Energy-saving data collaborative management system and method based on big data analysis

Abstract

The invention discloses an energy-saving data collaborative management system and method based on big data analysis, and relates to the technical field of energy-saving data collaborative management, and the method comprises the steps: evaluating the temperature change abnormal condition of an area in the refrigeration process of a central air conditioner, and obtaining a target area; analyzing the correlation degree between the monitoring index in the historical environment monitoring record and the people flow in the target area, and predicting the people flow change condition in the target area in the current period; analyzing the cold load demand condition of the target area in the current period to obtain cold load demand data; cold load demand data of different areas in a building in the current period and equipment energy-saving data of the central air conditioner are obtained, a control strategy of the central air conditioner is generated in combination with the cold load demand data, and refrigeration in the different areas is cooperatively managed based on the control strategy, so that it is guaranteed that cold load demands of all the areas are met, and the energy-saving performance of the central air conditioner is improved. And the energy consumption of the central air conditioner during working is greatly reduced, and the energy consumption is obviously reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving data collaborative management, and in particular to an energy-saving data collaborative management system and method based on big data analysis. Background Art

[0002] At present, large buildings rely on central air conditioning for cooling and refrigeration. Central air conditioning is installed in different areas of large buildings with corresponding air outlets, and the temperature and wind speed of the outlet are set in advance, so that the central air conditioning can cool different areas through different air outlets. Although this method can reduce the temperature of different areas in large buildings, the conditions in different areas of different large buildings are different. Some areas have fixed scenes and there will not be a large flow of people. Therefore, their cooling demand for central air conditioning is relatively stable. However, the cooling demand in some areas will change greatly with factors such as the flow of people. Therefore, this traditional method only relies on preset values ​​to control the temperature and wind speed of the air outlet, and allows the central air conditioning to operate according to preset parameters, thereby reducing the temperature of the area to achieve regional cooling. It cannot realize dynamic control of the central air conditioning according to the cooling needs of different areas, and cannot adapt to the cooling needs of different areas in a large area. Not only does it not have a good cooling effect, but it will also greatly increase the energy consumption during the operation of the central air conditioning, and cannot meet the energy-saving needs of the central air conditioning. Summary of the Invention

[0003] The purpose of the present invention is to provide an energy-saving data collaborative management system and method based on big data analysis to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a collaborative energy-saving data management method based on big data analysis, the method comprising:

[0005] Step S100: obtaining an area in a building where a central air conditioner is cooling, obtaining historical temperature change records within the area, evaluating abnormal temperature changes in the area during the central air conditioner cooling process, and obtaining a target area;

[0006] Step S200: Obtain historical records of changes in passenger flow and historical environmental monitoring records for the target area, analyze the correlation between monitoring indicators in the historical environmental monitoring records and passenger flow within the target area to obtain relevant data, obtain environmental monitoring data for the target area in the current cycle, and combine the relevant data to predict passenger flow changes within the target area in the current cycle to obtain passenger flow prediction data;

[0007] Step S300: Obtain heat source data of the target area in the current cycle, and analyze the cooling load demand status of the target area in the current cycle in combination with the crowd flow prediction data to obtain cooling load demand data;

[0008] Step S400: Obtain the cooling load demand data of different areas in the building in the current period, obtain the equipment energy saving data of the central air conditioner, and generate a control strategy of the central air conditioner in combination with the cooling load demand data, and based on the control strategy, the refrigeration in different areas is collaboratively managed.

[0009] Further, step S100 includes:

[0010] Step S101: Obtain each area in the building responsible for refrigeration by the central air conditioner, obtain each historical temperature change record of the area refrigerated by the central air conditioner, and set a unit time interval Obtain the average value of the temperature in the unit time interval area from the historical temperature change record every unit time interval, and collect to obtain a temperature data set of the historical temperature change record;

[0011] Step S102: Obtain the historical period to which the historical temperature change record belongs, and calculate the temperature trend slope β of the area in the historical period according to the temperature data set;

[0012] Step S103: Obtain the mean μ and variance δ of the temperature of each element in the temperature data set, construct the temperature vector A of the area in the historical period A = [β, μ, δ], and obtain the mean μ of the temperature of each area in the building in the historical period △ , the mean δ of the variance △ , and the mean β of the temperature trend slope △ , construct the temperature vector A of the building in the historical period A △ = [β △ , μ △ , δ △ ], and calculate the temperature change similarity B of the area and the building in the historical period:

[0013]

[0014] Step S104: Obtain the temperature change similarity of the area and the building in each historical period, and calculate the temperature change difference value C of the area and the building:

[0015]

[0016] Where j is the total number of each historical period; B i is the temperature change similarity of the area and the building in the i-th historical period;

[0017] Obtain the mean C △ of the temperature change difference value of each area and the building, and divide the absolute value of the difference between the temperature change difference value C and the mean C △ by the mean C △And record the temperature change abnormal degree of the region as a value a, when a is greater than a preset threshold, it is determined that the temperature change of the region in the central air conditioning refrigeration process is abnormal, and the region is recorded as a target region.

