CN117078024A - Dangerous waste quantity change detection system and method based on video analysis - Google Patents
Dangerous waste quantity change detection system and method based on video analysis Download PDFInfo
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Abstract
The invention discloses a system and a method for detecting the change of dangerous waste quantity based on video analysis, which relate to the technical field of dangerous waste quantity change detection and comprise the following steps: s1: acquiring relevant data of each enterprise in a target area, and analyzing the dangerous waste quantity change trend of each enterprise; s2: analyzing the change history data of the dangerous waste quantity of each enterprise in the target area, excavating the dangerous waste disposal rule of each enterprise, and establishing a dangerous waste transfer behavior discrimination model; s3: monitoring the change of the dangerous waste quantity of each enterprise in the target area, analyzing the risk index of each enterprise, and carrying out alarm prompt on the enterprise exceeding the preset risk index threshold; s4: providing a risk interpretation report for the risk enterprises marked in the step S3, tracking the scheduling management related data of the risk enterprises, and feeding back and recording the risk processing results. The enterprise dangerous waste quantity in the real-time monitoring area fluctuates, and the clear control of the dangerous waste disposal flow is realized.
Description
Technical Field
The invention relates to the technical field of dangerous waste quantity change detection, in particular to a system and a method for detecting dangerous waste quantity change based on video analysis.
Background
With the importance of environmental protection in the current society, whether enterprises can properly treat hazardous waste generated by production work becomes an important condition for enterprise development due to serious pollution and potential safety risks of the hazardous waste. The rapid development of industrialization is accompanied by an increasing yield of hazardous waste, and whether or not to manage the hazardous waste as specified involves business persistence and public impact of enterprises. More and more enterprises begin to use the software system to check the dangerous waste quantity condition of the enterprises, and as the types of dangerous waste are more, the hidden danger is large, the processing procedure is complicated, and whether the dangerous waste quantity change of the enterprises can be efficiently detected and recorded is one of the standards for judging the comprehensive capacity of the enterprises.
Current enterprise hazardous waste regulations are mainly focused on storage safety of hazardous waste, such as leakage problems, and storage specifications, such as excessive problems; however, intelligent analysis is not realized for the integration of regional dangerous waste data and the supervision of the dangerous waste disposal process, the regional dangerous waste data cannot be summarized in real time, the dangerous waste disposal process is not well controlled, the fluctuation of the dangerous waste quantity of enterprises is not timely processed, the fault tolerance rate of dangerous waste storage is low, and the efficient coordination of the dangerous waste disposal process cannot be realized; if the regulatory authorities examine that there is an offence to the disposal of hazardous waste by the enterprise, the serious situation can lead to the shutdown of the enterprise to rectify and even cancel the license.
Therefore, in order to solve the above problems or part of the problems, the present invention provides a system and a method for detecting the change of the dangerous waste amount based on video analysis.
Disclosure of Invention
The invention aims to provide a system and a method for detecting dangerous waste quantity change based on video analysis, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a method for detecting the change of dangerous waste quantity based on video analysis comprises the following steps:
s1: acquiring relevant data of each enterprise in a target area, and analyzing the dangerous waste quantity change trend of each enterprise;
s2: analyzing the change history data of the dangerous waste quantity of each enterprise in the target area, excavating the dangerous waste disposal rule of each enterprise, and establishing a dangerous waste transfer behavior discrimination model;
s3: monitoring the change of the dangerous waste quantity of each enterprise in the target area, analyzing the risk index of each enterprise, marking the enterprise exceeding the preset risk index threshold as a risk enterprise, and carrying out alarm prompt on the risk enterprise;
s4: providing a risk interpretation report for the risk enterprises marked in the step S3, tracking the scheduling management related data of the risk enterprises, and feeding back and recording the risk processing results.
Further, the step S1 includes:
step S1-1: acquiring enterprise data access rights in a target area of an access server, and reading dangerous waste related data of each enterprise, wherein the dangerous waste related data comprises the current dangerous waste quantity of the enterprise, the dangerous waste storage position of the enterprise, the upper limit of the preset dangerous waste quantity of the enterprise and an enterprise cooperation treatment unit list;
step S1-2: extracting historical data of the amount of the hazardous waste of the enterprise from a database or related data source, including enterprise names, time and the amount of the hazardous waste; analyzing the change trend of the amount of the dangerous waste of each enterprise according to the following steps:
step S1-2-1: the extracted data is cleaned, missing values in the data are removed, and the integrity and the accuracy of the data are ensured; converting the time data into a time sequence format; the time item can be subjected to format conversion according to the requirement, and the data are summarized according to the year or quarter so as to facilitate subsequent analysis;
step S1-2-2: decomposing the cleaned data, and splitting the original data Y (T) into a long-term trend T (T), a month fluctuation M (T), a cyclic fluctuation C (T) and an irregular fluctuation B (T) based on a seasonal decomposition method, wherein Y (T) =T (T) +M (T) +C (T) +B (T); wherein,
T（t）+C（t）=(Σk j=-ky t+j )/N ；
n represents the number of sample data, n=2k+1, y t+j The dangerous waste quantity at the time of t+j in the sample data is represented;
selecting historical data of the number of the enterprise hazardous waste in the last year as a sample, calculating the average number of trending data of each month, and taking the average number corresponding to each month to represent the month component of each month;
the detrending data = M (T) +b (T) = Y (T) -T (T) -C (T);
step S1-2-3: establishing a dangerous waste change trend prediction model:
S waste of =α+β 0 *t+β i *J i +λ；
Wherein S is Waste of Representing the amount of hazardous waste, alpha is the intercept term, beta 0 Coefficient, beta, representing the independent variable time t i Representing the coefficient corresponding to the ith month, J i Represents the month component of the ith month, i.e. [1,12 ]]The method comprises the steps of carrying out a first treatment on the surface of the Lambda represents the fluctuation residual, preset by the relevant staff.
