Industrial sewage real-time monitoring treatment method and system

Abstract

The invention relates to the technical field of industrial sewage monitoring, in particular to an industrial sewage real-time monitoring treatment method and system. The method comprises the following steps: firstly, acquiring and processing data of heavy metal parameter acquisition points to obtain a sewage quantity parameter matrix, a heavy metal parameter matrix, a heavy metal parameter time sequence matrix and a heavy metal parameter acquisition matrix; then calculating the heavy metal load according to the sewage amount and the heavy metal concentration data; acquiring and processing data in the heavy metal parameter matrix to obtain heavy metal types; constructing a heavy metal concentration monitoring value according to data in the heavy metal parameter acquisition matrix and the heavy metal parameter time sequence matrix; constructing a heavy metal joint matrix according to the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter acquisition matrix to obtain a spatial feature vector and a time feature vector; a water quality monitoring vector is constructed according to the data, a sewage treatment parameter recommendation model obtains the water quality monitoring vector and processes the water quality monitoring vector, and recommendation parameters are obtained and used for recommending industrial sewage treatment parameters to a sewage treatment plant.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial sewage monitoring, and particularly to a real-time monitoring and treatment method and system for industrial sewage. Background Technique

[0002] The sewage treatment plants in industrial parks are used to receive the sewage discharged by different enterprises during the production process. The discharge of sewage is intermittent, and the water volume and water quality of the sewage in the park may fluctuate greatly in a short period of time. For example, the sewage discharged during the peak production period of some enterprises has a high concentration, while less or lower-concentration sewage is discharged during the low period.

[0003] On the other hand, the existing sewage treatment processes may be difficult to adapt to the rapid changes in sewage water quality, and more sensitive monitoring of sewage water quality is required to improve the adaptability and flexibility of sewage treatment plants. Especially for sewage containing heavy metals, its response ability needs to be improved.

[0004] Therefore, a real-time monitoring and treatment method and system for industrial sewage are proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a real-time monitoring and treatment method and system for industrial sewage, which can improve the adaptability and flexibility of sewage treatment plants for sewage treatment by monitoring and analyzing heavy metals in sewage in real time and sensitively. First, obtain the sewage volume parameter matrix, heavy metal parameter matrix, heavy metal parameter time series matrix, and heavy metal parameter acquisition matrix, and then calculate the heavy metal load according to the data of sewage volume and heavy metal concentration; obtain the data in the heavy metal parameter matrix and process it to obtain the types of heavy metals; construct the heavy metal concentration monitoring value according to the data in the heavy metal parameter acquisition matrix and the heavy metal parameter time series matrix; construct the heavy metal joint matrix according to the heavy metal parameter matrix, the heavy metal parameter time series matrix, and the heavy metal parameter acquisition matrix to obtain the spatial feature vector and the time feature vector; construct the water quality monitoring vector according to the above data, and the sewage treatment parameter recommendation model obtains the water quality monitoring vector and processes it to obtain the recommended parameters for recommending the parameters for specifically treating industrial sewage to the sewage treatment plant.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A real-time monitoring and treatment method for industrial sewage, including:

[0008] Obtain the positions of N sewage receiving pipes of the sewage treatment plant and set heavy metal parameter acquisition points; obtain the data of the heavy metal parameter acquisition points and process it to obtain the sewage volume parameter matrix, the heavy metal parameter matrix, the heavy metal parameter time series matrix, and the heavy metal parameter acquisition matrix;

[0009] Further, the heavy metal parameter matrix, the heavy metal parameter time series matrix, and the heavy metal parameter acquisition matrix include:

[0010] The heavy metal parameter matrix L is an N×M×K matrix, where N represents the number of sewage receiving pipes in the sewage treatment plant, M represents the number of heavy metal types, K represents the number of the most recent K data collection times, and L nmk represents the data of the concentration of the m-th heavy metal in the n-th sewage receiving pipe at the k-th collection;

[0011] The heavy metal parameter time series matrix L is an N×M×K matrix, where N represents the number of sewage receiving pipes in the sewage treatment plant, M represents the number of heavy metal types, K represents the number of the most recent K data collection times, and L nmk represents the difference between the data of the concentration of the m-th heavy metal in the n-th sewage receiving pipe at the k-th collection and the data at the (k - 1)-th collection;

[0012] The heavy metal parameter acquisition matrix is an N×M×K matrix, where N represents the number of sewage receiving pipes in the sewage treatment plant, M represents the number of heavy metal types, K represents the number of the most recent K data collection times, represents the waiting time before the k-th collection of the concentration data of the m-th heavy metal in the n-th sewage receiving pipe.

