CN113495912A - Scattered pollution enterprise accurate prevention and control monitoring method and system based on electric power data - Google Patents
Scattered pollution enterprise accurate prevention and control monitoring method and system based on electric power data Download PDFInfo
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Abstract
The invention relates to the technical field of power monitoring, in particular to a scattered dirt enterprise accurate prevention and control monitoring method and system based on power data, which comprises the following steps of A1-A3: a1, matching with 'scattered pollution' enterprises: s11, matching with the file information of the electricity consumption customer through modes of accurate matching, text mining, regularization, single condition and multi-condition fuzzy matching, on-site checking according to longitude and latitude, experience checking of power supply personnel and the like; s12, judging the matching degree of the 'scattered pollution' enterprises according to the preset index statistical value; a2, monitoring and analyzing the electricity utilization condition of the 'scattered pollution' enterprise: s21, monitoring daily/monthly electricity consumption and daily electricity load conditions of the 'scattered pollution' enterprise within a period of time; s22, carrying out statistical analysis on the electricity utilization condition of the 'scattered pollution' enterprise; a3, identifying abnormal 'scattered pollution' enterprises. The invention constructs the scattered-pollutant enterprise identification model by using the industrial and commercial data and the power utilization characteristics of the scattered-pollutant enterprises, and realizes the accurate identification and treatment of the scattered-pollutant enterprises.
Description
Technical Field
The invention relates to the technical field of power monitoring, in particular to a power data-based method and a system for accurately preventing, controlling and monitoring a scattered-pollution enterprise.
Background
At present, various regions have a plurality of 'scattered sewage' enterprises, the enterprises have small scale, fast transfer, strong concealment and more sewage discharge, depend on manual investigation and regulation of environmental protection departments, and have long time consumption period, lower working efficiency, overlarge input cost and poor effect.
Disclosure of Invention
In order to solve the technical problems in the background art, on one hand, the invention provides an accurate prevention and control monitoring method for a scattered pollution enterprise based on electric power data, which comprises the following steps of A1-A3:
a1 matching with "scattered pollution" enterprises
S11, matching the external information such as enterprise name, county, country, organization code, address, longitude and latitude and the like in the annual scattered pollution enterprise renovation dynamic list published by the environmental protection department with the power consumption customer file information in the modes of accurate matching, text mining, regularization, single condition + multi-condition fuzzy matching, on-site checking according to longitude and latitude, experience checking of power supply personnel and the like;
s12, judging the matching degree of the 'scattered pollution' enterprises according to the preset index statistical value;
a2, monitoring and analyzing the power consumption condition of the enterprise of scattered pollution
S21, monitoring daily/monthly electricity consumption and daily electricity load conditions of the scattered pollution enterprises within a period of time, and displaying comparison conditions of the whole scattered pollution enterprises according to industry, region and time dimensions;
s22, carrying out statistical analysis on the electricity utilization condition of the 'scattered pollution' enterprise;
a3 enterprise recognizing abnormal scattered pollutants
And (3) setting abnormal 'scattered pollution' enterprise conditions by using the 'scattered pollution' enterprise power consumption monitoring result, and identifying and classifying abnormal enterprises.
Preferably, in S11, the information archive matching model is constructed by the following steps:
s1, performing accurate matching according to the highly accurate information such as the unified social credit codes, legal representatives and the like in the 'scattered and dirty' enterprise list data and the basic power file data of the power users;
s2, extracting key text information according to unstructured text information such as enterprise names and enterprise addresses in the 'scattered and dirty' enterprise list data by adopting techniques such as word segmentation, key word extraction based on a TF-IDF method and the like, and constructing a multi-angle hierarchical matching model by combining related information of a standard address library and adopting a regularization and fuzzy matching method to find out enterprises with most similar information;
s3, finding out the nearest enterprises in the specific range by using a kd-tree method according to the longitude and latitude in the 'scattered and polluted' enterprise list data;
s4, matching according to the main raw materials, main fuels and main products in the 'scattered pollution' enterprise list and by combining information such as the category of the power utilization industry and the like;
s5, fusing the four matching results to further improve the matching accuracy, and finally providing the matching result for relevant management personnel for rechecking and confirmation;
s6, the environmental protection + electric power department needs to obtain information such as user numbers, table numbers and the like through contacting with the scattered and polluted enterprises and then verify the information by the electric power department, wherein the information is not matched through the means.
