Central air-conditioning energy-saving system based on environmental data

Abstract

The invention discloses a central air conditioner energy-saving system based on environmental data. The central air conditioner energy-saving system comprises a data analysis module, and an environmental data acquisition module and a central control module which are connected with the data analysis module, relates to the technical field of air conditioner energy conservation, and solves the technical problem that the energy-saving effect of a central air conditioner is not ideal because the heat preservation effect of a regulation and control area is difficult to evaluate in the prior art. The energy efficiency coefficient of each regulation and control area is calculated based on environment data; setting the air supply priority of each regulation and control area based on the energy efficiency coefficient; generating a characteristic temperature change curve based on the characteristic temperatures of a plurality of continuous periods; and the heat preservation effect of each regulation and control area is obtained based on the characteristic temperature change curve of each regulation and control area. By calculating the energy efficiency coefficient of each regulation and control area, the central air conditioner is controlled to preferentially open the air outlet of the regulation and control area with the high energy efficiency coefficient, the energy loss in the process that the central air conditioner regulates and controls the temperature of the target area can be reduced, and the energy-saving effect of the central air conditioner can be improved.

Description

Technical Field

[0001] The invention belongs to the technical field of air-conditioning energy saving, and in particular is a central air-conditioning energy saving system based on environmental data. Background Art

[0002] With climate change and economic development, central air conditioning has become an indispensable equipment in modern buildings. However, the use of central air conditioning also consumes a lot of electricity resources, which brings a great burden to the energy environment. In order to solve this problem, in recent years, researchers have begun to adopt various energy-saving technologies and designed various new central air conditioners to reduce energy consumption.

[0003] The prior art (invention patent with publication number CN117190407B) discloses a central air-conditioning energy-saving control method and system, which can control the energy saving of the central air-conditioning to a certain extent by regularly acquiring the building environment information and the operating parameters of the terminal equipment, adjusting the chilled water flow rate according to the current building load level and actual temperature, humidity and chilled water supply and return temperature difference;

[0004] However, the prior art can only reduce the power of the water pump by adjusting the chilled water flow rate to achieve energy-saving effects, but the small power generated by the central air-conditioning during startup has not changed, so the energy-saving effect of the central air-conditioning is limited; in addition, the thermal insulation performance of the control area will also directly affect the operating power of the central air-conditioning. For example, when the doors and windows in the control area are not closed, the operating power of the central air-conditioning is higher, and the central air-conditioning cannot directly feedback problems in this regard, which makes it impossible for users to rectify the control area in time, resulting in less than ideal energy-saving effects of the central air-conditioning.

[0005] The present invention provides a central air-conditioning energy-saving system based on environmental data to solve the above technical problems. Summary of the invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a central air-conditioning energy-saving system based on environmental data, which is used to solve the problem in the prior art that the power of the water supply pump can only be reduced by adjusting the chilled water flow rate to achieve energy-saving effects, but the small power generated by the central air-conditioning during the startup movement has not changed, so the energy-saving effect of the central air-conditioning is limited; in addition, since the thermal insulation performance of the control area will also directly affect the operating power of the central air-conditioning, for example, when there are doors and windows in the control area that are not closed tightly, the operating power of the central air-conditioning is higher, and the central air-conditioning cannot directly feedback problems in this regard, which makes it impossible for users to rectify the control area in time, thereby causing the technical problem that the energy-saving effect of the central air-conditioning is not ideal.

