Intelligent adaptive air conditioner refrigerating machine energy-saving control method

Abstract

The invention relates to the technical field of air conditioner energy-saving control, and discloses an intelligent adaptive air conditioner refrigerator energy-saving control method which comprises the steps of forming an estimated temperature set, obtaining habit data of a target user for target equipment, generating an equipment habit model and analysis data or reference data, and adjusting the refrigeration storage amount of the target equipment. According to the intelligent adaptive energy-saving control method for the air conditioner refrigerating machine, after refrigerating and storing are conducted in the electricity utilization valley period, cold air is output through stored refrigerating energy in the electricity utilization peak period, and a user's use habit model for an air conditioner is formed; the refrigeration storage amount of the air conditioner in the electricity consumption valley period and the refrigeration energy source in the electricity consumption peak period are adjusted, the regional electricity consumption burden in the electricity consumption peak period is relieved, meanwhile, the use cost of the air conditioner by a user is reduced, the use experience of the user is improved, and the electric power condition of a regional power grid in the electricity consumption peak period and the electricity consumption valley period is balanced.

Description

Technical Field

[0001] The present invention relates to the technical field of air-conditioning energy-saving control, and in particular to an intelligent adaptive air-conditioning refrigerator energy-saving control method. Background Art

[0002] With the increase of global energy demand and the increasingly serious environmental problems, energy-saving technology has become the focus of attention of all walks of life. In particular, air-conditioning systems account for a considerable proportion of building energy consumption, especially in the hot summer season, the energy consumption of refrigeration machines is particularly prominent. Therefore, it is of great significance to develop an intelligent adaptive air-conditioning refrigeration machine energy-saving control method. The intelligent adaptive air-conditioning refrigeration machine energy-saving control method came into being under such a background. It can not only significantly reduce energy consumption and reduce environmental impact, but also improve user comfort and meet the dual needs of the market and policies. By integrating the Internet of Things, artificial intelligence and advanced sensor technology, this energy-saving control method will lead the air-conditioning industry into a new intelligent era.

[0003] The existing intelligent adaptive energy-saving control method for air-conditioning refrigeration machines cannot, according to the peak and valley periods of electricity consumption in the user's area, cool and store the energy during the valley period and then use the stored refrigeration energy to output cold air during the peak period. It cannot form a user's air-conditioning usage habit model based on the user's air-conditioning usage habits, the ambient temperature and the air-conditioning temperature adjusted by the user's habits. It cannot adjust the cooling time and storage amount of the air-conditioning during the valley period based on the user's air-conditioning usage habit model and the total amount of refrigeration energy that the air-conditioning can store. It cannot adjust the source of refrigeration performed by the air-conditioning during the peak period based on the user's air-conditioning usage habit model and the storage amount of the air-conditioning refrigeration energy. This causes the user to consume too much electric energy when using the air-conditioner, increases the regional electricity burden, and increases the user's air-conditioning usage cost. Its practicality has certain limitations. Summary of the invention

[0004] The present invention provides an intelligent adaptive air-conditioning refrigerator energy-saving control method for facilitating solving the problems mentioned in the background technology.

[0005] The present invention provides the following technical solution: an intelligent adaptive air-conditioning refrigeration machine energy-saving control method, comprising:

[0006] Get the current date;

[0007] Calculate the estimated date, estimated date = current date + 1;

[0008] Get all estimated temperatures within the estimated date to form an estimated temperature set;

[0009] Get historical temperature data;

[0010] The historical temperature data includes historical ambient temperature data and historical equipment temperature data;

[0011] Get historical electricity usage time data;

[0012] The historical electricity consumption time data includes peak electricity consumption data and valley electricity consumption data;

[0013] Obtain target user’s habit data on target devices;

[0014] If the target user has habit data, a device habit model is generated based on historical temperature data, historical power usage time data, and habit data;

[0015] Based on the estimated temperature set and equipment habit model, the energy storage analysis strategy is used to generate analysis data;

[0016] If there is no habit data for the target user, the reference data collection strategy is executed to form reference data;

[0017] According to the analysis data or reference data, energy storage data is formed through energy storage reference analysis strategy;

[0018] According to the energy storage data, adjust the refrigeration storage capacity of the target equipment;

[0019] According to the energy storage data, the estimated temperature set and the equipment habit model, the energy regulation data is formed through the energy regulation strategy;

[0020] The energy usage source of the target device is regulated according to the energy regulation data.

[0021] As an optional solution of the intelligent adaptive air-conditioning refrigeration machine energy-saving control method of the present invention, wherein: the acquisition of the habit data of the target user for the target device is specifically:

[0022] Get the current time;

[0023] Set the cycle duration;

[0024] Calculate the collection start time, collection start time = current time - cycle length;

[0025] The period between the collection start time and the current time is defined as the collection period;

[0026] Set the daily collection duration;

[0027] The collection start time is taken as the initial time, and the collection duration is calculated every other day to form a daily collection time.

[0028] Obtain all daily collection times within the collection period to form a daily time set;

[0029] The time period formed between every two adjacent elements in the daily time set is defined as the collection day cycle;

[0030] Obtain all the collected daily cycles to form a daily cycle set;

[0031] Set the collection duration;

[0032] The collection start time is taken as the initial time, and the collection duration is calculated for each interval to form a collection time.

[0033] Respectively obtain all the hourly collection times in each collection daily cycle in the daily cycle set to form an hourly collection time set;

[0034] Each hourly collection time set is matched with each element in the daily cycle set one by one to form a collection cycle set;

[0035] Obtain the historical ambient temperature data corresponding to each element in the collection period set, and define it as the historical ambient time temperature;

[0036] Each historical ambient temperature is matched with each element in the collection period set one by one to form a historical ambient temperature set;

[0037] Obtain the historical device temperature data corresponding to each element in the collection period set, and define it as the historical device time temperature;

[0038] Each historical device temperature is matched with each element in the collection period set one by one to form a historical set temperature set;

[0039] Get the estimated temperature set;

[0040] If the historical ambient temperature set contains all elements in the estimated temperature set, it is determined that the target user has habit data;

[0041] If the historical ambient temperature set does not contain all the elements in the estimated temperature set, it is determined that the target user does not have habit data.

[0042] As an optional solution of the intelligent adaptive air-conditioning refrigeration machine energy-saving control method of the present invention, wherein: the generating device habit model is specifically:

[0043] Get the collection cycle set;

[0044] Get the historical ambient temperature collection and historical set temperature collection;

[0045] Collect device usage data corresponding to each element in the cycle set;

[0046] Obtain peak power consumption data and off-peak power consumption data;

[0047] Extract the element containing the device usage data and define it as the usage element;

[0048] If the usage element is within the peak power usage data, it is defined as the peak usage element;

[0049] If the usage element is within the valley power consumption data, it is defined as the valley usage element;

[0050] Get all peak usage elements to form a peak usage set;

[0051] Extract the peak value and use the historical ambient temperature data corresponding to each element in the historical ambient temperature set to form a peak ambient temperature set;

[0052] Extract the historical equipment temperature data corresponding to each element in the peak usage set in the historical set temperature set to form a peak set temperature set;

[0053] Integrate the peak ambient temperature set and the peak set temperature set to form an equipment habit model.

