An intelligent detection method for wastewater discharge from electrophoresis production line

Abstract

The invention relates to the field of data processing, and in particular to an intelligent detection method for wastewater discharge of an electrophoresis production line. The method comprises: obtaining a pH data sequence and a total phosphorus content data sequence of a monitoring point of an electrophoresis wastewater discharge outlet; segmenting the pH data sequence according to a preset rule; for each segmented data, determining the initial abnormality of the pH value at each moment according to the correlation between the pH value at each moment in the segmented data and the total phosphorus content data sequence; determining a correction coefficient of the pH value at each moment; for each moment, correcting the initial abnormality of the pH value at the moment according to the correction coefficient, obtaining the final abnormality of the pH value at the moment, thereby determining a real pH value data sequence, and performing intelligent monitoring of wastewater discharge according to the real pH value data sequence. The invention solves the problem of missed detection caused by data deviation of the pH value obtained in the electrophoresis wastewater monitoring in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to an intelligent detection method for wastewater discharge from an electrophoresis production line. Background Art

[0002] Electrophoretic coating lines are a specialized coating method used to apply electrophoretic paint to substrates. Widely used in industries such as automotive and electronics, these lines generate wastewater, which contains a wide variety of pollutants and a complex composition. Therefore, prior to discharge, the wastewater must be treated to improve water quality and reduce environmental pollution. Furthermore, real-time water quality monitoring is essential during discharge. However, this data often contains significant noise, which can affect monitoring accuracy. Therefore, noise reduction is a primary consideration.

[0003] When identifying abnormal noise in the collected pH value data sequence, the existing technology mainly uses the local change deviation of the pH value data sequence for analysis. The pH value (Pondus Hydrogenii, PH) describes the acidity and alkalinity of the aqueous solution. However, in practice, the change of the pH value in electrophoresis wastewater is affected by the phosphate content, resulting in local deviation of the pH value, which in turn makes the pH value abnormality based on the local deviation unreliable. This needs to be optimized to obtain the true pH value of the wastewater. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide an intelligent detection method for wastewater discharge from an electrophoresis production line. The technical solutions adopted are as follows:

[0005] An intelligent detection method for wastewater discharge from an electrophoresis production line, the method comprising:

[0006] Obtain the pH data series and total phosphorus content data series of the electrophoresis wastewater discharge monitoring point;

[0007] Segmenting the PH data sequence according to preset rules;

[0008] For each segmented data, determining the initial abnormality degree of the pH value at each moment according to the correlation between the pH value at each moment in the segmented data and the total phosphorus content data sequence;

[0009] Determining a correction coefficient for the pH value at each moment based on the pH data sequence, the total phosphorus content data sequence, and the initial abnormality of the pH value at each moment;

[0010] For each moment, the initial abnormality of the pH value at that moment is corrected according to the correction coefficient to obtain the final abnormality of the pH value at that moment after correction;

[0011] A true pH value data sequence is determined based on the final abnormality degree of the pH value at each moment, and wastewater discharge is intelligently monitored based on the true pH value data sequence.

[0012] Furthermore, the step of obtaining the pH data sequence and the total phosphorus content data sequence of the electrophoresis wastewater discharge outlet monitoring point further includes:

[0013] Collect pH value data from the pH value measuring sensor placed at the electrophoresis wastewater discharge outlet monitoring point according to the preset sampling frequency;

[0014] Collect the total phosphorus content data detected by the total phosphorus detector at the same time as the data of the pH value measurement sensor;

[0015] Constructing a pH value data sequence according to the pH value data;

[0016] A total phosphorus content data sequence is constructed based on the total phosphorus content data.

[0017] Furthermore, segmenting the PH data sequence according to a preset rule further includes:

[0018] Construct a coordinate image based on the acquisition time series and pH value data series;

[0019] According to the coordinate image, each moment and the pH value data corresponding to the moment are used as input data, and the coordinate image is segmented according to the similarity of the pH value data using superpixel segmentation technology to output multiple segmented data.

[0020] Furthermore, for each segmented data, determining the initial abnormality of the pH value at each moment based on the correlation between the pH value at each moment in the segmented data and the total phosphorus content data sequence further includes:

[0021] For each segmented data, calculate the data difference between the pH value data at each moment in the segmented data and the pH value data at adjacent moments;

[0022] Calculate the data mean of the segmented data;

[0023] The abnormality degree of the pH value at each moment is calculated based on the data difference between the pH value data at each moment and the pH value data at adjacent moments and the data mean of the segmented data.

