CN116993227B - Heat supply analysis and evaluation method, system and storage medium based on artificial intelligence - Google Patents
Heat supply analysis and evaluation method, system and storage medium based on artificial intelligence Download PDFInfo
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Abstract
The invention discloses a heating analysis and evaluation method, a heating analysis and evaluation system and a storage medium based on artificial intelligence. The invention obtains the user heat supply data in different heat supply subareas; performing periodic fluctuation analysis based on the user heat supply data to obtain heat supply characteristic data of a heat supply subarea; according to the heat supply characteristic data and a preset clustering algorithm, carrying out regional heat supply characteristic fusion analysis of the heat supply subareas, and forming a plurality of heat supply regional groups; performing ARIMA-based data prediction analysis based on historical heat supply data of a plurality of heat supply area groups to obtain heat supply prediction data; and leading the heat supply prediction data into a heat supply map model for visual display, heat supply analysis and regional heat supply pressure prediction to obtain a heat supply pressure region and a heat supply regulation and control scheme. The invention can improve the heat supply analysis efficiency in the target heat supply area, effectively generate and manage the heat supply scheme of the internal area, and realize the accurate heat supply evaluation and regulation of the heat supply system.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a heating analysis and evaluation method, a heating analysis and evaluation system and a storage medium based on artificial intelligence.
Background
As urban advances and energy demands increase, management and operation of heating systems becomes increasingly important. The traditional heating system has the problems of energy waste, unstable operation, difficult diagnosis of faults, inaccurate heating prediction and the like. How to optimize the heating system by combining modern information means is becoming the subject of current research.
Furthermore, due to the prior art, the heat supply prediction and analysis process of the heat supply system by some heat supply enterprises is low in efficiency and low in accuracy, and the informatization means are not well utilized for system data analysis and regulation optimization. Therefore, there is a need for an efficient heating analysis and assessment method.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a heating analysis and evaluation method, a heating analysis and evaluation system and a storage medium based on artificial intelligence.
The first aspect of the invention provides a heating analysis and evaluation method based on artificial intelligence, which comprises the following steps:
constructing a three-dimensional-based heating map model based on the target heating area;
Dividing a plurality of heat supply subareas based on the target heat supply area, and acquiring user heat supply data in different heat supply subareas;
performing periodic fluctuation analysis based on the user heat supply data to obtain heat supply characteristic data of a heat supply subarea;
according to the heat supply characteristic data and a preset clustering algorithm, carrying out regional heat supply characteristic fusion analysis of the heat supply subareas, and forming a plurality of heat supply regional groups;
performing ARIMA-based data prediction analysis based on historical heat supply data of a plurality of heat supply area groups to obtain heat supply prediction data;
and leading the heat supply prediction data into a heat supply map model for visual display, heat supply analysis and regional heat supply pressure prediction to obtain a heat supply pressure region and a heat supply regulation and control scheme.
In this scheme, based on the target heat supply region, build the heat supply map model based on three-dimensional, specifically do:
acquiring heat exchange station area information, heat supply pipe network information and heat supply user area information in a target heat supply area;
the heat exchange station area information comprises the area size of the heat exchange station area and map contour information;
the heat supply pipe network information comprises space arrangement information of a heat supply pipe network and pipe network monitoring equipment information;
And carrying out map construction of a heat supply area and visual model fusion of a heat supply system based on the heat exchange station area information, the heat supply pipe network information and the heat supply user area information to obtain a three-dimensional heat supply map model.
In this scheme, divide out a plurality of heat supply subregions based on target heat supply region, acquire the user heat supply data in different heat supply subregions, specifically do:
acquiring the number and map distribution of the minimum analysis heating areas based on the target heating areas;
the minimum analytical heating zone comprises a residential zone, an enterprise zone and an industrial zone;
performing region fusion on the basis of the heat supply unit property of the minimum analysis heat supply region and map distribution to form a plurality of residential heat supply regions, a plurality of enterprise heat supply regions and a plurality of industrial heat supply regions;
taking the residential heating areas, the enterprise heating areas and the industrial heating areas as heating subareas, and associating corresponding heating unit properties with each heating subarea;
the heat supply unit property comprises residential heat supply, enterprise heat supply and industrial heat supply;
and acquiring heat supply statistical data of the minimum analysis heat supply area, carrying out calculation and analysis on the heat supply data of each heat supply subarea according to the heat supply statistical data, and obtaining user heat supply data of each heat supply subarea.
In this scheme, based on user heat supply data carries out periodic fluctuation analysis, obtains the heat supply characteristic data of heat supply subregion, specifically does:
performing data division of a preset time period based on the user heat supply data to obtain a plurality of periodic heat supply data;
carrying out data cleaning, abnormal value removal and missing value filling pretreatment on single periodic heat supply data;
performing fluctuation feature analysis and feature extraction based on linear regression on the single periodic heat supply data to obtain a first periodic fluctuation feature;
carrying out fluctuation feature analysis on all the periodic heat supply data to obtain a plurality of first periodic fluctuation features;
carrying out fluctuation feature analysis between the periodic heat supply data based on the plurality of first periodic fluctuation features and the user heat supply data, and carrying out fluctuation feature secondary extraction based on linear regression to obtain a second periodic fluctuation feature;
the heating characteristic data includes a first periodic fluctuation characteristic and a second periodic fluctuation characteristic for each heating subarea.
In this scheme, according to the heat supply characteristic data and a preset clustering algorithm, the regional heat supply characteristic fusion analysis of the heat supply subareas is performed, and a plurality of heat supply regional groups are formed, specifically:
Clustering a plurality of heating subareas by a DBSCAN clustering algorithm based on artificial intelligence;
the clustering process is as follows: selecting an unclassified heating subarea, and marking the unclassified heating subarea as a current heating subarea;
combining a heat supply map model, analyzing the heat supply characteristic data of the current heat supply subarea and the adjacent heat supply subareas to obtain total similarity, and if the total similarity is greater than a preset similarity, carrying out area fusion on the adjacent heat supply subareas and the current heat supply subareas to form a heat supply area group, and circularly judging other adjacent heat supply subareas;
the characteristic similarity analysis specifically comprises the steps of carrying out similarity calculation based on standard Euclidean distance on a first periodic fluctuation characteristic of a current heat supply subarea and a first periodic fluctuation characteristic of an adjacent heat supply subarea to obtain first similarity, carrying out similarity calculation based on standard Euclidean distance on a second periodic fluctuation characteristic of the current heat supply subarea and a second periodic fluctuation characteristic of the adjacent heat supply subarea to obtain second similarity, and calculating weighted average values of the first similarity and the second similarity based on a dynamic preset weight value to obtain total similarity;
and circularly analyzing the non-clustered heat supply subareas and performing cluster fusion analysis to finally obtain a plurality of heat supply area groups.