Estimating coefficients alpha and beta in the model by a least square method by taking time as an independent variable and the amount of dangerous waste as an independent variable 0 The introduced monthly component may represent the fluctuating impact of the monthly variation on the amount of hazardous waste, with fluctuating residuals being the remainder that cannot be interpreted by trend and monthly fluctuations, representing random fluctuations or other unaccounted for factors. Coefficient beta i The significance of (c) may be determined by calculating the p-value or confidence interval method, the coefficient beta i The magnitude of (2) represents the degree of influence of one unit month change on the amount of hazardous waste, the positive coefficient represents the influence as positive correlation, and the negative coefficient represents the influence as negative correlation.
Further, the step S2 includes:
step S2-1: acquiring dangerous waste change history data of enterprises in a target area, wherein the dangerous waste change history data comprise storage quantity change values and time points; cleaning and arranging the collected data, removing abnormal values or missing data, and performing standardized treatment to ensure the consistency and comparability of the data; sequencing the time points and the storage quantity change values to enable the storage quantity change values to be arranged according to a time sequence;
step S2-2: calculating the absolute value of the difference between the storage quantity change value of each time point and the storage quantity change value of the previous time point to obtain the fluctuation range of the storage quantity change value, wherein the u+1th fluctuation range is the number change value of the u+1th time point-the number change value of the u-th time point; storage amount change value = storage amount at the current point in time-storage amount at the previous point in time.
Observing the value of the fluctuation range, and if the value of the fluctuation range in the duration V is always less than or equal to c% of the storage capacity corresponding to each time point, considering that the quantity of dangerous wastes is relatively stable and no transfer occurs; if the fluctuation range value is greater than c% of the storage capacity corresponding to each time point in the duration V and the fluctuation range value occurs for a plurality of times in a short time, the time point is considered to have dangerous waste transfer behavior, and the time point with the dangerous waste transfer behavior is set as a dangerous waste transfer time point of a corresponding enterprise;
step S2-3: obtaining a sample set d= { (D) 1 ，p 1 ),(d 2 ，p 2 ),...,(d n ，p n ) And (d), wherein the i-th sample (d) i ，p i ) D in (d) i Time point p representing ith hazardous waste storage amount change i Representing a time point d i A change in the amount of hazardous waste stored; and marking the dangerous waste transfer time point as a leaf node, considering the class represented by the leaf node as the dangerous waste transfer behavior, and establishing a dangerous waste transfer behavior discrimination model based on a decision tree classification algorithm. When predicting a new sample, traversing the decision tree from the root node, selecting a corresponding branch according to the splitting rule on each node until reaching a leaf node, and judging whether dangerous waste transfer behaviors exist or not by taking the category represented by the node as a prediction result.
Further, the step S3 includes:
step S3-1: monitoring the dangerous waste quantity of each enterprise in the target area, and predicting the dangerous waste quantity transformation of each enterprise according to real-time monitoring data; according to the dangerous waste change trend in the step S1Predicting the dangerous waste quantity of each enterprise at the next time t 'by using the measurement model to obtain the dangerous waste quantity L at the time t' corresponding to any enterprise t’ ；
Step S3-2: judging the dangerous waste transfer behaviors of enterprises according to the dangerous waste transfer behavior judging model established in the step S2 by combining the predicted data in the step 3-1, and judging the predicted dangerous waste quantity L t’ Introducing the dangerous waste transfer behavior discrimination model established in the step S2; when any enterprise has a risk waste transfer tendency, adding 1 to an evaluation influence factor q of the risk index of the enterprise;
step S3-3: evaluating the risk index of the dangerous waste currently stored by each enterprise, setting E enterprises in the current target area, and calculating the risk index F of any enterprise E according to the following formula e ；
F e =(L t’ /L e )×η 0 +sgn(h-ε)×η 1 +w g ×η 2 +q×100% ；
Wherein L is e Representing a preset storage capacity upper limit of an enterprise e, sgn () representing a sign function, h representing an application passing rate of a history transfer application of the enterprise, epsilon representing a preset application passing rate threshold in a database; w (w) g Representing the waiting time of the g-th application in the historical transition application of the enterprise; η (eta) 0 、η 1 、η 2 The influence coefficients of each evaluation risk index are respectively represented, and are preset and stored into a database by related staff;
if a plurality of enterprises exist in the target area and dangerous waste transfer trend exists, and the number of dangerous waste treatment in the target area is limited, the possibility that the transfer application of the current enterprise does not pass is increased, and if the dangerous waste storage quantity of the enterprises is in a supercritical value, the delay of the transfer application can cause the enterprises to increase illegal risks;
step S3-4: judging risk indexes of enterprises, marking the enterprises with the risk indexes exceeding a preset threshold f as risk enterprises, and sending alarm prompt to the risk enterprises.
