Animal and plant ecological environment regulation control method based on artificial intelligence

Abstract

The invention provides an animal and plant ecological environment regulation control method based on artificial intelligence. The method comprises the following steps: acquiring current environment data, a current time point and growth image data of a target animal and plant in a season; performing growth stage analysis on the target animal and plant based on the growth image data, and determining a current growth stage of the target; inputting the target current growth stage and the current time point into an environmental parameter model to obtain target standard environmental parameters output by the environmental parameter model; performing difference value comparison on the current environment data and the target standard environment parameters to obtain a parameter difference value result; and performing comparative analysis based on a parameter difference result and a preset threshold value, and performing parameter regulation and control on the ecological environment based on a comparative analysis result. When the ecological environment is adjusted, an ordinary user does not need to deeply know complex environment requirement knowledge of animals and plants, and automatic change of the ecology of the animals and plants can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence control technology, and particularly to a method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence. Background Art

[0002] With the improvement of living standards, animal breeding and plant cultivation have gradually become popular hobbies, especially the breeding and cultivation of some rare animals and plants, which are highly sought after by enthusiasts. These rare animals and plants have unique and strict requirements for the living environment, and their growth and reproduction processes are closely related to many factors such as temperature, humidity, light, and soil conditions in the environment. Therefore, to ensure their healthy growth, it is crucial to create an ecological environment that meets their survival needs.

[0003] In the traditional simulation of the original ecological environment of animals and plants, it is mainly achieved through manual operation. However, different types of animals and plants have widely different requirements for environmental conditions, which requires breeders or cultivators to have profound professional knowledge and rich experience. For the vast majority of ordinary enthusiasts, it is difficult to acquire professional knowledge and achieve precise environmental control. At the same time, as the growth stage of animals and plants changes, the requirements for their growth environment will also change accordingly, further increasing the difficulty of ecological environment control. Summary of the Invention

[0004] The present invention provides a method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence, which can automatically change the ecology of animals and plants when regulating the ecological environment without requiring ordinary users to deeply understand the complex environmental requirement knowledge of animals and plants, reducing the professional threshold and the difficulty of environmental control, and facilitating the use of users.

[0005] In a first aspect, the present invention provides a method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence, including:

[0006] Obtaining the current environmental data, current time point, and growth image data of the target animals and plants in the current season;

[0007] Analyzing the growth stage of the target animals and plants based on the growth image data to determine the target current growth stage;

[0008] Inputting the target current growth stage and the current time point into an environmental parameter model to obtain the target standard environmental parameters output by the environmental parameter model; the target standard environmental parameters are the standard environmental parameters within a preset time interval immediately after the current time point; the environmental parameter model is trained based on sample target growth stage and time data and their corresponding standard environmental parameter label results;

[0009] Compare the current environmental data with the target standard environmental parameters to obtain a parameter difference result;

[0010] Based on the comparison and analysis of the parameter difference result with a preset threshold, perform parameter regulation on the ecological environment based on the comparison and analysis result.

[0011] In a second aspect, the present invention also provides an artificial intelligence-based animal and plant ecological environment regulation and control system, which is applied to the artificial intelligence-based animal and plant ecological environment regulation and control method as described in the first aspect; the artificial intelligence-based animal and plant ecological environment regulation and control system includes:

[0012] An acquisition unit for acquiring the current environmental data, the current time point, and the growth image data of the target animals and plants in the current season;

[0013] An analysis unit for analyzing the growth stage of the target animals and plants based on the growth image data to determine the target current growth stage;

[0014] A model prediction unit for inputting the target current growth stage and the current time point into an environmental parameter model to obtain the target standard environmental parameters output by the environmental parameter model; the target standard environmental parameters are the standard environmental parameters at a preset time interval immediately after the current time point; the environmental parameter model is trained based on the sample target growth stage and time data and their corresponding standard environmental parameter label results;

[0015] A comparison unit for comparing the difference between the current environmental data and the target standard environmental parameters to obtain a parameter difference result;

[0016] A regulation unit for comparing and analyzing the parameter difference result with a preset threshold, and performing parameter regulation on the ecological environment based on the comparison and analysis result.

[0017] In a third aspect, the present invention also provides an electronic device, including: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing the artificial intelligence-based animal and plant ecological environment regulation and control method as described in any one of the above.

[0018] In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium, in which a computer software program is stored, and when the computer software program is executed by a processor, the artificial intelligence-based animal and plant ecological environment regulation and control method as described in any one of the above is implemented.

[0019] Fifth aspect, the present invention further provides a computer program product, including a computer program, which when executed by a processor, implements the method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence as described in any one of the above.

