CN112633762A - Building energy efficiency obtaining method and equipment - Google Patents

Abstract

The invention is suitable for the technical field of computers, and provides a building energy efficiency obtaining method and equipment, wherein the method comprises the following steps: collecting data of a plurality of preset energy efficiency indexes of a target building; determining the information entropy of each preset energy efficiency index according to the data of the preset energy efficiency indexes, and determining a first weighted value of each preset energy efficiency index according to the information entropy of the preset energy efficiency indexes; determining a third weighted value of each preset energy efficiency index according to the first weighted value and the second weighted value of each preset energy efficiency index, wherein the second weighted value of each preset energy efficiency index is determined according to a hierarchical analysis algorithm; and determining the energy efficiency value of the target building according to the data of each preset energy efficiency index and the third weighted value of each preset energy efficiency index. The invention can improve the accuracy of building energy efficiency acquisition.

Description

Building energy efficiency acquisition method and equipment

technical field

The invention belongs to the field of computer technology, and in particular relates to a method and device for obtaining energy efficiency of a building.

Background technique

With the rapid development of social economy and science and technology, people's awareness of environmental protection and energy conservation has gradually increased, and more and more attention has been paid to the effective use of energy. As a result, the concept of an integrated energy system has attracted attention, and more and more buildings are based on an integrated energy system. The acquisition of building energy efficiency based on the integrated energy system plays an important role in the design of the building planning stage and the operation management of the energy equipment in the building operation stage.

At present, the energy efficiency indicators and weights are usually set by manual experience, and the energy efficiency of the building can be obtained by combining the data of the building. However, human experience is highly subjective, resulting in poor accuracy of the obtained building energy efficiency.

SUMMARY OF THE INVENTION

In view of this, the embodiments of the present invention provide a building energy efficiency acquisition method and device to solve the problem of poor accuracy of building energy efficiency acquisition in the prior art.

A first aspect of the embodiments of the present invention provides a method for obtaining energy efficiency of a building, including:

Collect data of multiple preset energy efficiency indicators of target buildings;

Determine the information entropy of each preset energy efficiency index according to the data of the multiple preset energy efficiency indexes, and determine the first weight value of each preset energy efficiency index according to the information entropy of the multiple preset energy efficiency indexes;

According to the first weight value and the second weight value of each preset energy efficiency index, the third weight value of each preset energy efficiency index is determined, wherein the second weight value of each preset energy efficiency index is determined according to the analytic hierarchy process algorithm;

The energy efficiency value of the target building is determined according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index.

A second aspect of the embodiments of the present invention provides a building energy efficiency acquisition device, including:

The collection module is used to collect data of multiple preset energy efficiency indicators of the target building;

a processing module, configured to determine the information entropy of each preset energy efficiency index according to the data of the multiple preset energy efficiency indexes, and determine the first weight of each preset energy efficiency index according to the information entropy of the multiple preset energy efficiency indexes value;

The processing module is further configured to determine the third weight value of each preset energy efficiency index according to the first weight value and the second weight value of each preset energy efficiency index, wherein the second weight value of each preset energy efficiency index is The weight value is determined according to the AHP algorithm;

The processing module is further configured to determine the energy efficiency value of the target building according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index.

A third aspect of the embodiments of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes all The steps of the method described in the first aspect above are implemented when the computer program is executed.

A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, wherein, when the computer program is executed by a processor, the implementation is as described in the first aspect above steps of the method.

Compared with the prior art, the embodiments of the present invention have the following beneficial effects: by collecting data of multiple preset energy efficiency indicators of the target building; and determine the first weight value of each preset energy efficiency index according to the information entropy of multiple preset energy efficiency indicators; determine each preset energy efficiency index according to the first weight value and the second weight value of each preset energy efficiency index The third weight value of each preset energy efficiency index, wherein the second weight value of each preset energy efficiency index is determined according to the analytic hierarchy process algorithm; according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index, the target building is determined The information entropy can be considered in the process of obtaining the energy efficiency of the building, and the weight value determined by the information entropy and the weight value obtained by the AHP algorithm are used to jointly determine the weight value of each preset energy efficiency index. The subjectivity of the AHP algorithm makes the weight value of each preset energy efficiency index more accurate, thereby improving the accuracy of building energy efficiency acquisition.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only for the present invention. In some embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

1 is a schematic structural diagram of a building energy efficiency acquisition system provided by an embodiment of the present invention;

2 is a schematic flowchart of a method for obtaining energy efficiency of a building provided by an embodiment of the present invention;

3 is a schematic flowchart of a method for obtaining building energy efficiency provided by another embodiment of the present invention;

4 is a schematic flowchart of a method for obtaining building energy efficiency provided by another embodiment of the present invention;

Fig. 5 is the realization flow chart of an implementation example of the present invention;

FIG. 6 is a schematic structural diagram of a building energy efficiency acquisition device according to an embodiment of the present invention;

FIG. 7 is a schematic diagram of an electronic device provided by an embodiment of the present invention.

Detailed ways

In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are set forth in order to provide a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

In order to illustrate the technical solutions of the present invention, the following specific embodiments are used for description.

With the rapid development of social economy and science and technology, people's awareness of environmental protection and energy saving has gradually increased, and more and more attention has been paid to the effective use of energy. Constant energy waste has gradually led to energy crisis and serious environmental pollution. Energy development is also faced with severe challenges such as unreasonable energy consumption structure, mismatch of energy supply and demand distribution, non-integration of various energy systems, and lack of reasonable investment return models. Traditional microgrids have been unable to solve various current problems. Therefore, how to distribute energy and make it efficiently used has become a problem that needs attention, which makes the concept of integrated energy system attract more and more attention. There have been many research achievements in modeling, planning, and operation optimization of the integrated energy system, and some actual buildings of the integrated energy system have been carried out, but there are gaps in the acquisition of energy efficiency. In order for an integrated energy system building to play its maximum role in the development of an integrated energy system, the energy efficiency acquisition of the building is essential. For example, in the building planning stage of the construction of an integrated energy system project, faced with several existing planning and construction schemes, what is the investment return ratio of each scheme, how big is the environmental impact, and which types of energy equipment are used to make the overall efficiency the highest, etc. All problems need to be obtained through energy efficiency to obtain specific direct information or indirect information to assist in making the most scientific and objective analysis and decision-making. For another example, how to manage the operation of energy equipment to maximize energy saving in the operation stage after building construction is completed, it is also necessary to obtain the energy efficiency of the building and control and adjust it according to the energy efficiency of the building.