In this scheme, historical heat supply data based on a plurality of heat supply regional groups carries out the data predictive analysis based on ARIMA, obtains heat supply predictive data, specifically does:
acquiring historical heat supply data in a heat supply area group, and carrying out data preprocessing on the historical heat supply data;
converting the historical heat supply data into time series data, and importing the time series data into an ARIMA prediction model to generate a corresponding Autocorrelation Chart (ACF) and a Partial Autocorrelation Chart (PACF);
determining ARIMA prediction model parameters according to the autocorrelation diagrams and the partial autocorrelation diagrams;
performing prediction training and model parameter adjustment of an ARIMA prediction model based on historical heat supply data;
acquiring real-time heat supply data of a heat supply region group in a latest preset time period, and importing the real-time heat supply data into an ARIMA prediction model to generate heat supply prediction data in prediction time;
analyzing all the heat supply area groups and obtaining heat supply prediction data of each heat supply area group.
In this scheme, will heat supply forecast data import heat supply map model carries out visual show, heat supply analysis and regional heat supply pressure prediction, obtains heat supply pressure region and heat supply regulation and control scheme, specifically does:
Based on the heat supply prediction data, carrying out heat supply load fluctuation analysis based on linear regression on the heat supply region groups to obtain a load fluctuation characteristic diagram, and obtaining a heat supply load fluctuation maximum value region and a heat supply load fluctuation minimum value region of each heat supply region group through the load fluctuation characteristic diagram;
extracting a corresponding high-low load prediction time period based on the heating load fluctuation maximum value and minimum value region;
analyzing the heating pressure areas in different prediction time periods based on the high-low load prediction time periods of different heating area groups;
and carrying out heat supply regulation analysis on each heat supply region group based on the high-low load prediction time periods and the heat supply pressure regions of different heat supply region groups, and generating a heat supply regulation scheme.
The second aspect of the present invention also provides a heating analysis and evaluation system based on artificial intelligence, the system comprising: the system comprises a memory and a processor, wherein the memory comprises an artificial intelligence-based heating analysis and evaluation program, and the artificial intelligence-based heating analysis and evaluation program realizes the following steps when being executed by the processor:
constructing a three-dimensional-based heating map model based on the target heating area;
dividing a plurality of heat supply subareas based on the target heat supply area, and acquiring user heat supply data in different heat supply subareas;
Performing periodic fluctuation analysis based on the user heat supply data to obtain heat supply characteristic data of a heat supply subarea;
according to the heat supply characteristic data and a preset clustering algorithm, carrying out regional heat supply characteristic fusion analysis of the heat supply subareas, and forming a plurality of heat supply regional groups;
performing ARIMA-based data prediction analysis based on historical heat supply data of a plurality of heat supply area groups to obtain heat supply prediction data;
and leading the heat supply prediction data into a heat supply map model for visual display, heat supply analysis and regional heat supply pressure prediction to obtain a heat supply pressure region and a heat supply regulation and control scheme.
In this scheme, based on the target heat supply region, build the heat supply map model based on three-dimensional, specifically do:
acquiring heat exchange station area information, heat supply pipe network information and heat supply user area information in a target heat supply area;
the heat exchange station area information comprises the area size of the heat exchange station area and map contour information;
the heat supply pipe network information comprises space arrangement information of a heat supply pipe network and pipe network monitoring equipment information;
and carrying out map construction of a heat supply area and visual model fusion of a heat supply system based on the heat exchange station area information, the heat supply pipe network information and the heat supply user area information to obtain a three-dimensional heat supply map model.
The third aspect of the present invention also provides a computer readable storage medium having embodied therein an artificial intelligence based heating analysis and assessment program which, when executed by a processor, implements the steps of an artificial intelligence based heating analysis and assessment method as described in any one of the above.
The invention discloses a heating analysis and evaluation method, a heating analysis and evaluation system and a storage medium based on artificial intelligence. The invention obtains the user heat supply data in different heat supply subareas; performing periodic fluctuation analysis based on the user heat supply data to obtain heat supply characteristic data of a heat supply subarea; according to the heat supply characteristic data and a preset clustering algorithm, carrying out regional heat supply characteristic fusion analysis of the heat supply subareas, and forming a plurality of heat supply regional groups; performing ARIMA-based data prediction analysis based on historical heat supply data of a plurality of heat supply area groups to obtain heat supply prediction data; and leading the heat supply prediction data into a heat supply map model for visual display, heat supply analysis and regional heat supply pressure prediction to obtain a heat supply pressure region and a heat supply regulation and control scheme. The invention can improve the heat supply analysis efficiency in the target heat supply area, effectively generate and manage the heat supply scheme of the internal area, and realize the accurate heat supply evaluation and regulation of the heat supply system.
Drawings
FIG. 1 shows a flow chart of an artificial intelligence based heating analysis and assessment method of the present application;
FIG. 2 shows a flow chart of the heating map model construction of the present application;
FIG. 3 illustrates a heating regulation scheme acquisition flow chart of the present application;
FIG. 4 shows a block diagram of an artificial intelligence based heating analysis evaluation system of the present application.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited to the specific embodiments disclosed below.
FIG. 1 shows a flow chart of an artificial intelligence based heating analysis and assessment method of the present application.
As shown in fig. 1, a first aspect of the present application provides a heating analysis and evaluation method based on artificial intelligence, including:
S102, constructing a three-dimensional-based heat supply map model based on a target heat supply area;
s104, dividing a plurality of heat supply subareas based on the target heat supply area, and acquiring user heat supply data in different heat supply subareas;
s106, carrying out periodic fluctuation analysis based on the user heat supply data to obtain heat supply characteristic data of a heat supply subarea;
s108, carrying out regional heat supply characteristic fusion analysis of the heat supply subareas according to the heat supply characteristic data and a preset clustering algorithm, and forming a plurality of heat supply regional groups;
s110, performing ARIMA-based data prediction analysis based on historical heat supply data of a plurality of heat supply area groups to obtain heat supply prediction data;
and S112, the heat supply prediction data is imported into a heat supply map model for visual display, heat supply analysis and regional heat supply pressure prediction, and a heat supply pressure region and a heat supply regulation and control scheme are obtained.
FIG. 2 shows a flow chart of the heating map model construction of the present invention.
According to the embodiment of the invention, a three-dimensional heat supply map model is constructed based on the target heat supply area, and the heat supply map model is specifically:
s202, acquiring heat exchange station area information, heat supply pipe network information and heat supply user area information in a target heat supply area;
S204, the heat exchange station area information comprises the area size of the heat exchange station area and map contour information;
s206, the heat supply pipe network information comprises space arrangement information of a heat supply pipe network and pipe network monitoring equipment information;
and S208, carrying out map construction of a heat supply area and visual model fusion of a heat supply system based on the heat exchange station area information, the heat supply pipe network information and the heat supply user area information to obtain a three-dimensional-based heat supply map model.
