A water treatment optimization method and system based on microalgae growth prediction

Abstract

The present invention relates to the field of biological sewage treatment technology, and in particular to a water treatment optimization method and system based on microalgae growth prediction. A real-time water pollution parameter set and real-time water environment data for a target water treatment area are obtained, and a real-time water environment simulation field is constructed using the real-time water environment data. A future growth model of microalgae is simulated and generated using the real-time water environment simulation field, and spatial hash coding analysis of the real-time water pollution parameter set is performed to optimize and treat polluted water quality. A real-time water pollution scale thermodynamic model for the target water treatment area is constructed by combining the real-time water pollution parameter set and the real-time water environment data. The real-time water pollution scale thermodynamic model is used to query and determine whether the water pollution scale in the local water treatment area is abnormal, and wastewater treatment with microalgae growth regulation is performed. The present invention can analyze the microalgae growth and sewage purification performance and perform corresponding water treatment operations on it, ensuring rational water management and effectively improving water quality.

Description

Technical Field

[0001] The present invention relates to the technical field of biological sewage treatment, and in particular to a water treatment optimization method and system based on microalgae growth prediction. Background Art

[0002] In the field of water treatment, microalgae are becoming an important water treatment material due to their excellent water purification capabilities and environmental friendliness. Microalgae can absorb nutrients from water through photosynthesis, particularly pollutants such as nitrogen and phosphorus, effectively reducing eutrophication and improving water quality. However, due to the complexity of the aquatic environment and microalgae growth conditions, existing water treatment technologies based on microalgae growth still have many limitations. On the one hand, traditional methods for predicting microalgae growth often rely on empirical or simple linear models, which struggle to accurately characterize the impact of multiple factors interacting on microalgae growth rate and metabolite production, significantly reducing the accuracy and reliability of microalgae growth predictions. On the other hand, traditional water treatment methods often fail to achieve optimal control of microalgae growth, making it difficult to maintain optimal treatment efficiency under varying environmental conditions, resulting in unstable treatment efficiency and high costs. Therefore, accurately predicting and optimizing water treatment processes based on microalgae growth has become a major challenge in the current water treatment field. Therefore, it is necessary to develop a water treatment optimization method that can achieve extremely high prediction accuracy, improve water treatment efficiency, and reduce costs to address these issues. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a water treatment optimization method and system based on microalgae growth prediction.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] A first aspect of the present invention provides a water treatment optimization method based on microalgae growth prediction, comprising the following steps:

[0006] S102: Obtaining several environmental parameters of the target water treatment area and several growth parameters of the microalgae, training a microalgae growth prediction model for each environmental parameter and each growth parameter through multiple regression calculation and residual splitting gradient optimization, and performing future predictions to obtain future growth trends and future metabolic yields of the microalgae at future water treatment nodes;

[0007] S104: extracting multiple historical concentration detection parameters of one or more pollutants in the water quality, and iterating the logistic mapping of each historical concentration detection parameter one by one based on the future growth trend and the future metabolic production limit to obtain the purification performance of the microalgae for the water quality in the target water treatment area;

[0008] S106: Acquire a real-time water quality pollution parameter set and real-time water environment data for the target water treatment area, construct a real-time water environment simulation field using the real-time water environment data, simulate and generate a future growth model of microalgae through the real-time water environment simulation field, and perform spatial hash coding analysis of the real-time water quality pollution parameter set to optimize the treatment of polluted water quality;

[0009] S108: Constructing a real-time water pollution scale thermodynamic model for the target water treatment area in combination with the real-time water pollution parameter set and the real-time water environment data. Querying the real-time water pollution scale thermodynamic model to determine whether the water pollution scale in the local water treatment area is abnormal is performed to perform wastewater treatment for microalgae growth regulation.

[0010] More specifically, the step S102 includes the following steps:

[0011] Obtaining a target water treatment area, and obtaining several environmental parameters of the target water treatment area and several growth parameters of microalgae in the target water treatment area through online sensors;

[0012] Acquiring growth characteristics of the microalgae, constructing a multiple linear regression equation based on the growth characteristics, introducing a least squares method, estimating the multiple linear regression equation based on the environmental parameters and the growth parameters using the least squares method, and obtaining a series of regression coefficients;

[0013] Determine the residual value of the multivariate linear regression equation for capturing the growth of microalgae under each environmental parameter and each growth parameter condition based on a series of regression coefficients, obtain multiple residual values, and calculate the splitting gain based on each combination of the environmental parameter and each growth parameter condition, and preset different splitting points based on the splitting gain;

[0014] Constructing a root node and presetting a minimum residual value, and traversing each splitting point one by one from the root node based on each residual value to split the environmental parameters and the growth parameters, thereby generating a gradient capture decision tree for microalgae growth;

[0015] Obtaining the residual difference between the residual value and the preset minimum residual value, presetting a gradient learning rate based on the residual difference, weighting the gradient capture decision tree by the gradient learning rate to optimize the prediction result, and continuously repeating the above-mentioned splitting and construction of the gradient capture decision tree and the iteration of the weighted optimization step, and finally outputting the trained microalgae growth prediction model;

[0016] The treatment tasks for the target water treatment area are obtained, and the future water treatment nodes of the target water treatment area are extracted based on the treatment tasks. The actual environmental parameters and actual growth parameters of the microalgae at the future water treatment nodes are obtained. The actual environmental parameters and actual growth parameters are predicted and calculated using the trained microalgae growth prediction model to obtain the future growth trend and future metabolic yield of the microalgae at the future water treatment nodes.

