CN103162346A - Central heating monitoring system based on cloud service and adjustment method thereof - Google Patents

Abstract

The invention discloses a central heating monitoring system based on cloud services and an adjustment method of the central heating system. The central heating monitoring system includes a cloud server and a heating terminal monitoring device communicatively connected to the cloud server; A user behavior function corresponding to a user. The user behavior function reflects the relationship between the parameters of the house heating equipment and the heating load, water supply temperature and time of each user. The cloud server predicts the heating load of each user and obtains the current water supply temperature of each user, and according to the above parameters The parameters of the room heating equipment are calculated and sent to each heating terminal monitoring equipment; the heating terminal monitoring equipment adjusts according to the parameters of the room heating equipment. The invention can optimize the heating system of each user as a whole, thereby reducing the load of the entire heating system, has high intelligence, good precision, and certain improvement in energy saving.

Description

Central heating monitoring system and central heating system adjustment method based on cloud service

technical field

The invention relates to intelligent heating technology, in particular to a central heating monitoring system and a central heating system adjustment method based on cloud services.

Background technique

The central heating area of my country's cities is at least 3 billion square meters. Most of the users in these heating areas do not have energy-saving systems that automatically adjust the heating temperature, which wastes energy and is uncomfortable.

One of the existing indoor heating terminal monitoring equipment adjustment methods is that the user manually adjusts the user operation panel to complete the adjustment of the flow rate of the water supply and return pipeline. This method requires manual adjustment by the user, which has a low degree of automation and high energy consumption.

Another indoor heating terminal monitoring device performs automatic adjustment through an indoor temperature sensor. After the user sets the indoor temperature, the detection value of the indoor temperature sensor is compared with the indoor temperature value set by the user, and the comparison result is sent to the control part. By controlling the electric regulating valve installed on the water supply pipe, the indoor temperature reaches the set target value. Although this technical solution can achieve the effect of energy saving and emission reduction to a certain extent, it is difficult to keep the actual indoor temperature at the user's set value through passive adjustment according to the detection value of the temperature sensor. Moreover, due to the conductivity of heat energy, the heating consumption of each unit is closely related to the location of the unit in the building, the situation of neighbors and the enclosure structure of the building itself in addition to the unit's own consumption. Therefore, this indoor heating terminal The adjustment accuracy and energy saving degree of monitoring equipment still need to be improved.

Contents of the invention

The purpose of the present invention is to propose a central heating monitoring system based on cloud services and its adjustment method. Based on the large-scale computing power of the cloud, the load of each user in the entire central heating system and the behavior patterns of individual users are coordinated, and the indoor heating terminal is improved. Monitor the adjustment accuracy and energy saving degree of the equipment.

The invention discloses a centralized heating monitoring system based on cloud services, which includes a cloud server and a heating terminal monitoring device communicating with the cloud server:

The cloud server includes a user behavior function acquisition module, a load and water temperature acquisition module and a parameter calculation module;

The user behavior function acquisition module is used to acquire the user behavior function corresponding to each user according to historical data, and the user behavior function is a function of the relationship between room heating equipment parameters and time, corresponding user heating load, and water supply temperature;

The load and water temperature acquisition module is used to predict the heating load of each user and obtain the current water supply temperature of each user;

The parameter calculation module is used to calculate room heating equipment parameters according to the predicted heating load of each user, current water supply temperature and time information, and send them to each heating terminal monitoring equipment;

The heating terminal monitoring equipment is used to acquire the current water supply temperature of the monitored user where the room heating equipment is located and adjust the room heating equipment according to the parameters of the room heating equipment.

Preferably, the load and water temperature acquisition module predicts the heating load of each user according to the meteorological data and the pre-acquired energy consumption function;

The energy consumption function is a function reflecting the relationship between the meteorological data and the heating load of each user, which is obtained by fitting the historical meteorological data and the historical data of the heating load of each user.

Preferably, the weather data includes temperature, humidity and illuminance.