[0024] Furthermore, for each segmented data, determining the initial abnormality of the pH value at each moment based on the correlation between the pH value at each moment in the segmented data and the total phosphorus content data sequence further includes:

[0025] For each moment, calculate the correlation coefficient between the pH value data and the total phosphorus content data at that moment;

[0026] The abnormality of the pH value at each moment is corrected according to the correlation coefficient between the pH value data at that moment and the total phosphorus content data to obtain the initial abnormality of the pH value at each moment.

[0027] Furthermore, the determining of the correction coefficient of the pH value at each moment based on the pH data sequence, the total phosphorus content data sequence and the initial abnormality degree of the pH value at each moment further includes:

[0028] For each segmented data, calculate and determine other segments with similar total phosphorus content to the segmented data as similar segments to the segmented data;

[0029] Calculate the influence of the difference between the segmented data and the PH data of each similar segment on the overall deviation of the PH of the segmented data;

[0030] Calculate the difference between the segmented data and the pH value data of similar segments;

[0031] Calculating the overall deviation degree of the pH value of the segmented data based on the influence of the difference between the segmented data and each similar segmented pH data on the overall deviation degree of the pH value of the segmented data and the difference between the segmented data and its similar segmented pH value data;

[0032] The correction coefficient of the pH value at each moment is determined according to the overall deviation degree of the pH value of the segmented data and the initial abnormal degree of the pH value at each moment.

[0033] Furthermore, for each moment, correcting the initial abnormality of the pH value at that moment according to the correction coefficient to obtain the final abnormality of the pH value at that moment after correction further includes:

[0034] For each moment, the correction direction is determined based on the comparison result of the pH value data at that moment and the pH value average of the segment data at that moment;

[0035] The initial abnormality degree of the pH value at the moment is corrected according to the correction direction and the correction coefficient to obtain the final abnormality degree of the pH value at the moment after correction.

[0036] Furthermore, the determining of the correction direction according to the comparison result of the pH value data at the moment and the pH value average of the segmented data at the moment further includes:

[0037] If the pH value data at that moment is less than the mean pH value of the segment data at that moment, then a correction coefficient is added to correct the initial abnormality of the pH value at that moment;

[0038] If the pH value data at that moment is greater than or equal to the pH value mean of the segmented data at that moment, the correction coefficient is subtracted to correct the initial abnormality of the pH value at that moment.

[0039] Furthermore, determining the true pH value data sequence based on the final abnormality degree of the pH value at each moment further includes:

[0040] If the final abnormality of the pH value at that moment is greater than the preset threshold, the pH value data at that moment is considered to be an abnormal pH value;

[0041] The abnormal pH values ​​will be deleted and the corresponding deleted positions will be interpolated and filled;

[0042] The real pH value data sequence is determined based on the pH values ​​at all moments after interpolation and padding.

[0043] Furthermore, the method further comprises:

[0044] Setting up monitoring points at multiple preset locations of wastewater discharge outlets;

[0045] A pH sensor and a water sample collection point are set up at the monitoring point;

[0046] Collect wastewater at the water sampling point according to a preset sampling frequency;

[0047] The total phosphorus content data of the wastewater is tested by a total phosphorus detector.

[0048] The present invention has the following beneficial effects: the present invention obtains the pH data sequence and the total phosphorus content data sequence of the monitoring point of the electrophoresis wastewater discharge outlet; segments the pH data sequence according to preset rules; for each segmented data, determines the initial abnormality of the pH value at each moment based on the correlation between the pH value at each moment in the segmented data and the total phosphorus content data sequence; determines the correction coefficient of the pH value at each moment based on the pH data sequence, the total phosphorus content data sequence and the initial abnormality of the pH value at each moment; for each moment, obtains the final abnormality of the pH value at that moment after correction based on the correction coefficient for the initial abnormality of the pH value at that moment; determines the real pH value data sequence based on the final abnormality of the pH value at each moment, and performs intelligent monitoring of wastewater discharge based on the real pH value data sequence; the present invention obtains data deviation by analyzing the change of pH value in electrophoresis wastewater and the phosphate content that has the greatest impact on it, which is beneficial to the intelligent monitoring of wastewater discharge of the electrophoresis production line; solves the problem of data deviation in pH value obtained in electrophoresis wastewater monitoring in the prior art, thereby causing missed detection. The present invention analyzes the phosphates that affect the pH value in wastewater, analyzes the relationship between the pH value data and the total phosphorus content data, corrects the pH value data, obtains a true pH value data sequence, monitors pH value anomalies, and thus monitors whether the wastewater discharge meets the standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 A flow chart of an intelligent detection method for wastewater discharge from an electrophoresis production line provided by one embodiment of the present invention;