[0018] Further, step S200 comprises:

[0019] Step S201: Obtain the historical flow record and the historical environment monitoring record of each historical period in the target region, preprocess the data in the historical flow record and the historical environment monitoring record, obtain the total number of people in the target region from the historical flow record every unit time, and collect to obtain a people flow dataset;

[0020] Step S202: Obtain the historical environment monitoring record in the target region, obtain the value of each monitoring index from the historical environment monitoring record every unit time, and obtain a monitoring dataset;

[0021] Step S203: Based on the people flow dataset and the monitoring dataset, calculate the correlation coefficient between each monitoring index and the total number of people in the target region in the historical period to which the historical flow record belongs, obtain the average value of the correlation coefficient between the monitoring index and the total number of people in the target region in each historical period, and obtain the correlation degree between the monitoring index and the total number of people in the target region;

[0022] Obtain the correlation degree between each monitoring index and the total number of people in the target region, and calculate the correlation proportion L of the monitoring index and the total number of people in the target region:

[0023]

[0024] Wherein, q x The correlation degree between the xth monitoring index and the total number of people in the target region; q is the correlation proportion of the monitoring index and the total number of people in the target region; n is the total number of monitoring indexes;

[0025] Set a value k, sort the correlation proportion of each monitoring index and the total number of people in the target region according to the size, select the first k monitoring indexes, and determine that the first k monitoring indexes are related to the total number of people in the target region, and record them as related monitoring indexes. Obtain and collect each related monitoring index related to the target region to obtain related data.

[0026] Step S204: Remove the value of the monitoring index marked as the related monitoring index from the monitoring data set of the historical environment monitoring record to obtain a related monitoring data set;

[0027] The historical flow data set of the historical flow record of each historical period of the target area is obtained, the related monitoring data set of the historical environment monitoring record of each historical period of the target area is obtained, the data of the season and the holiday in each historical period is obtained, and a flow prediction model y(t) of the target area is constructed:

[0028] y(t)=g(t)+s(t)+h(t)+r(t)+ε t ,

[0029] Wherein, g(t) is a trend item of the flow prediction model y(t); s(t) is a seasonal item of the flow prediction model y(t); h(t) is a holiday item of the flow prediction model y(t); r(t) is a monitoring index regression item of the flow prediction model y(t), wherein r(t) is composed of each related monitoring index in the target area; ε t is an error item of the flow prediction model y(t);

[0030] Step S205: obtaining the objective function of the flow prediction model y(t), training the flow prediction model y(t) to minimize the objective function, obtaining each key parameter of the flow prediction model y(t), and completing the construction of the flow prediction model y(t);

[0031] The flow prediction model y(t) is used to predict the flow in each unit time in the current period, and the flow prediction data of the target area is obtained.

[0032] The above steps obtain the related monitoring index of the target area, because in actual situation, the flow of different areas or even different buildings will affect the environment factors to produce different degree of change, but the environment contains each monitoring index, therefore, from the numerous monitoring indexes, the monitoring indexes related to the change of the flow of the target area are selected, which not only greatly saves the operation time and operation resources, but also improves the accuracy of the model prediction, and when the model is constructed, the influence factors under time and space are considered, therefore, the holiday and the season are also taken as the relationship factors of the model construction, thereby greatly improving the accuracy of the model prediction.

[0033] Further, step S300 comprises:

[0034] Step S301: obtaining heat source data of the target area in the current period, the heat source data comprising total heat P sum of static heat source of the target area in unit time;

[0035] Step S302: obtaining the flow prediction amount of the target area in each unit time in the current period, and calculating the refrigeration demand Q z of the target area in the zth unit time in the current period.cool , where the specific calculation formula is:

[0036] Get the expected outdoor temperature T within the z-th unit time (z,out) , obtain the crowd flow prediction data of the target area in the current cycle, and obtain the crowd flow prediction amount M within the z-th unit time from the crowd flow prediction data z , calculate the heat load Q of the target area in the zth unit time of the current cycle z total :

[0037]

[0038] Among them, w l and w e are the sensible heat dissipation of the human body and the latent heat dissipation of the human body; P (z,sum) is the total heat of the static heat source in the target area during the zth unit time; is the initial temperature of the target area at the zth position;

[0039] Step S303: When the central air conditioner suspends cooling the target area during the zth unit time, obtain the highest temperature reached by the target area during the zth unit time.

[0040] in, is the unit time, H is the heat consumed by unit volume of air to increase unit temperature; V is the volume of the target area;

[0041] Step S304: Calculate the cooling load demand Q of the target area in the zth unit time z cool :

[0042]

[0043] Among them, T △ is the target temperature of the target area in the current cycle;

[0044] Obtain and aggregate the cooling load demand of the target area in each unit time period in the current cycle to obtain the cooling load demand data of the target area in the current cycle;

[0045] The above steps analyze the heat load of the target area. The method adopted is different from the conventional method. Instead, the heat load of the target area is analyzed based on the predicted traffic flow and the heat conditions of the static heat source in the target area. This method dynamically adjusts the heat load of the area according to the predicted traffic flow, making the acquisition of the cooling load demand of the target area in the current cycle more accurate. At the same time, it also makes the subsequent control of the central air conditioning more accurate, greatly saving energy consumption during the operation of the central air conditioning.