[0054] As an optional solution of the intelligent adaptive air conditioning refrigeration machine energy-saving control method of the present invention, the energy storage analysis strategy is specifically:

[0055] Get the estimated temperature set;

[0056] Extract the element with the smallest value and the largest value in the estimated temperature set, and define them as the low temperature estimated element and the high temperature estimated element respectively;

[0057] Get device habit model;

[0058] Extract all historical ambient temperature data corresponding to each collection day cycle in the historical ambient temperature set to form historical daily ambient temperature sets respectively;

[0059] For each historical daily ring temperature set, extract the element with the smallest value and the element with the largest value in the set, and define them as the low temperature historical element and the high temperature historical element respectively;

[0060] Extract the historical daily ambient temperature set where low temperature historical elements = low temperature estimated elements, high temperature historical elements = high temperature estimated elements, and all elements in the estimated temperature set, and define them as the historical reference set;

[0061] The data collection date for each historical reference set was extracted and designated as the extraction date;

[0062] Extract all extraction dates within the collection period set to form a sampling time set;

[0063] Identify each element in the sampling time set as a target element in turn, and execute steps A1-A5;

[0064] Get the estimated energy consumption value of each element in the sampling time set;

[0065] Obtain the target temperature difference of each element in the sampling time set to form a temperature difference set;

[0066] Calculate the reference energy consumption value, which is the sum of the estimated energy consumption values ​​of all elements in the sampling time set.

[0067] As an optional solution of the intelligent adaptive air conditioning refrigeration machine energy-saving control method of the present invention, the execution steps A1-A5 are specifically as follows:

[0068] A1. Extract all peak usage elements corresponding to the target element in the peak usage set and define them as the peak sampling time;

[0069] A2. Extract all historical ambient temperature data corresponding to each peak sampling time in the peak ambient temperature set to form a peak sampling ambient temperature set;

[0070] A3. Extract all historical equipment temperature data corresponding to each peak sampling time in the peak temperature set set to form a peak sampling temperature set;

[0071] A4. If all elements of the peak sampling ambient temperature set are the same, execute B1-B6;

[0072] B1. Obtain the number of peak sampling times, which is defined as the peak sampling number;

[0073] B2. Randomly extract the historical ambient temperature data corresponding to an element in the peak sampling ambient temperature set and set it as the target ambient temperature data;

[0074] B3. Randomly extract the historical equipment temperature data corresponding to an element in the peak sampling temperature setting set and set it as the target temperature setting data;

[0075] B4. Obtain the energy consumption data of the target equipment under the target ambient temperature data and the target set temperature data, and define it as the target energy consumption data;

[0076] B5. Calculate the estimated energy consumption value, estimated energy consumption value = peak sampling number × 60 × target energy consumption data;

[0077] B6. Calculate the target temperature difference, target temperature difference = target ambient temperature data - target set temperature data;

[0078] A5. If the elements in the peak sampling ambient temperature set are different, execute C1-C7;

[0079] C1. Obtain the number of each element in the peak sampling ambient temperature set respectively, and define it as the number of elements;

[0080] C2, identifying each element in the peak sampling ambient temperature set as the target ambient temperature data in turn;

[0081] C3, identifying each element in the peak sampling temperature setting set as the target temperature setting data in turn;

[0082] C4. Obtain target energy consumption data of the target device under target ambient temperature data and target set temperature data;

[0083] C5. Calculate the element energy consumption value, element energy consumption value = number of elements corresponding to the target ambient temperature data × target energy consumption data × 60;

[0084] C6. Calculate the estimated energy consumption value, which is the sum of the energy consumption values ​​of all elements in the peak sampling ambient temperature set;

[0085] C7. Calculate the target temperature difference, target temperature difference = target ambient temperature data - target set temperature data.

[0086] As an optional solution of the intelligent adaptive air-conditioning refrigeration machine energy-saving control method of the present invention, the reference data collection strategy is specifically:

[0087] Get the estimated temperature set;

[0088] Get the peak ambient temperature collection;

[0089] Extract all historical ambient temperature data corresponding to each collection day cycle in the peak ambient temperature set to form a historical daily ambient temperature set;

[0090] Elements that exist in both the historical daily annular temperature set and the estimated temperature set are defined as overlapping elements;

[0091] Get the number of overlapping elements in each historical daily ambient temperature set, and define it as the overlapping number;

[0092] Extract the historical daily annular temperature set with the largest number of overlaps and set it as the reference daily annular temperature set;

[0093] Elements that only exist in the estimated temperature set but not in the reference daily annular temperature set are defined as independent elements;

[0094] Elements that exist in both the reference daily annular temperature set and the estimated temperature set are defined as reference elements;

[0095] Perform steps D1-D8 for each reference element;

[0096] Calculate a first estimated energy consumption value, where the first estimated energy consumption value = the sum of the energy consumptions of all reference elements in the estimated temperature set;

[0097] Obtain reference energy consumption data for each independent element;

[0098] Calculate the second estimated energy consumption value, where the second estimated energy consumption value = the sum of the independent estimated energy consumptions of all independent elements in the estimated temperature set;

[0099] Obtain the target temperature difference corresponding to each element in the estimated temperature set to form a temperature difference set;

[0100] Calculate the reference energy consumption value, the reference energy consumption value = the first estimated energy consumption value + the second estimated energy consumption value;

[0101] The steps D1-D8 are specifically:

[0102] D1. Extract all reference daily ambient temperature sets containing reference elements;

[0103] D2. Extract the data collection date corresponding to the reference element in the reference daily ambient temperature set and set it as the reference date;

[0104] D3. Execute E1-E6 for each reference date;

[0105] E1. Extract all peak usage elements in the peak usage set on the reference date and define them as reference usage elements;

[0106] E2. Extract the historical equipment temperature data corresponding to each reference usage element in the peak temperature setting set and set it as the reference temperature setting data;

[0107] E3. Obtain all reference temperature setting data within the reference date to form a reference temperature setting set;

[0108] E4. Obtain the type of element in the reference temperature set set and define it as the element category;

[0109] E5. Obtain the number of elements of each element category in the reference temperature set set, and define it as the reference element number;

[0110] E6. Integrate the element categories in the reference temperature set and the number of reference elements corresponding to each element category to form a category element set;

[0111] D4. Integrate the category element sets corresponding to all reference dates to form a set category set;

[0112] D5. Extract the element category with the largest number of reference elements and set it as the reference temperature setting element;

[0113] D6. Obtain target energy consumption data of the target equipment under reference elements and reference temperature setting elements;

[0114] D7. Calculate the reference energy consumption corresponding to the reference element, reference energy consumption = target energy consumption data × 60;

[0115] D8. Calculate the target temperature difference, target temperature difference = reference element - reference set temperature element.

