Low-carbon building energy-saving strategy generation method and system based on human behavior patterns

Abstract

The present invention provides a method and system for generating energy-saving strategies for low-carbon buildings based on human behavior patterns, which relate to the field of data processing technology, and include: obtaining all energy-consuming loads of a target building to determine corresponding monitoring equipment, wherein the monitoring equipment obtains the historical working habits and corresponding first attribute data corresponding to each energy-consuming load; adding corresponding energy labels to all energy-consuming loads according to the energy demand of the energy-consuming loads, wherein the energy labels include single energy labels and combined energy labels; if it is determined that the target building has non-energy-consuming loads, then determining the corresponding energy-consuming loads based on the comparison between the second attribute data of the non-energy-consuming loads and the first attribute data, and determining the predicted habits of the non-energy-consuming loads based on the historical habits of the energy-consuming loads; and calculating the historical habits of the energy-consuming loads of the combined energy labels, the historical habits of the non-energy-consuming loads, and the load to obtain energy-saving statistical information of the low-carbon building.

Description

Technical Field

[0001] The present invention relates to data processing technology, and in particular to a method and system for generating a low-carbon building energy-saving strategy based on human behavior patterns. Background Art

[0002] The demand for energy conservation, emission reduction and low-carbon technologies in the field of modern construction is gradually increasing. Buildings are one of the main sources of energy consumption and carbon emissions. How to achieve reasonable management and optimization of building energy consumption and reduce carbon emissions through intelligent means has become an important issue in building design and operation. In large-scale buildings and intelligent buildings, there are a large number of energy-consuming equipment, such as lighting, air conditioning, heating, etc. These equipment consume a lot of energy during use. If the use of various energy-consuming equipment in the building can be monitored in real time through intelligent means, and analyzed and optimized in combination with historical data, it will help achieve efficient energy management and energy conservation and emission reduction goals for buildings.

[0003] In the existing technology, building energy consumption management mainly relies on a single data source or basic statistical analysis methods, and often only analyzes energy-consuming loads. The usual practice is to evaluate and manage building energy consumption by recording the power consumption of energy-consuming loads and simple time statistics. However, the existing technology lacks analysis of non-energy-consuming loads in buildings (such as natural ventilation, light-guided lighting, etc.), resulting in incomplete and inaccurate energy consumption data.

[0004] Therefore, how to conduct a comprehensive analysis based on the energy-consuming load and non-energy-consuming load of the building to be analyzed, obtain relatively comprehensive energy consumption data, and achieve relatively accurate and effective control of building energy consumption and energy-saving goals has become an urgent problem to be solved. Summary of the invention

[0005] The embodiment of the present invention provides a method and system for generating low-carbon building energy-saving strategies based on human behavior patterns, which conducts a comprehensive analysis of the energy-consuming load and non-energy-consuming load of the building to be analyzed, obtains relatively comprehensive energy consumption data, and achieves relatively accurate and effective control of building energy consumption and energy-saving goals.

[0006] A first aspect of an embodiment of the present invention provides a method for generating a low-carbon building energy-saving strategy based on a human behavior pattern, comprising:

[0007] Obtain all energy-consuming loads of the target building to determine the corresponding monitoring equipment, and the monitoring equipment obtains the historical working habits and corresponding first attribute data corresponding to each energy-consuming load, wherein the first attribute data at least includes first location information, first energy supply information, and first load information;

[0008] Adding corresponding energy usage tags to all energy consumption loads according to the energy consumption requirements of the energy consumption loads, wherein the energy usage tags include single energy usage tags and combined energy usage tags;

[0009] If it is determined that the target building has a non-energy-consuming load, the corresponding energy-consuming load is determined based on the comparison between the second attribute data of the non-energy-consuming load and the first attribute data, and the predicted habit of the non-energy-consuming load is determined based on the historical habit of the energy-consuming load;

[0010] The energy-saving statistical information of low-carbon building energy is obtained by combining the historical habits of energy-consuming loads of energy-consuming labels, the historical habits of non-energy-consuming loads, and load calculation.

[0011] Optionally, in a possible implementation manner of the first aspect, the acquiring all energy-consuming loads of the target building determines a corresponding monitoring device, and the monitoring device acquires a historical working habit and corresponding first attribute data corresponding to each energy-consuming load, and the first attribute data at least includes first location information, first energy supply information, and first load information, including:

[0012] Count all energy-consuming loads of the target building to obtain corresponding location information and configure it to the monitoring device, wherein the location information includes the location of the building surface;

[0013] The monitoring device obtains the historical working time of the energy-consuming load to obtain the corresponding historical habits, obtains the energy supply source of the energy-consuming load to obtain the energy supply information, and obtains the electric energy waveform corresponding to the historical working time to obtain the load information.

[0014] Optionally, in a possible implementation of the first aspect, the monitoring device acquires historical working time of the energy consuming load to obtain corresponding historical habits, the monitoring device acquires a source of energy supply of the energy consuming load to obtain energy supply information, and acquires an electric energy waveform corresponding to the historical working time to obtain load information, including:

[0015] The monitoring device obtains the historical working time of the energy consumption load on each reference date to obtain at least one working time period, determines the average value of the central time point of all working time periods to obtain the first time point, and calculates the average value of the working time period to obtain the average time of each working time period;

[0016] Count the average time of all first time points to obtain the historical habits of the corresponding energy consumption load;

[0017] The monitoring device obtains the energy supply information of the energy-consuming load, wherein the energy supply includes at least one of light energy, electrical energy, and gas energy;

[0018] After counting the waveforms of the working time period, the average value is calculated to obtain the corresponding load information.

[0019] Optionally, in a possible implementation manner of the first aspect, adding corresponding energy usage tags to all energy consumption loads according to energy consumption requirements of the energy consumption loads, the energy usage tags including single energy usage tags and combined energy usage tags, including:

[0020] If the energy demand of the energy-consuming load is determined to be one of light energy, electrical energy, and gas energy, then a corresponding single energy label is added;

[0021] If it is determined that the energy demand of the energy-consuming load is multiple types of light energy, electrical energy, and gas energy, a corresponding combined energy label is added.