[0013] Further, the specific steps of data collection include:

[0014] The formula for the data collection trigger value is:

[0015]

[0016] where S trigger represents the data collection trigger value, ω 1 represents the weight coefficient of the heavy metal concentration change rate, ΔC represents the change amount of the heavy metal concentration per unit time, Δt represents the unit time, ω 2 represents the weight coefficient of the sewage flow rate change rate, ΔQ represents the sewage flow rate per unit time, ω 3 represents the weight coefficient of the risk factor R;

[0017] Obtain the current timestamp and the timestamp of the previous data collection and process them to obtain the first waiting collection time; when the first waiting collection time is less than the preset maximum waiting collection time, if the data collection trigger value is greater than the preset trigger threshold, immediately perform data collection and reset the first waiting collection time; when the first waiting collection time is equal to the preset maximum waiting collection time, immediately perform data collection and reset the first waiting collection time.

[0018] Obtain the data in the sewage volume parameter matrix and the heavy metal parameter matrix, and calculate the heavy metal load according to the data of the sewage volume and the heavy metal concentration;

[0019] Further, the calculation formula for the heavy metal load is:

[0020]

[0021] Among them, C unit (n,k) represents the heavy metal load of the nth sewage receiving pipe during the kth data collection, C(m,k) represents the data of the mth heavy metal concentration during the kth collection, M represents the number of heavy metal types, Q(n,k) represents the sewage volume of the nth sewage receiving pipe during the kth collection, and Q(k) represents the total sewage volume during the kth collection.

[0022] Obtain the data in the heavy metal parameter matrix and process it to obtain the heavy metal types;

[0023] Construct a concentration mutation monitoring value based on the data in the heavy metal parameter acquisition matrix and the heavy metal parameter time series matrix, and further obtain the heavy metal concentration monitoring value;

[0024] Further, the calculation formula for the heavy metal concentration monitoring value is:

[0025]

[0026] Among them, RE represents the heavy metal concentration monitoring value, α 1 represents the weight coefficient of the heavy metal concentration change rate, N represents the number of sewage receiving pipes, M represents the types of heavy metals, K represents the number of the most recent K data collections, θ 1 represents the difference between the data of the mth heavy metal concentration of the nth sewage receiving pipe during the kth collection and the data of the (k - 1)th collection of the weight coefficient, θ 2 represents the weight coefficient of the time difference, represents the waiting time before the kth collection of the mth heavy metal concentration of the nth sewage receiving pipe, represents the waiting time before the (k - 1)th collection of the mth heavy metal concentration of the nth sewage receiving pipe, α 2 represents the weight coefficient of the heavy metal parameter time series matrix, represents the maximum value in the heavy metal parameter time series matrix, represents the minimum value in the heavy metal parameter time series matrix, α 3 represents the weight coefficient of the concentration mutation monitoring value ADI nmk of.

[0027] Further, the calculation formula for the concentration mutation monitoring value is as follows:

[0028]

[0029] where ADI nmk represents the concentration mutation monitoring value of the m-th heavy metal concentration in the n-th sewage receiving pipe at the k-th collection, represents the difference between the data of the m-th heavy metal concentration in the n-th sewage receiving pipe at the k-th collection and the data of the (k - 1)-th collection, represents the difference between the data of the m-th heavy metal concentration in the n-th sewage receiving pipe at the (k - 1)-th collection and the data of the (k - 2)-th collection, and σ nm represents the variance of the m-th heavy metal concentration in the n-th sewage receiving pipe, and β represents the weight coefficient of the heavy metal parameter acquisition matrix, represents the maximum value in the heavy metal parameter acquisition matrix, represents the waiting time before the k-th collection of the m-th heavy metal concentration in the n-th sewage receiving pipe.

[0030] Construct a heavy metal joint matrix based on the heavy metal parameter matrix, the heavy metal parameter time series matrix, and the heavy metal parameter acquisition matrix, and perform feature extraction through principal component analysis, matrix decomposition, and convolutional network to obtain spatial feature vectors and time feature vectors;

[0031] Construct a water quality monitoring vector based on the heavy metal load, the heavy metal type, the heavy metal concentration monitoring value, the spatial feature vector, and the time feature vector. The sewage treatment parameter recommendation model obtains and processes the water quality monitoring vector to obtain recommended parameters for recommending specific parameters for treating industrial sewage to the sewage treatment plant.

[0032] The present invention also provides an industrial sewage real-time monitoring and treatment system, including:

[0033] A sewage parameter acquisition module, including a sewage volume acquisition unit and a water quality parameter acquisition unit, where the sewage volume acquisition unit is used to obtain a sewage volume parameter matrix, and the water quality parameter acquisition unit is used to obtain a heavy metal parameter matrix, a heavy metal parameter time series matrix, and a heavy metal parameter acquisition matrix;

[0034] A sewage parameter transmission module for transmitting the sewage volume parameter matrix, the heavy metal parameter matrix, the heavy metal parameter time series matrix, and the heavy metal parameter acquisition matrix to the processing center;

[0035] A heavy metal source monitoring module for obtaining the data in the sewage volume parameter matrix and the heavy metal parameter matrix, and calculating the heavy metal load according to the data of the sewage volume and the heavy metal concentration;

