CN113988711A - Power consumption data-based monitoring method for stopping or limiting production of sewage disposal enterprises in control state - Google Patents
Power consumption data-based monitoring method for stopping or limiting production of sewage disposal enterprises in control state Download PDFInfo
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Abstract
The application discloses blowdown enterprise stops or limit production monitoring method under management and control state based on power consumption data includes: acquiring historical power consumption data of a sewage enterprise to be monitored and penalty data of a corresponding environmental department on a production stop violation enterprise in a pipe control state; calculating the power utilization index to obtain power utilization index sample data; screening state discrimination variable sample data from the electricity utilization index sample data; adopting logistic regression to construct a production state monitoring model under a control state; and under the control state, acquiring the power utilization data of the sewage enterprise to be monitored in real time, calculating the power utilization index, and inputting the power utilization index into the production state monitoring model under the control state to obtain whether the enterprise executes the production stop monitoring result under the control state. The invention can more objectively reflect the production condition of the enterprise discharging the sewage by utilizing the non-falsifiable characteristic of the power utilization data, realize the automatic monitoring of whether the enterprise executes the production stop condition in the control state and save a large amount of manpower and material resources.
Description
Technical Field
The invention belongs to the technical field of monitoring of production states of pollution discharge enterprises, and relates to a method for monitoring the shutdown or limited production of the pollution discharge enterprises in a control state based on electricity consumption data.
Background
In actual production, due to the limiting factors such as environmental control, power supply and industrial management, relevant departments can directionally set up requirements for production stop and production limit of enterprises within a specified time limit, and supervise the execution conditions of the production stop/production limit of the enterprises. Due to the fact that the number of enterprises is large, the types of industries are large, the process is complicated, and energy is consumed for judging pollution sources; the supervision personnel are limited, the requirement on supervision strength is very high, manual investigation is time-consuming and labor-consuming, careless and careless, and the accuracy is not enough, so that the efficiency of the management system is difficult to give full play.
Aiming at the background of management and control, the invention aims at solving the problem that whether the power utilization data of an enterprise can be utilized to accurately study and judge the shutdown/limited production behaviors of the enterprise.
The electricity consumption data consumer has the characteristics of accurate observation and no tampering, can be closely connected with the operation condition of an enterprise, and can be used for analyzing and judging the shutdown/limited production behaviors of the enterprise.
Generally, in a management and control state, because a pollution discharge enterprise is in a production stop state and a production limit state, the power consumption of the enterprise should be reduced compared with a normal state. However, because the daily power consumption of an enterprise has a certain random fluctuation, the reduced power in the control state is generated by random fluctuation, or is caused by taking production halt or production limit measures, and further quantitative analysis is needed.
Disclosure of Invention
For solving the not enough among the prior art, this application provides the management and control state based on power consumption data and arranges that the enterprise stops or the limit production monitoring method of wasting discharge.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an enterprise outage or production limit monitoring method based on electricity consumption data under a control state is characterized in that:
the method comprises the following steps:
step 1: acquiring historical power consumption data of a sewage enterprise to be monitored and penalty data of a corresponding environmental department on a production stop violation enterprise in a pipe control state;
step 2: carrying out power utilization index calculation on historical power utilization data of a sewage enterprise to be monitored to obtain power utilization index sample data;
and step 3: screening state discrimination variable sample data from power consumption index sample data by combining penalty data of a production stop violation enterprise under a pipe control state of an environmental department;
and 4, step 4: based on the state discrimination variable sample data, adopting logistic regression to construct a production state monitoring model under a control state;
and 5: and under the control state, acquiring the power utilization data of the sewage enterprise to be monitored in real time, calculating the power utilization index, and inputting the power utilization index into the production state monitoring model under the control state to obtain whether the enterprise executes the production stop monitoring result under the control state.
The invention further comprises the following preferred embodiments:
preferably, in the step 1, pollution discharge enterprises with close relation between enterprise production and power consumption in various industries are screened and obtained as monitorable pollution discharge enterprises through a pollution discharge enterprise monitoring list provided by an environmental department;
before monitoring the pollution discharge enterprise to be monitored, judging whether the pollution discharge enterprise is a monitorable pollution discharge enterprise in advance, if so, monitoring the pollution discharge enterprise, and if not, quitting and sending a non-monitorable prompt.
