CN114662981B - Pollution source enterprise supervision method based on big data application - Google Patents
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Abstract
The invention relates to the technical field of environmental data monitoring and management, in particular to a pollution source enterprise supervision method, which comprises the following application steps: s1, in a monitoring period, carrying out independent monitoring setting on each operation facility in each operation stage, wherein the operation stage comprises a first operation stage and a second operation stage with sequential relevance, and the operation facilities comprise a first facility and a second facility; acquiring first data and second data through monitoring; s2, respectively carrying out regularity analysis processing on the obtained first data and second data to generate a first regularity analysis result and a second regularity analysis result; if the analysis result condition in the obtained analysis result is that the regularity is not available, enabling the corresponding operation facility to carry out first identification registration; and the remaining operating facilities sequentially associated downstream from the operating facility registered with the first identifier are registered with the second identifier. The method can provide clear checking guidance for supervision and law enforcement, and promotes the efficiency of supervision and law enforcement.
Description
Technical Field
The invention relates to the technical field of environmental data monitoring and management, in particular to a pollution source enterprise supervision method based on big data application.
Background
The pollution source automatic monitoring system who adopts at present stage is through installing the online monitoring system at the pollution source discharge port, to pH value, COD, SS, index such as ammonia nitrogen carry out real-time supervision, data acquisition, transmit the pollution source on-line monitoring platform of environmental protection department, environmental protection department logs in this platform and monitors the pollutant discharge condition of enterprise, however there are some problems in the operation process, pollution source on-line monitoring equipment trouble, the phenomenon that the pollution source on-line monitoring equipment operation was destroyed in the interference happens occasionally, lead to pollution source on-line monitoring data distortion, can not truly reflect the enterprise's blowdown condition completely:
(1) Some enterprises discharge sewage through private concealed pipes, and the data monitored by the pollution source online monitoring system arranged at the discharge port is actually unreal data;
(2) And some enterprises interfere with and even destroy the sampling and analysis system of the online monitoring system, resulting in inaccurate monitored data.
Disclosure of Invention
The invention aims to provide a pollution source enterprise supervision method for overcoming the defects of the prior art.
A pollution source enterprise supervision method based on big data application comprises the following steps:
s1, in a monitoring period, independently monitoring and setting each operation facility in each operation stage of pollution source treatment in an enterprise, wherein the operation stage comprises a first operation stage and a second operation stage with sequence relevance, and the operation facilities comprise a first facility corresponding to the first operation stage and a second facility corresponding to the second operation stage; acquiring first data related to a first facility and second data related to a second facility through monitoring;
s2, performing regularity analysis processing on the obtained first data and second data respectively to generate a first regularity analysis result related to the first data and a second regularity analysis result related to the second data;
if the analysis result condition in the obtained first rule analysis result and/or the second rule analysis result is not regular, enabling the corresponding operation facility to perform first identification registration; and the remaining operating facilities sequentially associated downstream from the operating facility registered with the first identifier are registered with the second identifier.
Further, the monitoring period comprises a first monitoring period and a second monitoring period, wherein the first monitoring period acquires a first period regular condition of the operating facility, and the second monitoring period acquires a second period regular condition of the operating facility; and setting a rule deviation standard, comparing the rule deviation of the first period rule situation and the second period rule situation, and enabling the corresponding operating facilities to register a third identifier when the rule deviation standard is not met after comparison.
Further, the first operation stage is a pollutant production stage for generating pollutants, and the second operation stage is a pollution control stage for treating pollutants; the third operation stage is a pollutant discharge stage for discharging pollutants; and the pollution source enterprise executes the pollution production stage, the pollution treatment stage and the pollution discharge stage in sequence in the pollution source processing execution process.