[0036] Heavy metal type monitoring module, which is used to obtain and process the data in the heavy metal parameter matrix to obtain the heavy metal types;

[0037] Heavy metal concentration monitoring module, which is used to construct a concentration mutation monitoring value according to the data in the heavy metal parameter acquisition matrix and the heavy metal parameter time series matrix, and further obtain the heavy metal concentration monitoring value;

[0038] Heavy metal spatio-temporal monitoring module, which constructs a heavy metal joint matrix according to the heavy metal parameter matrix, the heavy metal parameter time series matrix and the heavy metal parameter acquisition matrix, and performs feature extraction through principal component analysis, matrix decomposition and convolutional network to obtain spatial feature vectors and time feature vectors;

[0039] Sewage treatment parameter recommendation module, which is used to construct a water quality monitoring vector according to the heavy metal load, the heavy metal types, the heavy metal concentration monitoring value, the spatial feature vector and the time feature vector. The sewage treatment parameter recommendation model obtains the water quality monitoring vector and processes it to obtain recommended parameters, which are used to recommend specific parameters for treating industrial sewage to the sewage treatment plant.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] 1. By introducing the sewage volume parameter matrix, the heavy metal parameter matrix, the heavy metal parameter time series matrix and the heavy metal parameter acquisition matrix, as well as the dynamic trigger mechanism for data acquisition, accurate, efficient and sensitive industrial sewage monitoring is realized, the flexibility and accuracy of data acquisition are improved, data acquisition omission is avoided, the integrity of monitoring is guaranteed, and a solid data foundation is provided for real-time monitoring and intelligent decision-making.

[0042] 2. Through the data analysis of these dimensions of time, space and concentration such as heavy metal load, heavy metal types, heavy metal concentration monitoring value, heavy metal spatial feature vector and heavy metal time feature vector, the accuracy, real-time performance and efficiency of quality analysis and monitoring of the sewage received by the sewage treatment plant are significantly improved; combined with multi-dimensional data processing and analysis methods, comprehensive monitoring is realized, and the sewage quality analysis ability is improved.

[0043] 3. The water quality monitoring vector integrates the heavy metal load, types, concentration monitoring value, and spatial and time feature vectors, comprehensively reflects the pollution characteristics of the sewage. By constructing the water quality monitoring vector and the sewage treatment parameter recommendation model, and combining historical data and linear regression analysis, the scientificity and accuracy of parameter recommendation are improved; the water quality monitoring vector can reflect the latest characteristics of the sewage in real time, and the recommendation model can dynamically update the treatment parameters, improving the adaptability and flexibility of sewage treatment. Brief Description of the Drawings

[0044] Figure 1 Flow chart of an industrial sewage real - time monitoring and treatment method provided by an embodiment of the present invention;

[0045] Figure 2 Structural schematic diagram of the data processing process provided by an embodiment of the present invention;

[0046] Figure 3 Structural schematic diagram of an industrial sewage real - time monitoring and treatment system provided by an embodiment of the present invention. Detailed implementation manners

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] Embodiment 1

[0049] In order to improve the adaptability and flexibility of sewage treatment and be able to handle industrial sewage with different amounts and different heavy metal concentrations at different times, a sewage treatment plant in an industrial park introduced an industrial sewage real - time monitoring and treatment method provided by the present invention. The method flow is as Figure 1 shown, and the specific implementation manners are as follows:

[0050] First, obtain the positions of N sewage receiving pipes in the sewage treatment plant and set heavy metal parameter collection points; obtain and process the data at the heavy metal parameter collection points to obtain a sewage volume parameter matrix, a heavy metal parameter matrix, a heavy metal parameter time - series matrix, and a heavy metal parameter collection matrix. As Figure 2 shown is the data processing and transmission process in the whole method;

[0051] Furthermore, the sewage volume parameter matrix records the sewage volume of the nth sewage receiving pipe at the kth data collection. Table 1 shows the sewage volume data of some sewage receiving pipes at different collection time points;

[0052] Table 1. Sewage volume data

[0053]

[0054] Furthermore, the heavy metal parameter matrix, the heavy metal parameter time - series matrix, and the heavy metal parameter collection matrix include: the heavy metal parameter matrix L is an N×M×K matrix, where N represents the number of sewage receiving pipes in the sewage treatment plant, M represents the number of heavy metal types, K represents the number of the most recent K data collection times, L nmkIt represents the data of the concentration of the m-th heavy metal in the n-th sewage receiving pipe at the k-th collection;

[0055] The heavy metal parameter time series matrix is a matrix of N×M×K, where N represents the number of sewage receiving pipes in the sewage treatment plant, M represents the number of heavy metal types, and K represents the number of the most recent K data collection times. It represents the difference between the data of the concentration of the m-th heavy metal in the n-th sewage receiving pipe at the k-th collection and the data at the (k - 1)-th collection.