[0022] Optionally, in a possible implementation manner of the first aspect, if it is determined that the target building has a non-energy-consuming load, determining a corresponding energy-consuming load based on a comparison between the second attribute data of the non-energy-consuming load and the first attribute data, and determining a predicted habit of the non-energy-consuming load based on a historical habit of the energy-consuming load, includes:

[0023] If it is determined that the target building has a non-energy-consuming load, receiving second attribute data configured by the user for the non-energy-consuming load, the second attribute data including second location information and second function type information;

[0024] Based on the comparison between the second function type information of the non-energy consuming load and the first function type information of all the energy consuming loads, the energy consuming loads corresponding to the first function type information corresponding to the second function type information are determined to obtain a first load set;

[0025] The second position information of the non-energy-consuming load is compared with the first position information of the energy-consuming load in the first load set to determine the target energy-consuming load, and the predicted habit of the non-energy-consuming load is determined based on the historical habit of the target energy-consuming load.

[0026] Optionally, in a possible implementation manner of the first aspect, comparing the second position information of the non-energy consuming load with the first position information of the energy consuming load in the first load set to determine the target energy consuming load, and determining the predicted habit of the non-energy consuming load based on the historical habit of the target energy consuming load includes:

[0027] Obtaining the number of energy-consuming loads in the first load set and located on the same building surface as the non-energy-consuming load to obtain a first number;

[0028] If the first number is greater than or equal to the preset number, the target virtualized energy consumption load is calculated based on the energy consumption loads on the same building surface, and the prediction habit of the non-energy consumption load is determined based on the historical habit of the target virtualized energy consumption load;

[0029] If the first number is less than a preset number, a virtualized energy consumption load of the target is calculated based on energy consumption loads on different building surfaces, and a predicted habit of the non-energy consumption load is determined based on the historical habit of the target energy consumption load.

[0030] Optionally, in a possible implementation manner of the first aspect, if the first number is greater than or equal to a preset number, calculating based on energy consumption loads on the same building surface to obtain a virtualized energy consumption load of a target, and determining a predicted habit of a non-energy consumption load based on a historical habit of the energy consumption load of the target, includes:

[0031] If the first number is greater than or equal to the preset number, all first time points and working time periods of all energy-consuming loads in their historical habits are selected;

[0032] The median time point and the mean time period are obtained by performing mean calculation based on the first time point and the working time period, and the median time point and the mean time period of the target virtualized energy-consuming load are determined as the prediction habit of the non-energy-consuming load.

[0033] Optionally, in a possible implementation manner of the first aspect, if the first number is less than a preset number, calculating based on energy consumption loads on different building surfaces to obtain a virtualized energy consumption load of a target, and determining a predicted habit of a non-energy consumption load based on a historical habit of the energy consumption load of the target, includes:

[0034] If the first number is less than a preset number, energy-consuming loads on different building surfaces are selected and their position distance relationship with non-energy-consuming loads is calculated, and the energy-consuming loads are sorted in ascending order based on the position distance relationship to obtain a selection sequence;

[0035] Selecting energy-consuming loads in sequence based on the selection sequence to obtain a selection quantity, stopping the selection of energy-consuming loads after the sum of the selection quantity and the first quantity is equal to a preset quantity, and determining a surface distance relationship between a building surface of each energy-consuming load and a building surface of a non-energy-consuming load;

[0036] Based on the number of energy-consuming loads on the same building surface as the non-energy-consuming loads, the number of energy-consuming loads on different building surfaces, and the surface distance relationship, the median time point and mean time period are obtained, and the median time point and mean time period of the target virtualized energy-consuming load are determined as the prediction habits of the non-energy-consuming load.

[0037] Optionally, in a possible implementation of the first aspect, the calculating based on the number of energy-consuming loads on the same building surface as the non-energy-consuming load, the number of energy-consuming loads on different building surfaces, and the surface distance relationship to obtain the median time point and the average time period, and determining the median time point and the average time period of the target virtualized energy-consuming load as the prediction habit of the non-energy-consuming load includes:

[0038] Obtaining a reference weight offset value based on the number of energy-consuming loads on the same building surface and the number of energy-consuming loads on different building surfaces;

[0039] Different reference weights are obtained based on the surface distance relationship of the energy consumption load of different building surfaces, and the numerical range of each surface distance relationship has a preset reference weight;

[0040] The median time point and mean time period are calculated using the following formula,

[0041] ;

[0042] in, is the median time point, For the same building surface The time point of the energy consumption load, is the number of energy-consuming loads with the same building surface, is the number of energy loads on different building surfaces, is the first constant value, is the benchmark time weight, For different building surfaces The time point of the energy consumption load, For different building surfaces The reference weight of each energy-consuming load, is the quantity value at a time point, is the normalized value at the time point, is the length of the mean time period, For the same building surface The length of the time period of the energy-consuming load, is the second constant value, is the benchmark time weight, For different building surfaces The length of the time period of the energy-consuming load, For different building surfaces The reference weight of each energy-consuming load, is the number of time periods, is the normalized value of the time period.

[0043] Optionally, in a possible implementation of the first aspect, the energy-saving statistical information of the low-carbon building energy is obtained by calculating the historical habits of the energy-consuming loads, the historical habits of the non-energy-consuming loads, and the loads of the combined energy consumption tags, including:

[0044] Obtain the historical habits of energy consumption loads of combined energy consumption tags, and calculate the carbon emission information difference between the best energy-saving energy and the most energy-consuming energy in different energy consumption scenarios;

[0045] The first energy-saving sub-information is obtained by calculating the difference between the historical habits of the energy consumption load and the carbon emission information of the combined energy consumption label;

[0046] The energy consumption load of the same building surface and the load average of the energy consumption load of different building surfaces are counted, and the second energy-saving sub-information is calculated based on the load average and the historical habit of non-energy consumption load;

[0047] The first energy-saving sub-information and the second energy-saving sub-information are added together to obtain energy-saving statistical information.

[0048] A second aspect of an embodiment of the present invention provides a low-carbon building energy-saving strategy generation system based on human behavior patterns, comprising:

[0049] A monitoring module, used to obtain all energy-consuming loads of a target building to determine corresponding monitoring devices, wherein the monitoring devices obtain the historical working habits and corresponding first attribute data corresponding to each energy-consuming load, wherein the first attribute data at least includes first location information, first energy supply information, and first load information;

[0050] A label module, used to add corresponding energy labels to all energy-consuming loads according to the energy demand of the energy-consuming loads, wherein the energy labels include single energy labels and combined energy labels;

[0051] A prediction module, for determining a corresponding energy-consuming load based on the comparison between the second attribute data of the non-energy-consuming load and the first attribute data if it is determined that the target building has a non-energy-consuming load, and determining a predicted habit of the non-energy-consuming load based on a historical habit of the energy-consuming load;

[0052] The calculation module is used to calculate the historical habits of energy-consuming loads and non-energy-consuming loads of the combined energy consumption tags and obtain the energy-saving statistical information of low-carbon buildings.