[0017] More specifically, the step S104 includes the following steps:

[0018] Obtaining the treatment log and treatment tasks of the target water treatment area, and obtaining one or more pollutants in the water quality of the target water treatment area that need to be improved and treated according to the treatment tasks;

[0019] Extracting the future growth trend of microalgae and the future metabolic output of microalgae and multiple historical concentration detection parameters of one or more polluting components when the water quality is polluted through the treatment log, and obtaining a preset detection strategy for the online sensor;

[0020] Extracting the detection node intervals of the online sensor for each historical concentration detection parameter according to a preset detection strategy, and performing regular analysis based on the criterion of the future growth trend and future metabolic output of microalgae causing one or more pollutants in the target water treatment area to pollute the water quality at each historical concentration detection parameter at the detection node intervals, thereby obtaining a temporal regularity in which microalgae growth affects polluted water quality;

[0021] A variable control parameter matrix is constructed based on the future growth trend and the future metabolic output, and a logistic mapping algorithm is introduced. Based on a temporal pattern, multiple historical concentration detection parameters of one or more pollutants are mapped to the state variables of each variable control parameter in the variable control parameter matrix one by one in the logistic mapping algorithm to generate a logistic mapping recursive equation for the effect of microalgae growth on polluted water quality.

[0022] Iteratively solving the logistic mapping recursive equation and presetting a periodic reference threshold according to a time series rule. If the periodic changes of the future growth trend and the future metabolic output with respect to the historical concentration detection parameter converge to the periodic reference threshold, the iterative operation is stopped and a series of iterative trajectories are outputted.

[0023] A trajectory bifurcation diagram is drawn based on a series of iterative trajectories, and the trajectory directions in the trajectory bifurcation diagram are analyzed to determine the purification performance of the microalgae on the polluted water in the target water treatment area under the conditions of future growth trends and future metabolites.

[0024] More specifically, the step S106 includes the following steps:

[0025] The water quality of the target water treatment area is detected in real time by online sensors to obtain the real-time water pollution parameter set and real-time water environment data of the target water treatment area, and to construct the spatiotemporal discretization grid of the target water treatment area;

[0026] A central difference method is introduced to solve the difference equation of the real-time water environment data to obtain a solution of the real-time water environment difference equation, and the values of the grid points on the spatiotemporal discretization grid are cyclically updated by the solution of the real-time water environment difference equation to obtain a real-time water environment simulation field of the target water treatment area;

[0027] The future growth trend and future metabolic output of microalgae are simulated by using the real-time water environment simulation field to obtain a future growth model of microalgae, and a spatial hash grid unit and index table are constructed based on the purification performance;

[0028] Dividing the future growth model of the microalgae into a plurality of sub-model objects using a spatial hash grid unit, and mapping the coordinates of each sub-model object to an index table of the spatial hash grid unit to obtain a spatial hash value of each sub-model object;

[0029] Create a spatial hash table, store all sub-model objects belonging to a grid cell under the key of the grid cell based on the spatial hash value, and perform hash coding processing, and finally obtain a spatial hash coding table of the purification performance of the future growth model of microalgae;

[0030] A three-dimensional geographic terrain model of the target water treatment area is constructed. Based on the purification performance, a predetermined pollution absorption area is constrained and planned within the three-dimensional geographic terrain model. The aforementioned steps are repeated to perform hash coding on the predetermined pollution absorption area to obtain a spatial hash decoding sequence of the predetermined pollution absorption spatial area. The spatial hash coding table is used to analyze the microalgae purification water quality of the spatial hash decoding sequence for water treatment optimization.

[0031] More specifically, the method comprises constructing a three-dimensional geographic terrain model of the target water treatment area, constraining and planning a predetermined pollution absorption area within the three-dimensional geographic terrain model based on purification performance, repeating the above steps to hash-code the predetermined pollution absorption area to obtain a spatial hash decoding sequence of the predetermined pollution absorption spatial area, and using a spatial hash coding table to analyze the microalgae purification water quality of the spatial hash decoding sequence to optimize water treatment, specifically comprising the following steps:

[0032] Preset a purification radiation boundary threshold based on purification performance and construct a three-dimensional geographic terrain model of the target water treatment area. Use the purification radiation boundary threshold as a range constraint to plan the pollution absorption range within the three-dimensional geographic terrain model, and obtain the established pollution absorption range of the target water treatment area according to the future growth model of microalgae.

[0033] Extracting multiple pollutant concentration parameters of water quality in a predetermined pollution absorption area using a real-time water quality pollution parameter set of a target water treatment area, repeating the above-mentioned spatial hash code construction step based on the multiple pollutant concentration parameters to hash the regional spatial pollution degree of the predetermined pollution absorption area, and obtaining a spatial hash decoding sequence of the predetermined pollution absorption spatial area;

[0034] Determining whether the spatial hash decoding sequence can decode and output the spatial hash coding table; if not, obtaining a missing tampering code output by the spatial hash coding table that cannot be decoded using the spatial hash decoding sequence; and determining a purification performance gap of the polluted water quality in the target water treatment area due to microalgae growth based on the missing tampering code;

[0035] Based on the purification performance gap, the growth conditions of microalgae in the established pollution absorption space area are improved and optimized to enhance the water pollution purification efficiency of microalgae growth in the established pollution absorption space area.

[0036] More specifically, the step S108 includes the following steps:

[0037] constructing a random distribution structure of water environment flow in a three-dimensional geographical terrain model based on a real-time water environment simulation field of a target water treatment area, and extracting a random node array of the random distribution structure;

[0038] Obtaining neighbor nodes of each random node in the random node array, calculating weights between each random node and each neighbor node based on a real-time water quality pollution parameter set for the target water treatment area, generating weight values for each random node corresponding to a plurality of random node boundaries, and determining a random probability distribution of real-time water quality pollution based on a ratio between the weight values;

[0039] A maximum number of steps is preset, and the current random node is updated to a neighboring node selected with the maximum random probability according to the random probability distribution. The weight calculation between the random node and the neighboring node and the random node update steps are repeated to continuously execute the next round of random walk until the random walk reaches the maximum number of steps, thereby generating a real-time water quality pollution scale model for the target water treatment area;

[0040] Obtaining benchmark thermal color gamut rules for different water pollution scale ratings based on a big data network, performing color gamut assignment transformation on the real-time water pollution scale model using the benchmark thermal color gamut rules, and obtaining a real-time water pollution scale thermal model for the target water treatment area;

[0041] Obtaining an online sensor layout pattern of a target water treatment area, dividing the real-time water pollution scale thermal model into M sub-model area blocks based on the online sensor layout pattern, and obtaining a real-time thermal color gamut RGB lookup table for each sub-model area block;

[0042] Abnormal thermochromatic RGB values for unreasonable water pollution scales are preset based on the purification performance of microalgae for different water pollution scales. If the abnormal thermochromatic RGB value can be found in the real-time thermochromatic gamut RGB lookup table, the sub-model area block is marked as an abnormal water pollution scale area and a wastewater treatment operation for microalgae growth regulation is performed.