[0056] The heavy metal parameter collection matrix is a matrix of N×M×K, where N represents the number of sewage receiving pipes in the sewage treatment plant, M represents the number of heavy metal types, and K represents the number of the most recent K data collection times. It represents the waiting time before the k-th collection of the concentration data of the m-th heavy metal in the n-th sewage receiving pipe.

[0057] By introducing the sewage volume parameter matrix, the heavy metal parameter matrix, the heavy metal parameter time series matrix and the heavy metal parameter collection matrix, accurate, efficient and sensitive industrial sewage monitoring is realized, the flexibility and accuracy of data collection are improved, data collection omission is avoided, the integrity of monitoring is guaranteed, and a solid data foundation is provided for real-time monitoring and intelligent decision-making.

[0058] Furthermore, the specific steps of data collection include:

[0059] The formula for the data collection trigger value is:

[0060]

[0061] where S trigger represents the data collection trigger value, ω 1 represents the weight coefficient of the heavy metal concentration change rate, ΔC represents the change amount of the heavy metal concentration per unit time, Δt represents the unit time, ω 2 represents the weight coefficient of the sewage flow rate change rate, ΔQ represents the sewage flow rate per unit time, ω 3 represents the weight coefficient of the risk factor R;

[0062] Furthermore, the risk factor R is used to reset the data collection trigger value, and the calculation formula is:

[0063]

[0064] where λ > 0 represents the intercept, t represents the first waiting collection time, t 0 represents the preset maximum waiting collection time.

[0065] Obtain the current timestamp and the timestamp of the previous data collection and process them to obtain the first waiting collection time; when the first waiting collection time is less than the preset maximum waiting collection time, if the data collection trigger value is greater than the preset trigger threshold, immediately perform data collection and reset the first waiting collection time; when the first waiting collection time is equal to the preset maximum waiting collection time, immediately perform data collection and reset the first waiting collection time.

[0066] The data collection trigger value combines the change rate of heavy metal concentration, the change rate of sewage flow, and risk factors, and is dynamically adjusted through a weighting coefficient, which can more accurately reflect the trend and urgency of pollution changes; through the preset trigger threshold, not only the sensitivity and efficiency of collection are improved, but also the integrity and continuity of data are ensured; combining the dual control of the trigger threshold and the maximum collection interval can avoid wasting resources due to overly frequent collection and prevent data loss caused by too long a sampling interval.

[0067] Obtain the data in the sewage volume parameter matrix and the heavy metal parameter matrix, and calculate the heavy metal load according to the data of the sewage volume and heavy metal concentration.

[0068] Further, the calculation formula of the heavy metal load is:

[0069]

[0070] Among them, C unit (n,k) represents the heavy metal load of the nth sewage receiving pipe at the kth data collection, C(m,k) represents the data of the mth heavy metal concentration at the kth collection, M represents the number of heavy metal types, Q(n,k) represents the sewage volume of the nth sewage receiving pipe at the kth collection, and Q(k) represents the total sewage volume at the kth collection.

[0071] By calculating the sewage volume and heavy metal concentration to construct the heavy metal load of the sewage receiving pipe at different collection times, the heavy metal pollution burden can be accurately quantified, the pipes with serious pollution and their contributions to the overall discharge can be identified, and the precise positioning of key pollution sources can be realized.

[0072] Obtain the data in the heavy metal parameter matrix and process them to obtain the heavy metal types.

[0073] Further, according to the heavy metal data collected by the device, the heavy metal types can be determined.

[0074] Construct a concentration mutation monitoring value according to the data in the heavy metal parameter collection matrix and the heavy metal parameter time series matrix, and further obtain the heavy metal concentration monitoring value.

[0075] Further, the calculation formula of the heavy metal concentration monitoring value is:

[0076]

[0077] Among them, RE represents the monitored value of the heavy metal concentration, and α 1 represents the weight coefficient of the change rate of the heavy metal concentration, N represents the number of sewage receiving pipes, M represents the types of heavy metals, K represents the number of the most recent K data collection times, and θ 1 represents the difference between the data of the m-th heavy metal concentration of the n-th sewage receiving pipe collected at the k-th time and the data collected at the (k - 1)-th time of the weight coefficient, and θ 2 represents the weight coefficient of the time difference represents the waiting time before the k-th collection of the m-th heavy metal concentration of the n-th sewage receiving pipe represents the waiting time before the (k - 1)-th collection of the m-th heavy metal concentration of the n-th sewage receiving pipe, and α 2 represents the weight coefficient of the time series matrix of the heavy metal parameters represents the maximum value in the time series matrix of the heavy metal parameters represents the minimum value in the time series matrix of the heavy metal parameters, and α 3 represents the monitored value of concentration mutation ADI nmk of the weight coefficient

[0078] Through the multi-dimensional data integration of the change of heavy metal concentration, the monitored value of heavy metal concentration is constructed, which not only provides accurate and efficient dynamic monitoring capabilities, improves the sensitivity to the monitored data, but also improves the capabilities of pollution source tracing, risk warning and resource optimization, providing strong data support for the flexible operation of the sewage treatment plant

