Sewage treatment method and system based on anaerobic bacteria

Abstract

The invention relates to the technical field of sewage treatment, and discloses a sewage treatment method and system based on anaerobic bacteria, and the method comprises the following steps: obtaining the suspended matter concentration of each sewage detection area, comparing the suspended matter concentration with the standard suspended matter concentration, determining the initial addition amount of anaerobic bacteria according to the comparison result, and determining the initial addition amount of anaerobic bacteria according to the initial addition amount. Comparing the characteristic parameter sequence with a standard characteristic parameter sequence, judging whether to adjust the initial dosage according to a comparison result, establishing a dosage correlation model according to the environment data set when the initial dosage is judged to be adjusted, determining a dosage prediction adjustment factor based on the environment characteristic parameters and the dosage correlation model which have differences, and predicting the dosage according to the dosage prediction adjustment factor. The initial adding amount is adjusted according to the adding target adjusting factor, adding is conducted on the sewage pool according to the adjusted initial adding amount, sewage treatment is completed, the adding amount of anaerobic bacteria is effectively controlled, the method adapts to the sewage environment, and the sewage treatment efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to a sewage treatment method and system based on anaerobic bacteria. Background Art

[0002] With the acceleration of industrialization and urbanization, sewage discharge is increasing. Sewage treatment has become a key link in the field of environmental protection. Anaerobic organisms can convert sewage into a certain amount of biogas, thereby realizing the treatment and utilization of sewage resources. At present, the addition of anaerobic bacteria in the sewage treatment process has certain limitations. On the one hand, the addition of anaerobic bacteria mainly relies on manual addition, which is easily affected by the operator's experience, operating habits and working conditions. It is difficult to effectively control the addition amount of anaerobic bacteria, thereby affecting the stability and treatment efficiency of sewage treatment. On the other hand, the addition amount cannot effectively adapt to the sewage environment. When environmental parameters such as flow rate and temperature change, the pollutants in the sewage cannot be effectively degraded by relying solely on the manually set addition amount, which causes sludge swelling and reduces the efficiency of sewage treatment.

[0003] Therefore, it is necessary to design a sewage treatment method and system based on anaerobic bacteria to solve the problems existing in current technology. Summary of the Invention

[0004] In view of this, the present invention proposes a sewage treatment method and system based on anaerobic bacteria, which aims to solve the problem that the addition of anaerobic bacteria mainly depends on manual addition, is easily affected by the operator's experience, operating habits and working conditions, and is difficult to effectively control the addition amount of anaerobic bacteria. Moreover, the addition amount cannot effectively adapt to the sewage environment, and the pollutants in the sewage cannot be effectively degraded by relying solely on the manually set addition amount.

[0005] In one aspect, the present invention provides a sewage treatment method based on anaerobic bacteria, comprising:

[0006] Divide the sewage pool into several sewage detection areas, obtain the suspended solids concentration of each sewage detection area, compare the suspended solids concentration with the standard suspended solids concentration, and determine the initial dosage of anaerobic bacteria based on the comparison results;

[0007] Obtaining environmental characteristic parameters of the sewage pool and constructing a characteristic parameter sequence, comparing the characteristic parameter sequence with a standard characteristic parameter sequence, and determining whether to adjust the initial dosage based on the comparison result;

[0008] When it is determined that the initial dosage is to be adjusted, a dosage association model is established according to the environmental data set, and environmental characteristic parameters with differences are extracted, a dosage prediction adjustment factor is determined based on the environmental characteristic parameters with differences and the dosage association model, and the dosage prediction adjustment factor is compared with the historical data set, and a dosage target adjustment factor is determined according to the comparison result;

[0009] The initial dosage is adjusted according to the dosage target adjustment factor, and the sewage pool is dosed according to the adjusted initial dosage to complete sewage treatment.

[0010] Furthermore, when comparing the suspended solids concentration with the standard suspended solids concentration and determining the initial dosage of anaerobic bacteria according to the comparison result, the method includes:

[0011] A first treatment mark is set for a sewage detection area corresponding to a suspended solids concentration less than the standard suspended solids concentration, a second treatment mark is set for a sewage detection area corresponding to a suspended solids concentration equal to the standard suspended solids concentration, and a third treatment mark is set for a sewage detection area corresponding to a suspended solids concentration greater than the standard suspended solids concentration;

[0012] Obtaining the average suspended solids concentration of all sewage detection areas where the second treatment mark is established;

[0013] The initial dosage is determined according to the suspended matter concentration for establishing the first treatment mark, the suspended matter concentration for establishing the third treatment mark, and the average suspended matter concentration.

[0014] Furthermore, when determining the initial dosage according to the suspended solids concentration for establishing the first treatment mark, the suspended solids concentration for establishing the third treatment mark, and the average suspended solids concentration, the method includes:

[0015] Constructing the suspended matter concentrations of all sewage detection areas with the first treatment mark established as a first concentration set, and constructing the suspended matter concentrations of all sewage detection areas with the third treatment mark established as a third concentration set;

[0016] determining a first median and a first mean of the first set of concentrations, and determining a third median and a third mean of the third set of concentrations;

[0017] Extracting suspended matter concentrations greater than the first median in the first concentration set and constructing a first concentration value set, extracting suspended matter concentrations greater than the first average in the first concentration set and constructing a second concentration value set;

[0018] Extracting suspended matter concentrations greater than the third median in the third concentration set and constructing a third concentration value set, extracting suspended matter concentrations greater than the third mean in the third concentration set and constructing a fourth concentration value set;

[0019] The initial dosage is determined according to the first concentration value set, the second concentration value set, the third concentration value set, the fourth concentration value set and the average suspended matter concentration.

