Intelligent algae monitoring method and system based on floating bed process

Abstract

The invention discloses an intelligent algae monitoring method and system based on a floating bed process. The method comprises the following steps: constructing a floating bed area map model; collecting multi-dimensional algae and environmental parameters before and after floating bed regulation; performing linear regression prediction on the serialized parameters to form new parameter data, and vectorizing the parameters to obtain algae and environment feature vectors; introducing a hierarchical clustering algorithm to evaluate regional feature similarity, and analyzing an algae reproduction and migration direction and an environment matching region; and comparing and analyzing the algae migration trend and the environment change before and after regulation, evaluating the floating bed regulation effectiveness and optimizing the floating bed arrangement scheme. According to the method, intelligent algae monitoring and water ecology multi-point trend prediction are achieved, the algae migration law is accurately recognized, the effectiveness of floating bed setting is scientifically evaluated, water body deterioration is effectively relieved, the water ecology regulation and control efficiency is improved, and early warning hysteresis is reduced.

Description

Technical Field

[0001] This invention relates to the field of floating bed technology and ecological monitoring, and more specifically, to an intelligent algae monitoring method and system based on floating bed technology. Background Technology

[0002] Water source reservoirs are the core guarantee for urban and rural water supply, and their water quality safety is directly related to public health. At present, floating bed technology has become one of the key technologies for reservoir eutrophication, pollution control, and ecological regulation due to its eco-friendliness, stable algae control effect, and strong ecological regulation capacity. However, the current application of floating beds has significant technical shortcomings.

[0003] Existing floating bed technologies often only focus on the absorption and purification of a certain type of pollutants by aquatic plants, lacking a comprehensive assessment of ecological pollution such as algal growth from multiple dimensions. Furthermore, due to factors such as ecological competition, upstream and downstream migration of pollutants, and different spatial settings of floating beds, pollutant changes are complex and diverse, making it difficult to assess the trend of algal growth under ecological floating bed applications. Traditional technologies are often based on fixed-point and timed sampling and analysis, which often results in delayed early warnings. Moreover, predictive analysis methods cannot fully reflect the complex influencing factors of algal growth, making it difficult to achieve intelligent ecological monitoring and water body tracking and control. Summary of the Invention

[0004] This invention overcomes the shortcomings of the prior art and proposes an intelligent algae monitoring method and system based on floating bed technology.

[0005] The first aspect of this invention provides an intelligent algae monitoring method based on floating bed technology, comprising: S11: Construct a map model based on the ecological floating bed area; S12: During an ecological regulation period, multi-dimensional algal and ecological environment parameters of the floating bed area and adjacent areas are collected through monitoring units; S13: The multi-dimensional algal parameters and ecological environment parameters are serialized in time dimension. The data of algal parameters and environmental parameters are predicted for a preset time period through a linear regression prediction model. The predicted values ​​are inserted into the algal parameters and ecological environment parameters. The multi-dimensional parameters are vectorized to form algal change feature vectors and environmental change feature vectors. S14: Introduce a hierarchical clustering algorithm to evaluate the similarity of features in multiple regions of the floating bed based on the algal change feature vector and the environmental change feature vector as input. Analyze the algal reproduction and migration direction and the matching region of the environment based on the clustering results. S15: Within a post-ecological regulation cycle, multi-dimensional parameters of the floating bed area and adjacent areas are collected through monitoring units, and the direction of algal reproduction and migration and the matching area with the environment are analyzed. The algal reproduction and migration trend and environmental status change are assessed in comparison with the pre-ecological regulation cycle. The effectiveness of the floating bed based on ecological regulation is evaluated, and the floating bed setting scheme is optimized.

[0006] In this solution, S11 specifically refers to: A 3D map is constructed based on the shape, area, monitoring point layout, and floating bed installation plan of the ecological floating bed area, forming a visual map model. In the map model, each monitoring point corresponds to at least one monitoring unit and includes the floating bed setting area; The area where the floating bed is installed includes at least one floating bed and a water purification device.