Preferably, enterprises with the power ratio exceeding 80% in the energy structure are screened as monitorable pollution discharge enterprises through measuring the production and power consumption relationship density of the enterprises through the power ratio in the energy structure.
Preferably, in step 1, determining the sewage enterprise to be monitored and the control time thereof, and acquiring power consumption data of the sewage enterprise to be monitored in the previous week of control and penalty data of the production stop violation enterprise in a pipe control state by a corresponding environmental department;
the electricity consumption data specifically comprises: the date, the total daily power consumption of the pollution discharge enterprise and the power consumption of each period of 24 hours;
the penalty data specifically includes: penalty date, penalty reason, illegal date, electricity consumption data before and after penalty.
Preferably, the electricity utilization index comprises 7 days of average electricity consumption x before enterprise violation management and control1And standard deviation x of daily electricity consumption sample 7 days before enterprise violation management and control2The ratio x of the enterprise electricity consumption on the same management and control day to the average electricity consumption in the week before management and control3Enterprise control of the day festival and holiday variable x4The specific calculation formula is as follows:
average power consumption x of enterprise 7 days before violation management and control1：
ztTo control the daily electricity consumption of the previous day;
standard deviation x of daily electricity consumption sample 7 days before enterprise violation management and control2：
Ratio x of enterprise electricity consumption on the management and control day to average electricity consumption in the week before management and control3：
zt+1And showing that the electricity consumption of the current day is managed and controlled.
Enterprise control day festival and holiday variable x4: festival and holiday fetching1, the working day is 0.
Preferably, in step 3, a state discrimination variable y is introduced to indicate whether the enterprise executes production stopping in a management and control state;
the state discrimination variable y is a two-value variable, y equals 0 to indicate that the production is stopped and limited according to requirements in the control state, and y equals 1 to indicate that the production is not stopped and limited according to requirements.
Preferably, step 3 is specifically:
taking an enterprise illegal date in punishment data of the illegal enterprise stopping limited production in a pipe control state by an environmental department as a base period, acquiring power utilization indexes obtained by calculating power utilization data of the illegal date of the enterprise and the previous 7 days from the power utilization index sample data in the step 2, and taking the power utilization indexes as sample data with a state discrimination variable y being 1;
taking the important holiday as a control period, acquiring the electricity utilization index obtained by calculating the electricity utilization data 7 days before the important holiday from the electricity utilization index sample data in the step 2, and taking the electricity utilization index as the sample data with a state discrimination variable y being 0;
the important holidays are holidays with holidays more than 2 days.
Preferably, in step 4, a logistic regression model about the probability p that y is 1 and the independent variable is established by using the constructed state discrimination variable y is 1 as the dependent variable and each electricity index as the independent variable, and a regression coefficient of the model is determined by using the sample data of the state discrimination variable, so as to obtain the production state monitoring model in the control state.
Preferably, the logistic regression model is:
Logit(p)＝α0+α1x1+α2x2+α3x3+α4x4
wherein x is1Average power consumption and x in 7 days before enterprise violation management and control2Standard deviation and x of daily electricity consumption sample 7 days before enterprise violation management and control3For controlling the ratio x of the enterprise electricity consumption on the same day to the average electricity consumption in the week before the management3、x4Managing and controlling the day-festival and holiday variables for the enterprise; alpha is alpha0α1，α2，α3And alpha4The regression coefficients are represented.
Preferably, in the step 5, in the control state, acquiring the power consumption data of the sewage enterprise to be monitored in real time, calculating the power consumption index, and bringing the power consumption index into the monitoring model to obtain the probability prediction value of abnormal production of the sewage enterprise under the control state;
meanwhile, setting a probability threshold, and taking the predicted value of the state discrimination variable y as 1 when the predicted probability value is greater than the probability threshold, or taking the predicted value of the state discrimination variable y as 0;
and y is 0 to represent stopping and limiting production according to requirements in the control state, and y is 1 to represent that the production is not stopped and limited according to the requirements.
