CN115796693A - Beer production enterprise energy efficiency determination method and system and electronic equipment - Google Patents
Beer production enterprise energy efficiency determination method and system and electronic equipment Download PDFInfo
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Abstract
The invention discloses a method and a system for determining energy efficiency of a beer production enterprise and electronic equipment, and relates to the technical field of data processing. According to the beer production enterprise energy efficiency assessment method, the beer production enterprise energy efficiency determination model is constructed by adopting a PSO + AHP model and a fuzzy comprehensive evaluation method, the beer production enterprise energy efficiency is determined from multiple dimensions of local, micro, fine and integral, macro, comprehensive and quantitative and qualitative combination, the obtained assessment result is objective, the defects of the existing energy efficiency assessment method can be effectively improved, an analysis and optimization means is provided for the beer production enterprise to efficiently use energy, a basis is provided for further improving the energy utilization efficiency of the beer production enterprise, and the method has certain universality for other beer production enterprises in the brewing industry.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a system for determining energy efficiency of a beer production enterprise and electronic equipment.
Background
At present, a set of scientific and reasonable energy efficiency assessment method is not available, and the specific use condition of the energy of beer production enterprises in the brewing industry and the generated benefits can be accurately known, so that the problems of energy loss, energy waste and the like are caused.
Disclosure of Invention
The invention aims to provide a method, a system and electronic equipment for determining the energy efficiency of a beer production enterprise, which can accurately determine the energy efficiency of the beer production enterprise and further solve the problems of energy loss, waste and the like in the prior art.
In order to achieve the purpose, the invention provides the following scheme:
a beer production enterprise energy efficiency determination method comprises the following steps:
acquiring production data of beer production enterprises; the production data includes: steam consumption data, electricity consumption data, water consumption data and water vapor recovery data;
establishing an evaluation index system of the energy efficiency of the beer production enterprise based on the production data;
determining the weight of each evaluation index in the evaluation index system by adopting an analytic hierarchy process;
constructing a PSO + AHP model; the PSO + AHP model is obtained by applying a Particle Swarm Optimization (PSO) to an Analytic Hierarchy Process (AHP);
optimizing the weight by adopting the PSO + AHP model to obtain the optimized weight;
determining the degradation degree of each evaluation index in the evaluation index system;
determining the membership degree of each evaluation index in the evaluation index system according to the degradation degree by adopting a Fuzzy Comprehensive Evaluation (FCE) method to obtain a membership degree matrix;
determining the score of each evaluation index in the evaluation index system according to the membership matrix and a preset evaluation set matrix;
constructing a beer production enterprise energy efficiency determination model based on the scores and the optimized weights;
and determining the energy efficiency of the beer production enterprise based on the beer production enterprise energy efficiency determination model.
Preferably, the determining the weight of each evaluation index in the evaluation index system by using an analytic hierarchy process specifically includes:
constructing judgment matrixes of an index layer and a standard layer based on a scale theory of a 9-division method; the index layer is formed by each evaluation index in the evaluation index system; the standard layer is formed by set evaluation indexes;
carrying out column vector normalization processing on the judgment matrix to obtain a normalized matrix;
determining the average value of the row vectors of the normalized matrix to obtain a row vector matrix; the row vector matrix is used for representing the relative weight of each evaluation index in the evaluation index system.
Preferably, the method further comprises the following steps:
determining the maximum eigenvalue of the judgment matrix;
determining a consistency index based on the maximum characteristic value, the order of the judgment matrix and the average random consistency index; setting the average random consistency index based on the matrix order;
and when the consistency index does not meet the preset requirement, correcting the judgment matrix.
Preferably, the optimizing the weight by using the PSO + AHP model to obtain the optimized weight specifically includes:
and solving and optimizing the weight by constructing a fitness function by using a Python tool aiming at the PSO + AHP model.
