Industrial wastewater treatment data management method and system

Abstract

The present invention provides an industrial wastewater treatment data management method and system, which belongs to the field of data management technology, and specifically includes: determining the credibility of the treatment data of the emission source and the credible emission source according to the similarity between the data of the non-investigation period and the data of the investigation period, obtaining the historical emission data of the target emission source, and determining the reference emission source of the target emission source in combination with the industrial wastewater treatment equipment of the target emission source, determining the fraud probability of the target emission source by using the deviation between the real-time emission data and the historical emission data of the reference emission source and the credibility of the treatment data of the reference emission source; when the fraud probability of the target emission source does not meet the requirements, analyzing and processing the historical emission data of the target emission source, locating and marking the fraud risk data, and improving the reliability of the treatment data.

Description

Technical Field

[0001] The present invention belongs to the technical field of data management, and in particular relates to an industrial wastewater treatment data management method and system. Background Art

[0002] With the digital transformation of industrial wastewater treatment systems, the accuracy of industrial wastewater treatment management has been further improved. Specifically, in the invention patent application CN201310078622.1 "A Volatile Organic Compound Emission Control Data Management System and Monitoring Platform", after processing the relevant data of the emission source to be inspected uploaded by the user, the corresponding emission source directory list in the database storage unit is updated, and the monitoring and management of the volatile organic compound emission source is realized. However, it is not difficult to find through analysis that there are the following technical problems:

[0003] In actual use, some companies may use cheating equipment to conceal the actual discharge situation of industrial wastewater from emission sources. Therefore, if the cheating probability of different emission sources cannot be evaluated based on the analysis results of the emission data, and the emission data is managed based on the evaluation results of the cheating probability, the reliability of the monitoring data of industrial wastewater treatment cannot be guaranteed.

[0004] In response to the above technical problems, the present invention provides an industrial wastewater treatment data management method and system. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a method for managing industrial wastewater treatment data is provided.

[0007] An industrial wastewater treatment data management method specifically includes:

[0008] S1 determines the real-time emission data of the target emission source, and when it is determined that there is an abnormality in the real-time emission data of the target emission source by using the matching of the real-time emission data and the historical emission data of the target emission source, proceeds to the next step;

[0009] S2 obtains the historical emission data of industrial wastewater from different emission sources, divides the historical emission data into investigation period data and non-emission period data according to whether it is an on-site investigation period, and determines the credibility of the emission source governance data and the credible emission source based on the similarity between the non-investigation period data and the investigation period data;

[0010] S3: acquiring historical emission data of a target emission source, and determining a matching coefficient between the target emission source and different credible emission sources in combination with the industrial wastewater treatment equipment of the target emission source, and determining a reference emission source of the target emission source using the matching coefficient;

[0011] S4 determines the fraud probability of the target emission source by using the deviation between the real-time emission data and the historical emission data of the reference emission source and the credibility of the governance data of the reference emission source. When the fraud probability of the target emission source does not meet the requirements, the historical emission data of the target emission source is analyzed and processed to locate and mark the fraud risk data.

[0012] A further technical solution is that the real-time emission data is determined based on the monitoring data of the Internet of Things device of the target emission source.

[0013] A further technical solution is that the real-time emission data includes real-time emission amount and real-time emission data of different types of pollutants.

[0014] A further technical solution is to determine whether the real-time emission data of the target emission source is abnormal, specifically including:

[0015] Determine the real-time emission amount of the target emission source and the real-time emission data of different types of pollutants using the real-time emission data, and determine the matching emission data in the historical emission data according to the real-time emission amount;

[0016] Determine emission amounts and emission data of different types of pollutants at different historical matching emission times according to the matching emission data, and determine emission data deviation amounts at different historical matching emission times based on deviations of emission data of different types of pollutants at different historical matching emission times;

[0017] The data deviation emission times in the historical matching emission times are determined by using the emission data deviation amounts of different historical matching emission times, and the emission data anomaly values ​​of the target emission source are determined by using the proportion of the data deviation emission times in the historical matching emission times. The emission data anomaly values ​​are used to determine whether there is an anomaly in the real-time emission data of the target emission source.