Preferably, in S12, the preset indexes include 5 items of overall matching rate, regional matching rate, industry matching rate, rectification type matching rate, and unmatched enterprise statistics, where:
the integral matching rate is the number of 'scattered pollution' enterprises/the number of all 'scattered pollution' enterprises which can be matched with the power consumer;
the region matching rate is the number of 'scattered and dirty' enterprises which can be matched with the power users in a certain county/the number of all 'scattered and dirty' enterprises in the county;
the industry matching rate is the number of 'scattered pollution' enterprises which can be matched with power consumers in a certain industry/the number of all 'scattered pollution' enterprises in the industry;
the adjustment type matching rate is the number of 'scattered pollution' enterprises of a certain type/the number of all 'scattered pollution' enterprises of the type which can be matched with the power users;
and (4) unmatched enterprise statistics, namely statistics is carried out on the 'messy and dirty' enterprises which are not matched with the power users, and the enterprise situation is verified by both parties of the enterprise and the environmental protection department.
Preferably, in S22, the statistical types include:
b1 statistics of electricity consumption of integral scattered pollution
The method is embodied by an integral power consumption (day/month) curve, the integral scattered pollution power consumption fluctuation analysis judges the integral scattered pollution production operation change trend by the statistical values of 2 indexes including the integral scattered pollution power utilization ring ratio increase rate and the same ratio increase rate, and the correlation analysis of the power trend and the regional pollutant discharge trend is carried out;
b2 statistics of electricity consumption of regional scattered pollutants
The method is embodied by 2 index statistical values of area power consumption (daily/monthly) ratio and area power consumption ranking, and is used for judging the production power consumption condition of the area scattered pollution enterprise and carrying out the correlation analysis of the area scattered pollution enterprise power and the environmental pollution index;
b3 statistics of power consumption for scattered pollution in industry
The method is embodied by 2 index statistical values of the industry power consumption (daily/monthly) ratio and the industry power consumption ranking, and is used for judging the production power consumption condition of the industry scattered pollution enterprise and carrying out the correlation analysis of the industry scattered pollution enterprise power and the environmental pollution index;
b4 statistics of single scattered pollution electricity consumption
The method is embodied by 2 index statistical values of single power consumption (day/month) grade ratio and single enterprise power consumption ranking.
Preferably, the method further comprises the following steps:
a4, constructing an accurate recognition model of the 'scattered pollution' enterprise, wherein the construction steps are as follows:
c1, providing a 'scattered pollution' enterprise list according to an environmental protection department, performing data matching according to a scattered pollution enterprise matching model, corresponding to an electric power user file, performing scattered pollution enterprise characteristic analysis according to user file information, extracting the power utilization characteristics of the known 'scattered pollution' enterprise, and performing characteristic analysis including and not limited to attributes such as power consumption capacity, industry type, power utilization property, execution electricity price policy, peak-valley power consumption ratio, household date, power load level, power consumption (day, month and average), power consumption level, power consumption interval distribution, enterprise power utilization activity, power utilization interval, region and address;
c2, learning and training sample data based on the history of the 'scattered pollution' enterprise in the last 3 years, fully mining the characteristics of the 'scattered pollution' enterprise by utilizing a high-performance clustering algorithm and machine learning methods such as time sequence two-dimension and imaging, by methods such as K-Means and Gaussian mixture and by combining distribution analysis methods such as box line graphs, selecting a proper model type, model type and model algorithm by combining an abnormal data detection method, constructing a 'scattered pollution' enterprise accurate identification model based on a data center, and mining the typical power utilization rules of the 'scattered pollution' enterprise in different regions, different industries, different capacities and the like;
and C3, training the model by using historical electricity consumption data of 'scattered and dirty' enterprises, selecting the electricity consumption data with different time lengths to perform fitting training on the model, and continuously iterating and perfecting the model by methods such as cluster analysis and periodic analysis.