Preferably, for the PSO + AHP model, the optimized weights for solving and optimizing the weights by constructing a fitness function using a Python tool specifically include:
taking the corrected judgment matrix as an input layer and introducing the input layer into a particle swarm optimization algorithm to obtain a PSO + AHP model;
selecting parameters of the PSO + AHP model, and taking the weight as input data of the PSO + AHP model to obtain an output value meeting constraint conditions; the output value is used as the optimized weight.
Preferably, the beer production enterprise energy efficiency determination model is as follows:
C＝ω·Q＝ω·R·V T ；
wherein C is the energy efficiency score of the beer production enterprise, R is a membership matrix, V is an evaluation set matrix, Q is a score, and omega is the optimized weight.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the beer production enterprise energy efficiency assessment method, the beer production enterprise energy efficiency determination model is constructed by adopting a PSO + AHP model and a fuzzy comprehensive evaluation method, the beer production enterprise energy efficiency is determined from multiple dimensions of local, micro, fine and integral, macro, comprehensive and quantitative and qualitative combination, the obtained assessment result is objective, the defects of the existing energy efficiency assessment method can be effectively improved, an analysis and optimization means is provided for the beer production enterprise to efficiently use energy, a basis is provided for further improving the energy utilization efficiency of the beer production enterprise, and the method has certain universality for other beer production enterprises in the brewing industry.
Corresponding to the method for determining the energy efficiency of the beer production enterprise, the invention also provides the following implementation system:
a beer production enterprise energy efficiency determination system, comprising:
the production data acquisition module is used for acquiring production data of beer production enterprises; the production data includes: steam consumption data, electricity consumption data, water consumption data and water vapor recovery data;
the index system construction module is used for constructing an evaluation index system of the energy efficiency of the beer production enterprise based on the production data;
the weight determining module is used for determining the weight of each evaluation index in the evaluation index system by adopting an analytic hierarchy process;
the first model building module is used for building a PSO + AHP model; the PSO + AHP model is obtained by applying a particle swarm optimization algorithm to an analytic hierarchy process;
the weight optimization module is used for optimizing the weight by adopting the PSO + AHP model to obtain the optimized weight;
the degradation degree determining module is used for determining the degradation degree of each evaluation index in the evaluation index system;
the membership degree determining module is used for determining the membership degree of each evaluation index in the evaluation index system according to the degradation degree by adopting a fuzzy comprehensive evaluation method to obtain a membership degree matrix;
the score determining module is used for determining the score of each evaluation index in the evaluation index system according to the membership matrix and a preset evaluation set matrix;
the second model building module is used for building a beer production enterprise energy efficiency determining model based on the scores and the optimized weights;
and the energy efficiency determining module is used for determining the energy efficiency of the beer production enterprise based on the beer production enterprise energy efficiency determining model.
An electronic device, comprising:
a memory for storing computer logic control instructions; the computer logic control instruction is used for implementing the beer production enterprise energy efficiency determination method;
and the processor is connected with the memory and used for calling and executing the computer logic control instruction so as to determine the energy efficiency of the beer production enterprise.
Preferably, the memory is a computer readable storage medium.
Preferably, the processor is a computer.
Because the two implementation systems provided by the invention have the same technical effects as the method for determining the energy efficiency of the beer production enterprise provided by the invention, the detailed description is omitted here.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for determining energy efficiency of a beer producing enterprise according to the present invention;
FIG. 2 is a schematic diagram of an evaluation system according to an embodiment of the present invention;
FIG. 3 is a frame diagram of an embodiment of a method for determining energy efficiency of a beer production enterprise according to the present invention;
FIG. 4 is a flowchart of solution optimization for weights according to an embodiment of the present invention;
FIG. 5 is a comparison chart of the disposable index provided by the embodiment of the present invention;
FIG. 6 is a schematic structural diagram of an energy efficiency determination system for beer production enterprises according to the present invention;
fig. 7 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The invention aims to provide a method, a system and electronic equipment for determining the energy efficiency of a beer production enterprise, which can accurately determine the energy efficiency of the beer production enterprise, and further solve the problems of energy loss, waste and the like in the prior art.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1 and fig. 3, the method for determining energy efficiency of a beer production enterprise according to the present invention includes:
step 100: and acquiring production data of beer production enterprises. The production data includes: the data of steam consumption, electricity consumption, water vapor recovery and the like can be obtained through an online monitoring instrument. In addition, in order to further improve the accuracy of determining the energy efficiency of beer production enterprises, subjective factors influencing the energy efficiency of breweries can be analyzed and scored by combining with expert experience, and then the subjective factors can be used as a data base for constructing an evaluation index system. Such as new processes and new technologies and new equipment applications, professional scheduling, systems and assessments.