Further, the step S4 includes:
step S4-1: the alarm prompt sent to the risk enterprises comprises risk interpretation reports, the risk interpretation reports comprise alarm tracing, the enterprises verify the dangerous waste conditions of the enterprises according to the risk sources, if the data verification shows that no abnormal data exists, the enterprises manage the dangerous waste scheduling of the risk enterprises through a risk solution provided by the risk interpretation reports, and the risk indexes of the enterprises are reduced through the dangerous waste scheduling of the risk enterprises; if the data check shows that abnormal data exists, the related personnel of the enterprise initiate data correction, correct the abnormal data, and calculate the risk index of the enterprise again according to the corrected data;
step S4-2: updating the scheduled enterprise related data, recording the current scheduling, and marking the abnormal data in the data verification.
A system for detecting a change in a hazardous waste quantity based on video analysis, the system comprising: the system comprises a comprehensive situation detection module, a data visualization module, a risk discrimination module and a scheduling management and control module;
the comprehensive situation detection module is used for detecting the dangerous waste quantity change condition of a system access enterprise, integrating detection results and analyzing the comprehensive situation change according to data integration results;
the data visualization module is used for displaying the detection data in the comprehensive situation detection module to a system interface through a visualization technology;
the risk judging module is used for monitoring the dangerous and useless state of each enterprise, evaluating the real-time risk condition of each enterprise and judging whether the risk index of any enterprise exceeds a risk threshold value;
the scheduling management and control module is used for managing dangerous waste storage conditions of the risk enterprises according to the risk assessment result of the risk judging module and scheduling the dangerous wastes of the risk enterprises by combining the comprehensive situation monitoring data;
the output end of the comprehensive situation detection module is connected with the input ends of the data visualization module, the risk judging module and the scheduling management and control module, and the output end of the risk judging module is connected with the input ends of the data visualization module and the scheduling management and control module.
Further, the comprehensive situation detection module comprises a basic data importing unit, an enterprise data extracting unit and a change trend analyzing unit;
the basic data importing unit is used for importing basic data of each enterprise in the system, including enterprise names, enterprise positions and enterprise authorities;
the enterprise data extraction unit is used for extracting data related to the change of the quantity of the dangerous waste of each enterprise, integrating the extracted data and transmitting the integrated data to the change trend analysis unit;
the change trend analysis unit is used for analyzing situation changes of enterprises according to the dangerous waste quantity of the enterprises acquired by the real-time data acquisition unit and acquiring the change trend of the dangerous waste quantity of the enterprises.
Further, the data visualization module comprises a data visualization template and a visualization tool interface management unit;
the data visualization template is a visualization template designed in advance for related staff, and a user can flexibly adjust and fill data according to own requirements;
the visual tool interface management unit is used for packaging the interfaces of the bottom visual tool, centrally managing the interfaces of the visual tool and reducing direct operation and maintenance work of developers on the bottom interfaces; many data visualization tools provide an open interface or API that allows developers to programmatically use their functionality; the visual tool interfaces are uniformly managed, so that the management and maintenance processes of the interfaces are simplified, and meanwhile, the readability and maintainability of codes are improved, so that new visual tools are supported or new functions are added.
Further, the risk judging module comprises a dangerous waste state monitoring unit, a risk assessment unit and a risk warning unit;
the dangerous waste state monitoring unit is used for monitoring the real-time state data of dangerous wastes stored by each enterprise and transmitting the monitoring data to the risk assessment unit;
the risk assessment unit is used for assessing the risk index of the dangerous waste stored in the enterprise at present and calculating the risk index of each enterprise in the system;
the risk alarm unit is used for judging the risk indexes of enterprises, marking the enterprises with the risk indexes exceeding a preset threshold as risk enterprises, and sending an alarm through the system.
Further, the dispatch management and control module comprises a hazardous waste dispatch management unit and an enterprise data updating unit;
the hazardous waste scheduling management unit is used for managing hazardous waste scheduling of an inauguration enterprise, and reducing the risk index of the enterprise by scheduling the hazardous waste of the inauguration enterprise;
the enterprise data updating unit is used for updating the enterprise related data in the scheduled system and recording the current scheduling related data.
Compared with the prior art, the invention has the following beneficial effects:
the invention detects the dangerous waste quantity change condition of the system access enterprise through the comprehensive situation detection module, integrates the detection result, and analyzes the comprehensive situation change according to the data integration result; through the comprehensive situation detection module, the change condition of the dangerous waste quantity of the system access enterprise can be monitored and detected in real time, and abnormal conditions can be found in time.