[0007] To achieve the above-mentioned object, the first aspect of the present invention provides a central air-conditioning energy-saving system based on environmental data, comprising: a data analysis module, and an environmental data acquisition module and a central control module connected thereto;

[0008] The environmental data acquisition module is used to collect the operating data of the central air conditioner in real time; divide the target area into several control areas, and collect the environmental information and regional power consumption of each control area in real time; pre-process the environmental information to obtain environmental data; wherein the operating data includes operating power, chilled water flow, and chilled water temperature; the environmental information and environmental data both include the number of people, temperature, humidity, and solar radiation intensity;

[0009] The data analysis module is used to calculate the energy efficiency coefficient of each control area based on environmental data; set the air supply priority of each control area based on the energy efficiency coefficient; integrate the regional power consumption of several consecutive periods of each control area into a power consumption prediction sequence; and obtain the regional power consumption prediction value of each control area in the prediction period based on the power consumption prediction sequence;

[0010] The central control module is used to generate air conditioning control instructions based on the power consumption prediction value of each control area, and send the air conditioning control instructions to the central air conditioner for energy-saving control; control the working state of the air outlet of the central air conditioner based on the characteristic temperature of the control area; generate a characteristic temperature change curve based on the characteristic temperature of several consecutive cycles; obtain the thermal insulation effect of each control area based on the characteristic temperature change curve of each control area; wherein the air outlet working state includes the air outlet being opened and the air outlet being closed; the thermal insulation effect includes poor thermal insulation performance and good thermal insulation performance.

[0011] Preferably, the data analysis module communicates with and / or is electrically connected to the environmental data acquisition module and the central control module respectively.

[0012] Preferably, the target area is divided into several control areas, including:

[0013] Obtain the positions of several pipe outlets of the central air conditioner, take the position of each pipe outlet as the center, divide the target area into several control areas according to the set working radius, and mark the volume of the control area as Vi; where i = 1, 2, ..., n, n is the total number of control areas.

[0014] Preferably, the energy efficiency coefficient of each control area is calculated based on the environmental data, including:

[0015] Obtain the volume Vi of the control area i, and extract the number of people RYi, temperature WDi, humidity SDi and solar radiation intensity FSi from the environmental data of the control area i;

[0016] The energy efficiency coefficient NXi of the control area i is calculated by the formula NXi=(a×RYi+b×WDi+c×SDi+d×FSi) / Vi; wherein a, b, c, and d are all proportional coefficients greater than 0.

[0017] Preferably, the air supply priority of each control area is set based on the energy efficiency coefficient, including:

[0018] The energy efficiency coefficient of each control area is extracted, and the energy efficiency coefficient is sorted in descending order to obtain the air supply priority sequence. The central air conditioner sets the air outlet state of the control area with the highest energy efficiency coefficient in the air supply priority sequence to the air outlet open.

[0019] The present invention calculates the energy efficiency coefficient of each control area and sorts the energy efficiency coefficients to obtain an air supply priority sequence; since the control area with a low energy efficiency coefficient has a greater energy loss than the control area with a high energy efficiency coefficient, the present invention controls the central air-conditioning to preferentially open the air outlet of the control area with a high energy efficiency coefficient, which can reduce the energy loss in the process of the central air-conditioning regulating the temperature of the target area, and is beneficial to improving the energy-saving effect of the central air-conditioning.

[0020] Preferably, the obtaining of the regional power consumption prediction value of each control area in the prediction period based on the power consumption prediction sequence includes:

[0021] The power consumption prediction sequence is extracted and integrated into standard input data, and the standard input data is input into the regional power consumption prediction model to obtain the regional power consumption prediction value of each control area during the prediction period; among which, the regional power consumption prediction model is constructed based on the artificial intelligence model.

[0022] Preferably, the regional power consumption prediction model is constructed based on an artificial intelligence model, including:

[0023] Extract standard input data of several consecutive cycles and integrate them into several groups of training data and test data; use the training data to train the artificial intelligence model; use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally obtain a regional power consumption prediction model whose input is the standard input data of the latest several consecutive cycles and whose output is the regional power consumption prediction value of the prediction cycle; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0024] The present invention extracts historical air-conditioning operation data and corresponding historical environmental data and integrates them into standard input data, and uses the standard input data to train an artificial intelligence model. After the training is completed, a regional power consumption prediction model is obtained. By inputting the regional power consumption of several consecutive periods into the regional power consumption prediction model, a regional power consumption prediction value of the prediction period is obtained, which is beneficial to estimating in advance the regional power consumption prediction value required for the corresponding control area, and is convenient for the central air-conditioning to adjust the operation data in time according to the regional power consumption prediction value of each control area, thereby helping to improve the energy-saving effect of the central air-conditioning.