At present, the energy efficiency indicators and weights are usually set by manual experience, and the energy efficiency of the building can be obtained by combining the data of the building. However, human experience is highly subjective, resulting in poor accuracy of the obtained building energy efficiency.

The embodiment of the present invention collects the data of multiple preset energy efficiency indexes of the target building; determines the information entropy of each preset energy efficiency index according to the data of multiple preset energy efficiency indexes, and according to the information entropy of multiple preset energy efficiency indexes, Determine the first weight value of each preset energy efficiency index; determine the third weight value of each preset energy efficiency index according to the first weight value and the second weight value of each preset energy efficiency index, wherein each preset energy efficiency index The second weight value of the energy efficiency index is determined according to the analytic hierarchy process algorithm; the energy efficiency value of the target building is determined according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index, which can be considered in the process of obtaining the energy efficiency of the building Information entropy, the weight value determined by the information entropy and the weight value obtained by the AHP algorithm are used to jointly determine the weight value of each preset energy efficiency index. Combined with the objectivity of the information entropy and the subjectivity of the AHP algorithm, each preset energy efficiency The weight value of the indicator is more accurate, thereby improving the accuracy of building energy efficiency acquisition.

FIG. 1 is a schematic structural diagram of a building energy efficiency acquisition system according to an embodiment of the present invention. As shown in FIG. 1 , the system may include, but is not limited to, at least one of the following: energy consumption devices 11 deployed in buildings, devices 12 for storing building data, third-party data platforms 13 , electronic devices 14 and user terminals 15 .

The energy-consuming devices 11 deployed in the building may include, but are not limited to, at least one of the following: photovoltaic equipment, electrical equipment, access control equipment, monitoring equipment, air conditioners, elevators, lighting equipment, fire fighting equipment, and the like. The device 12 for storing building data may include, but is not limited to, at least one of the following: computer, mobile phone, server, etc. For example, building data may be stored on the device by building planning personnel or building management personnel. The third-party data platform 13 may include, but is not limited to, a building data management platform, a third-party data search platform, and the like. It should be noted that, for a building that has been constructed and is in operation, data can be collected by communicating with the energy-consuming equipment 11 deployed in the building, while for a building in the planning stage, energy efficiency cannot be obtained by communicating with the energy-consuming equipment 11 deployed in the building. Data is collected by communicating with the energy-consuming equipment 11, and data such as energy consumption and operation mode of each equipment can be collected through the equipment 12 storing building data and/or the third-party data platform 13 according to the planned equipment model.

The electronic device 14 may include, but is not limited to, at least one of the following: a terminal device, a server, and the like. When the electronic device 14 executes the computer program, the method provided by the embodiment of the present invention can be implemented. Specifically, the electronic device 14 collects the target by communicating with the energy-consuming device 11 deployed in the building, the device 12 storing building data, and the third-party data platform 13 . The data of multiple preset energy efficiency indexes of the building is processed according to the method provided by the embodiment of the present invention, and the energy efficiency value of the target building is obtained.

The user terminal 15 may include, but is not limited to, at least one of the following: a mobile phone, a tablet computer, a desktop computer, a vehicle-mounted terminal, a wearable device, and the like. The user can view the energy efficiency value of the target building obtained by the electronic device 14 through the user terminal 15 . For example, after obtaining the energy efficiency value of the target building, the electronic device 14 sends one or more of the energy efficiency value of the target building, the data of each preset energy efficiency index, and the third weight value to the user terminal, so that the user terminal can display it to the user terminal. User view. For another example, after receiving the query instruction input by the user, the user terminal 15 sends a query request to the electronic device 14, and the electronic device 14 sends the energy efficiency value of the target building, data of various preset energy efficiency indicators, and data to the user terminal 15 according to the query request. One or more of the third weight values. It should be noted that, when the electronic device 14 is a terminal device with a display screen, after obtaining the energy efficiency value of the target building, the electronic device 14 can display the energy efficiency value of the target building, the data of each preset energy efficiency index, and the third weight value. one or more of , for users to view.

FIG. 2 is a schematic flowchart of a method for obtaining energy efficiency of a building according to an embodiment of the present invention. The execution body of the method is the electronic device in FIG. 1 . As shown in Figure 2, the method includes:

S201: Collect data of multiple preset energy efficiency indicators of the target building.

In this embodiment, the target building is a building whose energy efficiency needs to be determined. There can be one or more target buildings, which are not limited here. The default energy efficiency index is the index that affects the energy efficiency of the building. The preset energy efficiency index may be preset by the user, or may be obtained by screening candidate indexes according to certain rules, which is not limited herein. For example, the preset energy efficiency indicators may include, but are not limited to, at least one of the following: internal rate of return, incremental investment static payback period, building waste emissions, building unit energy consumption emissions, building waste reduction rate, building availability Proportion of renewable energy, building energy system utilization rate, building heating/cooling efficiency, building exergy efficiency, building energy storage efficiency, average equipment utilization rate, building primary energy utilization rate, building energy saving rate.

The electronic equipment can communicate with one or more of the energy-consuming equipment deployed in the target building, the equipment storing the target building data, the third-party data platform, etc. to collect the initial data of the target building, and then according to the initial data of the target building. The data determines the data of each preset energy efficiency index. For example, initial data may include, but are not limited to, at least one of the following: fuel costs, labor costs, equipment maintenance costs and equipment depreciation, nitrogen, carbon, sulfide emissions, nitrogen, carbon, sulfide equivalent values, specific energy consumption The number of pollution equivalents produced, the energy consumption of the system, the annual power generation of renewable energy, the input and output power of the electric energy storage and storage elements, the system power supply, cooling capacity and heat supply, etc.

S202: Determine the information entropy of each preset energy efficiency index according to the data of the multiple preset energy efficiency indexes, and determine the first weight value of each preset energy efficiency index according to the information entropy of the multiple preset energy efficiency indexes.

In this embodiment, the number of the preset energy efficiency indicators is not limited herein. For example, if there are 17 preset energy efficiency indicators, first, according to the data of these 17 preset energy efficiency indicators, calculate the information entropy of each preset energy efficiency index, and then calculate the information entropy of each preset energy efficiency index according to the information entropy of these 17 preset energy efficiency indicators. The first weight value of a preset energy efficiency index.