[0046] Furthermore, step S400 includes:

[0047] Step S401: obtaining cooling load demand data of different areas in the building, and obtaining cooling load demands of different areas in the building in each unit time within the current cycle from the cooling load demand data;

[0048] Step S402: Obtaining energy-saving data for the central air conditioner. The energy-saving data includes a range of the central air conditioner's load rate ζ, a range of the difference between the outlet air temperature and the room temperature ψ, and a range of the outlet wind speed τ when the central air conditioner has the highest energy efficiency ratio.

[0049] Based on the cooling load demand of different areas in each unit time period within the current cycle, the wind speed and outlet temperature of different areas are adjusted in order of cooling load demand, and a control strategy for the central air conditioner is generated. Based on the control strategy, the cooling in different areas is coordinated and managed;

[0050] The specific adjustment strategy for the wind speed and outlet temperature of the air outlets in different areas is: make the load rate of the central air conditioner within the current cycle within the range ζ, the difference between the outlet temperature and the room temperature in different areas within the range ψ, and the wind speed of the air outlet in different areas within the range τ.

[0051] In order to better implement the above method, an energy-saving data collaborative management system based on big data analysis is also proposed. The system includes a temperature anomaly assessment module, a crowd flow prediction module, a cooling load demand analysis module, and an energy-saving collaborative management module.

[0052] The temperature anomaly assessment module is used to assess the abnormal temperature change of the area where the central air conditioner is cooling during the central air conditioner cooling process and obtain the target area;

[0053] The crowd flow prediction module is used to predict the changes in crowd flow in the target area within the current cycle and obtain crowd flow prediction data;

[0054] The cooling load demand analysis module is used to analyze the cooling load demand status of the target area in the current cycle and obtain cooling load demand data;

[0055] The energy-saving collaborative management module is used to obtain the energy-saving data of central air-conditioning equipment, and combine it with the cooling load demand data to generate the control strategy of the central air-conditioning and coordinate the cooling in different areas.

[0056] Furthermore, the temperature anomaly assessment module includes a temperature change similarity assessment unit and a temperature anomaly assessment unit;

[0057] The temperature change similarity assessment unit is used to assess the similarity of temperature changes between the region and the building in the historical period and calculate the similarity of temperature changes between the region and the building in the historical period;

[0058] The temperature anomaly assessment unit is used to calculate the temperature change difference between the area and the building based on the temperature change similarity, and based on the temperature change difference, assess the temperature change anomaly of the area during the central air-conditioning cooling process to obtain the target area.

[0059] Furthermore, the crowd flow prediction module includes a correlation analysis unit and a crowd flow prediction unit;

[0060] A correlation analysis unit is used to obtain historical environmental monitoring records in the target area, obtain the correlation between the monitoring indicators in the historical environmental monitoring records and the flow of people in the target area, and obtain relevant data;

[0061] The crowd flow prediction unit is used to obtain the historical crowd flow change records of the target area, and combine relevant data to predict the crowd flow changes in the target area in the current cycle to obtain crowd flow prediction data.

[0062] Furthermore, the cooling load demand analysis module includes a regional heat load acquisition unit and a cooling load demand analysis unit;

[0063] The regional heat load acquisition unit is used to acquire the heat source data of the target area in the current cycle, obtain the crowd flow prediction data of the target area in the current cycle, and calculate the heat load of the target area per unit time in the current cycle;

[0064] The cooling load demand analysis unit is used to analyze the cooling load demand of the target area in the current cycle according to the heat load of the target area in the unit time of the current cycle, and obtain the cooling load demand data of the target area in the current cycle.

[0065] Furthermore, the energy-saving collaborative management module includes an energy-saving collaborative management unit;

[0066] The energy-saving collaborative management unit is used to obtain the cooling load demand data of different areas in the building, obtain the energy-saving data of the central air-conditioning equipment, and combine the cooling load demand data to generate the control strategy of the central air-conditioning to collaboratively manage the cooling of different areas in the building.

[0067] Compared with the prior art, the present application has the beneficial effects that: the present application realizes intelligent regulation and control of the central air conditioner, by taking the region responsible for refrigeration of the central air conditioner, and according to the abnormal temperature change of the region, the target region is obtained, and the correlation degree of different monitoring indexes and the passenger flow is analyzed, and the influence factors such as holidays and seasons in the historical period are combined to construct a passenger flow prediction model of the target region, and the passenger flow of the target region in the current period is predicted, so as to analyze the cold load demand condition of the target region in the current period, and according to the cold load demand data of different regions in the building and the equipment energy saving data of the central air conditioner, the control strategy of the central air conditioner is generated, and the central air conditioner is controlled based on the control strategy, not only the cold load demand of each region is met, but also the energy consumption of the central air conditioner is greatly reduced, and the energy consumption is significantly saved. BRIEF DESCRIPTION OF DRAWINGS

[0068] Fig. 1 is the method flow chart of the energy saving data collaborative management method based on big data analysis of the present application;

[0069] Fig. 2 is the module schematic diagram of the energy saving data collaborative management system based on big data analysis of the present application. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0071] Embodiment: as shown in the present application provides a technical solution, the energy saving data collaborative management method based on big data analysis, the method comprises: Figs. 1-2

[0072] Step S100: obtaining the region in which the central air conditioner performs refrigeration in the building, obtaining the historical temperature change record in the region, evaluating the abnormal temperature change condition of the region in the refrigeration process of the central air conditioner, and obtaining the target region;

[0073] Among them, step S100 includes:

[0074] Step S101: obtaining each region responsible for refrigeration of the central air conditioner in the building, obtaining each historical temperature change record of the region in which the central air conditioner performs refrigeration, setting a unit time period ​The average temperature of the area per unit time period is obtained from the historical temperature change records at each unit time period, and the data is aggregated to obtain a temperature dataset of the historical temperature change records.

