A Method and System for Microalgae Feature Fusion Analysis Based on Deep Learning

Abstract

The present invention relates to a method and system for microalgae feature fusion analysis based on deep learning, belonging to the technical field of microalgae feature recognition and analysis. The present invention obtains the microalgae harvesting time node according to the microalgae growth and harvesting threshold range, obtains the real-time pollutant data at the current microalgae harvesting time node, finally performs pollution analysis based on the real-time pollutant data at the current microalgae harvesting time node, and generates relevant regulation strategies based on the pollution analysis results, and conducts regulation according to the relevant regulation strategies. By fusing a deep learning network and a generative adversarial network, the present invention can fuse the cell division rules of microalgae under different environments and the image features within a preset time to analyze the microalgae growth data in the target area, solve the pain points of image technology for the recognition of microalgae growth and reproduction, accurately grasp the stage of microalgae growth, and provide theoretical support for the recognition, regulation and removal of microalgae pollutants in the watershed water body.

Description

Technical Field

[0001] The present invention relates to the technical field of microalgae identification and analysis, and in particular to a microalgae feature fusion analysis method and system based on deep learning. Background Art

[0002] Microalgae can convert solar energy into renewable energy materials such as hydrogen, hydrocarbons, alcohols, and oils and store them in cells. As raw materials for biodiesel, alcohol-based fuels, biohydrogen production technology, microalgae hydrocarbon production, biofertilizers, and microalgae plastics, they can meet the needs of industrial and agricultural applications to a certain extent. However, due to the small size of microalgae cells, the density close to that of water, and the negative charge on the surface, it is difficult for microalgae to settle naturally and separate from water, making the cost of microalgae harvesting account for 20% to 30% of the total production cost, which seriously limits its large-scale and industrial production. Traditional microalgae harvesting technologies include physical methods (such as centrifugation, membrane filtration, and flotation), chemical methods (such as flocculation), and electromagnetic methods (such as electroflocculation and magnetic flocculation). The application of these technologies generally requires a lot of energy consumption and high operating costs to drive, and is easy to cause damage to microalgae cells. Therefore, it is necessary to use image recognition technology, spectral technology, etc. for identification. However, due to the lack of microalgae image data, the limitations of image technology, and changes in the culture environment, it is impossible to truly predict the reproduction of microalgae, resulting in the failure of microalgae to meet the predetermined requirements when harvested. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a method and system for fusion analysis of microalgae features based on deep learning.

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

[0005] The first aspect of the present invention provides a method for fusion analysis of microalgae features based on deep learning, comprising the following steps:

[0006] Acquire image feature data of microalgae culture solution in the target area, build a microalgae growth prediction model based on a deep neural network, and optimize the microalgae growth prediction model through a generative adversarial network;

[0007] Predicting the image feature data of the microalgae culture solution in the target area by using the microalgae growth prediction model to obtain microalgae growth data within a preset time;

[0008] Setting a microalgae growth and harvesting threshold range, obtaining a microalgae harvesting time node according to the microalgae growth data within the preset time and the microalgae growth and harvesting threshold range, and obtaining real-time pollutant data at the current microalgae harvesting time node;

[0009] Conduct pollution analysis based on the real-time pollutant data at the current microalgae harvesting time node, generate relevant regulation strategies based on the pollution analysis results, and perform regulation according to the relevant regulation strategies.

[0010] Further, in the microalgae feature fusion analysis method based on deep learning, obtain the microalgae culture solution image feature data in the target area, and construct a microalgae growth prediction model based on a deep neural network, specifically:

[0011] Obtain the microalgae culture solution image feature data in the target area, simulate the environment through environmental simulation technology, add noise for simulation, enhance the effect of the microalgae culture solution image feature data, and perform enhancement processing on the microalgae culture solution image feature data;

[0012] Obtain the enhanced microalgae culture solution image feature data, construct a data training set according to the enhanced microalgae culture solution image feature data, and construct a microalgae growth prediction model based on a deep neural network;

[0013] Input the data training set into the constructed microalgae growth prediction model, use binary cross-entropy as the loss function of the microalgae growth prediction model, and evaluate the microalgae growth prediction model using the binary cross-entropy loss function;

[0014] Obtain the convergence speed information. When the convergence speed information is greater than the preset convergence speed information, output the microalgae growth prediction model.