Preferably, the room heating equipment parameter is the opening degree of an electric regulating valve.

Preferably, the cloud server also includes a feedback adjustment module:

The feedback adjustment module is used to modify the user behavior function according to the user's manual adjustment parameters.

Preferably, the cloud server also includes an optimal adjustment module;

The optimal adjustment module is used to modify the user behavior functions of other users according to the user behavior functions of the most energy-saving users.

The invention also discloses a central heating system adjustment method based on cloud services, including:

The cloud server obtains a user behavior function according to historical data, and the user behavior function is a function reflecting the relationship between room heating equipment parameters and time, corresponding user heating load, and water supply temperature;

The cloud server predicts the heating load of each user and obtains the current water supply temperature of each user through the heating terminal monitoring equipment;

The cloud server calculates room heating equipment parameters based on the predicted heating load of each user, current water supply temperature and time information and sends them to each heating terminal monitoring device;

The room heating is adjusted according to the room heating parameters.

Preferably, the cloud server predicts the heating load of each user according to the meteorological data and the pre-acquired energy consumption function;

The energy consumption function is a function of the relationship between the meteorological data and the heating load of each user, which is obtained by fitting the historical weather data and the historical data of the heating load of each user.

Preferably, the weather data includes temperature, humidity and illuminance.

Preferably, the room heating equipment parameter is the opening degree of an electric regulating valve.

Preferably, the method also includes:

The user behavior function is corrected according to the user's manually adjusted parameters.

Preferably, the method also includes:

The user behavior functions of other users are corrected according to the user behavior functions of the most energy-saving user.

The present invention acquires user behavior functions through historical data, and periodically calculates current room heating equipment parameters based on the user behavior functions on the cloud server according to the predicted heating load and water supply temperature of each user, and adjusts according to the calculated room heating parameters. As a result, the adjustment of room heating equipment can not only conform to the user's behavior pattern, but also optimize the load of the entire heating system as a whole, with high intelligence, good precision, and a certain increase in energy saving.

Description of drawings

Fig. 1 is a schematic diagram of a central heating monitoring system based on cloud services according to a first embodiment of the present invention.

Fig. 2 is a schematic diagram of heating terminal monitoring equipment of the central heating monitoring system based on cloud services according to the first embodiment of the present invention.

Fig. 3 is a flowchart of a method for adjusting a central heating system based on cloud services according to a second embodiment of the present invention.

Explanation of main reference signs:

Cloud server 11, heating terminal monitoring equipment 12, heating pipeline 13, meteorological center 14, user behavior function acquisition module 111, load and water temperature acquisition module 112, parameter calculation module 113, optimal adjustment module 114, feedback adjustment module 115, communication module 121. Control circuit board 122, electric regulating valve 123, water temperature sensor 124, user operation setting knob 125

Detailed ways

The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and through specific implementation methods.

Fig. 1 is a schematic diagram of a central heating monitoring system based on cloud services according to a first embodiment of the present invention. As shown in Figure 1, the central heating monitoring system based on cloud services includes a cloud server 11 and local heating equipment. The local heating equipment includes a heating terminal monitoring device 12 and a heating pipeline 13 located in the user's room. The pipe 13 flows into the water flow of the indoor room heating equipment to control the temperature.

Specifically, the cloud server 11 includes a user behavior function acquisition module 111 , a load and water temperature acquisition module 112 , and a parameter calculation module 113 .

The user behavior function acquisition module 111 is used to acquire the user behavior function according to the historical data. The user behavior function is a function reflecting the relationship between room heating equipment parameters and time, each user's heating load, and water supply temperature.

The load and water temperature acquisition module 112 is used to predict the heating load of each user and acquire the current water supply temperature.

The parameter calculation module 113 is used to calculate the parameters of the room heating equipment according to the predicted heating load of each user, the current water supply temperature, and time information, and send it to the heating terminal monitoring equipment.