Displaying the detection data in the comprehensive situation detection module to a system interface through a data visualization module by a visualization technology; by integrating and analyzing the detection results, the comprehensive situation change situation of each enterprise in the system can be obtained, so that the overall situation of dangerous waste management can be better understood. And the detection data in the comprehensive situation detection module is displayed on a system interface in an intuitive manner by utilizing a data visualization technology, so that a user can conveniently view and understand related information.
Monitoring the dangerous waste state of each enterprise through a risk judging module, evaluating the real-time risk condition of each enterprise, and judging whether the risk index of any enterprise exceeds a risk threshold value; the risk judging module is used for monitoring and evaluating the dangerous waste state of each enterprise, so that whether the risk index of any enterprise exceeds the risk threshold value can be judged, and potential risks can be early warned and processed in time. The method has the advantages that the intelligent analysis is realized for the supervision of the regional dangerous waste data integration and the dangerous waste disposal flow, the integration of the regional dangerous waste data can control the dangerous waste disposal flow of enterprises more clearly, the dangerous waste quantity fluctuation of each enterprise is monitored in real time, the fault tolerance of the dangerous waste storage of the enterprises is improved, and the efficient coordination of the dangerous waste disposal flow is realized;
the dangerous waste storage condition of the inauguration enterprises is managed according to the risk assessment result of the risk judging module through the scheduling management and control module, and the dangerous waste of the inauguration enterprises is scheduled by combining the comprehensive situation monitoring data; the scheduling management and control module can prompt and manage dangerous waste storage conditions of the risk enterprises according to the risk assessment result of the risk judging module, so that the enterprises are ensured to avoid the dangerous waste storage quantity exceeding the standard and the safety compliance of the enterprise dangerous waste transfer process, and the regional comprehensive management and the enterprise sustainable development are facilitated.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a module structure of a hazardous waste quantity change detection system based on video analysis;
FIG. 2 is a flow chart of a method for detecting the change of the dangerous waste quantity based on video analysis.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is further described with reference to fig. 1, 2 and embodiments.
Example 1: as shown in fig. 1, the present embodiment provides a system for detecting a change in the amount of hazardous waste based on video analysis, the system comprising: the system comprises a comprehensive situation detection module, a data visualization module, a risk discrimination module and a scheduling management and control module;
the comprehensive situation detection module is used for detecting the dangerous waste quantity change condition of the system access enterprise, integrating detection results and analyzing the comprehensive situation change according to the data integration result; the comprehensive situation detection module comprises a basic data importing unit, an enterprise data extracting unit and a change trend analyzing unit;
the basic data importing unit is used for importing basic data of each enterprise in the system, including enterprise names, enterprise positions and enterprise authorities;
the enterprise data extraction unit is used for extracting the related data of the change of the dangerous waste quantity of each enterprise, integrating the extracted data and transmitting the integrated data to the change trend analysis unit;
the change trend analysis unit is used for analyzing the situation change of each enterprise according to the dangerous waste quantity of each enterprise acquired by the real-time data acquisition unit and acquiring the change trend of the dangerous waste quantity of each enterprise.
The data visualization module is used for displaying the detection data in the comprehensive situation detection module to the system interface through a visualization technology; the data visualization module comprises a data visualization template and a visualization tool interface management unit;
the data visualization template is a visualization template designed in advance for related staff, and a user can flexibly adjust and fill data according to own requirements; these templates include patterns and layouts of various charts and graphs, common data visualization templates include Infogram, canva, and the like.
The visual tool interface management unit is used for packaging the interfaces of the visual tools at the bottom layer, and centrally managing the interfaces of the visual tools, including D3.js, plotly, tableau, arcGIS and the like; direct operation and maintenance work of a developer on the bottom layer interface are reduced; many data visualization tools provide an open interface or API that allows developers to programmatically use their functionality; the visual tool interfaces are uniformly managed, so that the management and maintenance processes of the interfaces are simplified, and meanwhile, the readability and maintainability of codes are improved, so that new visual tools are supported or new functions are added.
The risk judging module is used for monitoring the dangerous and useless state of each enterprise, evaluating the real-time risk condition of each enterprise and judging whether the risk index of any enterprise exceeds a risk threshold value; the risk judging module comprises a dangerous waste state monitoring unit, a risk assessment unit and a risk warning unit;
the dangerous waste state monitoring unit is used for monitoring the dangerous waste real-time state data stored by each enterprise and transmitting the monitoring data to the risk assessment unit;
the risk assessment unit is used for assessing the risk index of the dangerous waste stored in the enterprise at present and calculating the risk index of each enterprise in the system;
the risk alarm unit is used for judging the risk index of each enterprise, marking the enterprise with the risk index exceeding a preset threshold as a risk enterprise, and sending an alarm through the system.
The scheduling management and control module is used for managing the dangerous waste storage condition of the risk enterprises according to the risk assessment result of the risk judging module and scheduling the dangerous waste of the risk enterprises by combining the comprehensive situation monitoring data; the scheduling management and control module comprises a hazardous waste scheduling management unit and an enterprise data updating unit;
the hazardous waste scheduling management unit is used for managing hazardous waste scheduling of the inauguration enterprises, and reducing the risk index of the enterprises by scheduling the hazardous waste of the inauguration enterprises;
the enterprise data updating unit is used for updating the enterprise related data in the scheduled system and recording the current scheduling related data.