[0051] Figure 2 A flowchart of determining the correction coefficient of the pH value at each moment provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0052] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of an intelligent detection method for wastewater discharge from an electrophoresis production line proposed by the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0053] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0054] In the present invention, when obtaining the target area (such as cysts and other tissues) in the ultrasound image through threshold segmentation, due to the influence of resolution during ultrasound imaging, a certain echo is generated on the screen while obtaining the image of the target area, which is hereinafter referred to as the noise interference area. Due to the interference of the noise area, it is usually difficult to directly segment the target area, which affects the doctor's judgment of the lesion tissue. The present invention analyzes the characteristic differences between the target area and the noise interference area, and adaptively performs threshold segmentation on the pixels of different windows to effectively solve this problem.

[0055] In the field, local deviations in pH value data sequences need to be analyzed during electrophoresis wastewater discharge. However, in practice, pH variations in electrophoresis wastewater are affected by phosphate content, leading to local pH deviations and potentially missed detections. The present invention corrects abnormalities at each moment by analyzing segmented pH deviations to obtain a true pH value data sequence, thereby enabling intelligent detection of wastewater discharge.

[0056] The specific scheme of the intelligent detection method for wastewater discharge from an electrophoresis production line provided by the present invention is described in detail below with reference to the accompanying drawings.

[0057] See also Figure 1 , which shows a flow chart of an intelligent detection method for wastewater discharge from an electrophoresis production line provided by one embodiment of the present invention, the method comprising:

[0058] Step S110: Obtain the pH data sequence and total phosphorus content data sequence of the electrophoresis wastewater discharge outlet monitoring point.

[0059] In this embodiment, monitoring points are set at multiple preset locations of the wastewater discharge outlet; pH sensors and water sample collection points are installed at the monitoring points; wastewater is collected at the water sample collection points according to a preset sampling frequency; and the total phosphorus content data of the wastewater is detected by a total phosphorus detector.

[0060] In an optional manner, step S110 further includes: collecting pH value data of a pH value measuring sensor placed at a monitoring point of the electrophoresis wastewater discharge outlet according to a preset sampling frequency; collecting total phosphorus content data detected by a total phosphorus detector at the same time as the data of the pH value measuring sensor; constructing a pH value data sequence based on the pH value data; and constructing a total phosphorus content data sequence based on the total phosphorus content data.

[0061] Taking the preset sampling frequency of 10 minutes as an example, the data of the pH sensor is obtained every 10 minutes. At the same time, wastewater is collected at the water sampling point, and the total phosphorus content at the same time is detected by the total phosphorus detector. The pH value data sequence of each monitoring point is collected. , total phosphorus content data series , time series ,in, Indicates time Corresponding pH value data; Indicates time The corresponding total phosphorus content data; N is the number of sampling times; the data of each monitoring point at each moment can be recorded as .

[0062] Step S120: Segment the PH data sequence according to preset rules.

[0063] In an optional manner, step S120 further includes: constructing a coordinate image based on the acquisition time series and the pH value data sequence; based on the coordinate image, taking each moment and the pH value data corresponding to the moment as input data, using superpixel segmentation technology to segment the coordinate image according to the similarity of the pH value data, and outputting multiple segmented data.

[0064] Specifically, with time as the x-axis, pH value as the y-axis, and (0,0) as the origin, a time-pH value coordinate system is established. As coordinates; time as x-axis, The value is the y-axis, (0,0) is the origin, and the time-total phosphorus content coordinate system is established. The coordinates are plotted on a program software such as MATLAB software, which shows a time-pH value coordinate image and a time-total phosphorus content coordinate image of the monitoring point. The data is segmented using a superpixel segmentation technique based on the coordinate image. Specifically, the superpixel segmentation technique is first used to group adjacent pixels in the coordinate image into superpixels. These superpixels are then regarded as nodes in a graph structure, thereby converting the original pixel image into a graph structure. In the converted graph, the edges between the nodes represent the connection relationship between the superpixels, and the weights of the edges represent the similarity between the superpixels. The pH value data corresponding to each moment is used as input data, and the superpixel segmentation technique is used to segment the coordinate image according to the similarity of the pH value data, outputting a plurality of segmented data.

