CN116739223B - Enterprise pollution discharge real-time supervision method and device, storage medium and electronic equipment - Google Patents
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Abstract
The present disclosure relates to an enterprise pollution discharge real-time supervision method, a device, a storage medium and an electronic apparatus, wherein the enterprise pollution discharge real-time supervision method is implemented by acquiring enterprise supervision data of each supervision enterprise of a plurality of supervision enterprises in a supervision area; determining reference enterprise feature data of the supervision area according to the enterprise supervision data of each supervision enterprise in the supervision area, wherein the reference enterprise feature data is used for complementing enterprise feature data of the supervision enterprise; determining target enterprise feature data of each supervision enterprise according to the reference enterprise feature data; according to the target enterprise characteristic data of each supervision enterprise, supervision problem enterprises in a plurality of supervision enterprises in the supervision area are determined, the coverage range of a remote supervision enterprise for pollution emission can be effectively enlarged, the automation degree of enterprise pollution emission supervision can be effectively improved, the manpower and material resources required by enterprise pollution emission supervision are reduced, and the supervision efficiency is improved.
Description
Technical Field
The disclosure relates to the technical field of environmental monitoring, in particular to a real-time supervision method and device for pollution discharge of enterprises, a storage medium and electronic equipment.
Background
With the deep progress of industrial development, the problem of environmental pollution is increasingly prominent. Industrial emission is the biggest source of environmental pollution, carries out real-time comprehensive supervision to industrial enterprise's pollution emission, and the behavior such as preventing steal and arrange, leak and discharge, exceeds standard discharge is the important way of effectively reducing environmental pollution. However, the current supervision of the pollution emission of industrial enterprises generally has the problems of large quantity, wide distribution, low supervision efficiency, limited coverage and the like of the supervision enterprises.
Disclosure of Invention
The invention aims to provide an enterprise pollution discharge real-time supervision method, an enterprise pollution discharge real-time supervision device, a storage medium and electronic equipment.
To achieve the above object, a first aspect of the present disclosure provides a method for monitoring and managing pollution discharge of an enterprise in real time, the method comprising:
acquiring enterprise supervision data of each supervision enterprise in a plurality of supervision enterprises in a supervision area, wherein the enterprise supervision data is used for describing production, pollution discharge, pollution control and pollution discharge inspection status information of the enterprises in a specified historical time period;
determining reference enterprise feature data of the supervision area according to the enterprise supervision data of each supervision enterprise in the supervision area, wherein the reference enterprise feature data is used for complementing enterprise feature data of the supervision enterprise;
Determining target enterprise feature data of each supervision enterprise according to the reference enterprise feature data;
and determining a supervision problem enterprise in a plurality of supervision enterprises in the supervision area according to the target enterprise characteristic data of each supervision enterprise.
Optionally, the determining the reference enterprise feature data of the supervision area according to the enterprise supervision data of each supervision enterprise in the supervision area includes:
performing verification processing on the enterprise supervision data of each supervision enterprise to obtain the standby supervision data after verification;
extracting enterprise data of a plurality of preset fields from the standby supervision data of each supervision enterprise to obtain enterprise feature data of each supervision enterprise;
and determining the reference enterprise feature data according to the enterprise feature data of each of a plurality of supervising enterprises in the supervising region.
Optionally, the determining the reference enterprise feature data according to the enterprise feature data of each of a plurality of supervising enterprises in the supervising region includes:
determining the number of field types corresponding to each enterprise feature data;
determining a target supervision enterprise with the largest field type number in a plurality of supervision enterprises in the supervision area;
And taking the enterprise characteristic data of the target supervision enterprise as the reference enterprise characteristic data.
Optionally, the verifying the enterprise supervision data of each supervision enterprise to obtain the verified standby supervision data includes:
carrying out format unification processing on the enterprise supervision data to obtain data to be verified, which corresponds to the enterprise supervision data;
determining numerical data and non-numerical data in the data to be verified;
and determining the standby supervision data after verification according to the numerical data and the non-numerical data.
Optionally, the determining the standby supervision data after verification according to the numerical data and the non-numerical data includes:
performing upper and lower limit value verification on the numerical data to obtain intermediate verification data; under the condition that different values with a field having a plurality of sources exist in the intermediate verification data, determining the average value of the different values, and taking the average value as verified target numerical data;
and for the non-numerical data, in the case that a matter is determined to comprise a plurality of description data of a plurality of sources, displaying the plurality of description data, receiving target description information designated by a user, and taking the target description information as verified target non-numerical data to obtain standby supervision data comprising the target numerical data and the target non-numerical data.
Optionally, the determining the target enterprise feature data of each of the supervising enterprises according to the reference enterprise feature data includes:
under the condition that the enterprise data corresponding to each preset field in the enterprise feature data of the supervision enterprise is not empty, determining that the enterprise feature data is enterprise feature data without completion;
under the condition that null values exist in enterprise data corresponding to the preset fields, determining the enterprise characteristic data as enterprise characteristic data to be complemented;
and complementing the enterprise feature data to be complemented according to the reference enterprise feature data to obtain the target enterprise feature data.
Optionally, the complementing the enterprise feature data to be complemented according to the reference enterprise feature data to obtain the target enterprise feature data includes:
determining the similarity of the enterprise to be complemented and the enterprise corresponding to the reference enterprise characteristic data according to the existing enterprise data in the enterprise characteristic data to be complemented and the reference enterprise characteristic data;
determining target completion data in the enterprise feature data to be completed according to the similarity;
and taking the existing enterprise data and the target completion data as the target enterprise characteristic data.
Optionally, the determining, according to the similarity, target completion data in the enterprise feature data to be completed includes:
and determining target completion data corresponding to the similarity from preset relationship data, wherein the preset relationship data comprises corresponding relations between a plurality of preset similarities and different completion data.