Further, in step S2, the regularity analyzing and processing procedure is as follows:
s2-1, acquiring a plurality of occurrence data of the operating facilities within the time m to form an original sequence K i ，K i ＝{k 1 ,k 2 ,...,k m }; when the original sequence K i In which there are at least two minimum periods p: (
And is
To represent
Rounding down), the operating facilities are judged to have regularity;
s2-2, when the operating facilities are judged to have regularity, carrying out data K on the operating facilities within a certain time i Offset by minimum period p, construct alignment sequence K' i ＝{k′ 1 ,k′ 2 ,...,k′ m-p Of which is K' i There are m-p data points in total;
s2-3, presetting a threshold value S through a formula
And
(Sim(K i ,K′ i ) Is K i And K' i Similarity of (c), cor (K) i ,K′ i ) Is K i And K' i Coefficient of correlation, cov (K) i ,K′ i ) Is K i And K' i Covariance of (2), var (K) i ) Is K i Variance of (2), var (K' i ) Is K' i Variance of) calculating the original sequence K i And comparative sequence K' i Similarity of (2) Sim (K) i ,K′ i ) The obtained similarities Sim (K) are compared one by one i ,K′ i ) And a threshold S, when one of the similarity Sim (K) is smaller than the threshold S i ,K′ i ) And when the data is larger than or equal to the threshold S, judging that the corresponding data generated by the operating facilities has regularity.
Further, the step S2 further includes the steps of: s2-4, and Sim with each item generated being greater than or equal to threshold S(K i ,K′ i ) Sorting and selecting the largest Sim (K) i ,K′ i ) The corresponding minimum period p is used as a target minimum period of the enterprise operation facility; s2-5, replacing the minimum period p in the step S2-2 with the obtained target minimum period to carry out operation, and repeatedly executing the steps S2-2 and S2-3 to continuously monitor corresponding data generated by operating facilities for regular judgment.
Further, in step S2-1, a data preprocessing operation is performed on the occurrence data acquired by the operating facility, where the data preprocessing operation includes one or more of missing value filling, noise smoothing, or deburring of the data.
Furthermore, checking the content of each condition of the registered identification, and canceling the corresponding identification registration when the identification result is confirmed to be inaccurate after checking.
Furthermore, a supervision standard is set for the classification or quantity condition of the identification registration; and according to the corresponding identification registration condition of the enterprise, performing hierarchical label registration on the enterprise according to the supervision standard.
The invention has the beneficial effects that:
1. by monitoring the data of the corresponding operation facilities in the corresponding operation stage and performing regularity analysis processing, whether abnormal operation exists in the monitoring period and whether abnormal operation occurs in the corresponding operation stage are effectively determined, so that clear checking guidance is provided for supervision and law enforcement, and the efficiency of supervision and law enforcement is promoted.
2. The unreasonable analysis result is effectively screened and marked by performing related monitoring application on specific operating facilities in different operating stages with sequential relevance, so that subsequent supervision and law enforcement application is provided, and counterfeiting situations of enterprises in pollution discharge and pollution control processes are effectively traced.
Detailed Description
In order to make the technical solution, objects and advantages of the present invention more apparent, the following examples further illustrate the present invention.
The invention discloses a pollution source enterprise supervision method based on big data application, which is used for periodically and continuously monitoring and analyzing data conditions generated by operation facility operation behaviors of operation stages in a pollution source enterprise. Through analysis, whether each stage of pollutant treatment of the corresponding enterprise has regularity of normal operation is determined, and when the analysis shows that a certain operation stage of the enterprise has no regularity, the operation stage behavior of the enterprise or series of irregular behaviors such as abnormal pollutant treatment, interference and the like exist, the condition needs to be identified and registered for subsequent examination or field check.
After comprehensive analysis, if the enterprises are confirmed to have irregular behaviors in the pollutant treatment process, the enterprises and the graded labels need to be registered and processed for confirmation of environmental protection supervision departments.
The application steps of the pollution source enterprise supervision method are as follows:
s1, monitoring and setting operation facilities of a pollution source enterprise in a monitoring period, wherein the operation facilities comprise a first facility set corresponding to a first operation stage, a second facility set corresponding to a second operation stage and a third facility set corresponding to a third operation stage; monitoring for acquisition of first data relating to a first facility, second data relating to a second facility, and third data relating to a third facility; the first data, the second data and the third data have sequential relevance.
And S2, respectively carrying out regularity analysis processing on the obtained first data, second data and third data to generate a first regularity analysis result related to the first data, a second regularity analysis result related to the second data and a third regularity analysis result related to the third data.
And if one or more analysis results in the rule analysis results are not regular, enabling the corresponding operation facilities to perform first identification registration. And registering with the second identifier the operating facility of the downstream stage associated with the operating facility registered with the first identifier.