[0020] Furthermore, when determining the initial dosage according to the first concentration value set, the second concentration value set, the third concentration value set, the fourth concentration value set and the average suspended solids concentration, the method includes:

[0021] Determining whether there is an intersection between the first concentration value set and the second concentration value set, and whether there is an intersection between the third concentration value set and the fourth concentration value set;

[0022] If yes, construct a suspended matter concentration set according to the intersection value, determine the concentration average of the suspended matter concentration set, and determine the average of the concentration average and the average suspended matter concentration as the target suspended matter concentration;

[0023] If not, combining the first concentration value set and the second concentration value set to construct a fifth concentration value set, and combining the third concentration value set and the fourth concentration value set to construct a sixth concentration value set, obtaining the first concentration value average of the fifth concentration value set and the second concentration value average of the sixth concentration value set, and determining the average of the first concentration value average, the second concentration value average, and the average suspended matter concentration as the target suspended matter concentration;

[0024] The initial dosage is determined according to the target suspended solids concentration.

[0025] Furthermore, when determining the initial dosage according to the target suspended solids concentration, the method includes:

[0026] Presetting a first preset target suspended solids concentration and a second preset target suspended solids concentration, wherein the first preset target suspended solids concentration is greater than the second preset target suspended solids concentration;

[0027] Presetting a first preset initial dosage, a second preset initial dosage, and a third preset initial dosage, wherein the first preset initial dosage is greater than the second preset initial dosage, and the second preset initial dosage is greater than the third preset initial dosage;

[0028] When the target suspended solids concentration is greater than the first preset target suspended solids concentration, determining the first preset initial dosage as the initial dosage;

[0029] When the target suspended solids concentration is less than or equal to the first preset target suspended solids concentration and greater than or equal to the second preset target suspended solids concentration, the second preset initial dosage is determined as the initial dosage;

[0030] When the target suspended matter concentration is less than the second preset target suspended matter concentration, the third preset initial dosage is determined as the initial dosage.

[0031] Furthermore, when obtaining the environmental characteristic parameters of the sewage pool and constructing a characteristic parameter sequence, comparing the characteristic parameter sequence with a standard characteristic parameter sequence, and judging whether to adjust the initial dosage according to the comparison result, the method includes:

[0032] Acquire the standard characteristic parameter sequence corresponding to the characteristic parameter sequence;

[0033] When the environmental characteristic parameters in the characteristic parameter series are all equal to the standard environmental characteristic parameters in the standard characteristic parameter series, it is determined that the initial dosage is not adjusted, and the sewage pool is dosed with the initial dosage to complete sewage treatment; otherwise, it is determined that the initial dosage is adjusted.

[0034] Furthermore, when establishing a dosing correlation model based on the environmental data set and extracting different environmental characteristic parameters, and determining the dosing prediction adjustment factor based on the different environmental characteristic parameters and the dosing correlation model, the method includes:

[0035] Extracting environmental characteristic parameters that are not equal to the standard environmental characteristic parameters from the characteristic parameter sequence and recording them as a difference characteristic parameter set;

[0036] The environmental data set is divided into a training set and a test set, and a grid search is used to find the model establishment parameters to establish a random forest model;

[0037] Using the training set to train the random forest model, substituting the test set into the trained random forest model and determining the accuracy of model prediction, when the accuracy reaches an accuracy threshold, determining the trained random forest model as the dosage association model; otherwise, continuing to train the random forest model until the accuracy threshold is reached;

[0038] The difference characteristic parameter set is substituted into the dosage association model to determine the dosage prediction adjustment factor.

[0039] Furthermore, when comparing the dosage prediction adjustment factor with the historical data set and determining the dosage target adjustment factor according to the comparison result, the method includes:

[0040] The historical data set includes a plurality of historical difference characteristic parameter sets and a plurality of historical dosage prediction adjustment factors, each historical difference characteristic parameter set corresponds to a historical dosage prediction adjustment factor;

[0041] When there is a historical difference characteristic parameter set in the historical data set that is consistent with the difference characteristic parameter set, obtaining a historical dosage prediction adjustment factor corresponding to the historical difference characteristic parameter set; if the corresponding historical dosage prediction adjustment factor is equal to the dosage prediction adjustment factor, determining the dosage prediction adjustment factor as the dosage target adjustment factor; otherwise, clustering the historical data set and the difference characteristic parameter set to determine a clustered data set, and determining the mean of the historical dosage prediction adjustment factors in the clustered data set as the dosage target adjustment factor;

[0042] When there is no historical difference characteristic parameter set consistent with the difference characteristic parameter set in the historical data set, the dosage prediction adjustment factor is determined as the dosage target adjustment factor.

[0043] Furthermore, when the initial dosage is adjusted according to the dosage target adjustment factor, the method includes:

[0044] The initial dosage is directly proportional to the dosage target adjustment factor.

