CN107292501B

CN107292501B - Method and equipment for evaluating quality of wastewater monitoring data

Info

Publication number: CN107292501B
Application number: CN201710433699.4A
Authority: CN
Inventors: 高嘉阳; 徐伟; 高柱; 毛佳茗
Original assignee: Jiangsu Shencai Technology Co ltd
Current assignee: Shencai Technology Co., Ltd
Priority date: 2017-06-09
Filing date: 2017-06-09
Publication date: 2020-11-13
Anticipated expiration: 2037-06-09
Also published as: CN107292501A

Abstract

The method comprises the steps of presetting at least one quality evaluation characteristic of wastewater monitoring data before quality evaluation is carried out on the wastewater monitoring data corresponding to enterprise wastewater; acquiring wastewater monitoring data corresponding to enterprise wastewater, and performing quality evaluation on the wastewater monitoring data to obtain characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data; the quality assessment value of the wastewater monitoring data is obtained based on the characteristic value and the weight corresponding to each quality assessment characteristic of the wastewater monitoring data, so that the quality assessment of the wastewater monitoring data is realized, a supervisor can judge whether the wastewater monitoring data of the enterprise is real and effective through the quality assessment value of the wastewater monitoring data, and the enterprise wastewater corresponding to the fake wastewater monitoring data is prevented from being discharged, so that the ecological environment is influenced.

Description

Method and equipment for evaluating quality of wastewater monitoring data

Technical Field

The application relates to the field of computers, in particular to a method and equipment for evaluating the quality of wastewater monitoring data.

Background

Most of the current industrial parks are composed of a plurality of dispersed enterprises, each enterprise can generate various industrial waste water, and pollution factors contained in the waste water discharged by different enterprises are different. Furthermore, industrial wastewater mainly contains two sources: firstly, electroplating waste water generated by electroplating of an integrated circuit device comprises electroplating waste liquid and electroplating cleaning waste water, and pollutant indexes are mainly embodied in acid, alkali and metal copper ions; secondly, the pretreatment of the electroplating process of the integrated circuit device and the waste gas treatment of the electroplating process lead to the current acid, alkali and phosphate of pollutants. The pollutants are required to be subjected to coagulation reaction with the wastewater by adding a chemical reaction agent, so that pollutants in the wastewater are separated from the wastewater to achieve the purpose of purification, and finally enter a sewage discharge pipeline.

In order to ensure that the water discharged through the sewage discharge pipeline conforms to the regulations, the discharged wastewater needs to be sampled and monitored, a wastewater sampling device is usually additionally arranged in front of a sewage discharge outlet of the sewage discharge pipeline to sample the wastewater in real time, and the wastewater sampled in real time is analyzed for pollutants to obtain wastewater monitoring data. In order to avoid purifying wastewater, most enterprises can manually intervene or use intelligent machines to intervene on wastewater which is not qualified in purification, so that wastewater monitoring data of the monitored discharged wastewater can meet the regulations, wastewater which is truly discharged with pollution factors and cannot be discharged according to the standards can be discharged, and the ecological environment is polluted, and therefore, the main subject of research in the industry is how to monitor whether the wastewater monitoring data of the enterprises are false.

Disclosure of Invention

An object of the present application is to provide a method and an apparatus for evaluating the quality of wastewater monitoring data, so as to solve the problem of data non-authenticity caused by the existence of false wastewater monitoring data or failure of monitoring equipment in enterprise wastewater in the prior art.

According to one aspect of the present application, there is provided a method of assessing the quality of wastewater monitoring data, the method comprising:

presetting at least one quality evaluation characteristic of wastewater monitoring data, wherein the quality evaluation characteristic comprises a monitoring frequency characteristic, a stability characteristic, a steep peak value characteristic, an intra-day monitoring value change frequency characteristic, an inter-day monitoring value change frequency characteristic, a random number characteristic, a video monitoring flow characteristic and a video monitoring invasion characteristic;

acquiring wastewater monitoring data corresponding to enterprise wastewater, performing quality evaluation on the wastewater monitoring data to obtain characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data, and calculating the characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data to obtain weights of all quality evaluation characteristics of the wastewater monitoring data;

and obtaining the quality evaluation value of the wastewater monitoring data based on the characteristic value and the weight corresponding to each quality evaluation characteristic of the wastewater monitoring data.

Further, in the above method, the obtaining of wastewater monitoring data corresponding to enterprise wastewater and the quality assessment of the wastewater monitoring data to obtain characteristic values corresponding to each quality assessment characteristic of the wastewater monitoring data includes:

selecting a target month, and acquiring daily corresponding wastewater monitoring data and monitoring days of enterprise wastewater generated during enterprise work within the target month;

respectively performing quality evaluation on the wastewater monitoring data corresponding to each day to obtain characteristic values corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day;

obtaining characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to the target month based on the monitoring days and the characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to each day;

and determining the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to the target month as the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data.

Further, in the above method, the quality evaluation of the wastewater monitoring data corresponding to each day is performed to obtain a characteristic value corresponding to the quality evaluation characteristic of the wastewater monitoring data corresponding to each day, and the method includes:

acquiring daily monitoring frequency of wastewater monitoring data corresponding to each day;

and comparing the daily monitoring frequency of the wastewater monitoring data corresponding to each day with the corresponding preset daily monitoring frequency threshold value to obtain the characteristic value corresponding to the monitoring frequency characteristic of the wastewater monitoring data corresponding to each day.

Further, in the above method, the quality evaluation of the wastewater monitoring data corresponding to each day is performed to obtain a characteristic value corresponding to the quality evaluation characteristic of the wastewater monitoring data corresponding to each day, and the method includes: acquiring a monitoring value of wastewater monitoring data corresponding to each day;

and comparing the monitoring value of the wastewater monitoring data corresponding to each day with the corresponding preset monitoring value threshold value to obtain the characteristic value corresponding to the stability characteristic of the wastewater monitoring data corresponding to each day.

Further, in the above method, the quality evaluation of the wastewater monitoring data corresponding to each day is performed to obtain a characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day, and the method includes:

performing compliance quality evaluation on the wastewater monitoring data corresponding to each day based on an outlier algorithm to obtain the number of outliers in the wastewater monitoring data corresponding to each day;

and determining a characteristic value corresponding to the steep peak characteristic of the wastewater monitoring data corresponding to each day based on the number of the outliers.

acquiring monitoring values of wastewater monitoring data in each day according to preset time intervals, calculating a difference value of the monitoring values acquired by two adjacent preset time intervals, and determining the number of the difference values and the number of the difference values as zero;

and obtaining a characteristic value corresponding to the daily monitoring value variation frequency characteristic of the daily corresponding wastewater monitoring data based on the number of the difference values and the number of the difference values which are zero.