The specific application and setting principle of the pollution source enterprise supervision method are as follows:
generally, the operation stage of pollutant treatment of a pollution source enterprise comprises three stages which are arranged in sequence: the method comprises a pollutant production stage for generating pollutants, a pollutant treatment stage for treating the pollutants and a pollutant discharge stage for discharging the pollutants, wherein the three stages have sequential relevance; therefore, the arrangement of the sewage producing stage, the sewage treating stage and the sewage discharging stage is substituted into the arrangement of the first stage, the second stage and the third stage for application. The first facility is a pollution production facility, the second facility is a pollution treatment facility, the third facility is a pollution discharge facility, the first data is pollution production data, the second data is pollution treatment data, the third data is pollution discharge data, the first law analysis result is a pollution production law analysis result, the second law analysis result is a pollution treatment law analysis result, and the third law analysis result is a pollution discharge law analysis result.
The regularity analyzing and processing process is applied by adopting a period analyzing algorithm, and the period analyzing algorithm comprises the following steps:
step 1: analyzing and extracting the characteristics of the enterprise operating facilities.
Extracting the characteristics of the enterprise operating facilities through the attribute vector, wherein the same enterprise is generally provided with n operating facilities in different operating stages, and the n operating facilities can be expressed as < K 1 ,K 2 ,...,K n ＞。
And 2, step: analysing a certain operating facility K i Whether the behavior of (2) is regular.
(1) Obtaining an original sequence K i ：
Acquiring data of a certain operating facility within a certain time, and performing data preprocessing operation on the acquired data, wherein the data preprocessing operation comprises one or more modes of missing value filling, noise smoothing or burr removing on the data.
The data after the preprocessing operation may be represented as K i ＝{k 1 ,k 2 ,...,k m Where m is the total length of data for the facility operating. If operating the facility K i There is regularity in the behavior of (a), meaning that K is within time m i The behavior of (c) may be repeated. Therefore, when the minimum period p is found to occur repeatedly, the facility K is operated i There is regularity in the behavior of; otherwise, if the minimum period p does not exist, the facility K is operated i There is no regularity in the behavior of (c). There are at least 2 minimum periods p in time m, so p must satisfy
And is
To represent
And rounding down.
(2) Construction comparative sequence K' i 。
Data K of operating facilities in a certain time i Offset by minimum period p, construct alignment sequence K' i ＝{k′ 1 ,k′ 2 ,...,k′ m-p H, wherein K' i There were a total of m-p data points.
(3) Calculating the original sequence K i And comparison sequence K' i Obtaining the target minimum period p'
Calculating the original sequence K i And comparison sequence K' i The similarity formula is as follows, wherein Sim (K) i ,K′ i ) Is K i And K' i Similarity of (c), cor (K) i ,K′ i ) Is K i And K' i Coefficient of correlation, cov (K) i ,K′ i ) Is K i And K' i Covariance of (1), var (K) i ) Is K i Variance of (2), var (K' i ) Is K' i The variance of (c).
Through the above calculation, the similarity Sim (K) is compared i ,K′ i ) And a threshold value S if all Sims (K) i ,K′ i ) If the value is less than the threshold value S, the operation of the enterprise operation facilities is not regular; on the contrary, if at least one Sim (K) is present i ,K′ i ) If the value is larger than or equal to the threshold value, the operation regularity of the enterprise operation facilities is indicated.
To satisfy the requirement of repeated comparison operation of sequence similarity, sim (K) satisfying the above condition is selected i ,K′ i ) Sorting and selecting the largest Sim (K) i ,K′ i ) The corresponding minimum period p' is used as the target minimum period of the enterprise operating facility.
And (3) replacing the minimum period p in the step (2) with the obtained target minimum period p' to carry out operation, and repeatedly executing the step (2) and the step (3) to continuously monitor corresponding data generated by the operating facilities by regular judgment.
And 3, step 3: and generating a label of the enterprise management facility and registering.
Judging according to the step 2, if the enterprise management equipment K i The behavior of the enterprise management and treatment facility is regular, the operation rule of the enterprise management and treatment facility is determined, and the future behavior characteristics of the enterprise can be predicted through past rules. If new data of the enterprise is calculated, the periodicity of the new data is determined, and then the periodicity is compared with the conventional operation rule to determine whether the enterprise is suspected of abnormally operating facilities.
