A green building carbon emission optimization method, system and storage medium

Abstract

The present application relates to the technical field of green buildings, and in particular to a green building carbon emission optimization method, system and storage medium, which includes: obtaining building construction information to obtain basic carbon indicators; calculating a stimulus calibration ratio based on the basic carbon indicators; obtaining the building type and the building energy-saving tendency, and generating a corresponding type coefficient set based on the building type and a tendency coefficient set based on the building energy-saving tendency; generating intelligent body variables based on the type coefficient set and the tendency coefficient set; collecting the current energy consumption data and the current time of the building, calculating the current energy consumption data with the intelligent body variables to generate a stimulus amount; comparing the stimulus amount with the stimulus calibration ratio corresponding to the current time to analyze the reaction trend, calling the corresponding reaction measures based on the reaction trend, and outputting the reaction measures as an optimization plan. The present application has the effect of improving the carbon emission optimization management of green buildings.

Description

Technical Field

[0001] The present application relates to the technical field of green buildings, and in particular to a green building carbon emission optimization method, system and storage medium. Background Art

[0002] Currently, low-carbon green construction of buildings is an important part of urban construction. In the construction and management of green buildings, many aspects need to be considered, such as air conditioning, water supply, lighting electricity, etc. These parts of the building will more or less increase the overall carbon emissions of the building. How to optimize the management of carbon emissions in buildings to improve the excellent construction of low-carbon green buildings is a serious problem in current development.

[0003] In current technology, real-time updated energy consumption data of buildings are often collected and managed through BIM or IoT. The collected energy consumption data of various types are compared with the preset limit values, and the need for corresponding energy-saving optimization is determined based on the comparison results.

[0004] However, there is a problem that, on the one hand, in the management of green buildings, the data collection and analysis processes are often carried out independently, such as judging whether the water consumption exceeds the prescribed amount, whether the electricity consumption exceeds the prescribed amount, etc., but the various types of energy consumption data cannot be connected and shared, resulting in information islands. On the other hand, the energy consumption alarm of green buildings can only be compared with fixed values, and it is impossible to achieve real-time comparison and analysis of energy consumption at any time. Even if the current energy consumption is extremely high, as long as it has not accumulated to the alarm value, no alarm will be generated and optimization suggestions will not be generated. Therefore, the current technology has a poor effect on carbon emission optimization for green buildings. Summary of the invention

[0005] In order to improve the carbon emission optimization management of green buildings, the present application provides a green building carbon emission optimization method, system and storage medium.

[0006] In the first aspect, the present application provides a green building carbon emission optimization method, which adopts the following technical solution:

[0007] A green building carbon emission optimization method comprises the following steps:

[0008] Obtaining building construction information to obtain basic carbon indicators, wherein the building construction information includes building material information, space design information, construction information, and energy consumption information;

[0009] Calculating a stimulation calibration ratio according to the basic carbon index;

[0010] Acquire a building type and a building energy-saving tendency, and generate a corresponding type coefficient set based on the building type and a tendency coefficient set based on the building energy-saving tendency;

[0011] Generate an agent variable based on the type coefficient set and the tendency coefficient set, wherein the agent variable is characterized by a sensitivity to the stimulus;

[0012] Collecting current energy consumption data and current time of the building, and calculating the current energy consumption data with the agent variable to generate a stimulus amount;

[0013] The stimulation amount is compared with the stimulation calibration ratio corresponding to the current time to analyze the reaction trend, corresponding reaction measures are retrieved based on the reaction trend, and the reaction measures are output as an optimization solution.

[0014] In some embodiments, obtaining building construction information to obtain basic carbon indicators includes the following steps:

[0015] Generate embodied carbon data based on the building materials information and construction information;

[0016] Generate an implicit coefficient inversely proportional to the value of the implicit carbon data based on the value of the implicit carbon data;

[0017] Predicting operating carbon data based on the space design information and energy consumption body information;

[0018] The basic carbon index is calculated according to the operating carbon data and the implicit coefficient.

