Airport energy consumption monitoring management system and method

Abstract

The invention discloses an airport energy consumption monitoring management system and method, and relates to the technical field of energy consumption monitoring management, and the method comprises the steps: training a first energy consumption prediction model when the first type of medium energy consumption data 1, 2 and 3 are the same; when the first-class medium energy consumption data I, the second-class medium energy consumption data II and the third-class medium energy consumption data I are different from one another and two of the second-class medium energy consumption data I, the second-class medium energy consumption data II and the third-class medium energy consumption data II are the same, a second energy consumption prediction model When two of the first-class medium energy consumption data I, the second-class medium energy consumption data II and the third-class medium energy consumption data III are the same and at least two of the second-class medium energy consumption data I, the second-class medium energy consumption data II and the third-class medium energy consumption data III are the same, a third energy consumption prediction model is trained; and judging the relationship of the energy consumption data of the three current same media so as to select a corresponding model from the first, second and third energy consumption prediction models for energy consumption prediction, and finally performing distribution adjustment control on airport energy. According to the airport energy consumption monitoring management system and method provided by the invention, the energy consumption monitoring efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of energy consumption monitoring and management, and in particular to an airport energy consumption monitoring and management system and method. Background Art

[0002] Airport energy consumption monitoring and management is of great significance in ensuring energy safety, improving energy utilization efficiency, reducing operating costs, improving management level and promoting sustainable development.

[0003] In the prior art, the detection and analysis of airport energy consumption only considered a single angle, and did not conduct detailed analysis under multiple circumstances, resulting in large errors in the detection results of energy consumption, reducing the work efficiency of the entire energy consumption monitoring and management. Summary of the invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the present application provides an airport energy consumption monitoring and management system and method.

[0005] In a first aspect, the present application provides a method for monitoring and managing airport energy consumption, the method comprising: Obtain three target detection areas for energy consumption detection, and perform energy consumption statistics on the same media and different media in the three target detection areas in the recent historical period to obtain the first type of energy consumption data set and the second type of energy consumption data set; If the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are the same, extracting the corresponding correlation between the first type of energy consumption data set and the second type of energy consumption data set to train a first energy consumption prediction model; If the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are different from each other, and two of the second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 in the second type of energy consumption data set are the same, then the first type of energy consumption data set and the second type of energy consumption data set are combined with features of corresponding correlation extraction and difference comparison to train a second energy consumption prediction model; If two of the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are identical to each other, and at least two of the second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 in the second type of energy consumption data set are identical to each other, then a second difference feature set is calculated; If two of the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are the same, and the second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 in the second type of energy consumption data set are the same, then a third difference feature set is statistically calculated, and a third energy consumption prediction model is trained according to the second difference feature set or the third difference feature set; Obtain the energy consumption data of the same medium at the current detection moment to obtain three current energy consumption data of the same medium, make a relationship judgment on the three current energy consumption data of the same medium, and select one of the corresponding models from the first energy consumption prediction model, the second energy consumption prediction model and the third energy consumption prediction model to perform energy consumption prediction, and finally perform airport energy distribution adjustment control, and output the energy distribution adjustment control result.

[0006] Preferably, three target detection areas for energy consumption detection are obtained, and all media that need to consume energy in the three target detection areas in the recent historical period are counted to obtain three types of energy-consuming medium sets; Extracting the same part of the three types of energy-consuming media from the three types of energy-consuming media, outputting the same media, and respectively counting the energy consumption data of the same media in the recent historical period to obtain the same medium energy consumption data, the same medium energy consumption data including the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the third type of medium energy consumption data 3; The first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the third type of medium energy consumption data 3 are combined into a first type of energy consumption data set; Extract the remaining three different media from the three types of energy consumption media, output the difference media, and respectively count the energy consumption data of the same media in the recent historical period to obtain the difference medium energy consumption data, the difference medium energy consumption data includes the second type medium energy consumption data 1, the second type medium energy consumption data 2, and the second type medium energy consumption data 3; The second-category medium energy consumption data one, the second-category medium energy consumption data two and the second-category medium energy consumption data three are combined into a second-category energy consumption data set.

[0007] Preferably, if the first type medium energy consumption data 1, the first type medium energy consumption data 2 and the first type medium energy consumption data 3 are all the same, then the correlation between the second type medium energy consumption data 1 and the first type medium energy consumption data 1 is extracted to obtain the correlation degree 1; Extracting the correlation between the second type of medium energy consumption data 2 and the first type of medium energy consumption data 2 to obtain correlation degree 2; Extracting the correlation between the second-category medium energy consumption data three and the first-category medium energy consumption data three to obtain correlation degree three; The first correlation degree, the second correlation degree and the third correlation degree are combined into a first correlation degree feature set; A first energy consumption prediction model is trained according to the first correlation feature set, the first type of energy consumption data set, and the second type of energy consumption data set.