[0007] In this solution, S12 specifically refers to: Within an ecological regulation period, multiple monitoring time points are set, and multi-dimensional algal parameters and ecological environment parameters of the floating bed area and adjacent areas are collected through monitoring units. Algal parameters are monitored through water sampling, including preset algal biomass, number of algal species, and preset algal percentage. Ecological and environmental parameters include dissolved oxygen, water temperature, pH value, total nitrogen, and total phosphorus.

[0008] In this solution, S13 specifically refers to: Among multidimensional algal parameters or ecological environment parameters, one parameter is selected for serialization to obtain the first sequence; A linear regression prediction model is introduced to predict the sequence of the first sequence. The sequence points are used as the dependent variable and time is used as the independent variable. The prediction is based on a preset step size and multiple predicted values ​​are obtained. By using intermediate interpolation, multiple predicted values ​​are inserted into the end of the first sequence to obtain the second sequence; The second sequence was linearly fitted based on linear regression, and the fitting coefficients were used as the regression values ​​of the parameters. The parameter regression change values ​​of multidimensional algal parameters and ecological environment parameters are calculated, and algal change feature vectors and environmental change feature vectors are formed based on algal and environmental dimensions, respectively.

[0009] In this solution, S14 specifically refers to: Calculate and obtain the algal change feature vector and environmental change feature vector corresponding to the floating bed area and the adjacent area; The algal change feature vector and the environmental change feature vector of each region are used as two clustering inputs. The algal and environmental change features of the region are clustered in a bottom-up manner using a hierarchical clustering algorithm. During the clustering process, a merging rule is set: determine whether the similarity of the change feature vectors corresponding to two regions is within a preset range, determine whether the two regions are adjacent regions, and introduce Euclidean distance calculation for similarity. If both judgments are yes, then the two regions are merged into one cluster. At the same time, the change feature vectors corresponding to the floating bed region can be clustered and merged multiple times. The clustering input is cyclically merged until a preset number of iterations is reached or the number of cluster regions exceeds a preset value, and the first clustering result and the second clustering result are obtained. In the first clustering result, the cluster in which the floating bed area is located is analyzed. The direction between the center point of the floating bed area and the center point of the other neighboring areas in the cluster is calculated and used as the direction of algal reproduction and migration. In the second clustering results, the clusters in which the floating bed area is located are analyzed, and the neighboring areas in the clusters are marked as environmental matching areas.

[0010] In this solution, S15 specifically refers to: Within a cycle following ecological regulation, multi-dimensional algal and environmental parameters of the floating bed area and adjacent areas are collected through monitoring units, and the direction of algal reproduction and migration and the matching area with the environment are analyzed. Before and after ecological regulation, the rate of change in algal reproduction and migration direction was analyzed and calculated, and the downward trend of the matching area was analyzed based on the number of environmental matching areas. The effectiveness of ecological regulation is assessed based on the rate of change in migration direction and the downward trend in the matching area; Optimize the floating bed setup based on the evaluation results.

[0011] This solution includes sending the evaluation results and the floating bed setup plan to a preset terminal.

[0012] In this scheme, the adjacent area includes multiple regions.

[0013] A second aspect of the present invention also provides an intelligent algae monitoring system based on floating bed technology. The system includes: a memory, a central processing unit (CPU), and a data interface. The memory includes an intelligent algae monitoring program based on floating bed technology. When executed by the CPU, the intelligent algae monitoring program based on floating bed technology performs the following steps: S11: Construct a map model based on the ecological floating bed area; S12: During an ecological regulation period, multi-dimensional algal and ecological environment parameters of the floating bed area and adjacent areas are collected through monitoring units; S13: The multi-dimensional algal parameters and ecological environment parameters are serialized in time dimension. The data of algal parameters and environmental parameters are predicted for a preset time period through a linear regression prediction model. The predicted values ​​are inserted into the algal parameters and ecological environment parameters. The multi-dimensional parameters are vectorized to form algal change feature vectors and environmental change feature vectors. S14: Introduce a hierarchical clustering algorithm to evaluate the similarity of features in multiple regions of the floating bed based on the algal change feature vector and the environmental change feature vector as input. Analyze the algal reproduction and migration direction and the matching region of the environment based on the clustering results. S15: Within a post-ecological regulation cycle, multi-dimensional parameters of the floating bed area and adjacent areas are collected through monitoring units, and the direction of algal reproduction and migration and the matching area with the environment are analyzed. The algal reproduction and migration trend and environmental status change are assessed in comparison with the pre-ecological regulation cycle. The effectiveness of the floating bed based on ecological regulation is evaluated, and the floating bed setting scheme is optimized.