[0019] In some of these embodiments,

[0020] In some embodiments, calculating the stimulation calibration ratio according to the basic carbon index includes the following steps:

[0021] Proportionally dividing the basic carbon index based on the time coefficient to obtain a unit time index;

[0022] Acquire stimulus factors, and acquire ideal proportions corresponding to different types of energy consumption based on the stimulus factors, wherein the stimulus factors include energy-saving stimulus, water-saving stimulus, and temperature-saving stimulus;

[0023] Based on the ideal proportion and the unit time scale, stimulation calibration ratios corresponding to different types of energy consumption are generated respectively.

[0024] In some embodiments, obtaining a building type and a building energy-saving tendency, and generating a corresponding type coefficient set based on the building type and generating a tendency coefficient set based on the building energy-saving tendency include the following steps:

[0025] The building types include residential, commercial, production, and sports. The type coefficient sets corresponding to different building types include several type coefficients with different numerical proportions. The type coefficients include energy-saving coefficients, water-saving coefficients, and temperature-saving coefficients.

[0026] The building energy-saving tendency is generated based on active planning requirement information and carbon pressure passively generated based on the basic carbon index. The building energy-saving tendency includes strong tendency, medium tendency and weak tendency. The tendency coefficient sets corresponding to different building energy-saving tendencies all contain tendency coefficients with different numerical values, wherein the tendency coefficients correspond to strong tendency, medium tendency and weak tendency from large to small, respectively.

[0027] In some embodiments, generating an agent variable based on the type coefficient set and the tendency coefficient set comprises the following steps:

[0028] Multiplying each of the type coefficients in the type coefficient set by the tendency coefficient to obtain a plurality of first variable values;

[0029] Multiplying each of the type coefficients in the type coefficient set by a corresponding weight coefficient to obtain a plurality of weight coefficients, and adding the plurality of weight coefficients and multiplying them by the tendency coefficient to obtain a second variable value, wherein the weight coefficients are characterized by the proportion of the energy consumption corresponding to the type coefficient in the current energy consumption data and are all less than 1;

[0030] The first variable value and the second variable value together constitute the agent variable.

[0031] In some embodiments, collecting the current energy consumption data and the current time of the building, and calculating the current energy consumption data with the agent variable to generate the stimulus amount includes the following steps:

[0032] Classifying the current energy consumption data to obtain various types of energy consumption components, the types including water consumption, energy consumption, and heating consumption;

[0033] Multiplying each of the energy consumption components by the first variable value of the corresponding type to obtain stimulation components of different types;

[0034] The current energy consumption data is multiplied by the second variable value to obtain a total stimulation amount.

[0035] In some embodiments, comparing the stimulus amount with the stimulus calibration ratio corresponding to the current time to analyze the reaction trend, and calling the corresponding reaction measure based on the reaction trend, includes the following steps:

[0036] Acquire a stimulation calibration ratio corresponding to a corresponding unit time scale based on the current time, and add up a plurality of stimulation calibration ratios to obtain a total stimulation calibration ratio;

[0037] Matching a plurality of the stimulus components to corresponding stimulus factors to obtain the corresponding stimulus calibration ratio and performing comparison, and generating a first reaction including an avoidance reaction and an approach reaction according to the comparison result;

[0038] Comparing the total stimulus amount with the total stimulus calibration ratio, and generating a second response including an avoidance response and an approach response according to the comparison result;

[0039] When there is an avoidance reaction in the first reaction, a corresponding single reaction measure is called based on the energy consumption type corresponding to the avoidance reaction; when the second reaction is the avoidance reaction, multiple reaction measures are called.

[0040] In some of the embodiments, after generating the reaction measure, the following steps are further included:

[0041] Collecting the change amount of the current energy consumption data after the reaction measure is optimized, and generating optimization feedback according to the change amount;

[0042] The response measures are adjusted according to the optimization feedback.

[0043] In the second aspect, the present application provides a green building carbon emission optimization system, which adopts the following technical solutions:

[0044] A green building carbon emission optimization system is used to implement the above-mentioned green building carbon emission optimization method.

[0045] In a third aspect, the present application provides a storage medium, which adopts the following technical solution:

[0046] A storage medium stores at least one instruction, at least one program, a code set or an instruction set, wherein the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the above method.

