A sewage treatment method and system based on simulation analysis

Abstract

The present invention discloses a sewage treatment method and system based on simulation analysis. First, the polluted water area is divided into K sub-areas, and within a measurement time period, microalgae are released and pollution monitoring is performed on each sub-area based on a preset microalgae control scheme, and measurement data and pollution situation data are collected. By periodically analyzing the migration, growth and pollution changes of microalgae, the migration coefficient, growth coefficient and pollution change rate of the sub-area are calculated. The migration coefficient and growth coefficient are used as independent variables, and the pollution change rate is used as the dependent variable. Linear fitting is performed, and the best fitting parameters are searched through a gradient descent optimization algorithm to obtain the fitting equation of each sub-area. Furthermore, the sub-areas are grouped based on migration and growth characteristics. Through the Bayesian optimization algorithm, with the goal of minimizing the pollution change rate, local and global optimal solutions are searched, and combined with microalgae resources, the optimal microalgae control scheme is set for each regional group to achieve efficient treatment of polluted water bodies.

Description

Technical Field

[0001] The present invention relates to the field of sewage data analysis, and more specifically, to a sewage treatment method and system based on simulation analysis. Background Art

[0002] Traditional wastewater treatment methods often suffer from inefficiencies, high costs, and high resource consumption, making them unable to meet the growing demand for environmental protection. In recent years, microalgae have attracted widespread attention in the field of water pollution treatment due to their efficient wastewater purification capabilities and potential for biomass energy production.

[0003] However, traditional water pollution and microalgae management analysis technologies often lack correlation analysis of microalgae migration and growth characteristics, regional pollution change analysis within water bodies, and localized microalgae solutions for different small areas, lacking a global solution for the entire region, resulting in insufficient microalgae regulation capabilities. Therefore, there is an urgent need for an efficient and intelligent wastewater treatment method based on microalgae. Summary of the Invention

[0004] The present invention overcomes the defects of the prior art and proposes a sewage treatment method and system based on simulation analysis.

[0005] A first aspect of the present invention provides a sewage treatment method based on simulation analysis, comprising:

[0006] Based on the basic information of the polluted water area, the polluted water area is divided into K water sub-areas;

[0007] During a measurement period, based on a preset microalgae control plan, each water body sub-area is monitored for microalgae release and pollution. Microalgae measurement data and pollution status data are collected from each water body sub-area. These data are periodically analyzed, and microalgae migration analysis, microalgae growth analysis, and pollution change analysis are performed on the water body sub-area. The migration coefficient, growth coefficient, and pollution change rate based on microalgae over multiple cycles are calculated.

[0008] The migration coefficient and growth coefficient are set as independent variables, the pollution change rate is used as the dependent variable, the weight coefficients Q1 and Q2 are set and applied to the independent variables and linear fitting is performed. Based on the gradient descent optimization algorithm, the best fitting values ​​of Q1 and Q2 are searched, and the pollution fitting equation of each water body sub-region is obtained;

[0009] The migration coefficient and growth coefficient of microalgae under multiple cycles are vectorized to obtain the migration characteristic vector and growth characteristic vector of each water body sub-region. The migration characteristic vector and growth characteristic vector are used as clustering inputs to perform regional clustering and form multiple regional groups.

[0010] Based on the Bayesian optimization algorithm, in the fitting equation, minimizing the pollution change rate is used as the objective function, and the migration coefficient and growth coefficient are used as decision variables. A local optimal search is performed for each regional group, and a global optimal search is performed for the entire polluted water area. Multiple local optimal solutions and a global optimal solution based on the migration coefficient and growth coefficient are searched out.

[0011] Based on the local optimal solution and a global optimal solution, multiple microalgae solutions are set. The pollution situation of each regional group is predicted through pollution data simulation. Combined with the microalgae resources, a corresponding microalgae solution is set for each regional group.

[0012] In this solution, based on the basic information of the polluted water area, the polluted water area is divided into K water sub-areas, specifically:

[0013] Obtain basic information on polluted water areas, including water area, water map outline, water depth information, and water pollution information;

[0014] Based on the basic information, the polluted water area is divided into regions to obtain K water body sub-regions;

[0015] Each of the water body sub-regions is within a preset spatial range, and the water body characteristics within a water body sub-region are consistent.

[0016] In this scheme, during a measurement period, based on a preset microalgae control scheme, microalgae release and pollution monitoring are performed in each water body sub-area. Microalgae measurement data and pollution status data of each water body sub-area are collected, and the microalgae measurement data and pollution status data are periodically analyzed. Microalgae migration analysis, microalgae growth analysis, and pollution change analysis are performed on the water body sub-area. The migration coefficient, growth coefficient, and pollution change rate based on microalgae over multiple cycles are calculated, specifically as follows:

[0017] During a measurement period, based on a preset microalgae control plan, each water body sub-area is subjected to microalgae release and pollution monitoring, and microalgae measurement data and pollution status data of each water body sub-area are collected;

[0018] Microalgae measurement data includes microalgae species, the number of various microalgae, and the total amount of microalgae. Pollution data includes the concentrations of various preset water pollutants.

[0019] The measurement time period was divided into multiple analysis periods, with a water body sub-area as the analysis unit. Based on each analysis period, the change rates of the number of microalgae species and the number of various microalgae between analysis periods were calculated to obtain P1 and P2, respectively. P1 represents the change rate of the number of microalgae species, and P2 represents the average change rate of the number of various microalgae.

[0020] Based on P1 and P2, a two-dimensional vector is set to obtain the first vector, and the average distance between the first vector and the first vector corresponding to the adjacent water body sub-region is calculated, and the average distance is used as the migration coefficient;

[0021] The distance calculation is applied to the standard Euclidean distance calculation between vectors, and the adjacent water body sub-region is the region that is geographically adjacent to the one water body sub-region;

[0022] The growth coefficient is obtained by taking the weighted average of P1 and P2;

[0023] Based on the pollution data, the pollutant concentration change analysis is performed for each analysis period, and the mean of the change rates of multiple pollutants is calculated to obtain the pollution change rate.