Further, in the above method, the quality evaluation of the wastewater monitoring data corresponding to each day is performed to obtain a characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day, and the method includes: acquiring monitoring values of the wastewater monitoring data in each day according to a preset time interval, averaging the monitoring values to obtain a monitoring average value of the wastewater monitoring data in each day, and determining a monitoring maximum value of the wastewater monitoring data in each day;

taking the ratio of the monitoring maximum value to the monitoring average value of the wastewater monitoring data in each day as the standardized maximum value of the wastewater monitoring data in each day;

comparing the standardized maximum values of two adjacent days to obtain a comparison result;

calculating a rate of change ratio between the comparison result and the normalized maximum value corresponding to the first of the two adjacent days;

and determining a characteristic value corresponding to the daytime monitoring value change frequency characteristic of the daily corresponding wastewater monitoring data based on the change rate ratio and the corresponding preset change rate threshold.

Further, in the above method, the quality evaluation of the wastewater monitoring data corresponding to each day is performed to obtain a characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day, and the method includes: selecting a target week in the target month, and acquiring a monitoring value of wastewater monitoring data in each day according to the preset time interval in the target week;

performing random number detection on all the monitoring values acquired in the target period based on a random number detection algorithm to obtain a random number detection result;

and determining a characteristic value corresponding to the random number characteristic of the daily corresponding wastewater monitoring data in the target week based on the random number detection result.

acquiring daily pollution discharge monitoring videos at a discharge port of enterprise wastewater, analyzing the pollution discharge monitoring videos to obtain video analysis results, and matching the video analysis results with corresponding wastewater monitoring data according to a time sequence to obtain matching results;

and obtaining a characteristic value corresponding to the video monitoring flow characteristic of the wastewater monitoring data corresponding to each day based on the matching result.

Further, in the above method, the quality evaluation of the wastewater monitoring data corresponding to each day is performed to obtain a characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day, and the method includes: acquiring daily pollution discharge monitoring video at a discharge port of enterprise wastewater, and repeating the following steps until obtaining a characteristic value corresponding to the video monitoring invasion characteristic of the wastewater monitoring data corresponding to each day:

traversing and judging whether personnel enter the pollution discharge monitoring video;

if yes, determining a time point when the personnel enter in a target day corresponding to the pollution discharge monitoring video;

calculating a first monitoring mean value of the wastewater monitoring data before the time point and a second monitoring mean value of the wastewater monitoring data after the time point within the target day;

if the first monitoring mean value and the second monitoring mean value meet preset determination conditions, determining a characteristic value corresponding to video monitoring intrusion characteristics of the wastewater monitoring data corresponding to the target day, wherein the preset determination conditions comprise:

mean1-mean2)/mean1> -A, and A is more than or equal to 0 and less than or equal to 1;

wherein the mean1 is the first monitored mean, the mean2 is the second monitored mean, and A is a monitored mean fluctuation threshold.

Further, in the above method, the quality assessment feature further includes: and the correlation characteristics are obtained by acquiring wastewater monitoring data corresponding to the enterprise wastewater and performing quality evaluation on the wastewater monitoring data to obtain characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data, and the method comprises the following steps:

acquiring a monthly consumption vector corresponding to each energy consumption data of an enterprise and a monthly monitoring data vector corresponding to the wastewater monitoring data;

calculating the correlation between each monthly consumption vector and the corresponding monthly monitoring data vector of the wastewater monitoring data two by two respectively to obtain the correlation coefficient between each energy consumption data of the enterprise and the wastewater monitoring data;

and selecting a maximum value from all the correlation coefficients, and determining the correlation coefficient corresponding to the maximum value as a characteristic value corresponding to the correlation characteristic of the wastewater monitoring data.

According to another aspect of the present application, there is also provided an apparatus for evaluating the quality of wastewater monitoring data, wherein the apparatus comprises:

one or more processors; and

a memory storing computer readable instructions that, when executed, cause the processor to perform the operations of the method as described above.

Compared with the prior art, the method and the device have the advantages that at least one quality evaluation characteristic of the wastewater monitoring data and the corresponding weight of the wastewater monitoring data are preset before the quality of the wastewater monitoring data corresponding to the enterprise wastewater is evaluated; acquiring wastewater monitoring data corresponding to enterprise wastewater, and performing quality evaluation on the wastewater monitoring data to obtain characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data; the quality assessment value of the wastewater monitoring data is obtained based on the characteristic value and the weight corresponding to each quality assessment characteristic of the wastewater monitoring data, so that the quality assessment of the wastewater monitoring data of the enterprise wastewater is realized, a supervisor can judge whether the wastewater monitoring data of the enterprise wastewater discharged by the enterprise is real and effective through calculating the quality assessment value of the wastewater monitoring data obtained by the supervisor, the enterprise can be supervised to discharge the discharged wastewater after sewage standard treatment, and the enterprise wastewater corresponding to the waste water monitoring data is prevented from being discharged, so that the ecological environment is influenced.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:

FIG. 1 illustrates a schematic flow chart of a method of evaluating the quality of wastewater monitoring data according to one aspect of the subject application;

FIG. 2 illustrates a correlation coefficient matrix Cor between each energy consumption data of a last year of enterprise F and the corresponding wastewater monitoring data in an embodiment of a method of assessing the quality of wastewater monitoring data according to an aspect of the subject application_xy；

FIG. 3 illustrates a computational flow of a method of evaluating the quality of wastewater monitoring data according to one aspect of the subject application. The same or similar reference numbers in the drawings identify the same or similar elements.

Detailed Description

The present application is described in further detail below with reference to the attached figures.

In a typical configuration of the present application, the terminal, the device serving the network, and the trusted party each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.

Fig. 1 illustrates a method for evaluating the quality of wastewater monitoring data according to one aspect of the present application, the method being applied to a network device side in a government or wastewater discharge regulatory body or a regulatory personnel, wherein the method comprises: step S11, step S12 and step S13, the concrete steps are as follows:

before the quality evaluation of the authenticity of the wastewater monitoring data of the enterprise wastewater is required, the step S11 presets at least one quality evaluation characteristic of the wastewater monitoring data; step S12 is to obtain wastewater monitoring data corresponding to enterprise wastewater, perform quality assessment on the wastewater monitoring data to obtain characteristic values corresponding to the quality assessment features of the wastewater monitoring data, and calculate characteristic values corresponding to the quality assessment features of the wastewater monitoring data to obtain weights of the quality assessment features of the wastewater monitoring data; and step S13, obtaining a quality assessment value of the wastewater monitoring data based on the characteristic values and weights corresponding to the quality assessment features of the wastewater monitoring data, so as to implement quality assessment on the wastewater monitoring data of the enterprise wastewater, so that a supervisor can judge whether the wastewater monitoring data of the enterprise wastewater discharged by the enterprise is real and effective by calculating the obtained quality assessment value of the wastewater monitoring data, and further can supervise the enterprise to discharge the discharged wastewater after performing pollution discharge standard treatment, thereby preventing the enterprise wastewater corresponding to the fake wastewater monitoring data from being discharged and further affecting the ecological environment.