[0047] The technical solution provided by the embodiments of the present application has the following technical effects:

[0048] Basic indicators are generated through the construction information that cannot be changed by the building, and the basic tone of carbon emission optimization is set by the basic indicators. At the same time, a perception and sensitivity system for stimuli is established according to the type of building and the corresponding energy-saving efforts of the building, so as to distinguish the carbon emission stimulus perception of different energy consumption types in different buildings. In this way, after collecting the current building energy consumption data, the amount of stimulation perceived by each part of the current energy consumption data can be first calculated through the intelligent body variables, and the stimulation amount can be compared with the basic tone of carbon emission optimization. According to the comparison results, it is judged whether optimization measures are needed, and the objects and contents of specific optimization measures are generated. In this way, various types of energy consumption data can be comprehensively managed and analyzed during the carbon emission data monitoring of green buildings, and the analysis and optimization of carbon emission data can be carried out in real time at any time to improve the optimization management effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the steps of a green building carbon emission optimization method provided in this embodiment. DETAILED DESCRIPTION

[0050] To more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. However, it should be understood by those of ordinary skill in the art that the present application can be implemented without these details. In some cases, in order to avoid unnecessary descriptions that make various aspects of the present application obscure, well-known methods, processes, systems, components and / or circuits that have been described at a higher level will not be described in detail. For those of ordinary skill in the art, it is obvious that various changes can be made to the embodiments disclosed in the present application, and without departing from the principles and scope of the present application, the general principles defined in the present application can be applied to other embodiments and application scenarios. Therefore, the present application is not limited to the embodiments shown, but conforms to the broadest scope consistent with the scope claimed for protection of the present application.

[0051] It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as there is no conflict between them.

[0052] In the description of this application, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used to distinguish the technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0053] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples.

[0054] like Figure 1 As shown, the embodiment of the present application discloses a green building carbon emission optimization method, comprising the following steps:

[0055] S100, obtain building construction information to obtain basic carbon indicators.

[0056] Building construction information is represented by information on the design, construction, building material selection, transportation, installation, and energy-consuming elements of the target building during the construction phase. This information is used to comprehensively determine the carbon emissions accumulated by the building in the past and the carbon emissions that need to be eliminated and controlled in the future.

[0057] S200, calculating the stimulation calibration ratio according to the basic carbon index.

[0058] The stimulus calibration ratio corresponding to the building can be calculated through the basic carbon index calculated above. The stimulus is the motivation of a person or system to actively improve and optimize energy consumption after monitoring a certain degree of energy consumption data. The stimulus calibration ratio is a preset value based on the current basic carbon index. When the basic carbon index is large, it means that the carbon pressure of the building is large, and the corresponding stimulus calibration ratio is relatively small, which can make the stimulus generation more sensitive and trigger subsequent response measures in a timely manner.

[0059] S300, obtaining a building type and a building energy-saving tendency, and generating a corresponding type coefficient set based on the building type and a tendency coefficient set based on the building energy-saving tendency.

[0060] A corresponding type coefficient set is generated according to different types of buildings. The set contains type coefficients for different energy consumption objects in different building types. This is because the proportion and focus of different energy consumption of different types of buildings are different. For example, most of the energy consumption in residential buildings is in water and electricity, most of the energy consumption in stadiums is in electricity, and most of the energy consumption in commercial buildings is in electricity and heating. Therefore, different types of buildings have different consideration coefficients for different energy consumption objects.

[0061] The tendency of building energy conservation is characterized by the fact that owners or managers of different buildings will take energy-saving measures of different intensities. On the one hand, this energy-saving measure is actively required by managers based on policies, and on the other hand, it is passively carried out by managers based on current energy consumption.

[0062] Energy-saving measures of different intensities correspond to different tendency coefficients, which are related to the sensitivity of subsequent stimulation of energy-saving measures.

[0063] S400, generating agent variables based on the type coefficient set and the tendency coefficient set.

[0064] The sensitivity to the stimulation is predicted and calculated based on the type coefficient set and the tendency coefficient set, which is used to indicate whether the acquired current energy consumption value is likely to trigger the stimulation more easily.

[0065] S500, collecting the current energy consumption data and current time of the building, and calculating the current energy consumption data and the agent variables to generate a stimulus amount.