[0116] As an optional solution of the intelligent adaptive air-conditioning refrigeration machine energy-saving control method of the present invention, wherein: the reference energy consumption data for each independent element is obtained separately, specifically:

[0117] Get the area where the target user is located and set it as the target area;

[0118] Obtain all users who use the target device in the target area and set them as reference users;

[0119] Get the estimated temperature corresponding to the independent element and set it as the independent estimated temperature;

[0120] Obtain the device temperature set by each reference user for the target device under the independent estimated temperature, and set it as the reference device temperature;

[0121] Get all reference device temperatures to form a reference user set;

[0122] Obtain reference element and reference temperature setting element;

[0123] Calculate the first reference ratio, first reference ratio = reference element ÷ reference temperature setting element;

[0124] Get the peak ambient temperature set and the peak set temperature set;

[0125] The elements in the peak ambient temperature set are extracted sequentially and identified as the first peak element;

[0126] The elements in the peak temperature set set are sequentially extracted and identified as the second peak elements;

[0127] Calculate the second reference ratio, the second reference ratio = the first peak element ÷ the second peak element;

[0128] integrating the first reference ratio and all second reference ratios to form a reference ratio set;

[0129] Get the number of each element in the reference ratio set and set it as the ratio number;

[0130] Extract the element with the largest number of ratios and define it as the contrast element;

[0131] If the first reference ratio = the comparison element, the first reference ratio is identified as the estimated ratio;

[0132] If the first reference ratio ≠ the comparison element, the comparison element is identified as the estimated ratio;

[0133] Calculate the estimated device temperature, estimated device temperature = independent estimated temperature / estimated ratio;

[0134] Calculate the estimated difference corresponding to each element in the reference user set, where the estimated difference = |reference device temperature - estimated device temperature|;

[0135] The reference device temperature corresponding to the estimated difference with the smallest value is extracted and identified as the estimated reference set temperature;

[0136] Obtain target energy consumption data of the target device at independent estimated temperature and estimated reference set temperature;

[0137] Calculate the individual predicted energy consumption, individual predicted energy consumption = target energy consumption data × 60;

[0138] Calculate the target temperature difference, target temperature difference = independent estimated temperature - estimated reference set temperature.

[0139] As an optional solution of the intelligent adaptive air-conditioning refrigeration machine energy-saving control method of the present invention, the energy storage reference analysis strategy is specifically:

[0140] Obtain the total refrigeration storage capacity of the target equipment and set it as the target storage capacity;

[0141] Obtain reference energy consumption value;

[0142] Obtain the refrigeration storage capacity corresponding to the reference energy consumption value and set it as the reference storage capacity;

[0143] Calculate the estimated storage capacity, estimated storage capacity = reference storage capacity × (1 + 20%);

[0144] If the estimated storage capacity is less than or equal to the target storage capacity, the estimated storage capacity is regarded as the target cooling capacity;

[0145] If the estimated storage capacity > the target storage capacity, the target storage capacity is regarded as the target cooling capacity;

[0146] Obtain the peak electricity consumption data of target users;

[0147] The starting time of extracting off-peak electricity consumption data is set as the energy storage starting time;

[0148] When the energy storage start time is reached, the target device starts cooling and storing;

[0149] When the target cooling capacity is reached, the target device stops cooling.

[0150] As an optional solution of the intelligent adaptive air-conditioning refrigeration machine energy-saving control method of the present invention, the energy regulation strategy is specifically:

[0151] Get estimated storage and target storage;

[0152] If the estimated storage capacity is less than or equal to the target storage capacity, the refrigeration storage source of the equipment is obtained and identified as the target energy use source;

[0153] If the estimated storage capacity > the target storage capacity, the target storage capacity is regarded as the target cooling capacity;

[0154] Obtain the energy consumption time corresponding to the target cooling capacity;

[0155] Get the temperature difference set and the estimated temperature set;

[0156] Each element in the temperature difference set is matched one by one with each element in the estimated temperature set to form an estimated temperature difference set;

[0157] Get the temperature difference energy consumption duration corresponding to each element in the estimated temperature difference set;

[0158] Sort the elements in the estimated temperature difference set from small to large according to the value of the temperature difference energy consumption time to form a selection order set;

[0159] Starting from the first element in the selection order set, select elements one by one and then add them backwards. Calculate the sum of all selected elements and define it as the sum of elements.

[0160] Calculate the duration difference, duration difference = temperature difference energy consumption duration - total of elements;

[0161] Get the sum of the elements with a duration difference ≥ 0 and the smallest duration difference, and set it as the target element sum;

[0162] Extract the target element and all elements in the corresponding selection order set to form a first use set;

[0163] Calculate the second usage set, where the second usage set = temperature difference set - first usage set;

[0164] Extract the time corresponding to each element in the first use set and set it as the first adjustment time;

[0165] Extracting the time corresponding to each element in the second usage set and setting it as the second adjustment time;

[0166] Acquire an energy usage source of a target device, where the energy usage source includes a first energy source and a second energy source;

[0167] When the first adjustment time is reached, the first energy source is used for cooling;

[0168] When the second adjustment time is reached, the second energy source is used for cooling.

[0169] The present invention has the following beneficial effects:

[0170] 1. The intelligent adaptive air-conditioning refrigeration machine energy-saving control method obtains the peak and low power consumption periods in the user's area, and makes the target equipment cool and store during the low power consumption period, and then uses the stored refrigeration energy to output cold air during the peak power consumption period. By obtaining the user's air conditioning usage habits, ambient temperature and air conditioning set temperature, a user's air conditioning usage habit model is formed, which reduces the regional electricity consumption burden during peak power consumption periods, reduces the user's air conditioning usage cost, improves the user's usage experience, and balances the power situation of the regional power grid during peak and low power consumption periods.

[0171] 2. If the user has habit data, the intelligent adaptive air-conditioning refrigeration machine energy-saving control method obtains the user's air-conditioning usage habit model to estimate the user's use time of refrigeration on the second day and the energy consumption of the air-conditioning under the estimated ambient temperature, set temperature and usage time, so as to adjust the storage capacity of the air-conditioning for refrigeration during the low-power consumption period and the energy source for refrigeration during the peak power consumption period, reduce the regional electricity consumption burden during the peak power consumption period, reduce the user's use cost of air conditioning, improve the user's use experience, and balance the power situation of the regional power grid during the peak and low power consumption periods.