Preferably, the method further comprises the following steps:
a5, constructing an identification model of a newly added scattered and dirty enterprise, wherein the construction steps are as follows:
d1, extracting the newly-added electric power users regularly, screening all non-resident users, and acquiring basic information according to characteristic fields in the scattered pollution model, including and not limited to electricity consumption capacity, industry type, electricity consumption property, execution electricity price policy, peak-valley electricity consumption proportion, household date, electric power load level, electricity consumption grade, electricity consumption interval distribution, enterprise electricity consumption activity, electricity consumption time period, region and address;
d2, analyzing and judging according to the scattered pollution model, and performing preliminary identification on suspected scattered pollution enterprises on the power users screened in the first step by combining formed benchmarking libraries formed by typical power consumption characteristics of different industries and different regions;
d3, carrying out scattered pollution feature display on suspected enterprises, and carrying out reconfirming and recognition by combining key indexes related to a scattered pollution enterprise model to form monitoring and early warning information of the enterprises with abnormal power consumption;
d4, forming the list data of the suspected messy enterprises to be checked and confirmed on site by the environmental protection supervisory personnel.
On the other hand, the invention provides an accurate prevention and control monitoring system for a scattered sewage enterprise based on electric power data, which comprises the following components:
the 'messy dirt' enterprise matching module: matching with the file information of the power utilization customers through external information such as enterprise names, counties, organization codes, addresses, longitudes and latitudes and the like in an enterprise renovation dynamic list of 'scattered pollution' in the year published by an environmental protection department in modes of accurate matching, text mining, regularization, single condition + multi-condition fuzzy matching, field verification according to the longitudes and latitudes, experience verification of power supply personnel and the like; judging the matching degree of the 'scattered pollution' enterprises through a preset index statistic value;
the power utilization condition monitoring and analyzing module of the 'scattered pollution' enterprise: monitoring daily/monthly electricity consumption and daily electricity load conditions of the scattered sewage enterprises within a period of time, and displaying comparison conditions of the whole scattered sewage enterprises according to industries, regions and time dimensions; carrying out statistical analysis on the power utilization condition of the 'scattered and dirty' enterprise;
the 'scattered pollution' enterprise abnormity identification module: and (3) setting abnormal 'scattered pollution' enterprise conditions by using the 'scattered pollution' enterprise power consumption monitoring result, and identifying and classifying abnormal enterprises.
Compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the power utilization characteristics of the scattered sewage enterprises are utilized to analyze and classify, the 'scattered sewage' enterprise accurate identification model is constructed through a big data analysis method, suspected 'scattered sewage' enterprises hidden in residential communities and small industrial parks are accurately researched and judged, the pertinence of the 'scattered sewage' enterprise investigation is improved, and the 'scattered sewage' enterprise accurate identification and treatment are realized. Meanwhile, power utilization monitoring is carried out on 'scattered sewage' enterprises identified by environmental protection departments, the production conditions of the enterprises are mastered in real time, the phenomena of private reproduction and stealing, draining and missing are found, and technical support is provided for relevant departments to screen the scattered sewage enterprises, accurately analyze the environment protection policy execution in-place conditions of the enterprises and the like.
Drawings
FIG. 1 is a schematic diagram of the construction of an information archive matching model in an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a construction of an "dirty and scattered" enterprise precise identification model in an embodiment of the present invention;
fig. 3 and fig. 4 are schematic diagrams of construction of an identification model of a newly added "dirty and scattered" enterprise in the embodiment of the present invention.
Detailed Description
On one hand, the invention provides an accurate prevention and control monitoring method for a scattered pollution enterprise based on electric power data, which comprises the following steps A1-A5:
a1 matching with "scattered pollution" enterprises
S11, matching the external information such as enterprise name, county, country, organization code, address, longitude and latitude and the like in the annual scattered pollution enterprise renovation dynamic list published by the environmental protection department with the file information of the power consumption customer in the modes of accurate matching, text mining, regularization, single condition + multi-condition fuzzy matching, field check according to longitude and latitude, experience check of power supply personnel and the like, and constructing an information file matching model, wherein the construction process is shown in figure 1, and the construction steps are as follows: s1, performing accurate matching according to the highly accurate information such as the unified social credit codes, legal representatives and the like in the 'scattered and dirty' enterprise list data and the basic power file data of the power users; s2, extracting key text information according to unstructured text information such as enterprise names and enterprise addresses in the 'scattered and dirty' enterprise list data by adopting techniques such as word segmentation, key word extraction based on a TF-IDF method and the like, and constructing a multi-angle hierarchical matching model by combining related information of a standard address library and adopting a regularization and fuzzy matching method to find out enterprises with most similar information; s3, finding out the nearest enterprises in the specific range by using a kd-tree method according to the longitude and latitude in the 'scattered and polluted' enterprise list data; s4, matching according to the main raw materials, main fuels and main products in the 'scattered pollution' enterprise list and by combining information such as the category of the power utilization industry and the like; s5, fusing the four matching results to further improve the matching accuracy, and finally providing the matching result for relevant management personnel for rechecking and confirmation; s6, the environmental protection + electric power department needs to obtain information such as user numbers, table numbers and the like through contacting with the scattered and polluted enterprises through the means which are not matched, and then the electric power department verifies the information;