Here, the wastewater monitoring data may be, but is not limited to, including: the system comprises waste water flow data, waste water overproof state data, waste water overproof upper limit data, waste water pollution factor concentration and the like, wherein the waste water pollution factor concentration can comprise Chemical Oxygen Demand (COD), ammonia nitrogen, total phosphorus, sulfide and the like.

In an embodiment of the present application, the determination of authenticity of the wastewater monitoring data of wastewater discharge of at least one enterprise is required, and then a quality assessment model of the wastewater monitoring data needs to be constructed first, where parameters to be constructed in the quality assessment model are quality assessment features corresponding to the wastewater monitoring data, and the quality assessment features may include: monitoring frequency characteristics, stability characteristics, steep peak value characteristics, daily monitoring value change frequency characteristics, daytime monitoring value change frequency characteristics, random number characteristics, video monitoring flow characteristics, video monitoring intrusion characteristics and correlation characteristics. In order to perform normalized comparison on the wastewater monitoring data of the wastewater discharge of at least one enterprise, at least one quality evaluation feature of the wastewater monitoring data is preset, and the wastewater monitoring data of all enterprises are subjected to quality evaluation from the dimension of the at least one quality evaluation feature.

Next, in the above embodiment of the application, in step S12, if the wastewater monitoring data corresponding to the enterprise wastewater of enterprise F is obtained, and the wastewater monitoring data is subjected to quality evaluation, to obtain feature values corresponding to each quality evaluation feature of the wastewater monitoring data, the feature value corresponding to the monitoring frequency feature is c1, the feature value corresponding to the stability feature is c2, the feature value corresponding to the steep peak feature is c3, the feature value corresponding to the intra-day monitoring value change frequency feature is c4, the feature value corresponding to the inter-day monitoring value change frequency feature is c5, the feature value corresponding to the random number feature is c6, the feature value corresponding to the video monitoring flow feature is c7, the feature value corresponding to the video monitoring feature is c8, and the feature value corresponding to the correlation feature is c9, so as to calculate the feature values of each quality evaluation feature of the wastewater monitoring data.

After obtaining the eigenvalues corresponding to the quality evaluation features corresponding to the wastewater monitoring data of the enterprise, in step S12, an entropy weight method is used to analyze the eigenvalues corresponding to the quality evaluation features corresponding to the wastewater monitoring data of one or more enterprises, and a weight (weight) of each preset quality evaluation feature is calculated, for example, the weight of the monitoring frequency feature calculated by the entropy weight method is w1, the weight of the stability feature is w2, the weight of the steep peak value feature is w3, the weight of the intra-day monitoring value variation frequency feature is w4, the weight of the inter-day monitoring value variation frequency feature is w5, the weight of the random number feature is w6, the weight of the video monitoring flow feature is w7, the weight of the video monitoring intrusion feature is w8, and the weight of the correlation feature is w 9. W1+ w2+ w3+ w4+ w5+ w6+ w7+ w8+ w9 is 1, and the weight corresponding to the quality assessment feature is determined through an entropy weight method, so that the quality assessment value of the wastewater monitoring data of each enterprise is calculated according to the normalized weight.

Next, in step S13, the quality assessment value VF of the wastewater monitoring data of the enterprise wastewater of the enterprise F is calculated according to each feature value and its corresponding weight as follows: VF ═ c1 × w1+ c2 × w2+ c3 × w3+ c4 × w4+ c5 × w5+ c6 × w6+ c7 × w7+ c8 × w8+ c9 × w 9;

the quality evaluation value of the wastewater monitoring data of the enterprise wastewater of the enterprise F can be obtained through the calculation formula of the quality evaluation value VF, so that the quality evaluation of the wastewater monitoring data of the enterprise wastewater is realized. If the quality assessment value VF corresponding to the enterprise F is higher, it is more indicated that the obtained wastewater monitoring data corresponding to the enterprise wastewater of the enterprise F is closer to the actual wastewater monitoring data, and it is more indicated that the probability of human intervention on the wastewater monitoring data of the enterprise F is lower, so that the enterprise wastewater monitoring data is more authentic, and it can be further ensured that the enterprise wastewater corresponding to the actual wastewater monitoring data reaches the pollution discharge standard without artificial falsification. If the quality assessment value VF corresponding to the enterprise F is lower, manual intervention or machine intervention is indicated to monitor the enterprise wastewater of the enterprise F, so that the wastewater monitoring data is not real, and the authenticity monitoring of the government or supervision and supervision personnel on the enterprise wastewater of the enterprise F is influenced.

Further, step S12 obtains wastewater monitoring data corresponding to the enterprise wastewater, and performs quality assessment on the wastewater monitoring data to obtain characteristic values corresponding to each quality assessment feature of the wastewater monitoring data, including:

Next, in the above embodiment of the application, in the step S12, if the wastewater monitoring data of a certain month in the enterprise F needs to be analyzed, the selected target month M is determined, and the wastewater monitoring data and the monitoring days corresponding to the enterprise wastewater generated by the enterprise F during working per day are obtained within the target month M; if the enterprise works for 20 days in the target month M, the monitoring days D of the enterprise wastewater is 20, and wastewater monitoring Data corresponding to each day when the enterprise works is also obtained, that is, wastewater monitoring Data of 20 days respectively, for example, the wastewater monitoring Data of the first day of the enterprise work is Data1 (including wastewater flow Data, wastewater exceeding state Data, wastewater exceeding upper limit Data, wastewater pollution factor concentration, and the like), the wastewater monitoring Data of the second day of the enterprise work is Data2, … …, and the wastewater monitoring Data of the twentieth day of the enterprise work is Data 20.

Then, respectively performing quality evaluation on the wastewater monitoring data corresponding to each day to obtain characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to each day, wherein the characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to each day are binary values and are respectively 0 or 1; for example, if the quality evaluation is performed on the Data2 of the wastewater monitoring Data on the second day of the enterprise operation, the characteristic value corresponding to the monitoring frequency characteristic of the wastewater monitoring Data corresponding to the second day of the enterprise operation is D2c1 equal to 1, the characteristic value corresponding to the stability characteristic is D2c2 equal to 1, the characteristic value corresponding to the steep peak value characteristic is D2c3 equal to 0, the characteristic value corresponding to the intra-day monitoring value fluctuation frequency characteristic is D2c4 equal to 1, the characteristic value corresponding to the inter-day monitoring value fluctuation frequency characteristic is D2c5 equal to 1, the characteristic value corresponding to the random number characteristic is D2c6 equal to 1, the characteristic value corresponding to the video monitoring flow rate characteristic is D2c7 equal to 0, the characteristic value corresponding to the video monitoring intrusion characteristic is D2c8 equal to 1, the quality evaluation method of the wastewater monitoring characteristic corresponding to the enterprise intrusion characteristic value corresponding to the second day of the enterprise operation on the other day within 20 days of the enterprise operation is consistent with the second day operation, and will not be described in detail herein.