Step 101: and constructing an evaluation index system of the energy efficiency of the beer production enterprise based on the production data. An evaluation index system constructed based on the production data and the subjective data acquired in step 100 is shown in fig. 2.
Step 102: and determining the weight of each evaluation index in the evaluation index system by adopting an analytic hierarchy process. The steps are implemented in the process of implementation, and mainly comprise the following steps:
according to the scale theory of the 9-division method shown in table 1, a judgment matrix J of the index layer and the standard layer is constructed:
J＝(a ij ) n×n (1)
in the formula, a ij Is the importance scale of factor i compared to factor j, a ij ＝1/a ji ，a ii =1, wherein i =1,2.. N. j =1,2.. N.
The concrete implementation steps are as follows:
1) Normalizing the column vector of the judgment matrix to obtain a normalized matrix
2) Calculating an arithmetic mean of row vectors of a normalization matrix
To obtain
I.e. the relative weight of each evaluation index in the evaluation index system.
3) Calculating the maximum eigenvalue lambda of the judgment matrix max 。
That is, a row vector obtained by multiplying matrix J obtained by equation (1) by equation (3), and dividing each element of the row vector by
The obtained n elements are summed and finally divided by n to obtain the maximum characteristic value lambda max 。
4) Calculating consistency index of judgment matrix
Wherein the content of the first and second substances,
wherein CR is a consistency index, λ max And n is the order of the judgment matrix. RI is an average random consistency index and can be found in Table 2.
When CR is less than or equal to 0.1, the consistency of the matrix is judged to be acceptable. When CR is greater than 0.1, the judgment matrix is corrected appropriately.
For example, the method of step 102 is applied to select relevant data and information of a certain beer producing enterprise, and the weights of the evaluation indexes in the evaluation index system shown in fig. 2 are obtained as shown in table 3.
TABLE 1 9 graduation
In Table 1, u i And u j Any two indicators in the evaluation indicator system shown in fig. 2.
TABLE 2 random consistency index table of 12-order judgment matrix
TABLE 3 AHP calculation result table for each level index weight and consistency index
As can be seen from Table 3, the single-layer analytical method has a one-time index of less than 0.1, and meets the requirement of consistency.
Step 103: and constructing a PSO + AHP model. The PSO + AHP model is obtained by applying a particle swarm optimization algorithm to an analytic hierarchy process.
Step 104: and optimizing the weight by adopting a PSO + AHP model to obtain the optimized weight. The weight calculated by the AHP is optimized in the step, and the problems that the AHP has subjectivity in weight determination and the comprehensive evaluation result is more scientific and reliable due to limited cognitive level of people and inconsistency of opinions among evaluation experts are mainly solved. Based on this, the implementation process of the step mainly comprises the following steps:
according to the relative importance of each index, a judgment matrix J = { a } is constructed ij } n×n Wherein i, j =1,2 ij Indicating index u i Relative index u j The importance of (c). Let omega k The weight of each index is theoretically omega according to the definition of the judgment matrix i /ω j ＝a ij And at this time, if the judgment matrix J has complete consistency, then:
namely:
it can be seen from equation (8) that the smaller the value at the left end of the equation, the higher the consistency of the determination matrix, and the establishment of equation (8) indicates that the determination matrix has complete consistency. Therefore, the problem of weight value determination and consistency check of each index can be summarized as the following optimization problem:
in the formula: CIF (n) is a consistency index function, ω k To optimize the variables.