Fig. 2 is a schematic diagram of a heating terminal monitoring device of a central heating system based on a cloud service according to a first embodiment of the present invention. As shown in Figure 2, the heating terminal monitoring device 12 includes a communication module 121, a control circuit board 122, an electric regulating valve 123 arranged on the heating pipe for adjusting the flow of hot water entering the room, and a water temperature sensor arranged on the heating pipe 124. Preferably, a user-operated setting knob 125 may also be included.

The communication module 121 is used to communicate with the cloud server to report the water supply temperature of the heating pipe at the current moment measured by the water temperature sensor 124 , and to receive the room heating equipment parameters calculated and obtained from the cloud server 11 .

The control circuit board 122 is connected with the communication module 121 and is used for controlling the electric regulating valve 123 to adjust according to the parameters of the room heating equipment transmitted by the communication module 121 .

The electric regulating valve 123 is used for regulating the water intake according to the control of the control circuit board 122 .

Preferably, the user-operated setting knob 125 is connected to the control circuit board 122 and is used to transmit the user's operation instruction to the control circuit board 122 .

In a preferred implementation of this embodiment, the parameter of the room heating equipment is the opening degree of the electric regulating valve 123 of the heating terminal monitoring equipment. The user behavior function acquisition module 111 of the cloud server establishes the relationship between the water supply temperature, the heating load of each user, and the parameters of the room heating equipment according to the stored historical data of the water supply temperature, the historical data of each user's heating load, and the user's manual operation history information. Functional relationship, ie, user behavior function. The acquisition of the historical data can be carried out in a cycle of two to three weeks, that is, within the cycle time, no automatic adjustment is performed, but the user is allowed to operate and adjust the indoor heating terminal monitoring equipment, thereby collecting historical data reflecting user behavior patterns data.

Preferably, at this stage, the user behavior function acquisition module 111 can be used to perform empirical curve fitting on the historical data of time, load, water supply temperature, and electric control valve opening, and calculate the heating load and water supply temperature of each user at different times. , the user adjusts the opening degree of the electric control valve, and the user behavior function is obtained as follows:

Phi=f(t)*(a1+a2*L+a3*L ² +b1*T+b2*T ² +c*L*T)

Among them, Phi is the opening degree of the electric control valve 123; f(t) is a function representing the adjustment time of the heating terminal monitoring equipment, and L is the heating load of each user, which can be calculated by the cloud server within the function acquisition cycle. Obtained; T is the water supply temperature, which is collected by the water temperature sensor 124 arranged on the heating pipeline; a1, a2, a3, b1, b2, c are fitting coefficients.

Thus, the cloud server can calculate and obtain room heating equipment parameters that conform to user behavior patterns according to time, heating load of each user, and water supply temperature.

In a preferred implementation of this embodiment, the load and water temperature acquisition module 112 of the cloud server 11 predicts the heating load of each user according to the meteorological data and the energy consumption function obtained in advance, and obtains the heating load through the communication module 121 of the heating terminal monitoring device 12. The heating terminal monitors the water supply temperature of the heating pipeline 13 of the equipment.

The energy consumption function is a function that reflects the relationship between the meteorological data and the heating load of each user, that is, the heating of each user conforms to the law that changes with the change of the meteorological data, and it is trained according to the prediction algorithm on the historical data or obtained by fitting. The energy consumption function can be obtained through training or fitting by setting a special module on the cloud server 11 using data from the meteorological center 14 and historical data accumulated for obtaining the user behavior function, or the above data can be trained separately Obtain or fit to obtain the described energy consumption function.

Preferably, weather information such as temperature, humidity, and illuminance can be calculated hourly in the cloud server 11 based on the weather forecast algorithm based on the weather information of the user's area and the weather information predicted by the weather forecast. The hourly calculation of meteorological parameters using meteorological forecasting algorithms can improve the efficiency of meteorological information acquisition to a certain extent.

The hourly temperature T is calculated according to the information of the highest outdoor temperature T _h and the lowest outdoor temperature T _l obtained from the weather forecast using the following formula:

T=T _h -α _t ×(T _h -T _l ),

In the formula, _αt is the preset temperature prediction coefficient at time t, which can be obtained by fitting according to the historical temperature information of the area.