[0025] Preferably, the method of controlling the working state of the air outlet of the central air conditioner based on the characteristic temperature of the control area includes:

[0026] Obtain the characteristic temperature of the control area; determine whether the characteristic temperature is within the preset target temperature range; if yes, the central air conditioner sets the air outlet working state of the corresponding control area to the air outlet closed; if no, the central air conditioner sets the air outlet working state of the corresponding control area to the air outlet open.

[0027] Preferably, obtaining the characteristic temperature of the control area includes:

[0028] Extract the temperature at the position farthest from the air outlet in the control area and mark it as the first temperature, mark the maximum temperature in the control area as the second temperature, and mark the lowest temperature in the control area as the third temperature; calculate the temperature average of the first temperature, the second temperature and the third temperature, and mark the temperature average as the characteristic temperature.

[0029] Preferably, the generating of the characteristic temperature variation curve based on characteristic temperatures of several consecutive cycles includes:

[0030] Extract characteristic temperatures of several consecutive cycles; draw characteristic temperature change curves with time as the independent variable and characteristic temperature as the dependent variable.

[0031] Preferably, the step of obtaining the heat preservation effect of each control area based on the characteristic temperature change curve of each control area includes:

[0032] T1: Extract characteristic temperature change curves of each control area;

[0033] T2: Calculate the first-order derivative function of the characteristic temperature change curve of each control area and take the absolute value to obtain the characteristic temperature change derivative function;

[0034] T3: Get the maximum derivative value in the characteristic temperature change derivative function;

[0035] T4: Determine whether the maximum derivative value is less than a preset derivative threshold; if yes, mark the thermal insulation effect as good thermal insulation performance; if no, mark the thermal insulation effect as poor thermal insulation performance.

[0036] The present invention draws the characteristic temperature change curve of each control area, and calculates the first-order derivative function of the characteristic temperature change curve to obtain the characteristic temperature change derivative, and obtains the thermal insulation effect of each control area according to the characteristic temperature change derivative; the characteristic temperature change derivative can reflect the changing speed of the characteristic temperature of each control area, which is conducive to improving the accuracy of evaluating the thermal insulation performance of the target area.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. The present invention calculates the energy efficiency coefficient of each control area and sorts the energy efficiency coefficients to obtain an air supply priority sequence; since the energy loss of the control area with a low energy efficiency coefficient is greater than that of the control area with a high energy efficiency coefficient, the present invention controls the central air conditioner to preferentially open the air outlet of the control area with a high energy efficiency coefficient, which can reduce the energy loss of the central air conditioner in the process of regulating the temperature of the target area, which is beneficial to improving the energy-saving effect of the central air conditioner.

[0039] 2. The present invention draws the characteristic temperature change curve of each control area, and calculates the first-order derivative function of the characteristic temperature change curve to obtain the characteristic temperature change derivative, and obtains the thermal insulation effect of each control area according to the characteristic temperature change derivative; the characteristic temperature change derivative can reflect the changing speed of the characteristic temperature of each control area, which is conducive to improving the accuracy of evaluating the thermal insulation performance of the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 It is a schematic diagram of the principle of the central air-conditioning energy-saving system of the present invention;

[0042] Figure 2 It is the overall flow chart of the central air-conditioning energy-saving system of the present invention;

[0043] Figure 3 This is a flow chart for obtaining the thermal insulation effect of each control area in the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] See also Figure 1-Figure 3 , the first aspect of the present invention provides a central air conditioning energy saving system based on environmental data, including: a data analysis module, and an environmental data acquisition module and a central control module connected thereto;