[0043] A second aspect of the present invention provides a water treatment optimization system based on microalgae growth prediction, the water treatment optimization system including a memory and a processor, the memory storing a water treatment optimization method program based on microalgae growth prediction, and when the water treatment optimization method program is executed by the processor, any one of the steps of the water treatment optimization method is implemented.

[0044] The present invention solves the technical defects existing in the background technology, and the beneficial technical effects of the present invention are:

[0045] Several environmental parameters of a target water treatment area and several growth parameters of microalgae are obtained. A microalgae growth prediction model is trained and future predictions are performed for each environmental parameter and each growth parameter through multiple regression calculation and residual splitting gradient optimization, thereby obtaining the future growth trend and future metabolic yield of the microalgae at the future water treatment node. Multiple historical concentration detection parameters of one or more pollutants in the water are extracted. Based on the future growth trend and future metabolic yield, each historical concentration detection parameter is subjected to a logistic mapping iteration, thereby obtaining the purification performance of the microalgae in the target water treatment area. A real-time water pollution parameter set and real-time water environment data of the target water treatment area are obtained, and a real-time water environment simulation field is constructed using the real-time water environment data. A future growth model of the microalgae is simulated and generated using the real-time water environment simulation field, and spatial hash coding analysis of the real-time water pollution parameter set is performed to optimize the treatment of polluted water quality. A real-time water pollution scale thermal model of the target water treatment area is constructed by combining the real-time water pollution parameter set and the real-time water environment data. The real-time water pollution scale thermal model is used to query and determine whether the water pollution scale of the local water treatment area is abnormal, and wastewater treatment with microalgae growth regulation is performed. The present invention can quickly and accurately analyze the purification performance of microalgae for polluted water in the target water treatment area by predicting the growth trend and metabolic output of microalgae at future time nodes, and further analyze and execute corresponding reasonable water treatment operations based on the purification performance, thereby utilizing the characteristics of microalgae in absorbing pollutants in water to efficiently purify sewage, ensure the rationalization of water management, and effectively improve water quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, they can also obtain drawings of other embodiments based on these drawings.

[0047] Figure 1 A first method flow chart of a water treatment optimization method based on microalgae growth prediction is shown;

[0048] Figure 2 A second method flow chart of a water treatment optimization method based on microalgae growth prediction is shown;

[0049] Figure 3 A system framework diagram of a water treatment optimization system based on microalgae growth prediction is shown;

[0050] Figure 4 A schematic diagram of analyzing microscopic images of microalgae according to the present invention is shown;

[0051] Figure 5 A schematic diagram of the model for capturing the microalgae growth gradient decision tree is shown;

[0052] Figure 6 The figure shows a brief block diagram of the data processing flow of a water treatment optimization system based on microalgae growth prediction of the present invention. DETAILED DESCRIPTION

[0053] 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.

[0054] 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.

[0055] The first aspect of the present invention provides a water treatment optimization method based on microalgae growth prediction, such as Figure 1 As shown, the following steps are included:

[0056] S102: Obtaining several environmental parameters of the target water treatment area and several growth parameters of the microalgae, training a microalgae growth prediction model for each environmental parameter and each growth parameter through multiple regression calculation and residual splitting gradient optimization, and performing future predictions to obtain future growth trends and future metabolic yields of the microalgae at future water treatment nodes;

[0057] S104: extracting multiple historical concentration detection parameters of one or more pollutants in the water quality, and iterating the logistic mapping of each historical concentration detection parameter one by one based on the future growth trend and the future metabolic production limit to obtain the purification performance of the microalgae for the water quality in the target water treatment area;

[0058] S106: Acquire a real-time water quality pollution parameter set and real-time water environment data for the target water treatment area, construct a real-time water environment simulation field using the real-time water environment data, simulate and generate a future growth model of microalgae through the real-time water environment simulation field, and perform spatial hash coding analysis of the real-time water quality pollution parameter set to optimize the treatment of polluted water quality;

[0059] S108: Constructing a real-time water pollution scale thermodynamic model for the target water treatment area in combination with the real-time water pollution parameter set and the real-time water environment data. Querying the real-time water pollution scale thermodynamic model to determine whether the water pollution scale in the local water treatment area is abnormal is performed to perform wastewater treatment for microalgae growth regulation.

[0060] More specifically, the step S102 includes the following steps:

[0061] Obtaining a target water treatment area, and obtaining several environmental parameters of the target water treatment area and several growth parameters of microalgae in the target water treatment area through online sensors;

[0062] Acquiring growth characteristics of the microalgae, constructing a multiple linear regression equation based on the growth characteristics, introducing a least squares method, estimating the multiple linear regression equation based on the environmental parameters and the growth parameters using the least squares method, and obtaining a series of regression coefficients;

[0063] Determine the residual value of the multivariate linear regression equation for capturing the growth of microalgae under each environmental parameter and each growth parameter condition based on a series of regression coefficients, obtain multiple residual values, and calculate the splitting gain based on each combination of the environmental parameter and each growth parameter condition, and preset different splitting points based on the splitting gain;

[0064] Constructing a root node and presetting a minimum residual value, and traversing each splitting point one by one from the root node based on each residual value to split the environmental parameters and the growth parameters, thereby generating a gradient capture decision tree for microalgae growth;

[0065] Obtaining the residual difference between the residual value and the preset minimum residual value, presetting a gradient learning rate based on the residual difference, weighting the gradient capture decision tree by the gradient learning rate to optimize the prediction result, and continuously repeating the above-mentioned splitting and construction of the gradient capture decision tree and the iteration of the weighted optimization step, and finally outputting the trained microalgae growth prediction model;

[0066] The treatment tasks for the target water treatment area are obtained, and the future water treatment nodes of the target water treatment area are extracted based on the treatment tasks. The actual environmental parameters and actual growth parameters of the microalgae at the future water treatment nodes are obtained. The actual environmental parameters and actual growth parameters are predicted and calculated using the trained microalgae growth prediction model to obtain the future growth trend and future metabolic yield of the microalgae at the future water treatment nodes.