[0066] The energy consumption data of the current building and the current time of the collection are collected. First, the current energy consumption data is associated with the agent variables to calculate the stimulus amount. The size of the stimulus amount is represented as a value used to determine whether it is necessary to trigger carbon emission optimization of the current building to achieve energy saving. The larger the stimulus amount, the closer the current energy consumption data is to the ideal load limit.

[0067] S600, comparing the stimulus amount with the stimulus calibration ratio corresponding to the current time to analyze the reaction trend, retrieving the corresponding reaction measure based on the reaction trend, and outputting the reaction measure as an optimization solution.

[0068] The stimulus amount is compared with the stimulus calibration ratio corresponding to the current time, and the comparison result is used to determine whether the stimulus amount corresponding to the current energy consumption is sufficient to trigger the intelligent agent or administrator to take response measures. If necessary, the generated response measures are used as an optimization method and sent to the administrator or intelligent agent. The administrator or intelligent agent can then optimize and improve the current carbon emissions through the optimization plan.

[0069] Through the above method, basic indicators are generated through the construction information that cannot be changed by the building, and the basic tone of carbon emission optimization is set through the basic indicators. At the same time, a perception and sensitivity system for stimuli is established according to the type of building and the corresponding energy-saving efforts of the building, so as to distinguish the carbon emission stimulus perception of different energy consumption types in different buildings. In this way, after collecting the current building energy consumption data, the amount of stimulation perceived by each part of the current energy consumption data can be first calculated through the intelligent body variables, and the stimulation amount can be compared with the basic tone of carbon emission optimization. According to the comparison results, it is judged whether optimization measures are needed, and the objects and contents of specific optimization measures are generated. In this way, various types of energy consumption data can be comprehensively managed and analyzed in the process of carbon emission data monitoring of green buildings, and the analysis and optimization of carbon emission data can be carried out in real time at any time to improve the optimization management effect.

[0070] In some other embodiments, obtaining building construction information to obtain basic carbon indicators includes the following steps:

[0071] S110, generating embodied carbon data based on building material information and construction information.

[0072] Embodied carbon includes carbon emissions generated during the construction and demolition of buildings, as well as carbon emissions generated during the manufacturing, transportation, assembly, and replacement of building materials.

[0073] S120, generating an implicit coefficient inversely proportional to the value of the implicit carbon data.

[0074] The size of embodied carbon represents the carbon emissions generated during the construction phase of the building. These carbon emissions can no longer be optimized after the building is completed or demolished. Therefore, it serves as the basic tone for considering the intensity of other carbon emissions that can be optimized in the future.

[0075] The larger the embodied carbon data, the smaller the corresponding generated implicit coefficient.

[0076] S130, predicting operating carbon data based on the space design information and energy consumption information.

[0077] Operational carbon refers to the carbon emissions generated by the building during its operation for power supply, lighting, heating, cooling, etc. First, the overall heat loss and lighting demand of the building are predicted through the spatial design information of the building. At the same time, the number and power of energy-consuming objects in the building are used to predict the operational carbon data corresponding to the maximum energy supply in the space. Energy-consuming objects include lamps, air conditioners, HVAC, water supply pipes, electrical equipment, etc.

[0078] S140, calculating the basic carbon index based on the operating carbon data and the implicit coefficient.

[0079] The predicted operating carbon data is multiplied by the implicit coefficient to obtain the basic carbon index. This step is to further adjust and optimize the predicted operating carbon data through the implicit data corresponding to the known implicit carbon, which is the basic tone of carbon emissions. The basic carbon index obtained can more accurately represent the basic value for comparison of whether the building needs to be optimized for carbon emissions in the future.

[0080] In other embodiments, calculating the stimulation calibration ratio according to the basic carbon index includes the following steps:

[0081] S210, dividing the basic carbon index proportionally based on the time coefficient to obtain a unit time index.

[0082] The basic carbon index represents the carbon emission information of the building for a long period of time in the future. In order to compare the real-time carbon emission data at different times later, the basic carbon index needs to be divided according to the time coefficient to generate a unit time standard.

[0083] The unit time label represents the carbon index information corresponding to a unit time, such as one minute, one hour, one day, etc.

[0084] S220, obtaining stimulus factors, and obtaining ideal proportions corresponding to different types of energy consumption based on the stimulus factors.