[0065] However, the abnormality of the pH value at each moment of the monitoring data is mainly manifested as a difference from the pH value at other moments. At the same time, there will be deviations due to the total phosphorus content. Therefore, the degree of abnormality of each pH value needs to be calculated. For each moment, the more prominent the pH value, the greater the degree of deviation. For each segmented data, the greater the difference in pH value with the segmented data with similar total phosphorus content, the greater the degree of deviation. Therefore, this embodiment corrects the above pH value data sequence by analyzing the abnormality of the pH value at each moment and the pH value deviation of each segmented data, thereby obtaining a true pH value data sequence.

[0066] Step S130: for each segmented data, the initial abnormality degree of the pH value at each moment is determined based on the correlation between the pH value at each moment in the segmented data and the total phosphorus content data sequence.

[0067] In an optional manner, step S130 further includes the steps of: for each segmented data, calculating the data difference between the pH value data at each moment in the segmented data and the pH value data at adjacent moments; calculating the data mean of the segmented data; and calculating the abnormality degree of the pH value at each moment based on the data difference between the pH value data at each moment and the pH value data at adjacent moments and the data mean of the segmented data.

[0068] The segmented data of step S120 is recorded as , for each moment The segment belongs to , pH value data is , then for the moment Data The degree of abnormality is reflected in the fact that it is more prominent compared with the data at adjacent moments. The greater the difference between the data at adjacent moments, the The more prominent it is, the greater the degree of abnormality; it is also reflected in the greater difference from the average value of the data in the segment. The greater the difference from the average value in the segment, the more the data deviates from the data in the segment, and therefore the greater the degree of abnormality. The formula for calculating the degree of abnormality of the pH value at each moment is as follows (1):

[0069] ; (1)

[0070] in, 、 、 For the moment and the pH value at two adjacent moments; 、 For the moment The difference between pH values ​​at adjacent moments; The absolute value of the sum of the two differences. The larger the value, the larger or smaller the data at the adjacent moments. The more prominent it is, the greater the degree of abnormality; For the moment Belonging segment The mean of the data within For the moment The difference between the data and the overall mean. The larger the value, the more The greater the difference between the data and the segmented data, the greater the degree of anomaly; for pH value at all times The abnormal degree of time).

[0071] Next, since phosphate content is the main factor affecting the pH value of wastewater, the more phosphate content, the lower the pH value, so the total phosphorus content can be supplemented according to the correlation between the total phosphorus content and the pH value at each moment. degree of abnormality.

[0072] In an optional manner, step S130 further includes the steps of: calculating the correlation coefficient between the pH value data and the total phosphorus content data at each moment; correcting the abnormality degree of the pH value at each moment based on the correlation coefficient between the pH value data and the total phosphorus content data at that moment, and obtaining the initial abnormality degree of the pH value at each moment.

[0073] The weaker the correlation between total phosphorus content and pH value, the The less accurate it is, the higher the abnormality should be. The correlation between the two can be obtained by comparing the changing trend of pH value in the pH value data sequence and the changing trend of total phosphorus content at the same moment. Since the changing trends of pH value and total phosphorus content at the same moment are opposite, there is a correlation. The more consistent the total phosphorus content at the same moment is with the average total phosphorus content at other moments with the same pH value, the more correlated the pH value at that moment is with the total phosphorus content. The correlation coefficient between the pH value data and the total phosphorus content is as follows (2):

[0074] ; (2)

[0075] in, 、 It's time pH value and total phosphorus content, 、 The next moment pH value and total phosphorus content, express Moment comparison The changing trend of pH value and total phosphorus content at the moment is positive, which means that one of pH value and total phosphorus content increases while the other decreases, and the changing trends are opposite, which is consistent with the trend of pH value changing with phosphoric acid content, so there is a correlation; is with The average total phosphorus content at the same pH value at the time, Indicates that under the same pH value, the time The difference between the total phosphorus content at the time and the total phosphorus content at other times, the larger the value, the greater the time. The more inconsistent the correlation between pH value and total phosphorus content is with the correlation at other times, the weaker the correlation between pH value and total phosphorus content is, and the correlation is inversely proportional; It's every moment Correlation coefficient between pH data and total phosphorus content.

[0076] According to the correlation coefficient between the pH value data and the total phosphorus content data at that moment, the abnormal degree of pH value at each moment is corrected as a supplement. The abnormal degree of the pH value at each moment is obtained , the formula is as follows (3):

[0077] ; (3)

[0078] in, It's every moment The correlation coefficient between pH value and total phosphorus content. The more the pH value is correlated with the total phosphorus content, the more accurate the pH value is and the smaller the abnormality is. The function is a normalization function, and the normalized result is [0,1]; for pH value at all times The degree of abnormality, for The initial abnormality of the pH value at the moment.