[0015] Further, in the microalgae feature fusion analysis method based on deep learning, optimize the microalgae growth prediction model through a generative adversarial network, specifically:

[0016] Obtain the cell division rule information of microalgae under various environmental reproduction conditions, construct microalgae biological constraint conditions based on the cell division rule information of microalgae under various environmental reproduction conditions, and train the microalgae growth prediction model based on the microalgae biological constraint conditions;

[0017] Through training, the generator generates an initial microalgae growth prediction result according to the enhanced microalgae culture solution image feature data;

[0018] Input the initial microalgae growth prediction result into the discriminator for judgment, and combine the microalgae biological constraint conditions to judge whether to accept the initial microalgae growth prediction result. If the judgment result of the discriminator is acceptance, output the initial microalgae growth prediction result;

[0019] If the judgment result of the discriminator is non-acceptance, the generator continues to generate the next prediction result until the final prediction result is output that meets the constraint conditions, and the optimization of the microalgae growth prediction model is completed.

[0020] Further, in the microalgae feature fusion analysis method based on deep learning, the microalgae growth prediction model is used to predict the microalgae culture solution image feature data in the target area, and the microalgae growth data within a preset time is obtained. Specifically:

[0021] Obtain the growth environment characteristics of microalgae in the target area, and count the microalgae data in the microalgae culture solution image feature data in the target area;

[0022] Use the growth environment characteristics of microalgae in the target area and the microalgae data in the microalgae culture solution image feature data in the target area as model input data;

[0023] Input the model input data into the microalgae growth prediction model for prediction, obtain the final prediction result when the discriminator accepts it, and output the final prediction result when the discriminator accepts it as the microalgae growth data within a preset time.

[0024] Further, in the microalgae feature fusion analysis method based on deep learning, set the microalgae growth and harvesting threshold range, and obtain the microalgae harvesting time node according to the microalgae growth data within the preset time and the microalgae growth and harvesting threshold range. Specifically:

[0025] Set the microalgae growth and harvesting threshold range, and obtain the microalgae growth data at each timestamp from the microalgae growth data within the preset time, and judge whether the microalgae growth data at each timestamp is within the microalgae growth and harvesting threshold range;

[0026] Use the timestamps when the microalgae growth data is within the microalgae growth and harvesting threshold range as the microalgae harvesting time nodes, use the timestamps when the microalgae growth data is not within the microalgae growth and harvesting threshold range as non-microalgae harvesting time nodes, and output the microalgae harvesting time nodes.

[0027] Further, in the microalgae feature fusion analysis method based on deep learning, perform pollution analysis according to the real-time pollutant data at the current microalgae harvesting time node, and generate relevant regulation strategies based on the pollution analysis results. Specifically:

[0028] Set the pollution data threshold, and judge whether the real-time pollutant data at the current microalgae harvesting time node is greater than the pollution data threshold;

[0029] When the real-time pollutant data at the current microalgae harvesting time node is greater than the pollution data threshold, construct a pollution retrieval label, and perform retrieval based on the pollution retrieval label to obtain pollution-related regulation measures;

[0030] Generate relevant control strategies according to the pollution-related control measures, and display the generated relevant control strategies in a preset manner.

[0031] The second aspect of the present invention provides a microalgae feature fusion analysis system based on deep learning, including a memory and a processor. The memory includes a program for the microalgae feature fusion analysis method based on deep learning. When the program for the microalgae feature fusion analysis method based on deep learning is executed by the processor, the steps of any of the microalgae feature fusion analysis methods based on deep learning are implemented.

[0032] The present invention solves the defects in the background technology and has the following beneficial effects:

[0033] The present invention obtains the image feature data of the microalgae culture solution in the target area, constructs a microalgae growth prediction model based on a deep neural network, optimizes the microalgae growth prediction model through a generative adversarial network, and then predicts the image feature data of the microalgae culture solution in the target area through the microalgae growth prediction model to obtain the microalgae growth data within a preset time. Thus, a microalgae growth harvesting threshold range is set, the microalgae harvesting time node is obtained according to the microalgae growth data within the preset time and the microalgae growth harvesting threshold range, and the real-time pollutant data at the current microalgae harvesting time node is obtained. Finally, pollution analysis is performed according to the real-time pollutant data at the current microalgae harvesting time node, and relevant control strategies are generated based on the pollution analysis results, and control is carried out according to the relevant control strategies. By integrating a deep learning network and a generative adversarial network, the present invention can analyze the microalgae growth data in the target area by integrating the cell division rules of microalgae under different environments and the image features within a preset time, solve the pain points of image technology for microalgae growth recognition, and make the harvesting of microalgae more reasonable. Description of the Drawings

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 Shows a part of the microalgae training data set.