[0067] It should be noted that environmental parameters include light intensity, temperature, humidity, pH, carbon dioxide concentration, and nutrient concentration, while growth parameters include cell density, chlorophyll content, biomass concentration, and specific metabolite concentrations. Microalgae can absorb nutrients (such as nitrogen and phosphorus) and heavy metals and other pollutants from water to purify water quality, while also producing oxygen and improving the aquatic ecosystem. Appropriate microalgae growth can achieve natural plant-based water purification, so accurate prediction of microalgae growth is necessary to improve water treatment quality. However, traditional methods for predicting microalgae growth often rely on empirical models or simple linear models, which make it difficult to accurately characterize the impact of multiple factors interacting on microalgae growth rate and metabolite production, effectively explore the complex nonlinear relationships implicit in the acquired data, and predict microalgae growth trends in real time. To address this issue, this method constructs a multivariate linear regression equation based on the growth characteristics of microalgae, following the standardized growth of microalgae, to solve for the acquired environmental and growth parameters. This allows for descriptive learning by predicting the dependent variable (growth parameter) using the regression coefficient of each independent variable (environmental parameter). Compared to traditional prediction methods, this method ensures more accurate and reasonable predictions of microalgae growth. To further improve the accuracy and robustness of prediction model training, this method splits a tree by fitting the residuals between the predicted and actual values of the multivariate linear regression equation. The residuals identify errors in the prediction model, allowing the next tree to compensate for and avoid these errors in microalgae growth predictions. This method splits several environmental and growth parameters based on the residual values, traversing each split point from the root node one by one. This ensures causal linearity between the independent variables affecting microalgae growth and the dependent variable, thereby reducing prediction errors and improving model performance.

[0068] It should be noted that this method uses a gradient learning rate weighted update of the residual gap to enable the new tree to correct the previous error to a certain extent. The gradient learning rate controls the contribution of each tree to the final prediction, avoiding overfitting, so that each tree attempts to correct the error of the previous tree. The construction of each tree helps the model gradually correct the error of the current prediction, thereby making the growth trend and metabolic yield of microalgae at future water treatment nodes more accurate, improving the accuracy of microalgae growth prediction. This method can make the prediction of microalgae growth based on environmental parameters and growth parameters more accurate through the gradient optimization method of multivariate linear regression and residual fitting splitting. Compared with traditional prediction models, it can further improve the credibility of the prediction results and reduce the water treatment optimization error caused by prediction error.

[0069] More specifically, the step S104 includes the following steps:

[0070] Obtaining the treatment log and treatment tasks of the target water treatment area, and obtaining one or more pollutants in the water quality of the target water treatment area that need to be improved and treated according to the treatment tasks;

[0071] Extracting the future growth trend of microalgae and the future metabolic output of microalgae and multiple historical concentration detection parameters of one or more polluting components when the water quality is polluted through the treatment log, and obtaining a preset detection strategy for the online sensor;

[0072] Extracting the detection node intervals of the online sensor for each historical concentration detection parameter according to a preset detection strategy, and performing regular analysis based on the criterion of the future growth trend and future metabolic output of microalgae causing one or more pollutants in the target water treatment area to pollute the water quality at each historical concentration detection parameter at the detection node intervals, thereby obtaining a temporal regularity in which microalgae growth affects polluted water quality;

[0073] A variable control parameter matrix is constructed based on the future growth trend and the future metabolic output, and a logistic mapping algorithm is introduced. Based on a temporal pattern, multiple historical concentration detection parameters of one or more pollutants are mapped to the state variables of each variable control parameter in the variable control parameter matrix one by one in the logistic mapping algorithm to generate a logistic mapping recursive equation for the effect of microalgae growth on polluted water quality.

[0074] Iteratively solving the logistic mapping recursive equation and presetting a periodic reference threshold according to a time series rule. If the periodic changes of the future growth trend and the future metabolic output with respect to the historical concentration detection parameter converge to the periodic reference threshold, the iterative operation is stopped and a series of iterative trajectories are outputted.

[0075] A trajectory bifurcation diagram is drawn based on a series of iterative trajectories, and the trajectory directions in the trajectory bifurcation diagram are analyzed to determine the purification performance of the microalgae on the polluted water in the target water treatment area under the conditions of future growth trends and future metabolites.

[0076] It should be noted that pollutants include nitrates, nitrogen, phosphorus, and heavy metals. Because microalgae can absorb nutrients (such as nitrogen and phosphorus) and heavy metals, as well as other pollutants, from water, they possess a certain degree of wastewater purification capacity. Therefore, their growth trends and metabolic output determine their ability to purify pollutants in wastewater. This purification performance is crucial for wastewater treatment optimization and directly impacts whether treatment results meet expectations. Therefore, it is necessary to analyze and calculate the performance of microalgae in purifying pollutants in wastewater based on predicted future growth trends and metabolic output. This method first analyzes and constructs a temporal pattern of how microalgae growth affects pollutant quality. As microalgae gradually absorb pollutants from wastewater over time, their concentrations change. During this process, the growth trends and metabolic products of the microalgae also change synchronously. Therefore, these changes in the growth trends and metabolic outputs of the microalgae determine the changes in the concentration of the pollutants being purified. This temporal pattern reveals that microalgae growth recursively on the concentration of pollutants in wastewater. Variable mapping that follows this pattern enables more accurate and reliable analysis and inference of purification performance. Then, the Logistic mapping algorithm is used to map the historical concentration detection parameters of the pollutants to the variable control parameter matrix constructed by future growth trends and future metabolic outputs based on the temporal law. This systematically describes the state behavior of the microalgae in response to changes in pollutant concentrations as they undergo continuous growth and metabolic changes. Since this state behavior is based on temporal characteristics, the current state description depends on the previous state. Therefore, this method iteratively solves the Logistic mapping recursive equation for the impact of microalgae growth on polluted water quality and stops the iteration operation when the periodic reference threshold is reached. The Logistic mapping algorithm, also known as the Logistic Mapping Algorithm in Chinese, is a recursive algorithm used to describe the state behavior of the temporal influence of certain dynamic variables on other dynamic variables. This can maintain a certain temporal and stable state in the purification behavior of microalgae growth affecting polluted water quality, avoid temporal oscillations that lead to a chaotic state of the system and affect the inference of the final purification performance, and greatly improve the calculation accuracy and reliability of the purification performance.