[0018] A further technical solution is that when the abnormal value of the emission data of the target emission source is greater than a preset abnormal threshold, it is determined that the real-time emission data of the target emission source is abnormal.

[0019] A further technical solution is that when there is no abnormality in the real-time emission data of the target emission source, the fraud probability of the target emission source is not evaluated temporarily.

[0020] A further technical solution is that the matching emission data is historical emission data in which the deviation between the emission of the target emission source and the real-time emission is within a preset range.

[0021] A further technical solution is that when the fraud probability of the target emission source meets the requirements, there is no need to locate and mark the fraud risk data of the target emission source.

[0022] A further technical solution is to locate and label fraud risk data, including

[0023] Analyze and process the historical emission data of the target emission source, locate and mark the fraud risk data

[0024] Determine emission data of different types of pollutants at different historical emission times based on the historical emission data of the target emission source, and determine the number of reference emission sources whose deviations do not meet the requirements based on the deviations between the emission data of different types of pollutants and the reference emission data of the reference emission source;

[0025] When the number of reference emission sources whose deviations do not meet the requirements does not meet the requirements, the historical emission data of the corresponding historical emission times are fraud risk data, and the fraud risk data are marked with preset abnormal labels.

[0026] On the other hand, an embodiment of the present application provides an industrial wastewater treatment data management system, which adopts the above-mentioned industrial wastewater treatment data management method, specifically including:

[0027] Abnormal assessment module, emission source screening module, reference emission source screening module, marking processing module;

[0028] The abnormality assessment module is responsible for determining the real-time emission data of the target emission source, and using the matching of the real-time emission data and the historical emission data of the target emission source to determine whether the real-time emission data of the target emission source is abnormal;

[0029] The emission source screening module is responsible for obtaining the historical emission data of industrial wastewater from different emission sources, and dividing the historical emission data into investigation period data and non-emission period data according to whether it is an on-site investigation period, and determining the credibility of the emission source governance data and the credible emission source according to the similarity between the non-investigation period data and the investigation period data;

[0030] The reference emission source screening module is responsible for obtaining the historical emission data of the target emission source, and determining the matching coefficient between the target emission source and different credible emission sources in combination with the industrial wastewater treatment equipment of the target emission source, and using the matching coefficient to determine the reference emission source of the target emission source;

[0031] The labeling processing module is responsible for determining the fraud probability of the target emission source by using the deviation between the real-time emission data and the historical emission data of the reference emission source and the credibility of the governance data of the reference emission source. When the fraud probability of the target emission source does not meet the requirements, the historical emission data of the target emission source is analyzed and processed to locate and label the fraud risk data.

[0032] The beneficial effects of the present invention are:

[0033] 1. In the present invention, historical emission data and industrial wastewater treatment equipment of the target emission source are used to determine the matching coefficient between the target emission source and different credible emission sources and the reference emission source of the target emission source, thereby achieving the determination of the matching coefficient with different credible emission sources from two perspectives: the similarity of the historical emission data of the target emission source and the similarity of the industrial wastewater treatment equipment, thereby achieving the screening of the reference emission source of the target emission source and laying the foundation for further accurate assessment of the fraud probability of the target emission source.

[0034] 2. In the present invention, the fraud probability of the target emission source is determined by utilizing the deviation of real-time emission data and the historical emission data of the reference emission source and the credibility of the governance data of the reference emission source. This avoids the technical problem of inaccurate evaluation results of the fraud probability due to the deviation of the emission data of a single reference emission source, thereby improving the accuracy of the evaluation of the fraud probability. At the same time, by further combining the credibility of the governance data of different reference emission sources, a comprehensive consideration of the credibility of the data of different reference emission sources is also achieved.

