CN117451963A - High-efficient detecting system of ammonia nitrogen waste water - Google Patents
High-efficient detecting system of ammonia nitrogen waste water Download PDFInfo
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Abstract
The invention discloses an ammonia nitrogen wastewater high-efficiency detection system, in particular to the wastewater detection field, which is characterized in that a detection pressure significance index and a detection disorder index are calculated by analyzing a static data set and are converted into intervention coefficients, the intervention coefficients are used for evaluating the static data quality of the recent wastewater ammonia nitrogen detection, and different signals are generated by comparing the intervention coefficients with an interference judgment threshold value to indicate whether external intervention is needed to adjust and correct the static data; therefore, the method improves the degree of automation of real-time monitoring and interference judgment of static data quality, is beneficial to improving the accuracy and reliability of ammonia nitrogen detection of wastewater, reduces the risk of false decision, improves the efficiency of wastewater treatment and the environmental protection level, analyzes the correlation and the significance between static and dynamic data after acquiring interference signals, and generates corresponding correction signals so as to more accurately adjust the data and improve the data quality.
Description
Technical Field
The invention relates to the field of wastewater detection, in particular to an ammonia nitrogen wastewater high-efficiency detection system.
Background
Ammonia nitrogen wastewater refers to wastewater containing ammonia nitrogen compounds. Ammonia nitrogen refers to the sum of ammonia gas and ammonia radical ions dissolved in water. Such contaminants are typically from various industrial processes, agricultural emissions, municipal sewage plant emissions, and other sources of wastewater.
The harm of ammonia nitrogen wastewater mainly comprises the following aspects: firstly, ammonia nitrogen is a harmful water quality pollutant, high-concentration ammonia nitrogen can cause water eutrophication, promote the growth of blue algae and other harmful algae, cause water algal bloom explosion, and cause water eutrophication. Secondly, ammonia nitrogen can also have a composite action with other pollutants in water to form more harmful substances, which cause long-term harm to water environment and ecological system. In addition, ammonia nitrogen wastewater also threatens the sustainable utilization of water resources, influences water supply quality, reduces the ecological health of water bodies, and even generates potential risks for human health.
Therefore, in order to ensure the health and sustainable development of the water environment, it is important to treat ammonia nitrogen wastewater. The necessity of ammonia nitrogen wastewater treatment is to reduce the pollution degree of water body, maintain the ecological balance of water body, protect human health and obey environmental regulations and standards. In order to treat ammonia nitrogen wastewater correctly and efficiently, a special detection technology is required to monitor and evaluate ammonia nitrogen concentration in real time. These specialized techniques include ammonia nitrogen sensors, spectroscopic analysis, chemical analysis, etc., which can provide accurate data, help determine wastewater treatment processes and control measures, ensure that wastewater emissions are within acceptable standards, and ultimately protect the quality of the aqueous environment and the health of the ecosystem. In summary, ammonia nitrogen wastewater treatment is an environmental and health requirement, and a dedicated detection technology is a key to realizing efficient treatment.
The general ammonia nitrogen wastewater detection flow comprises dynamic monitoring and static monitoring functions, and the comprehensive, accurate and timely performance of wastewater monitoring can be improved by using the dynamic monitoring and the static monitoring, so that the effectiveness of a wastewater treatment process, the environment compliance and the stability of facility operation are ensured. This is important for environmental protection, compliance with regulatory requirements, and for improved wastewater treatment efficiency.
In the existing detection technology, a static and dynamic detection mode is adopted;
the static monitoring mode is suitable for long-term trend analysis, and can help to know trends such as seasonal and annual changes. This provides effective support for making long-term environmental protection plans and decisions.
Real-time and high-resolution ammonia nitrogen data can be provided by a dynamic monitoring method, instantaneous changes and concentration gradients are captured, and long-term and fixed-point monitoring data is provided by a static monitoring method. By combining the two, a more comprehensive data set can be obtained, and various conditions of ammonia nitrogen concentration are covered. The dynamic monitoring can monitor the change of ammonia nitrogen concentration in real time, is favorable for finding abnormal conditions in time, such as sudden pollution events or leakage, so as to take urgent measures for treatment, and has the extremely beneficial effects that: the dynamic monitoring data can be used for correcting or verifying the static monitoring data, so that the accuracy of the data is improved, and the quality of the static monitoring data is ensured.