根据某一天的历史数据采集湿度变化趋势，利用天气预报得到的全天平均相对湿度信息对历史变化趋势进行修正：hourly humidity

The humidity change trend is collected according to the historical data of a certain day, and the historical change trend is corrected by using the whole-day average relative humidity information obtained from the weather forecast:

为某天τ时刻典型气象年历史湿度；

为某天的全天累计相对湿度；RH为天气预报得到的全天平均相对湿度。In the formula,

is the historical humidity of a typical meteorological year at time τ on a certain day;

is the cumulative relative humidity of a day; RH is the average relative humidity of the whole day obtained from the weather forecast.

The hourly illuminance is calculated by the following formula:

${\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; Q</span>}\frac{\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; sin</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}}<span\; class="notranslate">\; )</span>$

In the formula, Q _τ (t) is the hourly total radiation of the sun at time t; Q is the total daily radiation; t _r and t _d are the sunrise and sunset times obtained from the weather forecast, respectively.

Of course, those skilled in the art can understand that the above weather data can also be obtained directly from the outside through the weather center 14 that provides hourly weather forecasts.

According to the acquired historical meteorological data and the historical data of each user's heating load, the energy consumption function can be established by fitting or other methods.

Preferably, the historical meteorological and load data can be used as the training data of the load forecasting neural network algorithm, and the corresponding relationship between the cooling (heating) load and time, outdoor temperature, relative humidity and irradiance can be obtained through the training of the existing algorithm, and also That is, the energy consumption function is obtained, and the energy consumption function is stored in the cloud server 11 .

Therefore, when the load and water temperature acquisition module 112 of the cloud server 11 needs to adjust the room heating parameters (in a preferred mode, it can be adjusted periodically or triggered by an event), it can be based on the weather data and pre-acquired The energy consumption function predicts the heating load of individual users. Therefore, the subsequent parameter calculation module 113 can calculate the parameters of the room heating equipment according to the predicted heating load of each user, the current water supply temperature and time information and send it to the heating terminal monitoring device 12, and then the heating terminal monitoring device 12 performs corresponding parameter adjustment. For example, the adjustment of the opening degree of the electric control valve.

In this embodiment, the user behavior function is acquired through historical data, and the cloud server 11 periodically calculates the room heating equipment parameters based on the user behavior function according to the predicted heating load and water supply temperature of each user, and adjusts according to the room heating equipment parameters. As a result, the adjustment of room heating equipment can not only conform to the user's behavior pattern, but also optimize the load of the entire heating system as a whole, with high intelligence, good precision, and a certain increase in energy saving.

In a preferred implementation of this embodiment, the cloud server 11 may further include an optimal adjustment module 114, which modifies the user behavior functions of other users according to the user behavior functions of the most energy-saving user.

Specifically, the optimal adjustment module 114 selects the user behavior model records of the same building or similar buildings where the user is located according to the user behavior records of users in the same area on the cloud server, and analyzes the overall load of various heating systems and water supply temperature conditions. The user's control information on the parameters of the room heating equipment, and find the most energy-saving control method of the room heating equipment parameters from the records, and establish the optimal user behavior function. Then adjust the user behavior function of each user according to the optimal user behavior function, and use the corrected user behavior function to adjust the parameters of the user's room heating equipment. In this way, the user can still manually adjust the parameters of the room heating equipment according to his own comfort requirements, and the adjustment records are saved on the cloud server 11 of the cloud service center.

In a preferred implementation of this embodiment, the cloud server 11 may also include a module for dynamically adjusting the user behavior function. This module may be, for example, a feedback adjustment module 115, which is used to correct the user's behavior according to the user's manual adjustment parameters. Behavior function.