[0075] Step S102: Obtain the historical period to which the historical temperature change records belong, and calculate the temperature trend slope β of the region within the historical period based on the temperature data set;

[0076] For example, the specific formula for calculating the temperature trend slope β of a region in a historical period is:

[0077]

[0078] Among them, t i is the characteristic duration of the i-th element in the temperature dataset, where t △ is the average characteristic duration of each element in the temperature data set; T i is the average temperature of the i-th element in the temperature data set; T △ is the average temperature of each element in the temperature data set;

[0079] Step S103: Obtain the mean μ and variance δ of the temperature of each element in the temperature data set, construct the temperature vector A = [β, μ, δ] of the area in the historical period, and obtain the mean μ of the temperature of each area in the building in the historical period. △ , the mean of the variance δ △ and the mean β of the temperature trend slope △ , construct the temperature vector A of the building in the historical period △ =[β △ 、μ △ , δ △ ], calculate the similarity B of temperature changes between the area and the building in the historical period:

[0080]

[0081] Step S104: Obtain the similarity of temperature changes between the region and the building in each historical period, and calculate the temperature change difference value C between the region and the building:

[0082]

[0083] Where j is the total number of historical periods; B i is the similarity of temperature changes between the region and the building in the i-th historical period;

[0084] Get the mean value C of the temperature difference between each area and the building △ , the temperature change difference C and the mean C △ The absolute value of the difference divided by the mean C△ , and record it as the value α of the abnormal degree of temperature change in the area. When α is greater than the preset threshold, it is determined that the temperature change in the area during the central air-conditioning cooling process is abnormal, and the area is recorded as the target area;

[0085] Step S200: Obtain historical records of changes in passenger flow and historical environmental monitoring records for the target area, analyze the correlation between monitoring indicators in the historical environmental monitoring records and passenger flow within the target area to obtain relevant data, obtain environmental monitoring data for the target area in the current cycle, and combine the relevant data to predict passenger flow changes within the target area in the current cycle to obtain passenger flow prediction data;

[0086] Wherein, step S200 includes:

[0087] Step S201: Obtain historical pedestrian flow records and historical environmental monitoring records for each historical period in the target area, pre-process the data in the historical pedestrian flow records and historical environmental monitoring records, obtain the total number of pedestrian flows in the target area from the historical pedestrian flow records at each unit time, and aggregate them to obtain a pedestrian flow dataset;

[0088] For example, preprocessing includes outlier removal and normalization or standardization;

[0089] Step S202: Obtain historical environmental monitoring records in the target area, and obtain the values ​​of various monitoring indicators from the historical environmental monitoring records at unit time intervals to obtain a monitoring data set;

[0090] For example, various monitoring indicators include ambient temperature, humidity, and rainfall;

[0091] Step S203: Based on the pedestrian flow data set and the monitoring data set, the correlation coefficient between each monitoring indicator in the historical period to which the historical pedestrian flow records belong and the total number of pedestrian flows in the target area is calculated, and the average value of the correlation coefficient between the monitoring indicator in each historical period and the total number of pedestrian flows in the target area is obtained to obtain the degree of correlation between the monitoring indicator and the total number of pedestrian flows in the target area;

[0092] For example, the specific formula for calculating the correlation coefficient P between the monitoring indicator in the historical period to which the historical flow record belongs and the total flow of people in the target area can be:

[0093]

[0094] Among them, x i is the value of the monitoring indicator in the i-th element of the monitoring data set of historical environmental monitoring records; △ is the average value of the monitoring indicators in each element in the monitoring data set; iThe total number of people flow of the i-th element in the crowd flow dataset of historical crowd flow records; y △ The total number of people flows for each element in the people flow dataset; n is the total number of elements in the people flow dataset;

[0095] Obtain the correlation between each monitoring indicator and the total number of people in the target area, and calculate the correlation ratio L between the monitoring indicator and the total number of people in the target area:

[0096]

[0097] Among them, q x is the correlation between the xth monitoring indicator and the total number of people flowing into the target area; q is the correlation ratio between the monitoring indicator and the total number of people flowing into the target area; n is the total number of monitoring indicators;

[0098] Set a value k, sort the top k monitoring indicators by their relative proportion to the total number of people in the target area, and mark them. Determine whether the top k monitoring indicators are related to the total number of people in the target area and record them as relevant monitoring indicators. Obtain and aggregate all relevant monitoring indicators related to the target area to obtain relevant data.