[0046] Environmental data acquisition module: used to collect the operating data of the central air conditioner in real time; divide the target area into several control areas, and collect the environmental information and regional power consumption of each control area in real time; pre-process the environmental information to obtain environmental data; among which, the operating data includes operating power, chilled water flow, and chilled water temperature; environmental information and environmental data include the number of people, temperature, humidity, and solar radiation intensity;

[0047] Data analysis module: used to calculate the energy efficiency coefficient of each control area based on environmental data; set the air supply priority of each control area based on the energy efficiency coefficient; integrate the regional power consumption of several consecutive periods of each control area into a power consumption prediction sequence; obtain the regional power consumption prediction value of each control area in the prediction period based on the power consumption prediction sequence;

[0048] Central control module: used to generate air conditioning control instructions based on the power consumption prediction value of each control area, and send the air conditioning control instructions to the central air conditioner for energy-saving control; control the working state of the air outlet of the central air conditioner based on the characteristic temperature of the control area; generate a characteristic temperature change curve based on the characteristic temperature of several consecutive cycles; obtain the thermal insulation effect of each control area based on the characteristic temperature change curve of each control area; among which, the air outlet working state includes the air outlet open and the air outlet closed; the thermal insulation effect includes poor thermal insulation performance and good thermal insulation performance.

[0049] It should be noted that the temperature and humidity data in the environmental data of the control area are collected at the corresponding pipeline outlet.

[0050] In this embodiment, the data analysis module communicates with and / or is electrically connected to the environmental data acquisition module and the central control module respectively.

[0051] In this embodiment, the target area is divided into several control areas, including:

[0052] Obtain the positions of several pipe outlets of the central air conditioner, take the position of each pipe outlet as the center, divide the target area into several control areas according to the set working radius, and mark the volume of the control area as Vi; where i = 1, 2, ..., n, n is the total number of control areas.

[0053] Exemplarily, the central air conditioner is provided with three duct air outlets, namely, duct air outlet CFK1, duct air outlet CFK2 and duct air outlet CFK3, and the corresponding working radius of each duct air outlet is R1=8m, R2=10m, R3=6m; with the position of each duct air outlet as the center, the target area is divided into three control areas according to the set working radius.

[0054] In this embodiment, the energy efficiency coefficient of each control area is calculated based on the environmental data, including:

[0055] Obtain the volume Vi of the control area i, and extract the number of people RYi, temperature WDi, humidity SDi and solar radiation intensity FSi from the environmental data of the control area i;

[0056] The energy efficiency coefficient NXi of the control area i is calculated by the formula NXi=(a×RYi+b×WDi+c×SDi+d×FSi) / Vi; wherein a, b, c, and d are all proportional coefficients greater than 0.

[0057] For example, the influence coefficients are set to a=80, b=50, c=10, and d=2, and the volume of the control area 1 is V1=80m 3 , extract the environmental data of control area 1, the number of people RY1 = 5, temperature WD1 = 29 ° C, humidity SD1 = 58%, solar radiation intensity FS1 = 600W / m 2 ; The energy efficiency coefficient of control area 1 is calculated by the formula NX1=45.375.

[0058] In this embodiment, the air supply priority of each control area is set based on the energy efficiency coefficient, including:

[0059] The energy efficiency coefficient of each control area is extracted, and the energy efficiency coefficient is sorted in descending order to obtain the air supply priority sequence. The central air conditioner sets the air outlet state of the control area with the highest energy efficiency coefficient in the air supply priority sequence to the air outlet open.

[0060] Exemplarily, the energy efficiency coefficient NX1 of control area 1 is set to 45.375, the energy efficiency coefficient NX2 of control area 2 is set to 56.17, and the energy efficiency coefficient NX3 of control area 3 is set to 48.32. The energy efficiency coefficients are sorted in order from large to small to obtain an air supply priority sequence. The priority sequence is {control area 2, control area 3, control area 1}. The central air conditioner sets the air outlet status of the control area with the highest energy efficiency coefficient in the air supply priority sequence to air outlet open.