[0008] Preferably, if the first type medium energy consumption data 1, the first type medium energy consumption data 2 and the first type medium energy consumption data 3 are all different from each other, then the relationship between the second type medium energy consumption data 1, the second type medium energy consumption data 2 and the second type medium energy consumption data 3 is determined, and the determination information is output; According to the judgment information, if two of the second-type medium energy consumption data 1, the second-type medium energy consumption data 2 and the second-type medium energy consumption data 3 are the same, output one of the judgment results; Comparing the two identical medium energy consumption data in the first judgment result with the one different medium energy consumption data in the second judgment result to obtain a difference feature; According to the first judgment result, the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 are compared with each other to obtain the second difference feature; The first difference feature and the second difference feature are combined into a first difference feature set; The same two medium energy consumption data in the first judgment result are respectively corresponded to the corresponding two medium energy consumption data in the first type medium energy consumption data one, the first type medium energy consumption data two and the first type medium energy consumption data three to extract the correlation between them to obtain correlation degree four and correlation degree five; The association degree 4 and the association degree 5 are combined into a second association degree feature set; A second energy consumption prediction model is trained according to the first difference feature set, the second correlation feature set, the first type of energy consumption data set, and the second type of energy consumption data set.

[0009] Preferably, if two of the first-category medium energy consumption data 1, the first-category medium energy consumption data 2 and the first-category medium energy consumption data 3 are the same, the second judgment result is output; and if two of the second-category medium energy consumption data 1, the second-category medium energy consumption data 2 and the second-category medium energy consumption data 3 are the same, the third judgment result is output; Comparing the energy consumption data of the two same media in the third judgment result to obtain the third difference feature; According to the same two medium energy consumption data in the third judgment result, the difference between the corresponding two medium energy consumption data in the second judgment result is compared to obtain the fourth difference feature; The third difference feature and the fourth difference feature are combined into a second difference feature set.

[0010] Preferably, if two of the first-category medium energy consumption data 1, the first-category medium energy consumption data 2 and the first-category medium energy consumption data 3 are the same, the second judgment result is output; and if the second-category medium energy consumption data 1, the second-category medium energy consumption data 2 and the second-category medium energy consumption data 3 are all the same, the fourth judgment result is output; According to the same two medium energy consumption data in the second judgment result, the corresponding two medium energy consumption data in the fourth judgment result are respectively compared with another medium energy consumption data to obtain the fifth difference feature and the sixth difference feature; The fifth difference feature and the sixth difference feature are combined into a third difference feature set; The second difference feature set or the third difference feature set, the first type of energy consumption data set and the second type of energy consumption data set are used to train a third energy consumption prediction model.

[0011] Preferably, according to the same medium, the energy consumption data of each of the same media at the current detection moment is obtained to obtain the same medium energy consumption data to obtain three types of current same medium energy consumption data; Determine the relationship between the three current energy consumption data of the same medium. If the three current energy consumption data of the same medium are the same, input the three current energy consumption data of the same medium and the difference medium into the first energy consumption prediction model for testing, and output the energy consumption prediction result; If the three current energy consumption data of the same medium are all different, the three current energy consumption data of the same medium and the difference medium are input into the second energy consumption prediction model for testing, and the energy consumption prediction result is output; If two of the three current energy consumption data of the same medium are the same, the three current energy consumption data of the same medium and the difference medium are input into the third energy consumption prediction model for testing, and the energy consumption prediction result is output; Based on the energy consumption prediction results and the energy consumption data of three current identical media, the airport energy distribution adjustment control is performed, and the energy distribution adjustment control results are output.

[0012] In a second aspect, an airport energy consumption monitoring and management system includes: The historical energy consumption statistics unit is used to obtain three target detection areas for energy consumption detection, and to perform energy consumption statistics on the same media and the different media in the three target detection areas in the recent historical period to obtain the first type of energy consumption data set and the second type of energy consumption data set; The first model training unit is used to determine if the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are the same, then extract the corresponding correlation between the first type of energy consumption data set and the second type of energy consumption data set to train a first energy consumption prediction model; The second model training unit is used to determine that if the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are different from each other, and two of the second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 in the second type of energy consumption data set are the same, then the first type of energy consumption data set and the second type of energy consumption data set are subjected to corresponding correlation extraction and difference comparison combined features to train a second energy consumption prediction model; a data feature statistical unit, for determining that if two of the first-category medium energy consumption data 1, the first-category medium energy consumption data 2, and the first-category medium energy consumption data 3 in the first-category energy consumption data set are identical to each other, and at least two of the second-category medium energy consumption data 1, the second-category medium energy consumption data 2, and the second-category medium energy consumption data 3 in the second-category energy consumption data set are identical to each other, then a second difference feature set is statistically generated; The third model training unit is used to determine if two of the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are the same, and the second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 in the second type of energy consumption data set are the same, then a third difference feature set is statistically calculated, and a third energy consumption prediction model is trained according to the second difference feature set or the third difference feature set; The current energy consumption data prediction unit is used to obtain the energy consumption data of the same medium at the current detection moment to obtain the energy consumption data of the same medium to obtain three current energy consumption data of the same medium, and make a relationship judgment on the three current energy consumption data of the same medium to select one of the corresponding models from the first energy consumption prediction model, the second energy consumption prediction model and the third energy consumption prediction model to perform energy consumption prediction, and finally perform airport energy distribution adjustment control, and output the energy distribution adjustment control result.