[0014] A third aspect of the present invention also provides a computer-readable storage medium comprising an intelligent algae monitoring program based on a floating bed process, wherein when the intelligent algae monitoring program based on the floating bed process is executed by a central processing unit, it implements the steps of the intelligent algae monitoring method based on the floating bed process as described in any of the preceding claims.

[0015] This invention discloses an intelligent algae monitoring method and system based on floating bed technology, comprising: constructing a floating bed area map model; collecting multi-dimensional algae and environmental parameters before and after floating bed regulation; performing linear regression prediction on serialized parameters to form new parameter data, and vectorizing parameters to obtain algae and environmental feature vectors; introducing a hierarchical clustering algorithm to evaluate the similarity of regional features, analyzing the algae reproduction and migration direction and the matching area with the environment; comparing and analyzing the algae migration trend and environmental changes before and after regulation, evaluating the effectiveness of floating bed regulation, and optimizing the floating bed setting scheme. This invention realizes intelligent algae monitoring and multi-point trend prediction of aquatic ecology, accurately identifies algae migration patterns, scientifically evaluates the effectiveness of floating bed setting, effectively alleviates water body deterioration, improves the efficiency of aquatic ecological regulation, and reduces early warning lag. Attached Figure Description

[0016] Figure 1 A flowchart of an intelligent algae monitoring method based on floating bed technology according to the present invention is shown; Figure 2 The flowchart of the feature vector analysis of the present invention is shown; Figure 3 A block diagram of an intelligent algae monitoring system based on floating bed technology according to the present invention is shown. Detailed Implementation

[0017] To better understand the above-mentioned objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It is understood that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0019] Figure 1 A flowchart of an intelligent algae monitoring method based on floating bed technology according to the present invention is shown.

[0020] like Figure 1 As shown, the first aspect of the present invention provides an intelligent algae monitoring method based on floating bed technology, comprising: S11: Construct a map model based on the ecological floating bed area; S12: During an ecological regulation period, multi-dimensional algal and ecological environment parameters of the floating bed area and adjacent areas are collected through monitoring units; S13: The multi-dimensional algal parameters and ecological environment parameters are serialized in time dimension. The data of algal parameters and environmental parameters are predicted for a preset time period through a linear regression prediction model. The predicted values ​​are inserted into the algal parameters and ecological environment parameters. The multi-dimensional parameters are vectorized to form algal change feature vectors and environmental change feature vectors. S14: Introduce a hierarchical clustering algorithm to evaluate the similarity of features in multiple regions of the floating bed based on the algal change feature vector and the environmental change feature vector as input. Analyze the algal reproduction and migration direction and the matching region of the environment based on the clustering results. S15: Within a post-ecological regulation cycle, multi-dimensional parameters of the floating bed area and adjacent areas are collected through monitoring units, and the direction of algal reproduction and migration and the matching area with the environment are analyzed. The algal reproduction and migration trend and environmental status change are assessed in comparison with the pre-ecological regulation cycle. The effectiveness of the floating bed based on ecological regulation is evaluated, and the floating bed setting scheme is optimized.

[0021] This can be understood as referring to large drinking water source reservoirs, ecological ponds, shallow reservoirs, etc. Multi-dimensional parameters include algae parameters and ecological environment parameters. One pre-ecological regulation cycle and one post-ecological regulation cycle represent the monitoring periods before and after the addition of the floating bed.