S203: Determine a third weight value of each preset energy efficiency index according to the first weight value and the second weight value of each preset energy efficiency index, wherein the second weight value of each preset energy efficiency index is based on the analytic hierarchy process algorithm Sure.

In this embodiment, the weight value determined by the information entropy is called the first weight value, and the weight value determined by the AHP algorithm is called the second weight value. After obtaining the first weight value and the second weight value of each preset energy efficiency index, for each preset energy efficiency index, the first weight value and the second weight value of the preset energy efficiency index can be used to determine the The third weight value of the preset energy efficiency index. The third weight value is a weight value used subsequently for obtaining the building energy efficiency.

S204, according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index, determine the energy efficiency value of the target building.

In this embodiment, the energy efficiency value of the target building may be expressed as an energy efficiency score or an energy efficiency grade. The range of the energy efficiency level can be set by the user according to requirements, which is not limited here. For example, the energy efficiency value may be 85 points, or the energy efficiency value may be energy efficiency class three.

The embodiment of the present invention collects the data of multiple preset energy efficiency indexes of the target building; determines the information entropy of each preset energy efficiency index according to the data of multiple preset energy efficiency indexes, and according to the information entropy of multiple preset energy efficiency indexes, Determine the first weight value of each preset energy efficiency index; determine the third weight value of each preset energy efficiency index according to the first weight value and the second weight value of each preset energy efficiency index, wherein each preset energy efficiency index The second weight value of the energy efficiency index is determined according to the analytic hierarchy process algorithm; the energy efficiency value of the target building is determined according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index, which can be considered in the process of obtaining the energy efficiency of the building Information entropy, the weight value determined by the information entropy and the weight value obtained by the AHP algorithm are used to jointly determine the weight value of each preset energy efficiency index. Combined with the objectivity of the information entropy and the subjectivity of the AHP algorithm, each preset energy efficiency The weight value of the indicator is more accurate, thereby improving the accuracy of building energy efficiency acquisition.

FIG. 3 is a schematic flowchart of a method for obtaining energy efficiency of a building according to another embodiment of the present invention. Based on the embodiment shown in FIG. 2 , the specific implementation process of determining the information entropy of each preset energy efficiency index is described. As shown in Figure 3, the method includes:

S301: Collect data of multiple preset energy efficiency indicators of the target building.

S302, standardize the data of a plurality of preset energy efficiency indicators to generate an indicator matrix.

S303, for each preset energy efficiency index, determine the information entropy of the preset energy efficiency index according to the index matrix.

In this embodiment, there are multiple target buildings. Standardizing the data of a plurality of preset energy efficiency indexes may include: for each preset energy efficiency index, normalizing the data of the preset energy efficiency index of each target building. First, an index matrix is generated from the standardized data of each preset energy efficiency index of a plurality of target buildings, and then the information entropy of each preset energy efficiency index is calculated according to the index matrix and the calculation formula of information entropy.

For example, suppose the index matrix formed by n preset energy efficiency indexes and m target buildings is Y=(y _ij ) _n×m , i=1,2,...,n; j=1,2,... ., m. First, standardize the data of the preset energy efficiency indicators.

Among them, y _ij represents the data of the i-th preset energy efficiency index of the j-th target building, and E _ij represents the data obtained by normalizing y _ij .

According to the calculation formula of information entropy, the information entropy of the ith preset energy efficiency index is:

Wherein, when E _ij =0, let E _ij lnE _ij =0.

S304: Determine a first weight value of each preset energy efficiency index according to the information entropy of the multiple preset energy efficiency indexes.

S305: Determine a third weight value of each preset energy efficiency index according to the first weight value and the second weight value of each preset energy efficiency index, wherein the second weight value of each preset energy efficiency index is based on the analytic hierarchy process algorithm Sure.

S306, according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index, determine the energy efficiency value of the target building.

In this embodiment, by standardizing the data of a plurality of preset energy efficiency indexes to generate an index matrix, and then for each preset energy efficiency index, according to the index matrix, determine the information entropy of the preset energy efficiency index, which can be accurately determined. Information entropy of each preset energy efficiency index.

As an embodiment of the present invention, on the basis of any of the above embodiments, according to the information entropy of multiple preset energy efficiency indicators, determining the first weight value of each preset energy efficiency index may include:

For each preset energy efficiency index, the first difference is divided by the second difference to obtain a first weight value of the preset energy efficiency index, wherein the first difference is 1 and the information entropy of the preset energy efficiency index The second difference is the difference between the total number of preset energy efficiency indexes and the sum of information entropy of each preset energy efficiency index.

In this embodiment, set the information entropy of the ith preset energy efficiency index as H(i), and there are n preset energy efficiency indexes in total, then the first weight value of the ith preset energy efficiency index can be expressed as:

Finally, the weight vector W”=(w ₁ ”, w ₂ ”, . . . , wn ”) of the _n preset energy efficiency indicators is obtained.

As an embodiment of the present invention, on the basis of any of the above-mentioned embodiments, according to the first weight value and the second weight value of each preset energy efficiency index, determining the third weight value of each preset energy efficiency index may be include:

For each preset energy efficiency index, the product value of the first weight value of the preset energy efficiency index and the first preset coefficient and the product value of the second weight value of the preset energy efficiency index and the second preset coefficient are calculated. and to obtain the third weight value of the preset energy efficiency index.

In this embodiment, the values of the first preset coefficient and the second preset coefficient may be set according to requirements, which are not limited herein. Both the first preset coefficient and the second preset coefficient are less than 1. Optionally, the sum of the first preset coefficient and the second preset coefficient is equal to one. For example, the first preset coefficient may be 0.7, and the second preset coefficient may be 0.3.

In this embodiment, by setting the first preset coefficient and the second preset coefficient, on the one hand, the weight obtained by the information entropy and the weight obtained by the AHP algorithm can be integrated, and the objectivity of the weight obtained by the information entropy can be to adjust the subjectivity of the weights obtained by the AHP algorithm, so that the third weight value of each preset energy efficiency index is more appropriate. On the other hand, using the first preset coefficient and the second preset coefficient enables users to adjust the information The weight obtained by entropy and the weight obtained by the AHP algorithm in the third weight value are more widely applicable.

FIG. 4 is a schematic flowchart of a method for acquiring energy efficiency of a building provided by another embodiment of the present invention. Based on any of the above embodiments, the specific implementation process of determining the energy efficiency value of the target building according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index is described. As shown in Figure 4, the method includes:

S401: Collect data of multiple preset energy efficiency indicators of the target building.