[0085] The main types of energy consumption in the building are classified, and the corresponding stimulus factors are matched according to each classification, and the ideal proportion of different types of energy consumption is obtained through the stimulus factors.

[0086] Stimulus factors include energy-saving stimulus, water-saving stimulus, and temperature-saving stimulus, and the energy consumption types include water consumption, energy consumption, temperature consumption and other aspects.

[0087] The overall usage of different types of energy in buildings is different. Generally speaking, the energy consumption of buildings is greater than the temperature consumption and the water consumption, and the values ​​may be adjusted according to changes in seasons, temperature, and environment.

[0088] Then, based on prior investigations, tests and statistics, the ideal proportion of each type of energy consumption in a building can be obtained. In the embodiment of the present application, the proportion of water consumption, energy consumption and temperature consumption is 2:5:3.

[0089] S230, generating stimulation calibration ratios corresponding to different types of energy consumption based on the ideal proportion and the unit time scale.

[0090] The ideal proportion is used to divide the proportion of each unit time mark to specifically calculate the energy consumption of different types of energy consumption that can be ideally divided and used in unit time, and the energy consumption size is used as the stimulus calibration ratio.

[0091] In some other embodiments, obtaining a building type and a building energy-saving tendency, and generating a corresponding type coefficient set based on the building type and a tendency coefficient set based on the building energy-saving tendency, include the following steps:

[0092] Building types include residential, commercial, production, and sports. The type coefficient sets corresponding to different building types include several type coefficients with different numerical proportions. The type coefficients include energy-saving coefficients, water-saving coefficients, and temperature-saving coefficients.

[0093] The overall trend of each type of building for different energy consumption requirements is different, so the type coefficient set corresponding to each type of building contains the specific type coefficients of this type of building for different types of energy consumption. For example, the type coefficient set corresponding to the residential building contains the type coefficients of water saving coefficient 4: energy saving coefficient 4: temperature saving coefficient 2, and the type coefficient set in the production building is water saving coefficient 1: energy saving coefficient 7: temperature saving coefficient 2.

[0094] The size of the type coefficient represents the degree of stimulation that can be caused to different types of energy consumption data in a building.

[0095] The building energy-saving tendency is based on the active planning requirement information and the carbon pressure generated passively based on the basic carbon indicators. The building energy-saving tendency includes strong tendency, medium tendency and weak tendency. The tendency coefficient sets corresponding to different building energy-saving tendencies all contain tendency coefficients of different numerical values. Among them, the tendency coefficients correspond to strong tendency, medium tendency and weak tendency from large to small respectively.

[0096] The energy-saving tendency of buildings is characterized by the intensity of the building's energy consumption management and carbon energy conservation. A strong tendency is characterized by extremely strict supervision and control of energy consumption, while a weak tendency is characterized by extremely lax supervision and control of energy consumption.

[0097] The energy-saving tendency of a building takes into account the corresponding planning requirements of the building, such as policy requirements, internal management requirements, etc. On the one hand, it takes into account the carbon pressure generated by the basic carbon index. If the basic carbon index is smaller, it means that the building needs to control energy consumption more to avoid excessive energy consumption stimulation. In this case, energy consumption needs to be strictly regulated.

[0098] In some other embodiments, generating agent variables based on a type coefficient set and a tendency coefficient set includes the following steps:

[0099] S410: Multiply each type coefficient in the type coefficient set by the tendency coefficient to obtain a plurality of first variable values.

[0100] First, each type coefficient in the type coefficient set is multiplied by the tendency coefficient to obtain a plurality of first variable values.

[0101] The first variable value is characterized as an independent variable corresponding to different types of energy consumption data, which is used to subsequently calculate the stimulation amount of an independent individual for the collected real-time data.

[0102] S420, multiply each type coefficient in the type coefficient set by the corresponding weight coefficient to obtain a number of weighted coefficients, and add the several weighted coefficients and multiply them by the tendency coefficient to obtain a second variable value, where the weight coefficient represents the proportion of the energy consumption corresponding to the type coefficient in the current energy consumption data and is less than 1.

[0103] At the same time, each type coefficient in the type coefficient set is firstly multiplied by the weight coefficient to obtain a number of weighted coefficients, and then the several weighted coefficients are added together and multiplied by the tendency coefficient to obtain the second variable value.