[0022] It is worth mentioning that in aquatic environments with floating beds, the surrounding environment of the corresponding ecological floating islands exhibits certain environmental fluctuations due to environmental changes and fluctuations. Traditional single-monitoring unit and single-parameter analysis methods are insufficient to assess the changing trends of algae and environmental characteristics. Multi-dimensional parameters are often required for comprehensive evaluation, analysis of risk factors, and reduction of early warning lag. This invention can effectively solve the above problems. Furthermore, through this invention, it is possible to assess algae migration trends in multiple directions and analyze the trends of environmental status in multiple regions. The migration analysis results have a certain degree of foresight and early warning capability, and can accurately assess the probability of algae recurrence after the addition of floating beds, reduce early warning lag, and achieve intelligent ecological monitoring.

[0023] In addition, this invention can effectively assess the current state and migration trends of the ecological environment and algae, and use the assessment results to set up human and material resources for floating beds (floating islands), including setting the spatial location and quantity of floating beds, so as to achieve efficient utilization of floating beds.

[0024] According to an embodiment of the present invention, S11 specifically includes: A 3D map is constructed based on the shape, area, monitoring point layout, and floating bed installation plan of the ecological floating bed area, forming a visual map model. In the map model, each monitoring point corresponds to at least one monitoring unit and includes the floating bed setting area; The area where the floating bed is installed includes at least one floating bed and a water purification device.

[0025] This can be understood as follows: within the ecological floating bed area, multiple water bodies can be divided for analysis. The floating bed area can be located in one or more of these water bodies, and the floating island areas generally have a certain spacing to maximize the water purification effect. Centered on the floating bed area, there are usually several adjacent water bodies. These adjacent water bodies typically do not have floating islands and are used to assess the multi-point changes in the aquatic environment and algae before and after ecological floating island control within the floating bed area, and to analyze the effectiveness of the floating island control. The monitoring unit is used to monitor various water quality parameters and sample water for algae detection.

[0026] Water purification devices can be installed under floating beds with phosphorus removal ceramic pellets, which purify the water. Floating beds, also known as ecological floating islands, are a water regulation technology.

[0027] According to an embodiment of the present invention, step S12 specifically includes: Within an ecological regulation period, multiple monitoring time points are set, and multi-dimensional algal parameters and ecological environment parameters of the floating bed area and adjacent areas are collected through monitoring units. Algal parameters are monitored through water sampling, including preset algal biomass, number of algal species, and preset algal percentage. Ecological and environmental parameters include dissolved oxygen, water temperature, pH value, total nitrogen, and total phosphorus.

[0028] Here, it can be understood that the algae parameters are as follows: algae biomass (unit: mg / L), and the algae to be detected can be one or more types of algae such as cyanobacteria, green algae, and diatoms, which are selected based on the characteristics of the ecological area. The selection of preset algae is similar.

[0029] Figure 2 A flowchart of the feature vector analysis process of the present invention is shown.

[0030] According to an embodiment of the present invention, S13 specifically includes: Among multidimensional algal parameters or ecological environment parameters, one parameter is selected for serialization to obtain the first sequence; A linear regression prediction model is introduced to predict the sequence of the first sequence. The sequence points are used as the dependent variable and time is used as the independent variable. The prediction is based on a preset step size and multiple predicted values ​​are obtained. By using intermediate interpolation, multiple predicted values ​​are inserted into the end of the first sequence to obtain the second sequence; The second sequence was linearly fitted based on linear regression, and the fitting coefficients were used as the regression values ​​of the parameters. The parameter regression change values ​​of multidimensional algal parameters and ecological environment parameters are calculated, and algal change feature vectors and environmental change feature vectors are formed based on algal and environmental dimensions, respectively.

[0031] It can be understood that in the second sequence, the first part of the sequence is used to characterize the current state of algae and environment in the region, and the second part is used to characterize the relevant change trend. The second sequence is used to perform regression analysis on the changes in algae or environmental parameters, and the rate of change is represented by the regression coefficient. The change characteristics are analyzed in the form of multi-dimensional vectors to achieve effective analysis of the water quality change trend of the ecological floating island based on multiple dimensions.

[0032] The inserted data points are inserted into the subsequent data. For example, if the first sequence is [a1, a2...a5, a6, a7] and the preset values ​​are two: Y1 and Y2, then after the intermediate difference, the second sequence is [a1, a2...a5, a6, Y1, a7, Y2].