[0020] The method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence provided by the embodiments of the present invention determines the current growth stage of the target by accurately analyzing the growth image data of the target animals and plants, enabling accurate identification of changes in the growth stage of animals and plants, and then being able to accurately address the problem of environmental changes caused by changes in the growth stage, reducing the difficulty of environmental regulation while providing a reliable basis for subsequent regulation of the ecological environment. Further, in the environmental parameter model, the target standard environmental parameters for the next stage of the target animals and plants can be obtained more accurately based on the accurately identified growth stage of the animals and plants. During the subsequent automatic regulation process of the ecological environment, it is possible to accurately identify and predict whether the current environment needs to be automatically regulated based on the comparison between the more accurate target standard environmental parameters and the current environmental data, and perform automatic regulation according to the identification and prediction results. Therefore, when regulating the ecological environment in the embodiments of the present invention, ordinary users do not need to deeply understand the complex environmental requirement knowledge of animals and plants, and can realize automatic changes to the animal and plant ecology, reducing the professional threshold and the difficulty of environmental regulation, and facilitating the use of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic flowchart of the method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence provided by the embodiments of the present invention;

[0022] Figure 2 is a schematic structural diagram of the system for regulating and controlling the ecological environment of animals and plants based on artificial intelligence provided by the embodiments of the present invention;

[0023] Figure 3 is an embodiment diagram of the electronic device provided by the embodiments of the present invention;

[0024] Figure 4 is an embodiment diagram of the computer-readable storage medium provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0026] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless otherwise specifically defined.

[0027] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be practiced without these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0028] Referring to Figure 1 , Figure 1 is a schematic flow diagram of the method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence provided by the present invention. In the embodiments of the present invention, the execution subject of the method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence is a regulation and control system. Therefore, the method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence includes:

[0029] Step 10, obtaining the current environmental data, the current time point, and the growth image data in the season in which the target animals and plants are located.

[0030] Optionally, the regulation and control system first installs a plurality of sensors (such as indoor positions like environmental ecological boxes) in the ecological environment area, including temperature sensors, humidity sensors, light intensity sensors, etc., and when installing the sensors, they are evenly distributed in a manner of a preset installation interval (such as installing a corresponding set of sensors every 50 cm), so as to accurately collect the current environmental data in the ecological environment area. The current environmental data includes corresponding temperature data, humidity data, and light intensity data.

[0031] Furthermore, the adjustment control system is also equipped with a high-definition camera for collecting growth image data. The high-definition camera regularly collects the growth images of the target animals and plants. And the number of cameras installed is not limited during the installation process. The purpose is to comprehensively cover the target animals and plants in the growth environment and ensure that clear and complete image data can be collected and photographed. It should be noted that while collecting the current environmental data and growth image data, the current time point is also collected. The current time point includes the month, day, hour, minute, and second of the current season, so that the time points corresponding to the collected current environmental data and growth image data are more accurate, facilitating subsequent accurate analysis of the changes in environmental parameters at different times and their association with the growth stages of animals and plants.

[0032] In an embodiment, taking the current environmental data as temperature data, humidity data, and light intensity data as an example, and the target animals and plants as reptile lizards (bearded dragons), when the adjustment control system collects the current environmental data, current data point, and growth image data in the current season, the temperature sensor detects that the temperature in the current ecological environment area is 35°C, the humidity is 27%, the light intensity is 3000 lux, the current time point is 10:30 on September 24, 2025, and the growth image data includes the images of the bearded dragon crawling in different positions and different orientations of the bearded dragon. And the growth image data is collected every preset time (such as one hour) to avoid the problem of excessive storage caused by over-frequent collection. At the same time, the current environmental data is also collected every certain time (such as 1 minute).

[0033] Step 20: Analyze the growth stage of the target animals and plants based on the growth image data to determine the current growth stage of the target.

[0034] Optionally, after receiving the growth image data collected every preset time, the adjustment control system performs image processing and analysis on the growth image data, extracts the key features of the target animals and plants from the growth image data, and then through a series of analysis and judgments, finally determines the current growth stage of the target animals and plants, as specifically described in Steps 201 - 205.

[0035] In an embodiment, also taking the reptile lizard (bearded dragon) as an example, from the image taken at 10:30 on September 24, 2025, after image analysis and processing, it is determined that the bearded dragon is a sub-adult bearded dragon with a body length of 25 cm, a head width of 3 cm, and an age of about 8 months.

[0036] Step 30: Input the target current growth stage and the current time point into the environmental parameter model to obtain the target standard environmental parameters output by the environmental parameter model; the target standard environmental parameters are the standard environmental parameters within a preset time interval immediately after the current time point; the environmental parameter model is trained based on the sample target growth stage and time data and their corresponding standard environmental parameter label results.

[0037] Optionally, during the process of determining the environmental parameter model, the control system uses a neural network (such as a long short-term memory neural network, a recurrent neural network, etc.) to construct the model, and at the same time collects a large amount of sample data, including the sample target growth stage and time data and their corresponding standard environmental parameter label results. After determining the training data, use a multi-layer neural network to train the sample data, and finally construct the environmental parameter model. After determining the environmental parameter model, input the target current growth stage obtained in step 20 and the current time point collected in step 10 into the trained environmental parameter model. The environmental parameter model calculates and predicts based on the input data, and outputs the standard environmental parameters within a preset time interval immediately after the current time point. The preset time interval can be set according to actual needs (that is, after an ecological environment problem occurs, it needs to be processed within a certain time), such as 1 hour, 2 hours, etc. To achieve the regulation and processing of the environmental ecology within the preset time interval according to the needs of the target animals and plants, so as to avoid the problem of untimely regulation of the ecological environment.

