Industrial sewage real-time monitoring and processing method and system

Abstract

The present application relates to the technical field of industrial wastewater monitoring, in particular to an industrial wastewater real-time monitoring and processing method and system. First, the data of heavy metal parameter collection points are acquired and processed to obtain a wastewater quantity parameter matrix, a heavy metal parameter matrix, a heavy metal parameter time sequence matrix and a heavy metal parameter collection matrix; then, the heavy metal load is calculated according to the data of wastewater quantity and heavy metal concentration; the data in the heavy metal parameter matrix are acquired and processed to obtain heavy metal types; the heavy metal concentration monitoring value is constructed according to the data in the heavy metal parameter collection matrix and the heavy metal parameter time sequence matrix; the heavy metal combined matrix is constructed according to the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter collection matrix to obtain a spatial feature vector and a time feature vector; the water quality monitoring vector is constructed according to the above data, and the water quality monitoring vector is acquired and processed by the wastewater treatment parameter recommendation model to obtain a recommended parameter, which is used to recommend the parameter for processing industrial wastewater to a wastewater treatment plant.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial wastewater monitoring, in particular to an industrial wastewater real-time monitoring and processing method and system. BACKGROUND

[0002] The wastewater treatment plant of an industrial park is used to receive wastewater from different enterprises during the production process, and the discharge of wastewater is intermittent. The water quantity and quality of the wastewater in the park may fluctuate greatly in a short period of time. For example, the concentration of wastewater discharged by some enterprises is high during the production peak period, while less or lower concentration wastewater is discharged during the trough period.

[0003] On the other hand, the existing wastewater treatment process may be difficult to adapt to the rapid changes in wastewater quality, and more sensitive monitoring of wastewater quality is needed to improve the adaptability and flexibility of the wastewater treatment plant, especially for wastewater containing heavy metals, and the strain capacity needs to be improved.

[0004] Therefore, an industrial wastewater real-time monitoring and processing method and system are proposed. SUMMARY

[0005] The purpose of the present application is to provide an industrial wastewater real-time monitoring and processing method and system, which monitors and analyzes heavy metals in wastewater in real time and sensitively to improve the adaptability and flexibility of the wastewater treatment plant for wastewater treatment. First, the wastewater quantity parameter matrix, heavy metal parameter matrix, heavy metal parameter time sequence matrix and heavy metal parameter acquisition matrix are obtained, and then the heavy metal load is calculated according to the data of wastewater quantity and heavy metal concentration; the data in the heavy metal parameter matrix is obtained and processed to obtain the heavy metal species; the heavy metal concentration monitoring value is constructed according to the data in the heavy metal parameter acquisition matrix and the heavy metal parameter time sequence matrix; the heavy metal joint matrix is constructed according to the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter acquisition matrix, and the spatial feature vector and the time feature vector are obtained; the water quality monitoring vector is constructed according to the above data, and the wastewater treatment parameter recommendation model obtains the water quality monitoring vector and processes it to obtain the recommended parameter, which is used to recommend the specific parameter for treating industrial wastewater to the wastewater treatment plant.

[0006] To achieve the above purpose, the present application provides the following technical scheme:

[0007] An industrial wastewater real-time monitoring and processing method, comprising:

[0008] Obtaining the positions of N wastewater receiving pipelines of the wastewater treatment plant and setting heavy metal parameter acquisition points; obtaining the data of the heavy metal parameter acquisition points and processing to obtain a wastewater quantity parameter matrix, a heavy metal parameter matrix, a heavy metal parameter time sequence matrix and a heavy metal parameter acquisition matrix;

[0009] Further, the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter collection matrix comprise:

[0010] The heavy metal parameter matrix L is an NXMK matrix, wherein N represents the number of sewage receiving pipelines of the sewage treatment plant, M represents the number of heavy metal types, K represents the number of K times of recent data collection, L nmk represents the data of the concentration of the mth heavy metal of the nth sewage receiving pipeline at the kth collection;

[0011] The heavy metal parameter time sequence matrix L is an NXMK matrix, wherein N represents the number of sewage receiving pipelines of the sewage treatment plant, M represents the number of heavy metal types, K represents the number of K times of recent data collection, L nmk represents the difference between the data of the concentration of the mth heavy metal of the nth sewage receiving pipeline at the kth collection and the data at the k-1th collection;

[0012] The heavy metal parameter collection matrix is an NXMK matrix, wherein N represents the number of sewage receiving pipelines of the sewage treatment plant, M represents the number of heavy metal types, K represents the number of K times of recent data collection, represents the waiting time of the concentration data of the mth heavy metal of the nth sewage receiving pipeline before the kth collection.