Specifically, the feedback adjustment module 115, for users who have adjusted the parameters of the room heating equipment in the automatic control phase, according to the user's operation records on the room heating equipment and the corresponding time, load, and water supply temperature information, according to the method of the model recognition phase, the user behavior The model performs empirical curve fitting to obtain a new user behavior function, and replaces the original user behavior function with this user behavior function.

In this preferred mode, the user's behavior function can be continuously and dynamically adjusted through the user's manual feedback, so as to realize the intelligent dynamic adjustment of the room heating equipment.

The adjustment modules involved in the above two implementation manners can coexist or exist independently, and continuously and dynamically adjust user behavior functions.

Fig. 3 is a flowchart of a method for adjusting a central heating system based on cloud services according to a second embodiment of the present invention. As shown in Figure 3, the method includes:

Step 310, the cloud server acquires user behavior functions according to historical data, and the user behavior functions are functions that reflect the relationship between room heating equipment parameters and time, heating load of each user, and water supply temperature.

In a preferred implementation of this embodiment, the parameter of the room heating equipment is the opening degree of the electric regulating valve of the heating terminal monitoring equipment. The cloud server establishes the functional relationship between the water supply temperature, the heating load of each user and the parameters of the room heating equipment according to the stored historical data of the water supply temperature, the historical data of the heating load of each user, and the historical information of the user's manual operation, that is, the user Behavior function. The acquisition of the historical data can be carried out in a cycle of two to three weeks, that is, within the cycle time, no automatic adjustment is performed, but the user is allowed to operate and adjust the indoor heating terminal monitoring equipment, thereby collecting historical data reflecting user behavior patterns data.

Preferably, at this stage, empirical curve fitting can be performed on the historical data of time, load, water supply temperature, and opening of the electric regulating valve to calculate the user's adjustment of the opening of the electric regulating valve at different times, each user's heating load and water supply temperature. According to the law of degree, the user behavior function is obtained as follows:

Phi=f(t)*(a1+a2*L+a3*L ² +b1*T+b2*T ² +c*L*T)

Among them, Phi is the opening degree of the electric control valve; f(t) is a function representing the adjustment time of the heating terminal monitoring equipment, and L is the centralized heating load of each user, which can be calculated by collecting the load parameters of the heating system within the function acquisition period through the cloud server Obtained; T is the water supply temperature, which is collected by the water temperature sensor installed on the heating pipeline; a1, a2, a3, b1, b2, c are fitting coefficients.

Thus, the cloud server can calculate and obtain room heating equipment parameters that conform to user behavior patterns according to time, heating load of each user of the central heating system, and water supply temperature.

Step 320, the cloud server predicts the heating load of each user and obtains the current water supply temperature of each user through the heating terminal monitoring equipment.

In a preferred implementation of this embodiment, the cloud server predicts the heating load of each user according to the meteorological data and the pre-acquired energy consumption function, and obtains the water supply temperature of the heating pipeline of the heating terminal monitoring device through the communication module of the heating terminal monitoring device .

The energy consumption function is a function that reflects the relationship between the meteorological data and the heating load of each user, that is, the heating of each user conforms to the law that changes with the change of the meteorological data, and it is trained according to the prediction algorithm on the historical data or obtained by fitting. The energy consumption function can be obtained through training or fitting by setting a special module on the cloud server using the data from the weather center and the historical data accumulated to obtain the user behavior function, or the above data can be trained or simulated separately. Combined to obtain the energy consumption function.

Preferably, the weather information such as temperature, humidity, and illuminance can be calculated hourly in the cloud server based on the weather forecast algorithm according to the weather information of the user's area and the weather information predicted by the weather forecast. The hourly calculation of meteorological parameters using meteorological forecasting algorithms can improve the efficiency of meteorological information acquisition to a certain extent.

The hourly temperature T is calculated according to the information of the highest outdoor temperature T _h and the lowest outdoor temperature T _l obtained from the weather forecast using the following formula:

T=T _h -α _t ×(T _h -T _l ),

In the formula, _αt is the preset temperature prediction coefficient at time t, which can be obtained by fitting according to the historical temperature information of the area.

