CN113298422A - Pollution source enterprise illegal production monitoring method based on electricity consumption data - Google Patents
Pollution source enterprise illegal production monitoring method based on electricity consumption data Download PDFInfo
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Abstract
The invention relates to a pollution source enterprise illegal production monitoring method based on electricity consumption data, which belongs to the technical field of energy monitoring and early warning, and comprises the steps of installing electricity consumption information acquisition equipment at an electricity consumption bus of a pollution source enterprise, and acquiring electricity consumption data of the total load of the enterprise; analyzing the importance degree of different load characteristics on classification based on a decision tree algorithm, and then selecting the load characteristics with higher importance degree in the process for subsequent training of a simplified model; training a decision tree classifier by using the load characteristics with higher importance degree screened in the last step and taking the combination of the on-off states of the enterprise production equipment and the enterprise environment-friendly equipment in the historical period as a label; and inputting the new multidimensional monitoring data into the trained classifier, and identifying the production behavior state of the polluted enterprise. According to the invention, the production behavior of the pollution source enterprise can be monitored only by arranging the power utilization information acquisition device at the power utilization bus of the enterprise, so that the number and the installation cost of the power utilization monitoring equipment are greatly reduced.
Description
Technical Field
The invention relates to a pollution source enterprise illegal production monitoring method based on electricity consumption data, and belongs to the technical field of energy monitoring and early warning.
Background
With the development of society, people pay more and more attention to the improvement of environmental quality, and the control on pollutant emission in relevant regulations is very strict, but only a few large-scale enterprises with organized emission incorporate automatic monitoring, and most small and medium-sized enterprises lack a supervision means.
Due to the fact that the number of enterprises is large, the types of industries are large, the process is complicated, a large amount of manpower, material resources and financial resources are consumed in the traditional mode of judging different types of pollution sources, and the production behaviors of the pollution source enterprises can be intuitively reflected by the electricity consumption data of the pollution source enterprises. At present, the electricity utilization information acquisition technology can acquire and store information such as voltage, current, voltage unbalance degree, total harmonic distortion rate and the like, a data basis is provided for monitoring production behaviors of pollution source enterprises, and powerful support is provided for environmental protection supervision work of related departments. To enterprise's environmental protection supervision, the environmental protection supervision platform based on power consumption data can carry out incessant power consumption control to blowdown and pollution treatment equipment, judges the start-stop time point, can carry out the blowdown and treat pollution linkage control, realizes no dead angle control, has compensatied the painful point that manual detection pollutant discharged.
However, the existing environmental monitoring scheme based on electricity consumption data analysis needs to install monitoring equipment on pollution treatment equipment, start and stop of the pollution treatment equipment are directly monitored, and whether illegal production behaviors exist in pollution source enterprises is further judged by combining the start and stop conditions of production equipment.
The invention discloses a regional industrial gaseous pollutant monitoring method and a system thereof in Chinese patent with publication number CN110849421A, which comprises a monitoring center, at least one electric parameter detection module, a communication module and a pollutant detection module; the monitoring center is communicated with the communication module and the pollutant detection module, and the electric parameter detection module is electrically connected with the communication module. The method comprises the steps of monitoring the operation electrical parameters of the production equipment and the pollutant discharge amount, uploading the detection data to a monitoring center, and processing the received data by the monitoring center to obtain the distribution condition of the regional industrial gaseous pollutants.
Above-mentioned reference example needs to carry out the monitoring of power consumption to all production line equipment in workshop and environmental protection equipment, if production equipment or environmental protection equipment are in a large number, not only with high costs, arrange the trouble moreover, and can only learn regional industry gaseous pollutant's distribution situation, can't report to the police to the enterprise of violating the regulations, consequently need improve urgently.
Disclosure of Invention
In order to overcome the defects that the existing environment-friendly detection scheme based on power consumption data analysis needs more monitoring equipment to be installed and the installation cost is high, the invention designs a pollution source enterprise illegal production monitoring method based on power consumption data.