[0077] It should be noted that the bifurcation diagram depicted by the iterative trajectory records the system's steady-state or periodic trajectory for each historical concentration detection parameter. Different branches or trajectories can be seen in the bifurcation diagram, manifesting as the system jumping from one stable point to another, reflecting the microalgae's ability to purify pollutants in wastewater over time. This method can map the predicted future growth trends and future metabolic yield state variables to the historical concentration detection parameters of pollutants, thereby describing the dynamic behavior of microalgae growth in absorbing and changing pollutants in wastewater. This allows for more accurate and efficient inference and determination of the purification performance of microalgae growth on polluted water quality in the target water treatment area, providing a reliable analytical basis for subsequent wastewater treatment optimization based on microalgae growth, effectively improving the accuracy and rationality of water treatment optimization.

[0078] More specifically, the step S106 includes the following steps:

[0079] The water quality of the target water treatment area is detected in real time by online sensors to obtain the real-time water pollution parameter set and real-time water environment data of the target water treatment area, and to construct the spatiotemporal discretization grid of the target water treatment area;

[0080] A central difference method is introduced to solve the difference equation of the real-time water environment data to obtain a solution of the real-time water environment difference equation, and the values of the grid points on the spatiotemporal discretization grid are cyclically updated by the solution of the real-time water environment difference equation to obtain a real-time water environment simulation field of the target water treatment area;

[0081] The future growth trend and future metabolic output of microalgae are simulated by using the real-time water environment simulation field to obtain a future growth model of microalgae, and a spatial hash grid unit and index table are constructed based on the purification performance;

[0082] Dividing the future growth model of the microalgae into a plurality of sub-model objects using a spatial hash grid unit, and mapping the coordinates of each sub-model object to an index table of the spatial hash grid unit to obtain a spatial hash value of each sub-model object;

[0083] Create a spatial hash table, store all sub-model objects belonging to a grid cell under the key of the grid cell based on the spatial hash value, and perform hash coding processing, and finally obtain a spatial hash coding table of the purification performance of the future growth model of microalgae;

[0084] A three-dimensional geographic terrain model of the target water treatment area is constructed. Based on the purification performance, a predetermined pollution absorption area is constrained and planned within the three-dimensional geographic terrain model. The aforementioned steps are repeated to perform hash coding on the predetermined pollution absorption area to obtain a spatial hash decoding sequence of the predetermined pollution absorption spatial area. The spatial hash coding table is used to analyze the microalgae purification water quality of the spatial hash decoding sequence for water treatment optimization.

[0085] It should be noted that after a period of time, microalgae can continue to reproduce and grow in water to form microalgae clusters of different sizes and uneven distribution. Under the premise of a determined purification performance, the scale of sewage treated by microalgae clusters of different sizes is different. For example, the sewage pollution level in a local area of the target water treatment area is high, while the microalgae growth size in this local area is small. Therefore, based on the purification performance premise, it can be known that the microalgae has a poor absorption capacity for the pollutants in this local area, making it difficult to completely purify the water. Therefore, it is necessary to further optimize it to improve the water pollution situation in this local area. It can be seen from this that the water treatment optimization operation can be determined by analyzing the pollution purification degree of microalgae in the corresponding sewage area. To this end, this method constructs a real-time water environment simulation field of the target water treatment area by introducing the central difference method to solve the difference equation of the real-time water environment data. The real-time water environment simulation field is used to simulate the future growth trend and future metabolic output. It can more accurately visualize the real-time specifications of the future growth of microalgae, making the subsequent analysis and calculation of the pollution purification degree of the polluted area more accurate and stable.

[0086] It should be noted that the traditional method usually constructs a pollution purification performance model of microalgae growth based on the purification performance into the model formula and conducts a simple comparative analysis with the pollution degree model of the pollution area in which it is located. This not only increases the judgment error of microalgae purification of sewage, but also increases unnecessary calculation amount, making it difficult to ensure the efficiency and quality of water treatment optimization. Therefore, this method constructs a spatial hash grid unit and index table based on purification performance, then divides the future growth model of microalgae by the spatial hash grid unit and subdivides it into the index table of the spatial hash grid unit. The hash value can directly locate the grid unit and its neighborhood where the microalgae spatial growth is located, without traversing all model areas, making the overall pollution purification capacity under the microalgae spatial growth specification based on purification performance description more accurate. Then, a spatial hash table is created to store all sub-model objects belonging to a certain grid unit under the key of the grid unit for hash coding processing, so that the overall pollution purification capacity under the microalgae spatial growth specification is reflected in the form of a spatial hash coding table. Subsequently, only the table needs to be used for decoding and judgment, eliminating redundant calculation steps and improving the efficiency of water treatment optimization. Finally, the same method is used to calculate the spatial hash code sequence of the pollution level in the area. Using this spatial hash code sequence to decode the spatial hash code table, it is possible to quickly determine whether the microalgae spatial growth specifications can completely purify the water pollution in the area, thereby deciding the corresponding water treatment optimization operation. This method can analyze the purification error defects of calculating whether the microalgae growth scale can completely purify the water pollution level in the area, and make corresponding water treatment optimization decisions, thereby improving the rationality, efficiency, and accuracy of water treatment optimization, and ensuring that the water quality purification treatment under the conditions of microalgae growth achieves the expected results.