[0104] The second variable value is used to characterize the overall variable corresponding to the energy consumption data of a building as a whole, which is used to subsequently calculate the overall stimulus amount for the collected real-time data.

[0105] During the overall calculation, there are differences in the proportion of several different types of energy consumption in the energy consumption data. In order to reduce the impact of such differences, it is necessary to multiply each type coefficient by the corresponding weight coefficient to make up for the numerical difference caused by the type difference. The weight coefficient is represented by the proportion of a certain type of energy consumption in the current energy consumption data. For example, the higher the numerical proportion of energy-saving data in the current energy consumption data, the smaller the weight. In this way, the impact of data type differences can be reduced by multiplying the type coefficient by the weight coefficient.

[0106] S430, the first variable value and the second variable value together constitute an agent variable.

[0107] In some other embodiments, collecting the current energy consumption data and the current time of the building, and calculating the current energy consumption data with the agent variables to generate the stimulus amount includes the following steps:

[0108] S510, classify the current energy consumption data to obtain various types of energy consumption components, including water consumption, energy consumption, and heating consumption.

[0109] After the current energy consumption data is collected, it is first classified based on the type of energy consumption, and the type of classification corresponds to the type of type coefficient and the stimulus factor.

[0110] S520, multiplying each energy consumption component by its corresponding type of first variable value to obtain stimulation components of different types.

[0111] Firstly, independent stimulation amount calculations are performed on different types of energy consumption data, and different types of energy consumption components are multiplied by corresponding types of first variable values ​​to obtain different types of stimulation components.

[0112] This stimulus component represents the motivation for carbon emission optimization that can be triggered by a certain type of current energy consumption data of a building.

[0113] S530, multiplying the current energy consumption data by the second variable value to obtain a total stimulation amount.

[0114] The current energy consumption data is multiplied by the second variable value as a whole without classification to obtain the total amount of stimulation.

[0115] The total amount of stimulus is characterized by the motivation of the building's current energy consumption data to trigger carbon emission optimization as a whole.

[0116] Since the value of the second variable is multiplied by a weight coefficient less than 1 based on the value of the first variable, the total amount of stimulation calculated for the same current energy consumption data is generally less than the sum of all stimulation components.

[0117] In other embodiments, comparing the stimulus amount with the stimulus calibration ratio corresponding to the current time to analyze the reaction trend, and calling the corresponding reaction measure based on the reaction trend, includes the following steps:

[0118] S610, obtaining a stimulation calibration ratio corresponding to a corresponding unit time mark based on the current time, and adding up a plurality of stimulation calibration ratios to obtain a total stimulation calibration ratio.

[0119] The current time is obtained based on the time when the current energy consumption data is collected, and the corresponding initial time is selected according to the preset time coefficient, such as the current time is the day of the month, the hour of the first day, etc., and the corresponding stimulation calibration ratio is calculated according to the current time and the unit time mark.

[0120] Each stimulus calibration ratio corresponds to a different type of energy consumption data, so it is also necessary to add up several stimulus calibration ratios to obtain a total stimulus calibration ratio.

[0121] S620, matching a plurality of stimulus components with corresponding stimulus factors to obtain corresponding stimulus calibration ratios and performing comparisons, and generating a first reaction including an avoidance reaction and an approach reaction according to the comparison results.

[0122] During independent judgment, several stimulus components are compared with the stimulus calibration ratio of the same type to obtain a first reaction. The first reaction is the reaction corresponding to each type of energy consumption data in the current energy consumption data. The reaction is characterized by the corresponding performance when the stimulus amount corresponding to an energy consumption data causes the intelligent agent or building to perform a certain behavior, such as the need to optimize energy consumption or not.

[0123] The avoidance response is characterized by the need for the agent to take actions to avoid further triggering of stimulation after being stimulated, that is, the current energy consumption needs to be adjusted to avoid further stimulation; the approach response means that the stimulation generated by the agent makes it approach a certain quantity but has not yet been triggered, that is, the current energy consumption has not reached the level that needs to be optimized.

[0124] S630, comparing the total stimulus amount with the total stimulus calibration ratio, and generating a second response including an avoidance response and an approach response according to the comparison result.