It should be noted that, the visual model of heating system includes structural models such as heating pipeline, heat exchange station, pipe network monitoring facilities, through the heating map model, can grasp heating system's running condition and heat supply management condition in the target heating region more directly perceivedly and conveniently and carry out visual show to the heat supply forecast data, further improve heat supply informatization management level. The heat supply map model comprises a heat exchange station area, a heat supply pipe network area and a heat supply user area.
According to the embodiment of the invention, the target heating area is used for dividing a plurality of heating subareas, and the user heating amount data in different heating subareas is obtained specifically as follows:
acquiring the number and map distribution of the minimum analysis heating areas based on the target heating areas;
The minimum analytical heating zone comprises a residential zone, an enterprise zone and an industrial zone;
performing region fusion on the basis of the heat supply unit property of the minimum analysis heat supply region and map distribution to form a plurality of residential heat supply regions, a plurality of enterprise heat supply regions and a plurality of industrial heat supply regions;
taking the residential heating areas, the enterprise heating areas and the industrial heating areas as heating subareas, and associating corresponding heating unit properties with each heating subarea;
the heat supply unit property comprises residential heat supply, enterprise heat supply and industrial heat supply;
and acquiring heat supply statistical data of the minimum analysis heat supply area, carrying out calculation and analysis on the heat supply data of each heat supply subarea according to the heat supply statistical data, and obtaining user heat supply data of each heat supply subarea.
The minimum analysis heating area is specifically a minimum unit area capable of performing heating statistics, and includes a residential area, an enterprise area and an industrial area. The heat supply statistical data comprise heat supply quantity statistical data of each minimum analysis heat supply area in a first preset time, and heat supply quantity data of a residential heat supply area, an enterprise heat supply area and an industrial heat supply area can be obtained through fusion analysis of a plurality of minimum analysis heat supply area data. The user heating capacity data comprise heating time, user heating area, heating load and the like. The number of heating subareas is the total area number of a plurality of residential heating areas, a plurality of enterprise heating areas and a plurality of industrial heating areas. One heating sub-area includes at least one minimum analytical heating.
According to the embodiment of the invention, the periodic fluctuation analysis is performed based on the user heat supply data to obtain the heat supply characteristic data of the heat supply subarea, specifically:
performing data division of a preset time period based on the user heat supply data to obtain a plurality of periodic heat supply data;
carrying out data cleaning, abnormal value removal and missing value filling pretreatment on single periodic heat supply data;
performing fluctuation feature analysis and feature extraction based on linear regression on the single periodic heat supply data to obtain a first periodic fluctuation feature;
carrying out fluctuation feature analysis on all the periodic heat supply data to obtain a plurality of first periodic fluctuation features;
carrying out fluctuation feature analysis between the periodic heat supply data based on the plurality of first periodic fluctuation features and the user heat supply data, and carrying out fluctuation feature secondary extraction based on linear regression to obtain a second periodic fluctuation feature;
the heating characteristic data includes a first periodic fluctuation characteristic and a second periodic fluctuation characteristic for each heating subarea.
It should be noted that the preset time period is generally 1 to 7 days. Each heating subarea corresponds to a type of heating area, i.e. to a corresponding heating unit property. And carrying out joint calculation through the minimum unit statistical data in the embodiment to obtain the heat supply data of each heat supply subarea, wherein the joint is to carry out addition calculation of corresponding data on the heat supply data. The first periodic fluctuation feature is specifically a fluctuation feature in a short period, namely data fluctuation in a preset time period, the corresponding analyzed data unit is single periodic heat supply data, the second periodic fluctuation feature is data fluctuation analysis among a plurality of periodic heat supply data, and compared with the first periodic fluctuation feature, the analysis period is longer, and the obtained fluctuation feature is more long-term. In particular, to be practical, the first periodic wave feature is typically a wave analysis over a single day or week, while the second periodic wave feature is typically a seasonal or a month of heating data wave analysis.
According to the embodiment of the invention, the regional heating characteristic fusion analysis of the heating subareas is performed according to the heating characteristic data and a preset clustering algorithm, and a plurality of heating regional groups are formed, specifically:
clustering a plurality of heating subareas by a DBSCAN clustering algorithm based on artificial intelligence;
the clustering process is as follows: selecting an unclassified heating subarea, and marking the unclassified heating subarea as a current heating subarea;
combining a heat supply map model, analyzing the heat supply characteristic data of the current heat supply subarea and the adjacent heat supply subareas to obtain total similarity, and if the total similarity is greater than a preset similarity, carrying out area fusion on the adjacent heat supply subareas and the current heat supply subareas to form a heat supply area group, and circularly judging other adjacent heat supply subareas;
the characteristic similarity analysis specifically comprises the steps of carrying out similarity calculation based on standard Euclidean distance on a first periodic fluctuation characteristic of a current heat supply subarea and a first periodic fluctuation characteristic of an adjacent heat supply subarea to obtain first similarity, carrying out similarity calculation based on standard Euclidean distance on a second periodic fluctuation characteristic of the current heat supply subarea and a second periodic fluctuation characteristic of the adjacent heat supply subarea to obtain second similarity, and calculating weighted average values of the first similarity and the second similarity based on a dynamic preset weight value to obtain total similarity;
And circularly analyzing the non-clustered heat supply subareas and performing cluster fusion analysis to finally obtain a plurality of heat supply area groups.
It should be noted that, after the current heating subarea merges with an adjacent heating subarea, the adjacent heating subarea of the adjacent heating subarea also becomes an adjacent area of the current heating subarea, and further cluster analysis is performed, so that when heating characteristics of areas within a certain range of one heating subarea are similar to those of the heating subarea, area expansion and merging can be performed. It is worth mentioning that in some heat supply characteristics of enterprises and residential areas or enterprises and industrial areas, there are often areas with highly similar heat supply data characteristics, so that the heat supply analysis efficiency in the target heat supply area can be further improved by analyzing the heat supply characteristic data of the areas to perform area fusion, and the accurate heat supply scheme generation and management on the internal area can be effectively performed. The dynamic preset weight value can be dynamically adjusted based on the actual heating system condition and the regional fluctuation condition. The heating zone group comprises at least one heating sub-zone. According to the embodiment of the invention, the heat supply characteristic data is automatically clustered and grouped through a DBSCAN clustering algorithm based on artificial intelligence. And the accurate heat supply evaluation and regulation of a plurality of heat supply areas are realized later. In the similarity calculation based on the standard Euclidean distance between the first periodic fluctuation feature of the current heat supply subarea and the first periodic fluctuation feature of the adjacent heat supply subarea, if a plurality of first periodic fluctuation features exist in the heat supply subarea, the calculation can be performed through a mean feature result.