[0035] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings;

[0038] Figure 1 It is a flow chart of a method for managing data for industrial wastewater treatment;

[0039] Figure 2 It is a flow chart of a method for determining the credibility of emission source governance data;

[0040] Figure 3 A flow chart of a method for determining a matching coefficient between a target emission source and different credible emission sources;

[0041] Figure 4 A flow chart of a method for determining a probability of fraud for a target emission source;

[0042] Figure 5 It is a framework diagram of an industrial wastewater treatment data management system. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0044] Some companies may use cheating equipment to conceal the actual discharge situation of industrial wastewater from emission sources. Therefore, it is necessary to evaluate the cheating probability of emission sources based on the analysis results of emission data and label the cheating emission sources.

[0045] Determine if there are anomalies in the real-time emission data of the target emission sources: Target emission sources whose real-time emissions deviate from historical emissions by more than 5% are identified as target emission sources with anomalies in real-time emission data.

[0046] Credibility of governance data: It is determined based on the similarity between the data in the non-investigation period and the data in the investigation period. The credibility of governance data is determined by the deviation between the emissions in the non-investigation period and the emissions in the investigation period. If the deviation of emissions is 3%, the credibility of governance data is 0.97.

[0047] Credible emission sources: emission sources with governance data credibility greater than 0.98.

[0048] The matching coefficient between the target emission source and different credible emission sources is determined by using the deviation of historical emissions and the same proportion of industrial wastewater treatment equipment. When the deviation is 0.03 and the same proportion is 0.9, the matching coefficient is 0.9 multiplied by 0.97, which is 0.873.

[0049] The reference emission source is a credible emission source with a matching coefficient greater than 0.8.

[0050] Fraud probability: It is determined by using the deviation between the real-time emission data and the historical emission data of the reference emission source and the credibility of the governance data of the reference emission source. Specifically, when the deviations are 0.03, 0.04, and 0.05 respectively, and the credibility of the governance data is 0.99, 0.98, and 0.98 respectively, the fraud probability is 17%.

[0051] When the fraud probability is greater than 10%, the fraud probability of the target emission source does not meet the requirements. Example

[0052] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a method for industrial wastewater treatment data management is provided, which specifically includes:

[0053] S1 determines the real-time emission data of the target emission source, and when it is determined that there is an abnormality in the real-time emission data of the target emission source by using the matching of the real-time emission data and the historical emission data of the target emission source, proceeds to the next step;

[0054] Furthermore, the real-time emission data is determined based on monitoring data of the Internet of Things device of the target emission source.

[0055] It can be understood that the real-time emission data includes real-time emission amount and real-time emission data of different types of pollutants.

[0056] Specifically, determining that the real-time emission data of the target emission source is abnormal includes:

[0057] Determine the real-time emission amount of the target emission source and the real-time emission data of different types of pollutants using the real-time emission data, and determine the matching emission data in the historical emission data according to the real-time emission amount;

[0058] Determine emission amounts and emission data of different types of pollutants at different historical matching emission times according to the matching emission data, and determine emission data deviation amounts at different historical matching emission times based on deviations of emission data of different types of pollutants at different historical matching emission times;

[0059] The data deviation emission times in the historical matching emission times are determined by using the emission data deviation amounts of different historical matching emission times, and the emission data anomaly values ​​of the target emission source are determined by using the proportion of the data deviation emission times in the historical matching emission times. The emission data anomaly values ​​are used to determine whether there is an anomaly in the real-time emission data of the target emission source.

[0060] Furthermore, when the abnormal value of the emission data of the target emission source is greater than a preset abnormal threshold, it is determined that the real-time emission data of the target emission source is abnormal.

[0061] It is understandable that when there is no abnormality in the real-time emission data of the target emission source, the fraud probability of the target emission source will not be evaluated for the time being.