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

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

[0015]

[0016] Wherein, S trigger represents the data collection trigger value, ω1 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, ω2 represents the weight coefficient of the sewage flow change rate, ΔQ represents the sewage flow per unit time, ω3 represents the weight coefficient of the risk factor R;

[0017] 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 a preset maximum waiting collection time, if the data collection trigger value is greater than a preset trigger threshold, data collection is immediately performed, 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 immediately performed, and the first waiting collection time is reset.

[0018] The data in the sewage quantity parameter matrix and the heavy metal parameter matrix are obtained, and the heavy metal load is calculated according to the data of the sewage quantity and the heavy metal concentration.

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

[0020]

[0021] Wherein, C unit (n, k) represents the heavy metal load of the nth sewage receiving pipeline in the kth data collection, C(m, k) represents the data of the mth heavy metal concentration in the kth collection, M represents the number of heavy metal species, Q(n, k) represents the sewage quantity of the nth sewage receiving pipeline in the kth collection, and Q(k) represents the total sewage quantity in the kth collection.

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

[0023] According to the data in the heavy metal parameter collection matrix and the heavy metal parameter time sequence matrix, a concentration mutation monitoring value is constructed, and a heavy metal concentration monitoring value is further obtained.

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

[0025]

[0026] Wherein, RE represents the heavy metal concentration monitoring value, α1 represents the weight coefficient of the heavy metal concentration change rate, N represents the number of sewage receiving pipelines, M represents the number of heavy metal species, K represents the number of recent K data collections, θ1 represents the weight coefficient of the difference between the data collected in the kth collection and the data collected in the k-1th collection of the mth heavy metal concentration of the nth sewage receiving pipeline, θ2 represents the weight coefficient of the time difference, represents the waiting time of the mth heavy metal concentration of the nth sewage receiving pipeline before the kth collection, represents the waiting time of the mth heavy metal concentration of the nth sewage receiving pipeline before the k-1th collection, α2 represents the weight coefficient of the heavy metal parameter time sequence matrix, represents the maximum value in the heavy metal parameter time sequence matrix, represents the minimum value in the heavy metal parameter time sequence matrix, and α3 represents the weight coefficient of the concentration mutation monitoring value ADI nmk .

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

[0028]

[0029] Wherein, ADI nmk ​a concentration mutation monitoring value of the mth heavy metal concentration of the nth sewage receiving pipeline at the kth collection, a difference value of the mth heavy metal concentration of the nth sewage receiving pipeline between the kth collection and the k-1th collection, a difference value of the mth heavy metal concentration of the nth sewage receiving pipeline between the k-1th collection and the k-2th collection, nm a variance of the mth heavy metal concentration of the nth sewage receiving pipeline, and β represents a weight coefficient of the heavy metal parameter collection matrix, a maximum value in the heavy metal parameter collection matrix, a waiting time of the mth heavy metal concentration of the nth sewage receiving pipeline before the kth collection.

[0030] A heavy metal combined matrix is constructed according to the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter collection matrix, and feature extraction is performed through principal component analysis, matrix decomposition and convolution network to obtain a spatial feature vector and a time feature vector;

[0031] A water quality monitoring vector is constructed according to the heavy metal load, the heavy metal species, the heavy metal concentration monitoring value, the spatial feature vector and the time feature vector, and a sewage treatment parameter recommendation model obtains the water quality monitoring vector and processes to obtain a recommended parameter, which is used to recommend a specific treatment industrial sewage parameter to a sewage treatment plant.

[0032] The application further provides an industrial sewage real-time monitoring and processing system, comprising:

[0033] A sewage parameter collection module, comprising a sewage quantity collection unit and a water quality parameter collection unit, wherein the sewage quantity collection unit is used to obtain a sewage quantity parameter matrix, and the water quality parameter collection unit is used to obtain a heavy metal parameter matrix, a heavy metal parameter time sequence matrix and a heavy metal parameter collection matrix;

[0034] A sewage parameter transmission module, used to transmit the sewage quantity parameter matrix, the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter collection matrix to a processing center;

[0035] A heavy metal source monitoring module, used to obtain data in the sewage quantity parameter matrix and the heavy metal parameter matrix, and calculate a heavy metal load according to the data of sewage quantity and heavy metal concentration;

[0036] A heavy metal species monitoring module, used to obtain data in the heavy metal parameter matrix and process to obtain a heavy metal species;

[0037] The heavy metal concentration monitoring module is configured to construct a concentration mutation monitoring value according to data in the heavy metal parameter acquisition matrix and the heavy metal parameter time sequence matrix, and further obtain a heavy metal concentration monitoring value.

