Whole sewage treatment process supervision method and system based on Internet of Things

Abstract

The invention relates to the technical field of water treatment, and discloses a sewage treatment whole-process supervision method and system based on the Internet of Things, and the method comprises the steps: collecting actual dissolved oxygen data of a biochemical reaction tank in a sewage purification process; determining a dissolved oxygen abnormal point according to the acquired actual dissolved oxygen data, identifying the dissolved oxygen state in the biochemical reaction tank according to the position of the dissolved oxygen abnormal point and the dissolved oxygen abnormal deviation data, and in the dissolved oxygen abnormal state, taking the dissolved oxygen abnormal center point as a reference to identify the dissolved oxygen abnormal point; a circular screening area is constructed with a dissolved oxygen abnormal point gradually away from the dissolved oxygen abnormal center point as the circle center and the effective coverage radius of the aeration discs as the radius in sequence, the monitoring aeration discs in the multiple layers of circular screening areas are subjected to superposition processing, and the abnormality checking sequence of the monitoring aeration discs is constructed according to the superposition data of the monitoring aeration discs; according to the invention, the dissolved oxygen is effectively monitored, and the aeration disc causing the abnormal state of the dissolved oxygen is patrolled and positioned.

Description

Technical Field

[0001] The present invention relates to the technical field of water treatment, and particularly to a method and system for the whole-process supervision of sewage treatment based on the Internet of Things. Background Art

[0002] Sewage treatment refers to the treatment of wastewater containing various harmful substances to remove or reduce the pollutants therein so that it meets the specified discharge standards. It can effectively reduce the pollution of sewage to water bodies and protect the sustainable utilization of water resources. With the acceleration of the urbanization process, sewage treatment has become an important link in urban environmental protection.

[0003] The biochemical reaction tank is the core unit of the sewage treatment plant, and mainly realizes the degradation of pollutants through the metabolic activities of microorganisms. The pre-anoxic zone, anaerobic zone, anoxic zone and aerobic zone are arranged inside it to provide a suitable environment for different microorganisms.

[0004] And the dissolved oxygen data is the "regulator" of the biochemical treatment efficiency of the biochemical reaction tank. The dissolved oxygen concentration directly affects the microbial activity and treatment effect. Therefore, it is necessary to effectively monitor the dissolved oxygen in the biochemical reaction tank, and be able to timely check the abnormalities of the aeration discs when the dissolved oxygen concentration does not meet the requirements, so as to improve the sewage treatment stability of the biochemical reaction tank.

[0005] Based on this, the present application provides a method and system for the whole-process supervision of sewage treatment based on the Internet of Things. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for the whole-process supervision of sewage treatment based on the Internet of Things. By hierarchically dividing the biochemical reaction tank, DO sensors are arranged on different levels of the biochemical reaction tank, and the dissolved oxygen data in the biochemical reaction tank is comprehensively identified according to the DO sensors, so as to complete the positioning and identification of the dissolved oxygen abnormal area in the biochemical reaction tank, and process the position of the dissolved oxygen abnormal area and the dissolved oxygen abnormal deviation data, so as to realize the determination of the dissolved oxygen state of the biochemical reaction tank from two dimensions of spatial position and data deviation, and complete the positioning and inspection of the abnormal aeration disc according to the dissolved oxygen abnormal state of the biochemical reaction tank.

[0007] The purpose of the present invention can be achieved by the following technical solutions: In the first aspect, the present invention provides a method for the whole-process supervision of sewage treatment based on the Internet of Things, including the following steps: Collect the actual dissolved oxygen data of the biochemical reaction tank during the sewage purification process; Determine the dissolved oxygen abnormal points according to the collected actual dissolved oxygen data, and identify the dissolved oxygen state in the biochemical reaction tank according to the position of the dissolved oxygen abnormal points and the dissolved oxygen abnormal deviation data; The dissolved oxygen state in the biochemical reaction tank includes abnormal dissolved oxygen state and normal dissolved oxygen state; In the abnormal dissolved oxygen state, taking the abnormal dissolved oxygen center point as the reference, circular screening areas are constructed in turn with the abnormal dissolved oxygen points that are gradually farther away from the abnormal dissolved oxygen center point as the centers and the effective coverage radius of the aeration disc as the radius; The monitoring aeration discs within the multi-layer circular screening areas are subjected to coincidence processing, and the abnormal troubleshooting sequence of the monitoring aeration discs is constructed based on the coincidence data of the monitoring aeration discs.

