Sewage treatment result evaluation method

Abstract

The invention relates to the technical field of sewage data processing, in particular to a sewage processing result evaluation method which comprises the following steps: respectively acquiring COD data segments on the left side and the right side of a COD data point in a sewage COD data time sequence, and acquiring dissolved oxygen data segments corresponding to the COD data segments in a dissolved oxygen data time sequence; obtaining the stability of the COD data segment according to the data fluctuation condition in the COD data segment; calculating the correlation between the COD data segments and the corresponding dissolved oxygen data segments; obtaining the reliability of the COD data segments through the correlation between the COD data segments and the corresponding dissolved oxygen data segments and the stability of the COD data segments; the window length of the COD data point is preset, and after the window position is set based on the reliability of the COD data segments on the two sides of the COD data point, denoising of the COD data time sequence is achieved; and the sewage treatment evaluation result is obtained based on the denoised COD data time sequence, so that the accuracy of the sewage treatment evaluation result is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage data processing, and more specifically, to a sewage treatment result evaluation method. Background Art

[0002] Sewage contains a large amount of wastewater, organic matter, chemicals, etc. Direct discharge into the water body will cause water quality to deteriorate. Removing harmful substances in sewage through sewage treatment can improve water quality and reduce the harm of sewage to aquatic organisms and aquatic ecosystems.

[0003] In order to ensure the quality and effectiveness of sewage treatment, it is necessary to evaluate the results of sewage treatment. There are many research methods for sewage treatment effect evaluation in the prior art. For example, the patent application document with publication number CN117591890A discloses a sewage treatment evaluation system and method based on big data. The application obtains historical terminal record data in the sewage treatment system; the characteristic factor set analysis module in the big data evaluation system is used to analyze the characteristic factor set of the sewage treatment system, and the safety evaluation module is used to construct the operation safety evaluation module corresponding to the target characteristic factor and calculate the safety evaluation index; the inspection drone is used to collect the status data of the sewage treatment area; the evaluation response module locates the ceramic membrane and the acquisition sensor position, and stores the water quality and other data of the sewage treatment area; the Web display device displays the evaluation data, so as to find abnormal problems and deal with them.

[0004] Although the above-mentioned existing technology can realize sewage treatment evaluation by collecting historical terminal record data, in the process of transmitting sewage data, it will undergo multi-channel data collection, network transmission, etc., and noise data may be generated in this process, thereby affecting the accuracy of sewage data evaluation.

[0005] Based on this, how to accurately realize the evaluation of sewage treatment results is an urgent problem to be solved by technical personnel in this field. Summary of the invention

[0006] In order to solve the above technical problem of how to accurately implement the evaluation of sewage treatment results, the present invention proposes a sewage treatment result evaluation method, which includes the following steps: In the COD data time series of sewage, the COD data segments on the left and right sides of the COD data point are obtained respectively, and the dissolved oxygen data segment corresponding to the COD data segment is obtained in the dissolved oxygen data time series; the stability of the COD data segment is obtained through the data fluctuation in the COD data segment; the correlation between the COD data segment and the corresponding dissolved oxygen data segment is calculated: ; For the The correlation between each COD data segment and the corresponding dissolved oxygen data segment, For the The Pearson correlation coefficient between each COD data segment and the corresponding dissolved oxygen data segment, For the The number of COD data points in a COD data segment, , Respectively The maximum value of the COD data segment and the corresponding dissolved oxygen data segment, , Respectively The COD data segment and the corresponding dissolved oxygen data segment COD data points, The value of the dissolved oxygen data point, , Respectively The range of the COD data segment and the corresponding dissolved oxygen data segment, is an exponential function with e as the base; the reliability of the COD data segment is obtained through the correlation between the COD data segment and the corresponding dissolved oxygen data segment and the stability of the COD data segment; the window length of the COD data point is preset, the window position is set based on the reliability of the COD data segments on the left and right sides of the COD data point, and the COD data time series sequence is denoised through the data in the window; the sewage treatment evaluation result is obtained based on the denoised COD data time series sequence.