[0038] The heavy metal space-time monitoring module is configured to construct 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, and perform feature extraction through principal component analysis, matrix decomposition and a convolution network to obtain a spatial feature vector and a time feature vector.

[0039] The sewage treatment parameter recommendation module is configured 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, and obtain the water quality monitoring vector by a sewage treatment parameter recommendation model to process and obtain a recommended parameter, which is used to recommend a specific treatment industrial sewage parameter to a sewage treatment plant.

[0040] Compared with the prior art, the present application has the following beneficial effects:

[0041] 1. By introducing the sewage quantity parameter matrix, the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter acquisition matrix, and a dynamic triggering mechanism for data acquisition, precise, efficient and sensitive industrial sewage monitoring is achieved, the flexibility and accuracy of data acquisition are improved, data acquisition omission is avoided, the integrity of monitoring is ensured, and a solid data foundation is provided for real-time monitoring and intelligent decision-making.

[0042] 2. Through data analysis of the heavy metal load, the heavy metal type, the heavy metal concentration monitoring value, the heavy metal spatial feature vector and the heavy metal time feature vector in the time, space and concentration dimensions, 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 achieved, and the sewage quality analysis capability is improved.

[0043] 3. The water quality monitoring vector integrates the heavy metal load, type, concentration monitoring value, spatial and time feature vectors, comprehensively reflects the pollution characteristics of the sewage, and through construction of the water quality monitoring vector and the sewage treatment parameter recommendation model, and combined with 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, the recommendation model can dynamically update the treatment parameters, and the adaptability and flexibility of the treatment of the sewage are improved. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 A flowchart of an industrial sewage real-time monitoring and processing method provided by the present application embodiment;

[0045] Figure 2 A structural schematic diagram of a data processing process provided by an embodiment of the present application is shown in FIG. 1.

[0046] Figure 3 A structural schematic diagram of an industrial wastewater real-time monitoring and processing system provided by an embodiment of the present application is shown in FIG. 2. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0048] Embodiment one

[0049] In order to improve the adaptability and flexibility of wastewater treatment, and to cope with industrial wastewater of different times, different wastewater quantities and different heavy metal concentrations, a wastewater treatment plant in an industrial park introduces an industrial wastewater real-time monitoring and processing method provided by the present application, and the method flow is as shown in FIG. 3. Figure 1 The specific implementation manners are as follows:

[0050] First, the positions of N wastewater receiving pipelines of the wastewater treatment plant are acquired, and heavy metal parameter collection points are set; data of the heavy metal parameter collection points are acquired and processed to obtain a wastewater quantity parameter matrix, a heavy metal parameter matrix, a heavy metal parameter time sequence matrix and a heavy metal parameter collection matrix, as shown in FIG. 4. Figure 2 FIG. 4 shows the data processing and transmission process in the entire method;

[0051] Further, the wastewater quantity parameter matrix records the wastewater quantity of the nth wastewater receiving pipeline at the kth data collection time, and Table 1 shows the wastewater quantity data of some wastewater receiving pipelines at different collection time points.

[0052] Table 1, wastewater quantity data

[0053]

[0054] Further, the heavy metal parameter matrix, the heavy metal parameter time sequence 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 wastewater receiving pipelines of the wastewater treatment plant, M represents the number of heavy metal types, and K represents the number of recent K data collection times, L nmk represents the data of the concentration of the mth heavy metal of the nth wastewater receiving pipeline at the kth collection time;

[0055] The heavy metal parameter time sequence matrix is a matrix of NXMK, wherein N represents the number of sewage receiving pipelines of the sewage treatment plant, M represents the number of heavy metal species, and K represents the number of recent K data collection times, represents the difference value of the mth heavy metal concentration of the nth sewage receiving pipeline in the kth collected data and the k-1th collected data;

[0056] the heavy metal parameter collection matrix is a matrix of NXMK, wherein N represents the number of sewage receiving pipelines of the sewage treatment plant, M represents the number of heavy metal species, and K represents the number of recent K data collection times, represents the waiting time of the concentration data of the mth heavy metal of the nth sewage receiving pipeline before the kth collection.