[0024] In this scheme, the migration coefficient and growth coefficient are set as independent variables, the pollution change rate is used as the dependent variable, the weight coefficients Q1 and Q2 are set and applied to the independent variables and linear fitting is performed. Based on the gradient descent optimization algorithm, the best fitting values ​​of Q1 and Q2 are searched, and the pollution fitting equation for each water body sub-area is obtained, which is specifically:

[0025] The migration coefficient and growth coefficient are set as independent variables, the pollution change rate is used as the dependent variable, and the weight coefficients Q1 and Q2 are set to be applied to the independent variables to obtain the fitting equation based on linear regression;

[0026] Based on the gradient descent optimization algorithm, the difference between the fitting equation result value and the pollution change rate under multiple analysis cycles is used as the loss function. Through gradient descent iterative calculation, the optimal weight coefficients Q1 and Q2 are searched until the loss function converges to the preset range;

[0027] Record the best fitting values ​​of Q1 and Q2 at this time, and obtain the pollution fitting equation;

[0028] Fitting analysis is performed on multiple water body sub-regions to obtain the corresponding pollution fitting equations.

[0029] In this scheme, the migration coefficient and growth coefficient of microalgae under multiple cycles are vectorized to obtain the migration characteristic vector and growth characteristic vector of each water body sub-region. The migration characteristic vector and growth characteristic vector are used as clustering inputs to perform regional clustering grouping to form multiple regional groups, specifically:

[0030] Based on a water body sub-region, the migration coefficient and growth coefficient of microalgae under multiple analysis periods are vectorized to form multi-dimensional migration characteristic vectors and growth characteristic vectors;

[0031] Based on the DBSCAN clustering algorithm, the migration eigenvector and growth eigenvector of each water body sub-region are used as clustering inputs, and each water body sub-region is used as a clustering unit. K water body sub-regions are clustered and grouped. In the clustering process, the similarity of the migration eigenvector and growth eigenvector between water body sub-regions is comprehensively considered to form multiple region groups.

[0032] In this solution, based on the Bayesian optimization algorithm, in the fitting equation, the minimization of the pollution change rate is used as the objective function, and the migration coefficient and growth coefficient are used as decision variables. A local optimal search is performed for each regional group, and a global optimal search is performed for the entire polluted water area. Multiple local optimal solutions and a global optimal solution based on the migration coefficient and growth coefficient are searched out. Specifically,

[0033] For a regional group, the pollution fitting equation of a water body sub-region in the regional group is randomly selected as the current fitting equation;

[0034] Based on the Bayesian optimization algorithm, in the current fitting equation, the minimization of the pollution change rate is used as the objective function, the migration coefficient and the growth coefficient are used as decision variables, and the optimal parameters are iteratively searched within the preset decision variable range and the constraints are set. The optimal solution obtained is set as the local optimal solution.

[0035] generating multiple local optimal solutions based on multiple region groups;

[0036] For the overall polluted water area, the pollution fitting equations of K water body sub-areas are parameter-averaged to generate the mean fitting equation;

[0037] Based on the Bayesian optimization algorithm, in the mean fitting equation, the minimization of the pollution change rate is used as the objective function, the migration coefficient and the growth coefficient are used as decision variables, and the optimal parameters are iteratively searched within the preset decision variable range and the constraints are set. The optimal solution obtained is set as the global optimal solution.

[0038] Each optimal solution includes an optimal migration coefficient and an optimal growth coefficient.

[0039] In this solution, multiple microalgae solutions are set based on local optimal solutions and a global optimal solution. The pollution situation of each regional group is simulated and predicted through pollution data. In combination with microalgae resources, a corresponding microalgae solution is set for each regional group. Specifically:

[0040] For each local optimal solution and global optimal solution, the optimal migration coefficient and optimal growth coefficient are obtained. Based on the optimal migration coefficient and optimal growth coefficient, the microalgae input type analysis and input amount matching are carried out, and the microalgae plan is set in combination with the microalgae resources.

[0041] Each local optimal solution and global optimal solution corresponds to a microalgae solution;

[0042] In the pollution data, the pollution change rate of the water body sub-area is periodically analyzed, and the pollution change rate is serialized and simulated through the ARIMA model to generate a predicted pollution change rate;

[0043] The microalgae solution corresponding to the local optimal solution is applied to the corresponding regional group;

[0044] If the predicted pollution change rate of a water body sub-region is lower than the preset pollution rate, it will be marked as a general sub-region, and the microalgae solution corresponding to the global optimal solution will be applied to the general sub-region.

[0045] In this solution, each of the multiple area groups includes multiple water body sub-areas.

[0046] A second aspect of the present invention further provides a sewage treatment system based on simulation analysis, the system comprising: a memory and a processor, wherein the memory includes a sewage treatment program based on simulation analysis, and when the sewage treatment program based on simulation analysis is executed by the processor, the following steps are implemented:

[0047] Based on the basic information of the polluted water area, the polluted water area is divided into K water sub-areas;

[0048] During a measurement period, based on a preset microalgae control plan, each water body sub-area is monitored for microalgae release and pollution. Microalgae measurement data and pollution status data are collected from each water body sub-area. These data are periodically analyzed, and microalgae migration analysis, microalgae growth analysis, and pollution change analysis are performed on the water body sub-area. The migration coefficient, growth coefficient, and pollution change rate based on microalgae over multiple cycles are calculated.

[0049] The migration coefficient and growth coefficient are set as independent variables, the pollution change rate is used as the dependent variable, the weight coefficients Q1 and Q2 are set and applied to the independent variables and linear fitting is performed. Based on the gradient descent optimization algorithm, the best fitting values ​​of Q1 and Q2 are searched, and the pollution fitting equation of each water body sub-region is obtained;

[0050] The migration coefficient and growth coefficient of microalgae under multiple cycles are vectorized to obtain the migration characteristic vector and growth characteristic vector of each water body sub-region. The migration characteristic vector and growth characteristic vector are used as clustering inputs to perform regional clustering and form multiple regional groups.

[0051] Based on the Bayesian optimization algorithm, in the fitting equation, minimizing the pollution change rate is used as the objective function, and the migration coefficient and growth coefficient are used as decision variables. A local optimal search is performed for each regional group, and a global optimal search is performed for the entire polluted water area. Multiple local optimal solutions and a global optimal solution based on the migration coefficient and growth coefficient are searched out.

[0052] Based on the local optimal solution and a global optimal solution, multiple microalgae solutions are set. The pollution situation of each regional group is predicted through pollution data simulation. Combined with the microalgae resources, a corresponding microalgae solution is set for each regional group.

[0053] The third aspect of the present invention also provides a computer-readable storage medium, which includes a sewage treatment program based on simulation analysis. When the sewage treatment program based on simulation analysis is executed by a processor, the steps of the sewage treatment method based on simulation analysis as described in any one of the above items are implemented.