s12, judging the matching degree of the 'scattered pollution' enterprises through the preset index statistical value, analyzing the matching condition of the 'scattered pollution' enterprises in a certain area and power users according to 5 statistical indexes, reflecting the standard degree of the 'scattered pollution' enterprises in the area, wherein the preset indexes comprise 5 statistics items including an overall matching rate, an area matching rate, an industry matching rate, a rectification type matching rate and unmatched enterprises, and the method comprises the following steps:
the integral matching rate is the number of 'scattered pollution' enterprises/the number of all 'scattered pollution' enterprises which can be matched with the power consumer;
the region matching rate is the number of 'scattered and dirty' enterprises which can be matched with the power users in a certain county/the number of all 'scattered and dirty' enterprises in the county;
the industry matching rate is the number of 'scattered pollution' enterprises which can be matched with power consumers in a certain industry/the number of all 'scattered pollution' enterprises in the industry;
the adjustment type matching rate is the number of 'scattered pollution' enterprises of a certain type/the number of all 'scattered pollution' enterprises of the type which can be matched with the power users;
unmatched enterprise statistics, namely performing statistics on 'scattered pollution' enterprises which are not matched with power users, and verifying the enterprise condition together with both sides of an environmental protection department;
a2, monitoring and analyzing the power consumption condition of the enterprise of scattered pollution
S21, monitoring daily/monthly electricity consumption and daily electricity load conditions of the scattered pollution enterprises within a period of time, and displaying comparison conditions of the whole scattered pollution enterprises according to industry, region and time dimensions;
s22, statistically analyzing the power consumption condition of the 'scattered and dirty' enterprise, wherein the statistical type comprises the following steps:
b1 statistics of electricity consumption of integral scattered pollution
The method is embodied by an integral power consumption (day/month) curve, the integral scattered pollution power consumption fluctuation analysis judges the integral scattered pollution production operation change trend by the statistical values of 2 indexes including the integral scattered pollution power utilization ring ratio increase rate and the same ratio increase rate, and the correlation analysis of the power trend and the regional pollutant discharge trend is carried out; the ring ratio increase rate is (the total electricity consumption of the whole 'scattered sewage' in the current period-the total electricity consumption of the whole 'scattered sewage' in the previous period)/the total electricity consumption of the whole 'scattered sewage' in the previous period; the same-ratio growth rate is (the whole 'scattered sewage' current period total power consumption-the whole 'scattered sewage' same-period total power consumption in the same year)/the whole 'scattered sewage' same-period total power consumption in the same year;
b2 statistics of electricity consumption of regional scattered pollutants
The method is embodied by 2 index statistical values of area power consumption (daily/monthly) ratio and area power consumption ranking, and is used for judging the production power consumption condition of the area scattered pollution enterprise and carrying out the correlation analysis of the area scattered pollution enterprise power and the environmental pollution index; the area power consumption ratio is equal to the total electricity consumption of scattered dirt in the target area/the total electricity consumption of scattered dirt on the whole; the fluctuation analysis of the power consumption of the regional scattered pollution is mainly characterized in that the production and operation change trend of the scattered pollution in the key concerned region is judged through the statistical values of 2 indexes including the specific increase rate and the same specific increase rate of the power ring of the regional scattered pollution, and the correlation analysis of the power trend and the regional pollutant emission trend is carried out. The ring ratio increase rate is (the current total power consumption of the area scattered dirt-the previous total power consumption of the area scattered dirt)/the previous total power consumption of the area scattered dirt; the same-ratio growth rate is (the current-year same-period total power consumption of the regional scattered sewage)/the same-year same-period total power consumption of the regional scattered sewage;
b3 statistics of power consumption for scattered pollution in industry
The method is embodied by 2 index statistical values of the industry power consumption (daily/monthly) ratio and the industry power consumption ranking, and is used for judging the production power consumption condition of the industry scattered pollution enterprise and carrying out the correlation analysis of the industry scattered pollution enterprise power and the environmental pollution index; the ratio of the industrial power consumption is equal to the total power consumption of scattered dirt of the target industry/the total power consumption of the integral scattered dirt; the fluctuation analysis of the power consumption of the industry scattered pollution is mainly embodied by the statistical values of 2 indexes of the specific increase rate and the same-specific increase rate of the power ring for the industry scattered pollution. The method is used for judging the production and operation change trend of 'scattered pollution' in key industries and carrying out correlation analysis on the electric quantity trend and the industry pollutant emission trend; the ring ratio increase rate is (the current total power consumption of the industry scattered pollution-the previous total power consumption of the industry scattered pollution)/the previous total power consumption of the industry scattered pollution.