If the eigenvalue Mc1 corresponding to the monitoring frequency characteristic of the wastewater monitoring data corresponding to the target month M is required to be obtained within 20 days of the work of the enterprise F, for example, within 20 days of the work of the enterprise M, the eigenvalues corresponding to the monitoring frequency characteristic of the wastewater monitoring data corresponding to the target month M are counted first as D1c1, D2c1, … … and D20c1, respectively, and the number D of eigenvalues of all eigenvalues being 1 is counted, so that the eigenvalue Mc1 corresponding to the monitoring frequency characteristic of the wastewater monitoring data corresponding to the target month M is: mc1 ═ D/D; wherein, D is 20, and if D is 18, the characteristic value Mc1 corresponding to the monitoring frequency characteristic of the wastewater monitoring data corresponding to the target month M is 18/20 is 0.9; then, in step S12, determining the characteristic value Mc1 corresponding to the monitoring frequency characteristic of the wastewater monitoring data corresponding to the target month M as the characteristic value corresponding to the monitoring frequency characteristic of the wastewater monitoring data of enterprise F, that is, the characteristic value Mc1 corresponding to the monitoring frequency characteristic of the wastewater monitoring data of enterprise F; here, the calculation methods for calculating the characteristic values Mc2, Mc3, Mc4, Mc5, Mc6, Mc7 and Mc8 corresponding to the stability characteristic, the steep peak characteristic, the daily monitoring value change frequency characteristic, the daytime monitoring value change frequency characteristic, the random number characteristic, the video monitoring flow characteristic and the video monitoring intrusion characteristic respectively corresponding to the wastewater monitoring data of the enterprise F are consistent with the calculation method for calculating the characteristic value corresponding to the monitoring frequency characteristic of the wastewater monitoring data of the enterprise F as Mc1, and are not described herein again; and further, quality evaluation is carried out on the wastewater monitoring data corresponding to the enterprise F every day in the working day, and then characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data of the enterprise F are obtained.

Following the above embodiment of the present application, the step S12 performs quality evaluation on the wastewater monitoring data of the enterprise wastewater by the following three aspects: compliance quality assessment, time series analysis and big data verification. The step S12 is illustrated in the following aspects of compliance quality assessment, time series analysis, and big data verification, respectively.

On one hand, considering the discharge standard of the wastewater of the enterprise specified by the country, the wastewater monitoring data of the wastewater of the enterprise needs to be evaluated to monitor whether the wastewater discharge condition of the wastewater of the enterprise, the wastewater monitoring mode, the concentration of the wastewater pollution factor in the wastewater of the enterprise, etc. meet the relevant wastewater discharge standard of the country, so the step S12 of respectively performing quality evaluation on the wastewater monitoring data corresponding to each day to obtain the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day includes:

and performing compliance quality evaluation on the wastewater monitoring data corresponding to each day respectively to obtain characteristic values corresponding to the monitoring frequency characteristic, the stability characteristic and the steep peak value characteristic of the wastewater monitoring data corresponding to each day respectively. Therefore, the compliance quality assessment is respectively carried out on the daily corresponding wastewater monitoring data obtained in the monitoring process of the enterprise wastewater of the enterprise F, the quality assessment is carried out from three dimensions, namely the monitoring frequency assessment, the stability assessment and the steep peak value assessment, namely, after the compliance evaluation of the three dimensions is carried out on the wastewater monitoring data corresponding to the enterprise every day acquired by the enterprise F in the monitoring process, can respectively obtain the characteristic values corresponding to the monitoring frequency characteristic, the stability characteristic and the steep peak value characteristic of the wastewater monitoring data corresponding to each day, so that the supervisor can know whether the wastewater monitoring data of the enterprise wastewater of the enterprise F conforms to the national relevant wastewater discharge standard and whether the wastewater monitoring data conforms to the authenticity through the characteristic value, so that the monitored wastewater data of the monitoring enterprise can be reported to the government, the supervision department and the supervision personnel.

Next, in the above embodiments of the present application, in the relevant national wastewater discharge regulations, the working production period of an enterprise is within 8 hours, and it is necessary to sample the enterprise wastewater once for at least 1-2 hours, and record the wastewater monitoring data each time; if the work production cycle is longer than 8 hours, enterprise wastewater needs to be sampled at least every 2-4 hours, and wastewater monitoring data of each time is recorded, because some enterprises in reality do not comply with the national regulation of wastewater discharge sampling cycle, in order to save cost and reduce monitoring frequency, the method performs compliance quality assessment on wastewater monitoring data corresponding to each day in the step S12 to obtain characteristic values corresponding to monitoring frequency characteristics of the wastewater monitoring data corresponding to each day, and the method comprises the following steps:

For example, the preset daily monitoring frequency threshold corresponding to the daily wastewater monitoring data of the enterprise F may be a positive integer, where the positive integer is preferably 6, when the enterprise F works, the monitoring frequency of the daily wastewater monitoring data (including wastewater flow data, wastewater exceeding state data, wastewater exceeding upper limit data, wastewater pollution factor concentration, and the like) corresponding to the enterprise F during working is counted, and if the daily monitoring frequency of the daily wastewater monitoring data corresponding to each day is less than or equal to the corresponding preset daily monitoring frequency threshold, it indicates that the enterprise F does not reach the international frequency of monitoring wastewater according to regulations; for example, if the daily monitoring frequency of the wastewater pollution factor concentration is 8, and the daily monitoring frequency of the wastewater pollution factor concentration is 8 greater than the corresponding preset daily monitoring frequency threshold 6, it indicates that the daily monitoring of the wastewater corresponding to the daily monitoring frequency of 8 meets the regulation, the daily mark of sampling is 1, and the characteristic value corresponding to the monitoring frequency characteristic of the obtained daily corresponding wastewater monitoring data is 1; of course, if the daily monitoring frequency of the concentration of the wastewater pollution factor is 4 less than the corresponding preset daily monitoring frequency threshold 6, it indicates that the monitoring of the wastewater does not meet the regulations, the current day of sampling is marked as 0, and the characteristic value corresponding to the monitoring frequency characteristic of the wastewater monitoring data corresponding to each day is 0, so that the wastewater monitoring data of the enterprise F working on each day is monitored and evaluated.