[0045] Compared with the prior art, the beneficial effects of the present invention are: dividing the sewage pool into several sewage detection areas, obtaining the suspended solids concentration of each sewage detection area and comparing it with the standard suspended solids concentration, determining the initial dosage of anaerobic bacteria, avoiding human experience judgment, combining the environmental characteristic parameters of the sewage pool to construct a characteristic parameter series, and comparing it with the standard characteristic parameter series to determine whether to adjust the initial dosage, avoiding the risk of the dosage not being consistent with the actual sewage environment due to environmental changes, ensuring the stability of the sewage treatment process, and effectively avoiding fluctuations in treatment effects caused by excessive or insufficient dosage, determining the dosage prediction adjustment factor by establishing a dosage association model, and comparing it with the historical data set to obtain the dosage target adjustment factor, which can dynamically adjust the initial dosage of anaerobic bacteria, ensure the effective degradation of pollutants in sewage under different environmental conditions, and improve the efficiency of sewage treatment.

[0046] On the other hand, the present application also provides an anaerobic-based sewage treatment system for applying the above-mentioned anaerobic-based sewage treatment method, comprising:

[0047] The first processing module is configured to divide the sewage pool into a plurality of sewage detection areas, obtain the suspended solids concentration of each sewage detection area, compare the suspended solids concentration with a standard suspended solids concentration, and determine an initial dosage of anaerobic bacteria based on the comparison result;

[0048] The second processing module is configured to obtain environmental characteristic parameters of the sewage pool and construct a characteristic parameter sequence, compare the characteristic parameter sequence with a standard characteristic parameter sequence, and determine whether to adjust the initial dosage according to the comparison result;

[0049] a dosage determination module configured to, when determining to adjust the initial dosage, establish a dosage association model based on the environmental data set, extract different environmental characteristic parameters, determine a dosage prediction adjustment factor based on the different environmental characteristic parameters and the dosage association model, compare the dosage prediction adjustment factor with the historical data set, and determine a dosage target adjustment factor based on the comparison result;

[0050] The dosage adjustment module is configured to adjust the initial dosage according to the dosage target adjustment factor, and to perform the dosage to the sewage pool according to the adjusted initial dosage to complete the sewage treatment.

[0051] It is understandable that the above-mentioned sewage treatment method and system based on anaerobic bacteria have the same beneficial effects and will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0053] Figure 1 A flow chart of a sewage treatment method based on anaerobic bacteria provided in an embodiment of the present invention;

[0054] Figure 2 This is a functional block diagram of a sewage treatment system based on anaerobic bacteria provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0056] In some embodiments of the present application, see Figure 1 As shown, a sewage treatment method based on anaerobic bacteria comprises:

[0057] S100: Divide the sewage pool into several sewage detection areas, obtain the suspended solids concentration of each sewage detection area, compare the suspended solids concentration with the standard suspended solids concentration, and determine the initial dosage of anaerobic bacteria based on the comparison results.

[0058] S200: Obtain environmental characteristic parameters of the sewage pool and construct a characteristic parameter sequence, compare the characteristic parameter sequence with a standard characteristic parameter sequence, and determine whether to adjust the initial dosage based on the comparison result.

[0059] S300: When it is determined that the initial dosage is to be adjusted, a dosage association model is established based on the environmental data set, and different environmental characteristic parameters are extracted. Based on the different environmental characteristic parameters and the dosage association model, a dosage prediction adjustment factor is determined, and the dosage prediction adjustment factor is compared with the historical data set. Based on the comparison result, a dosage target adjustment factor is determined.

[0060] S400: The initial dosage is adjusted according to the dosage target adjustment factor, and the sewage pool is dosed according to the adjusted initial dosage to complete sewage treatment.

[0061] Specifically, the sewage pool is divided into multiple sewage detection areas, and the specific number is evenly divided according to the actual size of the sewage pool. The composition and concentration of sewage at different locations in the sewage pool are not evenly distributed. By obtaining the suspended solids concentration of each sewage detection area, the distribution of pollutants in the sewage pool can be accurately understood and compared with the standard suspended solids concentration. The standard suspended solids concentration represents the pollutant content under the ideal treatment state of sewage. The standard suspended solids concentration is determined according to the "Technical Specifications for Supervision and Management of Urban Sewage Treatment Plant Operation". If the suspended solids concentration is higher than the standard suspended solids concentration, it means that there are more pollutants in the sewage and a larger dosage is required. Otherwise, it should be appropriately reduced. The initial dosage thus determined is based on the preliminary setting of the pollutant content of the sewage itself. Environmental characteristic parameters (such as flow rate, temperature, pH value, etc.) have a certain impact on the activity and treatment efficiency of anaerobic bacteria. They are constructed as characteristic parameter series and compared with the standard characteristic parameter series. The standard characteristic parameter series is determined based on the ideal treatment environment of anaerobic bacteria and reflects the ideal environmental conditions when anaerobic bacteria exert their treatment effect. When there is a difference between the characteristic parameter series and the standard characteristic parameter series, it indicates that the current sewage pool environment may not be conducive to the normal operation of anaerobic bacteria, and it is necessary to consider adjusting the initial dosage to compensate for the impact of environmental factors on the treatment effect.