However, in the prior art, static data is usually corrected by directly using dynamic data, and correlation between the two is ignored and correction accuracy of the dynamic data to the static data is ignored. This approach limits the ability to improve the quality of the test data, especially when treating ammonia nitrogen wastewater tests, thus leading to possible distortion of the test results and failure to provide accurate abatement decisions. The severity of this problem is that environmental pollution needs to be timely and effectively ameliorated, and if the data cannot be accurately interpreted, we are prevented from taking necessary measures to protect the health of the water and the ecosystem. Therefore, a more detailed data analysis method and a more comprehensive data integration strategy are needed to cope with the ammonia nitrogen wastewater problem and ensure accurate environmental monitoring and treatment decisions.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide for calculating a detected pressure significance index and a detected disorder index by analyzing a set of static data and converting them into intervention coefficients for evaluating the quality of static data of recent wastewater ammonia nitrogen detection, generating different signals by comparing with an interference judgment threshold, indicating whether external intervention is required to adjust and correct the static data; therefore, the method improves the degree of automation of real-time monitoring and interference judgment of static data quality, is beneficial to improving the accuracy and reliability of ammonia nitrogen detection of wastewater, reduces the risk of false decision, improves the efficiency of wastewater treatment and the environmental protection level, analyzes the correlation and the significance between static and dynamic data after acquiring interference signals, and generates corresponding correction signals so as to more accurately adjust the data and improve the data quality, thereby solving the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: the system comprises an intervention monitoring unit, an intervention analysis unit, an intervention summarization unit, a correlation analysis unit and a correlation judgment unit;
the intervention monitoring unit is used for acquiring a static data set formed by a plurality of static monitoring data during ammonia nitrogen wastewater detection in unit time and sending the static data set to the intervention analysis unit;
the intervention analysis unit acquires pressure information and connection information of the static data set, wherein the pressure information comprises a detected pressure significant index, the connection information comprises a detected disorder index, the detected pressure significant index and the detected disorder index are processed to obtain a data analysis model, an intervention coefficient is generated, and the intervention coefficient is sent to the intervention summarization unit;
the intervention summarizing unit further processes the intervention coefficients to obtain intervention judgment signals, wherein the intervention judgment signals comprise an intervention signal and an intervention signal, and the intervention judgment signals are sent to the relevant analysis unit;
under the condition that interference signals are obtained, a correlation analysis unit obtains a dynamic data set formed by a plurality of dynamic monitoring data during ammonia nitrogen wastewater detection in unit time, processes the static data set and the dynamic data set to obtain a correlation coefficient and a significant coefficient, and sends the correlation coefficient and the significant coefficient to a correlation judgment unit;
the correlation judgment unit further analyzes the correlation coefficient and the significant coefficient to generate a supplementary signal, wherein the supplementary signal comprises an accurate correction signal, a low correction signal and a discard signal.
In a preferred embodiment, the acquisition logic to detect the pressure significance index is:
the calculation formula of the detected pressure significance index is as follows:wherein->In order to detect the significance index of the pressure,representing a continuous time point +.>Ammonia nitrogen concentration of->Represents the previous time point +.>Ammonia nitrogen concentration of->Represents the time interval between two adjacent time points, < >>Represents the average flow in the corresponding time period, +.>Indicating the time point sequence number.
In a preferred embodiment, the detection disorder index fetch logic is:
step S11, sequencing the static detection data according to time sequence, and ensuring that each data point is associated with a corresponding time point;
step S12, a calculation formula for detecting the disorder index is as follows:wherein->Indicating a detection disorder index, & gt>Data of the current time point and the lag time point, respectively,/->Representing the mean of the data.
In a preferred embodiment, after the intervention coefficients are obtained, the intervention coefficients are compared with an intervention judgment threshold;
if the intervention coefficient is smaller than the interference judgment threshold value, generating a release signal;
and if the intervention coefficient is greater than or equal to the interference judgment threshold value, generating an interference signal.
In a preferred embodiment, under the condition of obtaining an interference signal, a dynamic data set formed by a plurality of dynamic monitoring data during ammonia nitrogen wastewater detection in unit time is obtained, and the dynamic data set and the static data set are comprehensively analyzed to obtain a correlation coefficient and a significant coefficient, wherein the data in the dynamic data set and the static data set are acquired based on the same time point, namely, the corresponding dynamic data and static data are acquired at the same time point.