[0172] 3. If the user does not have habit data, the intelligent adaptive air-conditioning refrigeration machine energy-saving control method estimates the user's refrigeration use time on the next day and the energy consumption of the air conditioner under the estimated ambient temperature, set temperature and usage time by obtaining the user's used data and the usage data of other users using the same type of equipment in the user's area, so as to adjust the storage capacity of the air conditioner for refrigeration during the low-power consumption period and the energy source for refrigeration during the peak power consumption period, reduce the regional electricity consumption burden during the peak power consumption period, reduce the user's use cost of air conditioning, improve the user's use experience, and balance the power situation of the regional power grid during the peak and low power consumption periods. BRIEF DESCRIPTION OF THE DRAWINGS

[0173] Figure 1 This is a flow chart of the intelligent adaptive air-conditioning refrigeration machine energy-saving control method of the present invention. DETAILED DESCRIPTION

[0174] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0175] Embodiment 1, an intelligent adaptive air conditioning refrigeration machine energy saving control method, see Figure 1 ,include:

[0176] Get the current date;

[0177] Calculate the estimated date, estimated date = current date + 1;

[0178] Obtain all estimated temperatures within the estimated date to form an estimated temperature set, where the estimated temperature is the temperature at each hour of the day, such as 1:00, 2:00, etc.;

[0179] Get historical temperature data;

[0180] The historical temperature data includes historical ambient temperature data and historical equipment temperature data;

[0181] Get historical electricity usage time data;

[0182] The historical electricity usage time data includes peak electricity usage data and valley electricity usage data. The peak electricity usage data refers to the time when the user uses electricity during the peak electricity usage period in the area where the user is located, and the valley electricity usage data refers to the time when the user uses electricity during the valley electricity usage period in the area where the user is located;

[0183] Obtain target user’s habit data on target devices;

[0184] If the target user has habit data, a device habit model is generated based on historical temperature data, historical power usage time data, and habit data;

[0185] Based on the estimated temperature set and equipment habit model, the energy storage analysis strategy is used to generate analysis data;

[0186] If there is no habit data for the target user, the reference data collection strategy is executed to form reference data;

[0187] According to the analysis data or reference data, energy storage data is formed through energy storage reference analysis strategy;

[0188] According to the energy storage data, the refrigeration storage capacity of the target device is adjusted, where the refrigeration storage capacity is the amount of refrigeration and storage performed by the device during the off-peak period of electricity consumption;

[0189] According to the energy storage data, the estimated temperature set and the equipment habit model, the energy regulation data is formed through the energy regulation strategy;

[0190] According to the energy regulation data, the energy usage source of the target device is regulated, where the energy usage source is the energy supply source of the device, including refrigeration through power lines and compression equipment and refrigeration through refrigeration energy stored in the device.

[0191] Through the above method, according to the peak and off-peak periods of electricity consumption in the user's area, after cooling and storing during the off-peak period, the stored refrigeration energy is used to output cold air during the peak period of electricity consumption. According to the user's usage habits of the air conditioner, the ambient temperature and the set temperature of the air conditioner, a user's usage habit model of the air conditioner is formed. According to the user's usage habit model of the air conditioner, the storage amount of the air conditioner for refrigeration during the off-peak period is adjusted, and the energy source of the air conditioner for refrigeration during the peak period of electricity consumption is adjusted, thereby reducing the electric energy consumed by the user in the process of using the air conditioner, alleviating the regional electricity consumption burden during the peak period of electricity consumption, and at the same time reducing the user's use cost of the air conditioner, improving the user's usage experience, and balancing the power situation of the regional power grid during the peak and off-peak periods of electricity consumption.

[0192] Embodiment 2: This embodiment is an improvement made on the basis of embodiment 1. In the intelligent adaptive air-conditioning refrigeration machine energy-saving control method, the acquisition of the habit data of the target user on the target device is specifically:

[0193] Get the current time;

[0194] Set the cycle duration, which is 60 days;

[0195] Calculate the collection start time, collection start time = current time - cycle length;

[0196] The period between the collection start time and the current time is defined as the collection period;

[0197] Set the daily collection time, which is 24 hours;

[0198] The collection start time is taken as the initial time, and the collection duration is calculated every other day to form a daily collection time.

[0199] Obtain all daily collection times within the collection period to form a daily time set, where the time set includes the collection start time and the current time;

[0200] The time period formed between every two adjacent elements in the daily time set is defined as the collection day cycle;

[0201] Obtain all the collected daily cycles to form a daily cycle set;

[0202] Set the time acquisition duration, which is 60 minutes;

[0203] The collection start time is taken as the initial time, and the collection duration is calculated for each interval to form a collection time.

[0204] Respectively obtain all the hourly collection times in each collection daily cycle in the daily cycle set to form an hourly collection time set;

[0205] Each hourly collection time set is matched with each element in the daily cycle set one by one to form a collection cycle set;

[0206] Obtain the historical ambient temperature data corresponding to each element in the collection period set, and define it as the historical ambient time temperature;

[0207] Each historical ambient temperature is matched with each element in the collection period set one by one to form a historical ambient temperature set;

[0208] Obtain the historical device temperature data corresponding to each element in the collection period set, and define it as the historical device time temperature;

[0209] Each historical device temperature is matched with each element in the collection period set one by one to form a historical set temperature set;

[0210] Get the estimated temperature set;

[0211] If the historical ambient temperature set contains all elements in the estimated temperature set, it is determined that the target user has habit data;

[0212] If the historical ambient temperature set does not contain all the elements in the estimated temperature set, it is determined that the target user does not have habit data.

[0213] This embodiment also provides that the generating device habit model is specifically:

[0214] Get the collection cycle set;

[0215] Get the historical ambient temperature collection and historical set temperature collection;

[0216] The device usage data corresponding to each element in the collection period set is collected. The device usage data is the usage after the device is turned on. If the device is turned on, there is device usage data. If the device is turned off, there is no device usage data.

[0217] Obtain peak power consumption data and off-peak power consumption data;

[0218] Extract the element containing the device usage data and define it as the usage element;

[0219] If the usage element is within the peak power usage data, it is defined as the peak usage element;

[0220] If the usage element is within the valley power consumption data, it is defined as the valley usage element;

[0221] Get all peak usage elements to form a peak usage set;

[0222] Extract the historical ambient temperature data corresponding to each element in the peak usage set in the historical ambient temperature set to form a peak ambient temperature set;

[0223] Extract the historical equipment temperature data corresponding to each element in the peak usage set in the historical set temperature set to form a peak set temperature set;

[0224] Integrate the peak ambient temperature set and the peak set temperature set to form an equipment habit model.

[0225] Embodiment 3: This embodiment is an improvement made on the basis of Embodiment 2. In this embodiment, the energy storage analysis strategy is specifically as follows:

[0226] Get the estimated temperature set;

[0227] Extract the element with the smallest value and the largest value in the estimated temperature set, and define them as the low temperature estimated element and the high temperature estimated element respectively;

[0228] Get device habit model;

[0229] Extract all historical ambient temperature data corresponding to each collection day cycle in the historical ambient temperature set to form historical daily ambient temperature sets respectively;

[0230] For each historical daily ring temperature set, extract the element with the smallest value and the element with the largest value in the set, and define them as the low temperature historical element and the high temperature historical element respectively;

[0231] Extract the historical daily ambient temperature set of low temperature historical elements = low temperature estimated elements, high temperature historical elements = high temperature estimated elements, and all elements in the estimated temperature set, and define them as the historical reference set, that is, extract the historical usage data of the user in the historical use process, in which the daily maximum temperature and daily minimum temperature are the same as the predicted daily maximum temperature and daily minimum temperature on the day of this upcoming use, and the daily temperature change is similar to the predicted daily temperature change on the day of this upcoming use, as a reference for the user, so as to determine the cooling capacity of the equipment during the low electricity consumption period before use;

[0232] The data collection date for each historical reference set was extracted and designated as the extraction date;

[0233] Extract all extraction dates within the collection period set to form a sampling time set;

[0234] Identify each element in the sampling time set as a target element in turn, and execute steps A1-A5;

[0235] Get the estimated energy consumption value of each element in the sampling time set;

[0236] Obtain the target temperature difference of each element in the sampling time set to form a temperature difference set;

[0237] Calculate the reference energy consumption value, which is the sum of the estimated energy consumption values ​​of all elements in the sampling time set.