The method specifically comprises the following steps: setting the monitoring period with a first monitoring period and a second monitoring period of different time periods, and acquiring a first period regular condition of an operating facility in the first monitoring period and a second period regular condition of the operating facility in the second monitoring period by applying the step 2; setting a regular deviation standard, comparing the regular deviation of the first periodic regular condition and the second periodic regular condition, and if the compared regular deviation standard is not met, even if the second periodic regular condition is regular, the second periodic regular condition can still be considered as a condition that the operating facilities are suspected to be abnormally operated, and the corresponding operating facilities need to be subjected to third identification registration.
Preferably, the rule deviation criterion is set to 10%, that is, when the similarity sim of the second period is in the range of-1.1 × sim (first period) to 1.1 × sim (first period), the second period may be considered to be in compliance with the rule deviation criterion.
And through the application of the first identification registration, the identification registration is carried out on the data generated by the independently judged operating facilities without regularity so as to be applied to subsequent direct tracing audit and verification.
And through the application of the second identification registration, identifying and registering the data generated by the operation facilities in the downstream stage associated with the operation facilities which are judged to have no regularity, so as to be applied to the subsequent retrospective checking. For example: when the pollution production facility is analyzed to have no regularity in a monitoring period, the pollution production facility is subjected to first identification registration, and the pollution treatment facility and the pollution discharge facility in the downstream stages (the pollution treatment stage and the pollution discharge stage) are subjected to second identification registration.
In the subsequent checking process, the specific production application condition of the operation facility related to the first identifier registration is firstly confirmed, if the pollution control data and the pollution discharge data of the downstream of the pollution control facility are judged to be normal if the pollution control facility does not have the analysis regularity condition caused by the change of the real pollution control data, such as the false alarm caused by the analysis error, the false alarm caused by the fault of the data transmission module and the like, the pollution control data and the pollution discharge data of the downstream are judged to be normal, the pollution control data of the pollution control facility can be considered to be the reasonable condition, the data generated by the downstream facility are normal, and the second identifier registration condition of the pollution control facility and the pollution discharge facility is cancelled.
If the pollution production facility does not have the analysis regularity condition due to the real pollution production data change, but the analysis of the downstream pollution control data and the downstream pollution discharge data is judged to be normal, the data generated by the downstream facility can be considered to have abnormal possibility, and the corresponding pollution control facility and the corresponding pollution discharge facility in the monitoring period need to be further checked to confirm the real pollutant treatment result.
And through the application of the third identification registration, the data generated by the corresponding operating facilities with regular deviation in the previous and subsequent monitoring periods are subjected to identification registration so as to be applied to subsequent retrospective checking.
And when the identification result is confirmed to be inaccurate after checking, canceling the identification registration of the corresponding operating facility. Setting a supervision standard for the classification or quantity condition of the identification registration; and according to the corresponding identification registration condition of the enterprise, performing hierarchical label registration on the enterprise according to the supervision standard. For example, three standards are set in the supervision standard in a grading way according to the acquired quantity conditions of the first identifier, the second identifier and the third identifier, and the first-level label registration can be carried out on enterprises with the quantity conditions lower than the set acquired identifier, and the records are green; for enterprises with the number of acquired identifications higher than the set number but not 2 times higher than the set target, performing second-level label registration, wherein the registration is yellow; for businesses that are more than 2 times the set representative acquisition count, a third level of tag registration is made, with the registration being red. Subsequent environmental law enforcement departments can pay important attention to enterprises marked with red or yellow, so that clear checking guidance is provided for supervision and law enforcement, and the efficiency of supervision and law enforcement is promoted.
In a preferred embodiment, in the above-mentioned hierarchical tag application case, a comprehensive score of the enterprise may be calculated by a score aggregation function, and the tag setting for the enterprise hierarchy may be performed by setting the comprehensive score.
And 4, step 4: checking or checking the identification result on site according to the label information of the alarm, if the identification result exists really, processing the problem that the enterprise treatment facilities are not normal in operation, marking the alarm information as true and placing the true and the false into a case library for continuous optimization of an algorithm model; and if the identification result is not accurate, the alarm information is marked as false and is listed in the case base. And when the ratio of the number of the alarm information marks as false to the total number of the alarm information exceeds 5%, triggering a mechanism for recalculating all the steps, retraining the algorithm model by modifying the data of the training set and optimizing parameters by adjusting the value of the minimum period p, thereby providing the accuracy of the algorithm model.
