Sludge Concentration Detection Method Based on Scattered Light Signal

Abstract

This invention relates to the field of water pollution detection technology, specifically to a method for detecting sludge concentration based on scattered light signals. The method includes: detecting several wastewater samples at different settling times using a multi-path turbidimeter, acquiring transmitted and scattered light signal data for each sample under each optical path; constructing an optical path data matrix for each wastewater sample based on all transmitted and scattered light signal data, and decomposing the optical path data matrix; analyzing the decomposition results of all optical path data matrices to determine the offset coefficient and confidence coefficient for each wastewater sample; and weighting and correcting the sludge concentration detection results obtained by the multi-path turbidimeter using the offset coefficient and confidence coefficient of the wastewater samples to obtain the corrected sludge concentration. This invention improves the correction effect of sludge concentration detection results using scattered light signals, thereby improving the accuracy of sludge concentration detection.

Description

Technical Field

[0001] This invention relates to the field of water pollution detection technology, specifically to a method for detecting sludge concentration based on scattered light signals. Background Technology

[0002] In wastewater treatment processes, sludge concentration is a key parameter for optimizing core processes such as aeration, sedimentation, and sludge return. Current mainstream methods for sludge concentration detection rely on turbidimeters based on the principle of light scattering, which indirectly reflect sludge concentration by detecting the intensity of scattered light. However, sludge typically contains a complex and varied composition of substances, and during sludge concentration detection, stray light components are introduced due to the absorption and multiple scattering of light by these substances.

[0003] Existing turbidimeters mainly rely on multi-path detection systems and optical structure optimization to suppress stray light, and only provide basic data storage and query functions for scattered light signals. They lack effective processing and analysis of scattered light signals. Therefore, relying solely on static thresholds or hardware shielding cannot guarantee the accuracy of sludge concentration detection results in complex environments. Summary of the Invention

[0004] This invention provides a sludge concentration detection method based on scattered light signals to solve the existing problem that relying solely on static thresholds or hardware shielding cannot guarantee the accuracy of sludge concentration detection results in complex environments.

[0005] The sludge concentration detection method based on scattered light signals of the present invention adopts the following technical solution: One embodiment of the present invention provides a method for detecting sludge concentration based on scattered light signals, the method comprising the following steps: The wastewater samples to be tested were tested using a multi-path turbidimeter at several different settling times, and the transmitted light signal data and scattered light signal data of each wastewater sample under each optical path were obtained. Based on all transmitted and scattered light signal data for each wastewater sample, an optical path data matrix for the corresponding wastewater sample is constructed, and the optical path data matrix is ​​decomposed accordingly. The decomposition results of all optical path data matrices were analyzed to determine the offset coefficient and confidence coefficient of each wastewater sample. The offset coefficient is used to indicate the degree of deviation of the sludge concentration detection result of the corresponding wastewater sample under the influence of stray light, and the confidence coefficient is used to indicate the reliability of the sludge concentration detection result obtained after the corresponding wastewater sample has been subjected to a settling treatment for a corresponding settling time. By combining the offset coefficient and confidence coefficient of the wastewater sample, the sludge concentration detection results obtained by the multi-path turbidimeter are weighted and corrected to obtain the corrected sludge concentration of the wastewater to be tested.

[0006] Optionally, the specific method for obtaining the optical path data matrix of the wastewater sample is as follows: For any wastewater sample, all transmitted and scattered light signal data are acquired. An optical path data matrix is ​​constructed using these data, containing two columns: one for each transmitted light path and the other for each scattered light path. The number of rows in the optical path data matrix corresponds to the number of emitters in the turbidimeter. The same, thus obtaining The matrix.

[0007] Optionally, the specific method for decomposing the optical path data matrix includes: By performing singular value decomposition on the optical path data matrix of the wastewater sample, the left singular matrix, several singular values, and the right singular matrix corresponding to the optical path data matrix are obtained.

[0008] Optionally, the specific method for obtaining the offset coefficient of the wastewater sample is as follows: Based on the differences between the singular values ​​of the wastewater samples, the singular value dispersion of the corresponding wastewater samples is obtained; The singular value dispersion of all wastewater samples was fitted with an exponential decay curve to obtain the singular value dispersion decay curve. The offset coefficient of the wastewater sample is obtained based on the relative difference between the singular value dispersion of the wastewater sample and the singular value dispersion in the singular value dispersion decay curve corresponding to the settling time.