The beneficial effect that this application reached:
according to the invention, by utilizing the non-falsification characteristic of the electricity utilization data, the electricity utilization characteristics of individuals, the population, the abnormal state, the normal state and other modes in the pollution discharge enterprise are analyzed, the electricity utilization index is determined, the historical electricity utilization data and the penalty data of the corresponding environmental department on the illegal production stop enterprise in the pipe control state are combined, the production state monitoring model in the pipe control state is constructed, the detection and early warning of the abnormal behavior of the pollution discharge enterprise are realized, the production state of the pollution discharge enterprise can be reflected more objectively, the automatic monitoring of whether the enterprise executes the automatic monitoring of the illegal production stop condition in the pipe control state is realized, and a large amount of manpower and material resources are saved.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is sample data in an embodiment of the invention;
FIG. 3 is a probabilistic predictive result in an embodiment of the invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, the method for monitoring the outage or production limit of the sewage disposal enterprise based on the electricity consumption data in the management and control state of the invention comprises the following steps:
step 1: acquiring historical power consumption data of a sewage enterprise to be monitored and penalty data of a corresponding environmental department on a production stop violation enterprise in a pipe control state;
further, in specific implementation, a pollution discharge enterprise monitoring list provided by an environmental department is used for screening and acquiring the pollution discharge enterprises with close relation between enterprise production and power consumption in each industry as monitorable pollution discharge enterprises by adopting a hierarchical sampling method;
before monitoring the pollution discharge enterprise to be monitored, judging whether the pollution discharge enterprise is a monitorable pollution discharge enterprise in advance, if so, monitoring the pollution discharge enterprise, and if not, quitting and sending a non-monitorable prompt.
And screening enterprises with the electric power ratio exceeding 80% in the energy structure as monitorable pollution discharge enterprises by measuring the production and power consumption relation density of the enterprises through the electric power ratio in the energy structure.
the electricity consumption data specifically comprises: the date, the total daily power consumption of the pollution discharge enterprise and the power consumption of each period of 24 hours;
the penalty data specifically includes: penalty date, penalty reason, illegal date, electricity consumption data before and after penalty.
Step 2: carrying out power utilization index calculation on historical power utilization data of a sewage enterprise to be monitored to obtain power utilization index sample data;
because the power consumption numerical value of an enterprise shows certain random fluctuation every day, the power consumption quantity of the pollution discharge enterprise can also fluctuate systematically before and after management and control because the pollution discharge enterprise is stopped and limited, and the superposition of the two fluctuations brings difficulty for the research and judgment on whether the pollution discharge enterprise stops and limits the production according to the management and control requirements. In order to distinguish random fluctuation and systematic fluctuation, the electricity utilization indexes selected in the specific implementation of the invention comprise the average electricity utilization x in 7 days before the enterprise violation control1And standard deviation x of daily electricity consumption sample 7 days before enterprise violation management and control2The ratio x of the enterprise electricity consumption on the same management and control day to the average electricity consumption in the week before management and control3Enterprise control of the day festival and holiday variable x4；
The specific calculation formula is as follows:
average power consumption x of enterprise 7 days before violation management and control1：
ztTo control the daily electricity consumption of the previous day;
standard deviation x of daily electricity consumption sample 7 days before enterprise violation management and control2：
Ratio x of enterprise electricity consumption on the management and control day to average electricity consumption in the week before management and control3：
zt+1And showing that the electricity consumption of the current day is managed and controlled.
Enterprise control day festival and holiday variable x4: holiday 1 and workday 0.
Further, the holidays comprise saturday, sunday or national legal holidays such as national day festival, mid-autumn festival and the like.
And step 3: screening state discrimination variable sample data from power consumption index sample data by combining penalty data of a production stop violation enterprise under a pipe control state of an environmental department;
in specific implementation, a state discrimination variable y is introduced to indicate whether an enterprise executes production stopping in a control state or not;
the state discrimination variable y is a two-value variable, y equals 0 to indicate that the production is stopped and limited according to requirements in the control state, and y equals 1 to indicate that the production is not stopped and limited according to requirements.
In order to acquire real sample data capable of training a discriminant model.
And for the determination of y being 1, processing the data according to the penalty data of the environmental protection department on the pollution discharge enterpriseAnd (5) obtaining the product. For example: according to 99 illegal punishment lists provided by environmental protection bureau of hong Kong City in 2020 year under the condition of pollution discharge enterprise management and control, taking the illegal date of the enterprise as a base period, acquiring the electricity consumption data of the current day and the previous 7 days, and calculating x1-x4The corresponding state discrimination variable y is 1.