[0062] Specifically, the matching emission data is historical emission data in which the deviation between the emission of the target emission source and the real-time emission is within a preset range.

[0063] It should also be noted that the real-time emission data of the target emission source is determined to be abnormal, including:

[0064] Determine the real-time emission amount of the target emission source and the real-time emission data of different types of pollutants using the real-time emission data, and determine the matching emission data in the historical emission data according to the real-time emission amount;

[0065] Determining emission data of different types of pollutants in different historical matching emission times according to the matching emission data, and determining reference emission ranges of different types of pollutants under the real-time emission amount based on the emission data of different types of pollutants in different historical matching emission times;

[0066] The types of pollutants not within the reference emission range are determined through the reference emission range and the real-time emission data, and whether there is an abnormality in the real-time emission data of the target emission source is determined through the proportion of the types of pollutants not within the reference emission range.

[0067] Furthermore, when the proportion of the category is greater than the proportion of the preset category, it is determined that the real-time emission data of the target emission source is abnormal.

[0068] It should also be noted that the real-time emission data of the target emission source is determined to be abnormal, including:

[0069] S11: using the real-time emission data to determine the real-time emission amount of the target emission source and the real-time emission data of different types of pollutants, and determining matching emission data in the historical emission data according to the real-time emission amount;

[0070] S12 determines emission data of different types of pollutants in different historical matching emission times according to the matching emission data, determines reference emission ranges of different types of pollutants under the real-time emission amount based on the emission data of different types of pollutants in different historical matching emission times, and determines whether there is a type of pollutant whose real-time emission data is not within the reference emission range, if so, proceeds to the next step, if not, proceeds to step;

[0071] S13 regards the type of pollutant whose real-time emission data is not within the reference emission range as the suspected abnormal pollutant type, and determines whether the number of the suspected abnormal pollutant type meets the requirement. If so, proceed to the next step; if not, determine that the real-time emission data of the target emission source is abnormal;

[0072] S14 determines the real-time abnormal value of the target emission source based on the deviation between the real-time emission data of different suspected abnormal pollutant types and the endpoints of the reference emission range, and judges whether the real-time abnormal value meets the requirements. If so, proceed to the next step; if not, it is determined that the real-time emission data of the target emission source is abnormal;

[0073] S15 determines the emission data deviation amounts of different historical matching emission times based on the deviation of emission data of different types of pollutants in different historical matching emission times, determines the data deviation emission times in the historical matching emission times through the emission data deviation amounts of different historical matching emission times, and determines the emission data abnormal value of the target emission source in combination with the emission data deviation amounts of different data deviation emission times, and determines whether there is an abnormality in the real-time emission data of the target emission source through the emission data abnormal value.

[0074] S2 obtains the historical emission data of industrial wastewater from different emission sources, divides the historical emission data into investigation period data and non-emission period data according to whether it is an on-site investigation period, and determines the credibility of the emission source governance data and the credible emission source based on the similarity between the non-investigation period data and the investigation period data;

[0075] Furthermore, the historical emission data is divided into investigation period data and non-emission period data according to whether it is an on-site investigation period, specifically including:

[0076] The historical emission data of on-site inspectors during the on-site emission period will be used as the investigation period data, and the historical emission data that does not belong to the investigation period data will be used as the non-emission period data.

[0077] Specifically, Figure 2 As shown, the method for determining the credibility of the emission source governance data is:

[0078] Use the investigation period data to determine the historical emission data of different types of pollutants in different emission flow intervals and use them as reference emission data;

[0079] Determine the average value of historical emission data of different types of pollutants in different emission flow intervals during the non-inspection period based on the non-inspection period data, and determine the emission data credibility coefficient of different emission flow intervals by using the deviation between the average value of historical emission data of different types of pollutants and the reference emission data;

[0080] Based on the historical emission times in different emission flow intervals, the weight values ​​of different emission flow intervals are determined, and the credibility of the control data of the emission source is determined in combination with the credibility coefficients of the emission data in different emission flow intervals.