[0033] The feature vector can be represented as A = [change value 1, change value 2, change value 3...], where the change value is the change value of a certain parameter over multiple time periods (regression fitting coefficient), which is used to characterize the parameter change characteristics.

[0034] According to an embodiment of the present invention, step S14 specifically includes: Calculate and obtain the algal change feature vector and environmental change feature vector corresponding to the floating bed area and the adjacent area; The algal change feature vector and the environmental change feature vector of each region are used as two clustering inputs. The algal and environmental change features of the region are clustered in a bottom-up manner using a hierarchical clustering algorithm. During the clustering process, a merging rule is set: determine whether the similarity of the change feature vectors corresponding to two regions is within a preset range, determine whether the two regions are adjacent regions, and introduce Euclidean distance calculation for similarity. If both judgments are yes, then the two regions are merged into one cluster. At the same time, the change feature vectors corresponding to the floating bed region can be clustered and merged multiple times. The clustering input is cyclically merged until a preset number of iterations is reached or the number of cluster regions exceeds a preset value, and the first clustering result and the second clustering result are obtained. In the first clustering result, the cluster in which the floating bed area is located is analyzed. The direction between the center point of the floating bed area and the center point of the other neighboring areas in the cluster is calculated and used as the direction of algal reproduction and migration. In the second clustering results, the clusters in which the floating bed area is located are analyzed, and the neighboring areas in the clusters are marked as environmental matching areas.

[0035] It can be understood that the hierarchical clustering algorithm of this invention adopts an agglomerative hierarchical clustering form, progressively clustering data from bottom to top to form clusters. The change feature vectors corresponding to the floating bed regions can be clustered and merged multiple times; in a single clustering result, a floating bed region can belong to multiple clusters. The preset range is analyzed using the shortest distance method. The first and second clustering results correspond to the clustering results of the two clustering inputs, respectively. The first is for evaluating the algal change feature vectors of each region, and the second is for evaluating the environmental change feature vectors of each region. Whether two regions are adjacent is determined specifically by physical proximity.

[0036] Within a cycle before and after ecological regulation, studies are generally conducted using one floating bed area and multiple adjacent areas.

[0037] The clustering results include multiple clusters, each of which contains multiple regions. Here, we only study the clusters with floating bed regions.

[0038] In the cluster, the direction between the center point of the floating bed region and the center point of other neighboring regions is calculated and used as the direction of algal reproduction and migration. Specifically, in the cluster, multiple neighboring regions are analyzed, and the neighboring regions are merged into a large region. The direction of the line connecting the center of the floating bed region and the center point of the large region is set as the direction of algal reproduction and migration.

[0039] In the second clustering result, the clusters in which the floating bed area is located are analyzed, and the neighboring areas in the clusters are marked as environmental matching areas. Environmental matching areas can effectively reflect areas that have the same environmental change trend as the floating bed area.

[0040] According to an embodiment of the present invention, step S15 specifically includes: Within a cycle following ecological regulation, multi-dimensional algal and environmental parameters of the floating bed area and adjacent areas are collected through monitoring units, and the direction of algal reproduction and migration and the matching area with the environment are analyzed. Before and after ecological regulation, the rate of change in algal reproduction and migration direction was analyzed and calculated, and the downward trend of the matching area was analyzed based on the number of environmental matching areas. The effectiveness of ecological regulation is assessed based on the rate of change in migration direction and the downward trend in the matching area; Optimize the floating bed setup based on the evaluation results.

[0041] Here, it can be understood that the directional change can be in the range of [0, 180°], and the rate of change can be mapped to [0, 100%]. The greater the rate of change in migration direction, the more divergent the algae are. For the floating bed area, there is a downward trend of algae in multiple directions, which corresponds to the higher effectiveness of the floating bed in regulating algae. The downward trend of the environmental matching area can be judged by the change in the number of matching areas. If the number of matching areas decreases, it means that the ecological trend of the floating bed area is optimized to a certain extent, and there is an ecological difference trend compared with the multi-point area, which means that its ecological regulation is more effective.