S402: Determine the information entropy of each preset energy efficiency index according to the data of the multiple preset energy efficiency indexes, and determine the first weight value of each preset energy efficiency index according to the information entropy of the multiple preset energy efficiency indexes.

S403: Determine the third weight value of each preset energy efficiency index according to the first weight value and the second weight value of each preset energy efficiency index, wherein the second weight value of each preset energy efficiency index is based on the analytic hierarchy process algorithm Sure.

S404 , adopting a matter-element extension algorithm to determine the energy efficiency value of the target building according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index.

In this embodiment, the matter-element extension algorithm can be used to perform modeling to determine the energy efficiency value of the target building. Wherein, the weight value of each preset energy efficiency index in the matter-element extension algorithm adopts the third weight value of each preset energy efficiency index determined in the above embodiment.

The implementation steps of the matter-element extension algorithm can include:

Step 1: Determine the matter-element

Denote the energy efficiency value of the target building to be evaluated as N, its feature as c, and the feature value as v. Suppose N has multiple features C ₁ , C ₂ ,...C _n The corresponding magnitudes of these n features For v ₁ , v ₂ ,...v _n , it can be expressed as:

Among them, R is the n-dimensional matter-element, abbreviated as: R=(N, c, v)

c represents the n features of the matter-element to be evaluated: c=(c ₁ , c ₂ ,...c _n )

v represents the magnitude of n features: v=(v ₁ , v ₂ ,...v _n )

Step 2, determine the classic domain

The classical domain is determined according to the characteristics of the matter element to be evaluated and the interval in which its magnitude is located. Suppose the evaluation level is divided into m levels, which are represented by N _j (j=1...m), and the jth level is denoted by c _i (i=1...n) is represented, the i-th evaluation index is represented by v _ji (i=1...n), and the value range of the i-th evaluation index under level j is represented by The interval (a _ji , b _ji ) is represented, then N, c, and v are combined in the form of ordered triples to be the classical domain matter-element R _j :

Step 3: Determine the section domain

The node domain is represented by R _p , and v _pi is the magnitude range of the node domain matter-element about the feature c _i : v _pi =(a _pi ,b _pi )(i=1...n)

Among them, (a _pi , b _pi ) is (a _ji , b _ji )j=1...m, that is, the union of all ranges of the i-th index.

The nodal matter-element R _p can be expressed as:

(4) Determine the matter element to be evaluated

There are y target buildings, and the matter-element of the energy efficiency value N _x of the target building to be evaluated is expressed as R _x :

Wherein, v ₁ , v ₂ , . . . v _n represent the scores of each preset energy efficiency index of the target building, wherein the score is obtained from the standardized data of each preset energy efficiency index. For example, the corresponding score can be obtained according to the interval in which the data of each preset energy efficiency index after normalization falls. The energy efficiency value of the target building can be obtained by weighting and summing the scores of each preset energy efficiency index through the third weight value of each preset energy efficiency index. Wherein, the third weight value of each preset energy efficiency index can be obtained according to the steps of the foregoing embodiment, and details are not described herein again.

The embodiment of the present invention utilizes the entropy weight algorithm and the AHP to determine the weight value in the matter-element extension algorithm, thereby improving the accuracy of the matter-element extension algorithm and improving the accuracy of the energy efficiency evaluation of the target building.

Optionally, on the basis of any of the above embodiments, the process of determining the second weight value of each preset energy efficiency index by the AHP includes: establishing a hierarchical structure model; constructing a judgment matrix; Consistency test; hierarchical total ordering and consistency test, the specific implementation process is as follows:

Step 1: Establish a hierarchical structure model

When applying AHP to analyze and evaluate problems, first of all, the problem should be systematized, the factors should be layered, and a factor hierarchical structure model should be constructed. Under this model, complex problems are decomposed into components that form several levels of elements by attributes and relationships. The factors of the upper level act as the criterion to dominate the factors of the next level, and the lower level factors are the refinement of the upper level factors. These layers are generally divided into the following three categories: ① Goal layer: there is only one element in this layer, that is, the predetermined goal of analyzing and evaluating the problem; ② Criterion layer: This layer contains the intermediate links involved in achieving the goal, which can It consists of several levels, including the criteria and sub-criteria that need to be considered; ③ Scheme level: This level should belong to the lowest level in the hierarchical model, including various measures and decisions to achieve the target level factors. plan etc. In this embodiment, the target layer includes energy efficiency values of target buildings, the criterion layer includes various preset energy efficiency indicators, and the solution layer includes each target building.

Step 2: Construct the judgment matrix

After the hierarchical structure model is established, the affiliation of the upper and lower factors is determined. In order to determine the importance of the elements of each layer relative to the target of the previous layer, a judgment matrix needs to be constructed. According to the evaluation standard of artificial quantification, the factors of each level are compared in pairs. In this embodiment, the Saaty1-9 scale method is used, and a specific digital scale (1-9) is used to express the relative relationship between the factor x _i and the factor x _j . Importance, establish a judgment matrix A=(a _ij ) _n×n . The scale and meaning of the judgment matrix are shown in the following table:

Table 1 Judgment matrix scale and meaning

Step 3: Hierarchical ordering and consistency check

Hierarchical single ordering refers to the importance ordering of factors at the same level to the index factors of the previous level in AHP. Generally, the eigenvector w corresponding to the maximum eigenvalue _λmax is calculated by the judgment matrix A=(a _ij ) _n×n . to make sure.

即1) Normalize the judgment matrix A=(a _ij ) _n×n by column to obtain a matrix

which is

各行元素和的平均值，即2) Calculate the matrix

The average value of the sum of the elements in each row, that is

Calculated w=[w ₁ , w ₂ ,...,w _n ] ^T is the desired eigenvector.

3) Calculate the maximum eigenroot λ _max of the judgment matrix.

4) Consistency test: In order to ensure the rationality of the weight distribution obtained by applying the AHP, it is necessary to test the coordination between the importance of each element, that is, to carry out a consistency test to avoid conflicts between the relative importance of each factor. Case. First, the consistency index CI of the judgment matrix A is calculated, that is,

其中RI是判断矩阵A的平均随机一致性指标，RI的数值大小仅与矩阵的阶数有关，可以通过查表得到。The larger the value of CI, the worse the consistency of the judgment matrix. The random consistency ratio is defined as CR, that is,

Among them, RI is the average random consistency index of the judgment matrix A, and the numerical value of RI is only related to the order of the matrix, which can be obtained by looking up the table.