[0087] More specifically, the method comprises constructing a three-dimensional geographic terrain model of the target water treatment area, constraining and planning a predetermined pollution absorption area within the three-dimensional geographic terrain model based on purification performance, repeating the above steps to hash-code the predetermined pollution absorption area to obtain a spatial hash decoding sequence of the predetermined pollution absorption spatial area, and using a spatial hash coding table to analyze the microalgae purification water quality of the spatial hash decoding sequence to optimize water treatment, specifically comprising the following steps:

[0088] Preset a purification radiation boundary threshold based on purification performance and construct a three-dimensional geographic terrain model of the target water treatment area. Use the purification radiation boundary threshold as a range constraint to plan the pollution absorption range within the three-dimensional geographic terrain model, and obtain the established pollution absorption range of the target water treatment area according to the future growth model of microalgae.

[0089] Extracting multiple pollutant concentration parameters of water quality in a predetermined pollution absorption area using a real-time water quality pollution parameter set of a target water treatment area, repeating the above-mentioned spatial hash code construction step based on the multiple pollutant concentration parameters to hash the regional spatial pollution degree of the predetermined pollution absorption area, and obtaining a spatial hash decoding sequence of the predetermined pollution absorption spatial area;

[0090] Determining whether the spatial hash decoding sequence can decode and output the spatial hash coding table; if not, obtaining a missing tampering code output by the spatial hash coding table that cannot be decoded using the spatial hash decoding sequence; and determining a purification performance gap of the polluted water quality in the target water treatment area due to microalgae growth based on the missing tampering code;

[0091] Based on the purification performance gap, the growth conditions of microalgae in the established pollution absorption space area are improved and optimized to enhance the water pollution purification efficiency of microalgae growth in the established pollution absorption space area.

[0092] It should be noted that microalgae clusters of different sizes have a specific range of pollutant components they can treat. Therefore, to ensure the sewage treatment effect of each microalgae cluster in its area, this method uses a pre-set purification radiation boundary threshold constraint to plan a three-dimensional geographic terrain model of the target water treatment area. This method can obtain the effective range of radiation absorption that each microalgae growth cluster can maximize, thereby improving the accuracy of water treatment optimization decisions based on whether the growth scale of each microalgae cluster can fully purify the water pollution level in the area. If the spatial hash decoding sequence cannot decode the output spatial hash code table, it indicates that the purification capacity of the microalgae cluster is insufficient to completely remove the pollution level within the given pollution absorption area, indicating that the purification capacity of the microalgae cluster is far from sufficient and there is a purification performance gap. Therefore, the growth conditions of the microalgae can be optimized based on this purification performance gap to ensure that the microalgae are adequately grown and improve their purification adaptability and ability to different pollution levels. This achieves sewage treatment optimization based on the coupled effect of microalgae growth, significantly improving water treatment efficiency and quality, and reducing the tedious steps and optimization errors caused by the traditional method of unified global optimization without considering the performance of the microalgae growth scale distribution, resulting in high economic benefits.

[0093] More specifically, the step S108 is as follows: Figure 2 As shown, the specific steps include:

[0094] S202: constructing a random distribution structure of water environment flow in a three-dimensional geographical terrain model based on a real-time water environment simulation field of a target water treatment area, and extracting a random node array of the random distribution structure;

[0095] S204: Obtaining neighbor nodes of each random node in the random node array, calculating weights between each random node and each neighbor node based on the real-time water quality pollution parameter set of the target water treatment area, generating weight values for each random node corresponding to a plurality of random node boundaries, and determining a random probability distribution of real-time water quality pollution based on a ratio between the weight values;

[0096] S206: Preset a maximum number of steps, update the current random node to a neighbor node selected with the maximum random probability according to the random probability distribution, repeat the above steps of calculating the weights between the random node and the neighbor node and updating the random node, and continuously execute the next round of random walk until the random walk reaches the maximum number of steps, thereby generating a real-time water quality pollution scale model for the target water treatment area;

[0097] S208: Obtaining benchmark thermal color gamut rules for different water pollution scale ratings based on a big data network, performing color gamut assignment transformation on the real-time water pollution scale model using the benchmark thermal color gamut rules, and obtaining a real-time water pollution scale thermal model of the target water treatment area;

[0098] S210: Obtaining an online sensor layout pattern of the target water treatment area, dividing the real-time water pollution scale thermal model into M sub-model area blocks based on the online sensor layout pattern, and obtaining a real-time thermal color gamut RGB lookup table for each sub-model area block;

[0099] S212: Preset abnormal thermochromatic RGB values for unreasonable water pollution scales based on the purification performance of microalgae for different water pollution scales. If the abnormal thermochromatic RGB values can be found in the real-time thermochromatic RGB lookup table, mark the sub-model area block as an abnormal water pollution scale area and perform a wastewater treatment operation for microalgae growth regulation.

[0100] It should be noted that existing water treatment facilities are usually equipped with online sensors for real-time monitoring. Once a high level of pollutants in the water is detected, water treatment optimization operations can be quickly responded to. However, traditional real-time water quality monitoring methods used in water treatment have poor ability to discriminate the scale of water pollution. Moreover, because the water environment is always in a dynamic state of continuous flow, the pollutants in the water body will undergo a random distribution of pollution scales with the water flow. In this case, traditional methods are even more difficult to detect the degree and scale of pollution within the water body area, thereby affecting the accuracy and response rate of sewage treatment. To address this problem, the present method first constructs a random distribution architecture of water environment flow in a three-dimensional geographic terrain model. Then, using a set of real-time water quality pollution parameters, a random walk is performed on the random node array in this random distribution architecture, which is based on the weight of the current random node relative to the neighboring nodes. This method can describe the real-time water quality pollution scale of the pollutants in the sewage as the water flows continuously. This makes water quality monitoring follow the randomness driven by the water flow, which can improve the accuracy of online sensor detection and analysis of the real-time water quality pollution scale of the target water treatment area. Compared with traditional methods, it solves the problem of difficulty in accurately detecting the scale of pollution in the water body in real time.