The output end of the comprehensive situation detection module is connected with the input ends of the data visualization module, the risk discrimination module and the scheduling management and control module, and the output end of the risk discrimination module is connected with the input ends of the data visualization module and the scheduling management and control module.
Example 2: as shown in fig. 2, the present embodiment provides a method for detecting a change in a dangerous waste amount based on video analysis, which is implemented based on a system for detecting a change in a dangerous waste amount based on video analysis in the embodiment, and specifically includes the following steps:
s1: acquiring relevant data of each enterprise in a target area, and analyzing the dangerous waste quantity change trend of each enterprise;
step S1-1: acquiring enterprise data access rights in a target area of an access server, and reading dangerous waste related data of each enterprise, wherein the dangerous waste related data comprises the current dangerous waste quantity of the enterprise, the dangerous waste storage position of the enterprise, the upper limit of the preset dangerous waste quantity of the enterprise and an enterprise cooperation treatment unit list; the acquiring mode comprises that an enterprise autonomously fills in related data or an enterprise authorization system monitors an enterprise warehouse in real time, video data of enterprise hazardous waste is acquired through a camera, and the related data of the hazardous waste of the enterprise is acquired based on a video analysis mode;
step S1-2: extracting historical data of the amount of the hazardous waste of the enterprise from a database or related data source, including enterprise names, time and the amount of the hazardous waste; analyzing the change trend of the amount of the dangerous waste of each enterprise according to the following steps:
step S1-2-1: the extracted data is cleaned, missing values in the data are removed, and the integrity and the accuracy of the data are ensured; converting the time data into a time sequence format; the time item can be subjected to format conversion according to the requirement, and the data are summarized according to the year or quarter so as to facilitate subsequent analysis;
step S1-2-2: decomposing the cleaned data, and splitting the original data Y (T) into a long-term trend T (T), a month fluctuation M (T), a cyclic fluctuation C (T) and an irregular fluctuation B (T) based on a seasonal decomposition method, wherein Y (T) =T (T) +M (T) +C (T) +B (T); wherein,
T（t）+C（t）=(Σk j=-ky t+j )/N ；
n represents the number of sample data, n=2k+1, y t+j The dangerous waste quantity at the time of t+j in the sample data is represented;
selecting historical data of the number of the enterprise hazardous waste in the last year as a sample, calculating the average number of trending data of each month, and taking the average number corresponding to each month to represent the month component of each month;
the detrending data = M (T) +b (T) = Y (T) -T (T) -C (T);
step S1-2-3: establishing a dangerous waste change trend prediction model:
S waste of =α+β 0 *t+β i *J i +λ；
Wherein S is Waste of Representing the amount of hazardous waste, alpha is the intercept term, beta 0 Coefficient, beta, representing the independent variable time t i Representing the coefficient corresponding to the ith month, J i Represents the month component of the ith month, i.e. [1,12 ]]The method comprises the steps of carrying out a first treatment on the surface of the Lambda represents fluctuation residual error, and is preset by related staff;
estimating coefficients alpha and beta in the model by a least square method by taking time as an independent variable and the amount of dangerous waste as an independent variable 0 The introduced monthly component may represent the fluctuating impact of the monthly variation on the amount of hazardous waste, with fluctuating residuals being the remainder that cannot be interpreted by trend and monthly fluctuations, representing random fluctuations or other unaccounted for factors. Coefficient beta i The significance of (c) may be determined by calculating the p-value or confidence interval method, the coefficient beta i The magnitude of (2) represents the degree of influence of one unit month change on the amount of hazardous waste, the positive coefficient represents the influence as positive correlation, and the negative coefficient represents the influence as negative correlation.
S2: analyzing the change history data of the dangerous waste quantity of each enterprise in the target area, excavating the dangerous waste disposal rule of each enterprise, and establishing a dangerous waste transfer behavior discrimination model;
step S2-1: acquiring dangerous waste change history data of enterprises in a target area, wherein the dangerous waste change history data comprise storage quantity change values and time points; cleaning and arranging the collected data, removing abnormal values or missing data, and performing standardized treatment to ensure the consistency and comparability of the data; sequencing the time points and the storage quantity change values to enable the storage quantity change values to be arranged according to a time sequence;
step S2-2: calculating the absolute value of the difference between the storage quantity change value of each time point and the storage quantity change value of the previous time point to obtain the fluctuation range of the storage quantity change value, wherein the u+1th fluctuation range is the number change value of the u+1th time point-the number change value of the u-th time point; storage amount change value = storage amount at the current point in time-storage amount at the previous point in time.
Observing the value of the fluctuation range, and if the value of the fluctuation range in the duration V is always less than or equal to c% of the storage capacity corresponding to each time point, considering that the quantity of dangerous wastes is relatively stable and no transfer occurs; if the fluctuation range value is greater than c% of the storage capacity corresponding to each time point in the duration V and the fluctuation range value occurs for a plurality of times in a short time, the time point is considered to have dangerous waste transfer behavior, and the time point with the dangerous waste transfer behavior is set as a dangerous waste transfer time point of a corresponding enterprise; the change trend and the fluctuation condition of the data can be more intuitively observed by using a chart or a visualization tool to carry out visual display on the fluctuation condition of the data.
