes). The application environment parameters of each energy-consuming equipment include geographic location, meteorological parameters, etc. Design parameters include design power, measurement range, design energy consumption parameters, design energy efficiency, etc. Application site type parameters include shopping malls, supermarkets, hotels, office buildings , exhibition hall, computer room, industrial plant, residence, national grid, etc., the energy supply type parameters include coal, electricity, natural gas, petroleum, biomass energy, thermal energy, renewable energy, etc. The user inputs the energy consumption constraint parameters of the currently generated energy consumption model through the energy consumption constraint parameter setting unit 14, and then finds the corresponding historical energy consumption model in the historical energy consumption model database 130 according to these energy consumption constraint parameters (that is, the energy consumption constraint A historical energy consumption model whose parameters match the generated energy consumption model), and then judge whether the generated energy consumption model matches the corresponding historical energy consumption model, if it does not match, it means that the energy consumption is unreasonable and needs to be adjusted. For example, the annual energy consumption per unit area of the generated energy consumption model is 200-300kWh, while the annual energy consumption per unit area of the historical energy consumption model with the same energy consumption constraint parameters is about 100kWh, indicating that the energy consumption is unreasonable and needs to be adjusted.

A control mode adjustment unit 135, configured to adjust the on-site control mode of the on-site controller 11 for each energy-consuming device 10 when the judgment result of the first judging unit 132 or the second judging unit 134 is mismatched . The mismatch indicates that the energy consumption does not meet the requirements, and the on-site control mode needs to be adjusted to reduce energy consumption until the energy consumption matches, so as to achieve the optimal configuration of energy consumption. When the judgment result of the first judging unit 132 is a mismatch, it means that the energy consumption cannot meet the requirements set by the user and needs to be adjusted directly; Although the energy consumption can meet the requirements set by the user, it is still not optimal. Evaluation criteria such as energy consumption benchmark, efficiency benchmark, and performance benchmark are not considered. It is necessary to make adjustments to further reduce energy consumption. If the judgment result of the second judging unit 134 is a match, it means that the produced energy consumption model is reasonable and meets the requirements, then the generated energy consumption model is added to the historical energy consumption model database to enrich the history The data provide reference for subsequent energy management and control.

Of course, there are many modes of controlling the on-site controller 11 by the cloud computing management and control platform 13, and the above-mentioned embodiment only provides one of them.

For the convenience of users, the device monitoring system based on cloud computing in this embodiment can be made into an intuitive display interface, and the user only needs to manage and control through the display interface.

The advantage of using cloud computing management and control platform 13 for energy management control is very obvious. The scale and scalability of cloud computing make it possible to realize the centralized control of ultra-large-scale energy consumption. Management control, including building energy management control, power transportation energy management control, etc., has a wider range of applications; the virtualization characteristics of cloud computing make it unnecessary for each user to configure an independent energy management control platform when performing energy management control , but obtained on-demand in the "cloud", which greatly reduces the cost; the resource sharing feature of cloud computing makes the historical data in the entire control platform very rich, and can match the best historical data as a reference, so as to realize the optimal allocation of energy .

The following uses the energy management control of a certain building as an example to illustrate the application process of the cloud computing-based equipment monitoring system of this embodiment.

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

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

2. The working pressure of chilled water and cooling water system is 1.5Mpa, and the test pressure is working pressure plus 0.5Mpa. The working pressure of the hot water system is 1.5Mpa, and the experimental pressure is the working pressure plus 0.5Mpa.

3. An air enthalpy sensor is installed outdoors, which can maximize the use of fresh air for control according to changes in outdoor enthalpy and entropy, and achieve the goal of saving energy and reducing consumption. Examples of specific fresh air system control strategies throughout the year are as follows:

A. In the air-conditioning season, when the return air temperature is 4°C lower than the outdoor fresh air temperature (assumed) and the enthalpy of the return air is lower than the enthalpy of the fresh air by 4KJ/Kg (assumed). Dry, start the runner heat recovery device and enter the exhaust air Heat recovery mode; when the return air temperature is lower than the outdoor fresh air temperature by less than 4°C for a period of time, stop running the runner heat recovery device and switch to bypass fresh air mode.