According to the embodiment of the invention, the historical heat supply data based on the plurality of heat supply area groups is subjected to ARIMA-based data prediction analysis to obtain heat supply prediction data, which specifically comprises:
acquiring historical heat supply data in a heat supply area group, and carrying out data preprocessing on the historical heat supply data;
converting the historical heat supply data into time series data, and importing the time series data into an ARIMA prediction model to generate a corresponding Autocorrelation Chart (ACF) and a Partial Autocorrelation Chart (PACF);
determining ARIMA prediction model parameters according to the autocorrelation diagrams and the partial autocorrelation diagrams;
performing prediction training and model parameter adjustment of an ARIMA prediction model based on historical heat supply data;
acquiring real-time heat supply data of a heat supply region group in a latest preset time period, and importing the real-time heat supply data into an ARIMA prediction model to generate heat supply prediction data in prediction time;
analyzing all the heat supply area groups and obtaining heat supply prediction data of each heat supply area group.
The predicted time is a system user-set time. The analysis process of the heat supply prediction data is visually displayed on a heat supply map model
FIG. 3 illustrates a heating regulation scheme acquisition flow chart of the present invention.
According to the embodiment of the invention, the heat supply prediction data is imported into a heat supply map model for visual display, heat supply analysis and regional heat supply pressure prediction to obtain a heat supply pressure region and a heat supply regulation scheme, and the heat supply regulation scheme specifically comprises the following steps:
s302, carrying out heat supply load fluctuation analysis based on linear regression on the heat supply region groups based on the heat supply prediction data to obtain a load fluctuation characteristic diagram, and obtaining a heat supply load fluctuation maximum value region and a heat supply load fluctuation minimum value region of each heat supply region group through the load fluctuation characteristic diagram;
s304, extracting a corresponding high-low load prediction time period based on the heating load fluctuation maximum value and minimum value region;
s306, analyzing the heat supply pressure areas in different prediction time periods based on the high-low load prediction time periods of different heat supply area groups;
and S308, carrying out heat supply regulation analysis on each heat supply region group based on the high-low load prediction time periods and the heat supply pressure regions of different heat supply region groups, and generating a heat supply regulation scheme.
The load prediction period includes a prediction period of a load maximum value and minimum value region, that is, a high load and low load prediction period. The heat supply regulation and control scheme comprises the regulation and control of the load, heat supply quantity, heat power and the like of the heat exchange station, and the regulation and control of the temperature, flow, power and the like of a heat supply pipe network. The heating pressure zone may be all or part of the total zone area in the heating zone group.
According to an embodiment of the present invention, further comprising:
acquiring heating pressure areas of different prediction time periods;
acquiring a heat exchange station area and a heat supply pipe network area according to a map model;
according to the heat supply pressure area, the heat exchange station area and the heat supply pipe network area, carrying out heat supply route analysis and obtaining a heat supply route of the pressure area;
based on the pressure area heat supply route, carrying out heat supply transmission equipment retrieval in the route by combining a heat supply map model, and obtaining first heat supply equipment;
a heating plant monitoring and maintenance scheme is generated based on the first heating plant, the high-low load prediction period, and the heating pressure region.
It should be noted that the heat supply equipment monitoring and maintaining scheme includes monitoring and maintaining information such as a heat supply area, a heat supply equipment, a heat supply route, a heat supply time period, and the like.
According to the embodiment of the invention, route analysis of the heating equipment can be performed based on the predicted high-pressure heating area, especially, the flow route of the heating pipe network is further generated, and a heating equipment monitoring and maintaining scheme with certain timeliness and high efficiency and accuracy is further generated, so that high-level heating system management and maintenance are realized.
According to an embodiment of the present invention, further comprising:
Acquiring user heat supply data of a heat supply subarea;
carrying out long-time and short-time based data serialization operation on the user heat supply data to obtain long-time sequence data and short-time sequence data;
constructing a prediction model based on LSTM;
the long-term sequence data and the short-term sequence data are used as time sequence data to be imported into the prediction model for long-term and short-term data prediction training and data test verification, and long-term data prediction accuracy and short-term data prediction accuracy are obtained respectively;
and carrying out weight distribution based on the short-time data prediction accuracy and the long-time data prediction accuracy to obtain a dynamic preset weight value.
The long time and the short time are set by the user, the long time sequence data is heat supply data in a long period, and the short time sequence data is heat supply data in a short period. The dynamic preset weight value corresponds to the predicted weight of the long-time and short-time data, and further, the method is used for calculating the similarity weight of the first periodic fluctuation feature and the second periodic fluctuation feature in the embodiment, and the first periodic fluctuation feature and the second periodic fluctuation feature correspond to the fluctuation feature corresponding to the short-time period and the long-time period respectively. By calculating the dynamic preset weight value in real time in the method of the embodiment, the value of the overall similarity can be dynamically adjusted in the subsequent heat supply subarea clustering analysis, the clustering effect is improved, particularly, the heat supply data in a large-range area are analyzed and clustered, the effects of high efficiency and dynamic data adjustment are achieved, and the efficient management and analysis of the heat supply area and the heat supply data are further realized. The larger the data prediction accuracy is, the larger the corresponding weight value is.
FIG. 4 shows a block diagram of an artificial intelligence based heating analysis evaluation system of the present invention.
The second aspect of the present invention also provides an artificial intelligence based heating analysis and assessment system 4, comprising: a memory 41, a processor 42, said memory comprising an artificial intelligence based heating analysis evaluation program, said artificial intelligence based heating analysis evaluation program when executed by said processor performing the steps of:
constructing a three-dimensional-based heating map model based on the target heating area;
dividing a plurality of heat supply subareas based on the target heat supply area, and acquiring user heat supply data in different heat supply subareas;
performing periodic fluctuation analysis based on the user heat supply data to obtain heat supply characteristic data of a heat supply subarea;
according to the heat supply characteristic data and a preset clustering algorithm, carrying out regional heat supply characteristic fusion analysis of the heat supply subareas, and forming a plurality of heat supply regional groups;
performing ARIMA-based data prediction analysis based on historical heat supply data of a plurality of heat supply area groups to obtain heat supply prediction data;
and leading the heat supply prediction data into a heat supply map model for visual display, heat supply analysis and regional heat supply pressure prediction to obtain a heat supply pressure region and a heat supply regulation and control scheme.