[0054] The present invention discloses a sewage treatment method and system based on simulation analysis. First, the polluted water area is divided into K sub-areas, and within a measurement time period, microalgae are released and pollution monitoring is performed on each sub-area based on a preset microalgae control scheme, and measurement data and pollution situation data are collected. By periodically analyzing the migration, growth and pollution changes of microalgae, the migration coefficient, growth coefficient and pollution change rate of the sub-area are calculated. The migration coefficient and growth coefficient are used as independent variables, and the pollution change rate is used as the dependent variable. Linear fitting is performed, and the best fitting parameters are searched through a gradient descent optimization algorithm to obtain the fitting equation of each sub-area. Furthermore, the sub-areas are grouped based on migration and growth characteristics. Through the Bayesian optimization algorithm, with the goal of minimizing the pollution change rate, local and global optimal solutions are searched, and combined with microalgae resources, the optimal microalgae control scheme is set for each regional group to achieve efficient treatment of polluted water bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flow chart of a sewage treatment method based on simulation analysis according to the present invention is shown;

[0056] Figure 2 The flow chart of obtaining water body sub-regions of the present invention is shown;

[0057] Figure 3 A block diagram of a sewage treatment system based on simulation analysis according to the present invention is shown;

[0058] Figure 4 A schematic diagram of analyzing microscopic images of microalgae according to the present invention is shown;

[0059] Figure 5 FIG1 shows a simplified block diagram of the data processing flow of a sewage treatment system based on simulation analysis according to the present invention; DETAILED DESCRIPTION

[0060] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0062] Figure 1 A flow chart of a sewage treatment method based on simulation analysis of the present invention is shown.

[0063] like Figure 1 As shown, the first aspect of the present invention provides a sewage treatment method based on simulation analysis, comprising:

[0064] S102, based on the basic information of the polluted water area, dividing the polluted water area into K water body sub-areas;

[0065] S104: During a measurement period, based on a preset microalgae control plan, microalgae release and pollution monitoring are performed in each water body sub-area. Microalgae measurement data and pollution status data are collected from each water body sub-area. The microalgae measurement data and pollution status data are periodically analyzed. Microalgae migration analysis, microalgae growth analysis, and pollution change analysis are performed on the water body sub-area. The migration coefficient, growth coefficient, and pollution change rate of the microalgae are calculated over multiple periods.

[0066] S106, setting the migration coefficient and the growth coefficient as independent variables, the pollution change rate as the dependent variable, setting weight coefficients Q1 and Q2 to apply to the independent variables and performing linear fitting, searching for the best fitting values ​​of Q1 and Q2 based on the gradient descent optimization algorithm, and obtaining the pollution fitting equation for each water body sub-region;

[0067] S108, performing data vectorization based on the migration coefficient and growth coefficient of the microalgae under multiple cycles to obtain a migration characteristic vector and a growth characteristic vector for each water body sub-region, and performing regional clustering grouping using the migration characteristic vector and the growth characteristic vector as clustering input to form multiple regional groups;

[0068] S110, based on the Bayesian optimization algorithm, uses minimization of the pollution change rate as the objective function in the fitting equation, and uses the migration coefficient and growth coefficient as decision variables. A local optimal search is performed for each regional group, and a global optimal search is performed for the entire polluted water area. Multiple local optimal solutions and a global optimal solution based on the migration coefficient and growth coefficient are searched out.

[0069] S112, setting multiple microalgae solutions based on the local optimal solution and a global optimal solution, predicting the pollution situation of each regional group through pollution situation data simulation, and setting a corresponding microalgae solution for each regional group in combination with microalgae resources.

[0070] Figure 2 The flow chart of obtaining water body sub-regions of the present invention is shown;

[0071] According to an embodiment of the present invention, the polluted water area is divided into K water sub-areas based on the basic information of the polluted water area, specifically:

[0072] S202, obtaining basic information of the polluted water area, including water area, water map outline, water depth information, and water pollution information;

[0073] S204, dividing the polluted water area into regions based on the basic information to obtain K water body sub-regions;

[0074] S206: Each of the water body sub-regions is within a preset spatial range, and the water body characteristics within a water body sub-region are consistent.

[0075] It should be noted that the preset spatial range is specifically an area range. In addition, based on the needs of pollution research, areas with the same pollution characteristics can be grouped.

[0076] According to an embodiment of the present invention, during a measurement period, based on a preset microalgae control scheme, microalgae release and pollution monitoring are performed in each water body sub-region, microalgae measurement data and pollution status data of each water body sub-region are collected, the microalgae measurement data and pollution status data are periodically analyzed, and microalgae migration analysis, microalgae growth analysis, and pollution change analysis are performed on the water body sub-region. The migration coefficient, growth coefficient, and pollution change rate based on microalgae over multiple periods are calculated, specifically as follows:

[0077] During a measurement period, based on a preset microalgae control plan, each water body sub-area is subjected to microalgae release and pollution monitoring, and microalgae measurement data and pollution status data of each water body sub-area are collected;

[0078] Microalgae measurement data includes microalgae species, the number of various microalgae, and the total amount of microalgae. Pollution data includes the concentrations of various preset water pollutants.

[0079] The measurement time period was divided into multiple analysis periods, with a water body sub-area as the analysis unit. Based on each analysis period, the change rates of the number of microalgae species and the number of various microalgae between analysis periods were calculated to obtain P1 and P2, respectively. P1 represents the change rate of the number of microalgae species, and P2 represents the average change rate of the number of various microalgae.

[0080] Based on P1 and P2, a two-dimensional vector is set to obtain the first vector, and the average distance between the first vector and the first vector corresponding to the adjacent water body sub-region is calculated, and the average distance is used as the migration coefficient;

[0081] The distance calculation is applied to the standard Euclidean distance calculation between vectors, and the adjacent water body sub-region is the region that is geographically adjacent to the one water body sub-region;

[0082] The growth coefficient is obtained by taking the weighted average of P1 and P2;

[0083] Based on the pollution data, the pollutant concentration change analysis is performed for each analysis period, and the mean of the change rates of multiple pollutants is calculated to obtain the pollution change rate.

[0084] It should be noted that the preset microalgae control plan includes information such as the amount of microalgae resources added, the type of release, and the frequency of release. A variety of preset water pollutants include ammonia nitrogen, nitrates, heavy metals, polycyclic aromatic hydrocarbons, and other preset pollutants, which can be treated and degraded by microalgae. In the weighted average, the weight is the user-set value. The migration coefficient can effectively reflect the migration trend of microalgae in a sub-region. It is obtained based on the difference analysis of the microalgae quantity characteristics in related adjacent areas. The larger the value, the stronger the migration trend. The longer the time, the lower the richness of the microalgae in the region.