[0125] Secondly, the total amount of stimulation is compared with the total stimulus calibration ratio corresponding to the current time, and the second response is also generated based on the comparison result.

[0126] S640, when there is an avoidance reaction in the first reaction, a corresponding single reaction measure is called based on the energy consumption type corresponding to the avoidance reaction; when the second reaction is an avoidance reaction, multiple reaction measures are called.

[0127] When there is an avoidance reaction in the first reaction, it means that one or more types of energy consumption data in the building exceeds the limit, and it needs to be optimized through certain reaction measures to reduce carbon emissions. The reaction measures at this time are aimed at one or more independent types of energy consumption data, so they correspond to single reaction measures.

[0128] When there is an avoidance reaction in the second reaction, it means that the overall energy consumption of the building has reached or exceeded the overall energy consumption limit of the building, and it needs to be optimized through certain reaction measures to reduce carbon emissions. The reaction measures at this time are aimed at the overall energy consumption of the building, so it corresponds to multiple reaction measures.

[0129] In some other embodiments, after the reaction measures are generated, the following steps are also included:

[0130] S710, collecting the change amount of the current energy consumption data after the reaction measure optimization, and generating optimization feedback according to the change amount.

[0131] The acquisition of reaction measures requires building managers to set improvement plans for different types of energy consumption based on the experience of using such buildings and relevant literature, such as how to save water and how to save electricity. After the current energy consumption data is adjusted and optimized through reaction measures, the current energy consumption data for a period of time is recollected to calculate the change in energy consumption.

[0132] At the same time, the effect of improvement is judged based on the change in energy consumption to generate optimization feedback.

[0133] S720, adjusting the response measures according to the optimization feedback.

[0134] When the optimization feedback is good, it is considered that the reaction measures for improving carbon emissions are accurate and effective, and there is no need to adjust the reaction measures. However, when the optimization feedback is poor, the reaction measures need to be further adjusted to improve the optimization effect.

[0135] The embodiments of the present application also disclose a green building carbon emission optimization system for implementing the green building carbon emission optimization method as described above.

[0136] A storage medium is also disclosed in an embodiment of the present application, characterized in that the storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the method as described above.

[0137] The implementation principle is:

[0138] Basic indicators are generated through the construction information that cannot be changed by the building, and the basic tone of carbon emission optimization is set by the basic indicators. At the same time, a perception and sensitivity system for stimuli is established according to the type of building and the corresponding energy-saving efforts of the building, so as to distinguish the carbon emission stimulus perception of different energy consumption types in different buildings. In this way, after collecting the current building energy consumption data, the amount of stimulation perceived by each part of the current energy consumption data can be first calculated through the intelligent body variables, and the stimulation amount can be compared with the basic tone of carbon emission optimization. According to the comparison results, it is judged whether optimization measures are needed, and the objects and contents of specific optimization measures are generated. In this way, various types of energy consumption data can be comprehensively managed and analyzed during the carbon emission data monitoring of green buildings, and the analysis and optimization of carbon emission data can be carried out in real time at any time to improve the optimization management effect.

[0139] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise clearly stated in this document, the execution of these steps is not strictly limited in order and can be performed in other orders.