[0007] The present invention obtains the sewage treatment evaluation result after denoising the COD data time series, which can effectively reduce the influence of noise data on the accuracy of the sewage treatment evaluation result. In this process, the present invention takes into account that when constructing the window for COD data point filtering, data points with higher noise performance may be included in the COD data point window, resulting in poor filtering effect. Based on this, the present invention obtains more data points in the COD data segment with higher stability by analyzing the data fluctuation of the data segments on the left and right sides of the COD data point, reduces the influence of data points with higher noise performance on the filtering effect, and improves the accuracy of denoising the COD data time series; on this basis, the present invention also takes into account that some noise data may be similar to the COD data itself, so by further obtaining dissolved oxygen data that is correlated with the COD data, the stability of the COD data segment is further verified, which effectively improves the accuracy of the COD data point construction window, thereby effectively improving the accuracy of the sewage treatment evaluation result.

[0008] According to a sewage treatment result evaluation method provided by the present invention, a COD data segment and a corresponding dissolved oxygen data segment are obtained, which also includes: preprocessing the COD data and dissolved oxygen data after collecting them to obtain a COD data time series sequence and a dissolved oxygen data time series sequence.

[0009] The present invention takes into account the problem that there may be data missing in the originally collected COD data and dissolved oxygen data, and therefore improves the overall quality of the data through preprocessing to prepare for subsequent data processing.

[0010] According to a sewage treatment result evaluation method provided by the present invention, the stability of the COD data segment satisfies the relationship: ; For the The stability of each COD data segment, For the All data points in the COD data segment are consistent with the The mean of the absolute values ​​of the differences between the means of the COD data segments, For the The mean of the absolute values ​​of the differences between all adjacent data points in a COD data segment, is an exponential function with base e.

[0011] According to a sewage treatment result evaluation method provided by the present invention, the reliability of the COD data segment is obtained through the correlation between the COD data segment and the corresponding dissolved oxygen data segment and the stability of the COD data segment, including: multiplying the correlation between the COD data segment and the corresponding dissolved oxygen data segment by the stability of the COD data segment and then performing normalization processing to obtain the reliability of the COD data segment.

[0012] According to a sewage treatment result evaluation method provided by the present invention, the window position is set based on the reliability of the COD data segments on the left and right sides of the COD data point, including: ; is the length of the left end position of the window of the i-th COD data point, , are the reliability of the COD data segments on the left and right of the i-th COD data point, is the window length of COD data points, is the floor rounding symbol; ; is the length of the right end position of the window of the i-th COD data point.

[0013] The present invention provides an accurate window length allocation method, which can effectively avoid the influence of COD data points with higher noise levels on the denoising effect by allocating more window lengths to COD data segments with higher reliability and setting window positions.

[0014] According to a sewage treatment result evaluation method provided by the present invention, the denoising of the COD data time series sequence is achieved through the data in the window, including: using the window of each COD data point in the SG filtering algorithm to denoise the COD data time series sequence.

[0015] The present invention takes into account that the noise data in the COD data time series sequence will affect the accuracy of the sewage treatment evaluation results. Therefore, the COD data time series sequence is denoised by the SG filtering algorithm, which can effectively avoid the influence of noise data on the sewage treatment evaluation results and improve the accuracy of the sewage treatment evaluation results.

[0016] According to a sewage treatment result evaluation method provided by the present invention, the sewage treatment evaluation result is obtained based on the denoised COD data time series sequence, including: performing anomaly detection on the denoised COD data time series sequence through a box plot algorithm, and obtaining the sewage treatment evaluation result based on the comparison result of the proportion of abnormal data points in the COD data time series sequence with a preset threshold.

[0017] According to a sewage treatment result evaluation method provided by the present invention, the sewage treatment evaluation result is obtained, and then it also includes: generating an evaluation report based on the denoised COD data time series sequence and the corresponding sewage treatment evaluation result.

[0018] The present invention takes into account that sewage treatment is a time-consuming process and requires continuous collection of historical COD data time series and corresponding sewage treatment evaluation results for analysis. Therefore, an evaluation report is generated by combining the denoised COD data time series and the corresponding sewage treatment evaluation results to facilitate staff to analyze the long-term trends and periodic changes of sewage treatment.