Optionally, the complementing the enterprise feature data to be complemented according to the reference enterprise feature data to obtain the target enterprise feature data includes:
and inputting the enterprise characteristic data to be complemented and the reference enterprise characteristic data into a first preset machine learning model to obtain the target enterprise characteristic data output by the first preset machine learning model.
Optionally, the training method of the first preset machine learning model includes:
obtaining first training data, wherein the first training data comprises a plurality of groups of sample data, each group of sample data comprises reference sample data, sample data to be complemented and complement sample data,
and performing model training on a first preset initial model by taking the complement sample data as sample marking data to obtain the first preset machine learning model.
Optionally, the determining a regulatory problem enterprise in a plurality of regulatory enterprises in the regulatory domain according to the target enterprise characteristic data of each of the regulatory enterprises includes:
inputting the target enterprise characteristic data of each supervision enterprise into a second preset machine learning model to obtain enterprise types output by the second preset machine learning model, wherein the enterprise types comprise major pollution discharge problem enterprises, major pollution control problem enterprises, neutral enterprises and front enterprises;
taking the enterprise type in the plurality of supervision enterprises in the supervision area as a supervision enterprise of a key pollution discharge problem enterprise and/or a key pollution control problem enterprise as the supervision problem enterprise.
Optionally, the training method of the second preset machine learning model includes:
obtaining second training data, wherein the second training data comprises enterprise characteristic sample data and enterprise type marking data of a plurality of enterprises,
and performing model training on a second preset initial model through the second training data to obtain the second preset machine learning model.
A second aspect of the present disclosure provides an enterprise pollution discharge real-time supervision apparatus, the apparatus comprising:
The system comprises an acquisition module, a management module and a management module, wherein the acquisition module is configured to acquire enterprise management data of each of a plurality of management enterprises in a management area, and the enterprise management data is used for describing production, pollution discharge, pollution control and pollution discharge inspection status information of the enterprises in a specified historical time period;
a first determining module configured to determine reference enterprise feature data of the administrative area according to the enterprise administrative data of each administrative enterprise within the administrative area, the reference enterprise feature data being used to complement enterprise feature data of an administrative enterprise;
a second determination module configured to determine target enterprise feature data for each of the supervising enterprises from the reference enterprise feature data;
a third determination module configured to determine a regulatory problem enterprise of a plurality of regulatory enterprises within the regulatory domain based on the target enterprise characteristic data for each of the regulatory enterprises.
Optionally, the first determining module is configured to:
performing verification processing on the enterprise supervision data of each supervision enterprise to obtain the standby supervision data after verification;
extracting enterprise data of a plurality of preset fields from the standby supervision data of each supervision enterprise to obtain enterprise feature data of each supervision enterprise;
And determining the reference enterprise feature data according to the enterprise feature data of each of a plurality of supervising enterprises in the supervising region.
Optionally, the first determining module is configured to:
determining the number of field types corresponding to each enterprise feature data;
determining a target supervision enterprise with the largest field type number in a plurality of supervision enterprises in the supervision area;
and taking the enterprise characteristic data of the target supervision enterprise as the reference enterprise characteristic data.
Optionally, the first determining module is configured to:
carrying out format unification processing on the enterprise supervision data to obtain data to be verified, which corresponds to the enterprise supervision data;
determining numerical data and non-numerical data in the data to be verified;
and determining the standby supervision data after verification according to the numerical data and the non-numerical data.
Optionally, the first determining module is configured to:
performing upper and lower limit value verification on the numerical data to obtain intermediate verification data; under the condition that different values with a field having a plurality of sources exist in the intermediate verification data, determining the average value of the different values, and taking the average value as verified target numerical data;
And for the non-numerical data, in the case that a matter is determined to comprise a plurality of description data of a plurality of sources, displaying the plurality of description data, receiving target description information designated by a user, and taking the target description information as verified target non-numerical data to obtain standby supervision data comprising the target numerical data and the target non-numerical data.
Optionally, the second determining module is configured to:
under the condition that the enterprise data corresponding to each preset field in the enterprise feature data of the supervision enterprise is not empty, determining that the enterprise feature data is enterprise feature data without completion;
under the condition that null values exist in enterprise data corresponding to the preset fields, determining the enterprise characteristic data as enterprise characteristic data to be complemented;
and complementing the enterprise feature data to be complemented according to the reference enterprise feature data to obtain the target enterprise feature data.
Optionally, the second determining module is configured to:
determining the similarity of the enterprise to be complemented and the enterprise corresponding to the reference enterprise characteristic data according to the existing enterprise data in the enterprise characteristic data to be complemented and the reference enterprise characteristic data;
Determining target completion data in the enterprise feature data to be completed according to the similarity;
and taking the existing enterprise data and the target completion data as the target enterprise characteristic data.
Optionally, the second determining module is configured to:
and determining target completion data corresponding to the similarity from preset relationship data, wherein the preset relationship data comprises corresponding relations between a plurality of preset similarities and different completion data.
Optionally, the second determining module is configured to:
and inputting the enterprise characteristic data to be complemented and the reference enterprise characteristic data into a first preset machine learning model to obtain the target enterprise characteristic data output by the first preset machine learning model.
Optionally, the apparatus further comprises a first training module configured to:
obtaining first training data, wherein the first training data comprises a plurality of groups of sample data, each group of sample data comprises reference sample data, sample data to be complemented and complement sample data,
and performing model training on a first preset initial model by taking the complement sample data as sample marking data to obtain the first preset machine learning model.
Optionally, the third determining module is configured to:
inputting the target enterprise characteristic data of each supervision enterprise into a second preset machine learning model to obtain enterprise types output by the second preset machine learning model, wherein the enterprise types comprise major pollution discharge problem enterprises, major pollution control problem enterprises, neutral enterprises and front enterprises;
taking the enterprise type in the plurality of supervision enterprises in the supervision area as a supervision enterprise of a key pollution discharge problem enterprise and/or a key pollution control problem enterprise as the supervision problem enterprise.