[0013] Compared with the prior art, the present invention has the following characteristics and beneficial effects: By performing a differentiated analysis on all energy-consuming media in different areas of the airport, that is, respectively performing a judgment analysis on the relationship between the energy consumption data of the same media and the difference media, and performing analysis in a variety of different processing methods, that is, if the relationship between the first-category medium energy consumption data one, the first-category medium energy consumption data two and the first-category medium energy consumption data of the same media statistics are all the same, then the second-category medium energy consumption data one, the second-category medium energy consumption data two and the second-category medium energy consumption data corresponding to the difference media can be directly extracted to obtain the degree of influence correlation between medium energy consumption, so as to facilitate the subsequent regular change analysis of all medium energy consumption conditions in different areas of the airport at the current moment, and train the first energy consumption prediction model based on the above-mentioned feature statistics. In order to reduce the large errors in energy consumption prediction results caused by the characteristics of medium energy consumption changes in various cases, it is necessary to perform a diverse and more comprehensive analysis, that is, the first-category medium energy consumption data one, the first-category medium energy consumption data two and the first-category medium energy consumption data are all the same. When they are not the same, the relationship between the second-category medium energy consumption data 1, the second-category medium energy consumption data 2 and the second-category medium energy consumption data is judged. If there are two kinds of medium energy consumption data that are the same, an analysis process different from the training of the first energy consumption prediction model is performed to train the second energy consumption prediction model. When the first-category medium energy consumption data 1, the first-category medium energy consumption data 2 and the first-category medium energy consumption data are all different, and at least two kinds of medium energy consumption data among the second-category medium energy consumption data 1, the second-category medium energy consumption data 2 and the second-category medium energy consumption data are the same, an analysis process different from the above-mentioned training of the first energy consumption prediction model and the second energy consumption prediction model is performed to train the third energy consumption prediction model. Through the training of the three models, the use of a single prediction model in the traditional technology to analyze and process only one energy consumption situation and cause misjudgment is avoided, thereby improving the utilization rate of resources and the efficiency of energy consumption monitoring. Finally, according to the energy consumption prediction results, the airport energy is adjusted and controlled accordingly. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flowchart of the steps of a method for monitoring and managing airport energy consumption mainly embodied in this embodiment.

[0015] Figure 2 It is a structural block diagram of an airport energy consumption monitoring and management system mainly embodied in this embodiment. DETAILED DESCRIPTION

[0016] The present invention is further described in detail below in conjunction with the following examples.

[0017] Reference Figure 1 , a method for monitoring and managing airport energy consumption, the method comprising the following steps: S1. Obtain three target detection areas for energy consumption detection, and collect energy consumption data statistics for the same media and different media in the three target detection areas in the recent historical period to obtain the first type of energy consumption data set and the second type of energy consumption data set.

[0018] S2. If the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are the same, then the first type of energy consumption data set and the second type of energy consumption data set are subjected to corresponding correlation extraction to train a first energy consumption prediction model.

[0019] S3. If the first-category medium energy consumption data one, the first-category medium energy consumption data two and the first-category medium energy consumption data three in the first-category energy consumption data set are different from each other, and two of the second-category medium energy consumption data one, the second-category medium energy consumption data two and the second-category medium energy consumption data three in the second-category energy consumption data set are the same, then the first-category energy consumption data set and the second-category energy consumption data set are subjected to corresponding correlation extraction and difference comparison combined features to train a second energy consumption prediction model.

[0020] S4. If two of the first-category medium energy consumption data 1, the first-category medium energy consumption data 2 and the first-category medium energy consumption data 3 in the first-category energy consumption data set are identical to each other, and at least two of the second-category medium energy consumption data 1, the second-category medium energy consumption data 2 and the second-category medium energy consumption data 3 in the second-category energy consumption data set are identical to each other, then a second difference feature set is statistically calculated.

[0021] S5. If two of the first-category medium energy consumption data one, the first-category medium energy consumption data two and the first-category medium energy consumption data three in the first-category energy consumption data set are the same, and the second-category medium energy consumption data one, the second-category medium energy consumption data two and the second-category medium energy consumption data three in the second-category energy consumption data set are the same, then a third difference feature set is statistically calculated, and a third energy consumption prediction model is trained based on the second difference feature set or the third difference feature set.

[0022] S6. Obtain the energy consumption data of the same medium at the current detection moment to obtain the energy consumption data of the same medium to obtain three current energy consumption data of the same medium, make a relationship judgment on the three current energy consumption data of the same medium, and select one of the corresponding models from the first energy consumption prediction model, the second energy consumption prediction model and the third energy consumption prediction model to perform energy consumption prediction, and finally perform allocation adjustment control of airport energy, and output the energy allocation adjustment control result.

[0023] Specifically, by performing a differentiated analysis on all energy-consuming media in different areas of the airport, that is, respectively performing a judgment analysis on the energy consumption data relationship between the same media and the difference media, and performing analysis in a variety of different processing methods, that is, if the relationship between the first-category medium energy consumption data one, the first-category medium energy consumption data two and the first-category medium energy consumption data of the same media statistics are the same, then the second-category medium energy consumption data one, the second-category medium energy consumption data two and the second-category medium energy consumption data corresponding to the difference media can be directly extracted for correlation to obtain the degree of influence correlation between medium energy consumption, so as to facilitate the subsequent regular change analysis of all medium energy consumption conditions in different areas of the airport at the current moment, and train the first energy consumption prediction model based on the above-mentioned feature statistics. In order to reduce the large errors in energy consumption prediction results caused by the characteristics of medium energy consumption changes in various situations, it is necessary to perform a diverse and more comprehensive analysis, that is, the first-category medium energy consumption data one, the first-category medium energy consumption data two and the first-category medium energy consumption data When the energy consumption data of the first type of medium are different from those of the first type of medium, the relationship between the energy consumption data of the second type of medium and the energy consumption data of the second type of medium is determined; if there are two types of the energy consumption data that are the same, an analysis process different from the training of the first energy consumption prediction model is performed to train the second energy consumption prediction model; and when the energy consumption data of the first type of medium, the energy consumption data of the first type of medium and the energy consumption data of the first type of medium are different, and at least two types of the energy consumption data of the second type of medium, the energy consumption data of the second type of medium and the energy consumption data of the second type of medium are the same, an analysis process different from the above-mentioned training of the first energy consumption prediction model and the second energy consumption prediction model is performed to train the third energy consumption prediction model; through the training of the three models, the misjudgment caused by the use of a single prediction model in the traditional technology to analyze and process only one energy consumption situation is avoided, thereby improving the utilization rate of resources and the efficiency of energy consumption monitoring; finally, according to the energy consumption prediction results, the energy allocation of the airport is adjusted and controlled accordingly.