[0085] Based on each analysis period, the change rate of the number of microalgae species and the number of various microalgae between analysis periods is calculated to obtain P1. In P2, the change rate of the number of microalgae species between adjacent analysis periods is specifically calculated to obtain P1. The same is true for the number of microalgae. Finally, the change rate of the microalgae is averaged among the various microalgae to obtain P2.

[0086] According to an embodiment of the present invention, the migration coefficient and the growth coefficient are set as independent variables, the pollution change rate is used as the dependent variable, the weight coefficients Q1 and Q2 are set and applied to the independent variables and linear fitting is performed. Based on the gradient descent optimization algorithm, the best fitting values ​​of Q1 and Q2 are searched, and the pollution fitting equation for each water body sub-region is obtained, which is specifically:

[0087] The migration coefficient and growth coefficient are set as independent variables, the pollution change rate is used as the dependent variable, and the weight coefficients Q1 and Q2 are set to be applied to the independent variables to obtain the fitting equation based on linear regression;

[0088] Based on the gradient descent optimization algorithm, the difference between the fitting equation result value and the pollution change rate under multiple analysis cycles is used as the loss function. Through gradient descent iterative calculation, the optimal weight coefficients Q1 and Q2 are searched until the loss function converges to the preset range;

[0089] Record the best fitting values ​​of Q1 and Q2 at this time, and obtain the pollution fitting equation;

[0090] Fitting analysis is performed on multiple water body sub-regions to obtain the corresponding pollution fitting equations.

[0091] It should be noted that the pollution fitting equation specifically functions to explore the correlation between migration trends and growth trends and pollution changes, and the embodiment of the present invention performs fitting analysis on multiple sub-regions to explore the pollution characteristics and microalgae control characteristics of the water area.

[0092] The pollution fitting equation is specifically:

[0093] ;

[0094] in, is the pollution change rate, Q1 and Q2 are the fitting weight coefficients, is the migration coefficient, is the growth coefficient.

[0095] According to an embodiment of the present invention, the data is vectorized by the migration coefficient and growth coefficient of microalgae under multiple cycles to obtain the migration characteristic vector and growth characteristic vector of each water body sub-region, and the migration characteristic vector and growth characteristic vector are used as clustering inputs to perform regional clustering grouping to form multiple regional groups, specifically:

[0096] Based on a water body sub-region, the migration coefficient and growth coefficient of microalgae under multiple analysis periods are vectorized to form multi-dimensional migration characteristic vectors and growth characteristic vectors;

[0097] Based on the DBSCAN clustering algorithm, the migration eigenvector and growth eigenvector of each water body sub-region are used as clustering inputs, and each water body sub-region is used as a clustering unit. K water body sub-regions are clustered and grouped. In the clustering process, the similarity of the migration eigenvector and growth eigenvector between water body sub-regions is comprehensively considered to form multiple region groups.

[0098] It should be noted that both the migration feature vector and the growth feature vector include data in multiple dimensions, and the value of each dimension corresponds to the migration coefficient or the growth coefficient. In clustering, the similarity of the input feature vectors can be calculated using the standard Euclidean distance.

[0099] According to an embodiment of the present invention, the Bayesian optimization algorithm is based on the fitting equation, with minimization of the pollution change rate as the objective function, and migration coefficient and growth coefficient as decision variables. A local optimal search is performed for each regional group, and a global optimal search is performed for the entire polluted water area. Multiple local optimal solutions and a global optimal solution based on the migration coefficient and growth coefficient are searched out, specifically:

[0100] For a regional group, the pollution fitting equation of a water body sub-region in the regional group is randomly selected as the current fitting equation;

[0101] Based on the Bayesian optimization algorithm, in the current fitting equation, the minimization of the pollution change rate is used as the objective function, the migration coefficient and the growth coefficient are used as decision variables, and the optimal parameters are iteratively searched within the preset decision variable range and the constraints are set. The optimal solution obtained is set as the local optimal solution.

[0102] generating multiple local optimal solutions based on multiple region groups;

[0103] For the overall polluted water area, the pollution fitting equations of K water body sub-areas are parameter-averaged to generate the mean fitting equation;

[0104] Based on the Bayesian optimization algorithm, in the mean fitting equation, the minimization of the pollution change rate is used as the objective function, the migration coefficient and the growth coefficient are used as decision variables, and the optimal parameters are iteratively searched within the preset decision variable range and the constraints are set. The optimal solution obtained is set as the global optimal solution.

[0105] Each optimal solution includes an optimal migration coefficient and an optimal growth coefficient.

[0106] It should be noted that the preset decision variable range includes the reasonable variation range of the migration coefficient and the growth coefficient. The pollution fitting equations for the K water body sub-regions are parameter-averaged to generate a mean fitting equation. That is, the corresponding weight coefficients Q1 and Q2 are averaged, the averaged fitting equation is set, and the mean fitting equation is used as the fitting equation for the entire water body region for analysis.

[0107] According to an embodiment of the present invention, multiple microalgae solutions are set based on local optimal solutions and a global optimal solution. The pollution situation of each regional group is simulated and predicted through pollution situation data. In combination with microalgae resources, a corresponding microalgae solution is set for each regional group. Specifically,

[0108] For each local optimal solution and global optimal solution, the optimal migration coefficient and optimal growth coefficient are obtained. Based on the optimal migration coefficient and optimal growth coefficient, the microalgae input type analysis and input amount matching are carried out, and the microalgae plan is set in combination with the microalgae resources.

[0109] Each local optimal solution and global optimal solution corresponds to a microalgae solution;

[0110] In the pollution data, the pollution change rate of the water body sub-area is periodically analyzed, and the pollution change rate is serialized and simulated through the ARIMA model to generate a predicted pollution change rate;

[0111] The microalgae solution corresponding to the local optimal solution is applied to the corresponding regional group;

[0112] If the predicted pollution change rate of a water body sub-region is lower than the preset pollution rate, it will be marked as a general sub-region, and the microalgae solution corresponding to the global optimal solution will be applied to the general sub-region.