[0008] In a second aspect, the present invention provides an Internet of Things-based whole-process sewage treatment supervision system, including: A data acquisition module for acquiring the actual dissolved oxygen data of the biochemical reaction tank during the sewage purification process; A behavior recognition module for determining the abnormal dissolved oxygen points according to the acquired actual dissolved oxygen data, and recognizing the dissolved oxygen state in the biochemical reaction tank based on the positions of the abnormal dissolved oxygen points and the abnormal deviation data of the dissolved oxygen; The dissolved oxygen state in the biochemical reaction tank includes abnormal dissolved oxygen state and normal dissolved oxygen state; An abnormal construction module for constructing circular screening areas in the abnormal dissolved oxygen state, taking the abnormal dissolved oxygen center point as the reference, and using the abnormal dissolved oxygen points that are gradually farther away from the abnormal dissolved oxygen center point as the centers and the effective coverage radius of the aeration disc as the radius; A positioning and inspection module for performing coincidence processing on the monitoring aeration discs within the multi-layer circular screening areas, and constructing the abnormal troubleshooting sequence of the monitoring aeration discs based on the coincidence data of the monitoring aeration discs.

[0009] Advantages of the present invention: By hierarchically dividing the biochemical reaction tank, DO sensors are arranged on different levels of the biochemical reaction tank, and the dissolved oxygen data in the biochemical reaction tank are comprehensively recognized according to the DO sensors, so as to complete the positioning and recognition of the abnormal dissolved oxygen area in the biochemical reaction tank, and process the position of the abnormal dissolved oxygen area and the abnormal deviation data of the dissolved oxygen, so as to realize the determination of the dissolved oxygen state of the biochemical reaction tank from two dimensions of spatial position and data deviation; When the present invention determines that the dissolved oxygen state in the biochemical reaction tank is abnormal, it determines the center point of the dissolved oxygen abnormality, and takes the center point of the dissolved oxygen abnormality as a reference. Successively, it constructs circular screening areas with the dissolved oxygen abnormality points that are gradually farther away from the center point of the dissolved oxygen abnormality as the centers and the effective coverage radius of the aeration disc as the radii. It performs coincidence processing on the monitoring aeration discs within the multi-layer circular screening areas to obtain the first-level monitoring sequence group of the monitoring aeration discs. Then, taking the first-level monitoring sequence group as a reference, it processes each monitoring aeration disc in the first-level monitoring sequence group that has adjacent monitoring aeration discs to obtain the monitored values of the monitored adjacent aeration discs, and obtains the second-level monitoring sequence group of the monitoring aeration discs according to the coincidence times of the monitoring aeration discs and the monitored values of the monitored adjacent aeration discs, and constructs the abnormal investigation sequence of the monitoring aeration discs. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The present invention will be further described below with reference to the accompanying drawings.

[0011] Figure 1 is a flowchart of a method for the whole-process supervision of sewage treatment based on the Internet of Things according to an embodiment of the present invention; Figure 2 is a block diagram of a system for the whole-process supervision of sewage treatment based on the Internet of Things according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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 of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0013] Please refer to Figure 1 As shown, the present invention is a method for the whole-process supervision of sewage treatment based on the Internet of Things, including the following steps: Collect the actual dissolved oxygen data in the biochemical reaction tank during the sewage purification process; Determine the dissolved oxygen abnormality points according to the collected actual dissolved oxygen data, and identify the dissolved oxygen state in the biochemical reaction tank according to the positions of the dissolved oxygen abnormality points and the dissolved oxygen abnormality deviation data; The dissolved oxygen state in the biochemical reaction tank includes a dissolved oxygen abnormal state and a dissolved oxygen normal state; In the dissolved oxygen abnormal state, taking the center point of the dissolved oxygen abnormality as a reference, successively construct circular screening areas with the dissolved oxygen abnormality points that are gradually farther away from the center point of the dissolved oxygen abnormality as the centers and the effective coverage radius of the aeration disc as the radii; Perform coincidence processing on the monitoring aeration discs within the multi-layer circular screening areas, and construct the abnormal investigation sequence of the monitoring aeration discs based on the coincidence data of the monitoring aeration discs.