Step S2-3: obtaining a sample set d= { (D) 1 ，p 1 ),(d 2 ，p 2 ),...,(d n ，p n ) And (d), wherein the i-th sample (d) i ，p i ) D in (d) i Time point p representing ith hazardous waste storage amount change i Representing a time point d i A change in the amount of hazardous waste stored; and marking the dangerous waste transfer time point as a leaf node, considering the class represented by the leaf node as the dangerous waste transfer behavior, and establishing a dangerous waste transfer behavior discrimination model based on a decision tree classification algorithm. When predicting a new sample, traversing the decision tree from the root node, selecting a corresponding branch according to the splitting rule on each node until reaching a leaf node, and taking the category of the node as a prediction result.
S3: monitoring the change of the dangerous waste quantity of each enterprise in the target area, analyzing the risk index of each enterprise, marking the enterprise exceeding the preset risk index threshold as a risk enterprise, and carrying out alarm prompt on the risk enterprise;
step S3-1: monitoring the dangerous waste quantity of each enterprise in the target area, and according to the real-time monitoring data, monitoring the dangerous waste quantity of each enterpriseThe conversion is predicted; predicting the dangerous waste quantity of each enterprise at the next time t 'according to the dangerous waste change trend prediction model in the step S1 to obtain the dangerous waste quantity L at the time t' corresponding to any enterprise t’ ；
Step S3-2: judging the dangerous waste transfer behaviors of enterprises according to the dangerous waste transfer behavior judging model established in the step S2 by combining the predicted data in the step 3-1, and judging the predicted dangerous waste quantity L t’ Introducing the dangerous waste transfer behavior discrimination model established in the step S2; when any enterprise has a risk waste transfer tendency, adding 1 to an evaluation influence factor q of the risk index of the enterprise;
step S3-3: evaluating the risk index of the dangerous waste currently stored by each enterprise, setting E enterprises in the current target area, and calculating the risk index F of any enterprise E according to the following formula e ；
F e =(L t’ /L e )×η 0 +sgn(h-ε)×η 1 +w g ×η 2 +q×100% ；
Wherein L is e Representing a preset storage capacity upper limit of an enterprise e, sgn () representing a sign function, h representing an application passing rate of a history transfer application of the enterprise, epsilon representing a preset application passing rate threshold in a database; w (w) g Representing the waiting time of the g-th application in the historical transition application of the enterprise; η (eta) 0 、η 1 、η 2 The influence coefficients of each evaluation risk index are respectively represented, and are preset and stored into a database by related staff;
if a plurality of enterprises exist in the target area and dangerous waste transfer trend exists, and the number of dangerous waste treatment in the target area is limited, the possibility that the transfer application of the current enterprise does not pass is increased, and if the dangerous waste storage quantity of the enterprise is in a supercritical value, the delay of the transfer application can cause the enterprise to increase illegal risks;
step S3-4: judging risk indexes of enterprises, marking the enterprises with the risk indexes exceeding a preset threshold f as risk enterprises, and sending alarm prompt to the risk enterprises.
S4: providing a risk interpretation report for the risk enterprises marked in the step S3, tracking the scheduling management related data of the risk enterprises, and feeding back and recording the risk processing results;
step S4-1: the alarm prompt sent to the risk enterprises comprises risk interpretation reports, the risk interpretation reports comprise alarm tracing, the enterprises verify the dangerous waste conditions of the enterprises according to the risk sources, if the data verification shows that no abnormal data exists, the enterprises manage the dangerous waste scheduling of the risk enterprises through a risk solution provided by the risk interpretation reports, and the risk indexes of the enterprises are reduced through the dangerous waste scheduling of the risk enterprises; if the data check shows that abnormal data exists, the related personnel of the enterprise initiate data correction, correct the abnormal data, and calculate the risk index of the enterprise again according to the corrected data;
for example, a risk early warning sent to the enterprise prompts the enterprise to submit a transfer application, a risk solution provided by a risk interpretation report comprises a transfer application submitting window of a relevant organization or department in a target area, the enterprise enters a relevant interface by clicking a jump link, a dangerous waste quantity change detection system reads dangerous waste relevant data of the enterprise to generate a first application material, a control authority of an enterprise warehouse camera is initiated through an enterprise terminal to identify the dangerous waste quantity stored in the enterprise warehouse, whether the dangerous waste quantity in the first application material accords with the actual condition is checked, if a data discrepancy exists between a dangerous waste quantity detection result based on video analysis and the first application material, data anomaly prompt is carried out, and relevant personnel of the enterprise process abnormal data.