[0053] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a memory, a processor and a computer program, wherein the computer program is stored in the memory, and the processor runs the computer program to execute the method of the first aspect.

[0054] According to a fourth aspect of an embodiment of the present invention, a storage medium is provided, characterized in that a computer program is stored in the storage medium, and when the computer program is executed by a processor, it is used to implement the method of the first aspect.

[0055] Technical effects of the present invention:

[0056] 1. Automated monitoring and historical habit analysis

[0057] The technical solution of the present invention obtains the location information, energy supply information and load information of all energy-consuming loads in the target building, and uses the sensor network to monitor the working history habits of each energy-consuming load in real time to generate the historical working time period and load information of the energy-consuming load. Through the comprehensive analysis of these data, the working mode of the energy-consuming equipment can be accurately predicted, providing a scientific basis for energy-saving optimization. In a specific embodiment, the historical working time of the energy-consuming load on each reference date is obtained by monitoring the equipment, and the average value of the central time point of all working time periods is determined to obtain a representative first time point. This process not only reduces manual intervention and improves the efficiency and accuracy of data processing, but also can record and analyze the working habits of various energy-consuming loads in the building in detail, thereby providing strong data support for optimizing energy use, reducing energy consumption, and achieving the goal of low-carbon energy conservation.

[0058] 2. Comprehensive energy labeling and non-energy load analysis

[0059] The present invention adds corresponding energy tags, including single energy tags and combined energy tags, according to the energy demand of the energy-consuming load. This feature makes it possible to accurately identify and classify various energy-consuming loads in a building, thereby realizing refined management. For non-energy-consuming loads, the corresponding energy-consuming loads are determined by comparing their second attribute data with the first attribute data, and the usage habits of the non-energy-consuming loads are predicted based on the historical habits of the energy-consuming loads. For example, by comparing the location information and function type information, the attribute data of the non-energy-consuming loads is matched with the energy-consuming loads, the energy-consuming loads close to the non-energy-consuming loads are determined, and based on the historical habits of these energy-consuming loads, the usage habits of the non-energy-consuming loads are accurately predicted. This method not only improves the comprehensiveness of data processing, but also can reasonably predict and manage non-energy-consuming loads, providing comprehensive data support for energy-saving optimization of low-carbon buildings.

[0060] 3. Statistics and optimization of low-carbon building energy conservation

[0061] The present invention calculates and analyzes the historical habits and load information of the energy-consuming load and non-energy-consuming load of the combined energy-using tag through the sensor network and historical habit data, and obtains the energy-saving statistical information of the low-carbon building. By evaluating the difference in carbon emission information under different energy-using scenarios, the energy-saving potential and optimization strategy are calculated. For example, by analyzing the energy-consuming load data in a certain period, the usage pattern and habit of the load under different time periods and conditions are identified, the energy usage under different energy-using scenarios (such as peak hours and non-peak hours, etc.) is evaluated, and the carbon emission difference between the optimal energy-saving energy and the most energy-consuming energy under these scenarios is calculated to obtain a carbon emission information difference. Finally, the energy-saving sub-information of each part is comprehensively added to obtain the overall low-carbon building energy-saving statistical information. This process not only realizes the accurate evaluation of building energy consumption, but also provides intuitive energy-saving statistical information, which is convenient for further analysis and decision-making, thereby effectively optimizing building energy consumption, reducing carbon emissions, and achieving low-carbon energy-saving goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a flow chart of a method for generating a low-carbon building energy-saving strategy based on human behavior patterns provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0064] See also Figure 1 , is a flow chart of a method for generating a low-carbon building energy-saving strategy based on human behavior patterns provided by an embodiment of the present invention, including:

[0065] S1, obtain all energy-consuming loads of the target building to determine the corresponding monitoring equipment, the monitoring equipment obtains the historical working habits and corresponding first attribute data corresponding to each energy-consuming load, and the first attribute data at least includes first location information, first energy supply information and first load information.

[0066] This solution can accurately monitor the working habits of various energy-consuming loads in the building, and can also optimize energy use and reduce energy consumption through historical data analysis, thereby achieving the goal of energy conservation and emission reduction. This method has broad application prospects, especially in energy management and optimization in large-scale buildings or smart buildings.

[0067] It is worth mentioning that this solution divides the load into energy-consuming load and non-energy-consuming load. Exemplarily, for lighting, energy-consuming loads are, for example, electric lighting equipment, which needs to consume electric energy to achieve lighting; non-energy-consuming loads are, for example, optical lighting equipment, which can achieve lighting without consuming energy. In the prior art, when analyzing building carbon emissions and energy consumption, only energy-consuming loads are analyzed, and non-energy-consuming loads of this solution are not analyzed. In some embodiments, the corresponding monitoring equipment is determined by obtaining all energy-consuming loads of the target building, and the monitoring equipment obtains the historical working habits and corresponding first attribute data corresponding to each energy-consuming load, and the first attribute data includes at least first location information, first energy supply information and first load information, including:

[0068] S11, all energy-consuming loads of the target building are counted to obtain corresponding location information and configured to the monitoring device, wherein the location information includes the location of the building surface.

[0069] This solution will collect statistics on all energy-consuming loads in the target building, obtain their location information, and configure this information into the monitoring equipment. The location information includes the location of these energy-consuming loads in the building, describing the specific location of the energy-consuming loads, such as the side or top of a floor.

[0070] S12, the monitoring device obtains the historical working time of the energy-consuming load to obtain the corresponding historical habits, the monitoring device obtains the energy supply source of the energy-consuming load to obtain energy supply information, and obtains the electric energy waveform corresponding to the historical working time to obtain load information.

[0071] The monitoring device records the historical working time of each energy-consuming load, thereby obtaining its working habits. For example, the daily operating time and specific switching time of a certain device. The monitoring device records the energy supply information of the energy-consuming load, such as electricity. By recording the power waveform or other energy consumption data corresponding to the historical working time, the monitoring device obtains the load information of the load. These data can be used to calculate and analyze energy consumption data.

[0072] The monitoring device obtains the historical working time of the energy-consuming load to obtain the corresponding historical habits, obtains the energy supply source of the energy-consuming load to obtain energy supply information, and obtains the electric energy waveform corresponding to the historical working time to obtain load information, including:

[0073] S121, the monitoring device obtains the historical working time of the energy consumption load on each reference date to obtain at least one working time period, determines the average value of the central time point of all working time periods to obtain the first time point, and calculates the average value of the working time period to obtain the average time of each working time period.