Optionally, the apparatus further comprises a second training module configured to:
acquiring second training data, wherein the second training data comprises enterprise characteristic sample data and enterprise type labeling data of a plurality of enterprises;
and performing model training on a second preset initial model through the second training data to obtain the second preset machine learning model.
A third aspect of the present disclosure provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of the first aspect above.
A fourth aspect of the present disclosure provides an electronic device, comprising:
A memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method of the first aspect above.
According to the technical scheme, the reference enterprise feature data of the supervision area can be determined according to the enterprise supervision data of each supervision enterprise in the supervision area, and the reference enterprise feature data is used for complementing the enterprise feature data of the supervision enterprise; determining target enterprise feature data of each supervision enterprise according to the reference enterprise feature data; according to the target enterprise characteristic data of each supervision enterprise, the supervision problem enterprises in the plurality of supervision enterprises in the supervision area are determined, the target enterprise characteristic data of each supervision enterprise can be determined according to the reference enterprise characteristic data, so that the coverage range of a remote supervision enterprise for pollution emission can be effectively enlarged, and according to the target enterprise characteristic data of each supervision enterprise, the supervision problem enterprises in the plurality of supervision enterprises in the supervision area can be determined, so that the automation degree of enterprise pollution emission supervision can be effectively improved, the manpower and material resources required by enterprise pollution emission supervision can be reduced, and the supervision efficiency can be improved.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the description serve to explain, but do not limit the disclosure. In the drawings:
FIG. 1 is a flow chart of an enterprise blowdown real-time supervision method, shown in an exemplary embodiment of the present disclosure;
FIG. 2 is a flow chart of an enterprise emissions real-time supervision method according to the embodiment shown in FIG. 1;
FIG. 3 is a flow chart of another method of enterprise emissions real-time supervision, shown in accordance with the embodiment of FIG. 1;
FIG. 4 is a flow chart illustrating yet another method of enterprise emissions real-time supervision, in accordance with the embodiment shown in FIG. 1;
FIG. 5 is a block diagram of an enterprise emissions real-time supervision apparatus, as shown in another exemplary embodiment of the present disclosure;
FIG. 6 is a block diagram of an enterprise emissions real-time supervision apparatus, shown in accordance with the embodiment of FIG. 5;
FIG. 7 is a block diagram of an electronic device, shown in accordance with an exemplary embodiment;
fig. 8 is a block diagram of another electronic device, shown in accordance with an exemplary embodiment.
Detailed Description
Specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the disclosure, are not intended to limit the disclosure.
It should be noted that, all actions for acquiring signals, information or data in the present disclosure are performed under the condition of conforming to the corresponding data protection rule policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.
Before describing the specific embodiments of the present disclosure in detail, the following description is first made on application scenarios of the present disclosure, where the present disclosure may be applied to a remote monitoring process of pollution emission of an enterprise, and in the related art, there are two main technical means for remote monitoring of pollution emission of an industrial enterprise: one is heavy-point pollution source on-line monitoring, and the other is enterprise working condition electricity monitoring. The heavy-point pollution source on-line monitoring technology monitors the standard condition of the pollutant concentration in the waste gas and waste water discharged by enterprises by installing a monitoring sensor at the discharge port of the enterprises; the enterprise working condition electricity consumption monitoring technology is to divide the meter electricity for each production equipment and pollution control equipment of an enterprise, and monitor the running state of the equipment and the production stopping condition during the control period by monitoring the electricity consumption load change. However, the problems of limited coverage, incomplete supervision and limited applicability exist in both the heavy-point pollution source on-line monitoring and the enterprise working condition electricity monitoring. Therefore, the comprehensive supervision is required at present, a great deal of manpower and material resources are still required to be spent for the on-site inspection of the pollution sources, and in the inspection process, a great deal of on-site inspection without directivity exists, so that the inspection efficiency is low, and further waste of manpower and material resources can be caused.
In order to solve the technical problems, the present disclosure provides an enterprise pollution discharge real-time supervision method, an apparatus, a storage medium and an electronic device, where the enterprise pollution discharge real-time supervision method obtains enterprise supervision data of each supervision enterprise in a plurality of supervision enterprises in a supervision area, where the enterprise supervision data is used for describing status information of enterprise production, pollution discharge, pollution control and pollution discharge inspection in a specified historical time period; determining reference enterprise feature data of the supervision area according to the enterprise supervision data of each supervision enterprise in the supervision area, wherein the reference enterprise feature data is used for complementing enterprise feature data of the supervision enterprise; determining target enterprise feature data of each supervision enterprise according to the reference enterprise feature data; according to the target enterprise characteristic data of each supervision enterprise, the supervision problem enterprises in the plurality of supervision enterprises in the supervision area are determined, the target enterprise characteristic data of each supervision enterprise can be determined according to the reference enterprise characteristic data, so that the coverage range of a remote supervision enterprise for pollution emission can be effectively enlarged, and according to the target enterprise characteristic data of each supervision enterprise, the supervision problem enterprises in the plurality of supervision enterprises in the supervision area can be determined, so that the automation degree of enterprise pollution emission supervision can be effectively improved, the manpower and material resources required by enterprise pollution emission supervision can be reduced, and the supervision efficiency can be improved.
The technical scheme of the present disclosure is described in detail below with reference to specific embodiments.
FIG. 1 is a flow chart of an enterprise blowdown real-time supervision method, shown in an exemplary embodiment of the present disclosure; as shown in fig. 1, the method may include:
step 101, acquiring enterprise supervision data of each supervision enterprise in a plurality of supervision enterprises in a supervision area.
The enterprise supervision data is used for describing production, pollution discharge, pollution control and pollution discharge inspection status information of enterprises in a specified historical time period.