B. In the air-conditioning season, the exhaust fan of the fresh air fan maintains the minimum fresh air ratio. When the CO2 concentration of the return air is higher than the set value, increase the set value of the new exhaust air constant air volume valve (CAV) to increase the fresh air volume; when the return air CO2 concentration is lower than the lower limit of the set value, reduce the new exhaust constant air volume. The air volume setting value of the air volume valve (CAV) reduces the fresh air volume.

C. When the enthalpy value of the outdoor air is lower than the set enthalpy value for starting fresh air (the indoor design enthalpy value is assumed to be 5KJ/Kg.dry), enter the fresh air working condition, open the transition season fresh air valve of the air conditioning box, and close the return air valve , chain open all the total fresh air fan, the total exhaust fan.

When fresh air cannot completely eliminate indoor waste heat, control the opening of the water valve of the air conditioning box according to the indoor temperature;

When the fresh air can completely eliminate the indoor waste heat, close the water valve of the air conditioning box;

When the fresh air temperature is too low, adjust the fresh air valve and the return air valve according to the indoor temperature value, reduce the fresh air volume, increase the return air volume, and the fresh air fan and the supply fan operate with frequency conversion.

D. When running in fresh air condition, when the outdoor air enthalpy value is higher than the set enthalpy value for starting fresh air, stop the fresh air operation and enter the air-conditioning season working condition.

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

5. Reference value of outdoor parameters:

In summer, the calculated dry bulb temperature outside the air conditioner is 33.2°C

In summer, the outdoor calculated wet bulb temperature of the air conditioner is 26.4°C

The calculated outdoor temperature of ventilation in summer is 30°C

The average outdoor wind speed in summer is 1.9m/s

Calculate the dry bulb temperature outside the air conditioner in winter -12°C

Calculate the relative humidity outside the air conditioner in winter at 45%

Calculate the dry bulb temperature outside the heating in winter -9°C

Calculated outdoor temperature for ventilation in winter -5°C

The average outdoor wind speed in winter is 2.8m/s

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

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

2. The power consumption of hotels and hotels is slightly higher, and the annual power consumption per unit area is about 100-200kWh;

3. Shopping malls consume more power equipment, the number of lighting fixtures is large, and the air-conditioning system has a large capacity and long running time. Compared with other types of buildings, shopping malls consume a lot of electricity per unit area, basically 200~ 300kWh;

4. Comprehensive commercial buildings include complexes of various types of buildings, and the area ratios of various types of buildings are different, so the energy consumption changes are also different. The annual power consumption per unit area of comprehensive commercial buildings is 100-300kWh.

The energy management and control process of the equipment monitoring system based on cloud computing is as follows:

1. Complete the logging of detection sensors and data information through the field equipment layer. The field equipment layer includes the energy consumption parameter collector 12 (generally various sensors) and the field controller 11. The energy consumption parameter collector 12 mainly completes various signal collections , the on-site controller 11 mainly performs on-site control on the corresponding energy-consuming equipment.

All signals are directly connected to the IP network through the switch, and uploaded to the cloud computing-based equipment monitoring system through the internet (wireless or wired) for signal collection, storage, statistics and analysis database.

The relevant design parameters of energy-consuming equipment and buildings are logged in through the cloud computing platform, and the information enters the equipment signal collection, storage, statistics, analysis and model database of the cloud computing energy management and control system.

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

◆Various detailed status, fault, operation and other data of control points,

◆Alarm summary table

◆Record the energy consumption data of each equipment through electric metering sensors or through calculation

◆Relevant design parameters of all energy-consuming equipment and buildings

2. Realize data analysis and related control through the control and analysis layer

The field-level controller implements field-level control on the corresponding equipment according to the detection signal and the user's target setting parameters, and uploads various signals to the cloud computing energy management and control system for equipment signal collection, storage, statistics and Analyze the database.