[0057] By introducing the sewage quantity parameter matrix, the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter collection matrix, precise, efficient and sensitive industrial sewage monitoring is realized, the flexibility and precision 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] Further, the specific steps of data collection include:

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

[0060]

[0061] wherein, S trigger represents the data collection trigger value, ω1 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, ω2 represents the weight coefficient of the sewage flow rate, ΔQ represents the sewage flow per unit time, and ω3 represents the weight coefficient of the risk factor R;

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

[0063]

[0064] wherein, λ>0 represents the intercept, t represents the first waiting collection time, and t0 represents the preset maximum waiting collection time.

[0065] 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 a preset maximum waiting collection time, if the data collection trigger value is greater than a preset trigger threshold, data collection is immediately performed 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 immediately performed and the first waiting collection time is reset.

[0066] The data collection trigger value combines the heavy metal concentration change rate, the sewage flow rate change rate and the risk factor, and can more accurately reflect the trend and urgency of pollution change through dynamic adjustment of the weighting coefficient; through the preset trigger threshold, the sensitivity and efficiency of collection are improved, and the integrity and continuity of data are also ensured; the dual control of the trigger threshold and the maximum collection interval not only avoids too frequent collection and waste of resources, but also prevents missing data caused by too long sampling interval.

[0067] Data in the sewage quantity parameter matrix and the heavy metal parameter matrix are obtained, and the heavy metal load is calculated according to the data of sewage quantity and heavy metal concentration;

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

[0069]

[0070] wherein, C unit (n, k) represents the heavy metal load of the nth sewage receiving pipeline in the kth data collection, C(m, k) represents the data of the mth heavy metal concentration in the kth collection, M represents the number of heavy metal species, Q(n, k) represents the sewage quantity of the nth sewage receiving pipeline in the kth collection, and Q(k) represents the total sewage quantity in the kth collection.

[0071] By calculating the sewage quantity and the heavy metal concentration, the heavy metal load of the sewage receiving pipeline at different collection times is constructed, which can accurately quantify the heavy metal pollution burden, identify the seriously polluted pipeline and its contribution to the overall discharge, and realize the accurate positioning of the key pollution source.

[0072] Data in the heavy metal parameter matrix are obtained and processed to obtain the heavy metal species;

[0073] Further, according to the heavy metal data collected by the equipment, the heavy metal species can be determined;

[0074] The concentration mutation monitoring value is constructed according to the data in the heavy metal parameter collection matrix and the heavy metal parameter time sequence matrix, and the heavy metal concentration monitoring value is further obtained;

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

[0076]

[0077] wherein RE represents the heavy metal concentration monitoring value, a1 represents a weight coefficient of the heavy metal concentration change rate, N represents the number of sewage receiving pipelines, M represents the type of heavy metal, K represents the number of data collection times in the last K times, θ1 represents a weight coefficient of a difference between the data collected in the kth time and the data collected in the k-1th time of the mth heavy metal concentration of the nth sewage receiving pipeline, θ2 represents a weight coefficient of a time difference, represents a waiting time before the kth collection of the mth heavy metal concentration of the nth sewage receiving pipeline, represents a waiting time before the k-1th collection of the mth heavy metal concentration of the nth sewage receiving pipeline, a2 represents a weight coefficient of the heavy metal parameter time sequence matrix, represents a maximum value in the heavy metal parameter time sequence matrix, represents a minimum value in the heavy metal parameter time sequence matrix, and a3 represents a weight coefficient of the concentration mutation monitoring value ADI. nmk

[0078] By integrating multi-dimensional data of heavy metal concentration change, a heavy metal concentration monitoring value is constructed, which not only provides accurate and efficient dynamic monitoring capability, improves the sensitivity of monitoring data, but also improves the ability of pollution tracing, risk early warning and resource optimization, and provides strong data support for flexible operation of the sewage treatment plant.