Step S4-2: updating the scheduled enterprise related data, recording the scheduling, and marking the abnormal data in the data verification so as to facilitate the subsequent product optimization for improving the accuracy.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A dangerous waste quantity change detection method based on video analysis is characterized by comprising the following steps: the method comprises the following steps:
s1: acquiring relevant data of each enterprise in a target area, and analyzing the dangerous waste quantity change trend of each enterprise;
s2: analyzing the change history data of the dangerous waste quantity of each enterprise in the target area, excavating the dangerous waste disposal rule of each enterprise, and establishing a dangerous waste transfer behavior discrimination model;
s3: monitoring the change of the dangerous waste quantity of each enterprise in the target area, analyzing the risk index of each enterprise, marking the enterprise exceeding the preset risk index threshold as a risk enterprise, and carrying out alarm prompt on the risk enterprise;
s4: providing a risk interpretation report for the risk enterprises marked in the step S3, tracking the scheduling management related data of the risk enterprises, and feeding back and recording the risk processing results;
the S1 comprises the following steps:
step S1-1: acquiring enterprise data access rights in a target area of an access server, and reading dangerous waste related data of each enterprise, wherein the dangerous waste related data comprises the current dangerous waste quantity of the enterprise, the dangerous waste storage position of the enterprise, the upper limit of the preset dangerous waste quantity of the enterprise and an enterprise cooperation treatment unit list;
step S1-2: extracting historical data of the amount of the hazardous waste of the enterprise from a database or related data source, including enterprise names, time and the amount of the hazardous waste; analyzing the change trend of the amount of the dangerous waste of each enterprise according to the following steps:
step S1-2-1: cleaning the extracted data to remove missing values in the data; converting the time data into a time sequence format;
step S1-2-2: decomposing the cleaned data, and splitting the original data Y (T) into a long-term trend T (T), a month fluctuation M (T), a cyclic fluctuation C (T) and an irregular fluctuation B (T), wherein Y (T) =T (T) +M (T) +C (T) +B (T); wherein,
T（t）+C（t）=(Σk j=-ky t+j )/N ；
n represents the number of sample data, n=2k+1, y t+j The dangerous waste quantity at the time of t+j in the sample data is represented;
selecting historical data of the number of the enterprise hazardous waste in the last year as a sample, calculating the average number of trending data of each month, and taking the average number corresponding to each month to represent the month component of each month;
the detrending data = M (T) +b (T) = Y (T) -T (T) -C (T);
step S1-2-3: establishing a dangerous waste change trend prediction model:
S waste of =α+β 0 *t+β i *J i +λ；
Wherein S is Waste of Representing the amount of hazardous waste, alpha is the intercept term, beta 0 Coefficient, beta, representing the independent variable time t i Representing the coefficient corresponding to the ith month, J i Represents the month component of the ith month, i.e. [1,12 ]]The method comprises the steps of carrying out a first treatment on the surface of the Lambda represents the fluctuation residual, preset by the relevant staff.
2. The method for detecting the change of the dangerous waste quantity based on video analysis according to claim 1, wherein the method comprises the following steps: the step S2 comprises the following steps:
step S2-1: acquiring dangerous waste change history data of enterprises in a target area, wherein the dangerous waste change history data comprise storage quantity change values and time points; sequencing the time points and the storage quantity change values to enable the storage quantity change values to be arranged according to a time sequence;
step S2-2: calculating the absolute value of the difference between the storage quantity change value of each time point and the storage quantity change value of the previous time point to obtain the fluctuation range of the storage quantity change value, wherein the u+1th fluctuation range is the number change value of the u+1th time point-the number change value of the u-th time point;
if the value of the fluctuation range in the duration V is always less than or equal to c% of the storage capacity corresponding to each time point, the quantity of dangerous wastes is considered to be relatively stable, and no transfer occurs; if the fluctuation range value is larger than the corresponding storage capacity c of each time point in the duration V, the time point is considered to have dangerous waste transfer behavior, and the time point with the dangerous waste transfer behavior is set as the dangerous waste transfer time point of the corresponding enterprise;
step S2-3: obtaining a sample set d= { (D) 1 ，p 1 ),(d 2 ，p 2 ),...,(d n ，p n ) And (d), wherein the i-th sample (d) i ，p i ) D in (d) i Time point p representing ith hazardous waste storage amount change i Representing a time point d i A change in the amount of hazardous waste stored; and marking the time point with dangerous waste transfer behaviors as a leaf node, considering the class represented by the leaf node as the dangerous waste transfer behavior, and establishing a dangerous waste transfer behavior discrimination model based on a decision tree classification algorithm.