[0009] Optionally, the specific method for obtaining the singular value dispersion decay curve is as follows: A two-dimensional rectangular coordinate system is established, with the settling time of the sewage samples as the horizontal axis and the singular value dispersion of the sewage samples as the vertical axis, forming a scatter plot of all sewage samples in the two-dimensional rectangular coordinate system. By performing exponential decay fitting on the scatter plot in the two-dimensional rectangular coordinate system using the least squares method, the initial dispersion, decay coefficient, and steady-state dispersion are obtained, thus forming a singular value dispersion decay curve from the initial dispersion, decay coefficient, and steady-state dispersion.

[0010] Optionally, the specific method for obtaining the offset coefficient of the wastewater sample is as follows: The difference between the singular value dispersion and the steady-state dispersion of any wastewater sample is taken as the first dispersion difference value of the corresponding wastewater sample; the difference between the fitted value of the singular value dispersion and the steady-state dispersion in the singular value dispersion decay curve corresponding to the standing time of the wastewater sample is taken as the second dispersion difference value of the corresponding wastewater sample. The offset coefficient of the wastewater sample is obtained based on the first and second dispersion difference values. The first dispersion difference value is positively correlated with the offset coefficient, and the second dispersion difference value is negatively correlated with the offset coefficient.

[0011] Optionally, the specific method for obtaining the confidence coefficient is as follows: The projection consistency of the wastewater sample is obtained by taking the row vectors in the optical path data matrix of the wastewater sample and the column vectors in the corresponding right singular matrix. The optical path variation coefficient of the wastewater sample is obtained based on the data distribution in the transmission column of the wastewater sample. The confidence coefficient of the wastewater sample is obtained by combining the settling time, projection consistency, and optical path variation coefficient of the wastewater sample.

[0012] Optionally, the specific method for obtaining the confidence coefficient of the wastewater sample is as follows: Calculate the confidence factor based on the projection consistency and optical path variation coefficient of the wastewater sample; The settling penalty factor for wastewater samples is calculated based on the difference between the settling time of wastewater samples and the maximum settling time of all wastewater samples. The confidence factor was adjusted using a settling penalty factor to obtain the confidence coefficient of the wastewater sample.

[0013] Optionally, the specific method for obtaining the settling penalty factor of the wastewater sample is as follows: The ratio between the settling time of a wastewater sample and the maximum settling time of all wastewater samples is taken as the relative settling time of the wastewater sample. Based on the relative settling time, the settling penalty factor of the wastewater sample is obtained, and the settling penalty factor is negatively correlated with the relative settling time.

[0014] Optionally, the specific method for obtaining the corrected sludge concentration of the wastewater to be tested is as follows: The overall weight of the wastewater sample is obtained based on the offset coefficient and confidence coefficient. The offset coefficient is negatively correlated with the overall weight, and the confidence coefficient is positively correlated with the overall weight. The sludge concentration of each wastewater sample output by the multi-path turbidimeter is obtained. The corresponding sludge concentration is weighted by the comprehensive weight of all wastewater samples to obtain the corrected sludge concentration of the wastewater to be tested.

[0015] The beneficial effects of the technical solution of this invention are as follows: Traditional turbidity detection is easily affected by stray light interference such as bubbles and irregular distribution of suspended matter. However, the embodiments of this invention extract the offset coefficient by decomposing the optical path data matrix, quantitatively identify and compensate for the concentration deviation caused by stray light, and break through the bottleneck of single optical path detection being easily affected by environmental noise, making the detection results closer to the true value. At the same time, the confidence coefficient is introduced to evaluate the detection reliability under different settling times, transforming the empirical settling treatment into a mathematical reliability index, providing data support for the optimization of the detection process. Furthermore, unlike the conventional practice of fixed parameter correction, the weighted strategy of combining offset coefficient and confidence coefficient can adaptively adjust the correction weight according to the sample characteristics, which avoids the distortion of detection results caused by over-correction and prevents the error residue caused by under-correction, achieving a balance between detection accuracy and stability, and improving the accuracy of sludge concentration detection in high-concentration wastewater. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 The flowchart illustrates the steps of a sludge concentration detection method based on scattered light signals provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of a sludge concentration detection system based on scattered light signals provided in an embodiment of the present invention. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the sludge concentration detection method based on scattered light signals proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, 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 pertains.

[0020] The specific scheme of the sludge concentration detection method based on scattered light signals provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a sludge concentration detection method based on scattered light signals according to an embodiment of the present invention. The method includes the following steps: Step S001: Use a multi-path turbidimeter to test wastewater samples at several different settling times to obtain transmitted light signal data and scattered light signal data for each wastewater sample under each optical path.