[0074] The monitoring device records the working time of the energy-consuming load on each reference date to obtain multiple working time periods. The reference date can be every day of a month. Determine the central time point of all working time periods, and calculate the average value of these central time points to obtain a representative time point, called the first time point. Calculate the length of each working time period to obtain the average length of the working time period. The monitoring device records the working time period of each energy-consuming load on multiple consecutive reference dates. For example, from 8 am to 6 pm, the device records the working status of each hour. Count the central time points of all working time periods, for example, the central time from 8 am to 9 am is 8:30. Calculate the average value of these central time points, called the first time point. Count the length of each working time period, for example, a typical working time period is 2 hours. Calculate the average length of all working time periods to obtain the average working time.

[0075] S122, calculating the average time of all first time points to obtain the historical habits of the corresponding energy consumption loads.

[0076] All first time points are counted and their average time is calculated. This average time reflects the typical working time of the energy-consuming load, which is called historical habits. These data can be used to predict future energy consumption and make optimization adjustments.

[0077] S123, the monitoring device obtains the energy supply of the energy-consuming load to obtain energy supply information, where the energy supply includes at least one of light energy, electrical energy, and gas energy.

[0078] The monitoring equipment of this solution needs to record the energy supply information used by the energy-consuming load in real time. The energy supply includes at least one or more of light energy, electrical energy, and gas energy. This information helps to analyze the usage and efficiency of different energy sources and then formulate energy-saving strategies.

[0079] S124, calculating the waveforms of the working time period and then performing mean value calculation to obtain corresponding load information.

[0080] The electric energy waveform data during the working time period is recorded and statistically analyzed. The corresponding load information is obtained by calculating the mean of these waveform data. These load information can be used to evaluate the energy efficiency of the equipment and identify high energy consumption periods and high energy consumption equipment. Through the above steps, the present invention can record and analyze in detail the working habits, energy supply information and load information of various energy-consuming loads in the building, thereby providing strong data support for optimizing energy use, reducing energy consumption, and achieving the goal of low-carbon energy saving.

[0081] S2, adding corresponding energy usage tags to all energy consumption loads according to the energy consumption requirements of the energy consumption loads, wherein the energy usage tags include single energy usage tags and combined energy usage tags.

[0082] In some embodiments, the energy consumption tags corresponding to the energy consumption loads are added according to the energy consumption requirements of the energy consumption loads, and the energy consumption tags include single energy consumption tags and combined energy consumption tags, including:

[0083] S21, if it is determined that the energy demand of the energy-consuming load is one of light energy, electric energy, and gas energy, then a corresponding single energy label is added.

[0084] This solution will determine whether the energy demand of the energy-consuming load is one of light energy, electric energy, and gas energy. For example, if a device only uses electric energy, then its energy demand is a single energy demand. According to the judgment result, a corresponding single energy label is added to the energy-consuming load. For example, for a device that only uses electric energy, an electric energy label is added.

[0085] S22: If it is determined that the energy demand of the energy-consuming load is multiple types of light energy, electric energy, and gas energy, a corresponding combined energy label is added.

[0086] Determine whether the energy demand of the energy-consuming load is multiple of light energy, electric energy, and gas energy. For example, if a device uses both electric energy and gas energy, then its energy demand is a combined energy demand. According to the judgment result, add a corresponding combined energy label to the energy-consuming load. For example, for a device that uses both electric energy and gas energy, add an electric energy-gas energy combination label. Through the above steps, the embodiment of the present invention can accurately identify and classify the energy demand of various energy-consuming loads in a building.

[0087] S3, if it is determined that the target building has a non-energy-consuming load, the corresponding energy-consuming load is determined based on the comparison between the second attribute data of the non-energy-consuming load and the first attribute data, and the predicted habit of the non-energy-consuming load is determined based on the historical habit of the energy-consuming load.

[0088] This solution can effectively identify and process non-energy-consuming loads in the target building, and reasonably predict the usage habits of non-energy-consuming loads based on the attributes and historical habits of the corresponding energy-consuming loads.

[0089] In some embodiments, if it is determined that the target building has a non-energy-consuming load, the corresponding energy-consuming load is determined based on the comparison between the second attribute data of the non-energy-consuming load and the first attribute data, and the predicted habit of the non-energy-consuming load is determined based on the historical habit of the energy-consuming load, including:

[0090] S31: If it is determined that the target building has a non-energy-consuming load, receiving second attribute data configured by the user for the non-energy-consuming load, wherein the second attribute data includes second location information and second function type information.

[0091] If it is determined that the target building has a non-energy-consuming load, such as a light-guiding lighting device, the second attribute data about the non-energy-consuming load is received from the user. The second location information is, for example, the side of the third floor. The second function type information is, for example, a lighting function.

[0092] S32: Based on the comparison between the second function category information of the non-energy consuming loads and the first function category information of all the energy consuming loads, determine the energy consuming loads corresponding to the first function category information corresponding to the second function category information to obtain a first load set.

[0093] This solution will compare the second function category information (such as lighting function) of non-energy-consuming loads with the first function category information of all energy-consuming loads. The purpose of the comparison is to determine the first function category information corresponding to the second function category information to find the relevant energy-consuming loads. For example, assuming that the energy-consuming loads in the building include lamps, smart bulbs, etc., the first function category information of these devices is also lighting function. Through the comparison, the energy-consuming loads that meet the conditions are determined to form a first load set. The first load set is, for example, a load set corresponding to the lighting function.

[0094] S33, comparing the second position information of the non-energy consuming load with the first position information of the energy consuming load in the first load set to determine the target energy consuming load, and determining the predicted habit of the non-energy consuming load based on the historical habit of the target energy consuming load.

[0095] The second location information of the non-energy-consuming load (such as the side of the third floor) is compared with the first location information of each energy-consuming load in the first load set. The purpose of the comparison is to find energy-consuming loads that are geographically close. By comparing the location information, the energy-consuming load that is close to the non-energy-consuming load is determined as the target energy-consuming load. For example, there may be a lamp on the side of the third floor, and this lamp is the target energy-consuming load. Based on the historical usage habits of the target energy-consuming load (such as the lamp on the side of the third floor), the predicted habits of the non-energy-consuming load are determined. Historical habits may include information such as usage time periods. For example, if the lamp is usually turned on between 6 and 10 p.m., non-energy-consuming loads (such as decorations with lighting functions) can also be predicted to be used during the same time period.