Taking the temperature control of the air conditioning unit as an example, the on-site controller can control the air conditioning unit including:

A. Start and stop control: complete the start and stop control according to the start and stop command signal;

B. Adjustment and control of temperature and humidity: In winter, when the indoor or air supply temperature is higher than the set value (T=20°C), the small water valve is turned off through PID control, and the large water is turned on when the indoor or air supply temperature is lower than the set value valve. In summer, when the indoor or air supply temperature is higher than the set value (T=26°C), the opening of the large water valve is controlled by PID, and the small water valve is closed when the indoor or air supply temperature is lower than the set value; the same is done for humidity;

C. Control of fresh air volume: the air volume control is realized through the proportional adjustment of the air valve, and the air volume is kept at 50 cubic meters/person/hour;

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

◆Back to fan running status, fan airflow status, manual and automatic status monitoring, start-stop control;

◆Return fan inverter feedback, inverter monitoring, inverter adjustment control;

◆Return air temperature/humidity measurement, return air CO2 concentration measurement;

◆Support 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: through the adjustment of the inverter by the controller, when the required air supply volume in the room changes, the motor speed should be reduced as much as possible on the basis of ensuring the fresh air volume to achieve energy-saving control.

3. Equipment monitoring based on cloud computing

First, judge whether the collected parameters match the parameters set by the user on the cloud computing control analysis platform. If they match, maintain the existing control mode, calculate and superimpose the total energy consumption of the entire building and the energy consumption of each parameter index, and generate energy consumption model; if it does not match, the control mode needs to be adjusted in time. The main parameters considered are:

■ Total building energy consumption index;

■General energy consumption index;

■Total energy consumption indicators in special areas;

■HVAC system energy consumption indicators:

1) Energy consumption index of air conditioning and 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 index;

■Total water consumption indicators for buildings; and so on.

Then judge whether the generated energy consumption model conforms to the industry standard on the cloud computing operation data model platform. If not, the control mode needs to be adjusted to further reduce energy consumption. There are various historical energy consumption models that meet industry standards (design standards) in the cloud computing operation data model platform. Compare the generated energy consumption model with the corresponding historical energy consumption model. If the energy consumption is higher than the historical energy consumption model , you need to adjust the control mode, if it is lower than the historical energy consumption model, keep the existing control mode unchanged, and add the generated energy consumption model as the historical energy consumption model. Several common control models are given below for reference:

A. Indoor temperature and humidity control model: according to different building types, build temperature and humidity control models with different control details to improve control accuracy. The main basis is to set a floating set point (no longer a single fixed point) based on the thermal load compensation curve, that is, to automatically adjust the indoor temperature set point more effectively, so that it can save energy as much as possible within the allowable range of the building load. In this case, the site controller includes a network temperature and humidity controller; the energy consumption parameter collector includes a network temperature and humidity sensor; the control mode adjustment unit adjusts the control mode of the network temperature and humidity controller to be based on thermal load compensation The curve dynamic setting sets the temperature and humidity value.

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

Where possible, the indoor temperature and humidity control accuracy can be achieved as follows: the temperature is ±1.5°C, and the humidity is ±5%. In this way, it is possible to avoid the phenomenon that the room temperature is too cold in summer (lower than the standard setting value) or the room temperature is overheated in winter (higher than the standard setting value), thereby realizing energy saving and consumption reduction.

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 the appropriate enthalpy value, the fresh air system is turned on and the hot and cold water supply is stopped. Or when the enthalpy value is lower than a certain value, the free refrigeration system is turned on and the air conditioner is turned off.

C. Control model of fresh air volume

According to hygienic requirements, everyone in the building must ensure a certain amount of fresh air. But getting too much fresh air will increase the energy consumption of fresh air. Under the design working conditions (outdoor temperature 26°C in summer, relative temperature 60%, room temperature 22°C in winter, relative humidity 55%), 6.5kWh cooling capacity and 12.7kWh heat capacity are required to process one kilogram (kg) of outdoor fresh air, so it meets the Under the premise of indoor sanitation requirements, the fresh air volume is reduced, which has a significant energy-saving effect. There are several main control elements to implement the fresh air volume control model:

1) The fresh air volume is determined according to the allowable indoor carbon dioxide (CO2) concentration, and the allowable concentration of CO2 is generally 0.1% (1000ppm). According to the CO2 concentration in the indoor or return air, the fresh air volume is automatically adjusted to ensure the freshness of the indoor air. The building equipment automation system with relatively complete control functions can meet these control requirements. The air volume and wind speed are adjusted according to the carbon dioxide concentration, which reflects the actual situation in the room and can save energy to the greatest extent.

2) According to the changing law of personnel in the building, use statistical methods to establish a fresh air damper control model, and determine the operating procedure to control the fresh air damper in the corresponding time, so as to achieve the control of the fresh air volume.