[0024] The specific step S1 includes the following sub-steps: Three target detection areas for energy consumption detection are obtained, and all media that need to consume energy in the three target detection areas in the recent historical period are counted to obtain three types of energy-consuming medium sets.

[0025] The same partial media are extracted from the three types of energy-consuming media, and the same media are output. The energy consumption data of the same media in the recent historical period are respectively counted to obtain the same medium energy consumption data. The same medium energy consumption data includes energy consumption data one of the first type of media, energy consumption data two of the first type of media and energy consumption data three of the third type of media.

[0026] The first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the third type of medium energy consumption data 3 are combined into the first type of energy consumption data set.

[0027] The difference media among the remaining three are extracted from the three types of energy consumption media, and the difference media are output. The energy consumption data of the same media in the recent historical period are counted respectively to obtain the difference media energy consumption data. The difference media energy consumption data includes the second type medium energy consumption data one, the second type medium energy consumption data two and the second type medium energy consumption data three.

[0028] The second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 are combined into the second type of energy consumption data set.

[0029] Specifically, there are three target detection areas (if they are area A, area B, and area C, such as terminal buildings, runways and aprons, maintenance areas, and cargo areas), three types of energy-consuming medium sets (such as all energy-consuming media in area A are a1 and a2; all energy-consuming media in area B are b1 and b2; all energy-consuming media in area C are c1 and c2), where it should be noted that the energy-consuming media include electricity, natural gas or steam, fuel oil, water, and renewable energy. The same medium (if they are a1, b1, and c3, for example, all are electricity), the first type of medium energy consumption data 1, the first type of medium energy consumption data 2, and the third type of medium energy consumption data 3 (that is, the degree of energy consumption, for example, 60%, 40%, 30%, or values ​​therein), the difference medium (that is, a2; b2; c2 are different categories from each other), the second type of medium energy consumption data 1, the second type of medium energy consumption data 2, and the second type of medium energy consumption data 3 (if they are 60%, 40%, 30%, or values ​​therein).

[0030] The specific step S2 includes the following sub-steps: If the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 are all the same, then the correlation between the second type of medium energy consumption data 1 and the first type of medium energy consumption data 1 is extracted to obtain the correlation degree 1; Extracting the correlation between the second type medium energy consumption data 2 and the first type medium energy consumption data 2 to obtain correlation degree 2; Extracting the correlation between the second type medium energy consumption data three and the first type medium energy consumption data three to obtain correlation degree three; The first correlation degree, the second correlation degree and the third correlation degree are combined into a first correlation degree feature set.

[0031] A first energy consumption prediction model is trained according to the first correlation feature set, the first type of energy consumption data set, and the second type of energy consumption data set.

[0032] Specifically, if the first-category medium energy consumption data one, the first-category medium energy consumption data two and the first-category medium energy consumption data three are the same (if all are 60% electricity consumption), correlation one (for example, a2 is fuel and the energy consumption is 40%, then calculate the ratio between the two, and calculate the ratio between the two in other historical periods, and average the multiple ratio data obtained by statistics to obtain an average ratio, which is the correlation one, if it is 1 / 3), correlation two and correlation three (the explanation is the same as the correlation one, no further explanation is given here, and b2 and c2 are processed respectively), the first energy consumption prediction model (that is, based on the energy consumption data records counted in historical periods, and the statistics of the degree of correlation of energy consumption data between each medium, to train a machine learning model, such as an intuitive explanation: x, y, k, where x is energy consumption data, k is the correlation feature, y is the first energy consumption prediction model, where multiple energy consumption data and multiple correlation features obtained based on statistics hi in multiple historical periods, can be trained to obtain a linear function equation about x, y, and k).

[0033] The specific step S3 includes the following sub-steps: If the first type medium energy consumption data 1, the first type medium energy consumption data 2 and the first type medium energy consumption data 3 are all different from each other, then the relationship between the second type medium energy consumption data 1, the second type medium energy consumption data 2 and the second type medium energy consumption data 3 is determined, and the determination information is output.

[0034] According to the judgment information, if two of the second-category medium energy consumption data 1, the second-category medium energy consumption data 2 and the second-category medium energy consumption data 3 are the same, one of the judgment results is output.

[0035] The two identical medium energy consumption data in one judgment result are respectively compared with the one different medium energy consumption data in another judgment result to obtain a difference feature.