[0019] The present invention has the following beneficial effects: Based on the above technical solution, when determining the sewage treatment evaluation result, the present invention obtains the sewage treatment evaluation result by denoising the COD data time series, which can effectively reduce the influence of noise data on the accuracy of the sewage treatment evaluation result. In this process, the present invention obtains more data points in the COD data segment with higher stability by analyzing the data fluctuations of the data segments on the left and right sides of the COD data point, reduces the influence of the data points with higher noise performance on the filtering effect, and improves the accuracy of the denoising of the COD data time series; on this basis, the present invention also obtains dissolved oxygen data that is correlated with the COD data to further verify the stability of the COD data segment, eliminates some noise data that may be similar to the COD data itself, and effectively improves the accuracy of the COD data point construction window, thereby effectively improving the accuracy of the sewage treatment evaluation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 A schematic flow chart of a method for evaluating sewage treatment results provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0022] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] It should be noted that sewage contains a large amount of wastewater, organic matter, chemicals and microorganisms, etc. Direct discharge into the water body will lead to deterioration of water quality. Removing harmful substances in sewage through sewage treatment can improve water quality and reduce the harm of sewage to aquatic organisms and aquatic ecosystems. In order to ensure the quality and effectiveness of sewage treatment, it is necessary to evaluate the results of sewage treatment. Chemical oxygen demand (COD) is one of the parameters for measuring sewage pollution, and its abnormality can be used as an important indicator for evaluating the effect of sewage treatment.

[0024] In the process of transmitting sewage-related data, the existing technology will go through multi-channel data collection, network transmission, etc., which may generate noise data, thus affecting the accuracy of sewage data evaluation. The SG filtering algorithm is a digital signal processing technology that can achieve smooth denoising of data by selecting a sliding window in a local area to fit the data.

[0025] Based on this, an embodiment of the present invention discloses a method for evaluating sewage treatment results. The method uses an SG filtering algorithm to denoise chemical oxygen demand data, and accurately obtains sewage treatment evaluation results based on the denoised chemical oxygen demand data. Figure 1 As shown, Figure 1 A schematic flow chart of a method for evaluating sewage treatment results provided in an embodiment of the present invention, the method specifically comprises the following steps.

[0026] S1: Obtain the COD data segments on the left and right sides of the COD data point in the COD data time series of the sewage.

[0027] The initial length of the COD data segment may be the length of the COD data point window minus 1.

[0028] It should be noted that the SG filtering algorithm takes the data point that needs to be processed as the center when constructing the window, and obtains other data points on both sides of the data point in equal amounts. However, there may be potential noise data in the COD data time series. If such noise data is also used as data in the COD data point window, it will cause errors in data filtering, resulting in low accuracy of sewage treatment evaluation results.

[0029] Based on this, the embodiment of the present invention obtains the COD data segments on the left and right sides of the COD data point respectively, analyzes the data reliability in the chemical oxygen demand data segment, allocates the window length based on the data reliability in the chemical oxygen demand data segment, and sets the window position, so as to accurately obtain the window of the COD data point.

[0030] For example, in an embodiment of the present invention, obtaining a COD data segment also includes: preprocessing the collected COD data to obtain a COD data time series.

[0031] The preprocessing may be data curve fitting, data format conversion, etc., which may be specifically configured according to actual needs, and the embodiment of the present invention does not impose too many limitations thereon.

[0032] Specifically, COD data can be collected by a COD analyzer, and the value of the COD data collected at each collection moment is a COD data point, and all COD data points are arranged in the collection order to obtain a COD data time series. Among them, the collection time can be 2 hours, and the collection frequency can be once every 2 seconds; the collection time and collection frequency can be set according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this.

[0033] After obtaining the COD data segments on the left and right sides of the COD data point based on the above steps, the following steps can be continued to analyze the data changes in the COD data segments to obtain the data stability.

[0034] S2: The stability of the COD data segment is obtained through the data fluctuation in the COD data segment.