[0101] It should be noted that this method then transforms the real-time water quality pollution scale model into the form of a thermal color domain, so as to obtain a lookup table expressing the thermal color domain of different local areas in the target water treatment area, that is, a real-time thermal color domain RGB lookup table for each sub-model area block; if the real-time thermal color domain RGB lookup table can query abnormal thermal chromaticity RGB values, it means that the microalgae in the water treatment area corresponding to the sub-model area block cannot effectively handle the current real-time pollution scale, that is, the pollution scale of the water treatment area is an abnormal phenomenon and a rapid response is urgently needed to perform water treatment optimization, so it is marked as an abnormal water quality pollution scale area and a sewage treatment operation of microalgae growth regulation is performed. The RGB query method of the thermal color domain can greatly improve the decision-making and response rate of the sewage treatment operation, reduce the error amount caused by the direct calculation and comparison of the pollution scale by the traditional method, optimize the real-time monitoring quality of the online sensor, and have high reliability. This method can use the pollutant concentration data detected by random walk in the real-time water environment simulation field of the target water treatment area to ensure that the water quality pollution scale monitoring and analysis of the online sensor more closely follows the water environment movement state, and use the RGB query judgment of the thermal color range to quickly decide and respond to water treatment optimization operations with different water pollution scales, reduce the water treatment optimization errors caused by improper traditional monitoring, ensure the rationality of water treatment, and improve the quality and efficiency of water treatment optimization.

[0102] The second aspect of the present invention provides a water treatment optimization system based on microalgae growth prediction, such as Figure 3 As shown, the water treatment optimization system includes a memory 31 and a processor 32. The memory 31 stores a water treatment optimization method program based on microalgae growth prediction. When the water treatment optimization method program is executed by the processor 32, any one of the water treatment optimization method steps is implemented.

[0103] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A water treatment optimization method based on microalgae growth prediction, characterized in that: The following steps are involved: S102: Obtaining several environmental parameters of the target water treatment area and several growth parameters of the microalgae, training a microalgae growth prediction model for each environmental parameter and each growth parameter through multiple regression calculation and residual splitting gradient optimization, and performing future predictions to obtain future growth trends and future metabolic yields of the microalgae at future water treatment nodes; S104: extracting multiple historical concentration detection parameters of one or more pollutants in the water quality, and iterating the logistic mapping of each historical concentration detection parameter one by one based on the future growth trend and the future metabolic production limit to obtain the purification performance of the microalgae for the water quality in the target water treatment area; S106: Acquire a real-time water quality pollution parameter set and real-time water environment data for the target water treatment area, construct a real-time water environment simulation field using the real-time water environment data, simulate and generate a future growth model of microalgae through the real-time water environment simulation field, and perform spatial hash coding analysis of the real-time water quality pollution parameter set to optimize the treatment of polluted water quality; S108: Build a real-time water pollution scale thermodynamic model for the target water treatment area by combining the real-time water pollution parameter set and the real-time water environment data. Query the real-time water pollution scale thermodynamic model to determine whether the water pollution scale in the local water treatment area is abnormal and perform wastewater treatment for microalgae growth regulation. The step S104 specifically includes the following steps: Obtaining the treatment log and treatment tasks of the target water treatment area, and obtaining one or more pollutants in the water quality of the target water treatment area that need to be improved and treated according to the treatment tasks; Extracting the future growth trend of microalgae and the future metabolic output of microalgae and multiple historical concentration detection parameters of one or more polluting components when the water quality is polluted through the treatment log, and obtaining a preset detection strategy for the online sensor; Extracting the detection node intervals of the online sensor for each historical concentration detection parameter according to a preset detection strategy, and performing regular analysis based on the criterion of the future growth trend and future metabolic output of microalgae causing one or more pollutants in the target water treatment area to pollute the water quality at each historical concentration detection parameter at the detection node intervals, thereby obtaining a temporal regularity in which microalgae growth affects polluted water quality; A variable control parameter matrix is constructed based on the future growth trend and the future metabolic output, and a logistic mapping algorithm is introduced. Based on a temporal pattern, multiple historical concentration detection parameters of one or more pollutants are mapped to the state variables of each variable control parameter in the variable control parameter matrix one by one in the logistic mapping algorithm to generate a logistic mapping recursive equation for the effect of microalgae growth on polluted water quality. Iteratively solving the logistic mapping recursive equation and presetting a periodic reference threshold according to a time series rule. If the periodic changes of the future growth trend and the future metabolic output with respect to the historical concentration detection parameter converge to the periodic reference threshold, the iterative operation is stopped and a series of iterative trajectories are outputted. A trajectory bifurcation diagram is drawn based on a series of iterative trajectories, and the trajectory directions in the trajectory bifurcation diagram are analyzed to determine the purification performance of the microalgae on the polluted water in the target water treatment area under the conditions of future growth trends and future metabolites.

2. The water treatment optimization method based on microalgae growth prediction according to claim 1, characterized in that: The step S102 specifically includes the following steps: Obtaining a target water treatment area, and obtaining several environmental parameters of the target water treatment area and several growth parameters of microalgae in the target water treatment area through online sensors; Acquiring growth characteristics of the microalgae, constructing a multiple linear regression equation based on the growth characteristics, introducing a least squares method, estimating the multiple linear regression equation based on the environmental parameters and the growth parameters using the least squares method, and obtaining a series of regression coefficients; Determine the residual value of the multivariate linear regression equation for capturing the growth of microalgae under each environmental parameter and each growth parameter condition based on a series of regression coefficients, obtain multiple residual values, and calculate the splitting gain based on each combination of the environmental parameter and each growth parameter condition, and preset different splitting points based on the splitting gain; Constructing a root node and presetting a minimum residual value, and traversing each splitting point one by one from the root node based on each residual value to split the environmental parameters and the growth parameters, thereby generating a gradient capture decision tree for microalgae growth; Obtaining the residual difference between the residual value and the preset minimum residual value, presetting a gradient learning rate based on the residual difference, weighting the gradient capture decision tree by the gradient learning rate to optimize the prediction result, and continuously repeating the above-mentioned splitting and construction of the gradient capture decision tree and the iteration of the weighted optimization step, and finally outputting the trained microalgae growth prediction model; The treatment tasks for the target water treatment area are obtained, and the future water treatment nodes of the target water treatment area are extracted based on the treatment tasks. The actual environmental parameters and actual growth parameters of the microalgae at the future water treatment nodes are obtained. The actual environmental parameters and actual growth parameters are predicted and calculated using the trained microalgae growth prediction model to obtain the future growth trend and future metabolic yield of the microalgae at the future water treatment nodes.