[0113] It should be noted that in the aforementioned analysis of microalgae input types and matching of input amounts, migration trends and growth requirements are analyzed based on migration coefficients and growth coefficients, and the corresponding microalgae types, total amounts, and input frequency are set to form a microalgae plan. Each regional group corresponds to a local optimal solution.

[0114] The microalgae solution corresponding to each local optimal solution generated can be applied to multiple water sub-areas within the regional group. At the same time, based on the pollution situation, if the change rate of a sub-area is lower than the preset value, it can be applied based on the globally optimized microalgae solution. The globally optimized microalgae solution is a general solution, and the cost of the microalgae solution is often low. Therefore, it can be applied to areas where the degree of pollution change is not large, so as to achieve cost reduction and efficiency improvement of microalgae governance.

[0115] According to an embodiment of the present invention, each of the plurality of area groups includes a plurality of water body sub-areas.

[0116] It's worth noting that conventional water pollution and microalgae management analysis technologies often lack correlation analysis of microalgae migration and growth characteristics, regional pollution change analysis within water bodies, and lack localized microalgae solutions for different small areas, lacking a global solution for the entire region, resulting in insufficient microalgae control capabilities. The embodiments of the present invention can effectively address the aforementioned technical issues.

[0117] According to an embodiment of the present invention, the pollution change rate of the water body sub-region is periodically analyzed in the pollution situation data, and the pollution change rate is serialized and simulated and predicted by the ARIMA model to generate a predicted pollution change rate, which also includes:

[0118] Serialize the historical analysis pollution change rate data of multiple water body sub-regions to form training data;

[0119] Build an ARIMA model, import the training data into the ARIMA model for prediction training, and determine the model's p, d, and q parameters;

[0120] Based on multiple analysis cycles, the pollution change rate of the water body sub-area is numerically serialized to form a first sequence, and the first sequence is introduced into the ARIMA model for multiple predictions to generate multiple predicted values;

[0121] Multiple predicted values ​​are combined with the first sequence to form a pollution change rate sequence. The pollution change rate trend is predicted based on the pollution fitting equation of the water body sub-region. It is judged whether the pollution change rate sequence is consistent with the predicted trend of the pollution fitting equation. If they are consistent, the corresponding predicted value is marked as the predicted pollution change rate of the water body sub-region.

[0122] It should be noted that p represents the autoregressive order of the time series, d represents the differencing order of the time series, and q represents the moving average order of the time series. The consistency of the forecast trend is determined by performing a deviation analysis based on the forecast value and the forecast data of the equation.

[0123] In complex water environments, in addition to using the ARIMA model to predict pollution changes, the predicted data also needs to be screened to effectively identify multiple trends. Based on this, the present invention uses the ARIMA model to initially generate multiple pollution trends for subregions. Based on the fitting equation, predicted values ​​that converge within the relevant error range (i.e., predicted values ​​with consistent trends) are screened to obtain accurate multiple pollution trends. These trends can then be applied to the development of microalgae solutions for efficient water pollution control. Multiple predicted pollution change rates can be included for a single water subregion.

[0124] Figure 3 A block diagram of a sewage treatment system based on simulation analysis of the present invention is shown.

[0125] A second aspect of the present invention further provides a sewage treatment system 2 based on simulation analysis, the system comprising: a memory 31 and a processor 32, wherein the memory 31 includes a sewage treatment program based on simulation analysis, and when the sewage treatment program based on simulation analysis is executed by the processor 32, the following steps are implemented:

[0126] Based on the basic information of the polluted water area, the polluted water area is divided into K water sub-areas;

[0127] During a measurement period, based on a preset microalgae control plan, each water body sub-area is monitored for microalgae release and pollution. Microalgae measurement data and pollution status data are collected from each water body sub-area. These data are periodically analyzed, and microalgae migration analysis, microalgae growth analysis, and pollution change analysis are performed on the water body sub-area. The migration coefficient, growth coefficient, and pollution change rate based on microalgae over multiple cycles are calculated.

[0128] The migration coefficient and growth coefficient are set as independent variables, the pollution change rate is used as the dependent variable, the weight coefficients Q1 and Q2 are set and applied to the independent variables and linear fitting is performed. Based on the gradient descent optimization algorithm, the best fitting values ​​of Q1 and Q2 are searched, and the pollution fitting equation of each water body sub-region is obtained;

[0129] The migration coefficient and growth coefficient of microalgae under multiple cycles are vectorized to obtain the migration characteristic vector and growth characteristic vector of each water body sub-region. The migration characteristic vector and growth characteristic vector are used as clustering inputs to perform regional clustering and form multiple regional groups.

[0130] Based on the Bayesian optimization algorithm, in the fitting equation, minimizing the pollution change rate is used as the objective function, and the migration coefficient and growth coefficient are used as decision variables. A local optimal search is performed for each regional group, and a global optimal search is performed for the entire polluted water area. Multiple local optimal solutions and a global optimal solution based on the migration coefficient and growth coefficient are searched out.

[0131] Based on the local optimal solution and a global optimal solution, multiple microalgae solutions are set. The pollution situation of each regional group is predicted through pollution data simulation. Combined with the microalgae resources, a corresponding microalgae solution is set for each regional group.

[0132] According to an embodiment of the present invention, the polluted water area is divided into K water sub-areas based on the basic information of the polluted water area, specifically:

[0133] Obtain basic information on polluted water areas, including water area, water map outline, water depth information, and water pollution information;

[0134] Based on the basic information, the polluted water area is divided into regions to obtain K water body sub-regions;

[0135] Each of the water body sub-regions is within a preset spatial range, and the water body characteristics within a water body sub-region are consistent.

[0136] It should be noted that the preset spatial range is specifically an area range. In addition, based on the needs of pollution research, areas with the same pollution characteristics can be grouped.

[0137] According to an embodiment of the present invention, during a measurement period, based on a preset microalgae control scheme, microalgae release and pollution monitoring are performed in each water body sub-region, microalgae measurement data and pollution status data of each water body sub-region are collected, the microalgae measurement data and pollution status data are periodically analyzed, and microalgae migration analysis, microalgae growth analysis, and pollution change analysis are performed on the water body sub-region. The migration coefficient, growth coefficient, and pollution change rate based on microalgae over multiple periods are calculated, specifically as follows:

[0138] During a measurement period, based on a preset microalgae control plan, each water body sub-area is subjected to microalgae release and pollution monitoring, and microalgae measurement data and pollution status data of each water body sub-area are collected;

[0139] Microalgae measurement data includes microalgae species, the number of various microalgae, and the total amount of microalgae. Pollution data includes the concentrations of various preset water pollutants.