[0042] Here, the effectiveness of floating bed setup and ecological regulation trend are jointly evaluated by the rate of change of migration direction and the downward trend of matching area, and ecological trend analysis and algae early warning assessment are realized in multiple directions and in multiple regions.

[0043] According to an embodiment of the present invention, the evaluation results and the floating bed setting scheme are sent to a preset terminal.

[0044] It is understood here that the preset terminal includes computer terminals and mobile terminals.

[0045] According to an embodiment of the present invention, the adjacent area includes multiple regions.

[0046] According to an embodiment of the present invention, it further includes: The study was conducted based on three preset parameters: algal biomass, number of algal species, and preset algal percentage. One parameter was selected to obtain the first sequence corresponding to a pre-ecological regulation cycle and a post-ecological regulation cycle. The two sequences were then spliced ​​together to obtain the pre- and post-regulation sequences. A linear regression prediction model is introduced to make linear predictions on the sequence before and after regulation, obtain the numerical prediction trend, and judge the accuracy of the floating bed ecological regulation effectiveness assessment results based on the prediction trends of the three parameters. This can be understood as the three parameters corresponding to three pre- and post-regulation sequences. In this invention, the linear prediction trends of these three algal parameters are introduced to analyze the accuracy of the floating bed effectiveness assessment. In the effectiveness assessment, higher effectiveness indicates a better regulation trend, with the corresponding trends for the three parameters being: a predetermined decrease in algal biomass, a decrease or no change in the number of algal species, and a predetermined decrease in the proportion of algae. If the accuracy is low, additional monitoring points and units can be added, and algal and environmental parameters can be assessed to improve the ability to analyze potential risks and enhance the accuracy of the effectiveness assessment method.

[0047] Figure 3 A block diagram of an intelligent algae monitoring system based on floating bed technology according to the present invention is shown.

[0048] A second aspect of the present invention also provides an intelligent algae monitoring system based on floating bed technology. The system includes: a memory 101, a central processing unit 102, and a data interface 103. The data interface is used to connect to a preset terminal and perform data interaction. The memory includes an intelligent algae monitoring program based on floating bed technology. When the central processing unit executes the intelligent algae monitoring program based on floating bed technology, it performs the following steps: S11: Construct a map model based on the ecological floating bed area; S12: During an ecological regulation period, multi-dimensional algal and ecological environment parameters of the floating bed area and adjacent areas are collected through monitoring units; S13: The multi-dimensional algal parameters and ecological environment parameters are serialized in time dimension. The data of algal parameters and environmental parameters are predicted for a preset time period through a linear regression prediction model. The predicted values ​​are inserted into the algal parameters and ecological environment parameters. The multi-dimensional parameters are vectorized to form algal change feature vectors and environmental change feature vectors. S14: Introduce a hierarchical clustering algorithm to evaluate the similarity of features in multiple regions of the floating bed based on the algal change feature vector and the environmental change feature vector as input. Analyze the algal reproduction and migration direction and the matching region of the environment based on the clustering results. S15: Within a post-ecological regulation cycle, multi-dimensional parameters of the floating bed area and adjacent areas are collected through monitoring units, and the direction of algal reproduction and migration and the matching area with the environment are analyzed. The algal reproduction and migration trend and environmental status change are assessed in comparison with the pre-ecological regulation cycle. The effectiveness of the floating bed based on ecological regulation is evaluated, and the floating bed setting scheme is optimized.

[0049] This can be understood as referring to large drinking water source reservoirs, ecological ponds, shallow reservoirs, etc. Multi-dimensional parameters include algae parameters and ecological environment parameters. One pre-ecological regulation cycle and one post-ecological regulation cycle represent the monitoring periods before and after the addition of the floating bed.

[0050] It is worth mentioning that in aquatic environments with floating beds, the surrounding environment of the corresponding ecological floating islands exhibits certain environmental fluctuations due to environmental changes and fluctuations. Traditional single-monitoring unit and single-parameter analysis methods are insufficient to assess the changing trends of algae and environmental characteristics. Multi-dimensional parameters are often required for comprehensive evaluation, analysis of risk factors, and reduction of early warning lag. This invention can effectively solve the above problems. Furthermore, through this invention, it is possible to assess algae migration trends in multiple directions and analyze the trends of environmental status in multiple regions. The migration analysis results have a certain degree of foresight and early warning capability, and can accurately assess the probability of algae recurrence after the addition of floating beds, reduce early warning lag, and achieve intelligent ecological monitoring.