[0036] According to the first judgment result, the first type of medium energy consumption data one, the first type of medium energy consumption data two and the first type of medium energy consumption data three are compared with each other to obtain the second difference feature.

[0037] The first difference feature and the second difference feature are combined into a first difference feature set.

[0038] The same two medium energy consumption data in the first judgment result are respectively corresponded to the corresponding two medium energy consumption data in the first type of medium energy consumption data one, the first type of medium energy consumption data two and the first type of medium energy consumption data three, and the correlation between them is extracted to obtain correlation degree four and correlation degree five.

[0039] Relevance degree four and relevance degree five are combined into a second relevance feature set.

[0040] A second energy consumption prediction model is trained according to the first difference feature set, the second correlation feature set, the first type of energy consumption data set, and the second type of energy consumption data set.

[0041] Specifically, if the first-category medium energy consumption data one, the first-category medium energy consumption data two and the first-category medium energy consumption data three are different from each other (if they are 60%, 40%, and 30% respectively), if two of the second-category medium energy consumption data one, the second-category medium energy consumption data two and the second-category medium energy consumption data three are the same (if they are 20%, 20%, and 40% respectively), one of the difference features (20% and 20% respectively), the second difference feature (60%-30%=30%, 40%-30%=10% respectively), correlation degree four (that is, the energy consumption data of a2 and b2 are the same, then a1 and b1 are respectively processed according to the correlation degree steps, and the processing steps are the same as the explanation of the above correlation degree one, and no further explanation is given here, if they are 1 / 4 and 2 / 3 respectively), according to the first difference feature set, the second correlation feature set, the first-category energy consumption data set and the second-category energy consumption data set, a second energy consumption prediction model is trained (the same as the explanation of the above-mentioned first energy consumption prediction model, and no further explanation is given here).

[0042] The specific step S4 includes the following sub-steps: If two of the first-category medium energy consumption data 1, the first-category medium energy consumption data 2 and the first-category medium energy consumption data 3 are the same, output the second judgment result, and if two of the second-category medium energy consumption data 1, the second-category medium energy consumption data 2 and the second-category medium energy consumption data 3 are the same, output the third judgment result.

[0043] The three difference characteristics are obtained by comparing the energy consumption data of the same two media in the three judgment results.

[0044] According to the same two medium energy consumption data in the third judgment result, the difference between the corresponding two medium energy consumption data in the second judgment result is compared to obtain the fourth difference feature.

[0045] The third difference feature and the fourth difference feature are combined into a second difference feature set.

[0046] Specifically, if two of the first-category medium energy consumption data one, the first-category medium energy consumption data two and the first-category medium energy consumption data three are the same (if they are 50%, 30%, and 50% respectively), if two of the second-category medium energy consumption data one, the second-category medium energy consumption data two and the second-category medium energy consumption data three are the same (if they are 30%, 30%, and 40% respectively), their third difference characteristics (that is, 10% and 10% respectively, that is, the difference comparison between a2 and c2, and the difference comparison between b2 and c2), their fourth difference characteristics (based on the above-mentioned difference comparison between a2 and c2, and the difference comparison between b2 and c2, then the difference comparison between a1 and c1 will be carried out accordingly, and the difference comparison between b1 and c1 will be carried out accordingly, that is, 0% and 20% respectively).

[0047] The specific step S5 includes the following sub-steps: If two of the first-category medium energy consumption data one, the first-category medium energy consumption data two and the first-category medium energy consumption data three are the same, output the second judgment result, and if the second-category medium energy consumption data one, the second-category medium energy consumption data two and the second-category medium energy consumption data three are all the same, output the fourth judgment result.

[0048] According to the same two medium energy consumption data in the second judgment result, the corresponding two medium energy consumption data in the fourth judgment result are compared with another medium energy consumption data to obtain the fifth difference feature and the sixth difference feature.

[0049] The fifth difference feature and the sixth difference feature are combined into the third difference feature set.

[0050] The third energy consumption prediction model is trained by using the second difference feature set or the third difference feature set, the first type of energy consumption data set, and the second type of energy consumption data set.

[0051] Specifically, if two of the first type of medium energy consumption data one, the first type of medium energy consumption data two and the first type of medium energy consumption data three are the same (if they are 50%, 30%, and 50% respectively), if the second type of medium energy consumption data one, the second type of medium energy consumption data two and the second type of medium energy consumption data three are all the same (both are 40%), their five difference features (that is, the corresponding difference comparison between a2 and b2 is 0%) and their six difference features (that is, the corresponding difference comparison between c2 and b2 is 10%), the second difference feature set or the third difference feature set, the first type of energy consumption data set and the second type of energy consumption data set, train a third energy consumption prediction model (the explanation of the first energy consumption prediction model is the same as the above, and no further explanation is given here).

[0052] The specific step S6 includes the following sub-steps: According to the same medium, the energy consumption data of each of the same media at the current detection moment is obtained to obtain the same medium energy consumption data to obtain three current same medium energy consumption data.

[0053] The relationship between the three current energy consumption data of the same medium is determined. If the three current energy consumption data of the same medium are the same, the three current energy consumption data of the same medium and the difference medium are input into the first energy consumption prediction model for testing, and the energy consumption prediction result is output.

[0054] If the three current energy consumption data of the same medium are all different, the three current energy consumption data of the same medium and the difference medium are input into the second energy consumption prediction model for testing, and the energy consumption prediction result is output.