[0096] The step of comparing the second position information of the non-energy-consuming load with the first position information of the energy-consuming load in the first load set to determine the target energy-consuming load, and determining the predicted habit of the non-energy-consuming load based on the historical habit of the target energy-consuming load includes:

[0097] S331, obtaining the number of energy-consuming loads in the first load set and located on the same building surface as the non-energy-consuming loads to obtain a first number.

[0098] Determine the energy-consuming loads in the first load set that are located on the same building surface as the non-energy-consuming loads. For example, if the non-energy-consuming load is located on the side of the third floor, then count all energy-consuming loads located on the side of the third floor to obtain a first quantity.

[0099] S332, if the first number is greater than or equal to the preset number, the target virtualized energy consumption load is calculated based on the energy consumption load on the same building surface, and the predicted habit of the non-energy consumption load is determined based on the historical habit of the target virtualized energy consumption load.

[0100] The preset number is a threshold value used to determine whether the number of energy-consuming loads in the same building surface is sufficient. For example, the preset number can be set to 10. If the first number is greater than or equal to the preset number, it means that the reference number is sufficient, and then the target virtualized energy-consuming load is calculated based on the energy-consuming loads in the same building surface. The target virtualized energy-consuming load is not an actual load, but a virtual load proposed by this scheme based on multiple energy-consuming loads, and then the historical habits of the target virtualized energy-consuming load are calculated to determine the predicted habits of the non-energy-consuming load.

[0101] S333: If the first number is less than the preset number, the virtualized energy consumption load of the target is calculated based on the energy consumption loads on different building surfaces, and the predicted habits of the non-energy consumption load are determined based on the historical habits of the target energy consumption load.

[0102] If the number of energy-consuming loads on the same building surface is less than the preset number, it means that the reference data is insufficient and the accuracy of the predicted data will be low. Therefore, this solution will include the energy-consuming loads on other building surfaces in the calculation range to make up for the insufficient number. By combining the energy-consuming loads of each building surface, the target virtualized energy-consuming load is calculated. Similarly, based on the historical habits of the target virtualized energy-consuming load, the prediction habits of non-energy-consuming loads are determined.

[0103] In some embodiments, if the first number is greater than or equal to a preset number, a virtualized energy consumption load of a target is calculated based on energy consumption loads on the same building surface, and a prediction habit of a non-energy consumption load is determined based on a historical habit of the energy consumption load of the target, including:

[0104] If the first number is greater than or equal to a preset number, all first time points and working time periods of all energy-consuming loads in their historical habits are selected.

[0105] It can be understood that when the number is large enough, the present solution can extract the historical habit data of all energy-consuming loads within the same building surface, including all first time points and working time periods.

[0106] The median time point and the mean time period are obtained by performing mean calculation based on the first time point and the working time period, and the median time point and the mean time period of the target virtualized energy-consuming load are determined as the prediction habit of the non-energy-consuming load.

[0107] This solution will calculate the average of all first time points to obtain a median time point. This can reflect the time when these energy-consuming loads usually work. For example, if the first time points of the lights on the side of the three-story building are 10:00 and 14:00, respectively, then the median time point is 12:00. At the same time, this solution will calculate the average of all working time periods to obtain an average time period. This can reflect the average duration that these energy-consuming loads usually work. For example, if the working hours of the three lights are 2 hours and 3 hours, respectively, then the average time period is (2+3) / 2 = 2.5 hours. This solution will use the calculated median time point and average time period as the historical habits of the virtualized energy-consuming loads. Based on these habits, the predicted habits of non-energy-consuming loads can be determined, that is, their possible working times and durations.

[0108] In some embodiments, if the first number is less than a preset number, a virtualized energy consumption load of a target is calculated based on energy consumption loads on different building surfaces, and a predicted habit of a non-energy consumption load is determined based on a historical habit of the energy consumption load of the target, including:

[0109] If the first number is less than the preset number, the energy-consuming loads of different building surfaces are selected and the positional distance relationship between them and the non-energy-consuming loads is calculated, and the energy-consuming loads are sorted in ascending order based on the positional distance relationship to obtain a selection sequence. It is understandable that when the first number is less than the preset number, energy-consuming loads need to be selected from different building surfaces to make up the number. The positional distance relationship between the energy-consuming loads and the non-energy-consuming loads of each different building surface is calculated. The positional distance relationship can be a physical distance or other appropriate measurement method. The energy-consuming loads of different building surfaces are sorted in ascending order based on the positional distance relationship to form a selection sequence. The energy-consuming loads with closer distances are ranked first. It is worth mentioning that the closer the distance of the energy-consuming loads, the closer their habits may be, and they will be given priority in analysis.

[0110] The energy-consuming loads are selected in sequence based on the selection sequence to obtain a selected number, the selection of the energy-consuming loads is stopped after the sum of the selected number and the first number is equal to a preset number, and the surface distance relationship between the building surface of each energy-consuming load and the building surface of the non-energy-consuming load is determined.

[0111] This solution selects energy-consuming loads in sequence according to the selection sequence until the sum of the selected number and the first number equals the preset number. Determine the surface distance relationship between the building surface of each energy-consuming load and the building surface of the non-energy-consuming load. The surface distance relationship can be horizontal distance, vertical distance or other appropriate measurement. For example, the distance to lamp B is 1 floor distance, and the distance to lamp C is 2 floor distance. Then lamp B will be selected first.

[0112] Based on the number of energy-consuming loads on the same building surface as the non-energy-consuming loads, the number of energy-consuming loads on different building surfaces, and the surface distance relationship, the median time point and mean time period are obtained, and the median time point and mean time period of the target virtualized energy-consuming load are determined as the prediction habits of the non-energy-consuming load.

[0113] It is understandable that when the number of energy-consuming loads in the same building surface is less than the preset number, the energy-consuming loads of different building surfaces can be effectively utilized, and reasonable selection and calculation can be performed through the position distance relationship and the surface distance relationship to obtain virtualized energy-consuming loads, and the use habits of non-energy-consuming loads can be accurately predicted based on their historical habits. This method not only ensures the comprehensiveness of the prediction, but also adapts to the relationship between different building surfaces, providing a flexible and reliable solution.