[0238] The execution steps A1-A5 are specifically as follows:

[0239] A1. Extract all peak usage elements corresponding to the target element in the peak usage set and define them as the peak sampling time, that is, extract the usage time of the user during the peak power consumption period under similar ambient temperature during the historical usage process;

[0240] A2. Extract all historical ambient temperature data corresponding to each peak sampling time in the peak ambient temperature set to form a peak sampling ambient temperature set, that is, extract the temperature changes of users during the peak power consumption period during historical use;

[0241] A3. Extract all historical device temperature data corresponding to each peak sampling time in the peak temperature set set to form a peak sampling temperature set, that is, extract the user's adjustment of the device set temperature according to the temperature change of the ambient temperature during the peak power consumption period during the historical use process;

[0242] A4. If all elements of the peak sampling ambient temperature set are the same, execute B1-B6. If the ambient temperature when the user uses the device during the peak power consumption period is the same, the set temperature of the device will not change.

[0243] B1. Obtain the number of peak sampling times, which is defined as the peak sampling number;

[0244] B2. Randomly extract the historical ambient temperature data corresponding to an element in the peak sampling ambient temperature set and set it as the target ambient temperature data;

[0245] B3. Randomly extract the historical equipment temperature data corresponding to an element in the peak sampling temperature setting set and set it as the target temperature setting data;

[0246] B4. Obtain energy consumption data of the target device under the target ambient temperature data and the target set temperature data, and define the target energy consumption data as the target energy consumption data, wherein the energy consumption data is the energy consumption per minute of the air conditioner when the air conditioner is operated at an ambient temperature and set to a device temperature;

[0247] B5. Calculate the estimated energy consumption value, estimated energy consumption value = peak sampling number × 60 × target energy consumption data;

[0248] B6. Calculate the target temperature difference, target temperature difference = target ambient temperature data - target set temperature data;

[0249] A5. If the elements in the peak sampling ambient temperature set are different, execute C1-C7. If the ambient temperature changes when the user uses the device during the peak power consumption period, the set temperature of the device will also change accordingly, that is, each ambient temperature corresponds to a corresponding device set temperature. For example, when the user uses the device during the peak power consumption period, the ambient temperature is 35°C, 35°C, 33°C, 33°C, and the corresponding device set temperatures are 27°C, 27°C, 28°C, 28°C, and 28°C. The device set temperature corresponding to the ambient temperature of 35°C is 27°C, and the device set temperature corresponding to the ambient temperature of 33°C is 28°C.

[0250] C1. Get the number of each element in the peak sampling ambient temperature set respectively, and define it as the number of elements, that is, get the number of times each temperature in the set appears in the set. For example, if the elements in the set are 35℃, 35℃, 33℃, 33℃, and 33℃ respectively, then the number of elements at 33℃ is 3, and the number of elements at 35℃ is 2;

[0251] C2, identifying each element in the peak sampling ambient temperature set as the target ambient temperature data in turn;

[0252] C3, identifying each element in the peak sampling temperature setting set as the target temperature setting data in turn;

[0253] C4. Obtain target energy consumption data of the target device under target ambient temperature data and target set temperature data;

[0254] C5. Calculate the element energy consumption value, element energy consumption value = number of elements corresponding to the target ambient temperature data × target energy consumption data × 60;

[0255] C6. Calculate the estimated energy consumption value, which is the sum of the energy consumption values ​​of all elements in the peak sampling ambient temperature set;

[0256] C7. Calculate the target temperature difference, target temperature difference = target ambient temperature data - target set temperature data.

[0257] This embodiment also provides that the reference data collection strategy is specifically:

[0258] Get the estimated temperature set;

[0259] Get the peak ambient temperature collection;

[0260] Extract all historical ambient temperature data corresponding to each collection day cycle in the peak ambient temperature set to form a historical daily ambient temperature set;

[0261] Elements that exist in both the historical daily annular temperature set and the estimated temperature set are defined as overlapping elements;

[0262] Get the number of overlapping elements in each historical daily ambient temperature set, and define it as the overlapping number;

[0263] Extract the historical daily annular temperature set with the largest number of overlaps and set it as the reference daily annular temperature set;

[0264] Elements that only exist in the estimated temperature set but not in the reference daily annular temperature set are defined as independent elements;

[0265] Elements that exist in both the reference daily annular temperature set and the estimated temperature set are defined as reference elements;

[0266] Perform steps D1-D8 for each reference element;

[0267] Calculate a first estimated energy consumption value, where the first estimated energy consumption value = the sum of the energy consumptions of all reference elements in the estimated temperature set;

[0268] Obtain reference energy consumption data for each independent element;

[0269] Calculate the second estimated energy consumption value, where the second estimated energy consumption value = the sum of the independent estimated energy consumptions of all independent elements in the estimated temperature set;

[0270] Obtain the target temperature difference corresponding to each element in the estimated temperature set to form a temperature difference set;

[0271] Calculate the reference energy consumption value, the reference energy consumption value = the first estimated energy consumption value + the second estimated energy consumption value;

[0272] The steps D1-D8 are specifically:

[0273] D1. Extract all reference daily ambient temperature sets containing reference elements, that is, extract all historical data corresponding to the ambient temperature corresponding to the reference elements;

[0274] D2. Extract the data collection date corresponding to the reference element in the reference daily ambient temperature set and set it as the reference date;

[0275] D3. Execute E1-E6 for each reference date;

[0276] E1. Extract all peak usage elements in the peak usage set on the reference date and define them as reference usage elements;

[0277] E2. Extract the historical equipment temperature data corresponding to each reference usage element in the peak temperature setting set and set it as the reference temperature setting data;

[0278] E3. Obtain all reference temperature setting data within the reference date to form a reference temperature setting set;

[0279] E4. Obtain the types of elements in the reference temperature set set and define them as element categories. For example, if the elements in the set are 27°C, 27°C, 28°C, 28°C, and 28°C, the element categories are 27°C and 28°C.

[0280] E5. Obtain the number of elements of each element category in the reference temperature set set and define it as the reference element number. For example, if the elements in the set are 27°C, 27°C, 28°C, 28°C, and 28°C respectively, and the element categories are 27°C and 28°C, then the reference element number corresponding to the element category 27°C is 2, and the reference element number corresponding to the element category 28°C is 3;

[0281] E6. Integrate the element categories in the reference temperature set and the number of reference elements corresponding to each element category to form a category element set;

[0282] D4. Integrate the category element sets corresponding to all reference dates to form a set category set. For example, the reference dates include date 1, date 2, and date 3, where the category element set corresponding to date 1 is (element category 27°C, reference element quantity 3) and (element category 28°C, reference element quantity 2), the category element set corresponding to date 2 is (element category 27°C, reference element quantity 3) and (element category 26°C, reference element quantity 2), and the category element set corresponding to date 3 is (element category 25°C, reference element quantity 2) and (element category 26°C, reference element quantity 3). Then the set category set is (element category 25°C, reference element quantity 2), (element category 26°C, reference element quantity 5), (element category 27°C, reference element quantity 6), and (element category 28°C, reference element quantity 2);

[0283] D5. Extract the element category with the largest number of reference elements and set it as the reference temperature setting element;

[0284] D6. Obtain target energy consumption data of the target equipment under reference elements and reference temperature setting elements;

[0285] D7. Calculate the reference energy consumption corresponding to the reference element, reference energy consumption = target energy consumption data × 60;

[0286] D8. Calculate the target temperature difference, target temperature difference = reference element - reference set temperature element.