[0038] In one embodiment, taking the reptile lizard (bearded dragon) as an example, collect a large amount of bearded dragon breeding environment data at different growth stages and different time points, as well as the corresponding healthy growth status records, including environmental parameters such as temperature, humidity, and light intensity, and the growth indicators of bearded dragons (such as body length growth, weight change, etc.). Use deep learning algorithms to train these data to construct an environmental parameter model. The model structure uses a multi-layer neural network, and by continuously adjusting the network weights, the model can accurately predict the appropriate environmental parameters according to the input growth stage and time point. Then, encode the current sub-adult stage of the bearded dragon determined in step 20 and the time 10:30 on September 24, 2025 recorded in step 10 according to the format specified by the model, and input them into the trained environmental parameter model. After complex calculations and analyses, the environmental parameter model outputs the standard environmental parameters within a preset time interval (such as the next 5 minutes) immediately after the current time point. These parameters are obtained based on a large amount of data training and are the environmental conditions most suitable for the healthy growth of bearded dragons at this growth stage during this period (that is, temperature 32°C, humidity 30%, light intensity 2500 lux).

[0039] Step 40: Compare the difference between the current environmental data and the target standard environmental parameters to obtain the parameter difference result.

[0040] Optionally, the adjustment control system compares the current environmental data obtained in step 10 with the target standard environmental parameters obtained in step 30 by calculating the difference, that is, calculating the difference of each environmental parameter, such as the temperature difference ΔT w = T dan - T mu , the humidity difference ΔH w = H dan - H mu , the light intensity difference ΔG w = G dan - G mu . After the calculation, all the parameter differences are formed into a difference vector for subsequent comprehensive analysis and processing of multiple environmental parameters.

[0041] In one embodiment, taking the reptile lizard (bearded dragon) as an example, the current environmental data obtained in step 10 is a temperature of 35 °C, a humidity of 27%, and a light intensity of 3000 lux. The target standard environmental parameters obtained in step 30 are a temperature of 32 °C, a humidity of 30%, and a light intensity of 2500 lux. Calculate the difference of each environmental parameter one by one. Among them, the temperature difference ΔT w is 35 - 32 = 3 °C, the humidity difference ΔH w is 27 - 30 = -3%, the light intensity difference ΔG w is 3000 - 2500 = 500 lux. Then, the differences of each environmental parameter obtained by calculation are integrated into a parameter difference vector [3, -3, 500] for subsequent comprehensive analysis and processing of multiple environmental parameters.

[0042] Step 50, compare and analyze the parameter difference results with the preset thresholds, and adjust the parameters of the ecological environment based on the comparison and analysis results.

[0043] Optionally, after receiving the parameter difference result, the adjustment control system compares the parameter difference result with a preset threshold. Similarly, since the parameter difference result is in vector form, and the preset thresholds are set for each environmental parameter according to the growth requirements of the target animals and plants and the environmental characteristics, such that the preset threshold represents the allowable fluctuation range of the environmental parameter. When the parameter difference exceeds the threshold, environmental regulation is required. Among them, the preset threshold includes a first threshold, a second threshold, and a third threshold, and the first threshold, the second threshold, and the third threshold also exist in the corresponding vector form. When comparing, for the difference of each environment, it is judged whether it exceeds the corresponding preset threshold, and then according to the result of the comparison and analysis, the corresponding environmental regulation strategy is formulated. Finally, the device parameters in the ecological environment are regulated according to the environmental adjustment strategy, specifically as described in steps 501 - 503. If the difference of an environmental parameter exceeds the threshold, the corresponding regulation device is activated to adjust the parameters.

[0044] In an embodiment, for each environmental parameter to be regulated, such as light intensity, temperature, humidity, etc., the corresponding first threshold, second threshold, and third threshold are respectively set. Taking temperature as an example, assume that in a certain growth stage of the bearded dragon, the suitable temperature range is 30°C - 35°C. According to its sensitivity to temperature changes and adaptability, the first threshold is set to 1°C, the second threshold is set to 2°C, and the third threshold is set to 3°C. When the parameter difference result is less than the first threshold of 1°C, it indicates that the current parameter difference result is small and does not affect the overall environmental ecology, so no adjustment is required to avoid continuous adjustment problems during short-term fluctuations and reduce the calculation amount; when the temperature difference is between 1°C - 2°C, the parameter difference result and the first mode are used to regulate the ecological environment; when it is between 2°C - 3°C, the parameter difference result and the second mode are used to regulate the ecological environment; when it exceeds 3°C, the parameter difference result and the third mode are used to regulate the ecological environment.