For the determination that y is 0, exploratory analysis of data shows that during holidays of important festivals (such as spring festival, national day, five-one, mid-autumn and the like), the power utilization of the enterprise shows an obvious fluctuation due to vacation, and the fluctuation is also a characteristic that the enterprise stops production under a control state. Therefore, the electricity utilization data of the pollution discharge enterprises before and after the major holidays are selected, the holidays are regarded as the control period, and the data of the previous 7 days are calculated to obtain x1-x4The state discrimination variable of the sample data is set to y equal to 0.
In specific implementation, step 3 specifically comprises:
comparing with a punishment list of the pollution discharge enterprise provided by an environmental protection department, taking an enterprise illegal date in punishment data of the illegal enterprise stopping production in a pipe control state by the environmental protection department as a base period, and acquiring a power utilization index obtained by calculating power utilization data of the illegal date of the enterprise and the previous 7 days from the power utilization index sample data in the step 2, wherein the power utilization index is used as the sample data of which the state discrimination variable y is 1;
taking the important holiday as a control period, acquiring the electricity utilization index obtained by calculating the electricity utilization data 7 days before the important holiday from the electricity utilization index sample data in the step 2, and taking the electricity utilization index as the sample data with a state discrimination variable y being 0;
the important holidays are holidays with holidays more than 2 days.
And 4, step 4: based on the state discrimination variable sample data, a production state monitoring model under a control state is constructed by adopting logistic regression, and specifically:
establishing a logistic regression model about the probability p that y is 1 and the independent variable by taking the constructed state discrimination variable y is 1 as the dependent variable and each electricity utilization index as the independent variable;
further, the state discrimination variable sample data is adopted, a maximum likelihood estimation method is used for calculating and determining the regression coefficient of the model, and the production state monitoring model under the control state is obtained.
The logistic regression model is:
Logit(p)＝α0+α1x1+α2x2+α3x3+α4x4
wherein x is1Average power consumption and x in 7 days before enterprise violation management and control2Standard deviation and x of daily electricity consumption sample 7 days before enterprise violation management and control3For controlling the ratio x of the enterprise electricity consumption on the same day to the average electricity consumption in the week before the management3、x4Managing and controlling the day-festival and holiday variables for the enterprise; alpha is alpha0，α1，α2，α3And alpha4The regression coefficients are represented.
In specific implementation, two sets of state discrimination variable sample data of "y is 0" and "y is 1" are obtained as sample data according to the above method.
And taking y as a target variable, taking the value of the y as two classification variables of 0 and 1, wherein the independent variable is a continuous variable, and the Logistic regression analysis requirement is met.
Dependent variable y and independent variable x1，x2，x3，x4The functional relationship between can be expressed in the form:
wherein alpha is0α1，α2，α3And alpha4The regression coefficients are represented. With appropriate mathematical changes, the logistic regression model can be written as follows for the anomaly (y ═ 1) probability p and the independent variables.
Logit(p)＝α0+α1x1+α2x2+α3x3+α4x4
Wherein
The regression coefficient in the model can be obtained by utilizing the sample data obtained in the previous step and carrying out operation by using a maximum likelihood estimation method.
For example: fig. 2 shows partial sample data obtained for the data of the pollution discharge enterprises in the hong Kong city of the Living cloud.
The monitoring model obtained by calculation is as follows:
Logit(p)＝-2.58-3.06×10-7×x1+1.47×10-7x2+3.16×x3-5.69×10-9×x4
and 5: under the control state, acquire the power consumption data of waiting to monitor sewage enterprise in real time, carry out the power consumption index and calculate the production state monitoring model under the input control state, obtain whether the enterprise carries out the shutdown monitoring result under the control state, it is specific:
under a control state, acquiring power consumption data of a sewage enterprise to be monitored in real time, calculating power consumption indexes, and bringing the power consumption indexes into a monitoring model to obtain a probability prediction value of abnormal production of the sewage enterprise under the control state;
meanwhile, setting a probability threshold, and taking the predicted value of the state discrimination variable y as 1 when the predicted probability value is greater than the probability threshold, or taking the predicted value of the state discrimination variable y as 0;
and y is 0 to represent stopping and limiting production according to requirements in the control state, and y is 1 to represent that the production is not stopped and limited according to the requirements.