[0114] In the above embodiment, the calculation based on the number of energy-consuming loads on the same building surface as the non-energy-consuming load, the number of energy-consuming loads on different building surfaces, and the surface distance relationship obtains the median time point and the average time period, and determines the median time point and the average time period of the target virtualized energy-consuming load as the prediction habit of the non-energy-consuming load, including:

[0115] The reference weight offset value is obtained by comparing the number of energy-consuming loads on the same building surface and the number of energy-consuming loads on different building surfaces. It is understandable that the smaller the number of energy-consuming loads on the same building surface or the greater the number of energy-consuming loads on different building surfaces, the smaller the reference weight offset value will be, so that the corresponding

[0116] Different reference weights are obtained based on the surface distance relationship of the energy consumption load of different building surfaces, and the numerical interval of each surface distance relationship has a preset reference weight. It can be understood that the larger the numerical interval of the surface distance relationship, the smaller the corresponding preset reference weight can be.

[0117] The median time point and mean time period are calculated using the following formula,

[0118] ;

[0119] in, is the median time point, For the same building surface The time point of the energy consumption load, is the number of energy-consuming loads with the same building surface, is the number of energy loads on different building surfaces, is the first constant value, is the benchmark time weight, For different building surfaces The time point of the energy consumption load, For different building surfaces The reference weight of each energy-consuming load, is the quantity value at a time point, is the normalized value at the time point, is the length of the mean time period, For the same building surface The length of the time period of the energy-consuming load, is the second constant value, is the benchmark time weight, For different building surfaces The length of the time period of the energy-consuming load, For different building surfaces The reference weight of each energy-consuming load, is the number of time periods, is the normalized value of the time period.

[0120] In the above formula, Represents the benchmark weight offset value. The larger the number of energy-consuming loads on the same building surface, the smaller the number of energy-consuming loads on different building surfaces, and the larger the corresponding benchmark weight offset value. the less; Represents a combination of different reference weights.

[0121] The calculation principle of the time length of the mean time period is similar. Among them, the number value of the time point, the normalized value of the time point, the number value of the time period, and the normalized value of the time period can be preset by the user.

[0122] S4, the historical habits of energy-consuming loads of the combined energy consumption labels, the historical habits of non-energy-consuming loads, and the load calculations are used to obtain energy-saving statistical information of low-carbon buildings.

[0123] In some embodiments, the energy-saving statistical information of low-carbon building energy obtained by calculating the historical habits of energy-consuming loads, historical habits of non-energy-consuming loads, and loads of the combined energy consumption tags includes:

[0124] S41, obtaining the historical habits of the energy consumption load of the combined energy consumption tag, and calculating the carbon emission information difference between the optimal energy-saving energy and the most energy-consuming energy under different energy consumption scenarios.

[0125] This solution analyzes the energy consumption load data in a certain period to identify the usage patterns and habits of the load under different time periods and conditions. For example, in an office building, the electricity consumption habits during the day and at night are quite different, and these differences can be statistically analyzed through historical data. Evaluate energy usage under different energy usage scenarios (such as peak hours and off-peak hours, etc.). Calculate the carbon emission difference between the best energy-saving energy (such as electricity for heating) and the most energy-consuming energy (such as gas for heating) in these scenarios, and obtain a carbon emission information difference.

[0126] S42, obtaining first energy-saving sub-information based on historical habits of energy consumption loads of the combined energy consumption tags and carbon emission information difference calculation.

[0127] This solution can use the difference between the historical habit data and carbon emission information obtained in S41 to calculate the energy saving potential in various energy use scenarios. This calculation can involve optimizing the proportion of different energy sources to minimize carbon emissions. For example, by optimizing energy use strategies, reducing the use of high-carbon emission energy and increasing the use of low-carbon or zero-carbon emission energy.

[0128] S43, counting the energy consumption load of the same building surface and the load average of the energy consumption load of different building surfaces, and calculating the second energy-saving sub-information based on the load average and the historical habit of non-energy consumption load.

[0129] Energy consumption load refers to the amount of energy consumed by the building during operation, while the load mean refers to the average value of these energy consumption data. The historical habits of non-energy consumption loads refer to the historical patterns of loads that do not consume energy (such as natural ventilation, sunlight, etc.). Through these data, the second energy-saving sub-information can be calculated, reflecting the energy-saving effects under different building surfaces and load conditions.

[0130] S44, adding the first energy-saving sub-information and the second energy-saving sub-information to obtain energy-saving statistical information.

[0131] Finally, this solution adds the first energy-saving sub-information and the second energy-saving sub-information to obtain the energy-saving statistical information of the entire building. The final energy-saving statistical information is obtained by integrating the two parts of sub-information and is used to evaluate the overall energy-saving effect. Through the statistics and analysis of historical data, a comprehensive evaluation of building energy consumption can be carried out to provide a decision-making basis that is conducive to energy conservation. By calculating the difference in carbon emission information under different energy usage scenarios, the carbon emission characteristics of different energy sources can be clearly understood. By statistically analyzing the load mean, the energy consumption of the building under different usage conditions can be more accurately reflected, thereby optimizing the energy-saving strategy. The energy-saving statistical information finally obtained is a combination of different sub-information, which can provide a comprehensive evaluation of energy-saving effects.

[0132] The embodiment of the present invention provides a low-carbon building energy-saving strategy generation system based on human behavior patterns, including:

[0133] A monitoring module, used to obtain all energy-consuming loads of a target building to determine corresponding monitoring devices, wherein the monitoring devices obtain the historical working habits and corresponding first attribute data corresponding to each energy-consuming load, wherein the first attribute data at least includes first location information, first energy supply information, and first load information;

[0134] A label module, used to add corresponding energy labels to all energy-consuming loads according to the energy demand of the energy-consuming loads, wherein the energy labels include single energy labels and combined energy labels;

[0135] A prediction module, for determining a corresponding energy-consuming load based on the comparison between the second attribute data of the non-energy-consuming load and the first attribute data if it is determined that the target building has a non-energy-consuming load, and determining a predicted habit of the non-energy-consuming load based on a historical habit of the energy-consuming load;

[0136] The calculation module is used to calculate the historical habits of energy-consuming loads and non-energy-consuming loads of the combined energy consumption tags and obtain the energy-saving statistical information of low-carbon buildings.

[0137] An embodiment of the present invention provides an electronic device, the electronic device comprising: a processor, a memory and a computer program; wherein

[0138] The memory is used to store the computer program, and the memory may also be a flash memory. The computer program is, for example, an application program, a functional module, etc. for implementing the above method.

[0139] The processor is used to execute the computer program stored in the memory to implement each step performed by the device in the above method. For details, please refer to the relevant description in the above method embodiment.

[0140] Optionally, the memory can be independent or integrated with the processor.

[0141] When the memory is a device independent of the processor, the device may further include:

[0142] A bus is used to connect the memory and the processor.

[0143] The present invention also provides a storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the various embodiments described above.