[0045] In the embodiments of the present invention, the current growth stage of the target animals and plants is determined through precise analysis of the growth image data of the target animals and plants, enabling the precise identification of changes in the growth stage of the animals and plants. Furthermore, it can accurately address the problem of environmental changes caused by changes in the growth stage, reduce the difficulty of environmental regulation, and provide a reliable basis for subsequent regulation of the ecological environment. Further, in the environmental parameter model, the target standard environmental parameters of the next stage of the target animals and plants can be obtained more accurately based on the precisely identified growth stage of the animals and plants. During the subsequent automatic regulation process of the ecological environment, it is possible to accurately identify and predict whether the current environment needs to be automatically regulated based on the comparison between the more accurate target standard environmental parameters and the current environmental data, and perform automatic regulation according to the identification and prediction results. Therefore, in the embodiments of the present invention, when regulating the ecological environment, it is not necessary for ordinary users to deeply understand the complex environmental requirement knowledge of animals and plants, and the automatic change of the animal and plant ecology can be realized, reducing the professional threshold and the difficulty of environmental regulation, and facilitating the use of users.

[0046] In one embodiment, the descriptions of steps 201 - 205 are as follows:

[0047] Step 201: Determine the growth scenario where the target animals and plants are located based on the position of the target animals and plants in the growth image data.

[0048] Optionally, after the adjustment control system obtains the growth image data, it uses an image segmentation algorithm, such as the MaskR-CNN algorithm based on deep learning, to accurately segment the contour of the target animals and plants in the image. Then, through geometric shapes such as the circumscribed rectangle or the minimum bounding ellipse of the contour, the position coordinates or the center coordinates of the ellipse and the major and minor semi-axes of the target animals and plants in the image are determined. According to the position information, combined with the background characteristics of the image, such as whether the surrounding environment is grassland, forest land, water area, etc., the growth scenario is judged. If there is a large amount of green vegetation around the target animals and plants in the image and it is relatively evenly distributed, it can be initially judged as a grassland scenario; if there are tall trees surrounding, it may be a forest land scenario. Realizing the judgment of the growth scenario combined with the background characteristics helps to understand the living environment of the target animals and plants and provides additional information for analyzing their growth stage.

[0049] In one embodiment, taking the bearded dragon as an example, the MaskR-CNN algorithm based on deep learning is used to process the captured image of the bearded dragon, and the contour of the bearded dragon is accurately segmented. The circumscribed rectangle is determined through the contour to obtain the position coordinates. Suppose the coordinates of the circumscribed rectangle of the bearded dragon in a certain image are (100, 151, 250, 300), which indicates that the horizontal position range of the bearded dragon in the image is from the abscissa 100 to 250, and the vertical position range is from the ordinate 150 to 300. Then, observe the background characteristics of the image. If there are sand, low plants, and a heating lamp around the bearded dragon in the image, it can be judged that its growth scenario is a desert simulation scenario in an artificial breeding environment.

[0050] Step 202: Conduct a comprehensive analysis of the growth image data based on the growth scenario to obtain the species to which the target animals and plants belong.

[0051] Optionally, the control system establishes corresponding characteristic libraries of animal and plant species for different growth scenarios. In the grassland scenario, the plant species may mainly be herbaceous plants, and the characteristic library contains characteristic data such as the leaf shapes, colors, and textures of different herbaceous plants; the animals may include hares, voles, etc., and the characteristic library contains their body shapes, coat colors, movement postures, etc. Then, use image feature extraction algorithms, such as Scale-Invariant Feature Transform (SIFT), to extract the local feature points of the target animals and plants, and combine global features such as shape and color histogram. For plants, calculate the Fourier descriptors of the leaf shapes, and identify the plant species by comparing with the Fourier descriptors of different plant species in the characteristic library. For animals, analyze the Hu-moment features of their body contours and match them with the Hu-moment features of the animals in the characteristic library to determine the species.

[0052] In one embodiment, for the artificial breeding desert simulation scenario, a corresponding characteristic library of animal species is established. For bearded dragons, the characteristic library records characteristic data such as the shape of their triangular heads, the texture and arrangement of the body scales, the morphology of the limbs, and the length and thickness ratio of the tails. Use the Scale-Invariant Feature Transform (SIFT) algorithm to extract the local feature points in the bearded dragon images, such as the unique feature points that appear at the positions of their eyes, nostrils, and scale edges. At the same time, calculate the Hu-moment features of their body contours. Match these features with the bearded dragon features in the characteristic library. Since its unique head shape and body scale texture and other features highly match the bearded dragon features in the characteristic library, it is determined to be a bearded dragon.

[0053] Step 203: Extract morphological features from multiple generated image data to obtain feature nodes.

[0054] Optionally, during the morphological feature extraction of multiple generated image data by the adjustment control system, different methods are adopted according to different target animal and plant species. For example, for plant images, operations such as morphological erosion and dilation are used to process the segmented plant regions to highlight the morphological features of the plants. Edge detection algorithms, such as the Canny algorithm, are used to extract the edge contours of the plants. On the edge contours, feature nodes are selected according to preset geometric rules. For example, feature nodes are selected at the inflection points of the leaf edges, the connections between the petioles and the stems, etc. For animal images, if the animals have obvious bone structures or body joints, these structures are highlighted through image enhancement and segmentation techniques, and feature nodes are selected at the joint points, bone connection points, etc. For animals without obvious bone structures, feature nodes are selected according to the curvature changes of the body contours. The positions with larger curvatures may correspond to the turning points of the body and are set as feature nodes. At the same time, attributes are assigned to each feature node, such as the node position coordinates, node types (such as leaf inflection points, key nodes, etc.), and connection relationships with adjacent nodes.