[0035] It should be noted that if the data distribution in the COD data segment is more concentrated and the changes between adjacent data points in the COD data segment are more gradual, it means that the possibility of noise is less. Based on this, the embodiment of the present invention obtains the stability of the COD data segment by analyzing the data fluctuation in the COD data segment.

[0036] It is understandable that the lengths of the COD data segments on both sides of the COD data point obtained based on the above steps are equal. The embodiment of the present invention is explained by taking the calculation of the stability of any COD data segment as an example, but it does not mean that the embodiment of the present invention is limited to this.

[0037] For example, in the embodiment of the present invention, the stability of the COD data segment is determined, and the following relationship can be specifically referred to: ; In the formula, For the The stability of each COD data segment, For the All data points in the COD data segment are consistent with the The mean of the absolute values ​​of the differences between the means of the COD data segments, For the The mean of the absolute values ​​of the differences between all adjacent data points in a COD data segment, is an exponential function with base e.

[0038] In the above formula, The smaller the The more concentrated the distribution of values ​​in a COD data segment is, the more stable the data change is, so the possibility of noise data is lower and the stability of the COD data segment is higher; conversely, the smaller the value is, the more stable the data change is. The more discrete the distribution of values ​​in a COD data segment is, the more chaotic the data changes are, so the possibility of noise data is higher and the stability of the COD data segment is lower.

[0039] The smaller the The smoother the changes between adjacent data points in a COD data segment, the greater the credibility of the relatively stable data fluctuations in the COD data segment. The higher the stability of the COD data segment.

[0040] After obtaining the stability of each COD data segment based on the above formula, continue to perform the following steps.

[0041] S3: Obtain the dissolved oxygen data segment corresponding to the COD data segment in the dissolved oxygen data time series, and calculate the correlation between the COD data segment and the corresponding dissolved oxygen data segment.

[0042] It should be noted that, based on the above steps, the stability of the COD data segment can be obtained by analyzing the data fluctuation of the COD data segment itself, thereby obtaining the possibility of noise data in the COD data segment. However, the value of the noise data may be close to the real COD data, so the stability obtained only through the data change of the COD data itself cannot accurately identify the noise data close to the real data.

[0043] It should be further explained that in the sewage data, the higher the COD, the higher the concentration of organic matter in the sewage, and the microorganisms need more oxygen to degrade the organic matter, which leads to a decrease in the dissolved oxygen content in the sewage; conversely, the lower the COD, the lower the concentration of organic matter in the sewage, the lower the oxygen consumption of the microorganisms, and the COD and dissolved oxygen content show a good negative correlation.

[0044] Based on this, the embodiment of the present invention obtains dissolved oxygen data that has a negative correlation with the COD data segment, calculates the correlation between the two, and corrects the stability of the COD data segment through the correlation between the two.

[0045] By way of example, in an embodiment of the present invention, obtaining a dissolved oxygen data segment corresponding to a COD data segment in a dissolved oxygen data time series sequence includes: preprocessing the collected dissolved oxygen data to obtain a dissolved oxygen data time series sequence; and obtaining a corresponding dissolved oxygen data segment from the dissolved oxygen data time series sequence within the same collection period of the COD data segment.

[0046] The number of COD data points in the COD data segment and the number of dissolved oxygen data points in the dissolved oxygen data segment are the same and correspond one to one.

[0047] For example, dissolved oxygen data can be collected by a dissolved oxygen detector. The preprocessing, collection frequency and collection duration can refer to the preprocessing, collection frequency and collection duration of COD data. The specific settings can be made according to actual needs, and the embodiments of the present invention will not be described in detail here.

[0048] For example, in an embodiment of the present invention, the correlation between the COD data segment and the corresponding dissolved oxygen data segment is determined, and the specific correlation can be seen in the following relationship: ; In the formula, For the The correlation between each COD data segment and the corresponding dissolved oxygen data segment, For the The Pearson correlation coefficient between each COD data segment and the corresponding dissolved oxygen data segment, For the The number of COD data points in a COD data segment, , Respectively The maximum value of the COD data segment and the corresponding dissolved oxygen data segment, , Respectively The COD data segment and the corresponding dissolved oxygen data segment COD data points, The value of the dissolved oxygen data point, , Respectively The range of the COD data segment and the corresponding dissolved oxygen data segment, is an exponential function with base e.