In a preferred embodiment, the correlation coefficient acquisition logic is:
step S21, calculating the average value of the dynamic data set and the static data set respectively;
step S22, for each data point, calculating a difference value between the data point and a corresponding mean value;
step S23, calculating the sum of difference products of every pair of data points to obtain a related molecular part;
step S24, calculating standard deviations of the dynamic data set and the static data set respectively to obtain denominator parts of the correlation;
step S25, calculating the correlation coefficient using the following formula：Wherein->The correlation coefficient is represented by a correlation coefficient,representing +.>Dynamic data->Mean value representing dynamic data set, +.>Representing +.>Dynamic data->Mean value representing static data set, +.>Standard deviation of the representation dynamic data set +.>Standard deviation of the static data set +.>And the sequence numbers of the time points for acquiring the dynamic data and the static data are represented, and the dynamic data and the static data are acquired simultaneously at the same time point.
In a preferred embodiment, the significant coefficient acquisition logic is:
step S31, determining a significance level, for example, a significance level of 0.05, which means that the significance test is performed at a 95% confidence level;
step S32, obtaining a correlation coefficient;
step S33, calculating the degree of freedom for determiningThe shape of the distribution is calculated as: />Wherein->Is the number of data points in the dynamic data set, the degree of freedom is used to determine +.>The shape of the distribution;
in step S34,the statistic is used for evaluating the significance of the correlation coefficient, and the calculation formula is as follows: />In which, in the process,representing the correlation coefficient;
step S35, usingDegree of freedom and significance level of distribution, find +.>Distributing tables to obtain->Statistics corresponding +.>The value, i.e., the saliency coefficient;
in step S36 of the process of the present invention,the value represents the observed +.>Statistics or more extreme values. />The calculation mode of the value is as follows: />Wherein->Is a correlation->The absolute value of the statistic for measuring the intensity and direction of the correlation +.>Representation->Cumulative distribution function of distribution for representing->Distribution of statistics->Is->Degree of freedom of distribution for determining +.>The shape of the distribution.
In a preferred embodiment, where correlation coefficients and significance coefficients are obtained, the correlation coefficients are compared to a correlation threshold and the significance coefficients are compared to a significance level;
if the correlation coefficient is greater than or equal to the correlation threshold, generating an accurate correction signal;
if the correlation coefficient is greater than or equal to the correlation threshold and the significant coefficient is less than the significant level, or if the correlation coefficient is less than the correlation threshold but the significant coefficient is greater than or equal to the significant level, generating a low-level correction signal;
if the correlation coefficient is less than the correlation threshold and the significance coefficient is less than the significance level, generating a relinquish signal.
The invention relates to a technical effect and advantages of an ammonia nitrogen wastewater high-efficiency detection system, which are as follows:
1. and analyzing the static data set, and further processing to obtain an intervention coefficient by utilizing the calculation of the detected pressure significance index and the detected disorder index. The use of the intervention coefficients can help to analyze the authenticity and quality of static data when detecting ammonia nitrogen in the wastewater in a short period of time. And comparing the intervention coefficient with a preset interference judgment threshold value to generate an release signal and an interference signal, thereby providing a clear signal and helping an operator judge whether external intervention is needed to adjust and correct the static data during detection. And further, the automatization capability of real-time monitoring and interference judgment of static data quality is improved. By introducing the intervention coefficient and the interference judgment threshold, an operator can more quickly and accurately identify whether static data is abnormally interfered or not, so that corresponding actions are taken. The method is beneficial to improving the accuracy and reliability of ammonia nitrogen detection of the wastewater, promoting timely data correction, reducing the risks of data distortion and false decision, and further improving the efficiency of wastewater treatment and the environmental protection level;
2. in the case of obtaining the interference signal, first, a dynamic data set is constructed by acquiring dynamic data detected at the same time point as the static data set. Then, using this set of dynamic data and the set of static data, a calculation of correlation coefficients is performed for analyzing the linear relationship between the dynamic data and the static data, and a calculation of saliency coefficients is performed for evaluating whether this relationship is statistically significant, by comparing the correlation coefficients with a correlation threshold, and the saliency coefficients with a saliency level, an accurate correction signal, a low correction signal, and a discard signal are generated, helping the operator to determine when correction of the static data is needed, when correction interventions of different degrees are needed, or when no intervention is possible. This helps to improve data quality, reduce the risk of false decisions, and ensure the efficiency and reliability of wastewater treatment processes, thereby promoting advances in environmental monitoring and wastewater treatment technologies.