[0022] It should be noted that at wastewater treatment sites, sludge concentration detection faces the challenge of detecting multiple optically active substances in wastewater, such as suspended sludge, colloidal particles, bubbles, and foam. Their interactions result in highly nonlinear characteristics in the optical signal. Therefore, this step requires the construction of a statistically representative and comparable dataset.

[0023] Specifically, in order to implement the sludge concentration detection method based on scattered light signals proposed in this embodiment, it is first necessary to acquire the transmitted light signal data and scattered light signal data of each wastewater sample under each optical path. The specific process is as follows: First, the turbidity meter is used to perform real-time and continuous sludge concentration detection at the wastewater treatment site. The turbidity meter is equipped with a microcomputer, a high-precision low-power chip, a large-capacity memory, and an RS232 interface for connecting to a computer. It also has data storage, data query, and data printing functions, and can store more than 200,000 sets of measurement data. It has a power-off data retention function. In addition, it also has an independent multi-optical path detection system.

[0024] Then, the wastewater to be tested is injected into a clean, scratch-free sample bottle to complete the sampling, resulting in several wastewater samples (i.e., wastewater from the same source from the same sampling point is divided into several sub-samples and injected into sample bottles). Several settling time parameters are set, the same as the number of wastewater samples, and all settling time parameters form an arithmetic sequence as the settling time sequence, so that each wastewater sample corresponds to a settling time parameter, thereby performing settling treatment on the wastewater samples for the corresponding time. The settling treated wastewater samples are placed in the sample tank, and the optical signal data of all different light paths are acquired by the multi-optical path detection system of the benchtop turbidimeter.

[0025] The settling time sequence ranges from 1 to 30 minutes, with arithmetic intervals of 5 minutes.

[0026] Specifically, in the multi-path detection system of a multi-path benchtop turbidimeter, there are multiple transmitters and detectors, and the number of transmitters and detectors is usually the same, thus forming multiple optical paths. After the infrared light emitted by the transmitter passes through the sample, some of the light is absorbed, scattered, or reflected. The detector receives the remaining transmitted light intensity, thus forming transmitted light signal data. In addition to the transmitted light, some light is scattered due to the presence of particulate matter in the wastewater sample. Multiple detectors capture the intensity of scattered light at different angles, thus forming scattered light signal data.

[0027] For transmitted light signal data, as a fundamental physical quantity, it reflects the residual intensity of incident light after passing through the wastewater sample. Its attenuation is positively correlated with the suspended solids concentration, that is, the higher the suspended solids concentration, the greater the attenuation of transmitted light. Its role is that the system can preliminarily estimate the suspended solids content based on the proportional relationship between the transmittance of transmitted light and the sludge concentration. For scattered light signal data, as a complementary physical quantity, it captures the distribution of the ability of light to deflect in various directions after interacting with sludge particles in wastewater. It is especially sensitive to fine particles in the 90° direction, so as to make up for the defect of saturation failure of the transmission method under high turbidity.

[0028] Finally, for any wastewater sample, the transmitted light signal data corresponding to all transmitted light paths and the scattered light signal data corresponding to all scattered light paths are obtained. Gaussian filtering is performed on the transmitted light signal data and the scattered light signal data, and the Gaussian-filtered transmitted light signal data and scattered light signal data are standardized by the Min-Max normalization method.

[0029] Thus, several transmitted light signal data and scattered light signal data for each wastewater sample were obtained using the above method.

[0030] Step S002: Based on all transmitted light signal data and scattered light signal data of each wastewater sample, construct the optical path data matrix of the corresponding wastewater sample, and decompose the optical path data matrix respectively.

[0031] It should be noted that in a multi-path turbidimeter, multiple transmitter-detector pairs form optical path layouts with different spatial angles. The detection signals of each optical path contain both common sludge concentration information and scattering phase information at specific angles. Therefore, by organizing the multi-path data into a matrix form, it is reflected that under normal circumstances, all optical path signals should be driven by a common sludge concentration parameter, which is manifested as the rank-one characteristic of the matrix. However, the stray light generated by bubble specular reflection and multiple scattering noise has angle-dependent randomness, which is manifested as high-rank perturbation.

[0032] As an optional embodiment, the specific method for obtaining the optical path data matrix of the wastewater sample is as follows: For any wastewater sample, all transmitted and scattered light signal data are acquired. An optical path data matrix is ​​constructed using these data, containing two columns: one for each transmitted light path and the other for each scattered light path. The number of rows in the optical path data matrix corresponds to the number of emitters in the turbidimeter. The same, thus obtaining The matrix.

[0033] As an optional embodiment, the specific method for decomposing the optical path data matrix includes: performing singular value decomposition on the optical path data matrix of the sewage sample to obtain the left singular matrix, several singular values, and the right singular matrix corresponding to the optical path data matrix.