[0049] In the above formula, Indicates The correlation between the COD data segment and the corresponding dissolved oxygen data segment. The smaller the value, the higher the correlation between the COD data segment and the corresponding dissolved oxygen data segment. The closer the Pearson correlation coefficient between the COD data segment and the corresponding dissolved oxygen data segment is to -1, the The stronger the negative correlation between COD data and dissolved oxygen data in the collection period of the COD data segment, the lower the possibility of noise data in the COD data in this collection period. The probability that noise data exists in a COD data segment is lower.

[0050] Indicates In the COD data segment The COD data point is The relative size of the COD data segment, Indicates The dissolved oxygen data segment corresponding to the COD data segment The relative size of a dissolved oxygen data point in the dissolved oxygen data segment in which it is located.

[0051] Indicates The negative correlation between the COD data segment and the corresponding dissolved oxygen data segment is smaller, indicating that The stronger the negative correlation between COD data and dissolved oxygen data during the collection period of the COD data segment, the The possibility of noise data existing in a COD data segment is also lower.

[0052] After obtaining the correlation between the COD data and the dissolved oxygen data based on the above steps, the stability of the COD data segment can be corrected through the correlation between the COD data and the dissolved oxygen data, thereby achieving accurate denoising, that is, continue to perform the following steps.

[0053] S4: The reliability of the COD data segment is obtained through the correlation between the COD data segment and the corresponding dissolved oxygen data segment and the stability of the COD data segment; the window length of the COD data point is preset, and the window position is set based on the reliability of the COD data segments on the left and right sides of the COD data point, and the COD data time series sequence is denoised through the data in the window.

[0054] The window length of the COD data point is the number of COD data points contained in the window. The window length can be preset to 15 and can be specifically set according to actual needs. The embodiment of the present invention does not impose too many restrictions on this.

[0055] By way of example, in an embodiment of the present invention, the reliability of the COD data segment is obtained through the correlation between the COD data segment and the corresponding dissolved oxygen data segment and the stability of the COD data segment, including: multiplying the correlation between the COD data segment and the corresponding dissolved oxygen data segment by the stability of the COD data segment and then performing normalization processing to obtain the reliability of the COD data segment.

[0056] It should be noted that based on the above steps, the reliability of the COD data segments on the left and right sides of any COD data point can be obtained. The higher the reliability, the less likely it is that there is noise data in the COD data segment, and the higher the data authenticity. When constructing the corresponding COD data point filtering window, more COD data points can be obtained in the COD data segment with higher reliability. Based on this, a window for obtaining the COD data point can be constructed.

[0057] For example, in an embodiment of the present invention, the window position is set based on the reliability of the COD data segments on the left and right sides of the COD data point. For details, see the following relationship: ; In the formula, is the length of the left end position of the window of the i-th COD data point, , are the reliability of the COD data segments on the left and right of the i-th COD data point, is the window length of COD data points, The floor symbol.

[0058] ; In the formula, is the length of the right end position of the window of the i-th COD data point, is the length of the left end position of the window of the i-th COD data point, is the window length of COD data points.

[0059] After obtaining the window of each COD data point based on the above steps, the window of each COD data point can be used in the SG filtering algorithm for filtering and denoising.

[0060] For example, in an embodiment of the present invention, denoising of the COD data time series sequence is achieved through data in a window, including: using a window of each COD data point in an SG filtering algorithm to perform denoising on the COD data time series sequence.

[0061] Specifically, when the window of each COD data point is used in the SG filtering algorithm to denoise the COD data time series sequence, the polynomial order can be preset, and the window of the COD data point is fitted based on the preset polynomial order to obtain the polynomial coefficients; the filtered value of the COD data point is obtained by the polynomial coefficients and the data value in the window of the COD data point; similarly, the filtered values ​​of all COD data points in the COD data time series sequence are obtained.

[0062] The preset polynomial order can be set to 1, which can be set according to actual needs. The specific steps of denoising the COD data time series by the SG filtering algorithm can be implemented by the prior art, and the embodiments of the present invention are not described in detail here.