[0099] Step S204: removing the values ​​of the monitoring indicators marked as relevant monitoring indicators from the monitoring data set of the historical environmental monitoring records to obtain the relevant monitoring data set;

[0100] Obtain the crowd flow data set of historical crowd flow records in each historical period of the target area, obtain the relevant monitoring data set of historical environmental monitoring records in each historical period of the target area, obtain the data of seasons and holidays in each historical period, and construct the crowd flow prediction model y(t) of the target area:

[0101] y(t)=g(t)+s(t)+h(t)+r(t)+ε t ,

[0102] Among them, g(t) is the trend term of the human flow prediction model y(t); s(t) is the seasonal term of the human flow prediction model y(t); h(t) is the holiday term of the human flow prediction model y(t); r(t) is the monitoring index regression term of the human flow prediction model y(t), where r(t) is composed of various related monitoring indicators in the target area; ε t is the error term of the pedestrian flow prediction model y(t);

[0103] For example, g(t), h(t), r(t), and ε t The specific formula is:

[0104] g(t) is specifically:

[0105]

[0106] Among them, n is the total number of elements in the crowd flow dataset in the target area, δ i is the change in the growth rate of the total number of people in the i-th element of the people flow data set; γ i =-δ i ·t i ; k is the basic increase rate of the total number of people; m is the initial value of the total number of people;

[0107] a i (t) is the change point indicator function. The specific formula is:

[0108]

[0109] The specific formula of s(t) is:

[0110]

[0111] Where P = {daily, weekly, yearly}, and Fourier series is used for each item in P, s p (t):

[0112]

[0113] Among them, a p,n and b p,n are the coefficients of the Fourier series, which together determine the specific form of seasonal fluctuations. When p is weekly, N weekly =3;

[0114] The specific formula of h(t) is:

[0115]

[0116] Among them, κ i I is the holiday impact intensity parameter; Di is the preset indicator function; L is the total number of holiday events;

[0117] The specific formula of r(t) is:

[0118]

[0119] Among them, β k is the kth related monitoring indicator of the target area; x k (t) is the normalized value of the kth relevant monitoring indicator in the target area;

[0120] ε t :

[0121]

[0122] Step S205: Obtain the objective function of the people flow prediction model y(t), train the people flow prediction model y(t) to minimize the objective function, obtain the key parameters of the people flow prediction model y(t), and complete the construction of the people flow prediction model y(t);

[0123] For example, the specific formula of the objective function L of the people flow prediction model y(t) is:

[0124] L = [(y t -y′ t ) 2 ],

[0125] y t is the true value at the t time, and y′ t is the predicted value at the t time;

[0126] And the key parameters of the people flow prediction model y(t) converge, specifically:

[0127]

[0128] Θ = {k, δ, a n , b n , β, κ},

[0129] Wherein, k, δ, a n , b n , β and κ are the key parameters of the people flow prediction model y(t);

[0130] Using the people flow prediction model y(t) to predict the people flow in each unit time in the current period, the people flow prediction data of the target area is obtained;

[0131] Step S300: Obtain the heat source data of the target area in the current period, and combine the people flow prediction data to analyze the cold load demand condition of the target area in the current period, and obtain the cold load demand data;

[0132] Wherein, step S300 includes:

[0133] Step S301: Obtain the heat source data of the target area in the current period, and the heat source data includes the total heat P sum of the static heat source in the target area in the unit time;

[0134] Wherein, wherein, the static heat source includes the fixed heat brought by lighting and equipment operation;

[0135] Step S302: Obtain the predicted flow of people in the target area in each unit time in the current cycle, and calculate the cooling demand Q of the target area in the zth unit time in the current cycle. z cool , where the specific calculation formula is:

[0136] Get the expected outdoor temperature T within the z-th unit time (z,out) , obtain the crowd flow prediction data of the target area in the current cycle, and obtain the crowd flow prediction amount M within the z-th unit time from the crowd flow prediction data z , calculate the heat load Q of the target area in the zth unit time of the current cycle z total :

[0137]

[0138] Among them, w l and w e are the sensible heat dissipation of the human body and the latent heat dissipation of the human body; P (z,sum) is the total heat of the static heat source in the target area during the zth unit time; is the initial temperature of the target area at the zth position;

[0139] For example, w l and w e All can be queried through the preset tables in the platform;

[0140] For example, the initial temperature is usually the target temperature set by the central air conditioner for the target area;

[0141] Step S303: When the central air conditioner suspends cooling the target area during the zth unit time, obtain the highest temperature reached by the target area during the zth unit time.

[0142] in, is the unit time, H is the heat consumed by unit volume of air to increase unit temperature; V is the volume of the target area;

[0143] Step S304: Calculate the cooling load demand Q of the target area in the zth unit time z cool :

[0144]

[0145] Among them, T △ is the target temperature of the target area in the current cycle;

[0146] Obtain and aggregate the cooling load demand of the target area in each unit time period in the current cycle to obtain the cooling load demand data of the target area in the current cycle;

[0147] Step S400: Obtain cooling load demand data for different areas of the building during the current period, obtain energy-saving data for central air conditioners, and generate a control strategy for the central air conditioners based on the cooling load demand data. Based on the control strategy, collaborative management of cooling in different areas is performed.