[0055] Step 204: Construct a topological skeleton structure with the feature nodes as endpoints and the distances between the feature nodes as edges.

[0056] Optionally, after the adjustment control system determines the feature nodes, for the Euclidean distance between any two feature nodes, assuming the coordinates of feature node i are (x i , y i ), and the coordinates of feature node j are (x j , y j ), then the distance between them In this way, the distances between all pairs of feature nodes are calculated to form a distance matrix, and then according to the rules for constructing the topological skeleton structure, such as setting a distance threshold T yu , when the distance d ij between two feature nodes is less than or equal to T yu , an edge is established between these two nodes to connect them. At the same time, according to the physiological structure knowledge of the target animals and plants, the rationality of the connections between the nodes is determined. For example, for the bone joint points of animals, they are connected according to the actual connection relationships of the bones. According to the distance calculation results and the construction rules, connections are established between the feature nodes to generate a topological skeleton structure.

[0057] Step 205: Analyze the growth stage of the target animals and plants based on the species to which the target animals and plants belong and the topological skeleton structure, and determine the current growth stage of the target.

[0058] Optionally, the adjustment control system analyzes and determines the growth stage of the target animals and plants based on the species of the target animals and plants obtained in step 202 and the topological skeleton structure obtained in step 204, so as to obtain the accurate current growth stage of the target animals and plants, as specifically described in steps 2051 - 2054.

[0059] The embodiments of the present invention make full use of various information in the growth image data and combine different algorithms to improve the accuracy and reliability of growth stage analysis, provide accurate growth stage information for the regulation and control of the ecological environment of animals and plants, help formulate environmental regulation strategies more in line with their growth needs, and then adjust environmental parameters according to the environmental regulation strategies.

[0060] In one embodiment, the descriptions of steps 2051 - 2054 are as follows:

[0061] Step 2051, divide the topological skeleton structure based on the species of the target animals and plants to obtain multiple layers of skeletons.

[0062] Optionally, the adjustment control system first formulates corresponding topological skeleton structure division rules for different species of target animals and plants according to their anatomical structures and growth and development characteristics. For example, for plants, the topological skeleton can be divided according to the hierarchical relationship of roots, stems, and leaves; for animals, it can be divided according to different parts of the skeletal system, such as the head, torso, limbs, etc., to ensure the rationality and scientificity of the division. Then, the adjustment control system uses image processing and computer graphics technologies to divide the constructed topological skeleton structure according to the formulated division rules to obtain multiple layers of skeletons. Among them, for the topological skeleton of plants, by identifying the characteristic nodes and connecting edges representing roots, stems, and leaves, the entire topological skeleton is divided into a root layer skeleton, a stem layer skeleton, and a leaf layer skeleton. For animals, based on the skeletal joint points, the topological skeleton is divided into a head skeleton layer, a torso skeleton layer, and a limb skeleton layer, etc. And during the division process, the skeletons of each layer are marked and stored for subsequent analysis.

[0063] Step 2052, analyze and process the connection relationship characteristics between adjacent layers based on the multiple layers of skeletons to obtain layer characteristics.

[0064] Optionally, for two adjacent layers of skeletons in the multiple layers of skeletons, the adjustment control system analyzes the connection relationship characteristics, including the number of connecting edges between the two adjacent layers of skeletons, the angular characteristics of the connecting edges, and the average length characteristics of the connecting edges, etc. Among them, for the number of connecting edges, for example, between the head skeleton layer and the torso skeleton layer, count the number of connecting edges N from the characteristic nodes of the head skeleton layer to the characteristic nodes of the torso skeleton layer 1-2 ; for the angular characteristics of the connecting edges, for example, for the connecting edge between the head skeleton layer and the torso skeleton layer, calculate the included angle θ between the connecting edge and the main direction of the head skeleton layer 1-2, let the angle calculation function be where is expressed as the main direction vector of the head skeleton layer, is expressed as the connecting edge vector; for the average length feature of the connecting edge, measure the length L of the connecting edge between the head skeleton layer and the torso skeleton layer 1-2 , and obtain it by calculating the average value of the lengths of all connecting edges. Combine these connection relationship features into a hierarchical feature vector F conn = [N 1-2 , θ 1-2 , L 1-2 , …].

[0065] Step 2053: Analyze the variation law of the hierarchical features at different growth stages of the target animal or plant species, and construct a hierarchical feature model for each growth stage.