[0061] The present invention calculates the energy efficiency coefficient of each control area and sorts the energy efficiency coefficients to obtain an air supply priority sequence; since the control area with a low energy efficiency coefficient has a greater energy loss than the control area with a high energy efficiency coefficient, the present invention controls the central air-conditioning to preferentially open the air outlet of the control area with a high energy efficiency coefficient, which can reduce the energy loss in the process of the central air-conditioning regulating the temperature of the target area, and is beneficial to improving the energy-saving effect of the central air-conditioning.

[0062] In this embodiment, obtaining the regional power consumption prediction value of each control area in the prediction period based on the power consumption prediction sequence includes:

[0063] The power consumption prediction sequence is extracted and integrated into standard input data, and the standard input data is input into the regional power consumption prediction model to obtain the regional power consumption prediction value of each control area during the prediction period; among which, the regional power consumption prediction model is constructed based on the artificial intelligence model.

[0064] In this embodiment, the regional power consumption prediction model is constructed based on an artificial intelligence model, including:

[0065] Extract standard input data of several consecutive cycles and integrate them into several groups of original data, use 80% of the original data as training data and 20% as test data; use the training data to train the artificial intelligence model; use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally obtain a regional power consumption prediction model whose input is the standard input data of several recent consecutive cycles and whose output is the regional power consumption prediction value of the prediction cycle; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0066] The present invention extracts historical air-conditioning operation data and corresponding historical environmental data and integrates them into standard input data, and uses the standard input data to train an artificial intelligence model. After the training is completed, a regional power consumption prediction model is obtained. By inputting the regional power consumption of several consecutive periods into the regional power consumption prediction model, a regional power consumption prediction value of the prediction period is obtained, which is beneficial to estimating in advance the regional power consumption prediction value required for the corresponding control area, and is convenient for the central air-conditioning to adjust the operation data in time according to the regional power consumption prediction value of each control area, thereby helping to improve the energy-saving effect of the central air-conditioning.

[0067] In this embodiment, the working state of the air outlet of the central air conditioner is controlled based on the characteristic temperature of the control area, including:

[0068] Obtain the characteristic temperature of the control area; determine whether the characteristic temperature is within the preset target temperature range; if yes, the central air conditioner sets the air outlet working state of the corresponding control area to the air outlet closed; if no, the central air conditioner sets the air outlet working state of the corresponding control area to the air outlet open.

[0069] Exemplarily, the characteristic temperature of control area 1 is set to 28.3°C, and the preset target temperature range of control area 1 is [24°C, 26°C]; since the characteristic temperature is not within the preset target temperature range, the central air conditioner sets the outlet working state of control area 1 to outlet open.

[0070] In this embodiment, obtaining the characteristic temperature of the control area includes:

[0071] Extract the temperature at the position farthest from the air outlet in the control area and mark it as the first temperature, mark the maximum temperature in the control area as the second temperature, and mark the lowest temperature in the control area as the third temperature; calculate the temperature average of the first temperature, the second temperature and the third temperature, and mark the temperature average as the characteristic temperature.

[0072] Exemplarily, the temperature at the position farthest from the air outlet in the control area 1, that is, the first temperature D1 is set to 28°C, the maximum temperature in the control area, that is, the second temperature D2 is set to 31°C, and the lowest temperature in the control area, that is, the third temperature D3 is set to 26°C; the average temperature of the first temperature, the second temperature and the third temperature is calculated to be 28.3°C, and the temperature average value of 28.3°C is marked as the characteristic temperature.

[0073] In this embodiment, a characteristic temperature change curve is generated based on characteristic temperatures of several consecutive cycles, including:

[0074] Extract characteristic temperatures of several consecutive cycles; draw characteristic temperature change curves with time as the independent variable and characteristic temperature as the dependent variable.