Wherein the constraint conditions are as follows:
and when the CIF (n) function reaches an optimal value in the global range and the optimal value is less than 0.1, the constructed judgment matrix J is considered to have satisfactory consistency, and the corresponding optimal solution is the subjective weight to be solved. When the global minimum value is 0, judging the matrix JThere is complete consistency. According to the constraint conditions
The global minimum is known to be unique.
For the PSO + AHP model, a Python tool is used, and the weight is solved and optimized by constructing a fitness function, and a specific flow is shown in fig. 4.
Taking the judgment matrix of the AHP structure as an input layer to be brought into the PSO, and selecting the parameters of the PSO: the population size was 40, the number of iterations was 200, and the calculation results are shown in table 4.
TABLE 4PSO-AHP model each level index weight and consistency index calculation result table
The comparison of the calculation results in tables 3 and 4 gives a consistency index comparison chart shown in fig. 5. As can be seen from FIG. 5, the consistency index of the PSO + AHP model is obviously reduced, and the accuracy of the calculation result is improved.
Step 105: and determining the degradation degree of each evaluation index in the evaluation index system. The specific implementation process of the step is as follows:
and (3) defining the degradation degree g by simulating equipment fault evaluation, wherein the g is more than or equal to 0 and less than or equal to 1, namely the index is in the degree of high energy consumption and low energy efficiency, and the larger the g is, the closer to 1, the higher the energy consumption is, the lower the energy efficiency is, the closer to 0 is. Smaller g means lower energy consumption and higher energy efficiency.
The degree of deterioration of each index is determined.
The calculation formula for the optimum degradation degree as the amount decreases is:
in the formula, x isEvaluating the index parameter value, x min 、x max Is a threshold value for evaluating the critical interval of the index parameter.
The intermediate type evaluation indexes are as follows:
x a and x b The boundary value of the reasonable interval of the index parameter is evaluated.
The calculation formula for the more optimal degradation degree is as follows:
as can be seen from the energy efficiency evaluation index system shown in FIG. 2, the lower the consumption of steam, electricity and water, the lower the energy consumption, the higher the energy efficiency. The more new technology and new equipment are applied, the more optimized the professional scheduling is, the more the water vapor is recycled, the more the system and the examination are in place, and the higher the grading is, the lower the energy consumption is, and the higher the energy efficiency is. Therefore, the deterioration degree is calculated in an optimum manner as the amount of energy consumption of steam, electricity, and water is smaller by equation (11). The degradation degree is calculated according to the optimal type when the formula (13) adopted by a new process, a new technology and a new equipment application, professional scheduling, water vapor recovery, a system and examination are larger.
The calculation results of the deterioration degree obtained from the above calculation formula are shown in table 5.
TABLE 5 degradation degree calculation result table
According to the degradation degree of each index calculated as described above, the factors affecting the energy efficiency can be clearly recognized and grasped from a local and microscopic perspective.
Step 106: and determining the membership degree of each evaluation index in the evaluation index system according to the degradation degree by adopting a fuzzy comprehensive evaluation method to obtain a membership degree matrix. The method specifically comprises the following steps:
setting an evaluation set of an evaluation system:
V＝{1,0.67,0.33,0} (14)
degradation g obtained according to step 105 i And selecting a trapezoidal distribution function to obtain a membership matrix R.
The membership function for each rating scale is as follows:
according to the obtained degradation degree, the membership degree calculation formulas in formulas (15) to (18) are carried, so that a membership degree matrix can be obtained, wherein the membership degree matrix respectively comprises:
step 107: and determining the score of each evaluation index in the evaluation index system according to the membership matrix and the preset evaluation set matrix. Considering the final realization of quantitative scoring, the membership matrix R and the evaluation set matrix V are integrated to obtain the single score Q of each element index, which is as follows:
Q＝R·V T (26)
step 108: and constructing a beer production enterprise energy efficiency determination model based on the scores and the optimized weights.