[0034] Thus, the decomposition results of the optical path data matrix of the sewage sample were obtained through the above method.

[0035] Step S003: Analyze the decomposition results of all optical path data matrices to determine the offset coefficient and confidence coefficient of each wastewater sample.

[0036] It should be noted that the core challenge in sludge concentration detection at wastewater treatment sites lies in the coexistence of various optically active substances such as suspended sludge, colloidal particles, bubbles, and foam. Their interactions result in highly nonlinear optical signals. Specifically, the dynamic processes of bubble escape and particle settling cause stray light interference to decay exponentially over time, while excessive settling leads to sludge sedimentation and stratification, resulting in a loss of sample representativeness. The temporal characteristics of this physical process necessitate the ability to distinguish between the contributions of two opposing trends: improved signal quality and decreased sample representativeness. Therefore, this invention employs singular value decomposition (SVD) to decompose the optical path data matrix. Using the decomposition results to analyze the performance of different light signals, a singular value dispersion is constructed to quantify signal quality. Furthermore, curve fitting establishes a mapping relationship between the temporal changes in optical signals and the physical settling process, thereby separating the systematic error component caused by stray light from the random error component caused by insufficient sample representativeness.

[0037] As an optional embodiment, the specific method for obtaining the offset coefficient of the wastewater sample is as follows: based on the differences between the singular values ​​of the wastewater samples, the singular value dispersion of the corresponding wastewater sample is obtained; an exponential decay curve is fitted to the singular value dispersion of all wastewater samples to obtain the singular value dispersion decay curve; based on the relative difference between the singular value dispersion of the wastewater sample and the singular value dispersion of the corresponding settling time in the singular value dispersion decay curve, the offset coefficient of the wastewater sample is obtained.

[0038] As an optional embodiment, the specific method for calculating the singularity dispersion of the wastewater sample can be: ,in Indicates the first The dispersion of singular values ​​in a wastewater sample; Indicates the first The first largest singular value corresponding to the optical path data matrix of a wastewater sample; Indicates the first The second largest singular value corresponding to the optical path data matrix of a wastewater sample; This represents the predefined singular factor.

[0039] For singular factors In a specific embodiment, this value can be set to 0.1, and can be adjusted according to actual conditions. Furthermore, in sludge concentration detection scenarios, the ideal scattered light signal should maintain a stable physical correlation with the transmitted light signal, exhibiting low-rank characteristics of the matrix. However, stray light has randomness and direction sensitivity, introducing high-rank components. Therefore, SVD can decompose the matrix into a signal subspace and a noise subspace, i.e., the first largest singular value. The second largest singularity corresponds to the effective optical signal generated by the sludge particles. It mainly reflects stray light interference; therefore, the greater the singular value dispersion, the higher the proportion of stray light component and the worse the signal quality when the corresponding sewage sample is detected by a multi-path detection system.

[0040] As an optional embodiment, the specific method for obtaining the singular value dispersion decay curve is as follows: establish a two-dimensional rectangular coordinate system, take the settling time of the sewage sample as the horizontal axis of the two-dimensional rectangular coordinate system, and take the singular value dispersion of the sewage sample as the vertical axis of the two-dimensional coordinate system to form a scatter plot of all sewage samples in the two-dimensional rectangular coordinate system; perform exponential decay fitting on the scatter plot in the two-dimensional rectangular coordinate system using the least squares method to obtain the initial dispersion, decay coefficient, and steady-state dispersion, thereby constructing the singular value dispersion decay curve from the initial dispersion, decay coefficient, and steady-state dispersion.

[0041] As an optional embodiment, the formula for the singular value dispersion decay curve can be expressed as follows: ,in Indicates the resting time as The dispersion of singular values ​​in wastewater samples at that time; The initial dispersion, The attenuation coefficient is... The steady-state dispersion; Represents the natural constant.

[0042] For the singular value dispersion decay curve obtained by fitting the exponential decay, the curve model uses the process of bubble escape and large particle sedimentation during sludge settling to establish a mapping between the temporal changes of the optical signal and the physical process, providing a benchmark for the subsequent calculation of the offset coefficient.

[0043] As an optional embodiment, the specific method for obtaining the offset coefficient of the wastewater sample is as follows: the difference between the singular value dispersion and the steady-state dispersion of any wastewater sample is taken as the first dispersion difference value of the corresponding wastewater sample; the difference between the fitted value of the singular value dispersion and the steady-state dispersion in the singular value dispersion decay curve corresponding to the standing time of the wastewater sample is taken as the second dispersion difference value of the corresponding wastewater sample; the offset coefficient of the wastewater sample is obtained based on the first and second dispersion difference values, wherein the first dispersion difference value is positively correlated with the offset coefficient, and the second dispersion difference value is negatively correlated with the offset coefficient.