[0081] Furthermore, when the credibility of the governance data of the emission source meets the requirements, the emission source is determined to be a credible emission source.

[0082] It should be further explained that the method for determining the credibility of the emission source governance data is:

[0083] Use the investigation period data to determine the historical emission data of different types of pollutants in different emission flow intervals and use them as reference emission data;

[0084] Determine historical emission data of different types of pollutants in different emission flow intervals during the non-inspection period based on the non-inspection period data, and determine the deviation emission times in different emission flow intervals using the deviations between the historical emission data of different types of pollutants and the reference emission data;

[0085] The credibility coefficient of emission data in different emission flow intervals is determined by the proportion of deviation emission times in different emission flow intervals, and the weight values ​​of different emission flow intervals are determined based on the historical emission times in different emission flow intervals. The credibility of the control data of the emission source is determined in combination with the credibility coefficient of emission data in different emission flow intervals.

[0086] It should be further explained that the method for determining the credibility of the emission source governance data is:

[0087] S21 uses the investigation period data to determine historical emission data of different types of pollutants in different emission flow intervals, and uses the historical emission data as reference emission data;

[0088] S22 determines the historical emission data of different types of pollutants in different emission flow intervals in the non-investigation period based on the non-investigation period data, and uses the deviation between the historical emission data of different types of pollutants and the reference emission data to determine whether there is a historical emission number in different emission flow intervals that does not meet the requirements of the deviation. If so, proceed to the next step; if not, proceed to step S25;

[0089] S23 takes the historical emission times that do not meet the requirements as the deviation emission times, and determines whether there is an emission flow interval in which the deviation emission times do not meet the requirements. If so, it is determined that the emission source is not a credible emission source. If not, proceed to the next step.

[0090] S24: taking the emission flow interval in which the deviation emission number is greater than the preset emission number as the suspected abnormal emission interval, and judging whether the number of the suspected abnormal emission intervals meets the requirement, if so, proceeding to the next step, if not, determining that the emission source is not a credible emission source;

[0091] S25 determines the average values ​​of historical emission data of different types of pollutants in different emission flow intervals during the non-inspection period based on the data of the non-inspection period, and determines the emission data credibility coefficient of different emission flow intervals by using the deviation between the average values ​​of historical emission data of different types of pollutants and the reference emission data, determines the weight values ​​of different emission flow intervals based on the number of historical emissions in different emission flow intervals, and determines the credibility of the control data of the emission source in combination with the emission data credibility coefficient of different emission flow intervals.

[0092] S3: acquiring historical emission data of a target emission source, and determining a matching coefficient between the target emission source and different credible emission sources in combination with the industrial wastewater treatment equipment of the target emission source, and determining a reference emission source of the target emission source using the matching coefficient;

[0093] Specifically, Figure 3 As shown, the method for determining the matching coefficient between the target emission source and different credible emission sources is:

[0094] Based on the industrial wastewater treatment equipment of the target emission source, determining the equipment matching coefficient between the trusted data source and the target data source;

[0095] Based on the historical emission data of the target emission source, determine the historical emission times of the target emission source in different emission flow intervals, and determine the emission data matching coefficient between the trusted data source and the target data source according to the deviation of the historical emission times in different emission flow intervals;

[0096] The matching coefficient between the target emission source and different credible emission sources is determined by the weighted sum of the emission data matching coefficient and the equipment matching coefficient.

[0097] Furthermore, when the matching coefficient between the credible emission source and the target emission source is within a preset range, the credible emission source is determined to be a reference emission source.