Next, in the above-mentioned embodiment of the present application, in the national relevant wastewater discharge regulations, when an enterprise does not stop production, a monitoring instrument must be started up for 24 hours and return wastewater monitoring data, in order to ensure real-time monitoring of wastewater of the enterprise, so that the monitoring instrument is not allowed to go down when the enterprise does not stop production, when the monitoring instrument is not running, the returned wastewater monitoring data is zero, so in order to ensure authenticity evaluation of the wastewater monitoring data of the enterprise, compliance quality evaluation is performed on the wastewater monitoring data corresponding to each day in step S12, and a feature value corresponding to a stability feature of the wastewater monitoring data corresponding to each day is obtained, including:

acquiring a monitoring value of wastewater monitoring data corresponding to each day;

For example, under the condition that an enterprise does not stop production, wastewater generated by the enterprise cannot be zero, and further wastewater monitoring data for monitoring the wastewater of the enterprise does not have zero, so that the preset monitoring value threshold of the wastewater monitoring data corresponding to each day is preferably 0, the monitoring value of the wastewater monitoring data corresponding to each day is compared with the corresponding preset monitoring value threshold 0, if the monitoring value of the wastewater monitoring data corresponding to each day has a zero value, the wastewater monitoring data is interfered by a person or a machine, the sampling day corresponding to the zero value is marked as 0, that is, the characteristic value corresponding to the stability characteristic of the wastewater monitoring data corresponding to the sampling day corresponding to the zero value is 0; if the monitoring value of the wastewater monitoring data corresponding to each day is not a zero value, the wastewater monitoring data is proved to have authenticity, the sampling day corresponding to the non-zero value is marked as 1, namely the characteristic value corresponding to the stability characteristic of the wastewater monitoring data corresponding to the sampling day corresponding to the non-zero value is 1, the characteristic value corresponding to the stability characteristic of the wastewater monitoring data corresponding to each day is represented by the marked 0 or 1, and the stability evaluation of the wastewater monitoring data is realized.

Following the above examples of the present application, in the national relevant wastewater discharge regulations, the wastewater index should be gradually changed according to the wastewater properties, such as the concentration of wastewater pollution factors, etc., without steep peaks. If the peak value appears, it is likely that enterprise personnel find that the discharged enterprise wastewater exceeds the standard, and the wastewater monitoring data is subjected to manual intervention immediately so as to reduce the concentration of pollution factors and the like. In order to avoid the above-mentioned human intervention situation, if the wastewater monitoring data needs to be subjected to steep peak evaluation, the step S12 of performing compliance quality evaluation on the wastewater monitoring data corresponding to each day respectively to obtain a characteristic value corresponding to a steep peak characteristic of the wastewater monitoring data corresponding to each day includes:

Herein, the outlier algorithm may be a univariate outlier monitoring algorithm including, but not limited to, a normal distribution, and other outlier algorithms, as applicable to the present application, shall be included in the present application. For example by a preferred outlier algorithm: performing compliance quality evaluation on the wastewater monitoring data corresponding to each day by using a normally distributed unary outlier monitoring algorithm, monitoring outliers in the wastewater monitoring data corresponding to each day, further obtaining the number of the outliers in the wastewater monitoring data corresponding to each day, marking the sampling date corresponding to the unique number of the outliers as 0 if the number of the outliers is unique and exceeds the wastewater standard exceeding state data of the wastewater monitoring data, otherwise marking as 1, namely, if the number of the outliers is unique and exceeds the wastewater standard exceeding state data of the wastewater monitoring data, marking the characteristic value corresponding to the steep peak characteristic of the wastewater monitoring data corresponding to the sampling day corresponding to the unique number of the outliers as 0, and so on, further obtaining the characteristic value corresponding to the steep peak characteristic of the wastewater monitoring data corresponding to each day, and realizing steep peak evaluation on the wastewater monitoring data of enterprises, so as to ensure that the obtained wastewater monitoring data conforms to the national relevant wastewater discharge regulations.

On the other hand, in the enterprise wastewater of the enterprise, the wastewater pollution factor concentration in the wastewater monitoring data (including wastewater flow data, wastewater overproof state data, wastewater overproof upper limit data, wastewater pollution factor concentration and the like) is a list of data which changes along with the time change, and is statistically called as a time series. Through a series of time series analysis, the concentration of the pollution factor of the waste water can be judged whether the condition of artificial false or machine failure occurs. In the wastewater monitoring, instrument failure often occurs or due to human intervention, the concentration of the wastewater pollution factor obtained by measurement is not changed for a long time or fluctuates randomly around a certain value, or an upper range limit is set manually, so that the concentration of the wastewater pollution factor obtained by measurement is always fluctuated within a certain range, so that in the step S12, the quality evaluation is performed on the wastewater monitoring data corresponding to each day, and the characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to each day are obtained, which includes: the method comprises the steps of respectively carrying out time series evaluation on the wastewater monitoring data corresponding to each day to obtain characteristic values corresponding to the daily monitoring value change frequency characteristic, the daytime monitoring value change frequency characteristic and the random number characteristic of the wastewater monitoring data corresponding to each day, so that the time series evaluation on the wastewater monitoring data of enterprise wastewater generated in an enterprise every day is realized, the authenticity of the obtained wastewater monitoring data is distinguished, and the distortion of the measured wastewater monitoring data caused by instrument faults or manual intervention is avoided.

Next, in the above-mentioned embodiment of the present application, since the concentration of the wastewater pollution factor in the measurement of the enterprise wastewater is often unchanged for a long time, and the reason may be that the measurement instrument fails, the data of the finally obtained concentration of the wastewater pollution factor is always returned, and in order to avoid this situation, in the above-mentioned step S12, the time-series evaluation is performed on the wastewater monitoring data corresponding to each day, so as to obtain the characteristic value corresponding to the intra-day monitoring value variation frequency characteristic of the wastewater monitoring data corresponding to each day, including:

For example, the step S12 obtains the monitoring values of the wastewater monitoring data (including wastewater flow data, wastewater exceeding state data, wastewater exceeding upper limit data, wastewater pollution factor concentration, and the like) corresponding to each hour in each day according to a preset time interval (for example, the event interval may be any time interval of 0 to 24 hours, and the preferred time interval in the embodiment of the present application is 1 hour), when the wastewater flow data is not zero, calculates the difference between the monitoring values obtained in two adjacent preset time intervals (for example, the time interval is 1 hour), that is, calculates the difference between the monitoring values of the wastewater monitoring data (for example, wastewater pollution factor concentration, and the like) in two adjacent hours, and counts the number of the differences in each day, and determines the number of the differences in each day as zero; then based on the number of the difference values in each day and the number of the difference values being zero, obtaining the characteristic value corresponding to the daily monitoring value variation frequency characteristic of the wastewater monitoring data corresponding to each day, for example, when the ratio of the number of differences to zero per day is greater than or equal to a preset difference threshold (e.g., the preset difference threshold may be any fractional number from 0 to 1, and is preferably 0.9 in the present application), marking the characteristic value corresponding to the characteristic of the change frequency of the monitoring value in the day of sampling as 0, if the ratio of the number of the difference values to be zero in each day is less than the preset difference threshold value, and marking the characteristic value corresponding to the characteristic of the daily monitoring value change frequency of the sampling day as 1, and realizing the daily monitoring value change frequency evaluation of the monitoring values of the wastewater monitoring data obtained every day according to the preset time interval.