According to the embodiment of the invention, a three-dimensional heat supply map model is constructed based on the target heat supply area, and the heat supply map model is specifically:
acquiring heat exchange station area information, heat supply pipe network information and heat supply user area information in a target heat supply area;
the heat exchange station area information comprises the area size of the heat exchange station area and map contour information;
the heat supply pipe network information comprises space arrangement information of a heat supply pipe network and pipe network monitoring equipment information;
and carrying out map construction of a heat supply area and visual model fusion of a heat supply system based on the heat exchange station area information, the heat supply pipe network information and the heat supply user area information to obtain a three-dimensional heat supply map model.
It should be noted that, the visual model of heating system includes structural models such as heating pipeline, heat exchange station, pipe network monitoring facilities, through the heating map model, can grasp heating system's running condition and heat supply management condition in the target heating region more directly perceivedly and conveniently and carry out visual show to the heat supply forecast data, further improve heat supply informatization management level. The heat supply map model comprises a heat exchange station area, a heat supply pipe network area and a heat supply user area.
According to the embodiment of the invention, the target heating area is used for dividing a plurality of heating subareas, and the user heating amount data in different heating subareas is obtained specifically as follows:
acquiring the number and map distribution of the minimum analysis heating areas based on the target heating areas;
the minimum analytical heating zone comprises a residential zone, an enterprise zone and an industrial zone;
performing region fusion on the basis of the heat supply unit property of the minimum analysis heat supply region and map distribution to form a plurality of residential heat supply regions, a plurality of enterprise heat supply regions and a plurality of industrial heat supply regions;
taking the residential heating areas, the enterprise heating areas and the industrial heating areas as heating subareas, and associating corresponding heating unit properties with each heating subarea;
the heat supply unit property comprises residential heat supply, enterprise heat supply and industrial heat supply;
and acquiring heat supply statistical data of the minimum analysis heat supply area, carrying out calculation and analysis on the heat supply data of each heat supply subarea according to the heat supply statistical data, and obtaining user heat supply data of each heat supply subarea.
The minimum analysis heating area is specifically a minimum unit area capable of performing heating statistics, and includes a residential area, an enterprise area and an industrial area. The heat supply statistical data comprise heat supply quantity statistical data of each minimum analysis heat supply area in a first preset time, and heat supply quantity data of a residential heat supply area, an enterprise heat supply area and an industrial heat supply area can be obtained through fusion analysis of a plurality of minimum analysis heat supply area data. The user heating capacity data comprise heating time, user heating area, heating load and the like. The number of heating subareas is the total area number of a plurality of residential heating areas, a plurality of enterprise heating areas and a plurality of industrial heating areas. One heating sub-area includes at least one minimum analytical heating.
According to the embodiment of the invention, the periodic fluctuation analysis is performed based on the user heat supply data to obtain the heat supply characteristic data of the heat supply subarea, specifically:
performing data division of a preset time period based on the user heat supply data to obtain a plurality of periodic heat supply data;
carrying out data cleaning, abnormal value removal and missing value filling pretreatment on single periodic heat supply data;
performing fluctuation feature analysis and feature extraction based on linear regression on the single periodic heat supply data to obtain a first periodic fluctuation feature;
carrying out fluctuation feature analysis on all the periodic heat supply data to obtain a plurality of first periodic fluctuation features;
carrying out fluctuation feature analysis between the periodic heat supply data based on the plurality of first periodic fluctuation features and the user heat supply data, and carrying out fluctuation feature secondary extraction based on linear regression to obtain a second periodic fluctuation feature;
the heating characteristic data includes a first periodic fluctuation characteristic and a second periodic fluctuation characteristic for each heating subarea.
It should be noted that the preset time period is generally 1 to 7 days. Each heating subarea corresponds to a type of heating area, i.e. to a corresponding heating unit property. And carrying out joint calculation through the minimum unit statistical data in the embodiment to obtain the heat supply data of each heat supply subarea, wherein the joint is to carry out addition calculation of corresponding data on the heat supply data. The first periodic fluctuation feature is specifically a fluctuation feature in a short period, namely data fluctuation in a preset time period, the corresponding analyzed data unit is single periodic heat supply data, the second periodic fluctuation feature is data fluctuation analysis among a plurality of periodic heat supply data, and compared with the first periodic fluctuation feature, the analysis period is longer, and the obtained fluctuation feature is more long-term. In particular, to be practical, the first periodic wave feature is typically a wave analysis over a single day or week, while the second periodic wave feature is typically a seasonal or a month of heating data wave analysis.
According to the embodiment of the invention, the regional heating characteristic fusion analysis of the heating subareas is performed according to the heating characteristic data and a preset clustering algorithm, and a plurality of heating regional groups are formed, specifically:
clustering a plurality of heating subareas by a DBSCAN clustering algorithm based on artificial intelligence;
the clustering process is as follows: selecting an unclassified heating subarea, and marking the unclassified heating subarea as a current heating subarea;
combining a heat supply map model, analyzing the heat supply characteristic data of the current heat supply subarea and the adjacent heat supply subareas to obtain total similarity, and if the total similarity is greater than a preset similarity, carrying out area fusion on the adjacent heat supply subareas and the current heat supply subareas to form a heat supply area group, and circularly judging other adjacent heat supply subareas;
the characteristic similarity analysis specifically comprises the steps of carrying out similarity calculation based on standard Euclidean distance on a first periodic fluctuation characteristic of a current heat supply subarea and a first periodic fluctuation characteristic of an adjacent heat supply subarea to obtain first similarity, carrying out similarity calculation based on standard Euclidean distance on a second periodic fluctuation characteristic of the current heat supply subarea and a second periodic fluctuation characteristic of the adjacent heat supply subarea to obtain second similarity, and calculating weighted average values of the first similarity and the second similarity based on a dynamic preset weight value to obtain total similarity;
And circularly analyzing the non-clustered heat supply subareas and performing cluster fusion analysis to finally obtain a plurality of heat supply area groups.
It should be noted that, after the current heating subarea merges with an adjacent heating subarea, the adjacent heating subarea of the adjacent heating subarea also becomes an adjacent area of the current heating subarea, and further cluster analysis is performed, so that when heating characteristics of areas within a certain range of one heating subarea are similar to those of the heating subarea, area expansion and merging can be performed. It is worth mentioning that in some heat supply characteristics of enterprises and residential areas or enterprises and industrial areas, there are often areas with highly similar heat supply data characteristics, so that the heat supply analysis efficiency in the target heat supply area can be further improved by analyzing the heat supply characteristic data of the areas to perform area fusion, and the accurate heat supply scheme generation and management on the internal area can be effectively performed. The dynamic preset weight value can be dynamically adjusted based on the actual heating system condition and the regional fluctuation condition. The heating zone group comprises at least one heating sub-zone. According to the embodiment of the invention, the heat supply characteristic data is automatically clustered and grouped through a DBSCAN clustering algorithm based on artificial intelligence. And the accurate heat supply evaluation and regulation of a plurality of heat supply areas are realized later. In the similarity calculation based on the standard Euclidean distance between the first periodic fluctuation feature of the current heat supply subarea and the first periodic fluctuation feature of the adjacent heat supply subarea, if a plurality of first periodic fluctuation features exist in the heat supply subarea, the calculation can be performed through a mean feature result.