[0066] Optionally, the control system pre-collects a large number of samples of the target animal or plant species at different growth stages in advance. These samples not only include topological skeleton structure data, but also include corresponding growth stage information. For example, for bearded dragons, collect the change trends in the juvenile, sub-adult, and adult stages. Let the range of the number of connecting edges in the juvenile stage be [N 1-2-min , N 1-2-max , the angle range be [θ 1-2-min , θ 1-2-max , and the average length range be [L 1-2-min , L 1-2-max . Then, use statistical analysis methods, such as mean, standard deviation, etc., to determine the typical values and fluctuation ranges of the hierarchical features at each growth stage. Let the mean number of connecting edges in the juvenile stage be the standard deviation be σ N-1-2-juv , the mean of the angle range be the standard deviation be σ θ-1-2-juv , the mean of the average length be the standard deviation be σ L-1-2-juv , and construct the hierarchical feature model for the juvenile stage as

[0067] And so on, finally construct the hierarchical feature models for each growth stage.

[0068] In one embodiment, through the analysis of 100 juvenile bearded dragon samples, the mean number of connecting edges between the core skeleton layer and the branch skeleton layer the standard deviation σ N-1-2-juv = 0.8; the mean angle is the standard deviation σ θ-1-2-juv = 3°; the mean of the average length millimeters, the standard deviation σ L-1-2-juv= 1 mm, thus constructing a hierarchical feature model for the juvenile stage.

[0069] Step 2054: Compare the hierarchical features with the hierarchical feature models of each growth stage to determine the target current growth stage.

[0070] Optionally, for the target animals and plants to be analyzed, the control system obtains the current hierarchical features of the template animals and plants and the corresponding hierarchical feature vector F obtained through step 2052 conn , and the hierarchical feature vector F conn is compared with the hierarchical feature models of each growth stage. The Euclidean distance is used to calculate the similarity between the current feature vector and the feature vectors of each model, and the similarity values in the juvenile stage, sub-adult stage, and adult stage are obtained. Then, the sizes of the similarity values are compared. The growth stage corresponding to the model with the smallest similarity value is the target current growth stage.

[0071] In one embodiment, for a bearded dragon to be analyzed, the calculated number of connecting edges N 1-2-conn = 9, the angle θ 1-2-conn = 35°, and the average length L 1-2-conn = 12 mm. When compared with the juvenile stage model, the Euclidean distance: When compared with the sub-adult stage model, the Euclidean distance: When compared with the adult stage model, the Euclidean distance: Since the obtained d ya is the smallest, it is determined that the bearded dragon is in the sub-adult stage.

[0072] The embodiments of the present invention fully consider the body structure characteristics and growth laws of the bearded dragon species. Through quantitative analysis and model comparison, the growth stage can be accurately determined, providing strong support for targeted ecological environment regulation and control, and improving the scientificity and effectiveness of bearded dragon growth management.

[0073] In one embodiment, the descriptions of steps 501 - 503 are as follows:

[0074] Step 501: If the parameter difference result is greater than the first threshold and less than or equal to the second threshold, then within a preset time interval, the ecological environment is regulated based on the parameter difference result and the first mode.

[0075] Optionally, when the adjustment control system receives that the parameter difference result is between the first threshold and the second threshold, it indicates that the current environmental parameters cannot reach the target standard environmental parameters after a preset time interval, but the overall fluctuation is still within a small range. Therefore, when making adjustments at this time, the real-time outdoor environmental factors should be considered, and an energy-saving and environmentally friendly method should be adopted as much as possible to adjust the environmental ecological parameters so that they can reach the requirements of the target standard environmental parameters after the preset time interval, as specifically described in steps 5011 - 5015.

[0076] In one embodiment, taking the temperature parameter in the ecological environment of bearded dragons as an example, if the first threshold ΔT1 = 0.5 °C and the second threshold ΔT2 = 2 °C. When the actual temperature difference ΔT w satisfies ΔT1 < |ΔT w | ≤ ΔT2, the first mode is started for energy-saving regulation. The sign of ΔT w being positive indicates that the cooling method is adopted, and being negative indicates that the heating method is required, and the same applies to humidity and light intensity.

[0077] Step 502, if the parameter difference result is greater than the second threshold and less than or equal to the third threshold, then within the preset time interval, the ecological environment is regulated based on the parameter difference result and the second mode.

[0078] Optionally, when the adjustment control system receives that the parameter difference result is between the second threshold and the third threshold, it indicates that there is a medium-range difference between any one or more environmental factors in the current environmental parameters and the target standard environmental parameters after a preset time interval, but it is still within the acceptable range of animals and plants within the preset time interval. Therefore, when adjusting the environmental ecology at this time, the actual needs of the target animals and plants should be combined for adjustment. For example, when the temperature requirement is high, the temperature is adjusted first to reduce the impact of temperature on the target animals and plants. Although other factors have a medium-range difference, their impact on the target animals and plants is small. Therefore, when within this threshold range, it is still not necessary to start all equipment. Instead, after adjusting the factors with more serious impacts according to the actual needs, the next adjustment is made according to the subsequent results, as specifically described in steps 5021 - 5025, to achieve more flexible regulation.

[0079] In one embodiment, still taking the temperature parameter in the ecological environment of bearded dragons as an example, the third threshold ΔT3 = 4 °C. When the actual temperature difference ΔT w satisfies ΔT2 < |ΔT w | ≤ ΔT3, the second mode is started for flexible regulation.