The same-ratio growth rate is (the current-year same-period total power consumption of the industry scattered sewage)/the year-year same-period total power consumption of the industry scattered sewage;
b4 statistics of single scattered pollution electricity consumption
The method is embodied by 2 index statistical values of single power consumption (day/month) grade ratio and single enterprise power consumption ranking; according to the electricity consumption grade of a single 'scattered pollution' enterprise, statistics is carried out on the daily/monthly electricity consumption of the 'scattered pollution' enterprise, N grades are divided according to the electricity consumption, and the enterprise quantity ratio (area and industry ratio) of different electricity consumption grades is analyzed.
Ranking the power consumption of a single 'scattered pollution' enterprise: the electricity consumption (day/month) of a single enterprise is ranked, and the top 100 of the whole enterprise, the top N of different areas and industries are ranked.
The single scattered pollution power consumption fluctuation analysis is mainly embodied by the statistical values of 2 indexes of single scattered pollution power utilization ring ratio increase rate and unity ratio increase rate, is used for judging the production and operation change trend of key scattered pollution enterprises and carries out the correlation analysis of the power trend and the industry pollutant discharge trend; the ring ratio increase rate is (single 'scattered sewage' current total power consumption-single 'scattered sewage' previous total power consumption)/single 'scattered sewage' previous total power consumption; the same-ratio increase rate is (single 'scattered sewage' current-year same-period total power consumption-single scattered sewage)/single 'scattered sewage' year same-period total power consumption;
a3 enterprise recognizing abnormal scattered pollutants
And (3) setting abnormal 'scattered pollution' enterprise conditions by using the 'scattered pollution' enterprise power consumption monitoring result, and identifying and classifying abnormal enterprises. The abnormal 'scattered pollution' judgment standard is that the power consumption of the shutdown and banned type enterprises is monitored according to the requirements of the banned type enterprises (shutdown banning, lifting, reforming and entering the park), and if the power consumption exists, the abnormal enterprises are judged. And secondly, judging abnormal production enterprises through abnormal electricity consumption judgment, abnormal electricity change, abnormal ring ratio increase and abnormal same ratio increase. By counting abnormal 'scattered pollution' enterprises, the abnormal enterprises can be counted according to regions and industries, and the detail of abnormal enterprise inquiry can be realized. For the high-voltage abnormal users, the abnormal production enterprises can be monitored in the production period through the power utilization condition of the time points.
A4, constructing an accurate recognition model of the 'scattered pollution' enterprise, wherein the construction process is shown in FIG. 2, and the construction steps are as follows:
c1, providing a 'scattered pollution' enterprise list according to an environmental protection department, performing data matching according to a scattered pollution enterprise matching model, corresponding to an electric power user file, performing scattered pollution enterprise characteristic analysis according to user file information, extracting the power utilization characteristics of the known 'scattered pollution' enterprise, and performing characteristic analysis including and not limited to attributes such as power consumption capacity, industry type, power utilization property, execution electricity price policy, peak-valley power consumption ratio, household date, power load level, power consumption (day, month and average), power consumption level, power consumption interval distribution, enterprise power utilization activity, power utilization interval, region and address;
c2, learning and training sample data based on the history of the 'scattered pollution' enterprise in the last 3 years, fully mining the characteristics of the 'scattered pollution' enterprise by utilizing a high-performance clustering algorithm and machine learning methods such as time sequence two-dimension and imaging, by methods such as K-Means and Gaussian mixture and by combining distribution analysis methods such as box line graphs, selecting a proper model type, model type and model algorithm by combining an abnormal data detection method, constructing a 'scattered pollution' enterprise accurate identification model based on a data center, and mining the typical power utilization rules of the 'scattered pollution' enterprise in different regions, different industries, different capacities and the like; if the power consumption of different enterprises is classified, the change trend of the reported capacity and the daily and monthly average power consumption of the enterprises can be combined, and the enterprises can be classified into large, medium and common-grade enterprise scales; different enterprises are divided into power utilization active enterprises, power utilization inactive enterprises and the like through the power utilization activity; typical power utilization characteristics of different industries and different regions are constructed according to the regions and industries of the known scattered sewage enterprises, for example, the known scattered sewage enterprises are analyzed, the scattered sewage enterprises distributed in the river clearing region are mainly concentrated in the textile industry, and the accuracy of identification of the local scattered sewage enterprises can be improved by combining the prominent industry analysis of different regions;
and C3, training the model by using historical electricity consumption data of 'scattered and dirty' enterprises, selecting the electricity consumption data with different time lengths to perform fitting training on the model, and continuously iterating and perfecting the model by methods such as cluster analysis and periodic analysis.