[0079] for The degree of deviation of the pH value data at each moment is affected by the distribution of the segmented data. At this time, it is necessary to analyze the impact of the abnormal deviation of each segmented data on the deviation of the pH value data at each moment it contains.

[0080] Step S140: determining a correction coefficient for the pH value at each moment based on the pH data sequence, the total phosphorus content data sequence, and the initial abnormality degree of the pH value at each moment.

[0081] Figure 2 A flow chart for determining the correction coefficient of the pH value at each moment provided by one embodiment of the present invention is as follows: Figure 2 As shown, in an optional manner, step S140 further includes steps 1 to 5:

[0082] Step 1: For each segmented data, calculate and determine other segments with similar total phosphorus content to the segmented data as similar segments to the segmented data.

[0083] First, based on the segmented data of the pH value data, other segments with similar total phosphorus content are determined. Specifically, in step S120, the segmented data obtained by segmentation based on the similarity of the pH value data is segmented. ,in, Represents multiple segmented data; n is the number of segments; the total phosphorus content sequence is segmented according to the same time series as the pH data, and the total phosphorus content segment corresponding to each pH value segment data is obtained. The mean of the total phosphorus content segment corresponding to each pH value segment data is recorded as .

[0084] It is known that phosphate content affects pH value. At this time, the pH values ​​corresponding to similar total phosphorus content segments are also similar. Therefore, for the j-th segment, first search for other segments with similar total phosphorus content. The corresponding search conditions are: the more consistent the mean total phosphorus content of the segment time, the higher the similarity; the more consistent the total phosphorus content change trend, the higher the corresponding similarity; the formula for calculating the similarity between the total phosphorus content segment corresponding to each pH value segment data and the total phosphorus content of the j-th segment is as follows (4):

[0085] ; (4)

[0086] in, is the mean total phosphorus content in the jth segment, is the mean total phosphorus content of any other s-th segment, is the difference between the mean total phosphorus content of the jth segment and the remaining sth segments. The smaller the value, the more similar the total phosphorus content of the jth segment and the sth segment is, and the higher the similarity; is the jth segment Total phosphorus content at the time, is the sth segment Total phosphorus content at the time, is the jth segment and the sth segment The difference in total phosphorus content at the time is the mean of the total phosphorus content difference between the jth segment and the sth segment at all times. This value represents the overall total phosphorus content difference between the jth segment and the sth segment. The larger the value, the more inconsistent the total phosphorus content change trends between the jth segment and the sth segment, the smaller the similarity, and therefore the smaller the degree of similarity; Indicates the similarity between the total phosphorus content segment corresponding to the j-th pH value segment data and the total phosphorus content of the s-th segment; is the number of all sampling moments of the j-th segment and the s-th segment.

[0087] according to The size of can obtain multiple segments with similar total phosphorus content to the jth segment. Specifically, by presetting the similarity threshold, when If the value of is greater than the similarity threshold, the total phosphorus content segment is considered similar to the total phosphorus content of the j-th segment and is regarded as a similar segment to the j-th segment. At this point, multiple other segments with similar total phosphorus content to the j-th segment are obtained. Furthermore, the pH value segments corresponding to these multiple other segments are the similar segments to the j-th segment.

[0088] Step 2: Calculate the impact of the difference between the segmented data and the PH data of each similar segment on the overall deviation of the PH of the segmented data.

[0089] Furthermore, after determining the similar segment corresponding to the j-th segment, since the pH value is affected by the total phosphorus content, and the j-th segment and its similar segment have similar total phosphorus content, the pH value of the j-th segment and its similar segment are consistent.

[0090] At this time, the difference between the pH value data of the jth segment and its similar segments reflects the overall deviation degree of the pH value data of the jth segment, wherein the difference between the pH value data of multiple similar segments and the jth segment has different degrees of influence on the overall deviation degree of the pH value of the jth segment, which is specifically manifested as the higher the similarity between the total phosphorus content of any similar segment and the jth segment, the higher the degree of influence; the greater the correlation coefficient between any similar segment and the corresponding pH value of the jth segment, the higher the degree of influence.