According to the embodiment of the invention, the historical heat supply data based on the plurality of heat supply area groups is subjected to ARIMA-based data prediction analysis to obtain heat supply prediction data, which specifically comprises:
acquiring historical heat supply data in a heat supply area group, and carrying out data preprocessing on the historical heat supply data;
converting the historical heat supply data into time series data, and importing the time series data into an ARIMA prediction model to generate a corresponding Autocorrelation Chart (ACF) and a Partial Autocorrelation Chart (PACF);
determining ARIMA prediction model parameters according to the autocorrelation diagrams and the partial autocorrelation diagrams;
performing prediction training and model parameter adjustment of an ARIMA prediction model based on historical heat supply data;
acquiring real-time heat supply data of a heat supply region group in a latest preset time period, and importing the real-time heat supply data into an ARIMA prediction model to generate heat supply prediction data in prediction time;
analyzing all the heat supply area groups and obtaining heat supply prediction data of each heat supply area group.
The predicted time is a system user-set time. The analysis process of the heat supply prediction data is visually displayed on a heat supply map model
According to the embodiment of the invention, the heat supply prediction data is imported into a heat supply map model for visual display, heat supply analysis and regional heat supply pressure prediction to obtain a heat supply pressure region and a heat supply regulation scheme, and the heat supply regulation scheme specifically comprises the following steps:
Based on the heat supply prediction data, carrying out heat supply load fluctuation analysis based on linear regression on the heat supply region groups to obtain a load fluctuation characteristic diagram, and obtaining a heat supply load fluctuation maximum value region and a heat supply load fluctuation minimum value region of each heat supply region group through the load fluctuation characteristic diagram;
extracting a corresponding high-low load prediction time period based on the heating load fluctuation maximum value and minimum value region;
analyzing the heating pressure areas in different prediction time periods based on the high-low load prediction time periods of different heating area groups;
and carrying out heat supply regulation analysis on each heat supply region group based on the high-low load prediction time periods and the heat supply pressure regions of different heat supply region groups, and generating a heat supply regulation scheme.
The load prediction period includes a prediction period of a load maximum value and minimum value region, that is, a high load and low load prediction period. The heat supply regulation and control scheme comprises the regulation and control of the load, heat supply quantity, heat power and the like of the heat exchange station, and the regulation and control of the temperature, flow, power and the like of a heat supply pipe network. The heating pressure zone may be all or part of the total zone area in the heating zone group.
According to an embodiment of the present invention, further comprising:
Acquiring heating pressure areas of different prediction time periods;
acquiring a heat exchange station area and a heat supply pipe network area according to a map model;
according to the heat supply pressure area, the heat exchange station area and the heat supply pipe network area, carrying out heat supply route analysis and obtaining a heat supply route of the pressure area;
based on the pressure area heat supply route, carrying out heat supply transmission equipment retrieval in the route by combining a heat supply map model, and obtaining first heat supply equipment;
a heating plant monitoring and maintenance scheme is generated based on the first heating plant, the high-low load prediction period, and the heating pressure region.
It should be noted that the heat supply equipment monitoring and maintaining scheme includes monitoring and maintaining information such as a heat supply area, a heat supply equipment, a heat supply route, a heat supply time period, and the like.
According to the embodiment of the invention, route analysis of the heating equipment can be performed based on the predicted high-pressure heating area, especially, the flow route of the heating pipe network is further generated, and a heating equipment monitoring and maintaining scheme with certain timeliness and high efficiency and accuracy is further generated, so that high-level heating system management and maintenance are realized.
According to an embodiment of the present invention, further comprising:
acquiring user heat supply data of a heat supply subarea;
Carrying out long-time and short-time based data serialization operation on the user heat supply data to obtain long-time sequence data and short-time sequence data;
constructing a prediction model based on LSTM;
the long-term sequence data and the short-term sequence data are used as time sequence data to be imported into the prediction model for long-term and short-term data prediction training and data test verification, and long-term data prediction accuracy and short-term data prediction accuracy are obtained respectively;
and carrying out weight distribution based on the short-time data prediction accuracy and the long-time data prediction accuracy to obtain a dynamic preset weight value.
The long time and the short time are set by the user, the long time sequence data is heat supply data in a long period, and the short time sequence data is heat supply data in a short period. The dynamic preset weight value corresponds to the predicted weight of the long-time and short-time data, and further, the method is used for calculating the similarity weight of the first periodic fluctuation feature and the second periodic fluctuation feature in the embodiment, and the first periodic fluctuation feature and the second periodic fluctuation feature correspond to the fluctuation feature corresponding to the short-time period and the long-time period respectively. By calculating the dynamic preset weight value in real time in the method of the embodiment, the value of the overall similarity can be dynamically adjusted in the subsequent heat supply subarea clustering analysis, the clustering effect is improved, particularly, the heat supply data in a large-range area are analyzed and clustered, the effects of high efficiency and dynamic data adjustment are achieved, and the efficient management and analysis of the heat supply area and the heat supply data are further realized. The larger the data prediction accuracy is, the larger the corresponding weight value is.
The third aspect of the present invention also provides a computer readable storage medium having embodied therein an artificial intelligence based heating analysis and assessment program which, when executed by a processor, implements the steps of an artificial intelligence based heating analysis and assessment method as described in any one of the above.