[0044] As an optional embodiment, the specific method for calculating the offset coefficient of the wastewater sample can be: in, Indicates the first The offset coefficient of each wastewater sample; For the first The dispersion of singular values ​​in a wastewater sample; Indicates the first The settling time of a wastewater sample corresponds to the fitted value of the singular value dispersion in the singular value dispersion decay curve. The steady-state dispersion; Represents a linear normalization function; Indicates the preset hyperparameters; This represents the maximum value function.

[0045] In a specific embodiment of the present invention, the linear normalization function can be Min-Max normalization; for hyperparameters... In a specific embodiment, it can be set to 0.1, and can be adjusted according to the actual situation. For the offset coefficient, when the offset coefficient is larger, it indicates that the actual stray light is higher than the model expectation and the offset is serious. Conversely, when the offset coefficient is smaller, it indicates that the signal representativeness may be insufficient due to excessive sludge settling, resulting in excessive suppression of stray light. Therefore, the offset coefficient reflects the degree of deviation of stray light from the sludge concentration detection result, that is, the relative deviation between the actual dispersion and the ideal dispersion. The larger the offset coefficient value, the more significant the detection offset caused by stray light.

[0046] As an optional embodiment, the specific method for obtaining the confidence coefficient is as follows: the projection consistency of the wastewater sample is obtained based on the row vectors in the optical path data matrix of the wastewater sample and the column vectors of the corresponding right singular matrix; the optical path variation coefficient of the wastewater sample is obtained based on the data distribution in the transmission column of the wastewater sample; and the confidence coefficient of the wastewater sample is obtained by combining the standing time, projection consistency, and optical path variation coefficient of the wastewater sample.

[0047] Because the wastewater samples undergo a settling process before sludge concentration detection via a turbidimeter, the impact of the substances in the sludge on the infrared light emitted by the transmitter varies with the settling time. This results in a gradual decrease in stray light and consequently, a reduction in the offset effect on the sludge concentration detection results. However, the sludge sedimentation caused by the settling process leads to inaccurate transmission and scattering light signals obtained by the detector under different optical paths, thus reducing the confidence level of the sludge concentration detection results for each wastewater sample. Therefore, this method obtains the offset coefficient and confidence coefficient for each wastewater sample to correct the sludge concentration detection results, thereby ensuring the accuracy of the subsequent sludge concentration detection results for the wastewater to be tested.

[0048] As an optional embodiment, the specific calculation method for the projection consistency of the wastewater sample can be: in, Indicates the first Projection consistency of individual wastewater samples; For the first The optical path data matrix of the wastewater sample is the first The row vector of a row. Indicates inner product operation; Indicates the first The first column vector of the right singular matrix corresponding to the optical path data matrix of a wastewater sample; Indicates the row number of the optical path data matrix; This represents the absolute value function.

[0049] A higher value indicates more consistent signals across all optical paths and better spatial uniformity of the sample. This means that under short settling times, the sludge is evenly distributed and the detection conditions for each optical path are similar. Conversely, under long settling times, sludge stratification leads to increased differences in signals between the upper and lower layers of the optical path. The projection consistency is reduced, therefore it directly reflects the representativeness of the wastewater sample.

[0050] As an optional embodiment, the specific method for calculating the optical path variation coefficient of the wastewater sample can be as follows: ,in Indicates the first The optical path variation coefficient of a wastewater sample; and The first Standard deviation and mean of the transmission column for each wastewater sample; This indicates the preset safety factor.

[0051] It should be noted that, based on experience, the safety factor is preset to 0.1 to avoid the denominator being 0; the larger the optical path variation coefficient, the more significant the spatial difference of transmitted light, and the more severe the sludge settling and stratification.

[0052] As an optional embodiment, the specific method for obtaining the settling penalty factor of the wastewater sample is as follows: the ratio between the settling time of the wastewater sample and the maximum settling time of all wastewater samples is taken as the relative settling time of the wastewater sample, and the settling penalty factor of the wastewater sample is obtained based on the relative settling time, wherein the settling penalty factor is negatively correlated with the relative settling time.

[0053] As an optional embodiment, the specific calculation method for the settling penalty factor of the wastewater sample can be as follows: in, Indicates the first The settling penalty factor for each wastewater sample; Indicates the first The settling time for each wastewater sample; This indicates the maximum settling time for all wastewater samples; This indicates the preset adjustment parameters.