[0148] Wherein, step S400 includes:

[0149] Step S401: obtaining cooling load demand data of different areas in the building, and obtaining cooling load demands of different areas in the building in each unit time within the current cycle from the cooling load demand data;

[0150] For example, when a certain area is not a target area, the impact of human heat does not need to be calculated. The heat load Q of a certain area in the fth unit time in the current cycle is z total :

[0151]

[0152] P (f,sum) is the total heat of the static heat source in a certain area during the fth unit time; is the initial temperature of the target area at the fth position; T (f,out) is the expected outdoor temperature of a certain area in the fth unit time;

[0153] Step S402: Obtaining energy-saving data for the central air conditioner. The energy-saving data includes a range of the central air conditioner's load rate ζ, a range of the difference between the outlet air temperature and the room temperature ψ, and a range of the outlet wind speed τ when the central air conditioner has the highest energy efficiency ratio.

[0154] Based on the cooling load demand of different areas in each unit time period within the current cycle, the wind speed and outlet temperature of different areas are adjusted in order of cooling load demand, and a control strategy for the central air conditioner is generated. Based on the control strategy, the cooling in different areas is coordinated and managed;

[0155] The specific adjustment strategy for the wind speed and outlet temperature of the air outlets in different areas is as follows: the load rate of the central air conditioner in the current cycle is within the range ζ, the difference between the outlet temperature and the room temperature in different areas is within the range ψ, and the wind speed of the air outlets in different areas is within the range τ;

[0156] In order to better implement the above method, an energy-saving data collaborative management system based on big data analysis is also proposed. The system includes a temperature anomaly assessment module, a crowd flow prediction module, a cooling load demand analysis module, and an energy-saving collaborative management module.

[0157] The temperature anomaly assessment module is used to assess the abnormal temperature change of the area where the central air conditioner is cooling during the central air conditioner cooling process to obtain the target area;

[0158] The crowd flow prediction module is used to predict the changes in crowd flow in the target area within the current cycle and obtain crowd flow prediction data;

[0159] The cooling load demand analysis module is used to analyze the cooling load demand status of the target area in the current cycle and obtain cooling load demand data;

[0160] The energy-saving collaborative management module is used to obtain energy-saving data of central air-conditioning equipment and, combined with cooling load demand data, generate control strategies for central air-conditioning to collaboratively manage cooling in different areas.

[0161] Among them, the temperature anomaly assessment module includes a temperature change similarity assessment unit and a temperature anomaly assessment unit;

[0162] The temperature change similarity assessment unit is used to assess the similarity of temperature changes between the region and the building in the historical period and calculate the similarity of temperature changes between the region and the building in the historical period;

[0163] The temperature anomaly assessment unit is used to calculate the temperature change difference between the area and the building based on the temperature change similarity, and based on the temperature change difference, assess the temperature change anomaly of the area during the central air conditioning cooling process to obtain the target area;

[0164] Among them, the crowd flow prediction module includes a correlation analysis unit and a crowd flow prediction unit;

[0165] A correlation analysis unit is used to obtain historical environmental monitoring records in the target area, obtain the correlation between the monitoring indicators in the historical environmental monitoring records and the flow of people in the target area, and obtain relevant data;

[0166] The crowd flow prediction unit is used to obtain the historical crowd flow change records of the target area, and combine relevant data to predict the crowd flow changes in the target area in the current cycle to obtain crowd flow prediction data;

[0167] The cooling load demand analysis module includes a regional heat load acquisition unit and a cooling load demand analysis unit;

[0168] The regional heat load acquisition unit is used to acquire the heat source data of the target area in the current cycle, obtain the crowd flow prediction data of the target area in the current cycle, and calculate the heat load of the target area per unit time in the current cycle;

[0169] a cooling load demand analysis unit, configured to analyze the cooling load demand of the target area in the current cycle according to the heat load of the target area in the unit time of the current cycle, and obtain cooling load demand data of the target area in the current cycle;

[0170] Among them, the energy-saving collaborative management module includes an energy-saving collaborative management unit;

[0171] The energy-saving collaborative management unit is used to obtain the cooling load demand data of different areas in the building, obtain the energy-saving data of the central air-conditioning equipment, and combine the cooling load demand data to generate the control strategy of the central air-conditioning to collaboratively manage the cooling of different areas in the building.

[0172] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. The energy-saving data collaborative management method based on big data analysis is characterized by: The method comprises: Step S100: obtaining an area in a building where a central air conditioner is cooling, obtaining a historical temperature change record in the area, evaluating abnormal temperature changes in the area during the central air conditioner cooling process, and obtaining a target area; Step S200: Obtain historical records of changes in passenger flow and historical environmental monitoring records for the target area, analyze the correlation between monitoring indicators in the historical environmental monitoring records and passenger flow within the target area to obtain relevant data, obtain environmental monitoring data for the target area in the current cycle, and, in combination with the relevant data, predict passenger flow changes within the target area in the current cycle to obtain passenger flow prediction data; Step S300: obtaining heat source data of the target area in the current cycle, and analyzing the cooling load demand status of the target area in the current cycle in combination with the crowd flow prediction data to obtain cooling load demand data; Step S400: obtaining cooling load demand data of different areas in the building in the current cycle, obtaining energy-saving data of the central air conditioner, and generating a control strategy for the central air conditioner in combination with the cooling load demand data. Based on the control strategy, collaboratively manage the cooling in the different areas.