[0055] If two of the three current same medium energy consumption data are the same, the three current same medium energy consumption data and the difference medium are input into the third energy consumption prediction model for testing, and the energy consumption prediction result is output.

[0056] According to the energy consumption prediction results and the energy consumption data of three current identical media, the airport energy distribution adjustment control is carried out, and the energy distribution adjustment control results are output.

[0057] Specifically, for example, if the three current energy consumption data of the same medium are different (the energy consumption data of a1, b1, and c1, i.e., the energy consumption data initially monitored, so as to make subsequent more accurate energy consumption predictions based on the energy consumption data monitored by the same medium, so as to make further adjustments to the energy consumption allocation originally preset in the remaining medium, so as to improve energy utilization and energy conservation, if they are 50%, 50%, and 50% respectively), if the three current energy consumption data of the same medium are different, then the three current energy consumption data of the same medium and the difference medium are input into the second energy consumption prediction model for testing (then the energy consumption of the remaining medium is predicted through the third energy consumption prediction model, if the energy consumption prediction results are 30%, 20%, and 35%), if the three current energy consumption data of the same medium are different (if they are 50%, 40%, and 60% respectively, then the energy consumption of the remaining medium is predicted through the second energy consumption prediction model), if there is a difference among the three current energy consumption data of the same medium The energy consumption data of two current identical media are the same (if they are 40%, 40%, and 60% respectively, the energy consumption of the remaining media is predicted through the third energy consumption prediction model). If the energy consumption data of the three current identical media are not the same, the energy consumption data of the three current identical media and the difference media are input into the second energy consumption prediction model for testing (then the energy consumption of the remaining media is predicted through the third energy consumption prediction model, if the energy consumption prediction results are 30%, 20%, and 35%). For example, if the original preset electric energy distribution is 50%, 50%, and 50% respectively, and the fuel energy distribution is 50%, 30%, and 20% respectively, then according to the energy consumption prediction results: 30%, 20%, and 35%, the fuel energy distribution in area A is reduced to 30%, the fuel energy distribution in area B is reduced to 20%, and the fuel energy distribution in area C is increased to 35%, so as to adjust and control the reasonable distribution of airport energy and improve energy utilization.

[0058] An airport energy consumption monitoring and management system, by applying the above-mentioned airport energy consumption monitoring and management method, includes a historical energy consumption statistics unit, a first model training unit, a second model training unit, a third model training unit and a current energy consumption data prediction unit, with reference to Figure 2, three target detection areas for energy consumption detection are obtained through the historical energy consumption statistics unit, and the energy consumption data of the same medium and the different medium in the energy consumption medium required in the three target detection areas in the recent historical period are respectively statistically analyzed to obtain the first type of energy consumption data set and the second type of energy consumption data set; through the first model training unit, it is determined that if the first type of medium energy consumption data one, the first type of medium energy consumption data two and the first type of medium energy consumption data three in the first type of energy consumption data set are the same, then the first type of energy consumption data set and the second type of energy consumption data set are extracted for the corresponding correlation to train the first energy consumption prediction model; through the second model The training unit determines that if the first type of medium energy consumption data one, the first type of medium energy consumption data two and the first type of medium energy consumption data three in the first type of energy consumption data set are different from each other, and two of the second type of medium energy consumption data one, the second type of medium energy consumption data two and the second type of medium energy consumption data three in the second type of energy consumption data set are the same, then the first type of energy consumption data set and the second type of energy consumption data set are combined with features for corresponding correlation extraction and difference comparison to train a second energy consumption prediction model; the data feature statistical unit determines that if the first type of medium energy consumption data one, the first type of medium energy consumption data two and the second type of medium energy consumption data three in the first type of energy consumption data set are the same, then the first type of energy consumption data set and the second type of energy consumption data set are combined with features for corresponding correlation extraction and difference comparison to train a second energy consumption prediction model; If two of the first-category medium energy consumption data 1, the first-category medium energy consumption data 2 and the first-category medium energy consumption data 3 are the same, and at least two of the second-category medium energy consumption data 1, the second-category medium energy consumption data 2 and the second-category medium energy consumption data 3 in the second-category energy consumption data set are the same, then a second difference feature set is statistically obtained; and if two of the first-category medium energy consumption data 1, the first-category medium energy consumption data 2 and the first-category medium energy consumption data 3 in the first-category energy consumption data set are the same, and at least two of the second-category medium energy consumption data 1, the second-category medium energy consumption data 2 and the second-category medium energy consumption data 3 in the second-category energy consumption data set are the same, then a second difference feature set is statistically obtained; and if two of the first-category medium energy consumption data 1, the first-category medium energy consumption data 2 and the first-category medium energy consumption data 3 in the second-category energy consumption data set are the same, then ... a second difference feature set is statistically obtained; and if two of the first-category medium energy consumption data 1, the first-category medium energy consumption data 2 and the first-category medium energy consumption data 3 in the second-category energy consumption data set are the same If all three energy data are the same, a third difference feature set is statistically calculated, and a third energy consumption prediction model is trained based on the second difference feature set or the third difference feature set; the current energy consumption data prediction unit is used to obtain the energy consumption data of the same medium at the current detection moment to obtain three current energy consumption data of the same medium, and a relationship judgment is made on the three current energy consumption data of the same medium to select one of the corresponding models from the first energy consumption prediction model, the second energy consumption prediction model and the third energy consumption prediction model for energy consumption prediction, and finally the airport energy distribution adjustment control is performed to output the energy distribution adjustment control result.