Next, in the above embodiment of the present application, since some enterprises in the measurement of the wastewater of the enterprises set the measurement ranges for the concentrations of the wastewater pollution factors, so that the wastewater pollution factors always float within a certain range, to ensure that the wastewater pollution factors never exceed the standards, in order to avoid such human or machine intervention, in step S12, the wastewater monitoring data corresponding to each day is respectively subjected to time series evaluation, so as to obtain the characteristic values corresponding to the diurnal monitoring value variation frequency characteristics of the wastewater monitoring data corresponding to each day, including:

acquiring monitoring values of the wastewater monitoring data in each day according to a preset time interval, averaging the monitoring values to obtain a monitoring average value of the wastewater monitoring data in each day, and determining a monitoring maximum value of the wastewater monitoring data in each day;

For example, the step S12 obtains the monitoring values of the wastewater monitoring data in each day according to a preset time interval (for example, the time interval is 1 hour), and averages the monitoring values to obtain a mean monitoring value mean of the wastewater monitoring data (for example, the concentration of the wastewater pollution factor) in each day_tWherein t represents a working day in time sequence, i.e., the tth day of work, and the maximum monitoring value max of wastewater monitoring data (e.g., wastewater pollution factor concentration, etc.) is selected from all monitoring values for each day_tThen, the maximum monitoring value max of the daily wastewater monitoring data is determined_tAnd monitoring the average value max_tAs the normalized maximum value Smax of the daily wastewater monitoring data_t＝max_t/mean_tAnd comparing the normalized maximum values of two adjacent days, e.g. normalized maximum value Smax_tAnd Smax of the previous day_t-1Comparing to obtain a comparison result | Smax_t-Smax_t-1|/Smax_t-1Then, the comparison result is compared with a corresponding preset threshold value of the rate of change (which may include any decimal between 0 and 1, and is preferably 0.01 in the embodiment of the present application), if | Smax_t-Smax_t-1|/Smax_t-1If the data is less than or equal to 0.01, the characteristic value corresponding to the daytime monitoring value variation frequency characteristic of the wastewater monitoring data corresponding to the sampling day t is marked as 0, otherwise, the characteristic value is marked as 1And the evaluation of the change rate of the measured values between the days of the enterprise is realized.

Next, in the above embodiment of the present application, since some enterprises in the measurement of enterprise wastewater replace actual measurement values with random numbers randomly generated by machines, in order to avoid this, in step S12, time-series evaluation is performed on the wastewater monitoring data corresponding to each day, so as to obtain characteristic values corresponding to random number characteristics of the wastewater monitoring data corresponding to each day, including:

selecting a target week in the target month, and acquiring a monitoring value of wastewater monitoring data in each day according to the preset time interval in the target week;

For example, selecting a target Week in the target month M, obtaining monitoring values of wastewater monitoring data (e.g., wastewater pollution factor concentration, etc.) in each day in the target Week according to a time sequence of the preset time interval (e.g., the time interval is five minutes, half an hour, real-time obtaining, etc.), performing random number detection on all the monitoring values obtained in the target Week based on a random number detection algorithm (which may include but is not limited to a Box-Ljung test random data number detection algorithm, etc.) to obtain random number detection results, which are respectively yes or no, if the random number detection result is yes, feature values corresponding to random number features of wastewater monitoring data corresponding to each day in the target Week are all determined to be 0, if the random number detection result is no, feature values corresponding to random number features of wastewater monitoring data corresponding to each day in the target Week are all determined to be 1, the possibility of taking the wastewater monitoring data (such as wastewater pollution factor concentration and the like) of the obtained enterprise wastewater as random numbers is eliminated.

Next, in the above embodiment of the present application, since some enterprises may manually intervene in the actual measurement value of the wastewater monitoring data of the enterprise wastewater in the measurement of the enterprise wastewater, in order to avoid this, the quality evaluation is performed on the wastewater monitoring data corresponding to each day in step S12, and a feature value corresponding to the quality evaluation feature of the wastewater monitoring data corresponding to each day is obtained, including:

and respectively carrying out big data verification on the wastewater monitoring data corresponding to each day to obtain characteristic values respectively corresponding to the video monitoring flow characteristic and the video monitoring invasion characteristic of the wastewater monitoring data corresponding to each day.

For example, in step S12, multiple kinds of multidimensional big data are used to perform cross big data check on the wastewater monitoring data (which may include wastewater flow data, wastewater exceeding state data, wastewater exceeding upper limit data, wastewater pollution factor concentration, and the like) corresponding to each day of the enterprise, so as to obtain feature values of two dimensions of the wastewater monitoring data corresponding to each day, where the feature values are respectively corresponding to the video monitoring flow feature and the video monitoring intrusion feature, and further, whether the wastewater monitoring data is artificially forged can be determined by the feature values, so as to prevent the artificial data counterfeiting of the wastewater monitoring data actually used for monitoring the wastewater of the enterprise.

Next, in the above embodiment of the present application, starting from the video monitoring flow, the step S12 of performing big data verification on the wastewater monitoring data corresponding to each day respectively to obtain the characteristic value corresponding to the video monitoring flow characteristic of the wastewater monitoring data corresponding to each day includes:

For example, a daily pollution discharge monitoring video at a discharge port of enterprise wastewater is acquired, the pollution discharge monitoring video is analyzed by using a video analysis technology to obtain an analysis result of the video (for example, wastewater flow data and the like corresponding to each time point in the pollution discharge monitoring video), and then the video analysis result is matched with corresponding wastewater monitoring data (for example, wastewater flow data and the like) according to a time sequence (for example, wastewater flow data analyzed by a time point T in the pollution discharge monitoring video is matched with wastewater flow data sampled by an actual monitoring instrument at the time point T) to obtain a matching result; wherein the match result is a full match or a mismatch; if the matching results are completely matched, it indicates that the actually sampled wastewater monitoring data is consistent with the wastewater monitoring data analyzed by the pollution discharge monitoring video, and if the matching results are not matched, it indicates that the actually sampled wastewater monitoring data is inconsistent with the wastewater monitoring data analyzed by the pollution discharge monitoring video, and if the matching results are not matched, it indicates that the actually sampled wastewater monitoring data is not consistent with the wastewater monitoring data analyzed by the pollution discharge monitoring video, and the feature value corresponding to the video monitoring flow feature of the wastewater monitoring data corresponding to the sampling date is 0, so that the cross data analysis of the pollution discharge monitoring video and the corresponding wastewater monitoring data in the monitoring process of the enterprise wastewater is realized, and the authenticity of the acquired wastewater monitoring data, such as the wastewater flow data, is judged.