3. The method for detecting the change of the dangerous waste quantity based on video analysis according to claim 1, wherein the method comprises the following steps: the step S3 comprises the following steps:
step S3-1: monitoring the dangerous waste quantity of each enterprise in the target area, and predicting the dangerous waste quantity transformation of each enterprise according to real-time monitoring data; according to the dangerous waste change trend prediction model in the step S1, the dangerous waste number of each enterprise at the next time t' is calculatedPredicting the quantity to obtain the quantity L of dangerous wastes at t' corresponding to any enterprise t’ ；
Step S3-2: judging the dangerous waste transfer behaviors of enterprises according to the dangerous waste transfer behavior judging model established in the step S2 by combining the predicted data in the step 3-1, and adding 1 to the evaluation influence factor q of the risk index of any enterprise when the dangerous waste transfer tendency exists in the enterprise;
step S3-3: evaluating the risk index of the dangerous waste currently stored by each enterprise, setting E enterprises in the current target area, and calculating the risk index F of any enterprise E according to the following formula e ；
F e =(L t’ /L e )×η 0 +sgn(h-ε)×η 1 +w g ×η 2 +q×100% ；
Wherein L is e Representing a preset storage capacity upper limit of an enterprise e, sgn () representing a sign function, h representing an application passing rate of a history transfer application of the enterprise, epsilon representing a preset application passing rate threshold in a database; w (w) g Representing the waiting time of the g-th application in the historical transition application of the enterprise; η (eta) 0 、η 1 、η 2 The influence coefficients of each evaluation risk index are respectively represented, and are preset and stored into a database by related staff;
step S3-4: judging risk indexes of enterprises, marking the enterprises with the risk indexes exceeding a preset threshold f as risk enterprises, and sending alarm prompt to the risk enterprises.
4. The method for detecting the change of the dangerous waste quantity based on video analysis according to claim 1, wherein the method comprises the following steps: the step S4 comprises the following steps:
step S4-1: the alarm prompt sent to the risk enterprises comprises risk interpretation reports, wherein the risk interpretation reports comprise alarm tracing, the enterprises verify the dangerous waste conditions of the enterprises according to the risk sources, and if the data verification shows that no abnormal data exists, the enterprises manage dangerous waste scheduling of the risk enterprises through a risk solution provided by the risk interpretation reports; if the data check shows that abnormal data exists, the related personnel of the enterprise initiate data correction, correct the abnormal data, and calculate the risk index of the enterprise again according to the corrected data;
step S4-2: updating the scheduled enterprise related data, recording the current scheduling, and marking the abnormal data in the data verification.
5. Dangerous waste quantity change detection system based on video analysis, its characterized in that: the system comprises: the system comprises a comprehensive situation detection module, a data visualization module, a risk discrimination module and a scheduling management and control module;
the comprehensive situation detection module is used for detecting the dangerous waste quantity change condition of a system access enterprise, integrating detection results and analyzing the comprehensive situation change according to data integration results;
the data visualization module is used for displaying the detection data in the comprehensive situation detection module to a system interface through a visualization technology;
the risk judging module is used for monitoring the dangerous and useless state of each enterprise, evaluating the real-time risk condition of each enterprise and judging whether the risk index of any enterprise exceeds a risk threshold value;
the scheduling management and control module is used for managing dangerous waste storage conditions of the risk enterprises according to the risk assessment result of the risk judging module and scheduling the dangerous wastes of the risk enterprises by combining the comprehensive situation monitoring data;
the output end of the comprehensive situation detection module is connected with the input ends of the data visualization module, the risk judging module and the scheduling management and control module, and the output end of the risk judging module is connected with the input ends of the data visualization module and the scheduling management and control module.
6. The video analysis-based hazardous waste quantity change detection system according to claim 5, wherein: the comprehensive situation detection module comprises a basic data importing unit, an enterprise data extracting unit and a change trend analyzing unit;
the basic data importing unit is used for importing basic data of each enterprise in the system, including enterprise names, enterprise positions and enterprise authorities;
the enterprise data extraction unit is used for extracting data related to the change of the quantity of the dangerous waste of each enterprise, integrating the extracted data and transmitting the integrated data to the change trend analysis unit;
the change trend analysis unit is used for analyzing situation changes of enterprises according to the dangerous waste quantity of the enterprises acquired by the real-time data acquisition unit and acquiring the change trend of the dangerous waste quantity of the enterprises.
7. The video analysis-based hazardous waste quantity change detection system according to claim 5, wherein: the data visualization module comprises a data visualization template and a visualization tool interface management unit;
the data visualization template is a visualization template designed in advance by related staff, and the visualization tool interface management unit is used for packaging the interfaces of the bottom visualization tools and centrally managing the interfaces of the visualization tools.
8. The video analysis-based hazardous waste quantity change detection system according to claim 5, wherein: the risk judging module comprises a dangerous waste state monitoring unit, a risk assessment unit and a risk warning unit;
the dangerous waste state monitoring unit is used for monitoring the real-time state data of dangerous wastes stored by each enterprise and transmitting the monitoring data to the risk assessment unit;
the risk assessment unit is used for assessing the risk index of the dangerous waste stored in the enterprise at present and calculating the risk index of each enterprise in the system;
the risk alarm unit is used for judging the risk indexes of enterprises, marking the enterprises with the risk indexes exceeding a preset threshold as risk enterprises, and sending an alarm through the system.
9. The video analysis-based hazardous waste quantity change detection system according to claim 5, wherein: the dispatching management and control module comprises a hazardous waste dispatching management unit and an enterprise data updating unit;
the hazardous waste scheduling management unit is used for managing hazardous waste scheduling of an inauguration enterprise, and reducing the risk index of the enterprise by scheduling the hazardous waste of the inauguration enterprise;
the enterprise data updating unit is used for updating the enterprise related data in the scheduled system and recording the current scheduling related data.
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