The above description is only a preferred embodiment of the present invention, and those skilled in the art may still modify the described embodiment without departing from the implementation principle of the present invention, and the corresponding modifications should also be regarded as the protection scope of the present invention.
Claims (6)
1. The pollution source enterprise supervision method based on big data application is characterized by comprising the following steps:
s1, in a monitoring period, independently monitoring and setting each operation facility in each operation stage of pollution source treatment in an enterprise, wherein the operation stage comprises a first operation stage and a second operation stage with sequence relevance, and the operation facilities comprise a first facility corresponding to the first operation stage and a second facility corresponding to the second operation stage; acquiring first data related to a first facility and second data related to a second facility through monitoring;
s2, performing regularity analysis processing on the obtained first data and second data respectively to generate a first regularity analysis result related to the first data and a second regularity analysis result related to the second data;
if the analysis result condition in the obtained first rule analysis result and/or the second rule analysis result is not regular, enabling the corresponding operation facility to perform first identification registration; and the other operation facilities which are sequentially related to the downstream of the operation facility registered with the first identifier are registered with second identifiers;
in step S2, the regularity analysis processing procedure is as follows:
s2-1, acquiring a plurality of occurrence data of the operating facilities within the time m to form an original sequence K i ，K i ＝{k 1 ,k 2 ,...,k m }; when the original sequence K i When at least two minimum periods p exist, judging that the operating facilities have regularity; wherein the content of the first and second substances,
and is provided with
To represent
Rounding down;
s2-2, when the operating facilities are judged to have regularity, carrying out data K on the operating facilities within a certain time i Offset by minimum period p, construct alignment sequence K' i ＝{k' 1 ,k' 2 ,...,k' m-p Of which is K' i There are m-p data points in total;
s2-3, presetting a threshold value S through a formula
And
calculating the original sequence K i And comparative sequence K' i Similarity Sim (K) i ,K′ i ) The obtained similarities Sim (K) are compared one by one i ,K′ i ) And a threshold S, when one of the similarity Sim (K) is i ,K′ i ) When the data is larger than or equal to the threshold S, judging that the corresponding data generated by the operating facilities has regularity; wherein, sim (K) i ,K′ i ) Is K i And K' i Similarity of (c), cor (K) i ,K′ i ) Is K i And K' i Coefficient of correlation, cov (K) i ,K′ i ) Is K i And K' i Covariance of (1), var (K) i ) Is K i Variance of (1), var (K' i ) Is K' i The variance of (a);
s2-4, and Sim (K) for each item generated to be greater than or equal to threshold S i ,K′ i ) Sorting to select the largest Sim (K) i ,K′ i ) The corresponding minimum period p is used as a target minimum period of the enterprise operation facility;
s2-5, replacing the minimum period p in the step S2-2 with the obtained target minimum period to carry out operation, and repeatedly executing the steps S2-2 and S2-3 to continuously monitor corresponding data generated by operating facilities for regular judgment.
2. The method of claim 1, wherein the monitoring period comprises a first monitoring period during which a first periodic profile of the operating facility is obtained and a second monitoring period during which a second periodic profile of the operating facility is obtained; and setting a rule deviation standard, comparing the rule deviation of the first period rule situation and the second period rule situation, and enabling the corresponding operating facilities to register a third identifier when the rule deviation standard is not met after comparison.
3. The method as claimed in claim 1, wherein the first operation stage is a pollutant production stage for pollutant generation, and the second operation stage is a pollutant treatment stage for pollutant treatment; the third operation stage is a pollutant discharge stage for discharging pollutants; and the pollution source enterprise executes the pollution production stage, the pollution treatment stage and the pollution discharge stage in sequence in the pollution source processing execution process.
4. The pollution source enterprise supervision method according to claim 1, wherein in step S2-1, the data acquired by the operating facility is subjected to a data preprocessing operation, and the data preprocessing operation includes one or more of missing value filling, noise smoothing or deburring of the data.
5. The pollution source enterprise supervision method according to any one of claims 1 to 4, wherein the contents of each case where the registration with the identifier is performed are checked, and when the recognition result is confirmed to be inaccurate after the check, the corresponding identifier registration is cancelled.
6. The pollution source enterprise supervision method according to claim 5, wherein supervision criteria are set for identifying the category or quantity condition of the registration; and according to the corresponding identification registration condition of the enterprise, performing hierarchical label registration on the enterprise according to the supervision standard.
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