In order to achieve the purpose, the invention adopts the following technical scheme:
a pollution source enterprise illegal production monitoring method based on electricity consumption data is characterized by comprising the following steps: the method comprises the following steps:
a1: installing an intelligent monitoring terminal for acquiring enterprise electricity utilization information at an enterprise electricity utilization bus;
a2: the method comprises the steps that an intelligent monitoring terminal obtains enterprise electricity utilization data in a period of time as load characteristics and obtains start-stop state data of the environment-friendly equipment in corresponding time;
a3: setting a three-phase apparent power threshold, acquiring total load three-phase apparent power of an enterprise in corresponding time through load characteristics, and judging the start-stop state of production equipment according to whether the total load three-phase apparent power of the enterprise exceeds the threshold or not;
a4: whether an enterprise breaks the production behavior in the corresponding time is obtained according to the start-stop state data of the environment-friendly equipment and the start-stop state of the production equipment; the load characteristics are used as a sample, whether an enterprise has illegal production behavior within corresponding time is used as an attribute label of the sample, a plurality of samples are collected as an initial sample set, a decision tree algorithm and the initial sample set are used for carrying out primary training to obtain a simplified classifier model, and a plurality of load characteristics close to a root node are screened from a decision tree of the simplified classifier model to be used as secondary training samples;
a5: training the simplified classifier model again by using the secondary training sample and the corresponding attribute label to obtain a trained separator;
a6: and inputting the real-time enterprise electricity utilization data acquired by the intelligent monitoring terminal into the trained classifier, and judging whether the enterprise has an illegal production behavior in the corresponding time period.
Further, the enterprise electricity utilization information collected by the intelligent monitoring terminal in the step a1 includes: each phase voltage, each phase current, each phase power, each harmonic voltage, each harmonic current, a total harmonic voltage distortion rate, a total harmonic current distortion rate, voltage deviation, voltage unbalance and power factors, wherein each phase power comprises each phase active power, each phase reactive power and each phase apparent power.
Further, in the step a3, the determining, according to whether the total load three-phase apparent power of the enterprise exceeds the threshold, the start-stop state of the production equipment specifically includes determining that the production equipment is in the start state when the total load three-phase apparent power of the enterprise exceeds the set threshold, and otherwise determining that the production equipment is in the stop state.
Further, step a4 specifically includes setting a data set as S, where S includes a load feature X and a sample attribute tag of each sample, where the load feature X is the enterprise electricity consumption data acquired by the intelligent monitoring terminal in step a2, and the sample attribute tag is set to three different values Fi (i is 1,2,3), and F1, F2, and F3 respectively represent that no environmental protection equipment is started in the production process, and three different production scenarios are started and stopped in the production process;
if the number of the categories Fi is Fi |, and the number of the samples in S is S |, the entropy of S is defined as:
wherein Pi is the probability that any sample belongs to Fi, and is recorded as:
the data in the S is divided according to the load characteristics X, if the load characteristics X have m different classes, the S is divided into m subsets { S1, S2, …, Sm }, the load characteristics X is used for dividing the sample set S, and then the entropy of the subset Si of the S is weighted and calculated, wherein the formula is as follows:
the information gain obtained under the load characteristic X is:
Gain(S,X)＝Entropy(S)-EntropyX(S)
and finally, screening the characteristics X' of a plurality of nodes from top to bottom from the decision tree according to the requirement for the next step of simplifying the model.
Further, the step a5 specifically includes using the load characteristics X' screened in the step a4 as input quantities of the decision tree algorithm, and using a combination of start-stop states of the production equipment of the enterprise and the environmental protection equipment of the enterprise in the historical period as attribute tags of the algorithm, and retraining the simplified decision tree classifier.
Further, the step a6 specifically includes inputting the real-time enterprise power consumption information acquired by the intelligent monitoring terminal into the decision tree classifier trained in the step a5, and if the determination result is that the production behavior state of the environmental protection equipment is not started in the production process, performing environmental protection abnormity alarm, and performing field verification and management by a manager.