Next, in the above-mentioned embodiment of the present application, starting from the video monitoring at the discharge port where the artificial invasion enterprise wastewater exists, in step S12, the large data check is performed on the wastewater monitoring data corresponding to each day, so as to obtain the characteristic value corresponding to the video monitoring invasion characteristic of the wastewater monitoring data corresponding to each day, including:

acquiring daily pollution discharge monitoring video at a discharge port of enterprise wastewater, and repeating the following steps until obtaining a characteristic value corresponding to the video monitoring invasion characteristic of the wastewater monitoring data corresponding to each day:

if yes, determining the time point when the personnel enter in the target day corresponding to the pollution discharge monitoring video;

and if the first monitoring mean value and the second monitoring mean value meet preset determination conditions, determining a characteristic value corresponding to the video monitoring intrusion characteristic of the wastewater monitoring data corresponding to the target day.

For example, whether people enter the acquired pollution discharge monitoring video within the target month M is determined in a traversing manner; if so, determining the time point T when the person enters in the target day corresponding to the pollution discharge monitoring video in which the person enters_IntoCalculating said time point T within said target day_Into(e.g., 14:25) and said time point T, the first mean value mean1 of the wastewater monitoring data before (i.e., calculating the mean value of the wastewater monitoring data between 0:00 and 14:25 in the target day)_IntoA second mean value of wastewater monitoring data mean2 (i.e., a mean value of wastewater monitoring data between 14:25 and 24:00 within the target day is calculated); and if the first monitoring mean value mean1 and the second monitoring mean value mean2 in the target day meet preset determination conditions, determining a characteristic value corresponding to the video monitoring intrusion characteristic of the wastewater monitoring data corresponding to the target day, and checking the wastewater monitoring data with human intervention.

Further, the preset determination condition in the step S12 includes:

(mean1-mean2)/mean1> -A, and 0 ≦ A ≦ 1;

Here, the monitoring mean fluctuation threshold a may be any decimal between 0 and 1, in an embodiment of the present application, it is preferable that the monitoring mean fluctuation threshold a is 0.15, the smaller the monitoring mean fluctuation threshold a, the closer the month of the wastewater monitoring data of the day is to the actual sampling, and if the first monitoring mean1 and the second monitoring mean2 satisfy (mean1-mean2)/mean1> -0.15, it is determined that the feature value corresponding to the video monitoring intrusion feature of the wastewater monitoring data corresponding to the target day is 0; if the first monitoring mean value mean1 and the second monitoring mean value mean2 do not satisfy (mean1-mean1)/mean2> -0.15, determining that the characteristic value corresponding to the video monitoring intrusion characteristic of the wastewater monitoring data corresponding to the target day is 1, repeating the steps until the characteristic value corresponding to the video monitoring intrusion characteristic of the wastewater monitoring data corresponding to each day is obtained, and judging human intervention by the pollution discharge monitoring video and the wastewater monitoring data corresponding to the pollution discharge monitoring video.

In another aspect, to integrate the distinguishing of the authenticity of the acquired wastewater monitoring data from multiple dimensions, the quality assessment feature in embodiments of the present application further comprises: a correlation characteristic. Analyzing the correlation characteristics, wherein the step S12 of obtaining wastewater monitoring data corresponding to the enterprise wastewater and performing quality evaluation on the wastewater monitoring data to obtain characteristic values corresponding to quality evaluation characteristics of the wastewater monitoring data includes:

acquiring wastewater monitoring data corresponding to enterprise wastewater, and performing correlation evaluation on the wastewater monitoring data to obtain a characteristic value corresponding to correlation characteristics of the wastewater monitoring data. Further, the step S12 obtains a monthly consumption vector corresponding to each energy consumption data of the enterprise and a monthly monitoring data vector corresponding to the wastewater monitoring data; calculating the correlation between each monthly consumption vector and the corresponding monthly monitoring data vector of the wastewater monitoring data two by two respectively to obtain the correlation coefficient between each energy consumption data of the enterprise and the wastewater monitoring data; and selecting a maximum value from all the correlation coefficients, and determining the correlation coefficient corresponding to the maximum value as a characteristic value corresponding to the correlation characteristic of the wastewater monitoring data.

For example, the energy consumption data includes, but is not limited to, monthly water consumption, monthly electricity consumption, monthly coal consumption, monthly petroleum consumption and the like of the enterprise F, and in order to comprehensively consider the authenticity of the acquired wastewater monitoring data, multidimensional data correlation analysis evaluation needs to be performed between the energy consumption data and the wastewater monitoring data. For example, the energy consumption of the enterprise F is water, electric quantity, coal and petroleum, enterprise wastewater containing COD and ammonia nitrogen can be generated, and in order to ensure the accuracy of the calculated correlation characteristics, monthly wastewater monitoring of the enterprise F in the last year is takenMeasuring data, and obtaining a monthly consumption vector corresponding to each energy consumption data of the enterprise F, wherein the monthly consumption vectors are respectively as follows: monthly water consumption vector Vwater_i：Vwater₁，Vwater₂…Vwater₁₂Monthly power consumption vector Velec_iMonthly coal quantity vector Vcoal_iAnd monthly petroleum quantity vector Voil_iAnd correspondingly calculating monthly monitoring data vectors of wastewater monitoring data (such as wastewater flow data, wastewater pollution factor concentration and the like, wherein the wastewater pollution factor concentration is preferably COD concentration and ammonia nitrogen concentration), wherein the monthly monitoring data vectors are respectively as follows: monthly average COD vector Vcod_iAverage ammonia nitrogen vector Vnh of moon_iMonthly accumulated wastewater flow vector Vflow_iWherein the monthly average COD vector Vcod_iObtained by averaging the COD concentrations obtained in real time in months, the monthly accumulated wastewater flow vector Vflow_iObtaining real-time waste water flow data by month summing; where i is used to indicate the number of copies per month of the last year of business F, i.e., i ═ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12; then, in step S12, the correlation between each monthly consumption vector and the corresponding monthly monitoring data vector of the wastewater monitoring data is calculated in pairs to obtain a correlation coefficient matrix Cor between each energy consumption data of the enterprise and the corresponding wastewater monitoring data_xyAs shown in fig. 2, where x is 1, 2 and 3 and y is 1, 2, 3 and 4, and further according to the correlation coefficient matrix Cor_xyObtaining a correlation coefficient between each energy consumption data of the enterprise and the wastewater monitoring data; for example the correlation coefficient Cor₂₃For indicating the correlation between the coal usage amount of enterprise F in the year and the ammonia nitrogen concentration in the produced enterprise wastewater; then selecting a maximum value from the correlation coefficients, determining the correlation coefficient corresponding to the maximum value as a characteristic value corresponding to the correlation characteristic of the wastewater monitoring data, and if the correlation coefficient Cor is not the maximum value, determining the correlation coefficient corresponding to the maximum value as the characteristic value corresponding to the correlation characteristic of the wastewater monitoring data₂₁The maximum value MaxCor is the correlation coefficient MaxCor ═ Cor corresponding to the maximum value₂₁And determining a characteristic value MaxCor corresponding to the correlation characteristic of the wastewater monitoring data, and realizing correlation analysis between the energy consumption data and the corresponding wastewater monitoring data.