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FIG. 1 is a schematic structural diagram of an ammonia nitrogen wastewater high-efficiency detection system.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment 1, fig. 1 shows an ammonia nitrogen wastewater high-efficiency detection system of the invention, which comprises an intervention monitoring unit, an intervention analysis unit, an intervention summarizing unit, a correlation analysis unit and a correlation judgment unit;
the general ammonia nitrogen wastewater detection flow comprises dynamic monitoring and static monitoring functions, and the comprehensive, accurate and timely performance of wastewater monitoring can be improved by using the dynamic monitoring and the static monitoring, so that the effectiveness of a wastewater treatment process, the environment compliance and the stability of facility operation are ensured. This is important for environmental protection, compliance with regulatory requirements, and for improved wastewater treatment efficiency.
The intervention monitoring unit is used for transmitting a static data set formed by a plurality of static monitoring data during ammonia nitrogen wastewater detection in time to the intervention analysis unit;
the intervention analysis unit acquires pressure information and connection information of the static data set, wherein the pressure information comprises a detected pressure significant index, the connection information comprises a detected disorder index, the detected pressure significant index and the detected disorder index are processed to obtain a data analysis model, an intervention coefficient is generated, and the intervention coefficient is sent to the intervention summarization unit;
the intervention summarizing unit further processes the intervention coefficients to obtain intervention judgment signals, wherein the intervention judgment signals comprise an intervention signal and an intervention signal, and the intervention judgment signals are sent to the relevant analysis unit;
under the condition that interference signals are obtained, a correlation analysis unit obtains a dynamic data set formed by a plurality of dynamic monitoring data during ammonia nitrogen wastewater detection in unit time, processes the static data set and the dynamic data set to obtain a correlation coefficient and a significant coefficient, and sends the correlation coefficient and the significant coefficient to a correlation judgment unit;
the correlation judgment unit further analyzes the correlation coefficient and the significant coefficient to generate a supplementary signal, wherein the supplementary signal comprises an accurate correction signal, a low correction signal and a discard signal.
The operation process of the intervention monitoring unit comprises the following steps:
and acquiring a static data set formed by a plurality of static monitoring data during ammonia nitrogen wastewater detection in unit time, wherein the unit time refers to the closest time period before the current ammonia nitrogen condition of the wastewater is detected.
The operation process of the intervention analysis unit comprises the following steps:
in detecting ammonia nitrogen wastewater, it is very necessary and important to evaluate the quality of static data and to obtain the degree of loading in wastewater detection. This is because the concentration and water quality characteristics of ammonia nitrogen wastewater may fluctuate significantly due to changes in wastewater sources, treatment processes, or environmental conditions. Knowing the degree of loading at the time of detection can help determine the stability and reliability of the static data, as well as whether it is being disturbed by external factors. If the loading level is high, i.e. the ammonia nitrogen concentration in the wastewater fluctuates more or the wastewater treatment capacity is large, the static data is more susceptible, and more frequent monitoring and correction are needed to ensure the accuracy of the data. Thus, assessing static data quality and knowing the degree of loading helps to optimize the wastewater detection process, ensuring efficient wastewater management and environmental monitoring.
The acquisition logic of the detection pressure significance index is as follows:
the calculation formula of the detected pressure significance index is as follows:wherein->For detecting a stress significance index +.>Representing a continuous time point +.>Ammonia nitrogen concentration of->Represents the previous time point +.>Ammonia nitrogen concentration of->Represents the time interval between two adjacent time points, < >>Represents the average flow in the corresponding time period, +.>Indicating the time point sequence number.
The detection pressure significance index is used for indicating the detection load degree of the ammonia nitrogen wastewater, reflecting the detection load of the ammonia nitrogen wastewater in unit time, and when the value of the detection pressure significance index is larger, indicating that the ammonia nitrogen wastewater is detected to bear heavier load pressure, meaning that the fluctuation of the ammonia nitrogen concentration in the wastewater is larger, the wastewater treatment capacity is larger, the detection error is increased, and higher detection capability is needed to meet the detection requirement; on the contrary, the smaller the value of the detection pressure significance index is, the smaller the load pressure born by the ammonia nitrogen wastewater detection is, the fluctuation of the ammonia nitrogen concentration in the wastewater is smaller, the wastewater flow is also smaller, the detection error is smaller, and the current detection capability meets the wastewater detection standard requirement.