[0014] The process of obtaining the actual dissolved oxygen data is as follows: DO sensors are arranged at different spatial positions vertically in the biochemical reaction pool; At least 2 DO sensors are arranged at the top layer position of the biochemical reaction pool; At least 2 DO sensors are arranged at the middle layer position of the biochemical reaction pool; At least 2 DO sensors are arranged at the bottom layer position of the biochemical reaction pool; Moreover, the projections of the DO sensors arranged at the bottom, middle and top layer positions of the biochemical reaction pool are staggered vertically; Based on the Grubbs criterion, the processing of the DO sensors in the same layer of each biochemical reaction pool at the same moment: That is, through the formula the standardized distance G of each single data point from the data mean value in each layer is obtained; Among them, is the dissolved oxygen measurement value of the i-th DO sensor; is the sample mean value of the data set, and the data set is the number of DO sensors in the same layer; s is the sample standard deviation of the data set; Obtain the critical value of the standardized distance of each single data point corresponding to each DO sensor from the data mean value ; That is, through the formula the critical value of the standardized distance of a single data point from the data mean value is obtained ; Among them, N is the number of samples in the data set; is the t-part quantile with degrees of freedom N - 2, corresponding to the significance ; If the > of a single DO sensor, it means that the dissolved oxygen data of this DO sensor is abnormal, and this DO sensor is marked as an abnormal DO sensor.

[0015] Exemplarily: 3 DO sensors are arranged at the top of the biochemical reaction pool, and the dissolved oxygen data of the three DO sensors are measured at a certain moment, X1 = 2.3 mg / L, X2 = 2.5 mg / L, X3 = 1.1 mg / L; Calculate the mean value and standard deviation:

[0016]

[0017] Calculate the G value of each data point For example, X3 = 1.1 mg / L

[0018] Determine the critical value Set a = 0.01, N = 3, degrees of freedom df = N - 2 = 1; Look up the Grubbs critical value table. The standard Grubbs critical value table (partial) is shown in Table 1:

[0019] When N = 3, regardless of one-sided or two-sided tests, the critical value is 1.155 (because the sample is too small and the statistic distribution is unique); The data in Table 1 is from Grubbs' original paper Technometrics 1969 or standard statistical reference books (such as "Statistics in Chemical Analysis"); Verify through the cumulative distribution function (CDF) formula of the Grubbs distribution: For N = 3, Satisfy:

[0020] Through Monte Carlo simulation or numerical integration, it can be obtained that when = 1.155, the misjudgment probability of outliers is 1%; When G = 1.14 < = 1.155, the DO sensor corresponding to G3 meets the requirements and is recorded as a normal DO sensor; Otherwise, it is recorded as an abnormal DO sensor.