[0079] Furthermore, the calculation formula of the monitored value of concentration mutation is as follows

[0080]

[0081] nmk Among them, ADI represents the monitored value of concentration mutation at the k-th collection of the m-th heavy metal concentration of the n-th sewage receiving pipe represents the difference between the data of the m-th heavy metal concentration of the n-th sewage receiving pipe collected at the k-th time and the data collected at the (k - 1)-th time nm represents the variance of the m-th heavy metal concentration of the n-th pipe, and β represents the weight coefficient of the heavy metal parameter collection matrix represents the maximum value in the heavy metal parameter collection matrix Denote the waiting time before the k-th collection of the m-th heavy metal concentration in the n-th sewage receiving pipe.

[0082] By calculating the concentration mutation monitoring value, the accuracy of concentration mutation identification can be improved, the robustness and sensitivity of the monitoring index are enhanced, the mutation phenomenon of heavy metal concentration in sewage can be monitored comprehensively and accurately, and a real-time decision-making basis is provided for the sewage treatment plant.

[0083] Construct a heavy metal joint matrix based on the heavy metal parameter matrix, the heavy metal parameter time series matrix, and the heavy metal parameter acquisition matrix, and perform feature extraction through principal component analysis, matrix decomposition, and convolutional network to obtain a spatial feature vector and a time feature vector;

[0084] Further, construct a heavy metal joint matrix according to the heavy metal parameter matrix, the heavy metal parameter time series matrix, and the heavy metal parameter acquisition matrix Use the principal component analysis algorithm to process the heavy metal joint matrix to obtain a principal component matrix, perform matrix decomposition on the principal component matrix to obtain latent features W and H, where W represents spatial features and H represents time features, and use a spatio-temporal convolutional network to process the latent features to obtain a spatial feature vector and a time feature vector

[0085] Construct a water quality monitoring vector based on the heavy metal load, the heavy metal type, the heavy metal concentration monitoring value, the spatial feature vector, and the time feature vector. The sewage treatment parameter recommendation model obtains and processes the water quality monitoring vector to obtain recommended parameters for recommending specific parameters for treating industrial sewage to the sewage treatment plant.

[0086] Further, construct a historical water quality monitoring vector set and a corresponding historical recommended parameter set according to the historical data of the sewage treatment plant, establish a mapping relationship between the historical water quality monitoring vector set and the historical recommended parameter set according to the linear regression model, and use the least squares method to solve the model parameters to obtain the sewage treatment parameter recommendation model.

[0087] Table 2. Recommended parameters for some sewage treatment ponds

[0088]

[0089] Further, after constructing the sewage treatment parameter recommendation model, the sewage treatment parameter recommendation model obtains the water quality monitoring vector and processes it to obtain recommended parameters, including recommending appropriate treatment technologies such as physical treatment, chemical precipitation, biological treatment, and membrane separation technology, determining the optimal operating load of each device, and recommending the types of chemical agents to be added, such as flocculants, reducing agents, oxidizing agents, or chelating agents. Table 2 shows the recommended parameters for some sewage treatment ponds.

[0090] By combining historical data with real-time monitoring data and optimizing the sewage treatment parameters of the sewage treatment plant through a linear regression model and the least squares method, it is possible to customize a treatment plan for the sewage treatment plant according to the actual situation, reducing the trial-and-error cost; the constructed water quality monitoring vector includes heavy metal load, type, monitoring value, and spatio-temporal characteristics, comprehensively considering the spatial and temporal variation factors, making the monitoring more comprehensive, and the recommended results of the sewage treatment parameters are also more accurate, improving the flexibility and adaptability of the sewage treatment plant in the process of treating sewage.

[0091] Embodiment 2

[0092] The present invention also provides an industrial sewage real-time monitoring and treatment system, and the system structure is as Figure 3 shown, and the specific implementation method is as follows:

[0093] The sewage parameter collection module includes a sewage volume collection unit and a water quality parameter collection unit, where the sewage volume collection unit is used to obtain a sewage volume parameter matrix, and the water quality parameter collection unit is used to obtain a heavy metal parameter matrix, a heavy metal parameter time series matrix, and a heavy metal parameter collection matrix;

[0094] The sewage parameter transmission module is used to transmit the sewage volume parameter matrix, the heavy metal parameter matrix, the heavy metal parameter time series matrix, and the heavy metal parameter collection matrix to the processing center;

[0095] Further, the heavy metal parameter matrix, the heavy metal parameter time series matrix, and the heavy metal parameter collection matrix include:

[0096] The heavy metal parameter matrix L is a matrix of N×M×K, where N represents the number of sewage receiving pipes in the sewage treatment plant, M represents the number of heavy metal types, K represents the number of the most recent K data collection times, and L nmk represents the data of the concentration of the mth heavy metal in the nth sewage receiving pipe at the kth collection;

[0097] The heavy metal parameter time series matrix is a matrix of N×M×K, where N represents the number of sewage receiving pipes in the sewage treatment plant, M represents the number of heavy metal types, K represents the number of the most recent K data collection times, Denote the difference between the data of the m-th heavy metal concentration in the n-th sewage receiving pipe collected at the k-th time and the data collected at the (k - 1)-th time;

[0098] The heavy metal parameter acquisition matrix is a matrix of N×M×K, where N represents the number of sewage receiving pipes in the sewage treatment plant, M represents the number of heavy metal types, and K represents the number of the most recent K data acquisitions, Denote the waiting time before the k-th collection of the concentration data of the m-th heavy metal in the n-th sewage receiving pipe.