[0144] Among them, the storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, the storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also exist in a communication device as discrete components. The storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0145] The present invention also provides a program product, which includes an execution instruction, which is stored in a storage medium. At least one processor of a device can read the execution instruction from the storage medium, and at least one processor executes the execution instruction so that the device implements the methods provided in the above various embodiments.

[0146] In the above-mentioned terminal or server embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating low-carbon building energy-saving strategies based on human behavior patterns, characterized in that: include: Obtain all energy-consuming loads of the target building to determine the corresponding monitoring equipment, and the monitoring equipment obtains the historical working habits and corresponding first attribute data corresponding to each energy-consuming load, wherein the first attribute data at least includes first location information, first energy supply information, and first load information; Adding corresponding energy usage tags to all energy consumption loads according to the energy consumption requirements of the energy consumption loads, wherein the energy usage tags include single energy usage tags and combined energy usage tags; If it is determined that the target building has a non-energy-consuming load, the corresponding energy-consuming load is determined based on the comparison between the second attribute data of the non-energy-consuming load and the first attribute data, and the predicted habit of the non-energy-consuming load is determined based on the historical habit of the energy-consuming load; The historical habits of energy-consuming loads and non-energy-consuming loads of the combined energy consumption labels and load calculations are used to obtain energy-saving statistics of low-carbon buildings; The method of obtaining all energy-consuming loads of the target building to determine the corresponding monitoring equipment, wherein the monitoring equipment obtains the historical working habits and corresponding first attribute data corresponding to each energy-consuming load, wherein the first attribute data at least includes first location information, first energy supply information and first load information, including: Count all energy-consuming loads of the target building to obtain corresponding location information and configure it to the monitoring device, wherein the location information includes the location of the building surface; The monitoring device obtains the historical working time of the energy-consuming load to obtain the corresponding historical habits, the monitoring device obtains the energy supply source of the energy-consuming load to obtain the energy supply information, and obtains the electric energy waveform corresponding to the historical working time to obtain the load information; The step of adding corresponding energy usage tags to all energy consumption loads according to the energy consumption requirements of the energy consumption loads, wherein the energy usage tags include single energy usage tags and combined energy usage tags, including: If the energy demand of the energy-consuming load is determined to be one of light energy, electrical energy, and gas energy, then a corresponding single energy label is added; If the energy demand of the energy-consuming load is determined to be multiple types of light energy, electrical energy, and gas energy, then a corresponding combined energy label is added; If it is determined that the target building has a non-energy-consuming load, the corresponding energy-consuming load is determined based on the comparison between the second attribute data of the non-energy-consuming load and the first attribute data, and the predicted habit of the non-energy-consuming load is determined based on the historical habit of the energy-consuming load, including: If it is determined that the target building has a non-energy-consuming load, receiving second attribute data configured by the user for the non-energy-consuming load, the second attribute data including second location information and second function type information; Based on the comparison between the second function type information of the non-energy consuming load and the first function type information of all the energy consuming loads, the energy consuming loads corresponding to the first function type information corresponding to the second function type information are determined to obtain a first load set; Comparing the second position information of the non-energy-consuming load with the first position information of the energy-consuming load in the first load set to determine the target energy-consuming load, and determining the predicted habit of the non-energy-consuming load based on the historical habit of the target energy-consuming load; The step of comparing the second position information of the non-energy-consuming load with the first position information of the energy-consuming load in the first load set to determine the target energy-consuming load, and determining the predicted habit of the non-energy-consuming load based on the historical habit of the target energy-consuming load includes: Obtaining the number of energy-consuming loads in the first load set and located on the same building surface as the non-energy-consuming load to obtain a first number; If the first number is greater than or equal to the preset number, the target virtualized energy consumption load is calculated based on the energy consumption loads on the same building surface, and the prediction habit of the non-energy consumption load is determined based on the historical habit of the target virtualized energy consumption load; If the first number is less than a preset number, a virtualized energy consumption load of the target is calculated based on energy consumption loads on different building surfaces, and a predicted habit of the non-energy consumption load is determined based on the historical habit of the target energy consumption load.

2. The method for generating low-carbon building energy-saving strategies based on human behavior patterns according to claim 1 is characterized in that: The monitoring device obtains the historical working time of the energy-consuming load to obtain the corresponding historical habits, obtains the energy supply source of the energy-consuming load to obtain energy supply information, and obtains the electric energy waveform corresponding to the historical working time to obtain load information, including: The monitoring device obtains the historical working time of the energy consumption load on each reference date to obtain at least one working time period, determines the average value of the central time point of all working time periods to obtain the first time point, and calculates the average value of the working time period to obtain the average time of each working time period; Count the average time of all first time points to obtain the historical habits of the corresponding energy consumption load; The monitoring device obtains the energy supply information of the energy-consuming load, wherein the energy supply includes at least one of light energy, electrical energy, and gas energy; After counting the waveforms of the working time period, the average value is calculated to obtain the corresponding load information.

3. The method for generating low-carbon building energy-saving strategies based on human behavior patterns according to claim 1 is characterized in that: If the first number is greater than or equal to a preset number, a virtualized energy consumption load of a target is calculated based on energy consumption loads on the same building surface, and a prediction habit of a non-energy consumption load is determined based on a historical habit of the energy consumption load of the target, including: If the first number is greater than or equal to the preset number, all first time points and working time periods of all energy-consuming loads in their historical habits are selected; The median time point and the mean time period are obtained by performing mean calculation based on the first time point and the working time period, and the median time point and the mean time period of the target virtualized energy-consuming load are determined as the prediction habit of the non-energy-consuming load.

4. The method for generating low-carbon building energy-saving strategies based on human behavior patterns according to claim 1 is characterized in that: If the first number is less than a preset number, a virtualized energy consumption load of a target is calculated based on energy consumption loads on different building surfaces, and a prediction habit of a non-energy consumption load is determined based on a historical habit of the target energy consumption load, including: If the first number is less than a preset number, energy-consuming loads on different building surfaces are selected and their position distance relationship with non-energy-consuming loads is calculated, and the energy-consuming loads are sorted in ascending order based on the position distance relationship to obtain a selection sequence; Selecting energy-consuming loads in sequence based on the selection sequence to obtain a selection quantity, stopping the selection of energy-consuming loads after the sum of the selection quantity and the first quantity is equal to a preset quantity, and determining a surface distance relationship between a building surface of each energy-consuming load and a building surface of a non-energy-consuming load; Based on the number of energy-consuming loads on the same building surface as the non-energy-consuming loads, the number of energy-consuming loads on different building surfaces, and the surface distance relationship, the median time point and mean time period are obtained, and the median time point and mean time period of the target virtualized energy-consuming load are determined as the prediction habits of the non-energy-consuming load.