Compared with the prior art, the invention has the following characteristics and beneficial effects:
1. according to the invention, the intelligent detection terminal is arranged at the enterprise power utilization bus to acquire the enterprise power utilization information of the enterprise and the start-stop state data of the environmental protection equipment, the decision tree is constructed by using a decision tree algorithm to train the classifier model, and then the enterprise power utilization information subsequently monitored by the intelligent detection terminal is input into the trained classifier, so that whether the enterprise violates the rules or not can be judged.
2. The invention trains the classifier by a decision tree method, calculates the information gain of all possible characteristics from the root node to the node, selects the characteristic with the maximum information gain as the characteristic of the node, establishes different values of the characteristic on the child nodes, namely trains the multi-classification classifier, can directly select the production behavior state of 'no environmental protection equipment started in the production process' which is focused on, and is efficient and rapid.
3. The invention simplifies the index quantity used in the process of training the model by adopting the decision tree algorithm, and screens from top to bottom from the decision tree when selecting the node characteristics in the training process.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The present invention will be described in more detail with reference to examples.
As shown in fig. 1, the method for monitoring illegal production of pollution source enterprises based on electricity consumption data of the embodiment includes the following steps:
a1: installing an intelligent monitoring terminal for acquiring enterprise electricity utilization information at an enterprise electricity utilization bus;
a2: the intelligent monitoring terminal acquires enterprise electricity utilization data in a period of time as load characteristics and acquires start-stop state data of the environment-friendly equipment in corresponding time, the time scale of data acquired by the intelligent electricity utilization monitoring terminal is in the minute level, 1min, 3min and 5min are commonly used, and the frequency of acquiring the data is higher than the acquisition frequency of acquiring electricity utilization information in the existing monitoring scheme by 15 min;
a3: setting a three-phase apparent power threshold, acquiring total load three-phase apparent power of an enterprise in corresponding time through load characteristics, and judging the start-stop state of production equipment according to whether the total load three-phase apparent power of the enterprise exceeds the threshold or not;
a4: whether an enterprise breaks the production behavior in the corresponding time is obtained according to the start-stop state data of the environment-friendly equipment and the start-stop state of the production equipment; the load characteristics are used as a sample, whether an enterprise has illegal production behavior within corresponding time is used as an attribute label of the sample, a plurality of samples are collected as an initial sample set, a decision tree algorithm and the initial sample set are used for carrying out primary training to obtain a simplified classifier model, and a plurality of load characteristics close to a root node are screened from a decision tree of the simplified classifier model to be used as secondary training samples;
the decision tree is a basic algorithm in machine learning, the core of the decision tree algorithm is to apply information gain criterion selection characteristics on each node of the decision tree and recursively construct the decision tree, and the specific method is as follows: calculating the information gains of all possible characteristics from the root node to the node, selecting the characteristic with the maximum information gain as the characteristic of the node, and establishing the characteristic in the child nodes according to different values of the characteristic; then recursively calling the above method for the nodes to construct a decision tree; stopping until the information gains of all the characteristics are very small or no characteristics can be selected, and obtaining a final decision tree;
a5: training the simplified classifier model again by using the secondary training sample and the corresponding attribute label to obtain a trained separator;
a6: and inputting the real-time enterprise electricity utilization data acquired by the intelligent monitoring terminal into the trained classifier, and judging whether the enterprise has an illegal production behavior in the corresponding time period.
Specifically, the start-stop state data of the environmental protection equipment in the step a2 and the start-stop state of the production equipment in the step A3 are combined to divide the production behavior state of the enterprise into: environmental protection equipment is not started in the production process, and the environmental protection equipment is started and the production is stopped in the production process.
Further, the enterprise electricity utilization information collected by the intelligent monitoring terminal in the step a1 includes: each phase voltage, each phase current, each phase power, each harmonic voltage, each harmonic current, total harmonic voltage distortion rate, total harmonic current distortion rate, voltage deviation, voltage unbalance and power factors, wherein each phase power comprises each phase active power, each phase reactive power and each phase apparent power, and each phase apparent power is monitoring data for judging the start-stop state of the production equipment in the step A3.