[0062] It is understandable that when it is determined that the initial dosage needs to be adjusted, a dosage association model is established based on the environmental data set. This model, through analysis and learning of a large amount of environmental data, mines the potential relationship between environmental characteristic parameters and anaerobic bacteria dosage. After extracting the environmental characteristic parameters with differences, the dosage association model is used to calculate the dosage prediction adjustment factor. The prediction adjustment factor is an estimate of the dosage adjustment based on the current sewage pool environment differences. This is compared with the historical data set, which records the adjustments made to the initial dosage under different environmental differences in the past. Through comparison, it is possible to obtain accurate adjustment basis from historical experience, thereby determining the dosage target adjustment factor, so that the adjusted initial dosage can meet the actual sewage treatment needs and ensure that the anaerobic bacteria dosage matches the sewage conditions and environmental conditions. Compared with traditional manual experience judgment, the data-driven approach can mine potential patterns and relationships, providing a reliable basis for the final dosage, thereby achieving stable sewage treatment.

[0063] In some embodiments of the present application, when comparing the suspended solids concentration with the standard suspended solids concentration and determining the initial dosage of anaerobic bacteria based on the comparison results, it includes: establishing a first treatment mark for the sewage detection area corresponding to the suspended solids concentration less than the standard suspended solids concentration, establishing a second treatment mark for the sewage detection area corresponding to the suspended solids concentration equal to the standard suspended solids concentration, establishing a third treatment mark for the sewage detection area corresponding to the suspended solids concentration greater than the standard suspended concentration, obtaining the average suspended solids concentration of all sewage detection areas with the second treatment mark established, and determining the initial dosage based on the suspended solids concentration for establishing the first treatment mark, the suspended solids concentration for establishing the third treatment mark, and the average suspended solids concentration.

[0064] Specifically, marking the sewage detection areas according to the relationship between suspended solids concentration and the standard suspended solids concentration further refines the sewage conditions in the sewage pool. Establishing a sewage detection area with the first treatment mark indicates a low pollutant load and relatively low demand for anaerobic bacteria. The suspended solids concentration in the sewage detection area with the second treatment mark is equal to the standard suspended solids concentration, meaning that the pollutant content is ideal. The sewage detection area with the third treatment mark indicates a high pollutant load and requires more anaerobic bacteria to treat it. Obtaining the average suspended solids concentration in the second treatment mark area is to obtain a baseline reference value. On this basis, combining the lower suspended solids concentration of the first treatment mark and the higher suspended solids concentration of the third treatment mark, it is possible to comprehensively consider the pollutant conditions in different sewage detection areas within the sewage pool. By analyzing the suspended solids concentrations of these three types of treatment marks, an initial dosage is determined that can balance the treatment needs of each area, avoiding resource waste or insufficient treatment due to a "one-size-fits-all" dosage, and effectively improving the adaptability and stability of sewage treatment.

[0065] In some embodiments of the present application, when determining the initial dosage based on the suspended solids concentration for establishing the first treatment mark, the suspended solids concentration for establishing the third treatment mark, and the average suspended solids concentration, it includes: constructing the suspended solids concentrations of all sewage detection areas for establishing the first treatment mark into a first concentration set, and constructing the suspended solids concentrations of all sewage detection areas for establishing the third treatment mark into a third concentration set, determining the first median and the first average of the first concentration set, and determining the third median and the third average of the third concentration set, extracting the suspended solids concentrations greater than the first median in the first concentration set and constructing a first concentration value set, extracting the suspended solids concentrations greater than the first average in the first concentration set and constructing a second concentration value set, extracting the suspended solids concentrations greater than the third median in the third concentration set and constructing a third concentration value set, extracting the suspended solids concentrations greater than the third average in the third concentration set and constructing a fourth concentration value set, and determining the initial dosage based on the first concentration value set, the second concentration value set, the third concentration value set, the fourth concentration value set, and the average suspended solids concentration.

[0066] Specifically, after constructing corresponding concentration sets for suspended solids concentrations with different treatment marks, determine their corresponding medians and averages to ensure that the characteristics of the concentration distribution are grasped from both the overall and intermediate levels. Extract suspended solids concentrations in each concentration set that are greater than the corresponding median and average values. Focus on the parts of each concentration set with high suspended solids concentrations. These data can better reflect the pollution situation in areas with different treatment marks. For example: the first concentration set represents 80 mg / L, 100 mg / L, 120 mg / L, 130 mg / L, and 140 mg / L, the first median represents 120 mg / L, and the first average represents 114 mg / L. Then the first concentration value set represents 130 mg / L and 140 mg / L, and the second concentration value set represents 120 mg / L, 130 mg / L, and 140 mg / L. The first and second concentration value sets represent higher concentration conditions for treating pollutants with lower treatment marks, and the third and fourth concentration value sets represent higher concentration conditions for treating pollutants with higher treatment marks. Combined with the average suspended solids concentration, this can fully consider the concentration distribution of pollutants in different sewage detection areas, avoid the limitations of a single data indicator, and analyze the concentration data from multiple dimensions, so that the initial dosage can adapt to the actual pollution conditions in each sewage detection area.

[0067] In some embodiments of the present application, when determining the initial dosage based on the first concentration value set, the second concentration value set, the third concentration value set, the fourth concentration value set and the average suspended solids concentration, the method includes: determining whether there is an intersection between the first concentration value set and the second concentration value set, and whether there is an intersection between the third concentration value set and the fourth concentration value set; if so, constructing a suspended solids concentration set based on the intersection value, determining the concentration average of the suspended solids concentration set, and determining the average of the concentration average and the average of the average suspended solids concentration as the target suspended solids concentration; if not, merging the first concentration value set and the second concentration value set to construct a fifth concentration value set, and merging the third concentration value set and the fourth concentration value set to construct a sixth concentration value set, obtaining the first concentration value average of the fifth concentration value set and the second concentration value average of the sixth concentration value set, and determining the average of the first concentration value average, the second concentration value average and the average suspended solids concentration as the target suspended solids concentration, and determining the initial dosage based on the target suspended solids concentration.