Table 2 Stochastic consistency index RI value

In general, the smaller the CR, the better the consistency of the judgment matrix. If CR<0.10, it means that the judgment matrix has acceptable and satisfactory consistency, otherwise it should be adjusted and corrected.

Each item in the feature vector w=[w ₁ , w ₂ , . . . , _wn ] ^T that has passed the consistency check is determined as the second weight value of each preset energy efficiency index.

In this embodiment, the analytic hierarchy process takes into account the knowledge and experience of experts and the preferences of decision makers, but is highly subjective. The entropy weight method is based on the information entropy theory. The principle is that the smaller the information entropy of the index, the greater the degree of variation, the greater the amount of effective information provided by the index, and the greater the corresponding weight, and vice versa, the smaller the weight. When the value of the evaluated object on an index is the same, the entropy value reaches the maximum, indicating that the index does not provide any useful information and can be removed from the evaluation index system. Therefore, the result of the entropy weight method is more objective, but it cannot reflect the knowledge of experts and experience and opinions of decision makers. In this embodiment, the advantages of the two are combined, and a combination of subjective and objective index weights is obtained to evaluate the building energy efficiency, thereby improving the accuracy of the energy efficiency evaluation.

As an embodiment of the present invention, on the basis of any of the above-mentioned embodiments, the energy efficiency value of the target building is determined according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index, which may include , determine the score of each preset energy efficiency index according to the data of each preset energy efficiency index, and obtain the energy efficiency value of the target building by weighting and summing the scores of each preset energy efficiency index through the formula R=GW. Among them, R is the energy efficiency value of the target building, G is the score determined after the data of each preset energy efficiency index is standardized, and W is the third weight value of each preset energy efficiency index.

Optionally, the preset energy efficiency indexes can be divided into extremely large indexes and extremely small indexes. Before determining the energy efficiency value of the target building according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index, the above different types of indexes can be preprocessed and converted into extremely large indexes. Specifically, the formula for converting the data of the extremely small index into the data of the extremely large index is: X'=X _max -X. Among them, X' is the data of the index after preprocessing; X _max is the maximum value of the data of the index; X is the actual value of the data of the index.

As an embodiment of the present invention, on the basis of any of the above-mentioned embodiments, the data of a plurality of preset energy efficiency indicators of the target building are collected, including:

Collect initial data of the target building by communicating with the target device, wherein the target device includes at least one of the following: energy consumption equipment deployed in the target building, equipment for storing data of the target building, and a third-party data platform;

According to the initial data, determine the data of multiple preset energy efficiency indicators of the target building, wherein the preset energy efficiency indicators include at least one of the following: internal rate of return, incremental investment static payback period, building waste emissions, building unit energy Emissions consumption, building waste reduction rate, proportion of building renewable energy, building energy system utilization rate, building heating/cooling efficiency, building exergy efficiency, building energy storage efficiency, average equipment utilization rate, building primary energy utilization rate, Building energy efficiency;

Store the initial data and push the initial data to the user terminal.

In this embodiment, the preset energy efficiency indicators may include, but are not limited to, at least one of the following: economic energy efficiency indicators, environmental energy efficiency indicators, and physical energy efficiency indicators, wherein the economic energy efficiency indicators may include but are not limited to at least one of the following Items: Internal rate of return, incremental investment static payback period. Environmental energy efficiency indicators may include, but are not limited to, at least one of the following: building waste emissions, building unit energy consumption emissions, building waste emission reduction rate, and building renewable energy ratio. The physical energy efficiency evaluation index aims to reflect that the building can improve energy utilization efficiency, save energy, and reduce primary energy consumption after the establishment of an internal comprehensive energy system. Energy efficiency indicators are mainly established from three perspectives of energy transmission, energy conversion and energy utilization. Including but not limited to at least one of the following: smart building energy system utilization rate, building heating/cooling efficiency, building exergy efficiency, building energy storage efficiency, average equipment utilization rate, building primary energy utilization rate, and building energy saving rate. The following describes the calculation formulas for determining the data of each preset energy efficiency index of the target building according to the initial data:

(1) Building economic energy efficiency indicators

1.1 Internal rate of return calculation formula:

where IRR is the internal rate of return, B _p is the cash flow per period, and t is the number of periods.

1.2 The formula for calculating the static payback period of incremental investment:

Among them, I _re is the incremental investment required for the integrated energy system to output the same cooling, heat and electricity as the traditional sub-production system; C _tr and C _int are the annual operating costs of the traditional sub-production system and the integrated energy system, including fuel Costs, labor costs, equipment maintenance costs and equipment depreciation, etc.

(2) Energy efficiency indicators for building environment

2.1 Calculation formula of building waste discharge:

Among them, B is the total pollution equivalent number; m _x is the nitrogen, carbon and sulfide emissions; a _x is the nitrogen, carbon, and sulfide equivalent value.

2.2 Calculation formula of building unit energy consumption and emission:

The unit energy consumption emission of a building refers to the ratio of a certain pollutant emission to the energy consumption of the system in a certain period of time. Among them, C is the number of pollution equivalents produced by unit energy consumption; E is the energy consumption of the system.

2.3 The formula for calculating the emission reduction rate of building waste:

Among them, F ₀ represents the amount of waste generated by the energy consumed by the original output of the required cooling and heating power, and F ₁ represents the amount of waste generated by the energy consumed by the original output of the required cooling and heating power after the building is intelligently optimized. .

2.4 The formula for calculating the proportion of renewable energy in buildings:

Among them, E _renew represents the annual power generation of renewable energy in urban comprehensive energy, including solar energy, geothermal energy, biomass energy, wind energy, etc. Q _elc represents the total electricity generation in the city.

(3) Building physical energy efficiency indicators

3.1 Calculation formula of energy system utilization rate of smart buildings:

Among them, _Pe is the net output electricity of the system; _{Sh is the heat output of the system; S c is the} output cooling capacity of the system; _V is the natural gas consumption; QL is the low calorific value of the natural gas; Pi is the net input electricity of the system.

3.2 Calculation formula of building heating/cooling efficiency:

Among them, Q _r(l) is the heat/cooling capacity output by the system, V _r(l) is the natural gas consumption for producing heat/cooling capacity, and _pr(l) is the electricity consumption for producing heat/cooling capacity.