Further, in the step a3, the determining, according to whether the total load three-phase apparent power of the enterprise exceeds the threshold, the start-stop state of the production equipment specifically includes determining that the production equipment is in the start state when the total load three-phase apparent power of the enterprise exceeds the set threshold, and otherwise determining that the production equipment is in the stop state.
Further, step a4 specifically includes setting a data set as S, where S includes a load characteristic X and a sample attribute tag of each sample, where the load characteristic X is the enterprise electricity consumption data collected by the intelligent monitoring terminal in step a2, and the sample attribute tag is set to three different values Fi(i＝1,2,3)，F1、F2And F3Respectively representing three different production scenes of not starting environmental protection equipment in the production process, starting the environmental protection equipment in the production process and stopping the production;
setting class FiIs | FiIf the number of samples in S is | S |, the entropy of S is defined as:
wherein, PiIs that any sample belongs to FiThe probability of (d) is noted as:
the data in S is divided according to load characteristics X, the load characteristics X have m different classes, and then S is divided into m subsets { S1,S2,…,SmDividing the sample set S by the load characteristic X, and then dividing the subset S of SiThe entropy of (a) is weighted, and the formula is as follows:
in information theory, Entropy (Entropy) is a measure of uncertainty of a random variable, i.e., the larger the Entropy, the larger the uncertainty of the random variable;
the information gain obtained under the load characteristic X is:
Gain(S,X)＝Entropy(S)-EntropyX(S)
the information gain refers to the change of the information entropy before and after the sample set S is divided, and is the split measurement standard of the sample set S, and the obtained information gain is larger, and the classification capability of the sample set S is stronger; the ID3 algorithm of the decision tree is equivalent to the selection of a probability model by a maximum likelihood method;
finally, screening the characteristics X' of a plurality of nodes from top to bottom from the decision tree for the next simplified model to use according to the requirement;
in the decision tree obtained in the step a4, the more the features of the nodes closer to the root node are, the higher the importance degree is, and the features X' of a certain number of nodes are selected from the decision tree from top to bottom as required for the next step of training the simplified model.
Further, the step a5 specifically includes using the load characteristics X' screened in the step a4 as input quantities of the decision tree algorithm, and using a combination of start-stop states of the production equipment of the enterprise and the environmental protection equipment of the enterprise in a historical period (i.e., no environmental protection equipment is started in the production process/production is stopped) as an attribute label of the algorithm, and retraining the simplified decision tree classifier.
Particularly, the production behavior state predicted and judged by the logistic regression classifier may have some errors with the actual state, the effect evaluation mainly adopts Accuracy (Accuracy) as an evaluation index, and samples with the predicted classification of the classifier being consistent with the real classification are recorded as True, and samples with the predicted classification of the classifier being inconsistent with the real classification are recorded as False;
the accuracy represents the ratio of the correct classification number of the model to the total number of samples, and is calculated as follows:
further, the step a6 specifically includes inputting the real-time enterprise power consumption information acquired by the intelligent monitoring terminal into the decision tree classifier trained in the step a5, and if the determination result is that the production behavior state of the environmental protection equipment is not started in the production process, performing environmental protection abnormity alarm, and performing field verification and management by a manager.