When detecting ammonia nitrogen wastewater, it is important to evaluate the quality of static data and to obtain autocorrelation among time-series data points when detecting ammonia nitrogen wastewater. The autocorrelation analysis helps to see if there is a correlation between data points at different points in time in the static data, i.e., how relevant the data points are to themselves at different time lags. Time dependence and trend in wastewater detection data can be revealed, helping to determine the stability and reliability of the data. If the autocorrelation is high, indicating that there is a significant correlation between the data points, it helps to analyze whether the static data conforms to the assumption of independent co-distribution, helping to simplify the analysis and reduce redundant information in the data. Therefore, the evaluation of the autocorrelation is important for optimizing the quality and interpretation capability of ammonia nitrogen wastewater detection data, and helps to ensure the accuracy and the credibility of environmental monitoring.
The detection disorder index acquisition logic is:
step S11, sequencing the static detection data according to time sequence, and ensuring that each data point is associated with a corresponding time point;
step S12, a calculation formula for detecting the disorder index is as follows:wherein->Indicating a detection disorder index, & gt>Data of the current time point and the lag time point, respectively,/->Representing the mean of the data.
The detection disorder index is used for representing the autocorrelation among time sequence data points in the static monitoring data, namely the correlation degree between the data points and the self-body at different time lags, and reflects the disorder of the static data; if the detected disorder index is larger, the positive correlation exists between static data points, which means that when the value of one data point is larger, the value of the next data point is larger, or when the value of one data point is smaller, the value of the next data point is smaller, which means that the higher correlation exists between the current data point and the data point of the lag time point, which means that the change trend between the data points is more continuous in time, which contains repeated information, and the redundant information in the monitored data is easy to increase, the problem of over fitting is easy to cause, the problem of over fitting means that the model or the analysis method is over matched with training data, and the over fitting can reduce the generalization capability of the model, so that the prediction or analysis effect on new data is poor, and the detection precision is adversely affected;
when the smaller the detection disorder index is, the less continuous the change trend between the data points is, the weak correlation between the adjacent data points is helpful to reduce the influence of the autocorrelation on the monitored data, so that the data is more consistent with the assumption of independent same distribution, which is one of ideal situations in the statistical analysis, and is helpful to reduce the redundancy in the data, so that the analysis is more concise and efficient.
The detected pressure significance index and the detected disorder index are comprehensively processed to obtain an intervention coefficient, and for example, the intervention coefficient can be calculated by the following formula:wherein->Representing intervention coefficients->The index is a significant index of the detected pressure and a disorder index of the detected pressure, respectively, < >>Preset proportional coefficients of the detected pressure significance index and the detected disorder index are respectively +.>Are all greater than 0.
The intervention coefficient is used to represent the extent to which the static monitoring data acquired during the current time period requires external intervention to ensure its authenticity and quality. The magnitude of the intervention coefficient can be used for judging whether the intervention is needed to be carried out on the data so as to ensure the accuracy and the reliability of the data;
when the intervention coefficient is large, the monitored pressure significance index and the detected disorder index are relatively low, namely the monitored data are small in pressure and ordered. This indicates that the current static monitoring data is relatively reliable and real, and does not require external intervention. A larger intervention coefficient corresponds to a better data quality.
When the intervention coefficient is small, it means that the monitored pressure significance index and the detected disorder index are relatively high, i.e. the monitored data are more pressurized and less ordered. This indicates that current static monitoring data has a large uncertainty or problem, requiring external intervention to improve the authenticity and quality of the data. Smaller intervention coefficients correspond to poorer data quality.
The operation process of the intervention summarization unit comprises the following contents:
after the intervention coefficient is obtained, comparing the intervention coefficient with an interference judgment threshold value;
if the intervention coefficient is smaller than the interference judgment threshold value, the current static monitoring data is more reliable and real, external intervention is not needed, and an release signal is generated;
if the intervention coefficient is greater than or equal to the interference judgment threshold, the current static monitoring data has larger uncertainty or problem, external intervention is needed to improve the authenticity and quality of the data, and an interference signal is generated.