2. The energy-saving data collaborative management method based on big data analysis according to claim 1 is characterized in that: The step S100 includes: Step S101: Acquire each area of ​​the building where the central air conditioner is responsible for cooling, obtain each historical temperature change record of the area where the central air conditioner is cooling, and set the unit time Obtaining an average temperature within the area per unit time from the historical temperature change records at each unit time, and aggregating the data to obtain a temperature dataset of the historical temperature change records; Step S102: obtaining the historical period to which the historical temperature change record belongs, and calculating the temperature trend slope β of the region in the historical period based on the temperature data set; Step S103: Obtain the mean μ and variance δ of the temperature of each element in the temperature data set, construct the temperature vector A = [β, μ, δ] of the area in the historical period, and obtain the mean μ of the temperature of each area in the building in the historical period. △ , the mean of the variance δ △ and the mean β of the temperature trend slope △ , construct the temperature vector A of the building during the historical period △ =[β △ 、μ △ , δ △ ], calculate the temperature variation similarity B between the area and the building in the historical period: Step S104: Obtain the similarity of temperature changes between the region and the building in each historical period, and calculate the temperature change difference value C between the region and the building: Wherein, j is the total number of each historical period; B i is the similarity of temperature changes between the area and the building in the i-th historical period; Get the average value C of the temperature difference between each area and the building △ , the temperature change difference C and the mean C △ The absolute value of the difference divided by the mean C △ , and recorded as the value α of the abnormal degree of temperature change in the area. When the α is greater than the preset threshold, it is determined that there is an abnormality in the temperature change of the area during the central air-conditioning cooling process, and the area is recorded as the target area.

3. The energy-saving data collaborative management method based on big data analysis according to claim 1 is characterized in that: The step S200 includes: Step S201: Obtain historical crowd flow records and historical environmental monitoring records for each historical period of the target area, pre-process the data in the historical crowd flow records and historical environmental monitoring records, obtain the total number of crowd flows in the target area from the historical crowd flow records at each unit time, and aggregate them to obtain a crowd flow dataset; Step S202: Obtain historical environmental monitoring records in the target area, and obtain the values ​​of various monitoring indicators from the historical environmental monitoring records at unit time intervals to obtain a monitoring data set; Step S203: Based on the crowd flow data set and the monitoring data set, calculating the correlation coefficient between each monitoring indicator in the historical period to which the historical crowd flow record belongs and the total number of people flowing in the target area, obtaining the average value of the correlation coefficient between the monitoring indicator in each historical period and the total number of people flowing in the target area, and obtaining the degree of correlation between the monitoring indicator and the total number of people flowing in the target area; Obtain the correlation between each monitoring indicator and the total number of people flow in the target area, and calculate the correlation ratio L between the monitoring indicator and the total number of people flow in the target area: Among them, q x is the correlation between the xth monitoring indicator and the total number of people flowing into the target area; q is the correlation ratio between the monitoring indicator and the total number of people flowing into the target area; n is the total number of monitoring indicators; Set a value k, sort the monitoring indicators by size based on their correlation with the total number of people in the target area, select the top k monitoring indicators and mark them, determine whether the top k monitoring indicators are correlated with the total number of people in the target area, and record them as relevant monitoring indicators. Obtain and aggregate all relevant monitoring indicators related to the target area to obtain relevant data; Step S204: removing the values ​​of the monitoring indicators marked as relevant monitoring indicators from the monitoring data set of the historical environmental monitoring records to obtain a relevant monitoring data set; Obtain a crowd flow data set of historical crowd flow records in each historical period of the target area, obtain relevant monitoring data sets of historical environmental monitoring records in each historical period of the target area, obtain data on seasons and holidays in each historical period, and construct a crowd flow prediction model y(t) for the target area: y(t)=g(t)+s(t)+h(t)+r(t)+ε t , Among them, g(t) is the trend term of the crowd flow prediction model y(t); s(t) is the seasonal term of the crowd flow prediction model y(t); h(t) is the holiday term of the crowd flow prediction model y(t); r(t) is the monitoring index regression term of the crowd flow prediction model y(t), wherein r(t) is composed of various relevant monitoring indicators in the target area; ε t is the error term of the crowd flow prediction model y(t); Step S205: Obtaining the objective function of the crowd flow prediction model y(t), training the crowd flow prediction model y(t) to minimize the objective function, obtaining various key parameters of the crowd flow prediction model y(t), and completing the construction of the crowd flow prediction model y(t); The crowd flow prediction model y(t) is used to predict the crowd flow in each unit time in the current cycle to obtain crowd flow prediction data for the target area.