[0068] Specifically, the intersection between each concentration value set is determined to explore the potential correlation between concentration data of different dimensions. If there is an intersection, it means that the higher concentration data screened out under different calculation dimensions have overlapping parts. These overlapping data are more representative. Constructing them into a suspended solids concentration set and calculating the average value can accurately reflect the more prominent pollution conditions in different sewage detection areas. The target suspended solids concentration is determined by combining the average suspended solids concentration, so that the results fit the actual sewage treatment needs. If there is no intersection, the different concentration value sets are merged and the corresponding average values are calculated respectively. This is to integrate different dimensions as a whole to screen out higher concentration data. By combining these average values with the average suspended solids concentration, the pollution conditions in each area can also be fully considered. For example: the average value of the first concentration value is 90 mg / L, the average value of the second concentration value is 130 mg / L, and the average suspended solids concentration is 100 mg / L, then their average value is 106.67 mg / L, and 106.67 mg / L is determined as the target suspended solids concentration. Whether based on intersection data or comprehensive integration when there is no intersection, it ensures that the target suspended solids concentration can reflect the actual pollution status of the sewage, laying a reliable data foundation for determining the initial dosage.

[0069] In some embodiments of the present application, when determining the initial dosage according to the target suspended solids concentration, it includes: pre-setting a first preset target suspended solids concentration and a second preset target suspended solids concentration, the first preset target suspended solids concentration is greater than the second preset target suspended solids concentration, pre-setting a first preset initial dosage, a second preset initial dosage and a third preset initial dosage, the first preset initial dosage is greater than the second preset initial dosage, and the second preset initial dosage is greater than the third preset initial dosage; when the target suspended solids concentration is greater than the first preset target suspended solids concentration, the first preset initial dosage is determined as the initial dosage; when the target suspended solids concentration is less than or equal to the first preset target suspended solids concentration and greater than or equal to the second preset target suspended solids concentration, the second preset initial dosage is determined as the initial dosage; when the target suspended solids concentration is less than the second preset target suspended solids concentration, the third preset initial dosage is determined as the initial dosage.

[0070] Specifically, different gradients of target suspended solids concentrations (first preset target suspended solids concentration, second preset target suspended solids concentration) and preset initial dosages (first preset initial dosage, second preset initial dosage, third preset initial dosage) are pre-set to form a target suspended solids concentration-dosage correspondence rule. Once the calculated target suspended solids concentration is determined, it is compared with the preset target suspended solids concentration and the corresponding preset initial dosage is dynamically selected. This reduces reliance on human experience, avoids errors in human judgment, ensures that the initial dosage matches the degree of sewage pollution, improves sewage treatment efficiency, and reduces fluctuations in treatment results and resource waste caused by improper dosage.

[0071] In some embodiments of the present application, when obtaining the environmental characteristic parameters of a sewage pool and constructing a characteristic parameter series, comparing the characteristic parameter series with the standard characteristic parameter series, and judging whether to adjust the initial dosage based on the comparison results, it includes: obtaining the standard characteristic parameter series corresponding to the characteristic parameter series, when the environmental characteristic parameters in the characteristic parameter series are all equal to the standard environmental characteristic parameters in the standard characteristic parameter series, determining not to adjust the initial dosage, and dosing the sewage pool with the initial dosage to complete sewage treatment, otherwise, determining to adjust the initial dosage.

[0072] Specifically, the environmental characteristic parameters of the sewage pool (such as temperature, flow rate, pH value, etc.) are obtained, and each environmental characteristic parameter corresponds to a numerical value. The constructed characteristic parameter series comprehensively reflects the characteristics of the overall data of the current environmental status of the sewage pool, and is compared with the standard characteristic parameter series. Among them, the standard environmental characteristic parameters in the standard characteristic parameter series correspond one-to-one to the environmental characteristic parameters in the characteristic parameter series. When all the environmental characteristic parameters in the characteristic parameter series are completely equal to all the standard environmental characteristic parameters in the standard characteristic parameter series, it means that the environment of the current sewage pool is in an ideal state, and anaerobic bacteria can achieve good treatment effects at the initial dosage. There is no need to adjust the initial dosage, and the dosage can be directly added according to the initial dosage. If the environmental characteristic parameters and the standard environmental characteristic parameters are not equal, it indicates that the environment has changed, which may affect the activity and treatment efficiency of anaerobic bacteria. The initial dosage needs to be adjusted to ensure the sewage treatment effect and ensure that the anaerobic bacteria are in a relatively suitable environment, which effectively improves the stability and reliability of sewage treatment.