[0054] Regarding the specific calculation method for the static penalty factor, in one specific embodiment, the adjustment parameter can be set to 0.01 to avoid a denominator of 0 during the calculation process; additionally... This indicates the relative settling time of the wastewater sample. The longer the wastewater sample is settling, the greater its relative settling time, and the smaller the corresponding settling penalty factor. However, as sludge settles and accumulates with increasing settling time, the amount of suspended sludge in the wastewater sample decreases, making it impossible to effectively reflect the sludge concentration in the wastewater sample through the scattered light signal. Therefore, this indicates that the confidence level of the results obtained when detecting the sludge concentration of this wastewater sample is low.

[0055] As an optional embodiment, the specific method for calculating the confidence coefficient of the wastewater sample can be: in, Indicates the first Confidence coefficient of each wastewater sample; Indicates the first Projection consistency of individual wastewater samples; It is the hyperbolic tangent function; Indicates the first The settling penalty factor for each wastewater sample; Indicates the first The optical path variation coefficient of a wastewater sample.

[0056] The confidence coefficient comprehensively reflects the reliability of the detection results, indicating the need to balance signal quality and sample representativeness when detecting sludge concentration after wastewater samples have undergone settling treatment. It is defined as a weighted harmonic average of row space consistency and settling stratification index, incorporating a nonlinear penalty for settling time. The confidence factor... The calculation method is the harmonic mean method, specifically reflected in the following: and When any one of the indicators approaches 0 (i.e., there is a serious defect), the harmonic mean rapidly decays to 0, reflecting a veto mechanism, thereby achieving a balance between signal quality and sample representativeness. Therefore, the harmonic mean can only reach its maximum value when both indicators are at a high level and are close to each other. The closer the confidence coefficient is to 1, the more reliable the detection result is. In addition, the settling penalty factor is used to avoid excessively long settling time leading to excessive sludge sedimentation, which would reduce the confidence of the sludge concentration detection result.

[0057] Thus, the offset coefficient and confidence coefficient for each wastewater sample are obtained using the above method.

[0058] Step S004: Combine the offset coefficient and confidence coefficient of the wastewater sample to perform weighted correction on the sludge concentration detection results obtained by the multi-path turbidimeter, so as to obtain the corrected sludge concentration of the wastewater to be tested.

[0059] It should be noted that since the offset coefficient quantifies the systematic error component caused by stray light, while the confidence coefficient quantifies the random error component caused by the fluctuation due to insufficient sample representativeness, the two together determine the weight allocation of each sample data. Therefore, by using the offset coefficient and the confidence coefficient to perform weighted correction on the sludge concentration detection results of the turbidimeter, the maximum likelihood estimate of the overall mean of the wastewater sample can be achieved, thereby improving the accuracy of the sludge concentration detection results.

[0060] As an optional embodiment, the specific method for obtaining the corrected sludge concentration of the wastewater to be tested is as follows: based on the offset coefficient and confidence coefficient of the wastewater sample, the comprehensive weight of the corresponding wastewater sample is obtained, wherein the offset coefficient is negatively correlated with the comprehensive weight and the confidence coefficient is positively correlated with the comprehensive weight; the sludge concentration of each wastewater sample output by the multi-path turbidimeter is obtained, and the corresponding sludge concentration is weighted using the comprehensive weight of all wastewater samples to obtain the corrected sludge concentration of the wastewater to be tested.

[0061] As an optional embodiment, the specific method for calculating the comprehensive weight of the wastewater sample can be: ;in, Indicates the first The overall weight of each wastewater sample; Indicates the first Confidence coefficient of each wastewater sample; Indicates the first The offset coefficient of each wastewater sample; This is a preset constant; This represents the linear normalization function.

[0062] Regarding the specific calculation method of the comprehensive weight, the constant is... In one specific embodiment of the present invention, the value can be set to 0.1. In other embodiments, it can be adjusted according to the actual situation. The embodiments of the present invention do not impose specific limitations. In addition, the offset coefficient... The confidence coefficient characterizes the degree of detection bias caused by stray light. The reliability of the test results is characterized by both the weighting and the confidence coefficient. The weighting design reflects the principle of prioritizing low offset and high confidence. That is, the smaller the offset coefficient, the better the stray light suppression, and the higher the confidence coefficient, the stronger the representativeness of the sample, and the greater the corresponding weight.