[0080] Step 503, if the parameter difference result is greater than the third threshold, then within the preset time interval, the ecological environment is regulated based on the parameter difference result and the third mode.

[0081] Optionally, when the adjustment control system receives that the parameter difference result is greater than the third threshold, it indicates that there are factors in the current environmental parameters that seriously deviate from the target value within the preset time interval. Therefore, within the preset time interval, urgent and powerful regulation measures need to be taken to adjust the ecological environment value to the target standard environmental parameter range within the preset time interval, specifically as described in steps 5031 - 5033, so as to achieve emergency and rapid adjustment.

[0082] In one embodiment, taking the temperature parameter in the ecological environment of bearded dragons as an example, the third threshold ΔT3 = 4°C. When the actual temperature difference ΔT w satisfies ΔT3 < |ΔT w |, the third mode is activated for emergency regulation.

[0083] By setting different thresholds and corresponding different regulation modes in the embodiments of the present invention, precise regulation can be carried out according to the degree of deviation of environmental parameters, meeting the environmental requirements of target animals and plants in different situations; at the same time, for different degrees of deviation of environmental parameters, diversified regulation strategies are provided, enabling the ecological environment adjustment control system to adapt to various complex environmental change situations; finally, reasonable threshold setting and effective regulation modes ensure that target animals and plants are always in a suitable ecological environment, providing a strong guarantee for their healthy growth and being conducive to improving the growth quality of animals and plants.

[0084] In one embodiment, the descriptions of steps 5011 - 5015 are as follows:

[0085] Step 5011, perform data decomposition on the parameter difference result to obtain the temperature difference, humidity difference, and light intensity difference corresponding to the current environmental data.

[0086] Optionally, when the adjustment control system determines to activate the first mode for ecological environment regulation, after obtaining the overall parameter difference result, it decomposes it into specific differences of each environmental parameter. Among them, if the parameter difference result is stored in vector form, through a data parsing algorithm, each component in the comprehensive vector is separated to obtain the temperature difference ΔT w 、humidity difference ΔH w and light intensity difference ΔG w .

[0087] In one embodiment, the parameter difference result obtained for the bearded dragon breeding environment is [2°C, 5%, -300 lux]. By parsing this vector, the temperature difference can be directly obtained as 2°C (indicating that the current temperature is 2°C higher than the target temperature), the humidity difference is 5% (the current humidity is 5% higher than the target humidity), and the light intensity difference is -300 lux (the current light intensity is 300 lux lower than the target light intensity).

[0088] Step 5012: Analyze based on the difference in light intensity and the natural light intensity of the outdoor environment to determine the light adjustment strategy.

[0089] Optionally, after the control system finishes decomposing the parameter difference result, it starts to obtain the natural light intensity of the outdoor environment (i.e., the natural light intensity outside the ecological environment box or outside the ecological environment room, which is also obtained by the light sensor). After determining the natural light intensity of the outdoor environment, analyze it with the light intensity difference obtained in Step 5011 (that is, first judge the magnitude of the indoor natural light intensity and the outdoor natural light intensity, and then judge the magnitude of the light intensity difference). If the natural light intensity is high and the light intensity difference is negative (i.e., the current indoor light is insufficient), give priority to introducing more natural light, and natural light entering the room can be increased by opening windows or adjusting sunshade facilities, etc. If the natural light intensity is low and the light intensity difference is negative, artificial light sources need to be combined for supplementation.

[0090] Furthermore, when determining to introduce natural light, specific parameters such as the area of the opened window or the angle of adjusting the sunshade facility need to be calculated. For example, the relationship between the opened window area A0 and the light intensity |ΔG w | is A0 = k1 * |ΔG w |, where k1 represents the light supplement coefficient, a coefficient determined according to factors such as the window position, orientation, and indoor space layout, and can be determined by establishing a model through experimental data; if artificial light sources are needed to supplement light, calculate the power or quantity of the required artificial light sources. Let the light intensity of each supplementary light be I lamp , the number of required supplementary lights (Considering factors such as light attenuation, the actual quantity may need to be fine-tuned on this basis).

[0091] In an embodiment, the current target light intensity in the ecological box for raising bearded dragons is 3000 lux, the actual light intensity is 2700 lux, and the light intensity difference is -300 lux. The outdoor natural light intensity is 4000 lux at this time. Since the light intensity difference is negative, it is decided to introduce natural light. Through the previously established model, it is known that k1 = 0.01 square meters / lux, and the calculated area of the window to be opened A0 = 0.01 * 300 = 3 square meters, and the window is opened by the corresponding area to introduce more natural light.

[0092] Step 5013: Execute the light adjustment strategy and analyze the execution result of the light adjustment strategy to determine the temperature compensation coefficient and the humidity compensation coefficient.