[0140] The measurement time period was divided into multiple analysis periods, with a water body sub-area as the analysis unit. Based on each analysis period, the change rates of the number of microalgae species and the number of various microalgae between analysis periods were calculated to obtain P1 and P2, respectively. P1 represents the change rate of the number of microalgae species, and P2 represents the average change rate of the number of various microalgae.

[0141] Based on P1 and P2, a two-dimensional vector is set to obtain the first vector, and the average distance between the first vector and the first vector corresponding to the adjacent water body sub-region is calculated, and the average distance is used as the migration coefficient;

[0142] The distance calculation is applied to the standard Euclidean distance calculation between vectors, and the adjacent water body sub-region is the region that is geographically adjacent to the one water body sub-region;

[0143] The growth coefficient is obtained by taking the weighted average of P1 and P2;

[0144] Based on the pollution data, the pollutant concentration change analysis is performed for each analysis period, and the mean of the change rates of multiple pollutants is calculated to obtain the pollution change rate.

[0145] It should be noted that the preset microalgae control plan includes information such as the amount of microalgae resources added, the type of release, and the frequency of release. A variety of preset water pollutants include ammonia nitrogen, nitrates, heavy metals, polycyclic aromatic hydrocarbons, and other preset pollutants, which can be treated and degraded by microalgae. In the weighted average, the weight is the user-set value. The migration coefficient can effectively reflect the migration trend of microalgae in a sub-region. It is obtained based on the difference analysis of the microalgae quantity characteristics in related adjacent areas. The larger the value, the stronger the migration trend. The longer the time, the lower the richness of the microalgae in the region.

[0146] Based on each analysis period, the change rate of the number of microalgae species and the number of various microalgae between analysis periods is calculated to obtain P1. In P2, the change rate of the number of microalgae species between adjacent analysis periods is specifically calculated to obtain P1. The same is true for the number of microalgae. Finally, the change rate of the microalgae is averaged among the various microalgae to obtain P2.

[0147] According to an embodiment of the present invention, the migration coefficient and the growth coefficient are set as independent variables, the pollution change rate is used as the dependent variable, the weight coefficients Q1 and Q2 are set and applied to the independent variables and linear fitting is performed. Based on the gradient descent optimization algorithm, the best fitting values ​​of Q1 and Q2 are searched, and the pollution fitting equation for each water body sub-region is obtained, which is specifically:

[0148] The migration coefficient and growth coefficient are set as independent variables, the pollution change rate is used as the dependent variable, and the weight coefficients Q1 and Q2 are set to be applied to the independent variables to obtain the fitting equation based on linear regression;

[0149] Based on the gradient descent optimization algorithm, the difference between the fitting equation result value and the pollution change rate under multiple analysis cycles is used as the loss function. Through gradient descent iterative calculation, the optimal weight coefficients Q1 and Q2 are searched until the loss function converges to the preset range;

[0150] Record the best fitting values ​​of Q1 and Q2 at this time, and obtain the pollution fitting equation;

[0151] Fitting analysis is performed on multiple water body sub-regions to obtain the corresponding pollution fitting equations.

[0152] It should be noted that the pollution fitting equation specifically functions to explore the correlation between migration trends and growth trends and pollution changes, and the embodiment of the present invention performs fitting analysis on multiple sub-regions to explore the pollution characteristics and microalgae control characteristics of the water area.

[0153] The pollution fitting equation is specifically:

[0154] ;

[0155] in, is the pollution change rate, Q1 and Q2 are the fitting weight coefficients, is the migration coefficient, is the growth coefficient.

[0156] According to an embodiment of the present invention, the data is vectorized by the migration coefficient and growth coefficient of microalgae under multiple cycles to obtain the migration characteristic vector and growth characteristic vector of each water body sub-region, and the migration characteristic vector and growth characteristic vector are used as clustering inputs to perform regional clustering grouping to form multiple regional groups, specifically:

[0157] Based on a water body sub-region, the migration coefficient and growth coefficient of microalgae under multiple analysis periods are vectorized to form multi-dimensional migration characteristic vectors and growth characteristic vectors;

[0158] Based on the DBSCAN clustering algorithm, the migration eigenvector and growth eigenvector of each water body sub-region are used as clustering inputs, and each water body sub-region is used as a clustering unit. K water body sub-regions are clustered and grouped. In the clustering process, the similarity of the migration eigenvector and growth eigenvector between water body sub-regions is comprehensively considered to form multiple region groups.

[0159] It should be noted that both the migration feature vector and the growth feature vector include data in multiple dimensions, and the value of each dimension corresponds to the migration coefficient or the growth coefficient. In clustering, the similarity of the input feature vectors can be calculated using the standard Euclidean distance.

[0160] According to an embodiment of the present invention, the Bayesian optimization algorithm is based on the fitting equation, with minimization of the pollution change rate as the objective function, and migration coefficient and growth coefficient as decision variables. A local optimal search is performed for each regional group, and a global optimal search is performed for the entire polluted water area. Multiple local optimal solutions and a global optimal solution based on the migration coefficient and growth coefficient are searched out, specifically:

[0161] For a regional group, the pollution fitting equation of a water body sub-region in the regional group is randomly selected as the current fitting equation;

[0162] Based on the Bayesian optimization algorithm, in the current fitting equation, the minimization of the pollution change rate is used as the objective function, the migration coefficient and the growth coefficient are used as decision variables, and the optimal parameters are iteratively searched within the preset decision variable range and the constraints are set. The optimal solution obtained is set as the local optimal solution.

[0163] generating multiple local optimal solutions based on multiple region groups;

[0164] For the overall polluted water area, the pollution fitting equations of K water body sub-areas are parameter-averaged to generate the mean fitting equation;

[0165] Based on the Bayesian optimization algorithm, in the mean fitting equation, the minimization of the pollution change rate is used as the objective function, the migration coefficient and the growth coefficient are used as decision variables, and the optimal parameters are iteratively searched within the preset decision variable range and the constraints are set. The optimal solution obtained is set as the global optimal solution.

[0166] Each optimal solution includes an optimal migration coefficient and an optimal growth coefficient.

[0167] It should be noted that the preset decision variable range includes the reasonable variation range of the migration coefficient and the growth coefficient. The pollution fitting equations for the K water body sub-regions are parameter-averaged to generate a mean fitting equation. That is, the corresponding weight coefficients Q1 and Q2 are averaged, the averaged fitting equation is set, and the mean fitting equation is used as the fitting equation for the entire water body region for analysis.