[0036] Figure 2 Shows the overall flowchart of the microalgae feature fusion analysis method based on deep learning;

[0037] Figure 3Shows a partial flow chart of a microalgae feature fusion analysis method based on deep learning;

[0038] Figure 4 Shows a system block diagram of a microalgae feature fusion analysis system based on deep learning. Specific embodiments

[0039] In order to more clearly 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 should be noted that, without conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.

[0040] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0041] As Figure 2 shown, the first aspect of the present invention provides a microalgae feature fusion analysis method based on deep learning, including the following steps:

[0042] S102: Obtain the microalgae culture solution image feature data in the target area, construct a microalgae growth prediction model based on a deep neural network, and optimize the microalgae growth prediction model through a generative adversarial network;

[0043] S104: Predict the microalgae culture solution image feature data in the target area through the microalgae growth prediction model to obtain the microalgae growth data within a preset time;

[0044] S106: Set the microalgae growth harvesting threshold range, obtain the microalgae harvesting time node according to the microalgae growth data within the preset time and the microalgae growth harvesting threshold range, and obtain the real-time pollutant data at the current microalgae harvesting time node;

[0045] S108: Perform pollution analysis according to the real-time pollutant data at the current microalgae harvesting time node, generate relevant regulation strategies based on the pollution analysis results, and perform regulation according to the relevant regulation strategies.

[0046] It should be noted that by integrating the deep learning network and the generative adversarial network, the present invention can integrate the cell division rules of microalgae under different environments and the image features within a preset time to analyze the microalgae growth data in the target area, solve the pain points of image technology for microalgae growth recognition, and make the harvesting of microalgae more reasonable.

[0047] Further, in the microalgae feature fusion analysis method based on deep learning, image feature data of microalgae culture solution in the target area is obtained, and a microalgae growth prediction model is constructed based on a deep neural network, specifically as follows:

[0048] As Figure 1 shown, which shows a partial structural schematic diagram of microalgae and the processed image, obtain the image feature data of the microalgae culture solution in the target area, and simulate the environment through environmental simulation technology, add noise for simulation, enhance the effect of the image feature data of the microalgae culture solution, and enhance the image feature data of the microalgae culture solution;

[0049] Exemplarily, the environmental simulation technology processing process includes: operations such as contrast adjustment, brightness adjustment, hue adjustment, and gray-scale transformation, simulation and removal of noise including removal of Gaussian noise, removal of salt-and-pepper noise, etc., and image enhancement including enhancement processing methods such as sharpening processing, Laplacian algorithm processing, gamma transformation processing, and contrast-limited adaptive histogram equalization.

[0050] Obtain the image feature data of the microalgae culture solution after enhancement processing, construct a data training set according to the image feature data of the microalgae culture solution after enhancement processing, and construct a microalgae growth prediction model based on a deep neural network;

[0051] It should be noted that the deep neural network includes convolutional neural network, recurrent neural network, etc.

[0052] Input the data training set into the constructed microalgae growth prediction model, use binary cross-entropy as the loss function of the microalgae growth prediction model, and evaluate the microalgae growth prediction model using the binary cross-entropy loss function;

[0053] Obtain the convergence speed information, and when the convergence speed information is greater than the preset convergence speed information, output the microalgae growth prediction model.

[0054] As Figure 3 shown, further, in the microalgae feature fusion analysis method based on deep learning, the microalgae growth prediction model is optimized through a generative adversarial network, specifically as follows:

[0055] S202: Obtain the cell division law information of microalgae under various environmental reproduction conditions, construct microalgae biological constraint conditions based on the cell division law information of microalgae under various environmental reproduction conditions, and train the microalgae growth prediction model based on the microalgae biological constraint conditions;

[0056] It should be noted that the Generative Adversarial Networks (GAN) is a deep learning model and one of the most promising methods for unsupervised learning on complex distributions in recent years. The Generative Adversarial Networks can learn generation tasks in semi-supervised or unsupervised application scenarios. At present, the Generative Adversarial Networks have achieved amazing results in the fields of computer vision, natural language processing, etc. The generative adversarial model is one of the most promising methods for unsupervised learning on complex data distributions in recent years. The Generative Adversarial Network (GAN) is a generative model that learns through the mutual game of two neural networks. The Generative Adversarial Networks can learn generation tasks without using labeled data. The Generative Adversarial Networks consist of a generator and a discriminator. The generator randomly samples from the latent space as input, and its output results need to mimic the real samples in the training set as much as possible. The input of the discriminator is either the real sample or the output of the generator, and its purpose is to distinguish the output of the generator from the real samples as much as possible. In this method, by constructing the microalgae biological constraint conditions based on the cell division law information of microalgae under various environmental reproduction conditions, and through the constraints of the constraint conditions, the microalgae growth prediction model is optimized and trained based on the microalgae biological constraint conditions.