[0091] Based on this, the formula for determining the degree of influence of the difference between the jth segment and its vth similar segment on the overall PH deviation of the jth segment data is as follows (5):

[0092] ; (5)

[0093] in, is the similarity between the total phosphorus content of the jth segment and the vth similar segment. The higher the similarity, the higher the impact. is the mean pH value of the jth segment, is the mean PH value of the vth similar segment, It is the difference between the two pH value means. This value indicates the correlation between the pH values ​​of the two segments. The larger the difference, the less correlated the pH values ​​of the two segments are, and the smaller the degree of influence is, which is inversely proportional. It indicates the degree of influence of the difference between the j-th segment and its v-th similar segment on the overall deviation of the PH of the j-th segment data.

[0094] Step 3: Calculate the difference between the segmented data and its similar segmented pH value data.

[0095] Furthermore, the difference between the PH of the jth segment and its similar segments is mainly manifested as: the difference in PH values ​​at each moment. The greater the difference in PH values ​​at each moment, the greater the difference in PH between the jth segment and its similar segments; the greater the variance of the PH values ​​between the jth segment and its similar segments, the greater the difference in PH between the jth segment and its similar segments.

[0096] The formula for calculating the difference between the pH value data of the jth segment and its similar segments is as follows (6):

[0097] ; (6)

[0098] in, is the jth segment pH value at the moment, is the vth similar segment pH value at the moment, is the jth segment and the vth similar segment The pH value difference at each moment, is the mean of the pH value differences between the jth segment and the vth similar segment at all times, where n is the number of segments. This value represents the overall pH value difference between the jth segment and the vth similar segment. The larger the value, the greater the pH difference between the jth segment and its similar segments. is the variance of the pH value of the jth segment, is the variance of the pH value of the vth similar segment, It is the difference between the two variances. The larger the value, the greater the difference in pH values ​​between the two segments. is the difference between the pH value data of the jth segment and its vth similar segment; is the number of all sampling moments of the jth segment and the vth similar segment.

[0099] Step 4: Calculate the overall deviation of the pH value of the segmented data based on the influence of the difference between the segmented data and the pH data of each similar segment on the overall deviation of the pH value of the segmented data and the difference between the segmented data and the pH value data of similar segments.

[0100] Finally, based on the above-mentioned influence degree and the difference of PH, the formula for determining the overall deviation degree of PH value of the j-th segment data is as follows (7):

[0101] ; (7)

[0102] in, It indicates the degree of influence of the difference between the jth segment and its vth similar segment on the overall deviation of the PH of the jth segment data. is the difference between the pH value data of the jth segment and its vth similar segment, It indicates the influence of the difference between the vth similar segment and the jth segment on the deviation degree of the jth segment. represents the deviation of all similar segments of the j-th segment from the j-th segment, That is, the overall deviation of the pH value of the j-th segment data; is the number of similar segments to the jth segment.

[0103] Step 5: Determine the correction coefficient of the pH value at each moment based on the overall deviation degree of the pH value of the segmented data and the initial abnormality degree of the pH value at each moment.

[0104] The greater the overall deviation of the segmented data, the less true the pH value data within the segment will be. Therefore, the degree of abnormality at each moment needs to be corrected by the overall deviation of the segmented data to obtain the true degree of abnormality. Therefore, the correction coefficient is calculated first.

[0105] for The greater the abnormality of the pH value at the moment, the greater the correction coefficient; The greater the deviation of the segment to which the moment belongs, the greater the correction coefficient. The correction coefficient formula of the pH value at the moment is as follows (8):

[0106] ; (8)

[0107] in, for The initial abnormality of the pH value at the time, is the overall deviation of the PH value of the j-th segment data, yes The pH value at the moment, the overall expression The value to which the pH value should be corrected at this moment.

[0108] Step S150: For each moment, the initial abnormality of the pH value at that moment is corrected according to the correction coefficient to obtain the final abnormality of the pH value at that moment after correction.

[0109] In an optional manner, step S150 further includes: for each moment, determining the correction direction based on the comparison result of the pH value data at that moment and the pH value average of the segmented data at that moment; correcting the initial abnormality degree of the pH value at that moment according to the correction direction and the correction coefficient to obtain the final abnormality degree of the pH value at that moment after correction.

[0110] Furthermore, determining the correction direction based on the comparison result of the pH value data at the moment and the pH value average of the segmented data at the moment further includes: if the pH value data at the moment is less than the pH value average of the segmented data at the moment, adding a correction coefficient to correct the initial abnormality of the pH value at the moment; if the pH value data at the moment is greater than or equal to the pH value average of the segmented data at the moment, subtracting the correction coefficient to correct the initial abnormality of the pH value at the moment.