It should be noted that, the enterprise supervision data may include a plurality of pollution source on-line monitoring information, enterprise working condition power consumption information, enterprise power grid power consumption information, pollution discharge license information, emission list information, emergency emission reduction list information, ring review batch information and enterprise pollution discharge inspection record information, where the pollution source on-line monitoring information includes a monitoring point name, a monitoring point position, an enterprise position, a pollutant type, a pollutant discharge time, a discharge concentration and the like of the pollution source; the enterprise working condition electricity consumption comprises the operation time rules of enterprise pollution production facilities and pollution control facilities; the enterprise power grid electricity consumption information comprises total electricity consumption information in a monitoring area and electricity consumption rule information; the pollution discharge license information including pollutant information allowing discharge; the emission list information comprises a pollution discharge name, a pollution discharge type, emission quantity, emission positions, a treatment facility name, a treatment type and treatment efficiency; the emergency emission reduction list information comprises management and control measure information of enterprise production stopping and production limiting under three emergency situations of red, orange and yellow; the enterprise pollution discharge inspection record information comprises record information generated when the enterprise is subjected to pollution discharge inspection in a target historical time period.
Step 102, determining reference enterprise feature data of the supervision area according to the enterprise supervision data of each supervision enterprise in the supervision area, wherein the reference enterprise feature data is used for complementing the enterprise feature data of the supervision enterprise.
The reference enterprise feature data may be the enterprise monitoring data of a monitoring enterprise (e.g., may be the most comprehensive monitoring enterprise including enterprise feature data) among a plurality of monitoring enterprises in the monitoring area.
This step may be implemented by the embodiment shown in fig. 2, and fig. 2 is a flowchart of an enterprise pollution emission real-time supervision method according to the embodiment shown in fig. 1; as shown in fig. 2, this step 102 may include:
and 1021, checking the enterprise supervision data of each supervision enterprise to obtain the checked standby supervision data.
In the step, format unification processing can be performed on the enterprise supervision data to obtain data to be checked corresponding to the enterprise supervision data; determining numerical data and non-numerical data in the data to be checked; and determining the standby supervision data after verification according to the numerical data and the non-numerical data.
Wherein, whether the data belong to the numerical data can be determined by identifying whether the data to be checked comprise numerical values of various language types (such as Chinese, english, french and the like), the numerical data are determined under the condition that the numerical value of any language type is included, and the non-numerical data are determined under the condition that the numerical value of any language type is not included.
The above-described embodiment of determining the standby regulatory data after verification from the numeric data and the non-numeric data may be:
performing upper and lower limit value verification on the numerical data to obtain intermediate verification data; under the condition that different values with a field having a plurality of sources exist in the intermediate checking data, determining the average value of the different values, and taking the average value as checked target value type data;
in the case that it is determined that one item includes a plurality of description data of a plurality of sources for the non-numeric data, the plurality of description data are displayed, target description information designated by a user is received, and the target description information is used as verified target non-numeric data to obtain standby supervision data including the target numeric data and the target non-numeric data.
The multiple description data can be displayed through a preset window, the preset window can comprise tabs corresponding to each description data, a user can select the tab through clicking, and the selected tab information can be used as target description information designated by the user.
Step 1022, extracting enterprise data of a plurality of preset fields from the standby supervision data of each supervision enterprise to obtain enterprise feature data of each supervision enterprise.
For example, if the preset field includes: an enterprise basic feature class field (e.g., an enterprise property field, an enterprise belonging industry field, an enterprise location field, an enterprise performance rating field, an enterprise scale field, etc.), a production feature class field (e.g., a product type field, a yield field, a raw material usage field required for enterprise production, an auxiliary material usage field, a fuel usage field, etc.), a pollution discharge feature class field (e.g., a pollutant class field, a pollution factor field, a pollutant discharge amount field, a pollutant discharge concentration field, a discharge law field, a treatment facility class field, a pollution treatment efficiency field, a pollution treatment facility operation law field, an emergency emission reduction management measure field, a emission reduction field, etc.), a power usage feature class field (e.g., an enterprise total amount of electricity consumption field, an enterprise day/night electricity consumption field, an enterprise daily/month/year electricity consumption field, a light/season electricity consumption field, an autumn/spring/summer electricity consumption field, an enterprise pollution production facility consumption field, a pollution treatment facility electricity consumption field, etc.), a problem feature class field (e.g., an on-line monitoring superscale frequency field, a pollution control facility consumption field, a performance monitoring frequency field, an internal or a status superscale condition, a traffic condition (e.g., a real-time, etc.), a problem, etc. It should be noted that, recognizing and acquiring text content corresponding to a specified field from a segment of text through text recognition is common in the prior art, and an implementation process of extracting feature data corresponding to a preset field in the present disclosure may refer to related descriptions in the prior art, which is not repeated herein.
Step 1023, determining the reference enterprise feature data according to the enterprise feature data of each of the plurality of supervising enterprises in the supervising region.
Wherein, the number of field types corresponding to each enterprise feature data can be determined; determining a target supervision enterprise with the largest number of the field types in a plurality of supervision enterprises in the supervision area; the enterprise feature data of the target supervising enterprise is taken as the reference enterprise feature data.
And step 103, determining target enterprise characteristic data of each supervision enterprise according to the reference enterprise characteristic data.
This step may be implemented by the steps shown in fig. 3, fig. 3 being a flowchart of another method for monitoring and controlling emissions of an enterprise in real time, according to the embodiment shown in fig. 1; as shown in fig. 3, this step 103 may include:
step 1031, determining that the enterprise feature data is the enterprise feature data without completion under the condition that it is determined that the enterprise data corresponding to each of the preset fields in the enterprise feature data of the supervising enterprise is not empty.
Step 1032, determining that the enterprise feature data is to be complemented if it is determined that null values exist in the enterprise data corresponding to the preset fields.
And 1033, complementing the enterprise feature data to be complemented according to the reference enterprise feature data to obtain the target enterprise feature data.
In this step, one possible implementation manner is to determine, according to existing enterprise data in the enterprise feature data to be completed and the reference enterprise feature data, a similarity between the enterprise to be completed and an enterprise corresponding to the reference enterprise feature data; determining target completion data in the enterprise feature data to be completed according to the similarity; the existing enterprise data and the target completion data are used as the target enterprise feature data.