[0140] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A green building carbon emission optimization method, characterized in that: The following steps are involved: Obtaining building construction information to obtain basic carbon indicators, wherein the building construction information includes building material information, space design information, construction information, and energy consumption information; The stimulation calibration ratio is calculated according to the basic carbon index, specifically, Proportionally dividing the basic carbon index based on the time coefficient to obtain a unit time index; Acquire stimulus factors, and acquire ideal proportions corresponding to different types of energy consumption based on the stimulus factors, wherein the stimulus factors include energy-saving stimulus, water-saving stimulus, and temperature-saving stimulus; Based on the ideal proportion, the unit time mark is divided to calculate the energy consumption of different types of energy consumption ideally divided in the unit time to define as the stimulation calibration ratio; Acquire a building type and a building energy-saving tendency, and generate a corresponding type coefficient set based on the building type and a tendency coefficient set based on the building energy-saving tendency; An agent variable is generated based on the type coefficient set and the tendency coefficient set, and the agent variable is characterized by the sensitivity of the agent to the stimulus. Specifically, Multiplying each of the type coefficients in the type coefficient set by the tendency coefficient to obtain a plurality of first variable values; Multiplying each of the type coefficients in the type coefficient set by a corresponding weight coefficient to obtain a plurality of weight coefficients, and adding the plurality of weight coefficients and multiplying the plurality of weight coefficients by the tendency coefficient to obtain a second variable value, wherein the weight coefficients are characterized by the proportion of the energy consumption corresponding to the type coefficient in the current energy consumption data and are all less than 1; The first variable value and the second variable value together constitute the agent variable; Collect the current energy consumption data and current time of the building, calculate the current energy consumption data and the agent variable to generate a stimulus amount, specifically, Classifying the current energy consumption data to obtain various types of energy consumption components, the types including water consumption, energy consumption, and heating consumption; Multiplying each of the energy consumption components by the first variable value of the corresponding type to obtain stimulation components of different types; Multiplying the current energy consumption data by the second variable value to obtain a total stimulation amount; The stimulation amount is compared with the stimulation calibration ratio corresponding to the current time to analyze the reaction trend, corresponding reaction measures are retrieved based on the reaction trend, and the reaction measures are output as an optimization solution.

2. The green building carbon emission optimization method according to claim 1, characterized in that: Obtaining building construction information to obtain basic carbon indicators includes the following steps: Generate embodied carbon data based on the building materials information and construction information; Generate an implicit coefficient inversely proportional to the value of the implicit carbon data based on the value of the implicit carbon data; Predicting operating carbon data based on the space design information and energy consumption information; The basic carbon index is calculated according to the operating carbon data and the implicit coefficient.

3. The green building carbon emission optimization method according to claim 1, characterized in that: Acquiring a building type and a building energy-saving tendency, and generating a corresponding type coefficient set based on the building type and a tendency coefficient set based on the building energy-saving tendency, including the following steps: The building types include residential, commercial, production, and sports. The type coefficient sets corresponding to different building types include several type coefficients with different numerical proportions. The type coefficients include energy-saving coefficients, water-saving coefficients, and temperature-saving coefficients. The building energy-saving tendency is generated based on active planning requirement information and carbon pressure passively generated based on the basic carbon index. The building energy-saving tendency includes strong tendency, medium tendency and weak tendency. The tendency coefficient sets corresponding to different building energy-saving tendencies all contain tendency coefficients with different numerical values, wherein the tendency coefficients correspond to strong tendency, medium tendency and weak tendency from large to small, respectively.

4. The green building carbon emission optimization method according to claim 1, characterized in that: Comparing the stimulus amount with the stimulus calibration ratio corresponding to the current time to analyze a reaction trend, and calling a corresponding reaction measure based on the reaction trend, comprises the following steps: Acquire a stimulation calibration ratio corresponding to a corresponding unit time scale based on the current time, and add up a plurality of stimulation calibration ratios to obtain a total stimulation calibration ratio; Matching a plurality of the stimulus components to corresponding stimulus factors to obtain the corresponding stimulus calibration ratios and comparing the stimulus components with the corresponding stimulus calibration ratios, and generating a first reaction including an avoidance reaction and an approach reaction according to the comparison results; Comparing the total stimulus amount with the total stimulus calibration ratio, and generating a second response including an avoidance response and an approach response according to the comparison result; When there is an avoidance reaction in the first reaction, a corresponding single reaction measure is called based on the energy consumption type corresponding to the avoidance reaction; when the second reaction is the avoidance reaction, multiple reaction measures are called; The avoidance response is characterized by the behavior of the agent to avoid triggering the stimulus again after being stimulated, and the approach response is characterized by the stimulus generated by the agent to make it approach a certain quantity but not yet triggered.

5. The green building carbon emission optimization method according to claim 1, characterized in that: After generating the reaction measures, the following steps are also included: Collecting the change amount of the current energy consumption data after the reaction measure is optimized, and generating optimization feedback according to the change amount; The response measures are adjusted according to the optimization feedback.

6. A green building carbon emission optimization system, characterized in that: Used to implement the green building carbon emission optimization method as described in any one of claims 1-5.

7. A storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method as described in any one of claims 1-5.

Images