3.3 Calculation formula of building exergy efficiency:

Among them, _EO means that the original B _h , B _C , and B are the energy quality coefficients of heat energy, cold energy and natural gas, respectively, which are defined as the theoretical maximum ability of energy to be converted into electric energy, _EO is the net output power of the system, E _i is the net input power of the system.

3.4 Calculation formula of energy storage efficiency of electric buildings:

Among them, P _CO represents the input power of the electric energy storage and storage element, and _PCI represents the output power of the electric energy storage and electric storage element.

3.5 Calculation formula for average utilization of equipment:

Among them, T ₀ is the unit planned working time; T is the actual working time of the equipment in a unit time; _Ne is the number of energy link equipment in the integrated energy system. The planned working time is calculated by optimization.

3.6 Calculation formula of primary energy utilization rate of buildings:

Among them, Q _e , Q _c , and Q _h are the power supply, cooling and heat supply of the system, respectively; Q ₁ is the primary energy consumption of the system.

3.7 Calculation formula of building energy saving rate:

Among them, E ₀ is the fuel consumption of the traditional sub-production system (converted to standard coal), and E ₁ is the fuel consumption of the system after being transformed into a smart building (converted to standard coal).

Electronic devices can seamlessly integrate data from various sources, accept files from data gateways through wired or wireless networks, and accept files from third-party data platforms through interfaces such as OPC (OLE for Process Control, OLE for Process Control). , to collect initial data.

The electronic device can store the initial data locally or in a cloud storage space. In addition, electronic equipment can also perform preliminary analysis on massive initial data to achieve powerful computing performance, and automatically analyze equipment and energy consumption data based on artificial intelligence engines, detect and identify problems such as equipment operation and inefficiency, and automatically obtain the independent information of each equipment. Preliminary corresponding improvement suggestions. The electronic device can present the initial data, so that the user can access it through the user terminal anytime and anywhere, present the data to users with different levels of authority in a variety of data expansion ways, and control the access.

On the basis of any of the foregoing embodiments, after determining the energy efficiency value of the target building, the method may further include at least one of the following steps:

In the first embodiment, the energy efficiency value of the target building and the data of each preset energy efficiency index are displayed.

In this embodiment, the electronic device has a display, and the display can display the energy efficiency value of the target building and the data of various preset energy efficiency indicators, so that the user can view it.

In the second embodiment, the energy efficiency value of the target building and the data of each preset energy efficiency index are sent to the user terminal.

In this embodiment, the electronic device can send the energy efficiency value of the target building and the data of each preset energy efficiency index to the user terminal, so that the user can view it through the user terminal.

In the third embodiment, the operation mode of the energy equipment of the target building is controlled according to the energy efficiency value of the target building.

In this embodiment, the target building has been constructed and is in the operation stage, and the electronic device can control the operation mode of the energy equipment of the target building according to the energy efficiency value of the target building. For example, when the energy efficiency value of the target building exceeds the preset energy efficiency upper limit, the electronic device can control some or all of the energy devices of the target building to switch from the normal working mode to the energy-saving working mode to reduce energy consumption.

The above-mentioned building energy efficiency assessment method will be described below through an implementation example. Take 3 smart building-type integrated energy system buildings as examples to illustrate. Building 1 is a commercial building in the Yangtze River Delta region, with a total land area of about 24,100 m ² . Building 2 is an international airport in the central plain area, with a total construction area of 212,000 m ² and an integrated energy system station. Building 3 is a newly built school in the coastal area, covering a total area of 170,000 m ² , with 42 teaching classes. The energy station mainly supplies energy to the teaching building and dormitory building. It is a demonstration building that advocates the use of clean energy.

The main operating parameters of some systems in the three buildings are shown in the following table:

Table 3 Main operating parameters of some systems in the building

FIG. 5 shows the implementation process of this implementation example. For the specific implementation process, refer to the above-mentioned embodiment, which is only briefly described here, and will not be repeated here. The implementation process includes:

S501 , constructing an index system, that is, determining and collecting data of each preset energy efficiency index of the target building.

S502. Determine matter-element, classical domain, and section domain.

S503: Determine the matter element to be evaluated.

S504, construct a judgment matrix.

S505. Calculate the layered weight.

S506, determine whether the consistency check of each level is passed, if not, jump to S505, and if so, execute S507.

S507. Calculate the combined weight, that is, the second weight value of each preset energy efficiency index determined by the AHP in the above embodiment.

S508 , data standardization processing, that is, the standardization processing performed before the information entropy is determined in the above embodiment.

S509 , using the weight obtained by the information entropy to modify the weight obtained by the AHP to obtain the weight of each preset energy efficiency index, that is, in the above embodiment, the third weight value is determined from the first weight value and the second weight value.

S510. Determine the building energy efficiency value according to the data and the weight value of each preset energy efficiency index.

S511. End.

The determined data and scores of the preset energy efficiency indicators for each building are as follows:

Table 4 Data and scores of each preset energy efficiency index for buildings

Finally, according to the scores of the preset energy efficiency indicators of each building and the corresponding weight value, the energy efficiency value of each building is determined. The results are as follows:

Table 5 Energy efficiency value of each building

It can be seen that building 1 has the highest energy efficiency value, and building 2 has the lowest energy efficiency value.

In addition, the advantages and disadvantages of the building can be analyzed through the specific scores of each preset energy efficiency index. For example, the indicators with higher scores in Building 1 are the comprehensive energy utilization rate of smart buildings, the proportion of renewable energy in buildings, and the utilization rate of primary energy in buildings. Advantage indicators, while the building incremental investment payback period, building energy saving rate and other scores are low, indicating that its building energy saving rate and incremental payback period need to be improved and optimized. Although building 2 and building 3 have similar energy efficiency values, the advantages and disadvantages of the two buildings are different: the advantages and disadvantages of building 2 are mainly the annual value of building investment cost and building heating/cooling efficiency. The competitiveness of energy consumption indicators is relatively weak, such as comprehensive energy utilization rate, building exergy efficiency, and cooling efficiency index scores are all low, indicating that in order to obtain relatively considerable economic benefits, energy waste is more serious; and Building 3 is the most important The dominant indicator is the proportion of renewable energy in buildings, that is, the proportion of clean energy is large, and other energy consumption indicators such as building waste emission reduction rate are low. It can be seen from the above analysis that the evaluation system of the smart building energy efficiency evaluation method proposed in this embodiment can not only evaluate a system more comprehensively, but also can clarify the advantages and disadvantages of the system through specific index scores.