[0063] As an optional embodiment, the specific method for calculating the corrected sludge concentration of the wastewater to be tested can be: ,in This indicates the corrected sludge concentration of the wastewater to be tested; Indicates the first The overall weight of each wastewater sample; This indicates the output of the multi-path turbidimeter. Sludge concentration of each wastewater sample; This indicates the number of wastewater samples to be tested.

[0064] Furthermore, the corrected sludge concentration of the wastewater to be tested is used as the final output of the sludge concentration of the wastewater to be tested, and a weight distribution can also be output. And data quality labels, providing on-site operators with a reference for data reliability; for data quality labels, specifically: marking or The wastewater sample was an abnormal sample.

[0065] It should be noted that the threshold parameters of 0.9 and 0.3 set for abnormal samples are empirical values ​​set in one embodiment. They can be adjusted according to the actual situation in other embodiments. No specific limitation is made in the embodiments of this invention.

[0066] This concludes the embodiment.

[0067] It should be noted that the embodiments used in this example or The model is only used to represent negative correlations and the results of the constraint model output are in Within this range, in specific implementations, other models with the same purpose can be substituted; this embodiment is merely an example. or The description will be based on a model, without making specific limitations on it. This refers to the input of the model.

[0068] This invention proposes a sludge concentration detection system based on scattered light signals. Please refer to [link / reference]. Figure 2 The diagram illustrates a structural schematic of a sludge concentration detection system 200 based on scattered light signals according to an embodiment of the present invention. The system includes: The sample detection module 201 is used to detect wastewater samples of the wastewater to be tested at several different standing times using a multi-path turbidimeter, and to obtain the transmitted light signal data and scattered light signal data of each wastewater sample under each optical path. The matrix processing module 202 is used to construct the optical path data matrix of the corresponding sewage sample based on all transmitted light signal data and scattered light signal data of each sewage sample, and to decompose the optical path data matrix respectively. The offset confidence module 203 is used to analyze the decomposition results of all optical path data matrices and determine the offset coefficient and confidence coefficient of each sewage sample. The offset coefficient is used to indicate the degree of deviation of the sludge concentration detection result of the corresponding sewage sample under the influence of stray light, and the confidence coefficient is used to indicate the reliability of the sludge concentration detection result obtained after the corresponding sewage sample has been subjected to a settling treatment for a corresponding settling time. The concentration correction module 204 is used to perform weighted correction on the sludge concentration detection results obtained by the multi-path turbidimeter by combining the offset coefficient and confidence coefficient of the sewage sample, so as to obtain the corrected sludge concentration of the sewage to be tested.

[0069] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the sludge concentration detection system based on scattered light signals and the sludge concentration detection method based on scattered light signals provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0070] This invention also provides an electronic device. The electronic device may include a processor, a memory, and a program stored in the memory and executable on the processor.

[0071] When the program is executed by the processor, it can achieve Figure 1 Any steps in the corresponding method embodiments and the achievement of the same beneficial effects will not be repeated here.

[0072] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by hardware related to program instructions, and the program can be stored in a readable medium.

[0073] This invention also provides a readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described functions. Figure 1 Any step in the corresponding method embodiment can achieve the same technical effect, and will not be repeated here to avoid repetition.

[0074] The computer-readable storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0075] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0076] The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0077] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or terminal. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0078] This invention also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the sludge concentration detection method based on scattered light signals provided in the above embodiments.

[0079] 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 within the protection scope of the present invention.

Claims

1. A method for detecting sludge concentration based on scattered light signals, characterized in that, The method includes the following steps: The wastewater samples to be tested were tested using a multi-path turbidimeter at several different settling times, and the transmitted light signal data and scattered light signal data of each wastewater sample under each optical path were obtained. Based on all transmitted and scattered light signal data for each wastewater sample, an optical path data matrix for the corresponding wastewater sample is constructed, and the optical path data matrix is ​​decomposed accordingly. The decomposition results of all optical path data matrices were analyzed to determine the offset coefficient and confidence coefficient of each wastewater sample. The offset coefficient is used to indicate the degree of deviation of the sludge concentration detection result of the corresponding wastewater sample under the influence of stray light, and the confidence coefficient is used to indicate the reliability of the sludge concentration detection result obtained after the corresponding wastewater sample has been subjected to a settling treatment for a corresponding settling time. By combining the offset coefficient and confidence coefficient of the wastewater sample, the sludge concentration detection results obtained by the multi-path turbidimeter are weighted and corrected to obtain the corrected sludge concentration of the wastewater to be tested.