The invention discloses a heating analysis and evaluation method, a heating analysis and evaluation system and a storage medium based on artificial intelligence. The invention obtains the user heat supply data in different heat supply subareas; performing periodic fluctuation analysis based on the user heat supply data to obtain heat supply characteristic data of a heat supply subarea; according to the heat supply characteristic data and a preset clustering algorithm, carrying out regional heat supply characteristic fusion analysis of the heat supply subareas, and forming a plurality of heat supply regional groups; performing ARIMA-based data prediction analysis based on historical heat supply data of a plurality of heat supply area groups to obtain heat supply prediction data; and leading the heat supply prediction data into a heat supply map model for visual display, heat supply analysis and regional heat supply pressure prediction to obtain a heat supply pressure region and a heat supply regulation and control scheme. The invention can improve the heat supply analysis efficiency in the target heat supply area, effectively generate and manage the heat supply scheme of the internal area, and realize the accurate heat supply evaluation and regulation of the heat supply system.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. An artificial intelligence based heating analysis and evaluation method, which is characterized by comprising the following steps:
constructing a three-dimensional-based heating map model based on the target heating area;
dividing a plurality of heat supply subareas based on the target heat supply area, and acquiring user heat supply data in different heat supply subareas;
performing periodic fluctuation analysis based on the user heat supply data to obtain heat supply characteristic data of a heat supply subarea;
according to the heat supply characteristic data and a preset clustering algorithm, carrying out regional heat supply characteristic fusion analysis of the heat supply subareas, and forming a plurality of heat supply regional groups;
performing ARIMA-based data prediction analysis based on historical heat supply data of a plurality of heat supply area groups to obtain heat supply prediction data;
the heat supply prediction data is imported into a heat supply map model for visual display, heat supply analysis and regional heat supply pressure prediction, and a heat supply pressure region and a heat supply regulation scheme are obtained;
The periodic fluctuation analysis is performed based on the user heat supply data to obtain heat supply characteristic data of a heat supply subarea, wherein the heat supply characteristic data specifically comprises:
performing data division of a preset time period based on the user heat supply data to obtain a plurality of periodic heat supply data;
carrying out data cleaning, abnormal value removal and missing value filling pretreatment on single periodic heat supply data;
performing fluctuation feature analysis and feature extraction based on linear regression on the single periodic heat supply data to obtain a first periodic fluctuation feature;
carrying out fluctuation feature analysis on all the periodic heat supply data to obtain a plurality of first periodic fluctuation features;
carrying out fluctuation feature analysis between the periodic heat supply data based on the plurality of first periodic fluctuation features and the user heat supply data, and carrying out fluctuation feature secondary extraction based on linear regression to obtain a second periodic fluctuation feature;
the heat supply characteristic data comprises a first period fluctuation characteristic and a second period fluctuation characteristic of each heat supply subarea;
and carrying out regional heat supply characteristic fusion analysis of the heat supply subareas according to the heat supply characteristic data and a preset clustering algorithm, and forming a plurality of heat supply regional groups, wherein the method specifically comprises the following steps of:
Clustering a plurality of heating subareas by a DBSCAN clustering algorithm based on artificial intelligence;
the clustering process is as follows: selecting an unclassified heating subarea, and marking the unclassified heating subarea as a current heating subarea;
combining a heat supply map model, analyzing the heat supply characteristic data of the current heat supply subarea and the adjacent heat supply subareas to obtain total similarity, and if the total similarity is greater than a preset similarity, carrying out area fusion on the adjacent heat supply subareas and the current heat supply subareas to form a heat supply area group, and circularly judging other adjacent heat supply subareas;
the characteristic similarity analysis specifically comprises the steps of carrying out similarity calculation based on standard Euclidean distance on a first periodic fluctuation characteristic of a current heat supply subarea and a first periodic fluctuation characteristic of an adjacent heat supply subarea to obtain first similarity, carrying out similarity calculation based on standard Euclidean distance on a second periodic fluctuation characteristic of the current heat supply subarea and a second periodic fluctuation characteristic of the adjacent heat supply subarea to obtain second similarity, and calculating weighted average values of the first similarity and the second similarity based on a dynamic preset weight value to obtain total similarity;
circularly analyzing the non-clustered heat supply subareas and performing cluster fusion analysis to finally obtain a plurality of heat supply area groups;
The historical heat supply data based on the plurality of heat supply area groups are subjected to ARIMA-based data prediction analysis to obtain heat supply prediction data, and the heat supply prediction data specifically comprises:
acquiring historical heat supply data in a heat supply area group, and carrying out data preprocessing on the historical heat supply data;
converting the historical heat supply data into time sequence data, and importing the time sequence data into an ARIMA prediction model to generate a corresponding autocorrelation graph ACF and a partial autocorrelation graph PACF;
determining ARIMA prediction model parameters according to the autocorrelation diagrams and the partial autocorrelation diagrams;
performing prediction training and model parameter adjustment of an ARIMA prediction model based on historical heat supply data;
acquiring real-time heat supply data of a heat supply region group in a latest preset time period, and importing the real-time heat supply data into an ARIMA prediction model to generate heat supply prediction data in prediction time;
analyzing all the heat supply area groups and obtaining heat supply prediction data of each heat supply area group.
2. The artificial intelligence-based heating analysis and evaluation method according to claim 1, wherein the building of the three-dimensional heating map model based on the target heating area is specifically as follows:
Acquiring heat exchange station area information, heat supply pipe network information and heat supply user area information in a target heat supply area;
the heat exchange station area information comprises the area size of the heat exchange station area and map contour information;
the heat supply pipe network information comprises space arrangement information of a heat supply pipe network and pipe network monitoring equipment information;
and carrying out map construction of a heat supply area and visual model fusion of a heat supply system based on the heat exchange station area information, the heat supply pipe network information and the heat supply user area information to obtain a three-dimensional heat supply map model.
3. The heat supply analysis and evaluation method based on artificial intelligence according to claim 2, wherein the target heat supply area is divided into a plurality of heat supply subareas, and the heat supply data of users in different heat supply subareas is obtained specifically as follows:
acquiring the number and map distribution of the minimum analysis heating areas based on the target heating areas;
the minimum analytical heating zone comprises a residential zone, an enterprise zone and an industrial zone;
performing region fusion on the basis of the heat supply unit property of the minimum analysis heat supply region and map distribution to form a plurality of residential heat supply regions, a plurality of enterprise heat supply regions and a plurality of industrial heat supply regions;
Taking the residential heating areas, the enterprise heating areas and the industrial heating areas as heating subareas, and associating corresponding heating unit properties with each heating subarea;
the heat supply unit property comprises residential heat supply, enterprise heat supply and industrial heat supply;
and acquiring heat supply statistical data of the minimum analysis heat supply area, carrying out calculation and analysis on the heat supply data of each heat supply subarea according to the heat supply statistical data, and obtaining user heat supply data of each heat supply subarea.
4. The artificial intelligence-based heat supply analysis and evaluation method according to claim 1, wherein the heat supply prediction data is imported into a heat supply map model for visual display, heat supply analysis and regional heat supply pressure prediction to obtain a heat supply pressure region and a heat supply regulation scheme, specifically comprising the following steps:
based on the heat supply prediction data, carrying out heat supply load fluctuation analysis based on linear regression on the heat supply region groups to obtain a load fluctuation characteristic diagram, and obtaining a heat supply load fluctuation maximum value region and a heat supply load fluctuation minimum value region of each heat supply region group through the load fluctuation characteristic diagram;
extracting a corresponding high-low load prediction time period based on the heating load fluctuation maximum value and minimum value region;
Analyzing the heating pressure areas in different prediction time periods based on the high-low load prediction time periods of different heating area groups;
and carrying out heat supply regulation analysis on each heat supply region group based on the high-low load prediction time periods and the heat supply pressure regions of different heat supply region groups, and generating a heat supply regulation scheme.