The embodiments of the present invention have the following beneficial effects: (1) the proposed method for evaluating the energy efficiency of smart buildings considering big data and cloud architecture has more constituent units and more energy types, and for various energy forms in the integrated energy system, Consider the indicators and utilization efficiency of water, electricity, gas, heat and energy storage. (2) Make full use of big data and cloud architecture technology to complete data collection, data processing, data presentation and data storage. Data acquisition can seamlessly integrate data from various sources, via wired or wireless networks, and accept files from data gateways. Accept files from third-party systems through interfaces such as OPC. Data processing can store data in the cloud, and perform preliminary analysis on massive data to achieve powerful computing performance. Based on artificial intelligence engine, it can automatically analyze equipment and energy consumption data, detect and identify problems such as equipment operation and inefficiency, and automatically obtain various data. Preliminary suggestions for improvement of equipment. Data presentation can be accessed through Web and Mobile anytime, anywhere, present data to users with different levels of authority in a variety of data presentation methods, and control access. Big data and cloud architecture technologies focus on solving the comprehensive energy consumption of buildings (including water, electricity, gas, heat, etc.), and users can choose or superimpose them according to actual needs. (3) Through the designed comprehensive benefit evaluation tool, it can provide decision support tools for the investors and builders of integrated energy buildings, evaluate the comprehensive benefits of integrated energy buildings according to the specific situation, reduce the sunk cost of energy supplier decision-making, and improve its service level . Therefore, the embodiments of the present invention can scientifically evaluate various comprehensive energy utilization rates, so as to achieve the purpose of optimizing the comprehensive energy system of the building in time and reducing the cost of planning and construction of new buildings.

It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

FIG. 6 is a schematic structural diagram of a building energy efficiency acquisition device according to an embodiment of the present invention. As shown in FIG. 6 , the building energy efficiency acquisition device 60 includes a collection module 601 and a processing module 602 .

The collection module 601 is used to collect data of multiple preset energy efficiency indicators of the target building;

The processing module 602 is configured to determine the information entropy of each preset energy efficiency index according to the data of the multiple preset energy efficiency indexes, and determine the first weight value of each preset energy efficiency index according to the information entropy of the multiple preset energy efficiency indexes ;

The processing module 602 is further configured to determine the third weight value of each preset energy efficiency index according to the first weight value and the second weight value of each preset energy efficiency index, wherein the second weight of each preset energy efficiency index is The value is determined according to the AHP algorithm;

The processing module 602 is further configured to determine the energy efficiency value of the target building according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index.

In the embodiment of the present invention, the data of multiple preset energy efficiency indicators of the target building are collected; the information entropy of each preset energy efficiency index is determined according to the data of multiple preset energy efficiency indicators, and the information entropy of each preset energy efficiency index is determined according to the data of multiple preset energy efficiency indicators, and the information entropy of each preset energy efficiency index is determined according to the data of multiple preset energy efficiency indicators, and the entropy, to determine the first weight value of each preset energy efficiency index; according to the first weight value and the second weight value of each preset energy efficiency index, determine the third weight value of each preset energy efficiency index, wherein, each The second weight value of the preset energy efficiency index is determined according to the analytic hierarchy process algorithm; according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index, the energy efficiency value of the target building is determined, which can be obtained in the process of obtaining the building energy efficiency. Considering the information entropy, the weight value determined by the information entropy and the weight value obtained by the AHP algorithm are used to jointly determine the weight value of each preset energy efficiency index. The weight value of the energy efficiency index is set to be more accurate, thereby improving the accuracy of building energy efficiency acquisition.

Optionally, the processing module 602 is used for:

Standardize the data of multiple preset energy efficiency indicators to generate an indicator matrix;

For each preset energy efficiency index, the information entropy of the preset energy efficiency index is determined according to the index matrix.

Optionally, the processing module 602 is used for:

For each preset energy efficiency index, the first difference is divided by the second difference to obtain a first weight value of the preset energy efficiency index, wherein the first difference is 1 and the information entropy of the preset energy efficiency index The second difference is the difference between the total number of preset energy efficiency indexes and the sum of information entropy of each preset energy efficiency index.

Optionally, the processing module 602 is used for:

For each preset energy efficiency index, the product value of the first weight value of the preset energy efficiency index and the first preset coefficient and the product value of the second weight value of the preset energy efficiency index and the second preset coefficient are calculated. and to obtain the third weight value of the preset energy efficiency index.

Optionally, the processing module 602 is used for:

The matter-element extension algorithm is used to determine the energy efficiency value of the target building according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index.

Optionally, the collection module 601 is used for:

Collect initial data of the target building by communicating with the target device, wherein the target device includes at least one of the following: energy consumption equipment deployed in the target building, equipment for storing data of the target building, and a third-party data platform;

According to the initial data, determine the data of multiple preset energy efficiency indicators of the target building, wherein the preset energy efficiency indicators include at least one of the following: internal rate of return, incremental investment static payback period, building waste emissions, building unit energy Emissions consumption, building waste reduction rate, proportion of building renewable energy, building energy system utilization rate, building heating/cooling efficiency, building exergy efficiency, building energy storage efficiency, average equipment utilization rate, building primary energy utilization rate, Building energy efficiency;

The device also includes a sending module, which is used for:

Store the initial data and push the initial data to the user terminal.

Optionally, the device further includes a display module, and the display module is used for:

Display the energy efficiency value of the target building and the data of various preset energy efficiency indicators.

Optionally, the sending module is also used for:

Send the energy efficiency value of the target building and the data of various preset energy efficiency indicators to the user terminal.

Optionally, the processing module 602 is further configured to:

According to the energy efficiency value of the target building, control the operation mode of the energy equipment of the target building.

The building energy efficiency acquisition device provided in this embodiment can be used to execute the above method embodiments, and the implementation principle and technical effect thereof are similar, and details are not described herein again in this embodiment.