For an enterprise, to judge whether the power consumption is abnormal in the management and control period, the power consumption data of the current day and the previous 7 days of management and control can be collected, and then x is calculated by applying the method1-x4If the probability value is substituted into the monitoring model, a predicted value p, that is, a probability value of an enterprise anomaly (y is 1) in a control state is obtained, as shown in fig. 3.
Since the predicted value of y ═ 1 is obtained at this time, not the final decision value. Further setting a threshold value for the probability, and taking the y predicted value as 1 when the predicted probability value is greater than the threshold value; and when the predicted value is less than or equal to the threshold value, taking the predicted value of y as 0.
For example: when the set threshold is 0.5, when the prediction probability is higher than 0.5, it can be considered that an abnormal condition exists in the sewage disposal enterprise under the control state.
The invention provides a management and control enterprise list aiming at an environmental department and provides a suspected abnormal list by applying an obtained model for predicting according to given management and control time by using a framework number analysis platform in a national network system. Through actual verification, the prediction accuracy can be effectively improved.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (10)
1. An enterprise outage or production limit monitoring method based on electricity consumption data under a control state is characterized in that:
the method comprises the following steps:
step 1: acquiring historical power consumption data of a sewage enterprise to be monitored and penalty data of a corresponding environmental department on a production stop violation enterprise in a pipe control state;
step 2: carrying out power utilization index calculation on historical power utilization data of a sewage enterprise to be monitored to obtain power utilization index sample data;
and step 3: screening state discrimination variable sample data from power consumption index sample data by combining penalty data of a production stop violation enterprise under a pipe control state of an environmental department;
and 4, step 4: based on the state discrimination variable sample data, adopting logistic regression to construct a production state monitoring model under a control state;
and 5: and under the control state, acquiring the power utilization data of the sewage enterprise to be monitored in real time, calculating the power utilization index, and inputting the power utilization index into the production state monitoring model under the control state to obtain whether the enterprise executes the production stop monitoring result under the control state.
2. The method for monitoring the outage or production limitation of the sewage disposal enterprise under the control state based on the electricity consumption data as recited in claim 1, wherein:
in the step 1, screening and acquiring pollution discharge enterprises with close relation between enterprise production and power consumption in various industries as monitorable pollution discharge enterprises through a pollution discharge enterprise monitoring list provided by an environmental department;
before monitoring the pollution discharge enterprise to be monitored, judging whether the pollution discharge enterprise is a monitorable pollution discharge enterprise in advance, if so, monitoring the pollution discharge enterprise, and if not, quitting and sending a non-monitorable prompt.
3. The method for monitoring stoppage or production limitation of the sewage disposal enterprise under the control state based on the electricity consumption data as claimed in claim 2, wherein:
and screening enterprises with the electric power ratio exceeding 80% in the energy structure as monitorable pollution discharge enterprises by measuring the production and power consumption relation density of the enterprises through the electric power ratio in the energy structure.
4. The method for monitoring the outage or production limitation of the sewage disposal enterprise under the control state based on the electricity consumption data as recited in claim 1, wherein:
step 1, determining a sewage enterprise to be monitored and control time thereof, and acquiring power consumption data of the sewage enterprise to be monitored in the previous week of control and penalty data of a production stop violation enterprise in a pipe control state by a corresponding environmental department;
the electricity consumption data specifically comprises: the date, the total daily power consumption of the pollution discharge enterprise and the power consumption of each period of 24 hours;
the penalty data specifically includes: penalty date, penalty reason, illegal date, electricity consumption data before and after penalty.
5. The method for monitoring the outage or production limitation of the sewage disposal enterprise under the control state based on the electricity consumption data as recited in claim 1, wherein:
the electricity utilization indexes comprise 7 days of average electricity consumption x before enterprise violation management and control1And standard deviation x of daily electricity consumption sample 7 days before enterprise violation management and control2For controlling enterprises on the same dayRatio x of electric quantity to average electricity consumption of one week before management and control3Enterprise control of the day festival and holiday variable x4The specific calculation formula is as follows:
average power consumption x of enterprise 7 days before violation management and control1：
ztTo control the daily electricity consumption of the previous day;
standard deviation x of daily electricity consumption sample 7 days before enterprise violation management and control2：
Ratio x of enterprise electricity consumption on the management and control day to average electricity consumption in the week before management and control3：
zt+1The electricity consumption on the day is controlled and controlled;
enterprise control day festival and holiday variable x4: holiday 1 and workday 0.