[0073] In some embodiments of the present application, when establishing a dosage association model based on an environmental data set, extracting environmental characteristic parameters with differences, and determining a dosage prediction adjustment factor based on the environmental characteristic parameters with differences and the dosage association model, it includes: extracting environmental characteristic parameters that are not equal to standard environmental characteristic parameters in the characteristic parameter series, and recording them as a difference characteristic parameter set, dividing the environmental data set into a training set and a test set, using grid search to find model establishment parameters, establishing a random forest model, using the training set to train the random forest model, and substituting the test set into the trained random forest model and determining the accuracy of the model prediction; when the accuracy reaches the accuracy threshold, the trained random forest model is determined as the dosage association model; otherwise, the random forest model is continued to be trained until the accuracy threshold is reached, and the difference characteristic parameter set is substituted into the dosage association model to determine the dosage prediction adjustment factor.

[0074] Specifically, the environmental dataset contains environmental characteristic parameters of the sewage pool at different times, along with corresponding sample values, anaerobic bacterial activity, and other data. The dataset is divided into a training set and a test set. The split ratio is typically 7:3, ensuring that both the training and test sets contain diverse data to improve the model's generalization. A grid search is used to establish parameters, such as the number of trees and maximum tree depth, through an exhaustive search in the parameter space. A random forest model is trained using the training set data. This model integrates multiple decision trees and averages their predictions, improving its accuracy and stability. During training, the model attempts to learn patterns and relationships within the data to improve its prediction or classification capabilities. The test set is then fed into the trained random forest model to calculate its accuracy. Accuracy reflects the model's performance on unseen data and is an important metric for evaluating model performance. Once the model reaches a preset accuracy threshold, it is considered to have stably approached the global optimal solution. The trained random forest model is then designated as the dosing correlation model. The differential characteristic parameter set is then fed into the dosing correlation model to determine the dosing prediction adjustment factor. When the model fails to reach the preset accuracy threshold, the random forest model will continue to be trained until the accuracy threshold is reached, avoiding the risk of model prediction errors, allowing the dosage to fit the environment of the sewage pool and improving the efficiency of sewage treatment.

[0075] In some embodiments of the present application, when comparing the dosage prediction adjustment factor with the historical data set and determining the dosage target adjustment factor based on the comparison result, it includes: the historical data set includes several historical difference feature parameter sets and several historical dosage prediction adjustment factors, each historical difference feature parameter set corresponds to a historical dosage prediction adjustment factor, when there is a historical difference feature parameter set consistent with the difference feature parameter set in the historical data set, the historical dosage prediction adjustment factor corresponding to the historical difference feature parameter set is obtained, if the corresponding historical dosage prediction adjustment factor is equal to the dosage prediction adjustment factor, the dosage prediction adjustment factor is determined as the dosage target adjustment factor, otherwise, the historical data set and the difference feature parameter set are clustered to determine a clustered data set, and the mean of the historical dosage prediction adjustment factors in the clustered data set is determined as the dosage target adjustment factor, when there is no historical difference feature parameter set consistent with the difference feature parameter set in the historical data set, the dosage prediction adjustment factor is determined as the dosage target adjustment factor.

[0076] Specifically, the historical data set forms a data reserve. A matching search is performed in the historical data set. If a completely consistent historical difference feature parameter set is found, it means that the current environmental situation has traces in history. At this time, the historical dosage prediction adjustment factor is compared with the dosage prediction adjustment factor. If they are equal, it means that the current result is consistent with historical experience and can be directly determined as the dosage target adjustment factor. If they are not equal, a cluster analysis is performed on the historical data set and the difference feature parameter set. Similar data are integrated through clustering, and the mean of the historical dosage prediction adjustment factor in the clustered data set is used as the dosage target adjustment factor. Clustering analysis algorithms include K-Means algorithm, hierarchical clustering algorithm and density clustering algorithm. You can choose one of the clustering algorithms. Here, the K-Means algorithm is taken as an example. The difference feature parameter set and the historical data set are used as the data sets to be clustered. The historical dosage prediction adjustment factor corresponding to each data set in the data set to be clustered is extracted, the expected number of clusters k is determined to be 2, and the parameters of the Gaussian distribution are initialized. The probability that each data set in the data set to be clustered belongs to each Gaussian distribution is calculated to obtain the responsibility value. According to the responsibility value, the clustered data set corresponding to the difference feature parameter set is obtained. If no matching historical difference characteristic parameter set is found, it means that the difference characteristic parameter set is relatively special. Then the prediction adjustment factor will be used as the target adjustment factor to ensure the timeliness of the treatment strategy. While ensuring the timeliness of the treatment, it provides a basis for the accumulation of subsequent data.

[0077] In some embodiments of the present application, when the initial dosage is adjusted according to the dosage target adjustment factor, it includes: the initial dosage and the dosage target adjustment factor are in direct proportion.

[0078] Specifically, the initial dosage is adjusted based on the target dosage adjustment factor. Assuming the initial dosage is L and the target adjustment factor is P, the adjusted initial dosage is determined to be L*P. When a higher initial dosage is required, the proportional relationship between the initial dosage and the target dosage adjustment factor enables precise control of the initial dosage during the sewage treatment process, thereby improving sewage treatment efficiency.

[0079] In summary, the beneficial effects of the present invention are: dividing the sewage pool into several sewage detection areas, obtaining the suspended solids concentration of each sewage detection area and comparing it with the standard suspended solids concentration, determining the initial dosage of anaerobic bacteria, avoiding human experience judgment, combining the environmental characteristic parameters of the sewage pool to construct a characteristic parameter series, and comparing it with the standard characteristic parameter series to determine whether to adjust the initial dosage, avoiding the risk of the dosage not being consistent with the actual sewage environment due to environmental changes, ensuring the stability of the sewage treatment process, and effectively avoiding fluctuations in treatment effects caused by excessive or insufficient dosage, determining the dosage prediction adjustment factor by establishing a dosage association model, and comparing it with the historical data set to obtain the dosage target adjustment factor, which can dynamically adjust the initial dosage of anaerobic bacteria, ensure the effective degradation of pollutants in sewage under different environmental conditions, and improve the efficiency of sewage treatment.