FIG. 7 is a schematic diagram of an electronic device provided by an embodiment of the present invention. As shown in FIG. 7 , the electronic device 7 of this embodiment includes a processor 70 , a memory 71 , and a computer program 72 stored in the memory 71 and executable on the processor 70 , such as a building energy efficiency acquisition program. When the processor 70 executes the computer program 72 , the steps in each of the foregoing embodiments of the method for obtaining energy efficiency of a building are implemented, for example, steps 201 to 204 shown in FIG. 2 . Alternatively, when the processor 70 executes the computer program 72, the functions of the modules/units in the above-mentioned apparatus embodiments, for example, the functions of the modules 61 to 62 shown in FIG. 6 are implemented.

Exemplarily, the computer program 72 may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 71 and executed by the processor 70 to complete the this invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 72 in the electronic device 7 .

The electronic device 7 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. The electronic device 7 may include, but is not limited to, a processor 70 and a memory 71 . Those skilled in the art can understand that FIG. 7 is only an example of the electronic device 7, and does not constitute a limitation on the electronic device 7. It may include more or less components than the one shown, or combine some components, or different components For example, the electronic device 7 may also include an input and output device, a network access device, a bus, and the like.

The so-called processor 70 may be a central processing unit (Central Processing Unit, CPU), and may also be other general-purpose processors, digital signal processors (Digital Signal Processors, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 71 may be an internal storage unit of the electronic device 7 , such as a hard disk or a memory of the electronic device 7 . The memory 71 may also be an external storage device of the electronic device 7, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) equipped on the electronic device 7 card, flash card (Flash Card) and so on. Further, the memory 71 may also include both an internal storage unit of the electronic device 7 and an external storage device. The memory 71 is used to store the computer program and other programs and data required by the apparatus/terminal device. The memory 71 may also be used to temporarily store data that has been output or will be output.

Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. Module completion, that is, dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit, and the above-mentioned integrated units may adopt hardware. It can also be realized in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing from each other, and are not used to limit the protection scope of the present application. For the specific working processes of the units and modules in the above-mentioned system, reference may be made to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

In the foregoing embodiments, the description of each embodiment has its own emphasis. For parts that are not described or described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of the present invention.

In the embodiments provided by the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units. Or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The integrated modules/units, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed by the processor, the steps of the foregoing method embodiments can be implemented. . Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, and the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable media may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer-readable media Electric carrier signals and telecommunication signals are not included.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it is still possible to implement the foregoing implementations. The technical solutions described in the examples are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the within the protection scope of the present invention.

Claims (10)

1. a building energy efficiency acquisition method, is characterized in that, comprises: Collect data of multiple preset energy efficiency indicators of target buildings; Determine the information entropy of each preset energy efficiency index according to the data of the multiple preset energy efficiency indexes, and determine the first weight value of each preset energy efficiency index according to the information entropy of the multiple preset energy efficiency indexes; According to the first weight value and the second weight value of each preset energy efficiency index, the third weight value of each preset energy efficiency index is determined, wherein the second weight value of each preset energy efficiency index is determined according to the analytic hierarchy process algorithm; The energy efficiency value of the target building is determined according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index. 2. The building energy efficiency acquisition method according to claim 1, wherein the information entropy of each preset energy efficiency index is determined according to the data of the multiple preset energy efficiency indexes, comprising: standardizing the data of the plurality of preset energy efficiency indicators to generate an indicator matrix; For each preset energy efficiency index, the information entropy of the preset energy efficiency index is determined according to the index matrix. 3. The method for obtaining energy efficiency of a building according to claim 1, wherein the first weight value of each preset energy efficiency index is determined according to the information entropy of a plurality of preset energy efficiency indexes, comprising: For each preset energy efficiency index, the first difference is divided by the second difference to obtain a first weight value of the preset energy efficiency index, wherein the first difference is 1 and the difference between the preset energy efficiency index The difference between the information entropy, the second difference is the difference between the total number of preset energy efficiency indicators and the sum of the information entropy of each preset energy efficiency index. 4. The building energy efficiency acquisition method according to claim 1, wherein the third weight value of each preset energy efficiency index is determined according to the first weight value and the second weight value of each preset energy efficiency index, comprising: : For each preset energy efficiency index, the product value of the first weight value of the preset energy efficiency index and the first preset coefficient and the product value of the second weight value of the preset energy efficiency index and the second preset coefficient are calculated. and to obtain the third weight value of the preset energy efficiency index. 5 . The method for obtaining energy efficiency of a building according to claim 1 , wherein the energy efficiency value of the target building is determined according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index, comprising: 6 . : The matter-element extension algorithm is used to determine the energy efficiency value of the target building according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index. 6. The building energy efficiency acquisition method according to any one of claims 1-5, wherein the data of a plurality of preset energy efficiency indicators of the target building are collected, including: The initial data of the target building is collected by communicating with the target device, wherein the target device includes at least one of the following: energy consumption devices deployed in the target building, devices for storing data of the target building, Third-party data platform; According to the initial data, data of multiple preset energy efficiency indicators of the target building are determined, wherein the preset energy efficiency indicators include at least one of the following: internal rate of return, static payback period for incremental investment, building waste Emissions, building unit energy consumption emissions, building waste reduction rate, building renewable energy ratio, building energy system utilization, building heating/cooling efficiency, building exergy efficiency, building energy storage efficiency, average equipment utilization, Building primary energy utilization rate, building energy saving rate; The initial data is stored, and the initial data is pushed to the user terminal. 7. The building energy efficiency acquisition method according to any one of claims 1-5, wherein the method further comprises at least one of the following steps: Display the energy efficiency value of the target building and the data of various preset energy efficiency indicators; sending the energy efficiency value of the target building and the data of each preset energy efficiency index to the user terminal; According to the energy efficiency value of the target building, the operation mode of the energy equipment of the target building is controlled. 8. A building energy efficiency acquisition device, characterized in that, comprising: The collection module is used to collect data of multiple preset energy efficiency indicators of the target building; a processing module, configured to determine the information entropy of each preset energy efficiency index according to the data of the multiple preset energy efficiency indexes, and determine the first weight of each preset energy efficiency index according to the information entropy of the multiple preset energy efficiency indexes value; The processing module is further configured to determine the third weight value of each preset energy efficiency index according to the first weight value and the second weight value of each preset energy efficiency index, wherein the second weight value of each preset energy efficiency index is The weight value is determined according to the AHP algorithm; The processing module is further configured to determine the energy efficiency value of the target building according to the data of each preset energy efficiency index and the third weight value of each preset energy efficiency index. 9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the computer program as claimed in the claims The steps of any one of 1-7. 10. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1-7 are implemented .

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