According to the invention, the intervention coefficient is further obtained through processing by analyzing the static data set and calculating the detected pressure significance index and the detected disorder index. The use of the intervention coefficients can help to analyze the authenticity and quality of static data when detecting ammonia nitrogen in the wastewater in a short period of time. And comparing the intervention coefficient with a preset interference judgment threshold value to generate an release signal and an interference signal, thereby providing a clear signal and helping an operator judge whether external intervention is needed to adjust and correct the static data during detection. And further, the automatization capability of real-time monitoring and interference judgment of static data quality is improved. By introducing the intervention coefficient and the interference judgment threshold, an operator can more quickly and accurately identify whether static data is abnormally interfered or not, so that corresponding actions are taken. The method is beneficial to improving the accuracy and reliability of ammonia nitrogen detection of the wastewater, promoting timely data correction, reducing the risks of data distortion and false decision, and further improving the efficiency of wastewater treatment and the environmental protection level.
The strength and direction of the linear relationship between the two data sets can be measured using correlation, and a saliency check evaluates whether this relationship is statistically significant. If the correlation is high and the significance test indicates that the correlation is not occasional, then the dynamic data sets have a higher potential to correct the static data because they provide important information about how to correct the static data to more accurately reflect the actual situation. Conversely, if the correlation is low or insufficient, no intervention of the dynamic data is required, as the relationship between the two is weak or insufficient to support effective correction. Therefore, correlation and significance based analysis can provide a powerful basis for determining whether dynamic monitoring data can correct static monitoring data to ensure data quality and decision accuracy.
The operation process of the intervention summarization unit comprises the following contents:
under the condition of obtaining interference signals, a dynamic data set formed by a plurality of dynamic monitoring data during ammonia nitrogen wastewater detection in unit time is obtained, and the dynamic data set and the static data set are comprehensively analyzed to obtain a correlation coefficient and a significant coefficient, wherein the data in the dynamic data set and the static data set are acquired based on the same time point, namely, the corresponding dynamic data and static data are acquired at the same time point.
The acquisition logic of the correlation coefficient is as follows:
step S21, calculating the average value of the dynamic data set and the static data set respectively;
step S22, for each data point, calculating a difference value between the data point and a corresponding mean value;
step S23, calculating the sum of difference products of every pair of data points to obtain a related molecular part;
step S24, calculating standard deviations of the dynamic data set and the static data set respectively to obtain denominator parts of the correlation;
step S25, calculating a correlation coefficient using the following formula:wherein->The correlation coefficient is represented by a correlation coefficient,representing +.>Dynamic data->Mean value representing dynamic data set, +.>Representing +.>Dynamic data->Mean value representing static data set, +.>Standard deviation of the representation dynamic data set +.>Standard deviation of the static data set +.>And the sequence numbers of the time points for acquiring the dynamic data and the static data are represented, and the dynamic data and the static data are acquired simultaneously at the same time point.
The acquisition logic of the significant coefficient is as follows:
step S31, determining a significance level, for example, a significance level of 0.05, which means that the significance test is performed at a 95% confidence level;
step S32, obtaining a correlation coefficient;
step S33, calculating the degree of freedom for determiningThe shape of the distribution is calculated as: />Wherein->Is the number of data points in the dynamic data set, the degree of freedom is used to determine +.>The shape of the distribution;
in step S34,the statistic is used for evaluating the significance of the correlation coefficient, and the calculation formula is as follows: />In which, in the process,representing the correlation coefficient;
step S35, usingDegree of freedom and significance level of distribution, find +.>Distributing tables to obtain->Statistics corresponding +.>The value, i.e., the saliency coefficient;
in step S36 of the process of the present invention,the value represents the observed +.>Statistics or more extreme values. />The calculation mode of the value is as follows: />Wherein->Is a correlation->The absolute value of the statistic for measuring the intensity and direction of the correlation +.>Representation->Cumulative distribution function of distribution for representing->Distribution of statistics->Is->Degree of freedom of distribution for determining +.>The shape of the distribution.
It is necessary to analyze the relationship between the dynamic data set and the static data set at the corresponding time point using the correlation and the salience. Correlation measures the strength and direction of a linear relationship between two data sets, while a saliency check evaluates whether the relationship is statistically significant. By analyzing the correlation and significance, it can be determined whether the following dynamic monitoring data qualify to correct the necessity of static monitoring data: if the correlation is high and the significance test results in a significantly smaller significance coefficient than the significance level, it is indicated that there is a strong linear relationship between the dynamic data and the static data, and the necessity of correcting the static data is high because the dynamic data can provide more accurate correction information. Conversely, if the correlation is low or the significant coefficient from the significance test is large, it may not be necessary to correct the static data because the relationship between the two is weak or insignificant. Thus, correlation and significance based analysis can help us determine if dynamic monitoring data is needed to correct static monitoring data to improve data quality and decision accuracy.