The determining the target completion data in the enterprise feature data to be completed according to the similarity, where the enterprise to be completed is an enterprise corresponding to the enterprise feature data to be completed, may include: and determining target completion data corresponding to the similarity from preset relationship data, wherein the preset relationship data comprises corresponding relations between a plurality of preset similarities and different completion data.
For example, the preset relationship may include target completion data corresponding to enterprise feature data of each field in different similarity intervals, for example, when the similarity is [70%,80% ], the target completion data corresponding to the a field is a, the target completion data corresponding to the B field is B, and the target completion data corresponding to the D field is D, where A, B, D is respectively different preset fields, a, B, and D are respectively enterprise feature data corresponding to different preset fields, and when the similarity belongs to the similarity interval, the enterprise feature data corresponding to the different preset fields may be used as the target completion data of the field. For example, when the similarity is 75%, if the current B field is a field to be complemented, B may be used as the target complement data of the field. The above data are only for illustrating how to determine the target completion data according to the preset relationship, and are not intended to limit the specific protection scope.
Another possible implementation is: and inputting the enterprise feature data to be complemented and the reference enterprise feature data into a first preset machine learning model to obtain the target enterprise feature data output by the first preset machine learning model.
The training method of the first preset machine learning model may include: obtaining first training data, wherein the first training data comprises a plurality of groups of sample data, each group of sample data comprises reference sample data, sample data to be complemented and complement sample data, taking the complement sample data as sample marking data, and performing model training on a first preset initial model to obtain the first preset machine learning model.
It should be noted that the first preset initial model may be a neural network algorithm model, a decision tree model, or other machine learning model. And in the model training process, the reference sample data and the sample data to be complemented are taken as model input data, the complement data is output according to the input data, the complement sample data is taken as labeling data, a loss value is calculated, the model is optimized according to the loss value, the model optimizing process is circularly executed until the loss value is smaller than or equal to a preset loss threshold value, and the first preset machine learning model is obtained.
Step 104, determining a supervision problem enterprise in a plurality of supervision enterprises in the supervision area according to the target enterprise characteristic data of each supervision enterprise.
This step, which may be implemented by the embodiment shown in fig. 4, fig. 4 is a flowchart of yet another method for monitoring and managing emissions of an enterprise in real time, which is shown in accordance with the embodiment shown in fig. 1; as shown in fig. 4, this step 104 may include:
step 1041, inputting the target enterprise feature data of each supervising enterprise into a second preset machine learning model to obtain an enterprise type output by the second preset machine learning model, where the enterprise type includes a major pollution discharge problem enterprise, a major pollution control problem enterprise, a neutral enterprise and a front enterprise.
The training method of the second preset machine learning model comprises the following steps:
and acquiring second training data, wherein the second training data comprises enterprise characteristic sample data and enterprise type labeling data of a plurality of enterprises, and performing model training on a second preset initial model through the second training data so as to obtain the second preset machine learning model.
It should be noted that, the major pollution discharge problem enterprise is an enterprise with serious problems in the pollution discharge process, the major pollution control problem enterprise is an enterprise with serious problems in the pollution control process, the neutral enterprise is an enterprise with few times of pollution discharge and pollution control problems, the problem is not serious, and the front enterprise is an enterprise with no problems in pollution discharge and pollution control. The second pre-set initial model may also be a neural network algorithm model, a decision tree model, or other machine learning model. In the model training process, enterprise feature sample data of a supervision enterprise are taken as model input to acquire an enterprise type output by the second preset initial model, a loss value is calculated according to the enterprise type and enterprise type labeling data corresponding to the enterprise feature sample data, the second preset initial model is optimized according to the loss value, and the optimization process is sequentially carried out according to the enterprise feature sample data and the enterprise type labeling data of a plurality of enterprises in the second training data until the first preset machine learning model of the enterprise feature sample data is obtained under the condition that the loss value is smaller than or equal to a specified loss threshold value.
Step 1042, taking the enterprise type of the plurality of supervising enterprises in the supervision area as the important pollution discharge problem enterprise and/or the supervising enterprise of the important pollution control problem enterprise as the supervising problem enterprise.
After the supervision problem enterprise is obtained, priority inspection can be performed for the supervision problem enterprise, high-frequency spot inspection can be performed for a neutral enterprise, and low-frequency spot inspection can be performed for a front enterprise.
According to the technical scheme, the enterprise characteristic data of each supervision enterprise to be completed can be correspondingly completed according to the standard enterprise characteristic data, so that the target enterprise characteristic data of each supervision enterprise is obtained, the coverage range of a remote supervision enterprise for pollution emission can be effectively enlarged, and supervision problem enterprises in a plurality of supervision enterprises in a supervision area can be determined according to the target enterprise characteristic data of each supervision enterprise, therefore, the automation degree of enterprise pollution emission supervision can be effectively improved, the manpower and material resources required by enterprise pollution emission supervision are reduced, and the supervision efficiency is improved.
FIG. 5 is a block diagram of an enterprise emissions real-time supervision apparatus, as shown in another exemplary embodiment of the present disclosure; as shown in fig. 5, the apparatus may include:
An obtaining module 501 configured to obtain enterprise supervision data of each of a plurality of supervising enterprises in a supervising area, the enterprise supervision data describing status information of production, pollution discharge, pollution control and pollution discharge inspection of the enterprise within a specified historical period of time;
a first determining module 502 configured to determine, according to the enterprise administrative data of each administrative enterprise within the administrative area, reference enterprise feature data of the administrative area, the reference enterprise feature data being used to complement enterprise feature data of an administrative enterprise;
a second determining module 503 configured to determine target enterprise feature data for each of the supervising enterprises based on the reference enterprise feature data;
a third determination module 504 is configured to determine a regulatory problem enterprise of a plurality of regulatory enterprises within the regulatory domain from the target enterprise characteristic data of each of the regulatory enterprises.