[0057] S204: Through training, the generator generates an initial microalgae growth prediction result according to the enhanced microalgae culture solution image feature data;

[0058] S206: Input the initial microalgae growth prediction result into the discriminator for judgment, and combine the microalgae biological constraint conditions to judge whether to accept the initial microalgae growth prediction result. If the judgment result of the discriminator is acceptance, then output the initial microalgae growth prediction result;

[0059] S208: If the judgment result of the discriminator is non-acceptance, the generator continues to generate the next prediction result until the final prediction result is output that meets the constraint conditions, and the optimization of the microalgae growth prediction model is completed.

[0060] Exemplarily, the reproduction data includes data such as the volume ratio of microalgae per unit volume, the area ratio of microalgae per unit area, and the quantity information of microalgae in per unit volume.

[0061] It should be noted that the cell division rules of cells under different environmental reproduction conditions are different. The environmental reproduction conditions include temperature, humidity, salinity, chemical composition concentration data, etc. Due to the limitations of image technology, by integrating deep learning networks and generative adversarial networks, it is possible to integrate the cell division rules of microalgae under different environments and the image features within a preset time to analyze the microalgae growth data in the target area, solve the pain points of image technology for microalgae growth recognition, and make the harvesting of microalgae more reasonable.

[0062] Furthermore, in the microalgae feature fusion analysis method based on deep learning, the microalgae growth prediction model is used to predict the image feature data of the microalgae culture solution in the target area to obtain the microalgae growth data within a preset time. Specifically:

[0063] Obtain the growth environment characteristics of microalgae in the target area, and count the microalgae data in the image feature data of the microalgae culture solution in the target area;

[0064] Take the growth environment characteristics of microalgae in the target area and the microalgae data in the image feature data of the microalgae culture solution in the target area as the model input data;

[0065] Input the model input data into the microalgae growth prediction model for prediction, obtain the final prediction result when the discriminator accepts it, and output the final prediction result when the discriminator accepts it as the microalgae growth data within a preset time.

[0066] Furthermore, in the microalgae feature fusion analysis method based on deep learning, set the microalgae growth harvesting threshold range, and obtain the microalgae harvesting time node according to the microalgae growth data within a preset time and the microalgae growth harvesting threshold range. Specifically:

[0067] Set the microalgae growth harvesting threshold range, and obtain the microalgae growth data at each time stamp from the microalgae growth data within a preset time, and judge whether the microalgae growth data at each time stamp is within the microalgae growth harvesting threshold range;

[0068] Take the time stamps when the microalgae growth data is within the microalgae growth harvesting threshold range as the microalgae harvesting time nodes, take the time stamps when the microalgae growth data is not within the microalgae growth harvesting threshold range as non-microalgae harvesting time nodes, and output the microalgae harvesting time nodes.

[0069] It should be noted that taking the time stamps when the microalgae growth data is within the microalgae growth harvesting threshold range as the microalgae harvesting time nodes makes the harvesting time nodes meet the expected effect.

[0070] Further, in the microalgae feature fusion analysis method based on deep learning, pollution analysis is performed according to the real-time pollutant data at the current microalgae harvesting time node, and relevant regulation strategies are generated based on the pollution analysis results. Specifically:

[0071] Set a pollution data threshold, and determine whether the real-time pollutant data at the current microalgae harvesting time node is greater than the pollution data threshold;

[0072] When the real-time pollutant data at the current microalgae harvesting time node is greater than the pollution data threshold, construct a pollution retrieval label, perform a retrieval based on the pollution retrieval label, and obtain regulation measures related to pollution;

[0073] Generate relevant regulation strategies according to the regulation measures related to pollution, and display the generated relevant regulation strategies in a preset manner.

[0074] It should be noted that the pollution data includes pollutant types, concentration data of pollutants, etc. Through further determination before harvesting by this method, the rationality of microalgae harvesting can be improved.