[0099] Furthermore, the specific steps of data acquisition include:

[0100] The formula for the data acquisition trigger value is:

[0101]

[0102] where S trigger denotes the data acquisition trigger value, ω 1 denotes the weight coefficient of the heavy metal concentration change rate, ΔC denotes the change amount of the heavy metal concentration per unit time, Δt denotes the unit time, ω 2 denotes the weight coefficient of the sewage flow change rate, ΔQ denotes the sewage flow per unit time, ω 3 denotes the weight coefficient of the risk factor R;

[0103] Obtain the current timestamp and the timestamp of the previous data acquisition and process them to obtain the first waiting acquisition time; when the first waiting acquisition time is less than the preset maximum waiting acquisition time, if the data acquisition trigger value is greater than the preset trigger threshold, immediately perform data acquisition and reset the first waiting acquisition time; when the first waiting acquisition time is equal to the preset maximum waiting acquisition time, immediately perform data acquisition and reset the first waiting acquisition time. Table 3 shows the first waiting acquisition times of some sewage pipes and their heavy metal concentration acquisitions at the most recent acquisition time point.

[0104] Table 3. Some First Waiting Acquisition Times

[0105] Pipe A1 Pipe A2 Pipe A3 Pipe A4 Pipe A5 Lead (Pb) 5 min 10 min 15 min 3 min 20 min Cadmium (Cd) 4 min 9 min 12 min 4 min 20 min Chromium (Cr) 6 min 8 min 14 min 3 min 20 min Mercury (Hg) 5 min 11 min 13 min 3 min 20 min Arsenic (As) 7 min 10 min 16 min 4 min 20 min

[0106] The heavy metal source monitoring module is used to obtain the data in the sewage volume parameter matrix and the heavy metal parameter matrix, and calculate the heavy metal load according to the data of the sewage volume and the heavy metal concentration;

[0107] Furthermore, the calculation formula for the heavy metal load is:

[0108]

[0109] where C unit(n, k) represents the heavy metal load of the nth sewage receiving pipeline during the kth data collection, C(m, k) represents the data of the mth heavy metal concentration during the kth collection, M represents the number of heavy metal types, Q(n, k) represents the sewage volume of the nth sewage receiving pipeline during the kth collection, and Q(k) represents the total sewage volume during the kth collection.

[0110] The heavy metal type monitoring module is used to obtain and process the data in the heavy metal parameter matrix to obtain the heavy metal types;

[0111] The heavy metal concentration monitoring module is used to construct a concentration mutation monitoring value based on the data in the heavy metal parameter collection matrix and the heavy metal parameter time series matrix, and further obtain the heavy metal concentration monitoring value;

[0112] Furthermore, the calculation formula for the heavy metal concentration monitoring value is:

[0113]

[0114] where RE represents the heavy metal concentration monitoring value, α 1 represents the weight coefficient of the heavy metal concentration change rate, N represents the number of sewage receiving pipelines, M represents the types of heavy metals, K represents the number of the most recent K data collections, θ 1 represents the difference between the data of the mth heavy metal concentration of the nth sewage receiving pipeline during the kth collection and the data of the (k - 1)th collection of the weight coefficient, θ 2 represents the weight coefficient of the time difference, represents the waiting time before the kth collection of the mth heavy metal concentration of the nth sewage receiving pipeline, represents the waiting time before the (k - 1)th collection of the mth heavy metal concentration of the nth sewage receiving pipeline, α 2 represents the weight coefficient of the heavy metal parameter time series matrix, represents the maximum value in the heavy metal parameter time series matrix, represents the minimum value in the heavy metal parameter time series matrix, α 3 represents the weight coefficient of the concentration mutation monitoring value ADI nmk of.

[0115] Furthermore, the calculation formula for the concentration mutation monitoring value is:

[0116]

[0117] where ADI nmk represents the concentration mutation monitoring value of the mth heavy metal concentration of the nth sewage receiving pipeline during the kth collection, Denotes the difference between the data of the m-th heavy metal concentration of the n-th sewage receiving pipe collected at the k-th time and the data collected at the (k - 1)-th time. Denotes the difference between the data of the m-th heavy metal concentration of the n-th sewage receiving pipe collected at the (k - 1)-th time and the data collected at the (k - 2)-th time, σ nm Denotes the variance of the m-th heavy metal concentration of the n-th sewage receiving pipe, and β denotes the weight coefficient of the heavy metal parameter acquisition matrix. Denotes the maximum value in the heavy metal parameter acquisition matrix. Denotes the waiting time before the m-th heavy metal concentration of the n-th sewage receiving pipe is collected at the k-th time.