According to the technical scheme, the enterprise characteristic data of each supervision enterprise to be completed can be correspondingly completed according to the standard enterprise characteristic data, so that the target enterprise characteristic data of each supervision enterprise is obtained, the coverage range of a remote supervision enterprise for pollution emission can be effectively enlarged, and supervision problem enterprises in a plurality of supervision enterprises in a supervision area can be determined according to the target enterprise characteristic data of each supervision enterprise, therefore, the automation degree of enterprise pollution emission supervision can be effectively improved, the manpower and material resources required by enterprise pollution emission supervision are reduced, and the supervision efficiency is improved.
Optionally, the first determining module 502 is configured to:
checking the enterprise supervision data of each supervision enterprise to obtain the standby supervision data after checking;
extracting enterprise data of a plurality of preset fields from the standby supervision data of each supervision enterprise to obtain enterprise feature data of each supervision enterprise;
the baseline enterprise characteristic data is determined from the enterprise characteristic data of each of a plurality of supervising enterprises within the supervising region.
Optionally, the first determining module 502 is configured to:
determining the number of field types corresponding to each enterprise feature data;
determining a target supervision enterprise with the largest number of the field types in a plurality of supervision enterprises in the supervision area;
the enterprise feature data of the target supervising enterprise is taken as the reference enterprise feature data.
Optionally, the first determining module 502 is configured to:
carrying out format unification processing on the enterprise supervision data to obtain data to be verified, which corresponds to the enterprise supervision data;
determining numerical data and non-numerical data in the data to be checked;
and determining the standby supervision data after verification according to the numerical data and the non-numerical data.
Optionally, the first determining module 502 is configured to:
performing upper and lower limit value verification on the numerical data to obtain intermediate verification data; under the condition that different values with a field having a plurality of sources exist in the intermediate checking data, determining the average value of the different values, and taking the average value as checked target numerical data;
in the case that it is determined that one item includes a plurality of description data of a plurality of sources for the non-numeric data, the plurality of description data are displayed, target description information designated by a user is received, and the target description information is used as verified target non-numeric data to obtain standby supervision data including the target numeric data and the target non-numeric data.
Optionally, the second determining module 503 is configured to:
under the condition that the enterprise data corresponding to each preset field in the enterprise feature data of the supervision enterprise is not empty, the enterprise feature data is determined to be the enterprise feature data without completion;
under the condition that null values exist in enterprise data corresponding to the preset fields, determining the enterprise characteristic data as enterprise characteristic data to be complemented;
And complementing the enterprise feature data to be complemented according to the reference enterprise feature data to obtain the target enterprise feature data.
Optionally, the second determining module 503 is configured to:
determining the similarity of the enterprise to be complemented and the enterprise corresponding to the reference enterprise characteristic data according to the existing enterprise data in the enterprise characteristic data to be complemented and the reference enterprise characteristic data;
determining target completion data in the enterprise feature data to be completed according to the similarity;
the existing enterprise data and the target completion data are used as the target enterprise feature data.
Optionally, the second determining module 503 is configured to:
and determining target completion data corresponding to the similarity from preset relationship data, wherein the preset relationship data comprises corresponding relations between a plurality of preset similarities and different completion data.
Optionally, the second determining module 503 is configured to:
and inputting the enterprise feature data to be complemented and the reference enterprise feature data into a first preset machine learning model to obtain the target enterprise feature data output by the first preset machine learning model.
FIG. 6 is a block diagram of an enterprise emissions real-time supervision apparatus, shown in accordance with the embodiment of FIG. 5; as shown in fig. 6, the apparatus further comprises a first training module 505, the first training module 505 being configured to:
Acquiring first training data, wherein the first training data comprises a plurality of groups of sample data, each group of sample data comprises reference sample data, sample data to be complemented and complement sample data,
and performing model training on the first preset initial model by taking the complement sample data as sample labeling data to obtain the first preset machine learning model.
Optionally, the third determining module 504 is configured to:
inputting the target enterprise characteristic data of each supervision enterprise into a second preset machine learning model to obtain enterprise types output by the second preset machine learning model, wherein the enterprise types comprise major pollution discharge problem enterprises, major pollution control problem enterprises, neutral enterprises and front enterprises;
taking the enterprise type in the plurality of supervision enterprises in the supervision area as a supervision enterprise of a key pollution discharge problem enterprise and/or a key pollution control problem enterprise as the supervision problem enterprise.
Optionally, the apparatus further comprises a second training module 506 configured to:
acquiring second training data, wherein the second training data comprises enterprise characteristic sample data and enterprise type marking data of a plurality of enterprises;
and performing model training on a second preset initial model through the second training data to obtain a second preset machine learning model.
According to the technical scheme, the enterprise characteristic data of each supervision enterprise to be completed can be correspondingly completed according to the standard enterprise characteristic data, so that the target enterprise characteristic data of each supervision enterprise is obtained, the coverage range of a remote supervision enterprise for pollution emission can be effectively enlarged, and supervision problem enterprises in a plurality of supervision enterprises in a supervision area can be determined according to the target enterprise characteristic data of each supervision enterprise, therefore, the automation degree of enterprise pollution emission supervision can be effectively improved, the manpower and material resources required by enterprise pollution emission supervision are reduced, and the supervision efficiency is improved.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 7 is a block diagram of an electronic device, according to an example embodiment. As shown in fig. 7, the first electronic device 700 may include: a first processor 701, a first memory 702. The first electronic device 700 may also include one or more of a multimedia component 703, a first input/output interface 704, and a first communication component 705.
The first processor 701 is configured to control the overall operation of the first electronic device 700, so as to complete all or part of the steps in the enterprise pollution emission real-time supervision method. The first memory 702 is used to store various types of data to support operation at the first electronic device 700, which may include, for example, instructions for any application or method operating on the first electronic device 700, as well as application-related data, such as contact data, messages, pictures, audio, video, and the like. The first Memory 702 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia component 703 can include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the first memory 702 or transmitted through the first communication component 705. The audio assembly further comprises at least one speaker for outputting audio signals. The first input/output interface 704 provides an interface between the first processor 701 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The first communication component 705 is configured to perform wired or wireless communication between the first electronic device 700 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination of more of them, is not limited herein. The corresponding first communication component 705 may thus comprise: wi-Fi module, bluetooth module, NFC module, etc.