[0075] In this embodiment, the heat preservation effect of each control area is obtained based on the characteristic temperature change curve of each control area, including:

[0076] T1: Extract characteristic temperature change curves of each control area;

[0077] T2: Calculate the first-order derivative function of the characteristic temperature change curve of each control area and take the absolute value to obtain the characteristic temperature change derivative function;

[0078] T3: Get the maximum derivative value in the characteristic temperature change derivative function;

[0079] T4: Determine whether the maximum derivative value is less than a preset derivative threshold; if yes, mark the thermal insulation effect as good thermal insulation performance; if no, mark the thermal insulation effect as poor thermal insulation performance.

[0080] Exemplarily, the maximum derivative value of the characteristic temperature change derivative function of the control area 1 is set to 0.25℃ / min, and the derivative threshold is set to 0.20℃ / min; since the maximum derivative value is greater than the preset derivative threshold, the thermal insulation effect of the control area 1 is marked as poor thermal insulation performance; the staff investigates the reasons for the poor thermal insulation performance of the control area 1, such as checking whether the doors and windows of the control area 1 are closed, whether the curtains are not enabled when the solar radiation intensity is high, and whether there is abnormal heating of office appliances.

[0081] The present invention draws the characteristic temperature change curve of each control area, and calculates the first-order derivative function of the characteristic temperature change curve to obtain the characteristic temperature change derivative, and obtains the thermal insulation effect of each control area according to the characteristic temperature change derivative; the characteristic temperature change derivative can reflect the changing speed of the characteristic temperature of each control area, which is conducive to improving the accuracy of evaluating the thermal insulation performance of the target area.

[0082] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0083] Working principle of the present invention:

[0084] The present invention collects the operation data of the central air conditioner in real time; divides the target area into several control areas, and collects the environmental information and regional power consumption of each control area in real time; pre-processes the environmental information to obtain environmental data; calculates the energy efficiency coefficient of each control area based on the environmental data; sets the air supply priority of each control area based on the energy efficiency coefficient; integrates the regional power consumption of several consecutive periods of each control area into a power consumption prediction sequence; obtains the regional power consumption prediction value of each control area in the prediction period based on the power consumption prediction sequence; generates air conditioning control instructions based on the power consumption prediction value of each control area, and sends the air conditioning control instructions to the central air conditioner for energy-saving control; controls the working state of the air outlet of the central air conditioner based on the characteristic temperature of the control area; generates a characteristic temperature change curve based on the characteristic temperatures of several consecutive periods; and obtains the thermal insulation effect of each control area based on the characteristic temperature change curve of each control area.

[0085] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A central air conditioning energy-saving system based on environmental data, comprising: A data analysis module, and an environmental data acquisition module and a central control module connected thereto; characterized in that the environmental data acquisition module: is used to collect the operating data of the central air conditioner in real time; divides the target area into a number of control areas, and collects the environmental information and regional power consumption of each control area in real time; pre-processes the environmental information to obtain environmental data; wherein the operating data includes operating power, chilled water flow, and chilled water temperature; the environmental information and environmental data both include the number of people, temperature, humidity, and solar radiation intensity; The data analysis module is used to calculate the energy efficiency coefficient of each control area based on environmental data; set the air supply priority of each control area based on the energy efficiency coefficient; integrate the regional power consumption of several consecutive periods of each control area into a power consumption prediction sequence; and obtain the regional power consumption prediction value of each control area in the prediction period based on the power consumption prediction sequence; The central control module is used to generate air conditioning control instructions based on the power consumption prediction value of each control area, and send the air conditioning control instructions to the central air conditioner for energy-saving control; control the working state of the air outlet of the central air conditioner based on the characteristic temperature of the control area; generate a characteristic temperature change curve based on the characteristic temperature of several consecutive cycles; obtain the thermal insulation effect of each control area based on the characteristic temperature change curve of each control area; wherein the air outlet working state includes the air outlet being opened and the air outlet being closed; the thermal insulation effect includes poor thermal insulation performance and good thermal insulation performance.