[0287] The step of obtaining reference energy consumption data for each independent element is as follows:

[0288] Get the area where the target user is located and set it as the target area;

[0289] Obtain all users who use the target device in the target area and set them as reference users;

[0290] Get the estimated temperature corresponding to the independent element and set it as the independent estimated temperature;

[0291] Obtain the device temperature set by each reference user for the target device under the independent estimated temperature, and set it as the reference device temperature;

[0292] Get all reference device temperatures to form a reference user set;

[0293] Obtain reference element and reference temperature setting element;

[0294] Calculate the first reference ratio, first reference ratio = reference element ÷ reference temperature setting element;

[0295] Get the peak ambient temperature set and the peak set temperature set;

[0296] The elements in the peak ambient temperature set are extracted sequentially and identified as the first peak element;

[0297] The elements in the peak temperature set set are sequentially extracted and identified as the second peak elements;

[0298] Calculate the second reference ratio, the second reference ratio = the first peak element ÷ the second peak element;

[0299] integrating the first reference ratio and all second reference ratios to form a reference ratio set;

[0300] Get the number of each element in the reference ratio set and define it as the ratio number, that is, get the number of times each element in the set appears. For example, if the elements in the set are 1.3, 1.3, 1.2, 1.3 and 1.2, then the ratio number of element 1.3 is 3, and the ratio number of element 1.2 is 2;

[0301] Extract the element with the largest number of ratios and define it as the contrast element;

[0302] If the first reference ratio = the comparison element, the first reference ratio is identified as the estimated ratio;

[0303] If the first reference ratio ≠ the comparison element, the comparison element is identified as the estimated ratio;

[0304] Calculate the estimated device temperature, estimated device temperature = independent estimated temperature / estimated ratio;

[0305] Calculate the estimated difference corresponding to each element in the reference user set, where the estimated difference = |reference device temperature - estimated device temperature|;

[0306] The reference device temperature corresponding to the estimated difference with the smallest value is extracted and identified as the estimated reference set temperature;

[0307] Obtain target energy consumption data of the target device at independent estimated temperature and estimated reference set temperature;

[0308] Calculate the individual predicted energy consumption, individual predicted energy consumption = target energy consumption data × 60;

[0309] Calculate the target temperature difference, target temperature difference = independent estimated temperature - estimated reference set temperature.

[0310] Embodiment 4: This embodiment is an improvement made on the basis of embodiment 3. In this embodiment, the energy storage reference analysis strategy is specifically:

[0311] Obtain the total refrigeration storage capacity of the target equipment and set it as the target storage capacity;

[0312] Obtain reference energy consumption value;

[0313] Obtain the refrigeration storage capacity corresponding to the reference energy consumption value and set it as the reference storage capacity;

[0314] Calculate the estimated storage capacity, estimated storage capacity = reference storage capacity × (1 + 20%);

[0315] If the estimated storage capacity is less than or equal to the target storage capacity, the estimated storage capacity is regarded as the target cooling capacity;

[0316] If the estimated storage capacity > the target storage capacity, the target storage capacity is regarded as the target cooling capacity;

[0317] Obtain the peak electricity consumption data of target users;

[0318] The starting time of extracting off-peak electricity consumption data is set as the energy storage starting time;

[0319] When the energy storage start time is reached, the target device starts cooling and storing;

[0320] When the target cooling capacity is reached, the target device stops cooling.

[0321] This embodiment also provides that the energy regulation strategy is specifically:

[0322] Get estimated storage and target storage;

[0323] If the estimated storage capacity is less than or equal to the target storage capacity, the refrigeration storage source of the equipment is obtained and identified as the target energy use source;

[0324] If the estimated storage capacity > the target storage capacity, the target storage capacity is regarded as the target cooling capacity;

[0325] Obtaining the energy consumption duration corresponding to the target cooling capacity, wherein the energy consumption duration is the operating time of the device under the cooling storage capacity, using the stored energy at an ambient temperature, setting a device temperature for cooling, from the time the device is turned on until the stored energy is consumed;

[0326] Get the temperature difference set and the estimated temperature set;

[0327] Each element in the temperature difference set is matched one by one with each element in the estimated temperature set to form an estimated temperature difference set;

[0328] Obtain the temperature difference energy consumption duration corresponding to each element in the estimated temperature difference set, wherein the target temperature difference in the estimated temperature difference set is the difference between an ambient temperature and the device temperature corresponding to it, and the temperature difference energy consumption duration is the duration that the device can operate when the device is set to the device temperature for cooling at the ambient temperature;

[0329] Sort the elements in the estimated temperature difference set from small to large according to the value of the temperature difference energy consumption time to form a selection order set;

[0330] Starting from the first element in the selection order set, select elements one by one and then add them backwards. Calculate the sum of all selected elements and define it as the sum of elements.

[0331] Calculate the duration difference, duration difference = temperature difference energy consumption duration - total of elements;

[0332] Get the sum of the elements with a duration difference ≥ 0 and the smallest duration difference, and set it as the target element sum;

[0333] Extract the target element and all elements in the corresponding selection order set to form a first use set;

[0334] Calculate the second usage set, where the second usage set = temperature difference set - first usage set;

[0335] Extract the time corresponding to each element in the first use set and set it as the first adjustment time;

[0336] Extracting the time corresponding to each element in the second usage set and setting it as the second adjustment time;

[0337] Acquire an energy source used by a target device, the energy source including a first energy source and a second energy source, the first energy source is refrigeration through power lines and compression equipment, and the second energy source is refrigeration through refrigeration energy stored in the device;

[0338] When the first adjustment time is reached, the first energy source is used for cooling;

[0339] When the second adjustment time is reached, the second energy source is used for cooling.

[0340] In this embodiment, according to the peak and low electricity consumption periods in the area where the user is located, the refrigeration energy is cooled and stored during the low electricity consumption period, and then the stored refrigeration energy is used to output cold air during the peak electricity consumption period. According to the user's usage habits of the air conditioner, the ambient temperature and the set temperature of the air conditioner, a user's usage habit model of the air conditioner is formed. According to the user's usage habit model of the air conditioner, the storage capacity of the air conditioner for refrigeration during the low electricity consumption period is adjusted, and the energy source of the air conditioner for refrigeration during the peak electricity consumption period is adjusted, thereby reducing the electric energy consumed by the user in the process of using the air conditioner, alleviating the regional electricity consumption burden during the peak electricity consumption period, and at the same time reducing the user's cost of using the air conditioner, improving the user's usage experience, and balancing the power situation of the regional power grid during the peak and low electricity consumption periods.