[0079] Further, the calculation formula of the concentration mutation monitoring value is:

[0080]

[0081] wherein ADI nmk represents the concentration mutation monitoring value of the mth heavy metal concentration of the nth sewage receiving pipeline at the kth collection, represents a difference between the data collected in the kth time and the data collected in the k-1th time of the mth heavy metal concentration of the nth sewage receiving pipeline, represents a difference between the data collected in the k-1th time and the data collected in the k-2th time of the mth heavy metal concentration of the nth sewage receiving pipeline, and σ nm represents the variance of the mth heavy metal concentration of the nth pipeline, β represents a weight coefficient of the heavy metal parameter collection matrix, represents a maximum value in the heavy metal parameter collection matrix, represents a waiting time before the kth collection of the mth heavy metal concentration of the nth sewage receiving pipeline.

[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 improved, and the mutation phenomenon of the heavy metal concentration in the sewage is comprehensively and accurately monitored, thereby providing real-time decision basis for the sewage treatment plant.

[0083] According to the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter collection matrix, a heavy metal joint matrix is constructed, and feature extraction is performed through principal component analysis, matrix decomposition and convolution network to obtain a spatial feature vector and a time feature vector.

[0084] Further, according to the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter collection matrix, a heavy metal joint matrix is constructed The heavy metal joint matrix is processed by using a principal component analysis algorithm to obtain a principal component matrix, the principal component matrix is decomposed to obtain latent features W and H, wherein W represents a spatial feature and H represents a time feature, and the latent features are processed by using a space-time convolution network to obtain a spatial feature vector and a time feature vector

[0085] 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, a water quality monitoring vector is constructed, and a sewage treatment parameter recommendation model obtains the water quality monitoring vector and processes to obtain a recommended parameter, which is used to recommend specific parameters for treating industrial sewage to a sewage treatment plant.

[0086] Further, a historical water quality monitoring vector set and a corresponding historical recommended parameter set are constructed according to 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 a linear regression model, and a model parameter is solved by using a least square method to obtain the sewage treatment parameter recommendation model.

[0087] Table 2, recommended parameters of part of the sewage treatment tank

[0088]

[0089] Further, after the sewage treatment parameter recommendation model is constructed, the sewage treatment parameter recommendation model obtains the water quality monitoring vector and processes to obtain a recommended parameter, including recommending appropriate treatment technologies such as physical treatment, chemical precipitation, biological treatment and membrane separation technology, determining the best operation load of each equipment, and recommending the types of chemical agents to be added such as flocculants, reducing agents, oxidizing agents or chelating agents. As shown in Table 2, the recommended parameters of part of the sewage treatment tank are shown.

[0090] The historical data and real-time monitoring data are combined, the sewage treatment parameters of the sewage treatment plant are optimized through a linear regression model and a least square method, a treatment scheme can be customized for the sewage treatment plant according to actual conditions, and the trial and error cost is reduced; the water quality monitoring vector includes heavy metal load, type, monitoring value and space-time characteristics, and the spatial and temporal variation factors are comprehensively considered, so that the monitoring is more comprehensive, the recommended result of the sewage treatment parameter is more accurate, and the flexibility and adaptability of the sewage treatment plant in the sewage treatment process are improved.

[0091] Embodiment two

[0092] The application further provides an industrial sewage real-time monitoring and processing system, a system structure of which is shown in Figure 3 The specific implementation is as follows:

[0093] The sewage parameter acquisition module comprises a sewage quantity acquisition unit and a water quality parameter acquisition unit, wherein the sewage quantity acquisition unit is used to acquire a sewage quantity parameter matrix, and the water quality parameter acquisition unit is used to acquire a heavy metal parameter matrix, a heavy metal parameter time sequence matrix and a heavy metal parameter acquisition matrix;

[0094] The sewage parameter transmission module is used to transmit the sewage quantity parameter matrix, the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter acquisition matrix to a processing center;

[0095] Further, the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter acquisition matrix comprise:

[0096] The heavy metal parameter matrix L is an N*M*K matrix, wherein N represents the number of sewage receiving pipelines of the sewage treatment plant, M represents the number of heavy metal types, and K represents the number of recent K times of data acquisition, L nmk represents the data of the concentration of the mth heavy metal of the nth sewage receiving pipeline at the kth acquisition;

[0097] The heavy metal parameter time sequence matrix is an N*M*K matrix, wherein N represents the number of sewage receiving pipelines of the sewage treatment plant, M represents the number of heavy metal types, and K represents the number of recent K times of data acquisition, represents the difference between the data of the concentration of the mth heavy metal of the nth sewage receiving pipeline at the kth acquisition and the data of the concentration of the mth heavy metal of the nth sewage receiving pipeline at the k-1th acquisition;

[0098] The heavy metal parameter acquisition matrix is an N*M*K matrix, wherein N represents the number of sewage receiving pipelines of the sewage treatment plant, M represents the number of heavy metal types, and K represents the number of recent K times of data acquisition, The concentration data of the mth heavy metal of the nth sewage receiving pipeline waiting for the kth collection.