6. The method for monitoring the outage or production limitation of the sewage disposal enterprise under the control state based on the electricity consumption data as recited in claim 1, wherein:
in step 3, introducing a state discrimination variable y to indicate whether the enterprise executes production stopping in a control state or not;
the state discrimination variable y is a two-value variable, y equals 0 to indicate that the production is stopped and limited according to requirements in the control state, and y equals 1 to indicate that the production is not stopped and limited according to requirements.
7. The method for monitoring the outage or production limitation of the sewage disposal enterprise under the control state based on the electricity consumption data as recited in claim 1, wherein:
the step 3 specifically comprises the following steps:
taking an enterprise illegal date in punishment data of the illegal enterprise stopping limited production in a pipe control state by an environmental department as a base period, acquiring power utilization indexes obtained by calculating power utilization data of the illegal date of the enterprise and the previous 7 days from the power utilization index sample data in the step 2, and taking the power utilization indexes as sample data with a state discrimination variable y being 1;
taking the important holiday as a control period, acquiring the electricity utilization index obtained by calculating the electricity utilization data 7 days before the important holiday from the electricity utilization index sample data in the step 2, and taking the electricity utilization index as the sample data with a state discrimination variable y being 0;
the important holidays are holidays with holidays more than 2 days.
8. The method for monitoring the outage or production limitation of the sewage disposal enterprise under the control state based on the electricity consumption data as recited in claim 1, wherein:
and 4, establishing a logistic regression model about the probability p that y is 1 and the independent variable by taking the constructed state discrimination variable y which is 1 as the dependent variable and each electricity index as the independent variable, and determining a regression coefficient of the model by adopting the state discrimination variable sample data to obtain the production state monitoring model in the control state.
9. The method for monitoring the outage or production limitation of the sewage disposal enterprise under the control state based on the electricity consumption data as recited in claim 8, wherein:
the logistic regression model is:
Logit(p)＝α0+α1，x1+α2x2+α3x3+α4x4
wherein x is1Average power consumption and x in 7 days before enterprise violation management and control2Standard deviation and x of daily electricity consumption sample 7 days before enterprise violation management and control3For controlling the ratio x of the enterprise electricity consumption on the same day to the average electricity consumption in the week before the management3、x4Managing and controlling the day-festival and holiday variables for the enterprise; alpha is alpha0，α1，α2，α3And alpha4The regression coefficients are represented.
10. The method for monitoring the outage or production limitation of the sewage disposal enterprise under the control state based on the electricity consumption data as recited in claim 1, wherein:
step 5, acquiring power consumption data of the sewage enterprise to be monitored in real time in a control state, calculating power consumption indexes, and bringing the power consumption indexes into a monitoring model to obtain a probability prediction value of abnormal production of the sewage enterprise in the control state;
meanwhile, setting a probability threshold, and taking the predicted value of the state discrimination variable y as 1 when the predicted probability value is greater than the probability threshold, or taking the predicted value of the state discrimination variable y as 0;
and y is 0 to represent stopping and limiting production according to requirements in the control state, and y is 1 to represent that the production is not stopped and limited according to the requirements.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114693166A (en) * | 2022-04-22 | 2022-07-01 | 北京中科三清环境技术有限公司 | Method and device for monitoring execution condition of emergency emission reduction measure on line and electronic equipment |
| CN115879726A (en) * | 2022-12-23 | 2023-03-31 | 中国环境科学研究院 | Method for mutually optimizing and screening power consumption and emission data of enterprise |