In an exemplary embodiment, the first electronic device 700 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processor (Digital Signal Processor, abbreviated as DSP), digital signal processing device (Digital Signal Processing Device, abbreviated as DSPD), programmable logic device (Programmable Logic Device, abbreviated as PLD), field programmable gate array (Field Programmable Gate Array, abbreviated as FPGA), controller, microcontroller, microprocessor, or other electronic components for performing the enterprise pollution discharge real-time supervision method described above.
In another exemplary embodiment, a computer readable storage medium is also provided that includes program instructions that, when executed by a processor, implement the steps of the enterprise emissions real-time supervision method described above. For example, the computer readable storage medium may be the first memory 702 including program instructions described above that are executable by the first processor 701 of the first electronic device 700 to perform the enterprise emissions real-time supervision method described above.
Fig. 8 is a block diagram of another electronic device, shown in accordance with an exemplary embodiment. For example, the second electronic device 800 may be provided as a server. Referring to fig. 8, the second electronic device 800 includes a second processor 822, which may be one or more in number, and a second memory 832 for storing a computer program executable by the second processor 822. The computer program stored in the second memory 832 may include one or more modules each corresponding to a set of instructions. Further, the second processor 822 may be configured to execute the computer program to perform the enterprise emissions real-time supervision method described above.
In addition, the second electronic device 800 may further include a power component 826 and a second communication component 850, the power component 826 may be configured to perform power management of the second electronic device 800, and the second communication component 850 may be configured to enable communication, e.g., wired or wireless communication, of the second electronic device 800. In addition, the second electronic device 800 may also include a second input/output interface 858. The second electronic device 800 may operate based on an operating system stored in the second memory 832.
In another exemplary embodiment, a computer readable storage medium is also provided that includes program instructions that, when executed by a processor, implement the steps of the enterprise emissions real-time supervision method described above. For example, the non-transitory computer readable storage medium may be the second memory 832 including program instructions described above that are executable by the second processor 822 of the second electronic device 800 to perform the enterprise emissions real-time supervision method described above.
In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described method of enterprise emissions real-time supervision when executed by the programmable apparatus.
The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and all the simple modifications belong to the protection scope of the present disclosure.
In addition, the specific features described in the foregoing embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, the present disclosure does not further describe various possible combinations.
Moreover, any combination between the various embodiments of the present disclosure is possible as long as it does not depart from the spirit of the present disclosure, which should also be construed as the disclosure of the present disclosure.
Claims (15)
1. An enterprise pollution discharge real-time supervision method is characterized by comprising the following steps:
acquiring enterprise supervision data of each supervision enterprise in a supervision area, wherein the enterprise supervision data are used for describing production, pollution discharge, pollution control and pollution discharge inspection status information of the enterprise in a specified historical time period, and the enterprise supervision data comprise one or more of pollution source online monitoring information, enterprise working condition electricity consumption information, enterprise power grid electricity consumption information, pollution discharge license information, emission list information, emergency emission reduction list information, ring review batch information and enterprise pollution discharge inspection record information, wherein the pollution source online monitoring information comprises at least one of monitoring point names, monitoring point positions, enterprise positions, pollutant types, pollutant emission time and emission concentration of the pollution source; the enterprise working condition electricity consumption comprises the operation time rules of enterprise pollution production facilities and pollution control facilities; the enterprise power grid electricity consumption information comprises total electricity consumption information in a monitoring area and electricity consumption rule information; the pollution discharge license information includes pollutant information allowing emission; the emission list information comprises at least one of a pollution discharge name, a pollution discharge type, an emission amount, an emission position, a treatment facility name, a treatment type and a treatment efficiency; the emergency emission reduction list information comprises management and control measure information of enterprise production stopping and production limiting under three emergency situations of red, orange and yellow; the enterprise pollution discharge inspection record information comprises record information generated when the enterprise is subjected to pollution discharge inspection in a target historical time period;
Determining reference enterprise feature data of the supervision area according to the enterprise supervision data of each supervision enterprise in the supervision area, wherein the reference enterprise feature data is used for complementing enterprise feature data of the supervision enterprise;
determining target enterprise feature data of each supervision enterprise according to the reference enterprise feature data;
and determining a supervision problem enterprise in a plurality of supervision enterprises in the supervision area according to the target enterprise characteristic data of each supervision enterprise.
2. The method of real-time supervision of an enterprise emissions according to claim 1, wherein the determining the reference enterprise characteristic data for each supervising enterprise within the supervision area from the enterprise supervision data for the supervision area comprises:
performing verification processing on the enterprise supervision data of each supervision enterprise to obtain the standby supervision data after verification;
extracting enterprise data of a plurality of preset fields from the standby supervision data of each supervision enterprise to obtain enterprise feature data of each supervision enterprise;
and determining the reference enterprise feature data according to the enterprise feature data of each of a plurality of supervising enterprises in the supervising region.
3. The method of real-time supervision of an enterprise emissions according to claim 2, wherein the determining the baseline enterprise characteristic data from the enterprise characteristic data of each supervising enterprise of a plurality of supervising enterprises within the supervision area comprises:
determining the number of field types corresponding to each enterprise feature data;
determining a target supervision enterprise with the largest field type number in a plurality of supervision enterprises in the supervision area;
and taking the enterprise characteristic data of the target supervision enterprise as the reference enterprise characteristic data.
4. The method for real-time supervision of an enterprise emissions according to claim 2, wherein the verifying the enterprise supervision data of each supervising enterprise to obtain the verified standby supervision data comprises:
carrying out format unification processing on the enterprise supervision data to obtain data to be verified, which corresponds to the enterprise supervision data;
determining numerical data and non-numerical data in the data to be verified;
and determining the standby supervision data after verification according to the numerical data and the non-numerical data.