[0075] In addition, this method also includes: obtaining the microalgae surface charge characteristic data and the working parameter characteristic data of the current electromagnetic device, calculating the coupling characteristic between the magnetic field gradient and the microalgae surface charge based on the working parameter characteristic data of the current electromagnetic device and the microalgae surface charge characteristic data; introducing the particle swarm optimization algorithm, setting the number of iterations based on the particle swarm optimization algorithm, setting a coupling characteristic threshold, and determining whether the coupling characteristic between the magnetic field gradient and the microalgae surface charge is greater than the coupling characteristic threshold; when the coupling characteristic between the magnetic field gradient and the microalgae surface charge is greater than the coupling characteristic threshold, control the current electromagnetic device based on the working parameter characteristic data of the current electromagnetic device; when the coupling characteristic between the magnetic field gradient and the microalgae surface charge is not greater than the coupling characteristic threshold, adjust the working parameter characteristic data of the current electromagnetic device by iterating based on the number of iterations until the coupling characteristic between the magnetic field gradient and the microalgae surface charge is greater than the coupling characteristic threshold.

[0076] It should be noted that due to different microalgae surface charges, the electromagnetic device will have different capture efficiencies during microalgae harvesting. By using the particle swarm optimization algorithm to dynamically adjust the working parameter characteristic data (magnetic field gradient) of the current electromagnetic device according to the coupling characteristic between the magnetic field gradient and the microalgae surface charge, the capture efficiency of microalgae harvesting can be improved. Among them, the coupling characteristic between the magnetic field gradient and the microalgae surface charge refers to the phenomenon that through the action of the magnetic field, mutual interaction occurs between two or more objects. In the microalgae separation process, the magnetic field gradient can effectively affect the movement trajectory of microalgae cells, thereby improving the separation efficiency.

[0077] Such as Figure 4As shown in the figure, the second aspect of the present invention provides a microalgae feature fusion analysis system 4 based on deep learning, including a memory 41 and a processor 42. The memory 41 includes a program for the microalgae feature fusion analysis method based on deep learning. When the program for the microalgae feature fusion analysis method based on deep learning is executed by the processor 42, the technical solution of the above-mentioned microalgae feature fusion analysis method based on deep learning is realized.

[0078] In summary, the present invention obtains the image feature data of the microalgae culture solution in the target area, constructs a microalgae growth prediction model based on a deep neural network, optimizes the microalgae growth prediction model through a generative adversarial network, and then predicts the image feature data of the microalgae culture solution in the target area through the microalgae growth prediction model to obtain the microalgae growth data within a preset time. Thus, a microalgae growth harvesting threshold range is set, the microalgae harvesting time node is obtained according to the microalgae growth data within the preset time and the microalgae growth harvesting threshold range, and the real-time pollutant data at the current microalgae harvesting time node is obtained. Finally, pollution analysis is performed according to the real-time pollutant data at the current microalgae harvesting time node, and relevant regulation strategies are generated based on the pollution analysis results, and regulation is carried out according to the relevant regulation strategies. By integrating a deep learning network and a generative adversarial network, the present invention can analyze the microalgae growth data in the target area by fusing the cell division rules of microalgae under different environments and the image features within a preset time, solves the pain points of image technology for microalgae growth recognition, and makes the harvesting of microalgae more reasonable.

[0079] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed may be through some interfaces. The indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0080] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units; they may be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0081] In addition, in each embodiment of the present invention, each functional unit can be fully integrated into a processing unit, or each unit can be separately regarded as a unit, or two or more units can be integrated into one unit; the above integrated unit can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0082] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed 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 including the above method embodiments; and the aforementioned storage medium includes: removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs and other various media that can store program codes.

[0083] Alternatively, if the above integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the various embodiments of the present invention. And the aforementioned storage medium includes: removable storage devices, ROM, RAM, magnetic disks, or optical discs and other various media that can store program codes.