[0063] After denoising the COD data time series based on the above steps, an evaluation result can be obtained based on the denoised COD data time series.

[0064] S5: Obtain the sewage treatment assessment results based on the denoised COD data time series.

[0065] It should be noted that the abnormality of COD data can be used as an important indicator for evaluating the effect of sewage treatment. Based on this, the embodiment of the present invention performs abnormality detection on the denoised COD data time series to obtain its abnormality.

[0066] By way of example, in an embodiment of the present invention, a sewage treatment evaluation result is obtained based on the denoised COD data time series sequence, including: performing anomaly detection on the denoised COD data time series sequence through a box plot algorithm, and obtaining the sewage treatment evaluation result based on the comparison result of the proportion of abnormal data points in the COD data time series sequence with a preset threshold.

[0067] The preset threshold value may be set to 0.01; the threshold value may be specifically set according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this.

[0068] Specifically, when the denoised COD data time series sequence is used for anomaly detection through the box plot algorithm, the denoised COD data time series sequence can be sorted first, the median, upper quartile and lower quartile in the denoised COD data time series sequence can be obtained, and a box plot can be drawn based on the median, upper quartile and lower quartile, the inner limit in the box plot can be determined, and the COD data points outside the inner limit position can be recorded as abnormal data points.

[0069] Among them, the specific steps of performing anomaly detection on the denoised COD data time series by using the box plot algorithm can be implemented by the existing technology, and the embodiments of the present invention will not be described in detail here.

[0070] By way of example, in an embodiment of the present invention, a sewage treatment evaluation result is obtained based on a comparison result of the proportion of abnormal data points in the COD data time series sequence with a preset threshold, including: if the proportion of abnormal data points is greater than the preset threshold, the sewage treatment evaluation result is general; otherwise, the sewage treatment evaluation result is good.

[0071] It can be understood that if the proportion of abnormal data points is greater than the preset threshold, it means that there are more abnormal data points in the denoised COD data time series, and the abnormality of the COD data is high, indicating that the current sewage treatment has a poor effect on improving water quality. If the proportion of abnormal data points is not greater than the preset threshold, it means that there are fewer abnormal data points in the denoised COD data time series, and the abnormality of the COD data is low, indicating that the current sewage treatment has a good effect on improving water quality.

[0072] After obtaining the sewage treatment assessment results based on the above steps, a report can also be generated based on the historically collected COD data and the corresponding sewage treatment assessment results to facilitate the analysis of the long-term trends and cyclical changes in sewage treatment.

[0073] For example, in an embodiment of the present invention, a sewage treatment evaluation result is obtained, and then the method further includes: generating an evaluation report based on the denoised COD data time series and the corresponding sewage treatment evaluation result.

[0074] The specific steps of generating the evaluation report can be implemented by existing technologies, and will not be described in detail in the embodiment of the present invention.

[0075] It can be seen that in an embodiment of the present invention, when determining the sewage treatment evaluation result, the COD data segments on the left and right sides of the COD data point can be respectively obtained in the COD data time series sequence of the sewage, and the dissolved oxygen data segment corresponding to the COD data segment can be obtained in the dissolved oxygen data time series sequence; the stability of the COD data segment is obtained through the data fluctuation in the COD data segment; the correlation between the COD data segment and the corresponding dissolved oxygen data segment is calculated; the reliability of the COD data segment is obtained through the correlation between the COD data segment and the corresponding dissolved oxygen data segment and the stability of the COD data segment; the window length of the COD data point is preset, the window position is set based on the reliability of the COD data segments on the left and right sides of the COD data point, and the COD data time series sequence is denoised through the data in the window; the sewage treatment evaluation result is obtained based on the denoised COD data time series sequence.