[0111] In this step, you first need to determine the correction direction. When the pH value deviates to a smaller value, the correction coefficient should be added. When the pH value deviates to a larger value, the correction coefficient should be subtracted. ( is the mean of the pH value sequence), when the pH value deviates to a small value, .

[0112] From this we can The abnormal degree of pH value at the moment is corrected. The final abnormal degree of pH value at the moment is expressed as follows (9):

[0113] ; (9)

[0114] in yes The initial abnormality of the pH value at the time, yes Correction coefficient of pH value at the moment, Indicates the pH value correction direction at the i-th moment, that is, when hour, ,when hour, ; The function is a normalization function, and the normalized result is [0,1]; After correction The final abnormality of the pH value at that moment.

[0115] Step S160: determining a true pH value data sequence based on the final abnormality degree of the pH value at each moment, and performing intelligent monitoring of wastewater discharge based on the true pH value data sequence.

[0116] In an optional manner, step S160 further includes: if the final abnormality degree of the pH value at that moment is greater than a preset threshold, the pH value data at that moment is determined to be an abnormal pH value; in the PH data sequence determined in step S110, the abnormal pH value is deleted, and interpolation is performed at the corresponding deleted position; and the true pH value data sequence is determined based on the pH values ​​at all moments after the interpolation and padding.

[0117] For example, if the preset threshold is 0.9, it is considered The PH value data at the moment is an abnormal PH value. In the PH data sequence determined in step S110, the abnormal PH value is deleted and interpolated at the corresponding position, for example, uniformly assigned to 0, so as to determine the real PH value data sequence.

[0118] The method of this embodiment is adopted, by obtaining the pH data sequence and total phosphorus content data sequence of the monitoring point of the electrophoresis wastewater discharge outlet; the pH data sequence is segmented according to preset rules; for each segmented data, the initial abnormality of the pH value at each moment is determined based on the correlation between the pH value at each moment in the segmented data and the total phosphorus content data sequence; the correction coefficient of the pH value at each moment is determined based on the pH data sequence, the total phosphorus content data sequence and the initial abnormality of the pH value at each moment; for each moment, the final abnormality of the pH value at that moment is corrected based on the correction coefficient; the true pH value data sequence is determined based on the final abnormality of the pH value at each moment, and the wastewater discharge is intelligently monitored based on the true pH value data sequence. This method obtains data deviation by analyzing the changes in the pH value in the electrophoresis wastewater and the phosphate content that has the greatest impact on it, which is beneficial to the intelligent monitoring of wastewater discharge from the electrophoresis production line; and solves the problem of data deviation in the pH value obtained in the electrophoresis wastewater monitoring in the prior art, thereby causing missed detection. This method analyzes the phosphates that affect the pH value in wastewater, analyzes the relationship between the pH value data and the total phosphorus content data, corrects the pH value data, obtains the real pH value data sequence, monitors the pH value anomalies, and thus monitors the compliance of wastewater discharge with standards.

[0119] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. An intelligent detection method for wastewater discharge from an electrophoresis production line, characterized in that: The method comprises: Obtain the pH data series and total phosphorus content data series of the electrophoresis wastewater discharge outlet monitoring point; Segmenting the PH data sequence according to a preset rule; For each segmented data, according to the correlation between the pH value at each moment in the segmented data and the total phosphorus content data sequence, determine the initial abnormal degree of the pH value at each moment; Determining a correction coefficient of the pH value at each moment according to the pH data sequence, the total phosphorus content data sequence and the initial abnormality of the pH value at each moment; For each moment, the initial abnormality of the pH value at that moment is corrected according to the correction coefficient to obtain the final abnormality of the pH value at that moment after correction; Determine a true pH value data sequence according to the final abnormal degree of the pH value at each moment, and perform intelligent monitoring of wastewater discharge according to the true pH value data sequence; The method of determining the correction coefficient of the pH value at each moment according to the pH data sequence, the total phosphorus content data sequence and the initial abnormality of the pH value at each moment further includes: For each segmented data, calculate and determine other segments with similar total phosphorus content to the segmented data as similar segments to the segmented data; Calculate the influence of the difference between the segmented data and each similar segmented PH data on the overall deviation of the PH of the segmented data; Calculate the difference between the segmented data and the pH value data of similar segments; Calculate the overall deviation degree of the PH value of the segmented data according to the influence degree of the difference between the segmented data and each similar segmented PH data on the overall deviation degree of the PH value of the segmented data and the difference between the segmented data and its similar segmented PH value data; The correction coefficient of the pH value at each moment is determined according to the overall deviation degree of the pH value of the segmented data and the initial abnormal degree of the pH value at each moment.