[0168] According to an embodiment of the present invention, multiple microalgae solutions are set based on local optimal solutions and a global optimal solution. The pollution situation of each regional group is simulated and predicted through pollution situation data. In combination with microalgae resources, a corresponding microalgae solution is set for each regional group. Specifically,

[0169] For each local optimal solution and global optimal solution, the optimal migration coefficient and optimal growth coefficient are obtained. Based on the optimal migration coefficient and optimal growth coefficient, the microalgae input type analysis and input amount matching are carried out, and the microalgae plan is set in combination with the microalgae resources.

[0170] Each local optimal solution and global optimal solution corresponds to a microalgae solution;

[0171] In the pollution data, the pollution change rate of the water body sub-area is periodically analyzed, and the pollution change rate is serialized and simulated through the ARIMA model to generate a predicted pollution change rate;

[0172] The microalgae solution corresponding to the local optimal solution is applied to the corresponding regional group;

[0173] If the predicted pollution change rate of a water body sub-region is lower than the preset pollution rate, it will be marked as a general sub-region, and the microalgae solution corresponding to the global optimal solution will be applied to the general sub-region.

[0174] It should be noted that in the aforementioned analysis of microalgae input types and matching of input amounts, migration trends and growth requirements are analyzed based on migration coefficients and growth coefficients, and the corresponding microalgae types, total amounts, and input frequency are set to form a microalgae plan. Each regional group corresponds to a local optimal solution.

[0175] The microalgae solution corresponding to each local optimal solution generated can be applied to multiple water sub-areas within the regional group. At the same time, based on the pollution situation, if the change rate of a sub-area is lower than the preset value, it can be applied based on the globally optimized microalgae solution. The globally optimized microalgae solution is a general solution, and the cost of the microalgae solution is often low. Therefore, it can be applied to areas where the degree of pollution change is not large, so as to achieve cost reduction and efficiency improvement of microalgae governance.

[0176] According to an embodiment of the present invention, each of the plurality of area groups includes a plurality of water body sub-areas.

[0177] It's worth noting that conventional water pollution and microalgae management analysis technologies often lack correlation analysis of microalgae migration and growth characteristics, regional pollution change analysis within water bodies, and lack localized microalgae solutions for different small areas, lacking a global solution for the entire region, resulting in insufficient microalgae control capabilities. The embodiments of the present invention can effectively address the aforementioned technical issues.

[0178] The third aspect of the present invention also provides a computer-readable storage medium, which includes a sewage treatment program based on simulation analysis. When the sewage treatment program based on simulation analysis is executed by a processor, the steps of the sewage treatment method based on simulation analysis as described in any one of the above items are implemented.

[0179] Figure 4 A schematic diagram of analyzing microscopic images of microalgae according to the present invention is shown;

[0180] like Figure 4 As shown, the microalgae were measured and the number of samples was analyzed and counted based on the microscopic images.

[0181] Figure 5 FIG1 shows a simplified block diagram of the data processing flow of a sewage treatment system based on simulation analysis according to the present invention;

[0182] The present invention discloses a sewage treatment method and system based on simulation analysis. First, the polluted water area is divided into K sub-areas, and within a measurement time period, microalgae are released and pollution monitoring is performed on each sub-area based on a preset microalgae control scheme, and measurement data and pollution situation data are collected. By periodically analyzing the migration, growth and pollution changes of microalgae, the migration coefficient, growth coefficient and pollution change rate of the sub-area are calculated. The migration coefficient and growth coefficient are used as independent variables, and the pollution change rate is used as the dependent variable. Linear fitting is performed, and the best fitting parameters are searched through a gradient descent optimization algorithm to obtain the fitting equation of each sub-area. Furthermore, the sub-areas are grouped based on migration and growth characteristics. Through the Bayesian optimization algorithm, with the goal of minimizing the pollution change rate, local and global optimal solutions are searched, and combined with microalgae resources, the optimal microalgae control scheme is set for each regional group to achieve efficient treatment of polluted water bodies.

[0183] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0184] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0185] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0186] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0187] Alternatively, if the integrated units described above are implemented as software modules and sold or used as standalone products, they can also be stored on a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

[0188] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A sewage treatment method based on simulation analysis, characterized in that: include: Based on the basic information of the polluted water area, the polluted water area is divided into K water sub-areas; During a measurement period, based on a preset microalgae control plan, microalgae release and pollution monitoring are carried out in each water body sub-area. Microalgae measurement data and pollution status data of each water body sub-area are collected, and the microalgae measurement data and pollution status data are periodically analyzed. Microalgae migration analysis, microalgae growth analysis and pollution change analysis are also carried out in the water body sub-area. The migration coefficient, growth coefficient and pollution change rate based on microalgae over multiple cycles are calculated; Specifically also include: During a measurement period, based on a preset microalgae control plan, each water body sub-area is subjected to microalgae release and pollution monitoring, and microalgae measurement data and pollution status data of each water body sub-area are collected; Microalgae measurement data includes microalgae species, the number of various microalgae, and the total amount of microalgae. Pollution data includes the concentrations of various preset water pollutants. The measurement time period was divided into multiple analysis cycles, with a water body sub-area as the analysis unit. Based on each analysis cycle, the number of microalgae species and the number of various microalgae were calculated. The change rates between the analysis cycles were then analyzed to obtain P1 and P2, respectively. P1 represents the change rate of the number of microalgae species, and P2 represents the average change rate of the number of various microalgae. Based on P1 and P2, a two-dimensional vector is set to obtain the first vector, and the average distance between the first vector and the first vector corresponding to the adjacent water body sub-region is calculated, and the average distance is used as the migration coefficient; The distance calculation is applied to the standard Euclidean distance calculation between vectors, and the adjacent water body sub-region is the region that is geographically adjacent to the one water body sub-region; The growth coefficient is obtained by taking the weighted average of P1 and P2; Based on the pollution data, the pollutant concentration change analysis is performed for each analysis period, and the average of the change rates of multiple pollutants is calculated to obtain the pollution change rate; The migration coefficient and growth coefficient are set as independent variables, the pollution change rate is used as the dependent variable, the weight coefficients Q1 and Q2 are set and applied to the independent variables and linear fitting is performed. Based on the gradient descent optimization algorithm, the best fitting values ​​of Q1 and Q2 are searched, and the pollution fitting equation of each water body sub-region is obtained; The migration coefficient and growth coefficient of microalgae under multiple cycles are vectorized to obtain the migration characteristic vector and growth characteristic vector of each water body sub-region. The migration characteristic vector and growth characteristic vector are used as clustering inputs to perform regional clustering and form multiple regional groups. Based on the Bayesian optimization algorithm, in the fitting equation, minimizing the pollution change rate is used as the objective function, and the migration coefficient and growth coefficient are used as decision variables. A local optimal search is performed for each regional group, and a global optimal search is performed for the entire polluted water area. Multiple local optimal solutions and a global optimal solution based on the migration coefficient and growth coefficient are searched out. Based on the local optimal solution and a global optimal solution, multiple microalgae solutions are set. The pollution situation of each regional group is predicted through pollution data simulation. Combined with the microalgae resources, a corresponding microalgae solution is set for each regional group.