[0021] In this embodiment, the situation where there are multiple dissolved oxygen abnormal points in the biochemical reaction pool is processed. Specifically: Identify the states of all DO sensors in the biochemical reaction pool and mark the positions of all abnormal DO sensors in the biochemical reaction pool, which are recorded as dissolved oxygen abnormal points; Process all the dissolved oxygen abnormal points in the biochemical reaction pool to construct a dissolved oxygen abnormal area; The processing methods include but are not limited to convex hull, triangulation, or voxelization processing; Obtain the volume of the dissolved oxygen abnormal area, and perform a ratio processing on the volume of the dissolved oxygen abnormal area and the volume of the biochemical reaction pool to obtain the ratio of the dissolved oxygen abnormal area; Obtain the value corresponding to The difference is recorded as the abnormal G difference. The abnormal G difference of the abnormal DO sensor is corresponding to Perform ratio calculation to obtain the abnormal G difference ratio of the abnormal DO sensor; Sum up and average the abnormal G difference ratios of all abnormal DO sensors to obtain the total abnormal G difference ratio; Build a state classification model based on a convolutional neural network, and use the state classification model to identify the dissolved oxygen state in the biochemical reaction pool. Specifically: Obtain a sample data set for identifying the dissolved oxygen state in the biochemical reaction pool, use 70% of the sample data in the sample data set as the training set, and 30% of the sample data as the validation set; Construct a state classification model, and input the set of state parameters for identifying the dissolved oxygen state in the biochemical reaction pool in the training set into the convolutional neural network to train the classifier; Perform classification through the trained classifier to generate a classification result, conduct an accuracy test based on the classification result, and calculate the deviation rate between the classification result and the sample data in the validation set; Judge whether the deviation rate is less than the preset deviation rate threshold. If it is less, it proves that the accuracy of the classifier meets the preset standard, and output the state classification model; Use the state classification model to judge whether the dissolved oxygen state in the biochemical reaction pool belongs to the normal state. If it belongs, record the dissolved oxygen state in the biochemical reaction pool as the normal dissolved oxygen state. If it does not belong, record the dissolved oxygen state in the biochemical reaction pool as the abnormal dissolved oxygen state; Among them, the process of obtaining the set of state parameters for identifying the dissolved oxygen state in the biochemical reaction pool is as follows: In the historical period, based on any time point, count the ratio of the occupied area of the dissolved oxygen abnormal area and the total abnormal G difference ratio corresponding to each time point; Use the ratio of the occupied area of the dissolved oxygen abnormal area and the total abnormal G difference ratio at each time point as the source data to construct a set of state parameters for identifying the dissolved oxygen state in the biochemical reaction pool at different time points; Among them, it should be noted that: the ratio of the occupied area of the dissolved oxygen abnormal area reflects the position distribution of all dissolved oxygen abnormal points in the biochemical reaction pool. Construct a three-dimensional entity for all dissolved oxygen abnormal points. The larger the volume of the dissolved oxygen abnormal area (three-dimensional entity), the larger the area range where the dissolved oxygen in the biochemical reaction pool does not meet the standard, and the worse the treatment effect of the biochemical reaction pool on the sewage; The total abnormal G difference ratio reflects the abnormal degree of the dissolved oxygen corresponding to the abnormal area in the biochemical reaction pool. The greater the abnormal degree of the dissolved oxygen corresponding to the abnormal area, the more serious the abnormal treatment of the sewage by the biochemical reaction pool.

[0022] Based on the abnormal dissolved oxygen state, obtain the value corresponding to the difference, and record the dissolved oxygen abnormal point of the abnormal DO sensor corresponding to the maximum difference as the dissolved oxygen abnormal center point; Connect the dissolved oxygen anomaly center points to the remaining dissolved oxygen anomaly points respectively to obtain several dissolved oxygen anomaly connection segments; Vertically project each dissolved oxygen anomaly connection segment onto the bottom of the biochemical reaction tank to obtain dissolved oxygen anomaly projection segments; Sort all the dissolved oxygen anomaly projection segments in ascending order of segment length, and successively use the dissolved oxygen anomaly points as the centers and the effective coverage radius R of the aeration disk to construct multi-level circular screening areas according to the ascending order of the segment lengths of the dissolved oxygen anomaly projection segments; Specifically: The first-layer circular screening area: Use the dissolved oxygen anomaly center point as the center and the effective coverage radius R of the aeration disk as the radius to construct a circular screening area to obtain the first-layer circular screening area; Mark the aeration disks with their center points within the first-layer circular screening area as monitored aeration disks; Integrate all the monitored aeration disks within the first-layer circular screening area to obtain the L1-layer aeration disk group; The second-layer circular screening area: Based on the shortest dissolved oxygen anomaly projection segment length, obtain the dissolved oxygen anomaly point corresponding to this dissolved oxygen anomaly projection segment; Use this dissolved oxygen anomaly point as the center and the effective coverage radius R of the aeration disk as the radius to construct a circular screening area to obtain the second-layer circular screening area; Mark the aeration disks with their center points within the second-layer circular screening area as monitored aeration disks; Integrate all the monitored aeration disks within the second-layer circular screening area to obtain the L2-layer aeration disk group; And so on: Construct the nth-layer circular screening area: Based on the longest dissolved oxygen anomaly projection segment length, obtain the dissolved oxygen anomaly point corresponding to this dissolved oxygen anomaly projection segment; Use this dissolved oxygen anomaly point as the center and the effective coverage radius R of the aeration disk as the radius to construct a circular screening area to obtain the nth-layer circular screening area; Mark the aeration disks with their center points within the nth-layer circular screening area as monitored aeration disks; Integrate all the monitored aeration disks within the nth-layer circular screening area to obtain the Ln-layer aeration disk group; Overlap the L1-layer aeration disk group, L2-layer aeration disk group,..., Ln-layer aeration disk group, and record the number of times the monitored aeration disks are overlapped; And sort the monitored aeration disks in descending order of the number of overlaps to obtain the first-level monitoring sequence group of the monitored aeration disks; Mark the adjacent monitoring aeration discs for each monitoring aeration disc within the first-level monitoring sequence group, denoted as the monitored adjacent aeration discs, and sum and average the number of times the monitored adjacent aeration discs are overlapped to obtain the monitored value of the monitored adjacent aeration discs; Denote the number of overlaps of the monitoring aeration disc as Jzc, and denote the monitored value of the monitored adjacent aeration disc adjacent to the monitoring aeration disc as Jxc; That is, through the formula Calculate the monitoring base number of the monitoring aeration disc , where k is a preset proportionality coefficient; Among them, there are m groups of historical data, and each group of historical data includes the number of overlaps Jzc of the monitoring aeration disc, the monitored value Jxc of the monitored adjacent aeration disc adjacent to the monitoring aeration disc, and the monitoring base number of the monitoring aeration disc ; Fit the m groups of historical data using a linear model, and substitute the prepared historical data into the selected fitting model for fitting, and obtain the average value of the fitting coefficients as the preset proportionality coefficient k; Sort all the monitoring aeration discs in descending order according to the monitoring base number of the monitoring aeration disc to obtain the second-level monitoring sequence group of the monitoring aeration discs; Complete the abnormal investigation of the aeration discs in the biochemical reaction pool according to the order of the second-level monitoring sequence group of the monitoring aeration discs.