[0099] Further, the specific steps of data collection include:

[0100] The formula of the data collection trigger value is:

[0101]

[0102] Wherein, S trigger The data collection trigger value is represented, ω1 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, 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.

[0103] The current timestamp and the timestamp of the last data collection are obtained and processed 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, data collection is immediately performed, 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 immediately performed, and the first waiting collection time is reset. As shown in Table 3, the first waiting collection time of part of the sewage pipeline and its heavy metal concentration collection at the last collection time point is shown.

[0104] Table 3, part of the first waiting collection time

[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 quantity parameter matrix and the heavy metal parameter matrix, and calculate the heavy metal load according to the data of sewage quantity and heavy metal concentration.

[0107] Further, the formula for calculating the heavy metal load is:

[0108]

[0109] Wherein, C unit (n, k) represents the heavy metal load of the nth sewage receiving pipeline 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 species, Q(n, k) represents the sewage quantity of the nth sewage receiving pipeline at the kth collection, and Q(k) represents the total sewage quantity at the kth collection.

[0110] The heavy metal species monitoring module is used to obtain the data in the heavy metal parameter matrix and process it to obtain the heavy metal species.

[0111] a heavy metal concentration monitoring module configured to construct a concentration mutation monitoring value according to data in the heavy metal parameter acquisition matrix and the heavy metal parameter time sequence matrix, and further obtain a heavy metal concentration monitoring value;

[0112] Further, the calculation formula of the heavy metal concentration monitoring value is:

[0113]

[0114] wherein RE represents the heavy metal concentration monitoring value, a1 represents a weight coefficient of a heavy metal concentration change rate, N represents the number of sewage receiving pipelines, M represents the type of heavy metal, K represents the number of data acquisition times, θ1 represents a weight coefficient of a difference value of data acquired in the kth time and data acquired in the k-1th time of the mth heavy metal concentration of the nth sewage receiving pipeline, θ2 represents a weight coefficient of a time difference, represents a waiting time before the kth acquisition of the mth heavy metal concentration of the nth sewage receiving pipeline, represents a waiting time before the k-1th acquisition of the mth heavy metal concentration of the nth sewage receiving pipeline, a2 represents a weight coefficient of the heavy metal parameter time sequence matrix, represents a maximum value in the heavy metal parameter time sequence matrix, represents a minimum value in the heavy metal parameter time sequence matrix, and a3 represents a weight coefficient of the concentration mutation monitoring value ADI. nmk

[0115] Further, the calculation formula of the concentration mutation monitoring value is:

[0116]

[0117] wherein ADI nmk represents the concentration mutation monitoring value of the mth heavy metal concentration of the nth sewage receiving pipeline at the kth acquisition, represents a difference value of data acquired in the kth time and data acquired in the k-1th time of the mth heavy metal concentration of the nth sewage receiving pipeline, represents a difference value of data acquired in the k-1th time and data acquired in the k-2th time of the mth heavy metal concentration of the nth sewage receiving pipeline, σ nm represents a variance of the mth heavy metal concentration of the nth sewage receiving pipeline, β represents a weight coefficient of the heavy metal parameter acquisition matrix, represents a maximum value in the heavy metal parameter acquisition matrix, represents a waiting time before the kth acquisition of the mth heavy metal concentration of the nth sewage receiving pipeline.

[0118] ​​The heavy metal space-time monitoring module constructs a heavy metal joint matrix according to the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter collection matrix, and extracts features through principal component analysis, matrix decomposition and convolution network to obtain a spatial feature vector and a time feature vector.

[0119] Table 4, recommended parameters of part of sewage treatment tanks

[0120]

[0121] The sewage treatment parameter recommendation module is configured 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, and the sewage treatment parameter recommendation model obtains the water quality monitoring vector and processes it to obtain recommended parameters, which are used to recommend parameters for treating industrial sewage to a sewage treatment plant.