2. A central air conditioning energy saving system based on environmental data according to claim 1, characterized in that: The target area is divided into several control areas, including: Obtain the positions of several pipe outlets of the central air conditioner, take the position of each pipe outlet as the center, divide the target area into several control areas according to the set working radius, and mark the volume of the control area as Vi; where i = 1, 2, ..., n, n is the total number of control areas.

3. A central air conditioning energy saving system based on environmental data according to claim 2, characterized in that: The energy efficiency coefficient of each control area is calculated based on environmental data, including: Obtain the volume Vi of the control area i, and extract the number of people RYi, temperature WDi, humidity SDi and solar radiation intensity FSi from the environmental data of the control area i; The energy efficiency coefficient NXi of the control area i is calculated by the formula NXi=(a×RYi+b×WDi+c×SDi+d×FSi) / Vi; wherein a, b, c, and d are all proportional coefficients greater than 0.

4. A central air conditioning energy saving system based on environmental data according to claim 1, characterized in that: The air supply priority of each control area is set based on the energy efficiency coefficient, including: The energy efficiency coefficient of each control area is extracted, and the energy efficiency coefficient is sorted in descending order to obtain the air supply priority sequence. The central air conditioner sets the air outlet state of the control area with the highest energy efficiency coefficient in the air supply priority sequence to the air outlet open.

5. The central air conditioning energy saving system based on environmental data according to claim 1, characterized in that: The obtaining of the regional power consumption prediction value of each control area in the prediction period based on the power consumption prediction sequence includes: The power consumption prediction sequence is extracted and integrated into standard input data, and the standard input data is input into the regional power consumption prediction model to obtain the regional power consumption prediction value of each control area during the prediction period; among which, the regional power consumption prediction model is constructed based on the artificial intelligence model.

6. A central air conditioning energy saving system based on environmental data according to claim 5, characterized in that: The regional power consumption prediction model is constructed based on an artificial intelligence model and includes: Extract standard input data of several consecutive cycles and integrate them into several groups of training data and test data; use the training data to train the artificial intelligence model; use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally obtain a regional power consumption prediction model whose input is the standard input data of the latest several consecutive cycles and whose output is the regional power consumption prediction value of the prediction cycle; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

7. A central air conditioning energy saving system based on environmental data according to claim 1, characterized in that: The method of controlling the working state of the air outlet of the central air conditioner based on the characteristic temperature of the control area includes: Obtain the characteristic temperature of the control area; determine whether the characteristic temperature is within the preset target temperature range; if yes, the central air conditioner sets the air outlet working state of the corresponding control area to the air outlet closed; if no, the central air conditioner sets the air outlet working state of the corresponding control area to the air outlet open.

8. A central air conditioning energy saving system based on environmental data according to claim 7, characterized in that: The step of obtaining the characteristic temperature of the control area includes: Extract the temperature at the position farthest from the air outlet in the control area and mark it as the first temperature, mark the maximum temperature in the control area as the second temperature, and mark the lowest temperature in the control area as the third temperature; calculate the temperature average of the first temperature, the second temperature and the third temperature, and mark the temperature average as the characteristic temperature.

9. The central air conditioning energy saving system based on environmental data according to claim 1, characterized in that: The generating of a characteristic temperature variation curve based on characteristic temperatures of a plurality of continuous cycles comprises: Extract characteristic temperatures of several consecutive cycles; draw characteristic temperature change curves with time as the independent variable and characteristic temperature as the dependent variable.

10. The central air conditioning energy saving system based on environmental data according to claim 1, characterized in that: The obtaining of the thermal insulation effect of each control area based on the characteristic temperature change curve of each control area includes: T1: Extract characteristic temperature change curves of each control area; T2: Calculate the first-order derivative function of the characteristic temperature change curve of each control area and take the absolute value to obtain the characteristic temperature change derivative function; T3: Get the maximum derivative value in the characteristic temperature change derivative function; T4: Determine whether the maximum derivative value is less than a preset derivative threshold; if yes, mark the thermal insulation effect as good thermal insulation performance; if no, mark the thermal insulation effect as poor thermal insulation performance.

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