Namely, a PSO + AHP model and an FCE method are used for energy efficiency evaluation, and the specific implementation process is as follows:
weighting the single-term scores Q of the element indexes obtained based on the FCE model by using the index weights omega of the factors obtained by PSO + HP, so as to obtain a brewery energy efficiency quantitative evaluation model which is as follows:
C＝ω·Q＝ω·R·V T (27)
from the quantitative evaluation model formula of formula (26), the quantitative evaluation model results shown in table 6 can be obtained.
TABLE 6 quantitative evaluation model results Table
C U ＝ω U ·[C U1 ,C U2 ,C U3 ,C U4 ,C U5 ,C U6 ,C U7 ]＝0.941 (28)
Step 109: and determining the energy efficiency of the beer production enterprise based on the beer production enterprise energy efficiency determination model. Specifically, as can be seen from equation (28), when the evaluation score of the brewery energy efficiency is 0.941, and the value of V = {1,0.67,0.33,0} is { excellent, good, fair, poor }, the energy efficiency is estimated to be nearly excellent according to the calculation result, which is consistent with the actual situation that the brewery is a new enterprise.
If the energy efficiency is low, the specific evaluation is needed to specifically evaluate which link causes low energy efficiency or which energy consumption is too high, and the reason for leakage, the relative fall-behind of the process, technology and equipment, the optimization or recycling requirement of scheduling needs to be improved or the institutional assessment is not in place.
Based on the description, the energy efficiency assessment system provided by the invention performs problem analysis and carding summary of applying new technology and new equipment, professional scheduling, water vapor recycling and system and assessment according to the difference between actual energy consumption data and internal control indexes of a certain brewery (moved and newly built on the basis of old breweries), follows the principles of scientificity, comprehensiveness, universality and operability, systematicness and hierarchy, qualitative and quantitative combination and the like, selects links such as brewing, packaging, power and the like involved in the beer production process and relevant factors influencing energy efficiency according to the actual production of beer enterprises in the brewing industry, and constructs an energy efficiency assessment system framework with important characteristics of reflecting energy consumption of the brewery, namely the energy efficiency assessment index system, thereby meeting the actual application requirements.
In addition, the degradation degree-based PSO + AHP model and FCE method can evaluate the energy efficiency of the enterprise from multiple dimensions, and has the following advantages:
1) The deterioration degree can be used for carrying out local, microscopic and fine analysis and modification on all factors influencing the energy efficiency of the enterprise. The deterioration degree of 83 factors calculated according to the table 5 is a numerical value of 0-1 which is converted from all factors which affect different types and dimensions of enterprise energy efficiency, so that the influence of each process and each specific technical index on the energy efficiency is convenient to compare, the deterioration degree of the water consumption of the packaging listening line is 0.959 at most, the evaluation index is 0.173T/kl by comparing with other 12-month data, other months are basically near 0.04T/kl, and the evaluation result is matched with the actual condition of the enterprise. The new process, the new technology and the new equipment have good application conditions, the degradation degree is mostly below 0.5, the refrigeration efficiency of one item is required to be improved by 0.8, and the conditions of the three items are 0 respectively good for reducing the temperature difference between the pre-soaking tank and the main alkali washing tank, reducing the steam waste and optimizing the application of the saccharification process. The professional scheduling level is high, and the degradation degree is mostly below 0.2. The water vapor recovery effect is excellent, the deterioration degree is all 0.2 or less, and the recycling effect of the cooling water is also excellent in the case where the deterioration degree is 0. The system and the examination are carried out in place, and the deterioration degree is low and is 0.1 and 0.2. Therefore, the factors influencing the energy efficiency are clearly and comprehensively known and grasped from the local and microscopic angles.
2) The calculation results of the quantitative evaluation model are shown in the table 6, evaluation indexes with different dimensions and different properties are converted into values with the same value of 0-1, so that the influence of seven indexes, namely steam consumption, electricity consumption, water consumption, new technology, new equipment application, professional scheduling, water vapor recovery, system and assessment on the overall energy efficiency of an enterprise can be comprehensively evaluated on the same platform by the same scale, and the professional scheduling score is the highest in the table 6, so that the high scheduling and management level of the enterprise is indicated. The fact that the unit power consumption is low is traced to that the power consumption of the brewing fermentation and the power refrigeration is high, the power consumption of the brewing fermentation is 0.84kwh/kl, the power consumption of the power refrigeration is 10.24kwh/kl, and the power consumption is far higher than the power consumption of the brewing fermentation of other 12 months by 0.66kwh/kl and the power consumption of the power refrigeration is 4.12kwh/kl, and the next step can be pertinently started to reduce the power consumption and improve the energy efficiency.