根据某一天的历史数据采集湿度变化趋势，利用天气预报得到的全天平均相对湿度信息对历史变化趋势进行修正：hourly humidity

The humidity change trend is collected according to the historical data of a certain day, and the historical change trend is corrected by using the whole-day average relative humidity information obtained from the weather forecast:

为某天τ时刻典型气象年历史湿度；为某天的全天累计相对湿度；RH为天气预报得到的全天平均相对湿度。In the formula,

is the historical humidity of a typical meteorological year at time τ on a certain day; is the cumulative relative humidity of a day; RH is the average relative humidity of the whole day obtained from the weather forecast.

The hourly illuminance is calculated by the following formula:

${\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; Q</span>}\frac{\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; sin</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}}<span\; class="notranslate">\; )</span>$

In the formula, Q _τ (t) is the hourly total radiation of the sun at time t; Q is the total daily radiation; t _r and t _d are the sunrise and sunset times obtained from the weather forecast, respectively.

Of course, those skilled in the art can understand that the above weather data can also be obtained directly from the outside through a weather center that provides hourly weather forecasts.

According to the acquired historical meteorological data and the historical data of each user's heating load, the energy consumption function can be established by fitting or other methods.

Preferably, the historical meteorological and load data can be used as the training data of the load forecasting neural network algorithm, and the corresponding relationship between the cooling (heating) load and time, outdoor temperature, relative humidity and irradiance can be obtained through the training of the existing algorithm, and also That is, the energy consumption function is obtained, and the energy consumption function is saved in the cloud server.

Therefore, when the cloud server needs to adjust the room heating parameters (in a preferred way, it can be adjusted periodically or triggered by events), it can predict the heating load of each user according to the meteorological data and the pre-acquired energy consumption function .

Step 330, the cloud server calculates room heating equipment parameters according to the predicted heating load of each user, current water supply temperature, and time information, and sends them to the heating terminal monitoring equipment.

Step 340, the heating terminal monitoring equipment adjusts the room heating equipment according to the room heating equipment parameters.

In a preferred implementation of this embodiment, the method further includes step 350 (indicated by a dotted line in the figure), modifying the user behavior function of the current user according to the user behavior function of the most energy-saving user.

Specifically, according to the user behavior records of users in the same area on the cloud server, select the user behavior model records of the same building or similar buildings where the user is located, and analyze the user's response to the room heating equipment parameters under various user heating loads and water supply temperature conditions. control information, and find the most energy-efficient room heating equipment parameter control method from the records, and establish the optimal user behavior function. Then adjust the user behavior function of each user according to the optimal user behavior function, and use the corrected user behavior function to adjust the parameters of the user's room heating equipment. In this way, users can still manually adjust the parameters of the room heating equipment according to their own comfort requirements, and the adjustment records are saved on the cloud server of the cloud service center, and the user behavior model is modified according to the first correction method at this stage. Make corrections.

In a preferred implementation of this embodiment, the method further includes step 360 (indicated by a dotted line in the figure), modifying the user behavior function according to the user's manual adjustment parameters.

Specifically, for users who manually adjust the parameters of room heating equipment in the automatic control stage, the user behavior model is analyzed according to the user's operation records on the room heating equipment parameters and the corresponding time, load, and water supply temperature information according to the method of the model identification stage. A new user behavior function is obtained by fitting the experience curve, and the original user behavior function is replaced by the user behavior function, and then returns to step 320 .

The adjustment methods involved in the above two implementation manners can coexist or exist independently, continuously and dynamically adjust the user behavior function, and realize the intelligent dynamic adjustment of the heating terminal monitoring equipment.

In this embodiment, the user behavior function is acquired through historical data, and the cloud server 11 periodically calculates the room heating equipment parameters based on the user behavior function according to the predicted heating load and water supply temperature of each user, and adjusts according to the room heating equipment parameters. As a result, the adjustment of room heating equipment can not only conform to the user's behavior pattern, but also optimize the load of the entire heating system as a whole, with high intelligence, good precision, and a certain increase in energy saving.