5. The method for generating low-carbon building energy-saving strategies based on human behavior patterns according to claim 4 is characterized in that: The calculation based on the number of energy-consuming loads on the same building surface as the non-energy-consuming load, the number of energy-consuming loads on different building surfaces, and the surface distance relationship obtains the median time point and the average time period, and determines the median time point and the average time period of the target virtualized energy-consuming load as the prediction habit of the non-energy-consuming load, including: Obtaining a reference weight offset value based on the number of energy-consuming loads on the same building surface and the number of energy-consuming loads on different building surfaces; Different reference weights are obtained based on the surface distance relationship of the energy consumption load of different building surfaces, and the numerical range of each surface distance relationship has a preset reference weight; The median time point and mean time period are calculated using the following formula, ， in, is the median time point, For the same building surface The time point of the energy consumption load, is the number of energy-consuming loads with the same building surface, is the number of energy loads on different building surfaces, is the first constant value, is the benchmark time weight, For different building surfaces The time point of the energy consumption load, For different building surfaces The reference weight of each energy-consuming load, is the quantity value at a time point, is the normalized value at the time point, is the length of the mean time period, For the same building surface The length of the time period of the energy-consuming load, is the second constant value, is the benchmark time weight, For different building surfaces The length of the time period of the energy-consuming load, For different building surfaces The reference weight of each energy-consuming load, is the number of time periods, is the normalized value of the time period.

6. The method for generating low-carbon building energy-saving strategies based on human behavior patterns according to claim 5 is characterized in that: The energy-saving statistical information of low-carbon building energy obtained by calculating the historical habits of energy-consuming loads and non-energy-consuming loads of the combined energy consumption labels includes: Obtain the historical habits of energy consumption loads of combined energy consumption tags, and calculate the carbon emission information difference between the best energy-saving energy and the most energy-consuming energy in different energy consumption scenarios; The first energy-saving sub-information is obtained by calculating the difference between the historical habits of the energy consumption load and the carbon emission information of the combined energy consumption label; The energy consumption load of the same building surface and the load average of the energy consumption load of different building surfaces are counted, and the second energy-saving sub-information is calculated based on the load average and the historical habits of non-energy consumption loads; The first energy-saving sub-information and the second energy-saving sub-information are added together to obtain energy-saving statistical information.

7. A low-carbon building energy-saving strategy generation system based on human behavior patterns, characterized by: include: A monitoring module, used to obtain all energy-consuming loads of a target building to determine corresponding monitoring devices, wherein the monitoring devices obtain the historical working habits and corresponding first attribute data corresponding to each energy-consuming load, wherein the first attribute data at least includes first location information, first energy supply information, and first load information; A label module, used to add corresponding energy labels to all energy-consuming loads according to the energy demand of the energy-consuming loads, wherein the energy labels include single energy labels and combined energy labels; A prediction module, for determining a corresponding energy-consuming load based on the comparison between the second attribute data of the non-energy-consuming load and the first attribute data if it is determined that the target building has a non-energy-consuming load, and determining a predicted habit of the non-energy-consuming load based on a historical habit of the energy-consuming load; A calculation module is used to calculate the historical habits of energy-consuming loads and the historical habits of non-energy-consuming loads of the combined energy consumption tags and loads to obtain energy-saving statistical information of low-carbon buildings; The method of obtaining all energy-consuming loads of the target building to determine the corresponding monitoring equipment, wherein the monitoring equipment obtains the historical working habits and corresponding first attribute data corresponding to each energy-consuming load, wherein the first attribute data at least includes first location information, first energy supply information and first load information, including: Count all energy-consuming loads of the target building to obtain corresponding location information and configure it to the monitoring device, wherein the location information includes the location of the building surface; The monitoring device obtains the historical working time of the energy-consuming load to obtain the corresponding historical habits, the monitoring device obtains the energy supply source of the energy-consuming load to obtain the energy supply information, and obtains the electric energy waveform corresponding to the historical working time to obtain the load information; The step of adding corresponding energy usage tags to all energy consumption loads according to the energy consumption requirements of the energy consumption loads, wherein the energy usage tags include single energy usage tags and combined energy usage tags, including: If the energy demand of the energy-consuming load is determined to be one of light energy, electrical energy, and gas energy, then a corresponding single energy label is added; If the energy demand of the energy-consuming load is determined to be multiple types of light energy, electrical energy, and gas energy, then a corresponding combined energy label is added; If it is determined that the target building has a non-energy-consuming load, the corresponding energy-consuming load is determined based on the comparison between the second attribute data of the non-energy-consuming load and the first attribute data, and the predicted habit of the non-energy-consuming load is determined based on the historical habit of the energy-consuming load, including: If it is determined that the target building has a non-energy-consuming load, receiving second attribute data configured by the user for the non-energy-consuming load, the second attribute data including second location information and second function type information; Based on the comparison between the second function type information of the non-energy consuming load and the first function type information of all the energy consuming loads, the energy consuming loads corresponding to the first function type information corresponding to the second function type information are determined to obtain a first load set; Comparing the second position information of the non-energy-consuming load with the first position information of the energy-consuming load in the first load set to determine the target energy-consuming load, and determining the predicted habit of the non-energy-consuming load based on the historical habit of the target energy-consuming load; The step of comparing the second position information of the non-energy-consuming load with the first position information of the energy-consuming load in the first load set to determine the target energy-consuming load, and determining the predicted habit of the non-energy-consuming load based on the historical habit of the target energy-consuming load includes: Obtaining the number of energy-consuming loads in the first load set and located on the same building surface as the non-energy-consuming load to obtain a first number; If the first number is greater than or equal to the preset number, the target virtualized energy consumption load is calculated based on the energy consumption loads on the same building surface, and the prediction habit of the non-energy consumption load is determined based on the historical habit of the target virtualized energy consumption load; If the first number is less than a preset number, a virtualized energy consumption load of the target is calculated based on energy consumption loads on different building surfaces, and a predicted habit of the non-energy consumption load is determined based on the historical habit of the target energy consumption load.

8. An electronic device, characterized in that: include: A memory, a processor and a computer program, wherein the computer program is stored in the memory, and the processor runs the computer program to execute the method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a computer program, which is used to implement any one of the methods of claims 1 to 6 when executed by a processor.

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