Claims

1. An intelligent adaptive air conditioning refrigeration machine energy-saving control method, characterized in that: include: Get the current date; Calculate the estimated date, estimated date = current date + 1; Get all estimated temperatures within the estimated date to form an estimated temperature set; Get historical temperature data; The historical temperature data includes historical ambient temperature data and historical equipment temperature data; Get historical electricity usage time data; The historical electricity consumption time data includes peak electricity consumption data and valley electricity consumption data; Obtain target user’s habit data on target devices; If the target user has habit data, a device habit model is generated based on historical temperature data, historical power usage time data, and habit data; Based on the estimated temperature set and equipment habit model, the energy storage analysis strategy is used to generate analysis data; If there is no habit data for the target user, the reference data collection strategy is executed to form reference data; According to the analysis data or reference data, energy storage data is formed through energy storage reference analysis strategy; According to the energy storage data, adjust the refrigeration storage capacity of the target equipment; According to the energy storage data, the estimated temperature set and the equipment habit model, the energy regulation data is formed through the energy regulation strategy; The energy usage source of the target device is regulated according to the energy regulation data.

2. The intelligent adaptive air conditioning refrigeration machine energy-saving control method according to claim 1 is characterized in that: The step of obtaining the target user's habit data on the target device is as follows: Get the current time; Set the cycle duration; Calculate the collection start time, collection start time = current time - cycle length; The period between the collection start time and the current time is defined as the collection period; Set the daily collection duration; The collection start time is taken as the initial time, and the collection duration is calculated every other day to form a daily collection time. Obtain all daily collection times within the collection period to form a daily time set; The time period formed between every two adjacent elements in the daily time set is defined as the collection day cycle; Obtain all the collected daily cycles to form a daily cycle set; Set the collection duration; The collection start time is taken as the initial time, and the collection duration is calculated for each interval to form a collection time. Respectively obtain all the hourly collection times in each collection daily cycle in the daily cycle set to form an hourly collection time set; Each hourly collection time set is matched with each element in the daily cycle set one by one to form a collection cycle set; Obtain the historical ambient temperature data corresponding to each element in the collection period set, and define it as the historical ambient time temperature; Each historical ambient temperature is matched with each element in the collection period set one by one to form a historical ambient temperature set; Obtain the historical device temperature data corresponding to each element in the collection period set, and define it as the historical device time temperature; Each historical device temperature is matched with each element in the collection period set one by one to form a historical set temperature set; Get the estimated temperature set; If the historical ambient temperature set contains all elements in the estimated temperature set, it is determined that the target user has habit data; If the historical ambient temperature set does not contain all the elements in the estimated temperature set, it is determined that the target user does not have habit data.

3. The intelligent adaptive air conditioning refrigeration machine energy-saving control method according to claim 1 is characterized in that: The generating device habit model is specifically: Get the collection cycle set; Get the historical ambient temperature collection and historical set temperature collection; Collect device usage data corresponding to each element in the cycle set; Obtain peak power consumption data and off-peak power consumption data; Extract the element containing the device usage data and define it as the usage element; If the usage element is within the peak power usage data, it is defined as the peak usage element; If the usage element is within the valley power consumption data, it is defined as the valley usage element; Get all peak usage elements to form a peak usage set; Extract the historical ambient temperature data corresponding to each element in the peak usage set in the historical ambient temperature set to form a peak ambient temperature set; Extract the historical equipment temperature data corresponding to each element in the peak usage set in the historical set temperature set to form a peak set temperature set; Integrate the peak ambient temperature set and the peak set temperature set to form an equipment habit model.

4. The intelligent adaptive air conditioning refrigeration machine energy-saving control method according to claim 1, characterized in that: The energy storage analysis strategy is specifically as follows: Get the estimated temperature set; Extract the element with the smallest value and the largest value in the estimated temperature set, and define them as the low temperature estimated element and the high temperature estimated element respectively; Get device habit model; Extract all historical ambient temperature data corresponding to each collection day cycle in the historical ambient temperature set to form historical daily ambient temperature sets respectively; For each historical daily ring temperature set, extract the element with the smallest value and the element with the largest value in the set, and define them as the low temperature historical element and the high temperature historical element respectively; Extract the historical daily ambient temperature set of low temperature historical elements = low temperature estimated elements, high temperature historical elements = high temperature estimated elements, and all elements in the estimated temperature set, and define them as the historical reference set; The data collection date for each historical reference set was extracted and designated as the extraction date; Extract all extraction dates within the collection period set to form a sampling time set; Identify each element in the sampling time set as a target element in turn, and execute steps A1-A5; Get the estimated energy consumption value of each element in the sampling time set; Obtain the target temperature difference of each element in the sampling time set to form a temperature difference set; Calculate the reference energy consumption value, which is the sum of the estimated energy consumption values ​​of all elements in the sampling time set.

5. The intelligent adaptive air conditioning refrigeration machine energy-saving control method according to claim 4 is characterized in that: The execution steps A1-A5 are specifically as follows: A1. Extract all peak usage elements corresponding to the target element in the peak usage set and define them as the peak sampling time; A2. Extract all historical ambient temperature data corresponding to each peak sampling time in the peak ambient temperature set to form a peak sampling ambient temperature set; A3. Extract all historical equipment temperature data corresponding to each peak sampling time in the peak temperature set set to form a peak sampling temperature set; A4. If all elements of the peak sampling ambient temperature set are the same, execute B1-B6; B1. Obtain the number of peak sampling times, which is defined as the peak sampling number; B2. Randomly extract the historical ambient temperature data corresponding to an element in the peak sampling ambient temperature set and set it as the target ambient temperature data; B3. Randomly extract the historical equipment temperature data corresponding to an element in the peak sampling temperature setting set and set it as the target temperature setting data; B4. Obtain the energy consumption data of the target equipment under the target ambient temperature data and the target set temperature data, and define it as the target energy consumption data; B5. Calculate the estimated energy consumption value, estimated energy consumption value = peak sampling number × 60 × target energy consumption data; B6. Calculate the target temperature difference, target temperature difference = target ambient temperature data - target set temperature data; A5. If the elements in the peak sampling ambient temperature set are different, execute C1-C7; C1. Obtain the number of each element in the peak sampling ambient temperature set respectively, and define it as the number of elements; C2, identifying each element in the peak sampling ambient temperature set as the target ambient temperature data in turn; C3, identifying each element in the peak sampling temperature setting set as the target temperature setting data in turn; C4. Obtain target energy consumption data of the target device under target ambient temperature data and target set temperature data; C5. Calculate the element energy consumption value, element energy consumption value = number of elements corresponding to the target ambient temperature data × target energy consumption data × 60; C6. Calculate the estimated energy consumption value, which is the sum of the energy consumption values ​​of all elements in the peak sampling ambient temperature set; C7. Calculate the target temperature difference, target temperature difference = target ambient temperature data - target set temperature data.