Obviously, those skilled in the art should understand that each module or each step of the present invention described above can be realized by a general-purpose computing device, and they can be concentrated on a single computing device, or distributed on a network formed by multiple computing devices, Optionally, they can be implemented with executable program codes of computer devices, so that they can be stored in storage devices and executed by computing devices, or they can be made into individual integrated circuit modules, or multiple of them Modules or steps are implemented as a single integrated circuit module. As such, the present invention is not limited to any specific combination of hardware and software.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims (12)

1. A central heating monitoring system based on cloud services, including a cloud server and a heating terminal monitoring device communicating with the cloud server: The cloud server includes a user behavior function acquisition module, a load and water temperature acquisition module and a parameter calculation module; The user behavior function acquisition module is used to acquire the user behavior function corresponding to each user according to historical data, and the user behavior function is a function of the relationship between room heating equipment parameters and time, corresponding user heating load, and water supply temperature; The load and water temperature acquisition module is used to predict the heating load of each user and obtain the current water supply temperature of each user; The parameter calculation module is used to calculate room heating equipment parameters according to the predicted heating load of each user, current water supply temperature and time information, and send them to each heating terminal monitoring equipment; The heating terminal monitoring equipment is used to acquire the current water supply temperature of the monitored user where the room heating equipment is located and adjust the room heating equipment according to the parameters of the room heating equipment. 2. The central heating monitoring system based on cloud service according to claim 1, wherein the load and water temperature acquisition module predicts the heating load of each user according to the meteorological data and the pre-acquired energy consumption function; The energy consumption function is a function reflecting the relationship between the meteorological data and the heating load of each user, and the energy consumption function is obtained by fitting the historical meteorological data and the historical data of the heating load of each user. 3. The central heating monitoring system based on cloud service according to claim 2, wherein the meteorological data includes temperature, humidity and illuminance. 4. The central heating monitoring system based on cloud service according to claim 1, characterized in that, the room heating equipment parameter is the opening degree of an electric regulating valve. 5. The central heating monitoring system based on cloud service according to claim 1, wherein said cloud server also includes a feedback adjustment module: The feedback adjustment module is used to modify the user behavior function according to the user's manual adjustment parameters. 6. The central heating monitoring system based on cloud service according to claim 1, wherein the cloud server also includes an optimal adjustment module; The optimal adjustment module is used to modify the user behavior functions of other users according to the user behavior functions of the most energy-saving users. 7. A central heating system regulation method based on cloud services, comprising: The cloud server obtains a user behavior function according to historical data, and the user behavior function is a function reflecting the relationship between room heating equipment parameters and time, corresponding user heating load, and water supply temperature; The cloud server predicts the heating load of each user and obtains the current water supply temperature of each user through the heating terminal monitoring equipment; The cloud server calculates room heating equipment parameters based on the predicted heating load of each user, current water supply temperature and time information and sends them to each heating terminal monitoring device; The heating terminal monitoring equipment adjusts the room heating equipment according to the parameters of the room heating equipment. 8. The central heating system adjustment method based on cloud services according to claim 7, wherein the cloud server predicts the heating load of each user according to the meteorological data and the pre-acquired energy consumption function; The energy consumption function is a function of the relationship between the meteorological data and the heating load of each user, which is obtained by fitting the historical weather data and the historical data of the heating load of each user. 9. The central heating system regulation method based on cloud service according to claim 8, characterized in that the meteorological data includes temperature, humidity and illuminance. 10. The central heating system adjustment method based on cloud services according to claim 7, characterized in that the room heating equipment parameter is the opening degree of an electric control valve. 11. The central heating system regulation method based on cloud service according to claim 7, characterized in that, the method further comprises: The user behavior function is corrected according to the user's manually adjusted parameters. 12. The central heating system regulation method based on cloud service according to claim 7, characterized in that, the method further comprises: The user behavior functions of other users are corrected according to the user behavior functions of the most energy-saving user.

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