[0118] The heavy metal spatio-temporal monitoring module constructs a heavy metal joint matrix according to the heavy metal parameter matrix, the heavy metal parameter time series matrix, and the heavy metal parameter acquisition matrix, and performs feature extraction through principal component analysis, matrix decomposition, and convolutional network to obtain spatial feature vectors and time feature vectors.

[0119] Table 4. Recommended parameters for some sewage treatment ponds

[0120]

[0121] The sewage treatment parameter recommendation module is used to construct a water quality monitoring vector according to the heavy metal load, the heavy metal type, the heavy metal concentration monitoring value, the spatial feature vector, and the time feature vector. The sewage treatment parameter recommendation model obtains and processes the water quality monitoring vector to obtain recommended parameters for recommending specific parameters for treating industrial sewage to the sewage treatment plant.

[0122] Furthermore, a historical water quality monitoring vector set and a corresponding historical recommended parameter set are constructed according to the historical data of the sewage treatment plant, a mapping relationship between the historical water quality monitoring vector set and the historical recommended parameter set is established according to the linear regression model, and the model parameters are solved by the least squares method to obtain the sewage treatment parameter recommendation model. Shown in Table 4 are some recommended parameters for the sewage treatment plant for heavy metals.

[0123] Through an industrial sewage real-time monitoring and treatment system provided by the present invention, accurate and sensitive monitoring and analysis of industrial sewage are realized, and recommended parameters are generated through the sewage treatment parameter recommendation model, improving the adaptability and flexibility of the sewage treatment plant in treating industrial sewage.

[0124] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirits of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for real-time monitoring and treatment of industrial wastewater, characterized in that: include: Obtain the locations of N sewage receiving pipes of the sewage treatment plant and set the heavy metal parameter collection points; Obtain and process the data of heavy metal parameter collection points to obtain a sewage volume parameter matrix, a heavy metal parameter matrix, a heavy metal parameter time series matrix and a heavy metal parameter collection matrix; Obtaining data in the sewage volume parameter matrix and the heavy metal parameter matrix, and calculating the heavy metal load according to the sewage volume and heavy metal concentration data; Obtaining and processing the data in the heavy metal parameter matrix to obtain the types of heavy metals; Constructing concentration mutation monitoring values ​​according to the data in the heavy metal parameter acquisition matrix and the heavy metal parameter time series matrix, and further obtaining heavy metal concentration monitoring values; A heavy metal joint matrix is ​​constructed according to the heavy metal parameter matrix, the heavy metal parameter time series matrix and the heavy metal parameter acquisition matrix, and feature extraction is performed through principal component analysis, matrix decomposition and convolutional network to obtain spatial feature vectors and temporal feature vectors; A water quality monitoring vector is constructed according to the heavy metal load, the type of heavy metal, the heavy metal concentration monitoring value, the spatial characteristic vector and the time characteristic vector; the sewage treatment parameter recommendation model obtains and processes the water quality monitoring vector to obtain recommended parameters for recommending parameters for treating industrial sewage to the sewage treatment plant.

2. A method for real-time monitoring and treatment of industrial wastewater according to claim 1, characterized in that: The heavy metal parameter matrix, the heavy metal parameter timing matrix and the heavy metal parameter acquisition matrix include: The heavy metal parameter matrix L is a matrix of N×M×K, where N represents the number of sewage receiving pipes of the sewage treatment plant, M represents the number of heavy metal types, K represents the number of the most recent K data collection times, and L nmk It represents the data of the concentration of the mth heavy metal in the nth sewage receiving pipe at the time of the kth collection; The heavy metal parameter timing matrix It is a matrix of N×M×K, where N represents the number of sewage receiving pipes of the sewage treatment plant, M represents the number of heavy metal types, and K represents the number of the most recent K data collection times. It represents the difference between the data collected at the kth time and the data collected at the k-1th time for the mth heavy metal concentration of the nth sewage receiving pipe; The heavy metal parameter acquisition matrix It is a matrix of N×M×K, where N represents the number of sewage receiving pipes of the sewage treatment plant, M represents the number of heavy metal types, and K represents the number of the most recent K data collection times. It indicates the waiting time before the kth collection of the concentration data of the mth heavy metal in the nth sewage receiving pipe.