2. The sludge concentration detection method based on scattered light signals according to claim 1, characterized in that, The specific method for obtaining the optical path data matrix of the wastewater sample is as follows: For any wastewater sample, all transmitted and scattered light signal data are acquired. An optical path data matrix is ​​constructed using these data, containing two columns: one for each transmitted light path and the other for each scattered light path. The number of rows in the optical path data matrix corresponds to the number of emitters in the turbidimeter. The same, thus obtaining The matrix.

3. The sludge concentration detection method based on scattered light signals according to claim 2, characterized in that, The specific methods for decomposing the optical path data matrix are as follows: By performing singular value decomposition on the optical path data matrix of the wastewater sample, the left singular matrix, several singular values, and the right singular matrix corresponding to the optical path data matrix are obtained.

4. The sludge concentration detection method based on scattered light signals according to claim 1, characterized in that, The specific method for obtaining the offset coefficient of the wastewater sample is as follows: Based on the differences between the singular values ​​of the wastewater samples, the singular value dispersion of the corresponding wastewater samples is obtained; The singular value dispersion of all wastewater samples was fitted with an exponential decay curve to obtain the singular value dispersion decay curve. The offset coefficient of the wastewater sample is obtained based on the relative difference between the singular value dispersion of the wastewater sample and the singular value dispersion in the singular value dispersion decay curve corresponding to the settling time.

5. The sludge concentration detection method based on scattered light signals according to claim 4, characterized in that, The specific method for obtaining the singular value dispersion decay curve is as follows: A two-dimensional rectangular coordinate system is established, with the settling time of the sewage samples as the horizontal axis and the singular value dispersion of the sewage samples as the vertical axis, forming a scatter plot of all sewage samples in the two-dimensional rectangular coordinate system. By performing exponential decay fitting on the scatter plot in the two-dimensional rectangular coordinate system using the least squares method, the initial dispersion, decay coefficient, and steady-state dispersion are obtained, thus forming a singular value dispersion decay curve from the initial dispersion, decay coefficient, and steady-state dispersion.

6. The sludge concentration detection method based on scattered light signals according to claim 5, characterized in that, The specific method for obtaining the offset coefficient of the wastewater sample is as follows: The difference between the singular value dispersion and the steady-state dispersion of any wastewater sample is taken as the first dispersion difference value of the corresponding wastewater sample; the difference between the fitted value of the singular value dispersion and the steady-state dispersion in the singular value dispersion decay curve corresponding to the standing time of the wastewater sample is taken as the second dispersion difference value of the corresponding wastewater sample. The offset coefficient of the wastewater sample is obtained based on the first and second dispersion difference values. The first dispersion difference value is positively correlated with the offset coefficient, and the second dispersion difference value is negatively correlated with the offset coefficient.

7. The sludge concentration detection method based on scattered light signals according to claim 3, characterized in that, The specific method for obtaining the confidence coefficient is as follows: The projection consistency of the wastewater sample is obtained by taking the row vectors in the optical path data matrix of the wastewater sample and the column vectors in the corresponding right singular matrix. The optical path variation coefficient of the wastewater sample is obtained based on the data distribution in the transmission column of the wastewater sample. The confidence coefficient of the wastewater sample is obtained by combining the settling time, projection consistency, and optical path variation coefficient of the wastewater sample.

8. The sludge concentration detection method based on scattered light signals according to claim 1, characterized in that, The specific method for obtaining the confidence coefficient of the wastewater sample is as follows: Calculate the confidence factor based on the projection consistency and optical path variation coefficient of the wastewater sample; The settling penalty factor for wastewater samples is calculated based on the difference between the settling time of wastewater samples and the maximum settling time of all wastewater samples. The confidence factor was adjusted using a settling penalty factor to obtain the confidence coefficient of the wastewater sample.

9. The sludge concentration detection method based on scattered light signals according to claim 8, characterized in that, The specific method for obtaining the settling penalty factor of the wastewater sample is as follows: The ratio between the settling time of a wastewater sample and the maximum settling time of all wastewater samples is taken as the relative settling time of the wastewater sample. Based on the relative settling time, the settling penalty factor of the wastewater sample is obtained, and the settling penalty factor is negatively correlated with the relative settling time.

10. The sludge concentration detection method based on scattered light signals according to claim 1, characterized in that, The specific method for obtaining the corrected sludge concentration of the wastewater to be tested is as follows: The overall weight of the wastewater sample is obtained based on the offset coefficient and confidence coefficient. The offset coefficient is negatively correlated with the overall weight, and the confidence coefficient is positively correlated with the overall weight. The sludge concentration of each wastewater sample output by the multi-path turbidimeter is obtained. The corresponding sludge concentration is weighted by the comprehensive weight of all wastewater samples to obtain the corrected sludge concentration of the wastewater to be tested.

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