2. A sewage treatment method based on simulation analysis according to claim 1, characterized in that: Based on the basic information of the polluted water area, the polluted water area is divided into K water sub-areas, specifically: Obtain basic information on polluted water areas, including water area, water map outline, water depth information, and water pollution information; Based on the basic information, the polluted water area is divided into regions to obtain K water body sub-regions; Each of the water body sub-regions is within a preset spatial range, and the water body characteristics within a water body sub-region are consistent.

3. The sewage treatment method based on simulation analysis according to claim 1, characterized in that: The migration coefficient and growth coefficient are set as independent variables, the pollution change rate is used as the dependent variable, the weight coefficients Q1 and Q2 are set to be applied to the independent variables and linear fitting is performed. Based on the gradient descent optimization algorithm, the best fitting values ​​of Q1 and Q2 are searched, and the pollution fitting equation for each water body sub-region is obtained, which is specifically: The migration coefficient and growth coefficient are set as independent variables, the pollution change rate is used as the dependent variable, and the weight coefficients Q1 and Q2 are set to be applied to the independent variables to obtain the fitting equation based on linear regression; Based on the gradient descent optimization algorithm, the difference between the fitting equation result value and the pollution change rate under multiple analysis cycles is used as the loss function. Through gradient descent iterative calculation, the optimal weight coefficients Q1 and Q2 are searched until the loss function converges to the preset range; Record the best fitting values ​​of Q1 and Q2 at this time, and obtain the pollution fitting equation; Fitting analysis is performed on multiple water body sub-regions to obtain the corresponding pollution fitting equations.

4. The sewage treatment method based on simulation analysis according to claim 1, characterized in that: The data is vectorized by using the migration coefficient and growth coefficient of microalgae under multiple cycles to obtain the migration characteristic vector and growth characteristic vector of each water body sub-region. The migration characteristic vector and growth characteristic vector are used as clustering inputs to perform regional clustering grouping to form multiple regional groups, specifically: Based on a water body sub-region, the migration coefficient and growth coefficient of microalgae under multiple analysis periods are vectorized to form multi-dimensional migration characteristic vectors and growth characteristic vectors; Based on the DBSCAN clustering algorithm, the migration eigenvector and growth eigenvector of each water body sub-region are used as clustering inputs, and each water body sub-region is used as a clustering unit. K water body sub-regions are clustered and grouped. In the clustering process, the similarity of the migration eigenvector and growth eigenvector between water body sub-regions is comprehensively considered to form multiple region groups.

5. The sewage treatment method based on simulation analysis according to claim 1, characterized in that: Based on the Bayesian optimization algorithm, in the fitting equation, minimization of the pollution change rate is used as the objective function, and the migration coefficient and growth coefficient are used as decision variables. A local optimal search is performed for each regional group, and a global optimal search is performed for the entire polluted water area. Multiple local optimal solutions and a global optimal solution based on the migration coefficient and growth coefficient are searched out. Specifically, For a regional group, the pollution fitting equation of a water body sub-region in the regional group is randomly selected as the current fitting equation; Based on the Bayesian optimization algorithm, in the current fitting equation, the minimization of the pollution change rate is used as the objective function, the migration coefficient and the growth coefficient are used as decision variables, and the optimal parameters are iteratively searched within the preset decision variable range and the constraints are set. The optimal solution obtained is set as the local optimal solution. generating multiple local optimal solutions based on multiple region groups; For the overall polluted water area, the pollution fitting equations of K water body sub-areas are parameter-averaged to generate the mean fitting equation; Based on the Bayesian optimization algorithm, in the mean fitting equation, the minimization of the pollution change rate is used as the objective function, the migration coefficient and the growth coefficient are used as decision variables, and the optimal parameters are iteratively searched within the preset decision variable range and the constraints are set. The optimal solution obtained is set as the global optimal solution. Each optimal solution includes an optimal migration coefficient and an optimal growth coefficient.

6. A sewage treatment method based on simulation analysis according to claim 5, characterized in that: The method is to set multiple microalgae solutions based on local optimal solutions and a global optimal solution, simulate and predict the pollution situation of each regional group through pollution situation data, and set a corresponding microalgae solution for each regional group in combination with microalgae resources, specifically: For each local optimal solution and global optimal solution, the optimal migration coefficient and optimal growth coefficient are obtained. Based on the optimal migration coefficient and optimal growth coefficient, the microalgae input type analysis and input amount matching are carried out, and the microalgae plan is set in combination with the microalgae resources; Each local optimal solution and global optimal solution corresponds to a microalgae solution; In the pollution data, the pollution change rate of the water body sub-area is periodically analyzed, and the pollution change rate is serialized and simulated through the ARIMA model to generate a predicted pollution change rate; The microalgae solution corresponding to the local optimal solution is applied to the corresponding regional group; If the predicted pollution change rate of a water body sub-region is lower than the preset pollution rate, it will be marked as a general sub-region, and the microalgae solution corresponding to the global optimal solution will be applied to the general sub-region.

7. The sewage treatment method based on simulation analysis according to claim 1, characterized in that: Among the multiple area groups, each area group includes multiple water body sub-areas.

8. A sewage treatment system based on simulation analysis, characterized in that: The system includes: a memory and a processor, wherein the memory includes a sewage treatment program based on simulation analysis, and when the sewage treatment program based on simulation analysis is executed by the processor, the steps of the sewage treatment method based on simulation analysis as described in claim 1 are implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a sewage treatment program based on simulation analysis. When the sewage treatment program based on simulation analysis is executed by a processor, the steps of the sewage treatment method based on simulation analysis as described in any one of claims 1 to 7 are implemented.

Images