It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Claims (6)
1. A pollution source enterprise illegal production monitoring method based on electricity consumption data is characterized by comprising the following steps: the method comprises the following steps:
a1: installing an intelligent monitoring terminal for acquiring enterprise electricity utilization information at an enterprise electricity utilization bus;
a2: the method comprises the steps that an intelligent monitoring terminal obtains enterprise electricity utilization data in a period of time as load characteristics and obtains start-stop state data of the environment-friendly equipment in corresponding time;
a3: setting a three-phase apparent power threshold, acquiring total load three-phase apparent power of an enterprise in corresponding time through load characteristics, and judging the start-stop state of production equipment according to whether the total load three-phase apparent power of the enterprise exceeds the threshold or not;
a4: whether an enterprise breaks the production behavior in the corresponding time is obtained according to the start-stop state data of the environment-friendly equipment and the start-stop state of the production equipment; the load characteristics are used as a sample, whether an enterprise has illegal production behavior within corresponding time is used as an attribute label of the sample, a plurality of samples are collected as an initial sample set, a decision tree algorithm and the initial sample set are used for carrying out primary training to obtain a simplified classifier model, and a plurality of load characteristics close to a root node are screened from a decision tree of the simplified classifier model to be used as secondary training samples;
a5: training the simplified classifier model again by using the secondary training sample and the corresponding attribute label to obtain a trained separator;
a6: and inputting the real-time enterprise electricity utilization data acquired by the intelligent monitoring terminal into the trained classifier, and judging whether the enterprise has an illegal production behavior in the corresponding time period.
2. The monitoring method for illegal production of pollution source enterprises based on electricity consumption data according to claim 1, characterized in that: the enterprise electricity consumption information collected by the intelligent monitoring terminal in the step A1 includes: each phase voltage, each phase current, each phase power, each harmonic voltage, each harmonic current, a total harmonic voltage distortion rate, a total harmonic current distortion rate, voltage deviation, voltage unbalance and power factors, wherein each phase power comprises each phase active power, each phase reactive power and each phase apparent power.
3. The monitoring method for illegal production of pollution source enterprises based on electricity consumption data according to claim 1, characterized in that: in step a3, the determining the start-stop state of the production equipment according to whether the total load three-phase apparent power of the enterprise exceeds the threshold specifically includes determining that the production equipment is in the start state when the total load three-phase apparent power of the enterprise exceeds the set threshold, and otherwise determining that the production equipment is in the stop state.
4. The monitoring method for illegal production of pollution source enterprises based on electricity consumption data according to claim 1, characterized in that: step A4 specifically includes setting a data set as S, where S includes each data setThe method comprises the steps of A, sampling load characteristics X and sample attribute labels, wherein the load characteristics X are enterprise electricity utilization data collected by the intelligent monitoring terminal in the step A2, and the sample attribute labels are set to three different values Fi(i＝1,2,3)，F1、F2And F3Respectively representing three different production scenes of not starting environmental protection equipment in the production process, starting the environmental protection equipment in the production process and stopping the production;
setting class FiIs | FiIf the number of samples in S is | S |, the entropy of S is defined as:
wherein Pi is any sample belonging to FiThe probability of (d) is noted as:
the data in S is divided according to load characteristics X, the load characteristics X have m different classes, and then S is divided into m subsets { S1,S2,…,SmDividing the sample set S by the load characteristic X, and then dividing the subset S of SiThe entropy of (a) is weighted, and the formula is as follows:
the information gain obtained under the load characteristic X is:
Gain(S,X)＝Entropy(S)-EntropyX(S)
and finally, screening the characteristics X' of a plurality of nodes from top to bottom from the decision tree according to the requirement for the next step of simplifying the model.
5. The monitoring method for illegal production of pollution source enterprises based on electricity consumption data according to claim 1, characterized in that: step a5 specifically includes using the load characteristics X' screened in step a4 as input quantities of the decision tree algorithm, and using a combination of start-stop states of production equipment of the enterprise and environmental protection equipment of the enterprise in a historical period as an attribute label of the algorithm, and retraining the simplified decision tree classifier.
6. The monitoring method for illegal production of pollution source enterprises based on electricity consumption data according to claim 1, characterized in that: step a6 specifically includes inputting the real-time enterprise electricity consumption information acquired by the intelligent monitoring terminal into the decision tree classifier trained in step a5, and if the judgment result is that the production behavior state of the environmental protection equipment is not started in the production process, performing environmental protection abnormity alarm, and performing field verification and management by a manager.
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