[0098] It should also be noted that the method for determining the matching coefficient between the target emission source and different credible emission sources is:

[0099] Based on the industrial wastewater treatment equipment of the target emission source, determine the deviation quantity between the industrial wastewater treatment equipment of the trusted data source and the target data source, and judge whether the deviation quantity of the industrial wastewater treatment equipment meets the requirement. If so, proceed to the next step; if not, determine that the trusted emission source does not belong to the reference emission source;

[0100] Based on the historical emission data of the target emission source, determine the historical emission times of the target emission source in different emission flow intervals, and determine whether the trusted data source has an emission flow interval in which the deviation of the historical emission times does not meet the requirements according to the deviation of the historical emission times in different emission flow intervals; if not, proceed to the next step; if so, determine that the trusted emission source does not belong to the reference emission source;

[0101] Determine the emission data matching coefficient between the trusted data source and the target data source according to the deviation of the historical emission times in different emission flow intervals, and judge whether the emission data matching coefficient between the trusted data source and the target data source meets the requirements, if yes, proceed to the next step, if not, determine that the trusted emission source does not belong to the reference emission source;

[0102] Based on the industrial wastewater treatment equipment of the target emission source, the equipment matching coefficient between the trusted data source and the target data source is determined, and the matching coefficient between the target emission source and different trusted emission sources is determined by the weight sum of the emission data matching coefficient and the equipment matching coefficient.

[0103] S4 determines the fraud probability of the target emission source by using the deviation between the real-time emission data and the historical emission data of the reference emission source and the credibility of the governance data of the reference emission source. When the fraud probability of the target emission source does not meet the requirements, the historical emission data of the target emission source is analyzed and processed to locate and mark the fraud risk data.

[0104] Specifically, it should be noted that Figure 4 As shown, the method for determining the fraud probability of the target emission source is:

[0105] Determine the real-time emission amount of the target emission source and the real-time emission data of different types of pollutants based on the real-time emission data, and determine the reference emission data of the historical emission data of the reference emission source through the real-time emission amount of the target emission source;

[0106] Determine the deviation between the real-time emission data of different types of pollutants of the target emission source and the reference emission data of the reference emission source using the reference emission data of the reference emission source, and determine the deviation amount of pollutants between the target emission source and different reference emission sources using the deviation;

[0107] The deviation emission source in the reference emission source is determined by the pollutant deviation, and the fraud probability of the target emission source is determined by using the ratio of the sum of the credibility of the treatment data of the deviation emission source.

[0108] Furthermore, when the fraud probability of the target emission source meets the requirements, there is no need to locate and mark the fraud risk data of the target emission source.

[0109] It is understandable that the location and labeling of fraud risk data specifically includes:

[0110] Analyze and process the historical emission data of the target emission source, locate and mark the fraud risk data

[0111] Determine emission data of different types of pollutants at different historical emission times based on the historical emission data of the target emission source, and determine the number of reference emission sources whose deviations do not meet the requirements based on the deviations between the emission data of different types of pollutants and the reference emission data of the reference emission source;

[0112] When the number of reference emission sources whose deviations do not meet the requirements does not meet the requirements, the historical emission data of the corresponding historical emission times is fraud risk data, and the fraud risk data is marked with a preset abnormal label. Example

[0113] Second, as Figure 5 As shown, the embodiment of the present application provides an industrial wastewater treatment data management system, which adopts the above-mentioned industrial wastewater treatment data management method, specifically including:

[0114] Abnormal assessment module, emission source screening module, reference emission source screening module, marking processing module;

[0115] The abnormality assessment module is responsible for determining the real-time emission data of the target emission source, and using the matching of the real-time emission data and the historical emission data of the target emission source to determine whether the real-time emission data of the target emission source is abnormal;

[0116] The emission source screening module is responsible for obtaining the historical emission data of industrial wastewater from different emission sources, and dividing the historical emission data into investigation period data and non-emission period data according to whether it is an on-site investigation period, and determining the credibility of the emission source governance data and the credible emission source according to the similarity between the non-investigation period data and the investigation period data;

[0117] The reference emission source screening module is responsible for obtaining the historical emission data of the target emission source, and determining the matching coefficient between the target emission source and different credible emission sources in combination with the industrial wastewater treatment equipment of the target emission source, and using the matching coefficient to determine the reference emission source of the target emission source;

[0118] The labeling processing module is responsible for determining the fraud probability of the target emission source by using the deviation between the real-time emission data and the historical emission data of the reference emission source and the credibility of the governance data of the reference emission source. When the fraud probability of the target emission source does not meet the requirements, the historical emission data of the target emission source is analyzed and processed to locate and label the fraud risk data.