[0051] In addition, this invention can effectively assess the current state and migration trends of the ecological environment and algae, and use the assessment results to set up human and material resources for floating beds (floating islands), including setting the spatial location and quantity of floating beds, so as to achieve efficient utilization of floating beds.

[0052] When the system is running, it can perform one or more steps of the intelligent algae monitoring method based on the floating bed technology described above.

[0053] A third aspect of the present invention also provides a computer-readable storage medium comprising an intelligent algae monitoring program based on a floating bed process, wherein when the intelligent algae monitoring program based on the floating bed process is executed by a central processing unit, it implements the steps of the intelligent algae monitoring method based on the floating bed process as described in any of the preceding claims.

[0054] Summary 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 illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0055] The units described above as separate components may or may not be physically separate. The components shown 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 to achieve the purpose of this embodiment according to actual needs.

[0056] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0057] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0058] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0059] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent algae monitoring based on floating bed technology, characterized in that, include: S11: Construct a map model based on the ecological floating bed area; S12: During an ecological regulation period, multi-dimensional algal and ecological environment parameters of the floating bed area and adjacent areas are collected through monitoring units; S13: The multi-dimensional algal parameters and ecological environment parameters are serialized in time dimension. The data of algal parameters and environmental parameters are predicted for a preset time period through a linear regression prediction model. The predicted values ​​are inserted into the algal parameters and ecological environment parameters. The multi-dimensional parameters are vectorized to form algal change feature vectors and environmental change feature vectors. S14: Introduce a hierarchical clustering algorithm to evaluate the similarity of features in multiple regions of the floating bed based on the algal change feature vector and the environmental change feature vector as input. Analyze the algal reproduction and migration direction and the matching region of the environment based on the clustering results. S15: Within a post-ecological regulation cycle, multi-dimensional parameters of the floating bed area and adjacent areas are collected through monitoring units, and the direction of algal reproduction and migration and the matching area with the environment are analyzed. The algal reproduction and migration trend and environmental status change are assessed in comparison with the pre-ecological regulation cycle. The effectiveness of the floating bed based on ecological regulation is evaluated, and the floating bed setting scheme is optimized.

2. The intelligent algae monitoring method based on floating bed technology according to claim 1, characterized in that, Specifically, S11 is as follows: A 3D map is constructed based on the shape, area, monitoring point layout, and floating bed installation plan of the ecological floating bed area, forming a visual map model. In the map model, each monitoring point corresponds to at least one monitoring unit and includes the floating bed setting area; The area where the floating bed is installed includes at least one floating bed and a water purification device.

3. The intelligent algae monitoring method based on floating bed technology according to claim 1, characterized in that, Specifically, S12 is as follows: Within an ecological regulation period, multiple monitoring time points are set, and multi-dimensional algal parameters and ecological environment parameters of the floating bed area and adjacent areas are collected through monitoring units. Algal parameters are monitored through water sampling, including preset algal biomass, number of algal species, and preset algal percentage. Ecological and environmental parameters include dissolved oxygen, water temperature, pH value, total nitrogen, and total phosphorus.

4. The intelligent algae monitoring method based on floating bed technology according to claim 1, characterized in that, Specifically, S13 is: Among multidimensional algal parameters or ecological environment parameters, one parameter is selected for serialization to obtain the first sequence; A linear regression prediction model is introduced to predict the sequence of the first sequence. The sequence points are used as the dependent variable and time is used as the independent variable. The prediction is based on a preset step size and multiple predicted values ​​are obtained. By using intermediate interpolation, multiple predicted values ​​are inserted into the end of the first sequence to obtain the second sequence; The second sequence was linearly fitted based on linear regression, and the fitting coefficients were used as the regression values ​​of the parameters. The parameter regression change values ​​of multidimensional algal parameters and ecological environment parameters are calculated, and algal change feature vectors and environmental change feature vectors are formed based on algal and environmental dimensions, respectively.