A5, constructing an identification model of a newly-added scattered and dirty enterprise, wherein the construction process is shown in FIGS. 3-4, and the construction steps are as follows:
d1, extracting the newly-added electric power users regularly, screening all non-resident users, and acquiring basic information according to characteristic fields in the scattered pollution model, including and not limited to electricity consumption capacity, industry type, electricity consumption property, execution electricity price policy, peak-valley electricity consumption proportion, household date, electric power load level, electricity consumption grade, electricity consumption interval distribution, enterprise electricity consumption activity, electricity consumption time period, region and address;
d2, analyzing and judging according to the scattered pollution model, and performing preliminary identification on suspected scattered pollution enterprises on the power users screened in the first step by combining formed benchmarking libraries formed by typical power consumption characteristics of different industries and different regions;
d3, carrying out scattered pollution feature display on suspected enterprises, and carrying out reconfirming and recognition by combining key indexes related to a scattered pollution enterprise model to form monitoring and early warning information of the enterprises with abnormal power consumption;
d4, forming the list data of the suspected messy enterprises to be checked and confirmed on site by the environmental protection supervisory personnel.
On the other hand, the invention provides an accurate prevention and control monitoring system for a scattered sewage enterprise based on electric power data, which comprises the following components:
the 'messy dirt' enterprise matching module: matching with the file information of the power utilization customers through external information such as enterprise names, counties, organization codes, addresses, longitudes and latitudes and the like in an enterprise renovation dynamic list of 'scattered pollution' in the year published by an environmental protection department in modes of accurate matching, text mining, regularization, single condition + multi-condition fuzzy matching, field verification according to the longitudes and latitudes, experience verification of power supply personnel and the like; judging the matching degree of the 'scattered pollution' enterprises through a preset index statistic value;
the power utilization condition monitoring and analyzing module of the 'scattered pollution' enterprise: monitoring daily/monthly electricity consumption and daily electricity load conditions of the scattered sewage enterprises within a period of time, and displaying comparison conditions of the whole scattered sewage enterprises according to industries, regions and time dimensions; carrying out statistical analysis on the power utilization condition of the 'scattered and dirty' enterprise;
the 'scattered pollution' enterprise abnormity identification module: and (3) setting abnormal 'scattered pollution' enterprise conditions by using the 'scattered pollution' enterprise power consumption monitoring result, and identifying and classifying abnormal enterprises.
According to the invention, the power utilization characteristics of the scattered sewage enterprises are utilized to analyze and classify, the 'scattered sewage' enterprise accurate identification model is constructed by a big data analysis method, suspected 'scattered sewage' enterprises hidden in residential communities and small industrial parks are accurately researched and judged, the pertinence of the 'scattered sewage' enterprise investigation is improved, and the 'scattered sewage' enterprise accurate identification and treatment are realized. Meanwhile, power utilization monitoring is carried out on 'scattered sewage' enterprises identified by environmental protection departments, the production conditions of the enterprises are mastered in real time, the phenomena of private reproduction and stealing, draining and missing are found, and technical support is provided for relevant departments to screen the scattered sewage enterprises, accurately analyze the environment protection policy execution in-place conditions of the enterprises and the like.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (7)
1. The scattered-pollution enterprise accurate prevention and control monitoring method based on electric power data is characterized by comprising the following steps A1-A3:
a1 matching with "scattered pollution" enterprises
S11, matching the external information such as enterprise name, county, country, organization code, address, longitude and latitude and the like in the annual scattered pollution enterprise renovation dynamic list published by the environmental protection department with the power consumption customer file information in the modes of accurate matching, text mining, regularization, single condition + multi-condition fuzzy matching, on-site checking according to longitude and latitude, experience checking of power supply personnel and the like;
s12, judging the matching degree of the 'scattered pollution' enterprises according to the preset index statistical value;
a2, monitoring and analyzing the power consumption condition of the enterprise of scattered pollution
S21, monitoring daily/monthly electricity consumption and daily electricity load conditions of the scattered pollution enterprises within a period of time, and displaying comparison conditions of the whole scattered pollution enterprises according to industry, region and time dimensions;
s22, carrying out statistical analysis on the electricity utilization condition of the 'scattered pollution' enterprise;
a3 enterprise recognizing abnormal scattered pollutants
And (3) setting abnormal 'scattered pollution' enterprise conditions by using the 'scattered pollution' enterprise power consumption monitoring result, and identifying and classifying abnormal enterprises.