2. According to claim 1, the intelligent detection method for wastewater discharge of an electrophoresis production line is characterized in that: The step of obtaining the pH data sequence and the total phosphorus content data sequence of the electrophoresis wastewater discharge outlet monitoring point further comprises: Collect pH value data of the pH value measuring sensor placed at the monitoring point of the electrophoresis wastewater discharge outlet according to the preset sampling frequency; Collect the total phosphorus content data detected by the total phosphorus detector at the same time as the data of the pH value measurement sensor; Constructing a pH value data sequence according to the pH value data; A total phosphorus content data sequence is constructed based on the total phosphorus content data.

3. The intelligent detection method for wastewater discharge from an electrophoresis production line according to claim 2 is characterized in that: The segmenting of the PH data sequence according to a preset rule further comprises: Construct a coordinate image based on the acquisition time series and pH value data series; According to the coordinate image, each moment and the PH value data corresponding to the moment are used as input data, and the coordinate image is segmented according to the similarity of the PH value data using superpixel segmentation technology to output multiple segmented data.

4. The intelligent detection method for wastewater discharge from an electrophoresis production line according to claim 3 is characterized in that: The step of determining the initial abnormality of the pH value at each moment for each segmented data according to the correlation between the pH value at each moment in the segmented data and the total phosphorus content data sequence further comprises: For each segmented data, the data difference between the pH value data at each moment in the segmented data and the pH value data at adjacent moments is calculated; Calculate the data mean of the segmented data; The abnormal degree of the pH value at each moment is calculated based on the data difference between the pH value data at each moment and the pH value data at adjacent moments and the data mean of the segmented data.

5. The intelligent detection method for wastewater discharge from an electrophoresis production line according to claim 4 is characterized in that: The step of determining the initial abnormality of the pH value at each moment for each segmented data according to the correlation between the pH value at each moment in the segmented data and the total phosphorus content data sequence further comprises: For each moment, calculate the correlation coefficient between the pH value data and the total phosphorus content data at that moment; The abnormal degree of the pH value at each moment is corrected according to the correlation coefficient between the pH value data at that moment and the total phosphorus content data to obtain the initial abnormal degree of the pH value at each moment.

6. The intelligent detection method for wastewater discharge from an electrophoresis production line according to claim 5 is characterized in that: The step of correcting the initial abnormality of the pH value at each moment according to the correction coefficient to obtain the final abnormality of the pH value at the moment after correction further includes: For each moment, the correction direction is determined based on the comparison result between the pH value data at that moment and the pH value average of the segmented data at that moment; The initial abnormal degree of the pH value at that moment is corrected according to the correction direction and the correction coefficient to obtain the final abnormal degree of the pH value at that moment after correction.

7. The intelligent detection method for wastewater discharge from an electrophoresis production line according to claim 6 is characterized in that: The step of determining the correction direction based on the comparison result of the pH value data at the moment and the pH value average of the segmented data at the moment further includes: If the pH value data at that moment is less than the average pH value of the segment data at that moment, then a correction coefficient is added to correct the initial abnormal degree of the pH value at that moment; If the pH value data at that moment is greater than or equal to the pH value mean of the segmented data at that moment, the correction coefficient is subtracted to correct the initial abnormal degree of the pH value at that moment.

8. The intelligent detection method for wastewater discharge from an electrophoresis production line according to claim 7 is characterized in that: Determining the true pH value data sequence according to the final abnormal degree of the pH value at each moment further includes: If the final abnormal degree of the pH value at that moment is greater than the preset threshold, the pH value data at that moment is deemed to be an abnormal pH value; The abnormal PH values ​​are deleted and interpolation is performed at the corresponding deleted positions; The real pH value data sequence is determined based on the pH values ​​at all times after interpolation and padding.

9. An intelligent detection method for wastewater discharge from an electrophoresis production line according to any one of claims 1 to 8, characterized in that: The method further comprises: Setting up monitoring points at multiple preset locations at the wastewater discharge outlet; A pH sensor and a water sample collection point are installed at the monitoring point; Collecting wastewater at the water sampling point according to a preset sampling frequency; The total phosphorus content data of the wastewater is tested by a total phosphorus detector.

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