3) Using the ratio of fresh air and return air to adjust and affect the controlled temperature is not the main basis for adjusting the fresh air valve. The adjustment of temperature is mainly completed by the regulating valve of the surface cooler. If the adjustment of the air valve is also based on temperature, then in terms of control, the two Each device is affected by a parameter at the same time and they all try to stabilize the parameter at the same time. The result is that the system is self-excited, and it is difficult or impossible to achieve stability. Therefore, the dead zone value of the fresh air adjustment temperature can be enlarged to make the damper a coarse adjustment. , the water valve is fine-tuned. The fresh air in the air conditioning system should not be less than 10% of the supply air volume. Regardless of the volume of the room occupied by each person, the fresh air volume should be greater than or equal to 30m3/h.

D. The control model for the optimal start and stop of electromechanical equipment:

The cloud computing management control platform can shorten the unnecessary start-stop tolerance time of the air conditioner and achieve the purpose of energy saving under the premise of ensuring a comfortable environment through the calculation and adaptive control of the optimal start-stop time of the air-conditioning equipment; at the same time, the pre-cooling Or when preheating, closing the fresh air valve can not only reduce the capacity of the equipment, but also reduce the energy consumption of cooling or heating caused by fresh air. For low-power fans or fans with soft start, intermittent fan control methods can be considered. If used properly, the fan only runs for 40-50 minutes per hour, and the energy-saving effect is more obvious. After the air-conditioning equipment adopts the energy-saving operation algorithm, the running time becomes more reasonable. Data records show that the cumulative time of actual energy supply work of each air conditioner is only about 2 hours in 24 hours a day.

E. Lighting system control model

Timing switch control is implemented for public lighting equipment, and pre-program dimming control and window dimming control are performed according to work and rest time and outdoor light, which can greatly reduce energy consumption.

F. Peak-to-valley electricity price difference control model:

Making full use of the policy of peak and valley electricity prices, the cloud computing energy management and control platform system formulates a reasonable ice storage control strategy, and chooses to unload some relatively unimportant electromechanical equipment in the building to reduce the peak load during peak power consumption, or Measures such as investing in emergency generators and releasing stored cooling capacity can realize off-peak operation and reduce operating costs.

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

According to the heat exchange nature of the air conditioning system: a certain flow of water exchanges energy with the air supply air driven by the fan through the surface cooler, so the efficiency of energy exchange is not only related to the influence of wind speed and surface cooler temperature on thermal efficiency, but also to the cooling effect. Hot water 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 nearest ends of the air conditioning system (relative to the water supply and return water and water collectors of the air conditioning system) in different energy supply states and different operating states The analysis of the parameters shows that the air conditioning system has obvious dynamic characteristics. In the running state, the cloud computing energy management and control system dynamically adjusts the regulating valves of each air conditioner according to the actual needs of heat exchange, and the control flow changes accordingly. Therefore, the total supply The flow value of the return water is also constantly changing. In order to respond to this change, the pressure difference between the supply and return water must be adjusted accordingly to obtain a new balance. The variable flow control mathematical model (algorithm) is established through experiments and historical data, and the air-conditioning water supply and return system is changed from an open-loop system to a closed-loop system.

The measured data shows that when the flow of the air handler reaches the rated flow condition, the pressure at both ends of the regulating valve is only 0.66kg/cm2-1kg/cm2. Dynamically adjust the number of water supply pumps put into operation according to the actual number of air handlers in operation and the operating flow conditions, and assist the fine-tuning of the bypass valve to achieve variable flow control, which can avoid leakage, improve control accuracy, and reduce unnecessary flow. Loss and power redundancy, resulting in obvious energy saving effect. According to actual data calculation, the energy-saving effect is more than 25%. And the dynamic parameters of the water supply and return flow are used as feedback to adjust the operating conditions of the chiller to achieve obvious energy-saving and consumption-reducing effects.

Since the smart building scientifically uses the energy-saving control mode and algorithm of the cloud computing management control platform to dynamically adjust the operation of the equipment, it can effectively overcome the energy waste caused by the equipment capacity and power redundancy caused by the HVAC design. According to statistics, in the adjustment of the heating system, using the 48-hour daily average temperature forecast to determine the water supply and return water temperature of the boiler room, compared with heating by experience, can save about 3 % energy. Just 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 collect the indoor temperature, which is an important basis for the heating load, and the energy saved in the heating season is not large. less than 5%.