2. The method for accurately preventing, controlling and monitoring the scattered pollution enterprises based on the electric power data as claimed in claim 1, wherein in S11, an information archive matching model is constructed by the following steps:
s1, performing accurate matching according to the highly accurate information such as the unified social credit codes, legal representatives and the like in the 'scattered and dirty' enterprise list data and the basic power file data of the power users;
s2, extracting key text information according to unstructured text information such as enterprise names and enterprise addresses in the 'scattered and dirty' enterprise list data by adopting techniques such as word segmentation, key word extraction based on a TF-IDF method and the like, and constructing a multi-angle hierarchical matching model by combining related information of a standard address library and adopting a regularization and fuzzy matching method to find out enterprises with most similar information;
s3, finding out the nearest enterprises in the specific range by using a kd-tree method according to the longitude and latitude in the 'scattered and polluted' enterprise list data;
s4, matching according to the main raw materials, main fuels and main products in the 'scattered pollution' enterprise list and by combining information such as the category of the power utilization industry and the like;
s5, fusing the four matching results to further improve the matching accuracy, and finally providing the matching result for relevant management personnel for rechecking and confirmation;
s6, the environmental protection + electric power department needs to obtain information such as user numbers, table numbers and the like through contacting with the scattered and polluted enterprises and then verify the information by the electric power department, wherein the information is not matched through the means.
3. The method for accurately preventing, controlling and monitoring the scattered pollution enterprises based on the electric power data as claimed in claim 1, wherein in S12, the preset indexes include 5 items including an overall matching rate, a regional matching rate, an industry matching rate, a rectification type matching rate and unmatched enterprise statistics, wherein:
the integral matching rate is the number of 'scattered pollution' enterprises/the number of all 'scattered pollution' enterprises which can be matched with the power consumer;
the region matching rate is the number of 'scattered and dirty' enterprises which can be matched with the power users in a certain county/the number of all 'scattered and dirty' enterprises in the county;
the industry matching rate is the number of 'scattered pollution' enterprises which can be matched with power consumers in a certain industry/the number of all 'scattered pollution' enterprises in the industry;
the adjustment type matching rate is the number of 'scattered pollution' enterprises of a certain type/the number of all 'scattered pollution' enterprises of the type which can be matched with the power users;
and (4) unmatched enterprise statistics, namely statistics is carried out on the 'messy and dirty' enterprises which are not matched with the power users, and the enterprise situation is verified by both parties of the enterprise and the environmental protection department.
4. The method for accurately preventing, controlling and monitoring the scattered pollution enterprise based on the electric power data as claimed in claim 1, wherein in S22, the statistical type includes:
b1 statistics of electricity consumption of integral scattered pollution
The method is embodied by an integral power consumption (day/month) curve, the integral scattered pollution power consumption fluctuation analysis judges the integral scattered pollution production operation change trend by the statistical values of 2 indexes including the integral scattered pollution power utilization ring ratio increase rate and the same ratio increase rate, and the correlation analysis of the power trend and the regional pollutant discharge trend is carried out;
b2 statistics of electricity consumption of regional scattered pollutants
The method is embodied by 2 index statistical values of area power consumption (daily/monthly) ratio and area power consumption ranking, and is used for judging the production power consumption condition of the area scattered pollution enterprise and carrying out the correlation analysis of the area scattered pollution enterprise power and the environmental pollution index;
b3 statistics of power consumption for scattered pollution in industry
The method is embodied by 2 index statistical values of the industry power consumption (daily/monthly) ratio and the industry power consumption ranking, and is used for judging the production power consumption condition of the industry scattered pollution enterprise and carrying out the correlation analysis of the industry scattered pollution enterprise power and the environmental pollution index;
b4 statistics of single scattered pollution electricity consumption
The method is embodied by 2 index statistical values of single power consumption (day/month) grade ratio and single enterprise power consumption ranking.