3) Based on the PSO + AHP model and the FCE energy efficiency evaluation model, the overall, macroscopic and comprehensive understanding and mastering of the enterprise energy efficiency can be achieved, and each evaluation index embodies the combination of quantification and qualification. The PSO + AHP model and the FCE based on the degree of degradation evaluate the energy efficiency of the enterprise from multiple dimensions, and the Python experiment shows that the energy efficiency evaluation result is consistent with the actual condition of the enterprise, so that the defects of the existing energy efficiency evaluation method can be effectively improved, an analysis and optimization means is provided for the efficient use of energy by the enterprise, a basis is provided for further improving the energy utilization efficiency of the enterprise, and the method has certain universality for other beer production enterprises in the brewing industry.
Corresponding to the method for determining the energy efficiency of the beer production enterprise, the invention also provides the following implementation system:
a beer production enterprise energy efficiency determination system, as shown in fig. 6, the system comprising:
the production data acquiring module 600 is used for acquiring production data of beer production enterprises. The production data includes: steam consumption data, electricity consumption data, water consumption data and water vapor recovery data.
And the index system building module 601 is used for building an evaluation index system of the energy efficiency of the beer production enterprise based on the production data.
A weight determining module 602, configured to determine a weight of each evaluation index in the evaluation index system by using an analytic hierarchy process.
A first model building module 603, configured to build a PSO + AHP model. The PSO + AHP model is obtained by applying a particle swarm optimization algorithm to an analytic hierarchy process.
And a weight optimization module 604, configured to optimize the weight by using a PSO + AHP model to obtain an optimized weight.
A degradation degree determination module 605, configured to determine a degradation degree of each evaluation index in the evaluation index system.
And a membership degree determining module 606, configured to determine a membership degree of each evaluation index in the evaluation index system according to the degradation degree by using a fuzzy comprehensive evaluation method, so as to obtain a membership degree matrix.
And a score determining module 607, configured to determine a score of each evaluation index in the evaluation index system according to the membership matrix and the preset evaluation set matrix.
And a second model building module 608 for building a beer production enterprise energy efficiency determination model based on the scores and the optimized weights.
And the energy efficiency determining module 609 is used for determining the energy efficiency of the beer production enterprise based on the beer production enterprise energy efficiency determining model.
An electronic device, as shown in fig. 7, includes:
And a processor 701, coupled to the memory 700, for retrieving and executing computer logic control instructions to determine the energy efficiency of the beer producing enterprise.
Further, the memory in the electronic device may be a computer-readable storage medium. The processor may be a computer.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the description of the method part.
The principle and the embodiment of the present invention are explained by applying specific examples, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A beer production enterprise energy efficiency determination method is characterized by comprising the following steps:
acquiring production data of beer production enterprises; the production data includes: steam consumption data, electricity consumption data, water consumption data and water vapor recovery data;
establishing an evaluation index system of the energy efficiency of the beer production enterprise based on the production data;
determining the weight of each evaluation index in the evaluation index system by adopting an analytic hierarchy process;
constructing a PSO + AHP model; the PSO + AHP model is obtained by applying a particle swarm optimization algorithm to an analytic hierarchy process;
optimizing the weight by adopting the PSO + AHP model to obtain the optimized weight;
determining the degradation degree of each evaluation index in the evaluation index system;
determining the membership degree of each evaluation index in the evaluation index system according to the degradation degree by adopting a fuzzy comprehensive evaluation method to obtain a membership degree matrix;
determining the score of each evaluation index in the evaluation index system according to the membership matrix and a preset evaluation set matrix;
constructing a beer production enterprise energy efficiency determination model based on the scores and the optimized weights;
and determining the energy efficiency of the beer production enterprise based on the beer production enterprise energy efficiency determination model.