[0080] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides an anaerobic bacteria-based sewage treatment system for applying the above-mentioned anaerobic bacteria-based sewage treatment method, including:

[0081] The first processing module is configured to divide the sewage pool into several sewage detection areas, obtain the suspended solids concentration of each sewage detection area, compare the suspended solids concentration with the standard suspended solids concentration, and determine the initial dosage of anaerobic bacteria based on the comparison results.

[0082] The second processing module is configured to obtain environmental characteristic parameters of the sewage pool and construct a characteristic parameter series, compare the characteristic parameter series with the standard characteristic parameter series, and determine whether to adjust the initial dosage based on the comparison result.

[0083] The dosage determination module is configured to establish a dosage association model based on the environmental data set when determining to adjust the initial dosage, and extract different environmental characteristic parameters, determine the dosage prediction adjustment factor based on the different environmental characteristic parameters and the dosage association model, compare the dosage prediction adjustment factor with the historical data set, and determine the dosage target adjustment factor based on the comparison result.

[0084] The dosing adjustment module is configured to adjust the initial dosing amount according to the dosing target adjustment factor, and dosing the sewage pool according to the adjusted initial dosing amount to complete sewage treatment.

[0085] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware embodiments. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A sewage treatment method based on anaerobic bacteria, characterized in that: include: Divide the sewage pool into several sewage detection areas, obtain the suspended solids concentration of each sewage detection area, compare the suspended solids concentration with the standard suspended solids concentration, and determine the initial dosage of anaerobic bacteria based on the comparison results; Obtaining environmental characteristic parameters of the sewage pool and constructing a characteristic parameter sequence, comparing the characteristic parameter sequence with a standard characteristic parameter sequence, and determining whether to adjust the initial dosage based on the comparison result; When it is determined that the initial dosage is to be adjusted, a dosage association model is established according to the environmental data set, and environmental characteristic parameters with differences are extracted, a dosage prediction adjustment factor is determined based on the environmental characteristic parameters with differences and the dosage association model, and the dosage prediction adjustment factor is compared with the historical data set, and a dosage target adjustment factor is determined according to the comparison result; The initial dosage is adjusted according to the dosage target adjustment factor, and the sewage pool is dosed according to the adjusted initial dosage to complete sewage treatment.

2. The anaerobic sewage treatment method according to claim 1, characterized in that: When comparing the suspended solids concentration with the standard suspended solids concentration and determining the initial dosage of anaerobic bacteria according to the comparison result, the method includes: A first treatment mark is set for a sewage detection area corresponding to a suspended solids concentration less than the standard suspended solids concentration, a second treatment mark is set for a sewage detection area corresponding to a suspended solids concentration equal to the standard suspended solids concentration, and a third treatment mark is set for a sewage detection area corresponding to a suspended solids concentration greater than the standard suspended solids concentration; Obtaining the average suspended solids concentration of all sewage detection areas where the second treatment mark is established; The initial dosage is determined according to the suspended matter concentration for establishing the first treatment mark, the suspended matter concentration for establishing the third treatment mark, and the average suspended matter concentration.

3. The anaerobic sewage treatment method according to claim 2, characterized in that: When the initial dosage is determined according to the suspended solids concentration for establishing the first treatment mark, the suspended solids concentration for establishing the third treatment mark, and the average suspended solids concentration, the method includes: Constructing the suspended matter concentrations of all sewage detection areas with the first treatment mark established as a first concentration set, and constructing the suspended matter concentrations of all sewage detection areas with the third treatment mark established as a third concentration set; determining a first median and a first mean of the first set of concentrations, and determining a third median and a third mean of the third set of concentrations; Extracting suspended matter concentrations greater than the first median in the first concentration set and constructing a first concentration value set, extracting suspended matter concentrations greater than the first average in the first concentration set and constructing a second concentration value set; Extracting suspended matter concentrations greater than the third median in the third concentration set and constructing a third concentration value set, extracting suspended matter concentrations greater than the third mean in the third concentration set and constructing a fourth concentration value set; The initial dosage is determined according to the first concentration value set, the second concentration value set, the third concentration value set, the fourth concentration value set and the average suspended matter concentration.

4. The sewage treatment method based on anaerobic bacteria according to claim 3, characterized in that: When determining the initial dosage according to the first concentration value set, the second concentration value set, the third concentration value set, the fourth concentration value set and the average suspended matter concentration, the method includes: Determining whether there is an intersection between the first concentration value set and the second concentration value set, and whether there is an intersection between the third concentration value set and the fourth concentration value set; If yes, construct a suspended matter concentration set according to the intersection value, determine the concentration average of the suspended matter concentration set, and determine the average of the concentration average and the average suspended matter concentration as the target suspended matter concentration; If not, combining the first concentration value set and the second concentration value set to construct a fifth concentration value set, and combining the third concentration value set and the fourth concentration value set to construct a sixth concentration value set, obtaining the first concentration value average of the fifth concentration value set and the second concentration value average of the sixth concentration value set, and determining the average of the first concentration value average, the second concentration value average, and the average suspended matter concentration as the target suspended matter concentration; The initial dosage is determined according to the target suspended solids concentration.