[0076] In this way, the embodiment of the present invention can effectively reduce the influence of noise data on the accuracy of sewage treatment evaluation results by denoising the COD data time series to obtain its sewage treatment evaluation results. In this process, the embodiment of the present invention takes into account that when constructing the window for COD data point filtering, data points with higher noise performance may be included in the COD data point window, resulting in poor filtering effect. Based on this, the embodiment of the present invention analyzes the data fluctuations of the data segments on the left and right sides of the COD data point, obtains more data points in the COD data segment with higher stability, reduces the influence of data points with higher noise performance on the filtering effect, and improves the accuracy of denoising the COD data time series; on this basis, the embodiment of the present invention also takes into account that some noise data may be similar to the COD data itself, so by further obtaining dissolved oxygen data that is correlated with the COD data, the stability of the COD data segment is further verified, which effectively improves the accuracy of the COD data point window construction, thereby effectively improving the accuracy of the sewage treatment evaluation results.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating sewage treatment results, characterized in that: include: In the COD data time series of sewage, the COD data segments on the left and right sides of the COD data point are obtained respectively, and the dissolved oxygen data segment corresponding to the COD data segment is obtained in the dissolved oxygen data time series; the stability of the COD data segment is obtained through the data fluctuation in the COD data segment; Calculate the correlation between the COD data segment and the corresponding dissolved oxygen data segment: ； For the The correlation between each COD data segment and the corresponding dissolved oxygen data segment, For the The Pearson correlation coefficient between each COD data segment and the corresponding dissolved oxygen data segment, For the The number of COD data points in a COD data segment, , Respectively The maximum value of the COD data segment and the corresponding dissolved oxygen data segment, , Respectively The COD data segment and the corresponding dissolved oxygen data segment COD data points, The value of the dissolved oxygen data point, , Respectively The range of the COD data segment and the corresponding dissolved oxygen data segment, is an exponential function with base e; The reliability of the COD data segment is obtained through the correlation between the COD data segment and the corresponding dissolved oxygen data segment and the stability of the COD data segment; The window length of the COD data point is preset, and the window position is set based on the reliability of the COD data segments on the left and right sides of the COD data point. The COD data time series sequence is denoised through the data in the window; the sewage treatment evaluation result is obtained based on the denoised COD data time series sequence.

2. A method for evaluating sewage treatment results according to claim 1, characterized in that: Get the COD data segment and the corresponding dissolved oxygen data segment, which also includes: After collecting COD data and dissolved oxygen data, preprocessing is performed to obtain COD data time series and dissolved oxygen data time series.

3. A method for evaluating sewage treatment results according to claim 1, characterized in that: The stability of the COD data segment satisfies the relationship: ; For the The stability of each COD data segment, For the All data points in the COD data segment are consistent with the The mean of the absolute values ​​of the differences between the means of the COD data segments, For the The mean of the absolute values ​​of the differences between all adjacent data points in a COD data segment, is an exponential function with base e.

4. A method for evaluating sewage treatment results according to claim 1, characterized in that: The reliability of the COD data segment is obtained by the correlation between the COD data segment and the corresponding dissolved oxygen data segment and the stability of the COD data segment, including: The reliability of the COD data segment is obtained by multiplying the correlation between the COD data segment and the corresponding dissolved oxygen data segment by the stability of the COD data segment and then performing normalization processing.

5. A method for evaluating sewage treatment results according to claim 1, characterized in that: The window position is set based on the reliability of the COD data segments on the left and right sides of the COD data point, including: ; is the length of the left end position of the window of the i-th COD data point, , are the reliability of the COD data segments on the left and right of the i-th COD data point, is the window length of COD data points, is the floor rounding symbol; ; is the length of the right end position of the window of the i-th COD data point.

6. A method for evaluating sewage treatment results according to claim 5, characterized in that: The denoising of the COD data time series is achieved through the data in the window, including: In the SG filtering algorithm, the window of each COD data point is used to denoise the COD data time series.

7. A method for evaluating sewage treatment results according to claim 1, characterized in that: The sewage treatment assessment result is obtained based on the denoised COD data time series, including: The box plot algorithm is used to detect anomalies in the denoised COD data time series, and the sewage treatment assessment results are obtained based on the comparison between the proportion of abnormal data points in the COD data time series and the preset threshold.

8. A method for evaluating sewage treatment results according to claim 7, characterized in that: The sewage treatment evaluation result is obtained, and then further includes: An assessment report is generated based on the denoised COD data time series and the corresponding sewage treatment assessment results.

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