5. The method of real-time supervision of an enterprise emissions according to claim 4, wherein the determining the verified standby supervision data from the numeric data and the non-numeric data comprises:
Performing upper and lower limit value verification on the numerical data to obtain intermediate verification data; under the condition that different values with a field having a plurality of sources exist in the intermediate verification data, determining the average value of the different values, and taking the average value as verified target numerical data;
and for the non-numerical data, in the case that a matter is determined to comprise a plurality of description data of a plurality of sources, displaying the plurality of description data, receiving target description information designated by a user, and taking the target description information as verified target non-numerical data to obtain standby supervision data comprising the target numerical data and the target non-numerical data.
6. The method of real-time supervision of an enterprise emissions according to claim 2, wherein the determining target enterprise characteristic data for each of the supervising enterprises from the reference enterprise characteristic data comprises:
under the condition that the enterprise data corresponding to each preset field in the enterprise feature data of the supervision enterprise is not empty, determining that the enterprise feature data is enterprise feature data without completion;
under the condition that null values exist in enterprise data corresponding to the preset fields, determining the enterprise characteristic data as enterprise characteristic data to be complemented;
And complementing the enterprise feature data to be complemented according to the reference enterprise feature data to obtain the target enterprise feature data.
7. The method of real-time supervision of an enterprise emissions according to claim 6, wherein the complementing the enterprise feature data to be complemented according to the reference enterprise feature data to obtain the target enterprise feature data comprises:
determining the similarity of the enterprise to be complemented and the enterprise corresponding to the reference enterprise characteristic data according to the existing enterprise data in the enterprise characteristic data to be complemented and the reference enterprise characteristic data;
determining target completion data in the enterprise feature data to be completed according to the similarity;
and taking the existing enterprise data and the target completion data as the target enterprise characteristic data.
8. The method for monitoring and managing pollution discharge of an enterprise according to claim 7, wherein determining target completion data in the to-be-completed enterprise feature data according to the similarity comprises:
and determining target completion data corresponding to the similarity from preset relationship data, wherein the preset relationship data comprises corresponding relations between a plurality of preset similarities and different completion data.
9. The method of real-time supervision of an enterprise emissions according to claim 6, wherein the complementing the enterprise feature data to be complemented according to the reference enterprise feature data to obtain the target enterprise feature data comprises:
and inputting the enterprise characteristic data to be complemented and the reference enterprise characteristic data into a first preset machine learning model to obtain the target enterprise characteristic data output by the first preset machine learning model.
10. The method of claim 9, wherein the training method of the first preset machine learning model comprises:
obtaining first training data, wherein the first training data comprises a plurality of groups of sample data, each group of sample data comprises reference sample data, sample data to be complemented and complement sample data,
and performing model training on a first preset initial model by taking the complement sample data as sample marking data to obtain the first preset machine learning model.
11. The method of real-time supervision of an enterprise emissions according to claim 1, wherein the determining a supervisory problem enterprise of a plurality of supervisory enterprises within the supervisory area based on the target enterprise characteristic data for each of the supervisory enterprises comprises:
Inputting the target enterprise characteristic data of each supervision enterprise into a second preset machine learning model to obtain enterprise types output by the second preset machine learning model, wherein the enterprise types comprise major pollution discharge problem enterprises, major pollution control problem enterprises, neutral enterprises and front enterprises;
taking the enterprise type in the plurality of supervision enterprises in the supervision area as a supervision enterprise of a key pollution discharge problem enterprise and/or a key pollution control problem enterprise as the supervision problem enterprise.
12. The method of claim 11, wherein the training method of the second preset machine learning model comprises:
acquiring second training data, wherein the second training data comprises enterprise characteristic sample data and enterprise type labeling data of a plurality of enterprises;
and performing model training on a second preset initial model through the second training data to obtain the second preset machine learning model.
13. An enterprise pollution discharge real-time supervision device, characterized in that the device includes:
an acquisition module configured to acquire enterprise supervision data of each of a plurality of supervising enterprises in a supervision area, the enterprise supervision data describing status information of production, pollution control and pollution discharge inspection of the enterprises in a specified history period, the enterprise supervision data including one or more of pollution source online monitoring information, enterprise operating condition electricity information, enterprise grid electricity information, pollution discharge license information, emission list information, emergency emission reduction list information, ring review batch information and enterprise pollution discharge inspection record information, wherein the pollution source online monitoring information includes at least one of a monitoring point name, a monitoring point position, an enterprise position, a pollutant type, a pollutant emission time and an emission concentration of the pollution source; the enterprise working condition electricity consumption comprises the operation time rules of enterprise pollution production facilities and pollution control facilities; the enterprise power grid electricity consumption information comprises total electricity consumption information in a monitoring area and electricity consumption rule information; the pollution discharge license information includes pollutant information allowing emission; the emission list information comprises at least one of a pollution discharge name, a pollution discharge type, an emission amount, an emission position, a treatment facility name, a treatment type and a treatment efficiency; the emergency emission reduction list information comprises management and control measure information of enterprise production stopping and production limiting under three emergency situations of red, orange and yellow; the enterprise pollution discharge inspection record information comprises record information generated when the enterprise is subjected to pollution discharge inspection in a target historical time period;
A first determining module configured to determine reference enterprise feature data of the administrative area according to the enterprise administrative data of each administrative enterprise within the administrative area, the reference enterprise feature data being used to complement enterprise feature data of an administrative enterprise;
a second determination module configured to determine target enterprise feature data for each of the supervising enterprises from the reference enterprise feature data;
a third determination module configured to determine a regulatory problem enterprise of a plurality of regulatory enterprises within the regulatory domain based on the target enterprise characteristic data for each of the regulatory enterprises.
14. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor realizes the steps of the method according to any of claims 1-12.
15. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method of any one of claims 1-12.
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