6. The intelligent adaptive air conditioning refrigeration machine energy-saving control method according to claim 1, characterized in that: The reference data collection strategy is specifically as follows: Get the estimated temperature set; Get the peak ambient temperature collection; Extract all historical ambient temperature data corresponding to each collection day cycle in the peak ambient temperature set to form a historical daily ambient temperature set; Elements that exist in both the historical daily annular temperature set and the estimated temperature set are defined as overlapping elements; Get the number of overlapping elements in each historical daily ambient temperature set, and define it as the overlapping number; Extract the historical daily annular temperature set with the largest number of overlaps and set it as the reference daily annular temperature set; Elements that only exist in the estimated temperature set but not in the reference daily annular temperature set are defined as independent elements; Elements that exist in both the reference daily annular temperature set and the estimated temperature set are defined as reference elements; Perform steps D1-D8 for each reference element; Calculate a first estimated energy consumption value, where the first estimated energy consumption value = the sum of the energy consumptions of all reference elements in the estimated temperature set; Obtain reference energy consumption data for each independent element; Calculate the second estimated energy consumption value, where the second estimated energy consumption value = the sum of the independent estimated energy consumptions of all independent elements in the estimated temperature set; Obtain the target temperature difference corresponding to each element in the estimated temperature set to form a temperature difference set; Calculate the reference energy consumption value, the reference energy consumption value = the first estimated energy consumption value + the second estimated energy consumption value; The steps D1-D8 are specifically: D1. Extract all reference daily ambient temperature sets containing reference elements; D2. Extract the data collection date corresponding to the reference element in the reference daily ambient temperature set and set it as the reference date; D3. Execute E1-E6 for each reference date; E1. Extract all peak usage elements in the peak usage set on the reference date and define them as reference usage elements; E2. Extract the historical equipment temperature data corresponding to each reference usage element in the peak temperature setting set and set it as the reference temperature setting data; E3. Obtain all reference temperature setting data within the reference date to form a reference temperature setting set; E4. Obtain the type of element in the reference temperature set set and define it as the element category; E5. Obtain the number of elements of each element category in the reference temperature set set, and define it as the reference element number; E6. Integrate the element categories in the reference temperature set and the number of reference elements corresponding to each element category to form a category element set; D4. Integrate the category element sets corresponding to all reference dates to form a set category set; D5. Extract the element category with the largest number of reference elements and set it as the reference temperature setting element; D6. Obtain target energy consumption data of the target equipment under reference elements and reference temperature setting elements; D7. Calculate the reference energy consumption corresponding to the reference element, reference energy consumption = target energy consumption data × 60; D8. Calculate the target temperature difference, target temperature difference = reference element - reference set temperature element.

7. The intelligent adaptive air conditioning refrigeration machine energy-saving control method according to claim 6 is characterized in that: The reference energy consumption data for each independent element is obtained separately, specifically: Get the area where the target user is located and set it as the target area; Obtain all users who use the target device in the target area and set them as reference users; Get the estimated temperature corresponding to the independent element and set it as the independent estimated temperature; Obtain the device temperature set by each reference user for the target device under the independent estimated temperature, and set it as the reference device temperature; Get all reference device temperatures to form a reference user set; Obtain reference element and reference temperature setting element; Calculate the first reference ratio, first reference ratio = reference element ÷ reference temperature setting element; Get the peak ambient temperature set and the peak set temperature set; The elements in the peak ambient temperature set are extracted sequentially and identified as the first peak element; The elements in the peak temperature set set are sequentially extracted and identified as the second peak elements; Calculate the second reference ratio, the second reference ratio = the first peak element ÷ the second peak element; integrating the first reference ratio and all second reference ratios to form a reference ratio set; Get the number of each element in the reference ratio set and set it as the ratio number; Extract the element with the largest number of ratios and define it as the contrast element; If the first reference ratio = the comparison element, the first reference ratio is identified as the estimated ratio; If the first reference ratio ≠ the comparison element, the comparison element is identified as the estimated ratio; Calculate the estimated device temperature, estimated device temperature = independent estimated temperature / estimated ratio; Calculate the estimated difference corresponding to each element in the reference user set, where the estimated difference = |reference device temperature - estimated device temperature|; The reference device temperature corresponding to the estimated difference with the smallest value is extracted and identified as the estimated reference set temperature; Obtain target energy consumption data of the target device at independent estimated temperature and estimated reference set temperature; Calculate the predicted energy consumption: predicted energy consumption = target energy consumption data × 60; Calculate the target temperature difference, target temperature difference = independent estimated temperature - estimated reference set temperature.

8. The intelligent adaptive air conditioning refrigeration machine energy-saving control method according to claim 1, characterized in that: The energy storage reference analysis strategy is specifically: Obtain the total refrigeration storage capacity of the target equipment and set it as the target storage capacity; Obtain reference energy consumption value; Obtain the refrigeration storage capacity corresponding to the reference energy consumption value and set it as the reference storage capacity; Calculate the estimated storage capacity, estimated storage capacity = reference storage capacity × (1 + 20%); If the estimated storage capacity is less than or equal to the target storage capacity, the estimated storage capacity is regarded as the target cooling capacity; If the estimated storage capacity > the target storage capacity, the target storage capacity is regarded as the target cooling capacity; Obtain the peak electricity consumption data of target users; The starting time of extracting off-peak electricity consumption data is set as the energy storage starting time; When the energy storage start time is reached, the target device starts cooling and storing; When the target cooling capacity is reached, the target device stops cooling.

9. The intelligent adaptive air conditioning refrigeration machine energy-saving control method according to claim 1, characterized in that: The energy regulation strategy is specifically as follows: Get estimated storage and target storage; If the estimated storage capacity is less than or equal to the target storage capacity, the refrigeration storage source of the equipment is obtained and identified as the target energy use source; If the estimated storage capacity > the target storage capacity, the target storage capacity is regarded as the target cooling capacity; Obtain the energy consumption time corresponding to the target cooling capacity; Get the temperature difference set and the estimated temperature set; Each element in the temperature difference set is matched one by one with each element in the estimated temperature set to form an estimated temperature difference set; Get the temperature difference energy consumption duration corresponding to each element in the estimated temperature difference set; Sort the elements in the estimated temperature difference set from small to large according to the value of the temperature difference energy consumption time to form a selection order set; Starting from the first element in the selection order set, select elements one by one and then add them backwards. Calculate the sum of all selected elements and define it as the sum of elements. Calculate the duration difference, duration difference = temperature difference energy consumption duration - total of elements; Get the sum of the elements with a duration difference ≥ 0 and the smallest duration difference, and set it as the target element sum; Extract the target element and all elements in the corresponding selection order set to form a first use set; Calculate the second usage set, where the second usage set = temperature difference set - first usage set; Extract the time corresponding to each element in the first use set and set it as the first adjustment time; Extracting the time corresponding to each element in the second usage set and setting it as the second adjustment time; Acquire an energy usage source of a target device, where the energy usage source includes a first energy source and a second energy source; When the first adjustment time is reached, the first energy source is used for cooling; When the second adjustment time is reached, the second energy source is used for cooling.

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