5. The intelligent algae monitoring method based on floating bed technology according to claim 1, characterized in that, Specifically, S14 is as follows: Calculate and obtain the algal change feature vector and environmental change feature vector corresponding to the floating bed area and the adjacent area; The algal change feature vector and the environmental change feature vector of each region are used as two clustering inputs. The algal and environmental change features of the region are clustered in a bottom-up manner using a hierarchical clustering algorithm. During the clustering process, a merging rule is set: determine whether the similarity of the change feature vectors corresponding to two regions is within a preset range, determine whether the two regions are adjacent regions, and introduce Euclidean distance calculation for similarity. If both judgments are yes, then the two regions are merged into one cluster. At the same time, the change feature vectors corresponding to the floating bed region can be clustered and merged multiple times. The clustering input is cyclically merged until a preset number of iterations is reached or the number of cluster regions exceeds a preset value, and the first clustering result and the second clustering result are obtained. In the first clustering result, the cluster in which the floating bed area is located is analyzed. The direction between the center point of the floating bed area and the center point of the other neighboring areas in the cluster is calculated and used as the direction of algal reproduction and migration. In the second clustering results, the clusters in which the floating bed area is located are analyzed, and the neighboring areas in the clusters are marked as environmental matching areas.

6. The intelligent algae monitoring method based on floating bed technology according to claim 1, characterized in that, Specifically, S15 is as follows: Within a cycle following ecological regulation, multi-dimensional algal and environmental parameters of the floating bed area and adjacent areas are collected through monitoring units, and the direction of algal reproduction and migration and the matching area with the environment are analyzed. Before and after ecological regulation, the rate of change in algal reproduction and migration direction was analyzed and calculated, and the downward trend of the matching area was analyzed based on the number of environmental matching areas. The effectiveness of ecological regulation is assessed based on the rate of change in migration direction and the downward trend in the matching area; Optimize the floating bed setup based on the evaluation results.

7. The intelligent algae monitoring method based on floating bed technology according to claim 6, characterized in that, include: The evaluation results and floating bed setup plan are sent to the preset terminal.

8. The intelligent algae monitoring method based on floating bed technology according to claim 1, characterized in that, The adjacent area includes multiple areas.

9. An intelligent algae monitoring system based on floating bed technology, characterized in that, The system includes: a memory, a central processing unit, and a data interface. The memory includes an intelligent algae monitoring program based on floating bed technology. When the central processing unit executes the intelligent algae monitoring program based on floating bed technology, it performs the following steps: S11: Construct a map model based on the ecological floating bed area; S12: During an ecological regulation period, multi-dimensional algal and ecological environment parameters of the floating bed area and adjacent areas are collected through monitoring units; S13: The multi-dimensional algal parameters and ecological environment parameters are serialized in time dimension. The data of algal parameters and environmental parameters are predicted for a preset time period through a linear regression prediction model. The predicted values ​​are inserted into the algal parameters and ecological environment parameters. The multi-dimensional parameters are vectorized to form algal change feature vectors and environmental change feature vectors. S14: Introduce a hierarchical clustering algorithm to evaluate the similarity of features in multiple regions of the floating bed based on the algal change feature vector and the environmental change feature vector as input. Analyze the algal reproduction and migration direction and the matching region of the environment based on the clustering results. S15: Within a post-ecological regulation cycle, multi-dimensional parameters of the floating bed area and adjacent areas are collected through monitoring units, and the direction of algal reproduction and migration and the matching area with the environment are analyzed. The algal reproduction and migration trend and environmental status change are assessed in comparison with the pre-ecological regulation cycle. The effectiveness of the floating bed based on ecological regulation is evaluated, and the floating bed setting scheme is optimized.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes an intelligent algae monitoring program based on floating bed technology. When the intelligent algae monitoring program based on floating bed technology is executed by a central processing unit, it implements the steps of the intelligent algae monitoring method based on floating bed technology as described in any one of claims 1 to 8.

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