[0122] Further, a historical water quality monitoring vector set and a corresponding historical recommended parameter set are constructed according to 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 a linear regression model, and a model parameter is solved by using a least square method to obtain the sewage treatment parameter recommendation model. As shown in Table 4, part of recommended parameters of the sewage treatment plant for heavy metals are shown.

[0123] The industrial sewage real-time monitoring and processing system provided by the application realizes accurate and sensitive monitoring and analysis of industrial sewage, and generates recommended parameters through a sewage treatment parameter recommendation model, thereby improving the adaptability and flexibility of the sewage treatment plant in treating industrial sewage.

[0124] Although the embodiments of the application have been shown and described, it is understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the application, and the scope of the application is defined by the appended claims and their equivalents.

Claims

1. A real-time monitoring and treatment method for industrial wastewater, characterized in that, The method comprises the following steps: Acquiring sewage treatment plant The position of the sewage receiving pipeline is acquired, and a heavy metal parameter acquisition point is set. The data of the heavy metal parameter acquisition point is acquired and processed 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. The heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter acquisition matrix comprise: The heavy metal parameter matrix for The matrix of Indicates the number of sewage receiving pipes of the sewage treatment plant, Indicates the number of heavy metal types, Indicates recent Number of data collection times, Indicates the The first sewage receiving pipe The concentration of heavy metals in The data collected at the time of the first collection; The heavy metal parameter time series matrix for The matrix of Indicates the number of sewage receiving pipes of the sewage treatment plant, Indicates the number of heavy metal types, Indicates recent Number of data collection times, Indicates the The first sewage receiving pipe The heavy metal concentration in The data collected and The difference between the data collected said heavy metal parameter acquisition matrix is a matrix, wherein represents the number of sewage receiving pipes of the sewage treatment plant, represents the number of heavy metal species, represents the number of the latest data acquisition times, represents the number of the root sewage receiving pipe, the concentration data of the heavy metal of the root sewage receiving pipe before the time of the latest data acquisition The data in the heavy metal parameter matrix is acquired and processed to obtain a heavy metal species. ； wherein, represents the first wastewater receiving pipe at the first data collection of heavy metal load, represents the first data collection of heavy metal concentration of the first data collection, represents the number of heavy metal species, represents the first wastewater receiving pipe at the first data collection of wastewater volume, represents the first data collection of total wastewater volume; A concentration mutation monitoring value is constructed according to the data in the heavy metal parameter acquisition matrix and the heavy metal parameter time sequence matrix, and a heavy metal concentration monitoring value is further obtained. A heavy metal joint matrix is constructed according to the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter acquisition matrix, and feature extraction is performed through principal component analysis, matrix decomposition and convolution network to obtain a spatial feature vector and a time feature vector. A water quality monitoring vector is constructed according to the heavy metal load, the heavy metal species, the heavy metal concentration monitoring value, the spatial feature vector and the time feature vector; and a sewage treatment parameter recommendation model acquires the water quality monitoring vector and processes it to obtain a recommended parameter, which is used to recommend a parameter for treating industrial sewage to a sewage treatment plant. The specific steps of data acquisition comprise:

2. The industrial wastewater real-time monitoring and processing method according to claim 1, characterized in that, The formula of the data acquisition trigger value is: The current timestamp and the timestamp of the last data acquisition are acquired and processed to obtain a first waiting acquisition time; when the first waiting acquisition time is less than a preset maximum waiting acquisition time, if the data acquisition trigger value is greater than a preset trigger threshold, data acquisition is immediately performed, and the first waiting acquisition time is reset; when the first waiting acquisition time is equal to the preset maximum waiting acquisition time, data acquisition is immediately performed, and the first waiting acquisition time is reset. ； wherein, represents the data collection trigger value, represents a weight coefficient of the heavy metal concentration change rate, represents a change amount of the heavy metal concentration per unit time, represents a unit time, represents a weight coefficient of the sewage flow rate change rate, represents a sewage flow rate per unit time, represents a weight coefficient of the risk factor . The formula of the heavy metal concentration monitoring value is:

3. The industrial wastewater real-time monitoring and processing method according to claim 1, characterized in that, The formula of the concentration mutation monitoring value is: ； in, Indicates the heavy metal concentration monitoring value, The weight coefficient representing the rate of change of heavy metal concentration, Indicates the number of sewage receiving pipes, Indicates the type of heavy metal. Indicates recent Number of data collection times, Indicates the The first sewage receiving pipe The heavy metal concentration in The data collected and The difference between the collected data The weight coefficient of represents the weight coefficient of the time difference, Indicates the The first sewage receiving pipe The heavy metal concentration in The waiting time before the next collection, Indicates the The first sewage receiving pipe The heavy metal concentration in The waiting time before the next collection, 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, Indicates the concentration mutation monitoring value The weight coefficient of .