[0119] By using the above embodiments, the present invention achieves the following beneficial effects:

[0120] 1. In the present invention, historical emission data and industrial wastewater treatment equipment of the target emission source are used to determine the matching coefficient between the target emission source and different credible emission sources and the reference emission source of the target emission source, thereby achieving the determination of the matching coefficient with different credible emission sources from two perspectives: the similarity of the historical emission data of the target emission source and the similarity of the industrial wastewater treatment equipment, thereby achieving the screening of the reference emission source of the target emission source and laying the foundation for further accurate assessment of the fraud probability of the target emission source.

[0121] 2. In the present invention, the fraud probability of the target emission source is determined by utilizing the deviation of real-time emission data and the historical emission data of the reference emission source and the credibility of the governance data of the reference emission source. This avoids the technical problem of inaccurate evaluation results of the fraud probability due to the deviation of the emission data of a single reference emission source, thereby improving the accuracy of the evaluation of the fraud probability. At the same time, by further combining the credibility of the governance data of different reference emission sources, a comprehensive consideration of the credibility of the data of different reference emission sources is also achieved.

[0122] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0123] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0124] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for managing industrial wastewater treatment data, characterized in that: Specifically include: Determine the real-time emission data of the target emission source, and when it is determined that there is an abnormality in the real-time emission data of the target emission source by using the matching of the real-time emission data and the historical emission data of the target emission source, proceed to the next step; Obtain historical emission data of industrial wastewater from different emission sources, divide the historical emission data into investigation period data and non-emission period data according to whether it is an on-site investigation period, and determine the credibility of the emission source governance data and the credible emission source based on the similarity between the non-investigation period data and the investigation period data; Acquire historical emission data of a target emission source, and determine a matching coefficient between the target emission source and different credible emission sources in combination with the industrial wastewater treatment equipment of the target emission source, and use the matching coefficient to determine a reference emission source of the target emission source; The fraud probability of the target emission source is determined by using the deviation between the real-time emission data and the historical emission data of the reference emission source and the credibility of the governance data of the reference emission source. When the fraud probability of the target emission source does not meet the requirements, the historical emission data of the target emission source is analyzed and processed to locate and mark the fraud risk data; The method for determining the matching coefficient between the target emission source and different credible emission sources is: Based on the industrial wastewater treatment equipment of the target emission source, determine the deviation amount between the credible emission source and the industrial wastewater treatment equipment of the target emission source, and judge whether the deviation amount of the industrial wastewater treatment equipment meets the requirement. If so, proceed to the next step; if not, determine that the credible emission source does not belong to the reference emission source; Based on the historical emission data of the target emission source, determine the historical emission times of the target emission source in different emission flow intervals, and determine whether the deviation of the historical emission times of the credible emission source does not meet the required emission flow interval according to the deviation of the historical emission times in different emission flow intervals. If not, proceed to the next step. If so, determine that the credible emission source does not belong to the reference emission source. Determine the emission data matching coefficient between the credible emission source and the target emission source according to the deviation of the historical emission times in different emission flow intervals, and judge whether the emission data matching coefficient between the credible emission source and the target emission source meets the requirements, if yes, proceed to the next step, if not, determine that the credible emission source does not belong to the reference emission source; Based on the industrial wastewater treatment equipment of the target emission source, determine the equipment matching coefficient between the credible emission source and the target emission source, and determine the matching coefficient between the target emission source and different credible emission sources by the weight sum of the emission data matching coefficient and the equipment matching coefficient; The method for determining the fraud probability of the target emission source is: Determine the real-time emission amount of the target emission source and the real-time emission data of different types of pollutants based on the real-time emission data, and determine the reference emission data of the historical emission data of the reference emission source through the real-time emission amount of the target emission source; Determine the deviation between the real-time emission data of different types of pollutants of the target emission source and the reference emission data of the reference emission source using the reference emission data of the reference emission source, and determine the deviation amount of pollutants between the target emission source and different reference emission sources using the deviation; The deviation emission source in the reference emission source is determined by the pollutant deviation, and the fraud probability of the target emission source is determined by using the ratio of the sum of the credibility of the treatment data of the deviation emission source.