[0059] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for monitoring and managing airport energy consumption, characterized in that: include: Obtain three target detection areas for energy consumption detection, and perform energy consumption statistics on the same media and different media in the three target detection areas in the recent historical period to obtain the first type of energy consumption data set and the second type of energy consumption data set; If the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are the same, extracting the corresponding correlation between the first type of energy consumption data set and the second type of energy consumption data set to train a first energy consumption prediction model; If the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are different from each other, and two of the second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 in the second type of energy consumption data set are the same, then the first type of energy consumption data set and the second type of energy consumption data set are combined with features of corresponding correlation extraction and difference comparison to train a second energy consumption prediction model; If two of the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are identical to each other, and at least two of the second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 in the second type of energy consumption data set are identical to each other, then a second difference feature set is calculated; If two of the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are the same, and the second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 in the second type of energy consumption data set are the same, then a third difference feature set is statistically calculated, and a third energy consumption prediction model is trained according to the second difference feature set or the third difference feature set; Obtain the energy consumption data of the same medium at the current detection moment to obtain three current energy consumption data of the same medium, make a relationship judgment on the three current energy consumption data of the same medium, and select one of the corresponding models from the first energy consumption prediction model, the second energy consumption prediction model and the third energy consumption prediction model to perform energy consumption prediction, and finally perform airport energy distribution adjustment control, and output the energy distribution adjustment control result.

2. The airport energy consumption monitoring and management method according to claim 1 is characterized in that: The steps of obtaining three target detection areas for energy consumption detection, and performing energy consumption data statistics on the same media and the different media in the three target detection areas in the recent historical period to obtain the first type of energy consumption data set and the second type of energy consumption data set are specifically as follows: Obtain three target detection areas for energy consumption detection, and count all media that need to consume energy in the three target detection areas in the recent historical period to obtain three types of energy-consuming medium sets; Extracting the same part of the three types of energy-consuming media from the three types of energy-consuming media, outputting the same media, and respectively counting the energy consumption data of the same media in the recent historical period to obtain the same medium energy consumption data, the same medium energy consumption data including the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the third type of medium energy consumption data 3; The first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the third type of medium energy consumption data 3 are combined into a first type of energy consumption data set; Extract the remaining three different media from the three types of energy consumption media, output the difference media, and respectively count the energy consumption data of the same media in the recent historical period to obtain the difference medium energy consumption data, the difference medium energy consumption data includes the second type medium energy consumption data 1, the second type medium energy consumption data 2, and the second type medium energy consumption data 3; The second-category medium energy consumption data one, the second-category medium energy consumption data two and the second-category medium energy consumption data three are combined into a second-category energy consumption data set.

3. The airport energy consumption monitoring and management method according to claim 2 is characterized in that: If the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are the same, the first type of energy consumption data set and the second type of energy consumption data set are subjected to corresponding correlation extraction to train a first energy consumption prediction model, specifically comprising: If the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 are all the same, extracting the correlation between the second type of medium energy consumption data 1 and the first type of medium energy consumption data 1 to obtain correlation degree 1; Extracting the correlation between the second type of medium energy consumption data 2 and the first type of medium energy consumption data 2 to obtain correlation degree 2; Extracting the correlation between the second-category medium energy consumption data three and the first-category medium energy consumption data three to obtain correlation degree three; The first correlation degree, the second correlation degree and the third correlation degree are combined into a first correlation degree feature set; A first energy consumption prediction model is trained according to the first correlation feature set, the first type of energy consumption data set, and the second type of energy consumption data set.

4. The airport energy consumption monitoring and management method according to claim 2 is characterized in that: If the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are different from each other, and two of the second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 in the second type of energy consumption data set are the same, then the first type of energy consumption data set and the second type of energy consumption data set are subjected to corresponding correlation extraction and difference comparison combined features to train the second energy consumption prediction model, specifically, the following steps: If the first type medium energy consumption data 1, the first type medium energy consumption data 2 and the first type medium energy consumption data 3 are all different from each other, then the relationship between the second type medium energy consumption data 1, the second type medium energy consumption data 2 and the second type medium energy consumption data 3 is determined, and the determination information is output; According to the judgment information, if two of the second-category medium energy consumption data 1, the second-category medium energy consumption data 2 and the second-category medium energy consumption data 3 are the same, outputting one of the judgment results; Comparing the two identical medium energy consumption data in the first judgment result with the one different medium energy consumption data in the second judgment result to obtain a difference feature; According to the first judgment result, the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 are compared with each other to obtain the second difference feature; The first difference feature and the second difference feature are combined into a first difference feature set; The same two medium energy consumption data in the first judgment result are respectively corresponded to the corresponding two medium energy consumption data in the first type medium energy consumption data one, the first type medium energy consumption data two and the first type medium energy consumption data three to extract the correlation between them to obtain correlation degree four and correlation degree five; The association degree 4 and the association degree 5 are combined into a second association degree feature set; A second energy consumption prediction model is trained according to the first difference feature set, the second correlation feature set, the first type of energy consumption data set, and the second type of energy consumption data set.