2. The method for determining energy efficiency of a beer production enterprise according to claim 1, wherein the determining the weight of each evaluation index in the evaluation index system by using an analytic hierarchy process specifically comprises:
constructing a judgment matrix of an index layer and a standard layer based on a scale theory of a 9-division method; the index layer is formed by each evaluation index in the evaluation index system; the standard layer is formed by set evaluation indexes;
carrying out column vector normalization processing on the judgment matrix to obtain a normalized matrix;
determining the average value of the row vectors of the normalization matrix to obtain a row vector matrix; the row vector matrix is used for representing the relative weight of each evaluation index in the evaluation index system.
3. The method of determining energy efficiency of a beer producing enterprise of claim 2, further comprising:
determining the maximum eigenvalue of the judgment matrix;
determining a consistency index based on the maximum characteristic value, the order of the judgment matrix and the average random consistency index; setting the average random consistency index based on the matrix order;
and when the consistency index does not meet the preset requirement, correcting the judgment matrix.
4. The method for determining energy efficiency of a beer production enterprise according to claim 3, wherein the optimizing the weight by using the PSO + AHP model to obtain the optimized weight specifically comprises:
and for the PSO + AHP model, using a Python tool to solve and optimize the weight by constructing a fitness function.
5. The method according to claim 4, wherein the step of solving and optimizing the optimized weights by constructing a fitness function using a Python tool for the PSO + AHP model specifically comprises:
taking the corrected judgment matrix as an input layer and introducing the input layer into a particle swarm optimization algorithm to obtain a PSO + AHP model;
selecting parameters of the PSO + AHP model, and taking the weight as input data of the PSO + AHP model to obtain an output value meeting constraint conditions; the output value is used as the optimized weight.
6. The method for determining the energy efficiency of the beer production enterprise according to claim 1, wherein the beer production enterprise energy efficiency determination model is:
C＝ω·Q＝ω·R·V T ；
wherein C is the energy efficiency score of the beer production enterprise, R is a membership matrix, V is an evaluation set matrix, Q is a score, and omega is the optimized weight.
7. A beer production enterprise energy efficiency determination system, comprising:
the production data acquisition module is used for acquiring production data of beer production enterprises; the production data includes: steam consumption data, electricity consumption data, water consumption data and water vapor recovery data;
the index system construction module is used for constructing an evaluation index system of the energy efficiency of the beer production enterprise based on the production data;
the weight determining module is used for determining the weight of each evaluation index in the evaluation index system by adopting an analytic hierarchy process;
the first model building module is used for building a PSO + AHP model; the PSO + AHP model is obtained by applying a particle swarm optimization algorithm to an analytic hierarchy process;
the weight optimization module is used for optimizing the weight by adopting the PSO + AHP model to obtain the optimized weight;
the degradation degree determining module is used for determining the degradation degree of each evaluation index in the evaluation index system;
the membership degree determining module is used for determining the membership degree of each evaluation index in the evaluation index system according to the degradation degree by adopting a fuzzy comprehensive evaluation method to obtain a membership degree matrix;
the score determining module is used for determining the score of each evaluation index in the evaluation index system according to the membership matrix and a preset evaluation set matrix;
the second model building module is used for building a beer production enterprise energy efficiency determining model based on the scores and the optimized weights;
and the energy efficiency determination module is used for determining the energy efficiency of the beer production enterprise based on the beer production enterprise energy efficiency determination model.
8. An electronic device, comprising:
a memory for storing computer logic control instructions; the computer logic control instructions are used for implementing the beer production enterprise energy efficiency determination method according to any one of claims 1-6;
and the processor is connected with the memory and used for calling and executing the computer logic control instruction so as to determine the energy efficiency of the beer production enterprise.
9. The electronic device of claim 8, wherein the memory is a computer-readable storage medium.
10. The electronic device of claim 8, wherein the processor is a computer.
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