4. The industrial wastewater real-time monitoring and processing method according to claim 1, characterized in that, The method comprises the following steps: ； in, Indicates the The first sewage receiving pipe The heavy metal concentration in The concentration mutation monitoring value at the time of the first collection, Indicates the The first sewage receiving pipe The heavy metal concentration in The data collected and The difference between the collected data, Indicates the The first sewage receiving pipe The heavy metal concentration in The data collected and The difference between the collected data, Indicates the The first sewage receiving pipe The variance of heavy metal concentrations, represents the weight coefficient of the heavy metal parameter acquisition matrix, represents the maximum value in the heavy metal parameter acquisition matrix, Indicates the The first sewage receiving pipe The heavy metal concentration in The waiting time before the next collection.

5. An industrial wastewater real-time monitoring and treatment system, characterized in that, The sewage parameter acquisition module comprises a sewage quantity acquisition unit and a water quality parameter acquisition unit, wherein the sewage quantity acquisition unit is used to acquire a sewage quantity parameter matrix, and the water quality parameter acquisition unit is used to acquire a heavy metal parameter matrix, a heavy metal parameter time sequence matrix and a heavy metal parameter acquisition matrix. The heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter acquisition matrix comprise: The sewage parameter transmission module is used to transmit the sewage quantity parameter matrix, the heavy metal parameter matrix, the heavy metal parameter time sequence matrix and the heavy metal parameter acquisition matrix to a processing center. The heavy metal parameter matrix for The matrix of Indicates the number of sewage receiving pipes of the sewage treatment plant, Indicates the number of heavy metal types, Indicates recent Number of data collection times, Indicates the The first sewage receiving pipe The concentration of heavy metals in The data collected at the time of the first collection; said heavy metal parameter time series matrix is a matrix, wherein denotes the number of sewage intake pipes of the sewage treatment plant, denotes the number of heavy metal species, denotes the most recent data collection number, denotes the root sewage intake pipe, denotes the difference between the data collected in the th collection and the data collected in the th collection. said heavy metal parameter acquisition matrix is a matrix, wherein denotes the number of sewage receiving pipes of a sewage treatment plant, denotes the number of heavy metal species, denotes the number of the most recent data acquisition times, denotes the number of the sewage receiving pipe, denotes the concentration data of the heavy metal of the sewage receiving pipe before the time of the last data acquisition The heavy metal source monitoring module is used to acquire the data in the sewage quantity parameter matrix and the heavy metal parameter matrix, and calculate a heavy metal load according to the data of the sewage quantity and the heavy metal concentration; the formula of the heavy metal load is: The heavy metal species monitoring module is used to acquire the data in the heavy metal parameter matrix and process it to obtain a heavy metal species. ； wherein, represents the sewage receiving pipe at the heavy metal load at the represents the data of the concentration of the heavy metal at the represents the number of heavy metal species, represents the sewage quantity at the sewage receiving pipe at the represents the total sewage quantity at the sewage receiving pipe at the The heavy metal source monitoring module is used to acquire the data in the sewage quantity parameter matrix and the heavy metal parameter matrix, and calculate a heavy metal load according to the data of the sewage quantity and the heavy metal concentration; the formula of the heavy metal load is: a heavy metal concentration monitoring module configured to construct a concentration mutation monitoring value according to data in the heavy metal parameter acquisition matrix and the heavy metal parameter time sequence matrix, and further obtain a heavy metal concentration monitoring value; a heavy metal space-time monitoring module configured to construct 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, and perform feature extraction through principal component analysis, matrix decomposition and convolution network to obtain a spatial feature vector and a time feature vector; a sewage treatment parameter recommendation module configured to construct a water quality monitoring vector according to the heavy metal load, the heavy metal species, the heavy metal concentration monitoring value, the spatial feature vector and the time feature vector; and a sewage treatment parameter recommendation model configured to obtain the water quality monitoring vector and process the same to obtain a recommended parameter, which is used to recommend a specific treatment industrial sewage parameter to a sewage treatment plant.

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