5. The sewage treatment method based on anaerobic bacteria according to claim 4, characterized in that: When determining the initial dosage according to the target suspended solids concentration, the method includes: Presetting a first preset target suspended solids concentration and a second preset target suspended solids concentration, wherein the first preset target suspended solids concentration is greater than the second preset target suspended solids concentration; Presetting a first preset initial dosage, a second preset initial dosage, and a third preset initial dosage, wherein the first preset initial dosage is greater than the second preset initial dosage, and the second preset initial dosage is greater than the third preset initial dosage; When the target suspended solids concentration is greater than the first preset target suspended solids concentration, determining the first preset initial dosage as the initial dosage; When the target suspended solids concentration is less than or equal to the first preset target suspended solids concentration and greater than or equal to the second preset target suspended solids concentration, the second preset initial dosage is determined as the initial dosage; When the target suspended matter concentration is less than the second preset target suspended matter concentration, the third preset initial dosage is determined as the initial dosage.

6. The sewage treatment method based on anaerobic bacteria according to claim 5, characterized in that: When obtaining the environmental characteristic parameters of the sewage pool and constructing a characteristic parameter sequence, comparing the characteristic parameter sequence with a standard characteristic parameter sequence, and judging whether to adjust the initial dosage according to the comparison result, the method includes: Acquire the standard characteristic parameter sequence corresponding to the characteristic parameter sequence; When the environmental characteristic parameters in the characteristic parameter series are all equal to the standard environmental characteristic parameters in the standard characteristic parameter series, it is determined that the initial dosage is not adjusted, and the sewage pool is dosed with the initial dosage to complete sewage treatment; otherwise, it is determined that the initial dosage is adjusted.

7. The anaerobic sewage treatment method according to claim 6, characterized in that: When establishing a dosing correlation model according to an environmental data set, extracting environmental characteristic parameters with differences, and determining a dosing prediction adjustment factor based on the environmental characteristic parameters with differences and the dosing correlation model, the method includes: Extracting environmental characteristic parameters that are not equal to the standard environmental characteristic parameters from the characteristic parameter sequence and recording them as a difference characteristic parameter set; The environmental data set is divided into a training set and a test set, and a grid search is used to find the model establishment parameters to establish a random forest model; Using the training set to train the random forest model, substituting the test set into the trained random forest model and determining the accuracy of model prediction, when the accuracy reaches an accuracy threshold, determining the trained random forest model as the dosage association model; otherwise, continuing to train the random forest model until the accuracy threshold is reached; The difference characteristic parameter set is substituted into the dosage association model to determine the dosage prediction adjustment factor.

8. The sewage treatment method based on anaerobic bacteria according to claim 7, characterized in that: When comparing the dosage prediction adjustment factor with the historical data set and determining the dosage target adjustment factor based on the comparison result, the following steps are included: The historical data set includes a plurality of historical difference characteristic parameter sets and a plurality of historical dosage prediction adjustment factors, each historical difference characteristic parameter set corresponds to a historical dosage prediction adjustment factor; When there is a historical difference characteristic parameter set in the historical data set that is consistent with the difference characteristic parameter set, obtaining a historical dosage prediction adjustment factor corresponding to the historical difference characteristic parameter set; if the corresponding historical dosage prediction adjustment factor is equal to the dosage prediction adjustment factor, determining the dosage prediction adjustment factor as the dosage target adjustment factor; otherwise, clustering the historical data set and the difference characteristic parameter set to determine a clustered data set, and determining the mean of the historical dosage prediction adjustment factors in the clustered data set as the dosage target adjustment factor; When there is no historical difference characteristic parameter set consistent with the difference characteristic parameter set in the historical data set, the dosage prediction adjustment factor is determined as the dosage target adjustment factor.

9. The sewage treatment method based on anaerobic bacteria according to claim 8, characterized in that: When the initial dosage is adjusted according to the dosage target adjustment factor, the method includes: The initial dosage is directly proportional to the dosage target adjustment factor.

10. An anaerobic bacteria-based sewage treatment system, used for applying the anaerobic bacteria-based sewage treatment method according to any one of claims 1 to 9, characterized in that: include: The first processing module is configured to divide the sewage pool into a plurality of sewage detection areas, obtain the suspended solids concentration of each sewage detection area, compare the suspended solids concentration with a standard suspended solids concentration, and determine an initial dosage of anaerobic bacteria based on the comparison result; The second processing module is configured to obtain environmental characteristic parameters of the sewage pool and construct a characteristic parameter sequence, compare the characteristic parameter sequence with a standard characteristic parameter sequence, and determine whether to adjust the initial dosage according to the comparison result; a dosage determination module configured to, when determining to adjust the initial dosage, establish a dosage association model based on the environmental data set, extract different environmental characteristic parameters, determine a dosage prediction adjustment factor based on the different environmental characteristic parameters and the dosage association model, compare the dosage prediction adjustment factor with the historical data set, and determine a dosage target adjustment factor based on the comparison result; The dosing adjustment module is configured to adjust the initial dosing amount according to the dosing target adjustment factor, and dosing the sewage pool according to the adjusted initial dosing amount to complete sewage treatment.

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