3. A method for real-time monitoring and treatment of industrial wastewater according to claim 2, characterized in that: The specific steps of data collection include: The formula for the data collection trigger value is: Among them, S trigger represents the data collection trigger value, ω1 represents the weight coefficient of the heavy metal concentration change rate, ΔC represents the change in heavy metal concentration per unit time, Δt represents the unit time, ω2 represents the weight coefficient of the sewage flow rate change rate, ΔQ represents the sewage flow per unit time, and ω3 represents the weight coefficient of the risk factor R; The current timestamp and the timestamp of the last data collection are obtained and processed to obtain a first waiting collection time; when the first waiting collection time is less than the preset maximum waiting collection time, if the data collection trigger value is greater than the preset trigger threshold, data collection is performed immediately and the first waiting collection time is reset; when the first waiting collection time is equal to the preset maximum waiting collection time, data collection is performed immediately and the first waiting collection time is reset.

4. The method for real-time monitoring and treatment of industrial wastewater according to claim 1, characterized in that: The calculation formula for the heavy metal load is: Among them, C unit (n,k) represents the heavy metal load of the nth sewage receiving pipe at the kth data collection, C(m,k) represents the data of the mth heavy metal concentration at the kth collection, M represents the number of heavy metal types, Q(n,k) represents the sewage volume of the nth sewage receiving pipe at the kth collection, and Q(k) represents the total sewage volume at the kth collection.

5. The method for real-time monitoring and treatment of industrial wastewater according to claim 1, characterized in that: The calculation formula of the heavy metal concentration monitoring value is: Wherein, RE represents the monitoring value of the heavy metal concentration, α1 represents the weight coefficient of the change rate of the heavy metal concentration, N represents the number of sewage receiving pipes, M represents the type of heavy metal, K represents the number of the most recent K data collection times, and θ1 represents the difference between the data collected at the kth time and the data collected at the k-1th time for the mth heavy metal concentration of the nth sewage receiving pipe. The weight coefficient of θ2 represents the weight coefficient of time difference. It indicates the waiting time before the kth collection of the mth heavy metal concentration of the nth sewage receiving pipe. represents the waiting time before the k-1th collection of the mth heavy metal concentration of the nth sewage receiving pipe, α2 represents the weight coefficient of the heavy metal parameter time series matrix, represents the maximum value in the heavy metal parameter time series matrix, represents the minimum value in the heavy metal parameter time series matrix, and α3 represents the concentration mutation monitoring value ADI nmk The weight coefficient of .

6. A method for real-time monitoring and treatment of industrial wastewater according to claim 5, characterized in that: The calculation formula of the concentration mutation monitoring value is: Among them, ADI nmk represents the concentration mutation monitoring value of the m-th heavy metal concentration of the n-th sewage receiving pipe at the k-th collection, It represents the difference between the data collected at the kth time and the data collected at the k-1th time for the mth heavy metal concentration of the nth sewage receiving pipe. represents the difference between the data collected at the k-1th time and the data collected at the k-2th time for the mth heavy metal concentration of the nth sewage receiving pipe, σ nm represents the variance of the mth heavy metal concentration of the nth sewage receiving pipe, β represents the weight coefficient of the heavy metal parameter acquisition matrix, represents the maximum value in the heavy metal parameter acquisition matrix, It indicates the waiting time before the kth collection of the mth heavy metal concentration of the nth sewage receiving pipe.

7. A real-time monitoring and treatment system for industrial wastewater, characterized in that: include: A sewage parameter collection module, comprising a sewage volume collection unit and a water quality parameter collection unit, wherein the sewage volume collection unit is used to obtain a sewage volume parameter matrix, and the water quality parameter collection unit is used to obtain a heavy metal parameter matrix, a heavy metal parameter time series matrix, and a heavy metal parameter collection matrix; A sewage parameter transmission module, used for transmitting the sewage volume parameter matrix, the heavy metal parameter matrix, the heavy metal parameter timing matrix and the heavy metal parameter acquisition matrix to a processing center; A heavy metal source monitoring module, used to obtain data in the sewage volume parameter matrix and the heavy metal parameter matrix, and calculate the heavy metal load based on the sewage volume and heavy metal concentration data; A heavy metal type monitoring module is used to obtain and process the data in the heavy metal parameter matrix to obtain the heavy metal type; A heavy metal concentration monitoring module, used to construct a concentration mutation monitoring value according to the data in the heavy metal parameter acquisition matrix and the heavy metal parameter time series matrix, and further obtain a heavy metal concentration monitoring value; A heavy metal spatiotemporal monitoring module, which constructs a heavy metal joint matrix according to the heavy metal parameter matrix, the heavy metal parameter time series matrix and the heavy metal parameter acquisition matrix, and extracts features through principal component analysis, matrix decomposition and convolutional network to obtain spatial feature vectors and temporal feature vectors; The sewage treatment parameter recommendation module is used to construct a water quality monitoring vector based on the heavy metal load, the heavy metal type, the heavy metal concentration monitoring value, the spatial feature vector and the time feature vector; the sewage treatment parameter recommendation model obtains the water quality monitoring vector and processes it to obtain recommended parameters for recommending specific parameters for treating industrial sewage to the sewage treatment plant.

Images