The operation process of the related judging unit comprises the following steps:
in the case of obtaining a correlation coefficient and a significance coefficient, comparing the correlation coefficient with a correlation threshold value, and comparing the significance coefficient with a significance level;
if the correlation coefficient is equal to or greater than the correlation threshold and the significance coefficient is equal to or greater than the significance level, a strong linear relationship exists between the dynamic data and the static data, and the relationship is statistically significant. This means that dynamic data is very suitable for correcting static data and the accuracy of the correction is high, generating a precise correction signal;
if the correlation coefficient is greater than or equal to the correlation threshold and the significant coefficient is less than the significant level, or if the correlation coefficient is less than the correlation threshold but the significant coefficient is greater than or equal to the significant level, the method indicates that dynamic data is worth correcting static data, but the correction accuracy is limited, and a low-level correction signal is generated;
if the correlation coefficient is less than the correlation threshold and the significance coefficient is less than the significance level, this indicates that the linear relationship between the dynamic data and the static data is weak and that this relationship is statistically insignificant. In this case, there is no obvious benefit in using dynamic data to correct static data, the accuracy of the correction is very low, and a discard signal is generated.
Under the condition of obtaining interference signals, firstly, a dynamic data set is constructed by obtaining dynamic data detected at the same time point as the static data set. Then, using this set of dynamic data and the set of static data, a calculation of correlation coefficients is performed for analyzing the linear relationship between the dynamic data and the static data, and a calculation of saliency coefficients is performed for evaluating whether this relationship is statistically significant, by comparing the correlation coefficients with a correlation threshold, and the saliency coefficients with a saliency level, an accurate correction signal, a low correction signal, and a discard signal are generated, helping the operator to determine when correction of the static data is needed, when correction interventions of different degrees are needed, or when no intervention is possible. This helps to improve data quality, reduce the risk of false decisions, and ensure the efficiency and reliability of wastewater treatment processes, thereby promoting advances in environmental monitoring and wastewater treatment technologies.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
In the several embodiments provided in this application, it should be understood that the disclosed systems and apparatuses may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. The high-efficiency detection system for the ammonia nitrogen wastewater is characterized by comprising an intervention monitoring unit, an intervention analysis unit, an intervention summarizing unit, a correlation analysis unit and a correlation judgment unit;
the intervention monitoring unit is used for acquiring a static data set formed by a plurality of static monitoring data during ammonia nitrogen wastewater detection in unit time and sending the static data set to the intervention analysis unit;
the intervention analysis unit acquires pressure information and connection information of the static data set, wherein the pressure information comprises a detected pressure significant index, the connection information comprises a detected disorder index, the detected pressure significant index and the detected disorder index are processed to obtain a data analysis model, an intervention coefficient is generated, and the intervention coefficient is sent to the intervention summarization unit;
the intervention summarizing unit further processes the intervention coefficients to obtain intervention judgment signals, wherein the intervention judgment signals comprise an intervention signal and an intervention signal, and the intervention judgment signals are sent to the relevant analysis unit;
under the condition that interference signals are obtained, a correlation analysis unit obtains a dynamic data set formed by a plurality of dynamic monitoring data during ammonia nitrogen wastewater detection in unit time, processes the static data set and the dynamic data set to obtain a correlation coefficient and a significant coefficient, and sends the correlation coefficient and the significant coefficient to a correlation judgment unit;
the correlation judgment unit further analyzes the correlation coefficient and the significant coefficient to generate a supplementary signal, wherein the supplementary signal comprises an accurate correction signal, a low correction signal and a discard signal.
2. The high-efficiency ammonia nitrogen wastewater detection system according to claim 1, wherein:
the acquisition logic of the detection pressure significance index is as follows:
the calculation formula of the detected pressure significance index is as follows:wherein->For detecting a stress significance index +.>Representing a continuous time point +.>Ammonia nitrogen concentration of->Representation ofBefore time point +.>Ammonia nitrogen concentration of->Represents the time interval between two adjacent time points, < >>Represents the average flow in the corresponding time period, +.>Indicating the time point sequence number.