[0084] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A microalgae feature fusion analysis method based on deep learning, characterized in that: The following steps are involved: Acquire image feature data of microalgae culture solution in the target area, build a microalgae growth prediction model based on a deep neural network, and optimize the microalgae growth prediction model through a generative adversarial network; Predicting the image feature data of the microalgae culture solution in the target area by using the microalgae growth prediction model to obtain microalgae growth data within a preset time; Setting a microalgae growth and harvesting threshold range, obtaining a microalgae harvesting time node according to the microalgae growth data within the preset time and the microalgae growth and harvesting threshold range, and obtaining real-time pollutant data at the current microalgae harvesting time node; Performing pollution analysis according to the real-time pollutant data at the current microalgae harvesting time node, generating relevant control strategies based on the pollution analysis results, and performing control according to the relevant control strategies; The image feature data of the microalgae culture solution in the target area is obtained, and a microalgae growth prediction model is constructed based on a deep neural network. Specifically: Acquire the microalgae culture solution image feature data in the target area, and simulate the environment through environmental simulation technology, add noise for simulation, enhance the effect of the microalgae culture solution image feature data, and enhance the microalgae culture solution image feature data; Acquire enhanced processed microalgae culture solution image feature data, construct a data training set according to the enhanced processed microalgae culture solution image feature data, and construct a microalgae growth prediction model based on a deep neural network; Inputting the data training set into the microalgae growth prediction model, using binary cross entropy as the loss function of the microalgae growth prediction model, and evaluating the microalgae growth prediction model using the binary cross entropy loss function; Acquiring convergence speed information, and when the convergence speed information is greater than preset convergence speed information, outputting the microalgae growth prediction model; The microalgae growth prediction model is optimized by generating adversarial networks, specifically: Acquiring information on the cell division law of microalgae under various environmental reproduction conditions, constructing microalgae biological constraints based on the information on the cell division law of microalgae under various environmental reproduction conditions, and training the microalgae growth prediction model based on the microalgae biological constraints; Through training, the generator generates initial microalgae growth prediction results based on the enhanced microalgae culture solution image feature data; Inputting the initial microalgae growth prediction result into a discriminator for judgment, and judging whether to accept the initial microalgae growth prediction result in combination with the microalgae biological constraint condition, and outputting the initial microalgae growth prediction result if the judgment result of the discriminator is acceptance; If the judgment result of the discriminator is not accepted, the generator continues to generate the next prediction result until the constraint conditions are met and the final prediction result is output, and the optimization of the microalgae growth prediction model is completed.

2. The method for fusion analysis of microalgae features based on deep learning according to claim 1, characterized in that: The microalgae growth prediction model is used to predict the microalgae culture solution image feature data in the target area to obtain microalgae growth data within a preset time, specifically: Acquire the growth environment characteristics of microalgae in the target area, and count the microalgae data in the microalgae culture solution image feature data in the target area; Using the growth environment characteristics of the microalgae in the target area and the microalgae data in the microalgae culture solution image feature data in the target area as model input data; The model input data is input into the microalgae growth prediction model for prediction, a final prediction result when accepted by the discriminator is obtained, and the final prediction result when accepted by the discriminator is output as microalgae growth data within a preset time.

3. The microalgae feature fusion analysis method based on deep learning according to claim 1, characterized in that: The microalgae growth and harvesting threshold range is set, and the microalgae harvesting time node is obtained according to the microalgae growth data within the preset time and the microalgae growth and harvesting threshold range, specifically: Setting a microalgae growth harvesting threshold range, and obtaining microalgae growth data of each timestamp from the microalgae growth data within the preset time, and determining whether the microalgae growth data of each timestamp is within the microalgae growth harvesting threshold range; The timestamp of the microalgae growth data within the microalgae growth harvesting threshold range is used as the microalgae harvesting time node, and the timestamp of the microalgae growth data not within the microalgae growth harvesting threshold range is used as the non-microalgae harvesting time node, and the microalgae harvesting time node is output.

4. The method for fusion analysis of microalgae features based on deep learning according to claim 1, characterized in that: Pollution analysis is performed according to the real-time pollutant data at the current microalgae harvesting time node, and relevant control strategies are generated based on the pollution analysis results, specifically: Setting a pollution data threshold, and determining whether the real-time pollutant data at the current microalgae harvesting time node is greater than the pollution data threshold; When the real-time pollutant data at the current microalgae harvesting time node is greater than the pollution data threshold, a pollution search tag is constructed, and a search is performed based on the pollution search tag to obtain control measures related to pollution; A relevant control strategy is generated according to the pollution-related control measures, and the generated relevant control strategy is displayed in a preset manner.

5. A microalgae feature fusion analysis system based on deep learning, characterized in that: It comprises a memory and a processor, wherein the memory comprises a microalgae feature fusion analysis method program based on deep learning, and when the microalgae feature fusion analysis method program based on deep learning is executed by the processor, the steps of the microalgae feature fusion analysis method based on deep learning as described in any one of claims 1 to 4 are implemented.

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