[0023] Among them, the process of obtaining the effective coverage radius R of the aeration disc is as follows: Based on the oxygen transfer efficiency (OTE): Calculate the effective coverage radius through the standard oxygen transfer rate (SOTR):

[0024] Among them, Is the standard oxygen transfer rate (kgO 2 / h); Is the ratio of the oxygen transfer coefficient of sewage to fresh water (usually 0.4 - 0.8); Is the safety factor (1.2 - 1.5); Is the volumetric oxygen mass transfer coefficient (h -1 ); Is the saturated dissolved oxygen concentration (mg / L); Exemplary: For a certain microporous aeration disc, SOTR = 0.25 kgO 2 / h, = 5 h -1 , = 9 mg / L, then the coverage radius R ≈ 1.2 m, corresponding to the coverage area πR2 ≈4.5 m 2 。

[0025] Example 2 Please refer to Figure 2 As shown, the present invention is a whole-process supervision system for sewage treatment based on the Internet of Things, including: A data acquisition module for acquiring the actual dissolved oxygen data in the biochemical reaction tank during the sewage purification process; A behavior recognition module for determining the dissolved oxygen abnormal points according to the acquired actual dissolved oxygen data, and recognizing the dissolved oxygen state in the biochemical reaction tank according to the positions of the dissolved oxygen abnormal points and the dissolved oxygen abnormal deviation data; The dissolved oxygen state in the biochemical reaction tank includes a dissolved oxygen abnormal state and a dissolved oxygen normal state; An abnormal construction module for, in the case of a dissolved oxygen abnormal state, taking the dissolved oxygen abnormal center point as a reference, and successively constructing circular screening areas with the dissolved oxygen abnormal points gradually away from the dissolved oxygen abnormal center point as the centers and the effective coverage radius of the aeration disk as the radii; A positioning and inspection module for overlapping the monitored aeration disks in the multi-layer circular screening areas, and constructing an abnormal inspection sequence of the monitored aeration disks based on the overlapping data of the monitored aeration disks.