4. The energy-saving data collaborative management method based on big data analysis according to claim 3 is characterized in that: The step S300 includes: Step S301: Obtain the heat source data of the target area in the current cycle, the heat source data including the total heat P of the static heat source of the target area in a unit time. sum ; Step S302: Obtain the predicted flow of people in the target area in each unit time in the current cycle, and calculate the cooling demand Q of the target area in the zth unit time in the current cycle. z cool , where the specific calculation formula is: Get the expected outdoor temperature T within the zth unit time (z,out) , obtain the crowd flow prediction data of the target area in the current cycle, and obtain the crowd flow prediction amount M in the z-th unit time from the crowd flow prediction data z , calculate the heat load Q of the target area in the zth unit time of the current cycle z total : Among them, w l and w e are the sensible heat dissipation of the human body and the latent heat dissipation of the human body; P (z,sum) is the total heat of the static heat source in the target area during the z-th unit time; is the initial temperature of the target area at the zth position; Step S303: When the central air conditioner suspends cooling the target area during the zth unit time, obtain the maximum temperature reached by the target area during the zth unit time. in, is the unit time, H is the heat consumed by the unit volume of air to increase the unit temperature; V is the volume of the target area; Step S304: Calculate the cooling load demand Q of the target area in the zth unit time z cool : Among them, T △ is the target temperature of the target area in the current cycle; The cooling load demand of the target area in each unit time in the current cycle is obtained and aggregated to obtain cooling load demand data of the target area in the current cycle.

5. The energy-saving data collaborative management method based on big data analysis according to claim 4 is characterized in that: The step S400 includes: Step S401: obtaining cooling load demand data of different areas in the building, and obtaining cooling load demands of different areas in the building in each unit time within a current cycle from the cooling load demand data; Step S402: Obtaining energy-saving data of the central air conditioner, the energy-saving data including a range of a load rate ζ of the central air conditioner, a range of a difference between the outlet air temperature and the room temperature ψ, and a range of a wind speed at the air outlet τ when the central air conditioner has the highest energy efficiency ratio; According to the cooling load demands of the different zones in each unit time period during the current cycle, the wind speed and outlet temperature of the air outlets of the different zones are adjusted in order of the cooling load demands, a control strategy for the central air conditioner is generated, and based on the control strategy, the cooling in the different zones is collaboratively managed; The specific adjustment strategy for the wind speed and outlet temperature of the air outlets in the different areas is: to make the load rate of the central air conditioner in the current cycle within the range ζ, the difference between the outlet temperature and the room temperature in the air outlets in the different areas within the range ψ, and the wind speed of the air outlets in the different areas within the range τ.

6. An energy-saving data collaborative management system based on big data analysis, used to execute the energy-saving data collaborative management method based on big data analysis according to any one of claims 1 to 5, characterized in that: The system includes a temperature anomaly assessment module, a crowd flow prediction module, a cooling load demand analysis module, and an energy-saving collaborative management module; The temperature anomaly assessment module is used to assess the abnormal temperature change of the area where the central air conditioner is cooling during the central air conditioner cooling process to obtain the target area; The crowd flow prediction module is used to predict the change of crowd flow in the target area in the current cycle and obtain crowd flow prediction data; The cooling load demand analysis module is used to analyze the cooling load demand status of the target area in the current cycle to obtain cooling load demand data; The energy-saving collaborative management module is used to obtain the energy-saving data of the central air-conditioning equipment, and generate a control strategy for the central air-conditioning in combination with the cooling load demand data, so as to collaboratively manage the cooling in the different areas.

7. The energy-saving data collaborative management system based on big data analysis according to claim 6 is characterized in that: The temperature anomaly assessment module includes a temperature change similarity assessment unit and a temperature anomaly assessment unit; The temperature change similarity evaluation unit is used to evaluate the similarity of temperature changes between the region and the building in the historical period, and calculate the similarity of temperature changes between the region and the building in the historical period; The temperature anomaly assessment unit is used to calculate the temperature change difference value between the area and the building based on the temperature change similarity, and to assess the temperature change anomaly condition of the area during the central air-conditioning cooling process based on the temperature change difference value to obtain the target area.

8. The energy-saving data collaborative management system based on big data analysis according to claim 6 is characterized in that: The crowd flow prediction module includes a correlation analysis unit and a crowd flow prediction unit; The correlation analysis unit is used to obtain historical environmental monitoring records in the target area, obtain the correlation between the monitoring indicators in the historical environmental monitoring records and the flow of people in the target area, and obtain relevant data; The crowd flow prediction unit is used to obtain the historical crowd flow change records of the target area, and combine the relevant data to predict the crowd flow change situation in the target area in the current cycle to obtain crowd flow prediction data.

9. The energy-saving data collaborative management system based on big data analysis according to claim 6 is characterized in that: The cooling load demand analysis module includes a regional heat load acquisition unit and a cooling load demand analysis unit; The regional heat load acquisition unit is used to acquire heat source data of the target area in the current cycle, acquire crowd flow prediction data of the target area in the current cycle, and calculate the heat load of the target area per unit time in the current cycle; The cooling load demand analysis unit is configured to analyze the cooling load demand of the target area in the current cycle according to the heat load of the target area in the unit time of the current cycle, and obtain cooling load demand data of the target area in the current cycle.

10. The energy-saving data collaborative management system based on big data analysis according to claim 6, characterized in that: The energy-saving collaborative management module includes an energy-saving collaborative management unit; The energy-saving collaborative management unit is used to obtain cooling load demand data of different areas in the building, obtain energy-saving data of the central air-conditioning equipment, and generate a control strategy for the central air-conditioning in combination with the cooling load demand data to collaboratively manage the cooling of different areas in the building.

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