| CN117609926A (en) * | 2024-01-23 | 2024-02-27 | 中科三清科技有限公司 | Pollution discharge mechanism production state determining method and device based on power data |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108764584A (en) * | 2018-06-05 | 2018-11-06 | 国网浙江省电力有限公司 | A kind of enterprise electrical energy replacement potential evaluation method |
| CN111210170A (en) * | 2020-04-21 | 2020-05-29 | 国网四川省电力公司电力科学研究院 | Environment-friendly management and control monitoring and evaluation method based on 90% electricity distribution characteristic index |
| CN111523794A (en) * | 2020-04-21 | 2020-08-11 | 国网四川省电力公司电力科学研究院 | Environment-friendly management and control measure response studying and judging method based on power utilization characteristics of pollution emission enterprises |
| CN111524032A (en) * | 2020-04-21 | 2020-08-11 | 国网四川省电力公司电力科学研究院 | Environment-friendly response quantification method and device based on enterprise electricity consumption data |
| CN111522864A (en) * | 2020-04-21 | 2020-08-11 | 国网四川省电力公司电力科学研究院 | Enterprise production mode recognition and transfer production early warning method based on electricity consumption data |
| CN112307435A (en) * | 2020-10-30 | 2021-02-02 | 三峡大学 | Method for judging and screening abnormal electricity consumption based on fuzzy clustering and trend |
| CN112949700A (en) * | 2021-02-19 | 2021-06-11 | 国网北京市电力公司 | Method and device for identifying execution strength of enterprise yield limit policy |
| CN113095953A (en) * | 2021-03-29 | 2021-07-09 | 福建亿力优能电力科技有限公司 | Enterprise environment-friendly monitoring method based on electricity consumption data |
| CN113298422A (en) * | 2021-06-16 | 2021-08-24 | 福建亿力优能电力科技有限公司 | Pollution source enterprise illegal production monitoring method based on electricity consumption data |
| CN113495912A (en) * | 2021-07-05 | 2021-10-12 | 晟至技术有限公司 | Scattered pollution enterprise accurate prevention and control monitoring method and system based on electric power data |
-
2021
- 2021-11-22 CN CN202111388973.3A patent/CN113988711A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108764584A (en) * | 2018-06-05 | 2018-11-06 | 国网浙江省电力有限公司 | A kind of enterprise electrical energy replacement potential evaluation method |
| CN111210170A (en) * | 2020-04-21 | 2020-05-29 | 国网四川省电力公司电力科学研究院 | Environment-friendly management and control monitoring and evaluation method based on 90% electricity distribution characteristic index |
| CN111523794A (en) * | 2020-04-21 | 2020-08-11 | 国网四川省电力公司电力科学研究院 | Environment-friendly management and control measure response studying and judging method based on power utilization characteristics of pollution emission enterprises |
| CN111524032A (en) * | 2020-04-21 | 2020-08-11 | 国网四川省电力公司电力科学研究院 | Environment-friendly response quantification method and device based on enterprise electricity consumption data |
| CN111522864A (en) * | 2020-04-21 | 2020-08-11 | 国网四川省电力公司电力科学研究院 | Enterprise production mode recognition and transfer production early warning method based on electricity consumption data |
| CN112307435A (en) * | 2020-10-30 | 2021-02-02 | 三峡大学 | Method for judging and screening abnormal electricity consumption based on fuzzy clustering and trend |
| CN112949700A (en) * | 2021-02-19 | 2021-06-11 | 国网北京市电力公司 | Method and device for identifying execution strength of enterprise yield limit policy |
| CN113095953A (en) * | 2021-03-29 | 2021-07-09 | 福建亿力优能电力科技有限公司 | Enterprise environment-friendly monitoring method based on electricity consumption data |
| CN113298422A (en) * | 2021-06-16 | 2021-08-24 | 福建亿力优能电力科技有限公司 | Pollution source enterprise illegal production monitoring method based on electricity consumption data |
| CN113495912A (en) * | 2021-07-05 | 2021-10-12 | 晟至技术有限公司 | Scattered pollution enterprise accurate prevention and control monitoring method and system based on electric power data |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114693166A (en) * | 2022-04-22 | 2022-07-01 | 北京中科三清环境技术有限公司 | Method and device for monitoring execution condition of emergency emission reduction measure on line and electronic equipment |
| CN115879726A (en) * | 2022-12-23 | 2023-03-31 | 中国环境科学研究院 | Method for mutually optimizing and screening power consumption and emission data of enterprise |
| CN115879726B (en) * | 2022-12-23 | 2023-10-31 | 中国环境科学研究院 | Enterprise production condition and emission monitoring method |
| CN117609926A (en) * | 2024-01-23 | 2024-02-27 | 中科三清科技有限公司 | Pollution discharge mechanism production state determining method and device based on power data |
| CN117609926B (en) * | 2024-01-23 | 2024-04-16 | 中科三清科技有限公司 | Pollution discharge mechanism production state determining method and device based on power data |
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