[0026] One of the core points of the present invention is to hierarchically divide the biochemical reaction tank, set DO sensors on different levels of the biochemical reaction tank, and comprehensively identify the dissolved oxygen data in the biochemical reaction tank according to the DO sensors, complete the positioning and identification of the dissolved oxygen abnormal area in the biochemical reaction tank, and process according to the position of the dissolved oxygen abnormal area and the dissolved oxygen abnormal deviation data, so as to realize the determination of the dissolved oxygen state of the biochemical reaction tank from two dimensions of spatial position and data deviation, with high accuracy; One of the core points of the present invention is that when the dissolved oxygen state of the biochemical reaction tank is abnormal, determine the dissolved oxygen abnormal center point, and take the dissolved oxygen abnormal center point as a reference, and successively construct circular screening areas with the dissolved oxygen abnormal points gradually away from the dissolved oxygen abnormal center point as the centers and the effective coverage radius of the aeration disk as the radii, overlap the monitored aeration disks in the multi-layer circular screening areas to obtain the first-level monitoring sequence group of the monitored aeration disks, and then take the first-level monitoring sequence group as a reference, process each monitored aeration disk in the first-level monitoring sequence group with adjacent monitored aeration disks to obtain the monitored values of the monitored adjacent aeration disks, and obtain the second-level monitoring sequence group of the monitored aeration disks according to the overlapping times of the monitored aeration disks and the monitored values of the monitored adjacent aeration disks, and construct an abnormal inspection sequence of the monitored aeration disks, so as to facilitate the staff to orderly inspect the aeration disks in the biochemical reaction tank and improve the inspection efficiency of the staff.

[0027] The above has described in detail an embodiment of the present invention, but the above content is only a preferred embodiment of the present invention and cannot be considered as defining the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention shall still fall within the scope covered by the patent of the present invention.

Claims

1. A method for monitoring the whole process of sewage treatment based on the Internet of Things, characterized in that: The following steps are involved: Collect actual dissolved oxygen data in the biochemical reaction tank during the sewage purification process; Determine the dissolved oxygen abnormal point based on the actual dissolved oxygen data collected, and identify the dissolved oxygen state in the biochemical reaction tank based on the location of the dissolved oxygen abnormal point and the dissolved oxygen abnormal deviation data; The dissolved oxygen state in the biochemical reaction tank includes an abnormal dissolved oxygen state and a normal dissolved oxygen state; Under abnormal dissolved oxygen conditions, a circular screening area is constructed with the abnormal dissolved oxygen center point as the reference, the abnormal dissolved oxygen points gradually away from the abnormal dissolved oxygen center point as the center of the circle, and the effective coverage radius of the aeration disk as the radius; The monitoring aeration disks in the multi-layer circular screening area are overlapped, and the abnormality troubleshooting sequence of the monitoring aeration disks is constructed with the overlapped data of the monitoring aeration disks.

2. According to the method for monitoring the whole process of sewage treatment based on the Internet of Things according to claim 1, it is characterized in that: The actual process of obtaining dissolved oxygen data is as follows: DO sensors at different spatial layers are arranged in the biochemical reaction pool along the vertical direction; Based on the Grubbs criterion, the DO sensors in the same layer in each biochemical reaction pool are processed at the same time to obtain the standardized distance G of a single data point in each layer deviating from the data mean; Get the critical value of the standardized distance of a single data point corresponding to each DO sensor in the same layer from the data mean ; In a single DO sensor > When the DO sensor is abnormal, the DO sensor is recorded as an abnormal DO sensor.

3. According to the method for monitoring the whole process of sewage treatment based on the Internet of Things according to claim 1, it is characterized in that: The abnormal dissolved oxygen point is the location of the abnormal DO sensor in the biochemical reaction tank.

4. According to claim 1, a method for monitoring the entire process of sewage treatment based on the Internet of Things is characterized in that: The process of identifying the dissolved oxygen status in the biochemical reaction tank is as follows: Establish a state classification model based on convolutional neural network; Inputting the state parameter set for identifying the dissolved oxygen state of the biochemical reaction tank in the training set into the convolutional neural network training classifier; Generate classification results through classification using the trained classifier, perform accuracy test based on the classification results, and calculate the deviation rate between the classification results and the sample data in the validation set; Determine whether the deviation rate is less than a preset deviation rate threshold. If so, it proves that the accuracy of the classifier meets the preset standard, and outputs a state classification model; The state classification model is used to determine whether the dissolved oxygen state in the biochemical reaction tank is normal. If it is, the dissolved oxygen state in the biochemical reaction tank is recorded as a normal dissolved oxygen state; if not, the dissolved oxygen state in the biochemical reaction tank is recorded as an abnormal dissolved oxygen state.