2. The industrial wastewater treatment data management method according to claim 1, characterized in that: The real-time emission data is determined based on the monitoring data of the Internet of Things device of the target emission source.

3. The industrial wastewater treatment data management method according to claim 1, characterized in that: The real-time emission data includes real-time emission amount and real-time emission data of different types of pollutants.

4. The industrial wastewater treatment data management method according to claim 1, characterized in that: Determine that the real-time emission data of the target emission source is abnormal, including: Determine the real-time emission amount of the target emission source and the real-time emission data of different types of pollutants using the real-time emission data, and determine the matching emission data in the historical emission data according to the real-time emission amount; Determine emission amounts and emission data of different types of pollutants at different historical matching emission times according to the matching emission data, and determine emission data deviation amounts at different historical matching emission times based on deviations of emission data of different types of pollutants at different historical matching emission times; The data deviation emission times in the historical matching emission times are determined by using the emission data deviation amounts of different historical matching emission times, and the emission data anomaly values ​​of the target emission source are determined by using the proportion of the data deviation emission times in the historical matching emission times. The emission data anomaly values ​​are used to determine whether there is an anomaly in the real-time emission data of the target emission source.

5. The industrial wastewater treatment data management method according to claim 1, characterized in that: When there is no abnormality in the real-time emission data of the target emission source, the fraud probability assessment of the target emission source will not be performed temporarily.

6. The industrial wastewater treatment data management method according to claim 1, characterized in that: The historical emission data is divided into investigation period data and non-emission period data according to whether it is an on-site investigation period, including: The historical emission data of on-site inspectors during the on-site emission period will be used as the investigation period data, and the historical emission data that does not belong to the investigation period data will be used as the non-emission period data.

7. The industrial wastewater treatment data management method according to claim 1, characterized in that: When the matching coefficient between the credible emission source and the target emission source is within a preset range, the credible emission source is determined to be a reference emission source.

8. An industrial wastewater treatment data management system, using an industrial wastewater treatment data management method according to any one of claims 1 to 7, characterized in that: Specifically include: Abnormal assessment module, emission source screening module, reference emission source screening module, marking processing module; The abnormality assessment module is responsible for determining the real-time emission data of the target emission source, and using the matching of the real-time emission data and the historical emission data of the target emission source to determine whether the real-time emission data of the target emission source is abnormal; The emission source screening module is responsible for obtaining the historical emission data of industrial wastewater from different emission sources, and dividing the historical emission data into investigation period data and non-emission period data according to whether it is an on-site investigation period, and determining the credibility of the emission source governance data and the credible emission source according to the similarity between the non-investigation period data and the investigation period data; The reference emission source screening module is responsible for obtaining the historical emission data of the target emission source, and determining the matching coefficient between the target emission source and different credible emission sources in combination with the industrial wastewater treatment equipment of the target emission source, and using the matching coefficient to determine the reference emission source of the target emission source; The labeling processing module is responsible for determining the fraud probability of the target emission source by using the deviation between the real-time emission data and the historical emission data of the reference emission source and the credibility of the governance data of the reference emission source. When the fraud probability of the target emission source does not meet the requirements, the historical emission data of the target emission source is analyzed and processed to locate and label the fraud risk data.

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