5. The power data-based accurate prevention, control and monitoring method for the scattered pollution enterprises according to claim 1, further comprising:
a4, constructing an accurate recognition model of the 'scattered pollution' enterprise, wherein the construction steps are as follows:
c1, providing a 'scattered pollution' enterprise list according to an environmental protection department, performing data matching according to a scattered pollution enterprise matching model, corresponding to an electric power user file, performing scattered pollution enterprise characteristic analysis according to user file information, extracting the power utilization characteristics of the known 'scattered pollution' enterprise, and performing characteristic analysis including and not limited to attributes such as power consumption capacity, industry type, power utilization property, execution electricity price policy, peak-valley power consumption ratio, household date, power load level, power consumption (day, month and average), power consumption level, power consumption interval distribution, enterprise power utilization activity, power utilization interval, region and address;
c2, learning and training sample data based on the history of the 'scattered pollution' enterprise in the last 3 years, fully mining the characteristics of the 'scattered pollution' enterprise by utilizing a high-performance clustering algorithm and machine learning methods such as time sequence two-dimension and imaging, by methods such as K-Means and Gaussian mixture and by combining distribution analysis methods such as box line graphs, selecting a proper model type, model type and model algorithm by combining an abnormal data detection method, constructing a 'scattered pollution' enterprise accurate identification model based on a data center, and mining the typical power utilization rules of the 'scattered pollution' enterprise in different regions, different industries, different capacities and the like;
and C3, training the model by using historical electricity consumption data of 'scattered and dirty' enterprises, selecting the electricity consumption data with different time lengths to perform fitting training on the model, and continuously iterating and perfecting the model by methods such as cluster analysis and periodic analysis.
6. The power data-based accurate prevention, control and monitoring method for the scattered pollution enterprises according to claim 5, further comprising:
a5, constructing an identification model of a newly added scattered and dirty enterprise, wherein the construction steps are as follows:
d1, extracting the newly-added electric power users regularly, screening all non-resident users, and acquiring basic information according to characteristic fields in the scattered pollution model, including and not limited to electricity consumption capacity, industry type, electricity consumption property, execution electricity price policy, peak-valley electricity consumption proportion, household date, electric power load level, electricity consumption grade, electricity consumption interval distribution, enterprise electricity consumption activity, electricity consumption time period, region and address;
d2, analyzing and judging according to the scattered pollution model, and performing preliminary identification on suspected scattered pollution enterprises on the power users screened in the first step by combining formed benchmarking libraries formed by typical power consumption characteristics of different industries and different regions;
d3, carrying out scattered pollution feature display on suspected enterprises, and carrying out reconfirming and recognition by combining key indexes related to a scattered pollution enterprise model to form monitoring and early warning information of the enterprises with abnormal power consumption;
d4, forming the list data of the suspected messy enterprises to be checked and confirmed on site by the environmental protection supervisory personnel.
7. Scattered dirty enterprise accurate prevention and control monitoring system based on electric power data, its characterized in that includes:
the 'messy dirt' enterprise matching module: matching with the file information of the power utilization customers through external information such as enterprise names, counties, organization codes, addresses, longitudes and latitudes and the like in an enterprise renovation dynamic list of 'scattered pollution' in the year published by an environmental protection department in modes of accurate matching, text mining, regularization, single condition + multi-condition fuzzy matching, field verification according to the longitudes and latitudes, experience verification of power supply personnel and the like; judging the matching degree of the 'scattered pollution' enterprises through a preset index statistic value;
the power utilization condition monitoring and analyzing module of the 'scattered pollution' enterprise: monitoring daily/monthly electricity consumption and daily electricity load conditions of the scattered sewage enterprises within a period of time, and displaying comparison conditions of the whole scattered sewage enterprises according to industries, regions and time dimensions; carrying out statistical analysis on the power utilization condition of the 'scattered and dirty' enterprise;
the 'scattered pollution' enterprise abnormity identification module: and (3) setting abnormal 'scattered pollution' enterprise conditions by using the 'scattered pollution' enterprise power consumption monitoring result, and identifying and classifying abnormal enterprises.
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