3. The high-efficiency ammonia nitrogen wastewater detection system according to claim 2, wherein:
the detection disorder index acquisition logic is:
step S11, sequencing the static detection data according to time sequence, and ensuring that each data point is associated with a corresponding time point;
step S12, a calculation formula for detecting the disorder index is as follows:wherein->Indicating a detection disorder index, & gt>Data of the current time point and the lag time point, respectively,/->Representing the mean of the data.
4. The high-efficiency ammonia nitrogen wastewater detection system according to claim 3, wherein:
after the intervention coefficient is obtained, comparing the intervention coefficient with an interference judgment threshold value;
if the intervention coefficient is smaller than the interference judgment threshold value, generating a release signal;
and if the intervention coefficient is greater than or equal to the interference judgment threshold value, generating an interference signal.
5. The high-efficiency ammonia nitrogen wastewater detection system according to claim 4, wherein:
under the condition of obtaining interference signals, a dynamic data set formed by a plurality of dynamic monitoring data during ammonia nitrogen wastewater detection in unit time is obtained, and the dynamic data set and the static data set are comprehensively analyzed to obtain a correlation coefficient and a significant coefficient, wherein the data in the dynamic data set and the static data set are acquired based on the same time point, namely, the corresponding dynamic data and static data are acquired at the same time point.
6. The high-efficiency ammonia nitrogen wastewater detection system according to claim 5, wherein:
the acquisition logic of the correlation coefficient is as follows:
step S21, calculating the average value of the dynamic data set and the static data set respectively;
step S22, for each data point, calculating a difference value between the data point and a corresponding mean value;
step S23, calculating the sum of difference products of every pair of data points to obtain a related molecular part;
step S24, calculating standard deviations of the dynamic data set and the static data set respectively to obtain denominator parts of the correlation;
step S25, calculating a correlation coefficient using the following formula:wherein->Representing the correlation coefficient>Representing +.>Dynamic data->Mean value representing dynamic data set, +.>Representing +.>Dynamic data->Mean value representing static data set, +.>Representing the standard deviation of the dynamic data set,standard deviation of the static data set +.>And the sequence numbers of the time points for acquiring the dynamic data and the static data are represented, and the dynamic data and the static data are acquired simultaneously at the same time point.
7. The high-efficiency ammonia nitrogen wastewater detection system according to claim 6, wherein:
the acquisition logic of the significant coefficient is as follows:
step S31, determining a significance level;
step S32, obtaining a correlation coefficient;
step S33, calculating the degree of freedom for determiningThe shape of the distribution is calculated as: />Wherein->Is the number of data points in the dynamic data set, the degree of freedom is used to determine +.>The shape of the distribution;
in step S34,the statistic is used for evaluating the significance of the correlation coefficient, and the calculation formula is as follows: />Wherein->Representing the correlation coefficient;
step S35, usingDegree of freedom and significance level of distribution, find +.>Distributing tables to obtain->Statistics corresponding +.>The value, i.e., the saliency coefficient;
in step S36 of the process of the present invention,the value represents the case where the null hypothesis is trueObserved->Probability of statistic or more extreme value, +.>The calculation mode of the value is as follows: />Wherein->Is a correlation->The absolute value of the statistic for measuring the intensity and direction of the correlation +.>Representation->Cumulative distribution function of distribution for representing->Distribution of statistics->Is->Degree of freedom of distribution for determining +.>The shape of the distribution.
8. The high-efficiency ammonia nitrogen wastewater detection system according to claim 7, wherein:
in the case of obtaining a correlation coefficient and a significance coefficient, comparing the correlation coefficient with a correlation threshold value, and comparing the significance coefficient with a significance level;
if the correlation coefficient is greater than or equal to the correlation threshold, generating an accurate correction signal;
if the correlation coefficient is greater than or equal to the correlation threshold and the significant coefficient is less than the significant level, or if the correlation coefficient is less than the correlation threshold but the significant coefficient is greater than or equal to the significant level, generating a low-level correction signal;
if the correlation coefficient is less than the correlation threshold and the significance coefficient is less than the significance level, generating a relinquish signal.
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| CN117786584A (en) * | 2024-02-27 | 2024-03-29 | 西安中创博远网络科技有限公司 | Big data analysis-based method and system for monitoring and early warning of water source pollution in animal husbandry |
| CN117786584B (en) * | 2024-02-27 | 2024-04-30 | 西安中创博远网络科技有限公司 | Big data analysis-based method and system for monitoring and early warning of water source pollution in animal husbandry |
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