As shown in fig. 3, after the monitoring frequency characteristic, the stability characteristic, the steep peak characteristic, the intra-day monitoring value variation frequency characteristic, the inter-day monitoring value variation frequency characteristic, the random number characteristic, the video monitoring flow characteristic, the video monitoring intrusion characteristic, and the correlation characteristic are calculated and obtained in step S12, the quality evaluation value V of the wastewater monitoring data is obtained_AThe following formula:

V_Ac1 w1+ c2 w2+ c3 w3+ c4 w4+ c5 w5+ c6 w6+ c7 w7+ c8 w8+ c9 w 9; obtaining a quality assessment value V of wastewater monitoring data at each drain outlet of enterprise F_ApAnd p is the number of the water outlets of the enterprise F. In order to enhance the authenticity monitoring of the wastewater monitoring data and prevent the damage to the ecological environment, the quality evaluation value V of the wastewater monitoring data at each water discharge outlet is used_ApComparing the obtained data and obtaining a quality evaluation value minV of the wastewater monitoring data at the worst water outlet_ApQuality assessment value V as the wastewater monitoring data of the enterprise F_A＝minV_ApThe utility model discloses a waste water monitoring data of enterprise's waste water carries out quality assessment, so that the supervisor can be through the quality assessment value of the waste water monitoring data that obtains of calculation, judge whether the waste water monitoring data of this enterprise's exhaust waste water has the authenticity, the supervisor can select the enterprise that score is low to carry out the on-the-spot inspection, very big improvement the accuracy of supervision, and then can supervise the enterprise and carry out blowdown standard treatment with exhaust waste water and just discharge, the waste water of the enterprise that the waste water monitoring data of avoiding making fake is discharged out, and then influence ecological environment.

In addition, this application embodiment still provides an equipment of aassessment waste water monitoring data quality, and this equipment includes:

one or more processors; and

a memory storing computer readable instructions that, when executed, cause the processor to perform the operations of the foregoing method.

For example, the computer readable instructions, when executed, cause the one or more processors to: presetting at least one quality evaluation characteristic of wastewater monitoring data before evaluating the quality of the wastewater monitoring data corresponding to the enterprise wastewater; acquiring wastewater monitoring data corresponding to enterprise wastewater, performing quality evaluation on the wastewater monitoring data to obtain characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data, and calculating the characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data to obtain weights of all quality evaluation characteristics of the wastewater monitoring data; the quality evaluation value of the wastewater monitoring data is obtained based on the characteristic value and the weight corresponding to each quality evaluation characteristic of the wastewater monitoring data, so that the quality evaluation of the wastewater monitoring data of enterprise wastewater is realized, a supervisor can judge whether the wastewater monitoring data of the enterprise wastewater discharged by the enterprise has authenticity or not through calculating the quality evaluation value of the wastewater monitoring data obtained by the supervisor, the supervisor can select the enterprise with low score to carry out on-site inspection, the supervision accuracy is greatly improved, the enterprise can be supervised to carry out pollution discharge standard treatment on the discharged wastewater to discharge, and the enterprise wastewater corresponding to the fake wastewater monitoring data is prevented from being discharged, so that the ecological environment is influenced.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

It should be noted that the present application may be implemented in software and/or a combination of software and hardware, for example, implemented using Application Specific Integrated Circuits (ASICs), general purpose computers or any other similar hardware devices. In one embodiment, the software programs of the present application may be executed by a processor to implement the steps or functions described above. Likewise, the software programs (including associated data structures) of the present application may be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Additionally, some of the steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.

In addition, some of the present application may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or techniques in accordance with the present application through the operation of the computer. Program instructions which invoke the methods of the present application may be stored on a fixed or removable recording medium and/or transmitted via a data stream on a broadcast or other signal-bearing medium and/or stored within a working memory of a computer device operating in accordance with the program instructions. An embodiment according to the present application comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform a method and/or a solution according to the aforementioned embodiments of the present application.

It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the apparatus claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Claims

1. A method of assessing the quality of wastewater monitoring data, wherein the method comprises:

obtaining a quality evaluation value of the wastewater monitoring data based on the characteristic value and the weight corresponding to each quality evaluation characteristic of the wastewater monitoring data;

feeding back the quality evaluation value of the wastewater monitoring data to a supervisor so that the supervisor can judge whether the wastewater monitoring data is authentic according to the quality evaluation value of the wastewater monitoring data;

acquiring wastewater monitoring data corresponding to enterprise wastewater, performing quality assessment on the wastewater monitoring data, and obtaining characteristic values corresponding to each quality assessment characteristic of the wastewater monitoring data, wherein the characteristic values comprise: selecting a target month, and acquiring daily corresponding wastewater monitoring data and monitoring days of enterprise wastewater generated during enterprise work within the target month; respectively performing quality evaluation on the wastewater monitoring data corresponding to each day to obtain characteristic values corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day; obtaining characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to the target month based on the monitoring days and the characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to each day; determining characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to the target month as characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data;

wherein, carry out quality assessment to the waste water monitoring data that corresponds every day respectively, obtain the characteristic value that each quality assessment feature of the waste water monitoring data that corresponds every day corresponds, include: acquiring daily pollution discharge monitoring video at a discharge port of enterprise wastewater, and repeating the following steps until obtaining a characteristic value corresponding to the video monitoring invasion characteristic of the wastewater monitoring data corresponding to each day:

(mean1-mean2)/mean1> -A, and 0 ≦ A ≦ 1;

2. The method of claim 1, wherein the quality evaluation of the wastewater monitoring data corresponding to each day to obtain the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day comprises:

3. The method of claim 1, wherein the quality evaluation of the wastewater monitoring data corresponding to each day to obtain the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day comprises:

4. The method of claim 1, wherein the quality evaluation of the wastewater monitoring data corresponding to each day to obtain the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day comprises:

5. The method of claim 1, wherein the quality evaluation of the wastewater monitoring data corresponding to each day to obtain the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day comprises:

6. The method of claim 1, wherein the quality evaluation of the wastewater monitoring data corresponding to each day to obtain the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day comprises:

7. The method of claim 1, wherein the quality evaluation of the wastewater monitoring data corresponding to each day to obtain the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day comprises:

8. The method of claim 1, wherein the quality evaluation of the wastewater monitoring data corresponding to each day to obtain the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day comprises:

9. The method of claim 1, wherein the quality assessment feature further comprises: and the correlation characteristics are obtained by acquiring wastewater monitoring data corresponding to the enterprise wastewater and performing quality evaluation on the wastewater monitoring data to obtain characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data, and the method comprises the following steps:

10. An apparatus for assessing the quality of wastewater monitoring data, wherein the apparatus comprises:

one or more processors; and

a memory storing computer readable instructions that, when executed, cause the processor to perform the operations of the method of any of claims 1 to 9.