3. The water treatment optimization method based on microalgae growth prediction according to claim 1, characterized in that: The step S106 specifically includes the following steps: The water quality of the target water treatment area is detected in real time by online sensors to obtain the real-time water pollution parameter set and real-time water environment data of the target water treatment area, and to construct the spatiotemporal discretization grid of the target water treatment area; A central difference method is introduced to solve the difference equation of the real-time water environment data to obtain a solution of the real-time water environment difference equation, and the values of the grid points on the spatiotemporal discretization grid are cyclically updated by the solution of the real-time water environment difference equation to obtain a real-time water environment simulation field of the target water treatment area; The future growth trend and future metabolic output of microalgae are simulated by using the real-time water environment simulation field to obtain a future growth model of microalgae, and a spatial hash grid unit and index table are constructed based on the purification performance; Dividing the future growth model of the microalgae into a plurality of sub-model objects using a spatial hash grid unit, and mapping the coordinates of each sub-model object to an index table of the spatial hash grid unit to obtain a spatial hash value of each sub-model object; Create a spatial hash table, store all sub-model objects belonging to a grid cell under the key of the grid cell based on the spatial hash value, and perform hash coding processing, and finally obtain a spatial hash coding table of the purification performance of the future growth model of microalgae; A three-dimensional geographic terrain model of the target water treatment area is constructed. Based on the purification performance, a predetermined pollution absorption area is constrained and planned within the three-dimensional geographic terrain model. The aforementioned steps are repeated to perform hash coding on the predetermined pollution absorption area to obtain a spatial hash decoding sequence of the predetermined pollution absorption spatial area. The spatial hash coding table is used to analyze the microalgae purification water quality of the spatial hash decoding sequence for water treatment optimization.

4. The water treatment optimization method based on microalgae growth prediction according to claim 3, characterized in that: The method comprises constructing a three-dimensional geographic terrain model of the target water treatment area, constraining and planning a predetermined pollution absorption area within the three-dimensional geographic terrain model based on purification performance, repeating the above steps to hash code the predetermined pollution absorption area to obtain a spatial hash decoding sequence of the predetermined pollution absorption spatial area, and using a spatial hash coding table to analyze the microalgae purification water quality of the spatial hash decoding sequence to optimize water treatment, specifically comprising the following steps: Preset a purification radiation boundary threshold based on purification performance and construct a three-dimensional geographic terrain model of the target water treatment area. Use the purification radiation boundary threshold as a range constraint to plan the pollution absorption range within the three-dimensional geographic terrain model, and obtain the established pollution absorption range of the target water treatment area according to the future growth model of microalgae. Extracting multiple pollutant concentration parameters of water quality in a predetermined pollution absorption area using a real-time water quality pollution parameter set of a target water treatment area, repeating the above-mentioned spatial hash code construction step based on the multiple pollutant concentration parameters to hash the regional spatial pollution degree of the predetermined pollution absorption area, and obtaining a spatial hash decoding sequence of the predetermined pollution absorption spatial area; Determining whether the spatial hash decoding sequence can decode and output the spatial hash coding table; if not, obtaining a missing tampering code output by the spatial hash coding table that cannot be decoded using the spatial hash decoding sequence; and determining a purification performance gap of the polluted water quality in the target water treatment area due to microalgae growth based on the missing tampering code; Based on the purification performance gap, the growth conditions of microalgae in the established pollution absorption space area are improved and optimized to enhance the water pollution purification efficiency of microalgae growth in the established pollution absorption space area.

5. The water treatment optimization method based on microalgae growth prediction according to claim 1, characterized in that: The step S108 specifically includes the following steps: constructing a random distribution structure of water environment flow in a three-dimensional geographical terrain model based on a real-time water environment simulation field of a target water treatment area, and extracting a random node array of the random distribution structure; Obtaining neighbor nodes of each random node in the random node array, calculating weights between each random node and each neighbor node based on a real-time water quality pollution parameter set for the target water treatment area, generating weight values for each random node corresponding to a plurality of random node boundaries, and determining a random probability distribution of real-time water quality pollution based on a ratio between the weight values; A maximum number of steps is preset, and the current random node is updated to a neighboring node selected with the maximum random probability according to the random probability distribution. The weight calculation between the random node and the neighboring node and the random node update steps are repeated to continuously execute the next round of random walk until the random walk reaches the maximum number of steps, thereby generating a real-time water quality pollution scale model for the target water treatment area; Obtaining benchmark thermal color gamut rules for different water pollution scale ratings based on a big data network, performing color gamut assignment transformation on the real-time water pollution scale model using the benchmark thermal color gamut rules, and obtaining a real-time water pollution scale thermal model for the target water treatment area; Obtaining an online sensor layout pattern of a target water treatment area, dividing the real-time water pollution scale thermal model into M sub-model area blocks based on the online sensor layout pattern, and obtaining a real-time thermal color gamut RGB lookup table for each sub-model area block; Abnormal thermochromatic RGB values for unreasonable water pollution scales are preset based on the purification performance of microalgae for different water pollution scales. If the abnormal thermochromatic RGB value can be found in the real-time thermochromatic gamut RGB lookup table, the sub-model area block is marked as an abnormal water pollution scale area and a wastewater treatment operation for microalgae growth regulation is performed.

6. A water treatment optimization system based on microalgae growth prediction, characterized in that: The water treatment optimization system includes a memory and a processor. The memory stores a water treatment optimization method program based on microalgae growth prediction. When the water treatment optimization method program is executed by the processor, the water treatment optimization method steps according to any one of claims 1 to 5 are implemented.

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