5. An artificial intelligence based heating analysis and assessment system, comprising: the system comprises a memory and a processor, wherein the memory comprises an artificial intelligence-based heating analysis and evaluation program, and the artificial intelligence-based heating analysis and evaluation program realizes the following steps when being executed by the processor:
constructing a three-dimensional-based heating map model based on the target heating area;
dividing a plurality of heat supply subareas based on the target heat supply area, and acquiring user heat supply data in different heat supply subareas;
performing periodic fluctuation analysis based on the user heat supply data to obtain heat supply characteristic data of a heat supply subarea;
according to the heat supply characteristic data and a preset clustering algorithm, carrying out regional heat supply characteristic fusion analysis of the heat supply subareas, and forming a plurality of heat supply regional groups;
performing ARIMA-based data prediction analysis based on historical heat supply data of a plurality of heat supply area groups to obtain heat supply prediction data;
The heat supply prediction data is imported into a heat supply map model for visual display, heat supply analysis and regional heat supply pressure prediction, and a heat supply pressure region and a heat supply regulation scheme are obtained;
the periodic fluctuation analysis is performed based on the user heat supply data to obtain heat supply characteristic data of a heat supply subarea, wherein the heat supply characteristic data specifically comprises:
performing data division of a preset time period based on the user heat supply data to obtain a plurality of periodic heat supply data;
carrying out data cleaning, abnormal value removal and missing value filling pretreatment on single periodic heat supply data;
performing fluctuation feature analysis and feature extraction based on linear regression on the single periodic heat supply data to obtain a first periodic fluctuation feature;
carrying out fluctuation feature analysis on all the periodic heat supply data to obtain a plurality of first periodic fluctuation features;
carrying out fluctuation feature analysis between the periodic heat supply data based on the plurality of first periodic fluctuation features and the user heat supply data, and carrying out fluctuation feature secondary extraction based on linear regression to obtain a second periodic fluctuation feature;
the heat supply characteristic data comprises a first period fluctuation characteristic and a second period fluctuation characteristic of each heat supply subarea;
And carrying out regional heat supply characteristic fusion analysis of the heat supply subareas according to the heat supply characteristic data and a preset clustering algorithm, and forming a plurality of heat supply regional groups, wherein the method specifically comprises the following steps of:
clustering a plurality of heating subareas by a DBSCAN clustering algorithm based on artificial intelligence;
the clustering process is as follows: selecting an unclassified heating subarea, and marking the unclassified heating subarea as a current heating subarea;
combining a heat supply map model, analyzing the heat supply characteristic data of the current heat supply subarea and the adjacent heat supply subareas to obtain total similarity, and if the total similarity is greater than a preset similarity, carrying out area fusion on the adjacent heat supply subareas and the current heat supply subareas to form a heat supply area group, and circularly judging other adjacent heat supply subareas;
the characteristic similarity analysis specifically comprises the steps of carrying out similarity calculation based on standard Euclidean distance on a first periodic fluctuation characteristic of a current heat supply subarea and a first periodic fluctuation characteristic of an adjacent heat supply subarea to obtain first similarity, carrying out similarity calculation based on standard Euclidean distance on a second periodic fluctuation characteristic of the current heat supply subarea and a second periodic fluctuation characteristic of the adjacent heat supply subarea to obtain second similarity, and calculating weighted average values of the first similarity and the second similarity based on a dynamic preset weight value to obtain total similarity;
Circularly analyzing the non-clustered heat supply subareas and performing cluster fusion analysis to finally obtain a plurality of heat supply area groups;
the historical heat supply data based on the plurality of heat supply area groups are subjected to ARIMA-based data prediction analysis to obtain heat supply prediction data, and the heat supply prediction data specifically comprises:
acquiring historical heat supply data in a heat supply area group, and carrying out data preprocessing on the historical heat supply data;
converting the historical heat supply data into time sequence data, and importing the time sequence data into an ARIMA prediction model to generate a corresponding autocorrelation graph ACF and a partial autocorrelation graph PACF;
determining ARIMA prediction model parameters according to the autocorrelation diagrams and the partial autocorrelation diagrams;
performing prediction training and model parameter adjustment of an ARIMA prediction model based on historical heat supply data;
acquiring real-time heat supply data of a heat supply region group in a latest preset time period, and importing the real-time heat supply data into an ARIMA prediction model to generate heat supply prediction data in prediction time;
analyzing all the heat supply area groups and obtaining heat supply prediction data of each heat supply area group.
6. The artificial intelligence based heating analysis and assessment system according to claim 5, wherein the building of the three-dimensional based heating map model based on the target heating area is specifically:
Acquiring heat exchange station area information, heat supply pipe network information and heat supply user area information in a target heat supply area;
the heat exchange station area information comprises the area size of the heat exchange station area and map contour information;
the heat supply pipe network information comprises space arrangement information of a heat supply pipe network and pipe network monitoring equipment information;
and carrying out map construction of a heat supply area and visual model fusion of a heat supply system based on the heat exchange station area information, the heat supply pipe network information and the heat supply user area information to obtain a three-dimensional heat supply map model.
7. A computer readable storage medium, characterized in that it comprises therein an artificial intelligence based heating analysis evaluation program, which, when executed by a processor, implements the steps of the artificial intelligence based heating analysis evaluation method according to any one of claims 1 to 4.
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| CN115095907A (en) * | 2022-07-15 | 2022-09-23 | 唐山学院 | Intelligent heat supply energy-saving regulation and control method and system based on deep learning and storage medium |
| US11487273B1 (en) * | 2021-04-30 | 2022-11-01 | Dalian University Of Technology | Distributed industrial energy operation optimization platform automatically constructing intelligent models and algorithms |
| CN116255665A (en) * | 2023-03-07 | 2023-06-13 | 南京华助智能科技有限公司 | Heat supply combined control method and system based on load prediction of heat supply network system |
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| US11487273B1 (en) * | 2021-04-30 | 2022-11-01 | Dalian University Of Technology | Distributed industrial energy operation optimization platform automatically constructing intelligent models and algorithms |
| CN115095907A (en) * | 2022-07-15 | 2022-09-23 | 唐山学院 | Intelligent heat supply energy-saving regulation and control method and system based on deep learning and storage medium |
| CN116255665A (en) * | 2023-03-07 | 2023-06-13 | 南京华助智能科技有限公司 | Heat supply combined control method and system based on load prediction of heat supply network system |
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