H. The control model of spring transition mode and autumn transition mode:

1) The historical outdoor calculation (dry bulb) temperature record of the region, and 2) whether the outdoor daily average temperature has reached 10°C. When the above two conditions are met, it will enter the spring transitional season mode. At this time, the system will automatically adjust the fresh air volume of the air conditioning unit according to the schedule to ensure indoor comfort.

When the maximum outdoor temperature exceeds 26°C, the system will adopt the control mode of autumn transitional season, adopt the method of night purge, make full use of the cool outdoor air to purify the room and take away the residual heat in the room. The blowing time can be adjusted according to the climate change. The night sweeping system is mainly based on the heat load curve, rather than the main use time program.

1) The historical outdoor (dry-bulb) temperature records of the region, and 2) whether the daily average outdoor temperature reaches 8°C. 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 operating heat and humidity load curve and schedule. However, if the maximum outdoor temperature is lower than 15°C, the system will adopt the control mode of the transitional season in spring and cancel the method of purging at night.

I. Control model using equivalent temperature and area

The human body is more sensitive to the temperature response, but the response to the relative humidity is much slower. The human body’s response to the relative humidity is relatively slow between 35% and 65%, but after exceeding 65% or lower than 35%, the human body’s response to humidity The reaction is very intense and so on principle. In the process of energy management control, not only temperature is used as the control index, but comfort is used as the control index, that is, the equivalent temperature is used as the control index (T=25°C, φ=50%). In addition to using the equivalent temperature as the control index, it is also necessary to use the method of regional control, that is, the human body feels more comfortable with the external environment in a certain area, so it is not necessary to control the equivalent temperature at one point, but to control it. Within a certain range, this can make the system easier to stabilize and can save energy very effectively. With this technology alone, the annual energy saving can save another 10% on the basis of ordinary strategies.

There are many types of model algorithms for the cloud computing management control platform, which are mainly divided into periodic algorithms and event-triggered algorithms. The regular algorithms include: algebraic calculation, total value calculation, equipment running time, Boolean operation, data integration, piecewise linear function , Maximum and minimum value records, etc. The event trigger algorithm includes: report task and display event, station group control, area or group alarm, combined structure alarm, etc. When using, select the algorithm according to the specific needs and establish the control model.

The above embodiments are only exemplary embodiments of the utility model, and are not intended to limit the utility model, and the protection scope of the utility model is defined by the appended claims. Those skilled in the art can make various modifications or equivalent replacements to the utility model within the spirit and protection scope of the utility model, and such modifications or equivalent replacements should also be deemed to fall within the protection scope of the utility model.

Claims (5)

1. A device monitoring system based on cloud computing, characterized in that, comprising: The on-site controller is used to perform on-site control of each energy-consuming device according to the user-set parameters and transmit the user-set parameters to the cloud computing management and control platform; An energy consumption parameter collector, configured to collect parameters related to the energy consumption of each energy-consuming device and transmit them to the cloud computing management and control platform; A cloud computing management and control platform, configured to adjust the on-site control of the on-site controller to the various energy-consuming equipment according to the collected parameters related to the energy consumption of the various energy-consuming equipment and the parameters set by the user model; The on-site controller and the cloud computing management control platform, and the energy consumption parameter collector and the cloud computing management control platform communicate with each other through a communication network. 2. The equipment monitoring system based on cloud computing according to claim 1, wherein the parameters set by the user and the collected parameters related to the energy consumption of each energy-consuming equipment are transmitted to Cloud computing management and control platform, the communication network is any one of wireless Internet, wired Internet, GPRS and 3G. 3. The device monitoring system based on cloud computing according to claim 1, characterized in that, both the energy consumption parameter collector and the on-site controller are devices with IP addresses. 4. The device monitoring system based on cloud computing according to claim 1, wherein the on-site controller comprises a network temperature and humidity controller; and the energy consumption parameter collector comprises a network temperature and humidity sensor. 5. The equipment monitoring system based on cloud computing according to claim 1, wherein the on-site controller comprises a network air volume controller; and the energy consumption parameter collector comprises a carbon dioxide concentration sensor. the

Images