5. According to claim 4, a method for monitoring the entire process of sewage treatment based on the Internet of Things is characterized in that: The process of obtaining the state parameter set for identifying the dissolved oxygen state of the biochemical reaction tank is as follows: In the historical period, based on any time point, the percentage of abnormal dissolved oxygen areas and the total ratio of abnormal G differences corresponding to each time point are counted; Taking the proportion of abnormal dissolved oxygen areas and the total ratio of abnormal G differences at each time point as the source data, a set of state parameters for identifying the dissolved oxygen status of the biochemical reaction tank at different time points was constructed.

6. According to the method for monitoring the whole process of sewage treatment based on the Internet of Things according to claim 5, it is characterized in that: The percentage of the abnormal dissolved oxygen area is the ratio of the volume of the abnormal dissolved oxygen area to the volume of the biochemical reaction tank; The abnormal DO sensor The value corresponding to The abnormal G difference is calculated by the difference between the abnormal DO sensor and the corresponding abnormal G difference. Perform ratio calculation to obtain the abnormal G difference ratio of the abnormal DO sensor; The abnormal G difference ratios of all abnormal DO sensors are summed and averaged to obtain the total abnormal G difference ratio.

7. The method for monitoring the whole process of sewage treatment based on the Internet of Things according to claim 2 is characterized in that: Based on the abnormal dissolved oxygen status, obtain the abnormal DO sensor The value corresponding to The abnormal dissolved oxygen point of the abnormal DO sensor corresponding to the maximum difference is recorded as the abnormal dissolved oxygen center point; Connect the dissolved oxygen anomaly center point with the other dissolved oxygen anomaly points respectively to obtain several dissolved oxygen anomaly line segments; Each dissolved oxygen abnormal line segment is vertically projected onto the bottom of the biochemical reaction tank to obtain a dissolved oxygen abnormal projection line segment; All the dissolved oxygen anomaly projection line segments are sorted in ascending order of line segment length, and according to the order of dissolved oxygen anomaly projection line segments in ascending order of line segment length, a multi-level circular screening area is constructed with the dissolved oxygen anomaly point as the center and the aeration disk effective coverage radius R.

8. The method for monitoring the whole process of sewage treatment based on the Internet of Things according to claim 7 is characterized in that: The aeration disk located in any circular screening area is recorded as the monitoring aeration disk; The number of overlaps of the monitoring aeration disks in the multi-level circular screening area is obtained, and the monitoring aeration disks are sorted in descending order according to the number of overlaps to obtain a primary monitoring sequence group of the monitoring aeration disks.

9. A method for monitoring the entire process of sewage treatment based on the Internet of Things according to claim 8, characterized in that: Each monitoring aeration disk in the first-level monitoring sequence group that has an adjacent monitoring aeration disk is marked as a monitoring adjacent aeration disk, and the number of times the monitoring adjacent aeration disks are overlapped is summed and averaged to obtain the monitored value of the monitoring adjacent aeration disk; The overlap times of the monitoring aeration disk and the monitored values ​​of the adjacent monitoring aeration disks adjacent to the monitoring aeration disk are processed to obtain the monitoring base number of the monitoring aeration disk; Complete the abnormal inspection of the aeration disk in the biochemical reaction tank according to the sequence of the secondary monitoring sequence group for monitoring the aeration disk.

10. A sewage treatment whole process monitoring system based on the Internet of Things, characterized in that: include: Data acquisition module, used to collect actual dissolved oxygen data of the biochemical reaction tank during the sewage purification process; The behavior recognition module is used to determine the dissolved oxygen abnormal point according to the actual dissolved oxygen data collected, and to identify the dissolved oxygen state in the biochemical reaction tank according to the location of the dissolved oxygen abnormal point and the dissolved oxygen abnormal deviation data; The dissolved oxygen state in the biochemical reaction tank includes an abnormal dissolved oxygen state and a normal dissolved oxygen state; An abnormal construction module is used to construct a circular screening area under abnormal dissolved oxygen state, taking the abnormal dissolved oxygen center point as the reference, taking the abnormal dissolved oxygen points gradually away from the abnormal dissolved oxygen center point as the center of the circle, and taking the effective coverage radius of the aeration disk as the radius; The positioning inspection module is used to perform overlap processing on the monitoring aeration disks in the multi-layer circular screening area, and to construct an abnormal troubleshooting sequence for the monitoring aeration disks based on the overlap data of the monitoring aeration disks.

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