5. The airport energy consumption monitoring and management method according to claim 2 is characterized in that: If two of the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are identical to each other, and at least two of the second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 in the second type of energy consumption data set are identical to each other, then the step of calculating the second difference feature set is specifically as follows: If two of the first-category medium energy consumption data 1, the first-category medium energy consumption data 2 and the first-category medium energy consumption data 3 are the same, output the second judgment result; and if two of the second-category medium energy consumption data 1, the second-category medium energy consumption data 2 and the second-category medium energy consumption data 3 are the same, output the third judgment result; Comparing the energy consumption data of the two same media in the third judgment result to obtain the third difference feature; According to the same two medium energy consumption data in the third judgment result, the difference between the corresponding two medium energy consumption data in the second judgment result is compared to obtain the fourth difference feature; The third difference feature and the fourth difference feature are combined into a second difference feature set.

6. The airport energy consumption monitoring and management method according to claim 5 is characterized in that: If two of the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are identical to each other, and the second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 in the second type of energy consumption data set are identical to each other, then a third difference feature set is statistically obtained, and the steps of training a third energy consumption prediction model according to the second difference feature set or the third difference feature set are specifically as follows: If two of the first-category medium energy consumption data 1, the first-category medium energy consumption data 2 and the first-category medium energy consumption data 3 are the same, output the second judgment result; and if the second-category medium energy consumption data 1, the second-category medium energy consumption data 2 and the second-category medium energy consumption data 3 are all the same, output the fourth judgment result; According to the same two medium energy consumption data in the second judgment result, the corresponding two medium energy consumption data in the fourth judgment result are respectively compared with another medium energy consumption data to obtain the fifth difference feature and the sixth difference feature; The fifth difference feature and the sixth difference feature are combined into a third difference feature set; The second difference feature set or the third difference feature set, the first type of energy consumption data set and the second type of energy consumption data set are used to train a third energy consumption prediction model.

7. The airport energy consumption monitoring and management method according to claim 6 is characterized in that: The steps of obtaining the energy consumption data of the same medium at the current detection moment, obtaining the energy consumption data of the same medium, obtaining three current energy consumption data of the same medium, performing relationship judgment on the three current energy consumption data of the same medium, selecting one of the corresponding models from the first energy consumption prediction model, the second energy consumption prediction model and the third energy consumption prediction model for energy consumption prediction, and finally performing the allocation adjustment control of the airport energy, and outputting the energy allocation adjustment control result, are specifically as follows: According to the same medium, the energy consumption data of each of the same mediums at the current detection moment is obtained to obtain the same medium energy consumption data to obtain three types of current same medium energy consumption data; Determine the relationship between the three current energy consumption data of the same medium. If the three current energy consumption data of the same medium are the same, input the three current energy consumption data of the same medium and the difference medium into the first energy consumption prediction model for testing, and output the energy consumption prediction result; If the three current energy consumption data of the same medium are all different, the three current energy consumption data of the same medium and the difference medium are input into the second energy consumption prediction model for testing, and the energy consumption prediction result is output; If two of the three current energy consumption data of the same medium are the same, the three current energy consumption data of the same medium and the difference medium are input into the third energy consumption prediction model for testing, and the energy consumption prediction result is output; Based on the energy consumption prediction results and the energy consumption data of three current identical media, the airport energy distribution adjustment control is performed, and the energy distribution adjustment control results are output.

8. An airport energy consumption monitoring and management system, characterized in that: The system is used to implement an airport energy consumption monitoring and management method as described in any one of claims 1 to 7, comprising: The historical energy consumption statistics unit is used to obtain three target detection areas for energy consumption detection, and to perform energy consumption statistics on the same media and the different media in the three target detection areas in the recent historical period to obtain the first type of energy consumption data set and the second type of energy consumption data set; The first model training unit is used to determine if the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are the same, then extract the corresponding correlation between the first type of energy consumption data set and the second type of energy consumption data set to train a first energy consumption prediction model; The second model training unit is used to determine that if the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are different from each other, and two of the second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 in the second type of energy consumption data set are the same, then the first type of energy consumption data set and the second type of energy consumption data set are subjected to corresponding correlation extraction and difference comparison combined features to train a second energy consumption prediction model; a data feature statistical unit, for determining that if two of the first-category medium energy consumption data 1, the first-category medium energy consumption data 2, and the first-category medium energy consumption data 3 in the first-category energy consumption data set are identical to each other, and at least two of the second-category medium energy consumption data 1, the second-category medium energy consumption data 2, and the second-category medium energy consumption data 3 in the second-category energy consumption data set are identical to each other, then a second difference feature set is statistically generated; The third model training unit is used to determine if two of the first type of medium energy consumption data 1, the first type of medium energy consumption data 2 and the first type of medium energy consumption data 3 in the first type of energy consumption data set are the same, and the second type of medium energy consumption data 1, the second type of medium energy consumption data 2 and the second type of medium energy consumption data 3 in the second type of energy consumption data set are the same, then a third difference feature set is statistically calculated, and a third energy consumption prediction model is trained according to the second difference feature set or the third difference feature set; The energy consumption prediction and energy control unit is used to obtain the energy consumption data of the same medium at the current detection moment to obtain the energy consumption data of the same medium to obtain three current energy consumption data of the same medium, make a relationship judgment on the three current energy consumption data of the same medium, and select one of the corresponding models from the first energy consumption prediction model, the second energy consumption prediction model and the third energy consumption prediction model to perform energy consumption prediction, and finally perform the distribution adjustment control of the airport energy, and output the energy distribution adjustment control result.

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