[0093] Optionally, after the adjustment control system executes the lighting adjustment strategy in step 5012, during the execution process, the indoor lighting intensity change is monitored in real time through sensors to ensure the expected lighting adjustment effect. And during the lighting adjustment process, the temperature and humidity sensors are used simultaneously to monitor the changes in indoor temperature and humidity. Since the change in lighting intensity may cause fluctuations in indoor temperature and humidity, for example, increasing natural lighting may raise the indoor temperature and lower the humidity. Record the change values of temperature and humidity for a period of time before and after the lighting adjustment. Let the temperature before lighting adjustment be T 11 , the stable temperature after adjustment is T 22 , the temperature variable value ΔT b =T 22 -T 11 ; the humidity before lighting adjustment is H 11 , the stable temperature after adjustment is H 22 , the temperature variable value ΔH b =H 22 -H 11 ; according to the change values of temperature and humidity, calculate the temperature compensation coefficient and humidity compensation coefficient. The temperature compensation coefficient α is used to measure the degree of influence of lighting adjustment on temperature, and the calculation formula is represents the temperature change amount caused by the change in unit lighting intensity. The humidity compensation coefficient β calculation formula is represents the humidity change amount caused by the change in unit lighting intensity.

[0094] Step 5014, compensate the temperature difference based on the temperature compensation coefficient to obtain the target temperature difference, and compensate the humidity difference based on the humidity compensation coefficient to obtain the target humidity difference.

[0095] Optionally, the adjustment control system compensates the temperature difference obtained in step 5011 according to the obtained temperature compensation coefficient. Let the original temperature difference be ΔT ori , the target temperature difference ΔT tar after compensation, the calculation formula is ΔT tar =ΔT ori -α*ΔT w . Similarly, for the humidity difference, let the original humidity difference be ΔH ori , the target humidity difference ΔH tar after compensation, the calculation formula is ΔH tar =ΔH ori -β*ΔT w .

[0096] Step 5015, analyze the ventilation intensity based on the temperature and humidity data of the outdoor environment, the target temperature difference and the target humidity difference, determine the ventilation adjustment strategy, and adjust the equipment parameters of the ventilation equipment based on the ventilation adjustment strategy.

[0097] Optionally, after obtaining the compensated target temperature difference and target humidity difference, the adjustment control system starts to obtain the temperature and humidity data of the outdoor environment from the outdoor temperature and humidity sensor, i.e., the outdoor temperature T out and the outdoor humidity H out . At this time, the adjustment control system analyzes the relationship between the ventilation intensity and the target temperature difference, the target humidity difference, and the outdoor temperature and humidity. If the target temperature difference ΔT tar is positive, indicating that the indoor temperature needs to be reduced. At this time, the relatively low-temperature outdoor air can be introduced to cool down. The ventilation volume V dan can be calculated by deriving from the heat exchange formula. Let the indoor air volume be V room , the specific heat capacity of air be C p , and the desired temperature adjustment time be Δt0, then where T room represents the current indoor temperature.

[0098] Furthermore, if the target humidity difference ΔH tar is positive, indicating that the indoor humidity needs to be increased. When the outdoor humidity H out is greater than the current indoor humidity H room , the ventilation volume can be appropriately increased to introduce the high-humidity outdoor air; if ΔH tar is negative, the ventilation volume is reduced or a dehumidification device is used for assistance. Let the ventilation volume adjustment coefficient be k V-H . When ΔH tar is positive and H out >H room , When ΔH w is negative, Finally, the ventilation volume V fin =V dan *(1 + k V-H ). According to the calculated ventilation volume V fin , adjust the equipment parameters of the ventilation equipment, such as the fan speed, the opening degree of the ventilation port, etc.

[0099] Furthermore, it should also be noted that when the temperature and humidity data of the outdoor environment are both less than the indoor temperature and humidity data, and at this time the target temperature difference is negative and heating is required, and the target humidity difference is also negative and humidification is required. When adjusting the ventilation strategy at this time, there is no need to turn on the ventilation equipment. Instead, directly use the indoor environment heating equipment and humidification equipment at low power to directly adjust the temperature and humidity to ensure that the target standard environmental parameter requirements are met within the preset time interval.

[0100] In the embodiments of the present invention, energy conservation and environmental protection are always taken as the core. The natural light and outdoor environmental resources are preferentially utilized to regulate the environmental parameters, reducing the energy consumption of artificial equipment and the impact on the environment, and fully reflecting the concept of energy conservation and environmental protection. And through a series of data decomposition, analysis and calculation, and compensation mechanisms, the environmental parameters such as light, temperature and humidity are accurately regulated to meet the strict requirements of target animals and plants for the environment at different growth stages, which is beneficial to their healthy growth.

[0101] In one embodiment, the descriptions of steps 5021 - 5025 are as follows:

[0102] Step 5021: Based on the requirements of the target animals and plants at the target current growth stage, determine the first adjustment factor, and match it with the parameter difference result to obtain the first adjustment value; the first adjustment factor is any environmental factor in the current environmental data.

[0103] Optionally, when the adjustment control system determines to start the second mode for environmental ecological regulation, first, after determining the species of the target animals and plants, according to the different requirements of the target animals and plants for environmental factors at different growth stages, determine the first adjustment factor. For example, juvenile bearded dragons are more sensitive to temperature, and their suitable temperature range is relatively narrow. Therefore, the temperature factor is determined as the first adjustment factor. After determining the first adjustment factor, the adjustment control system extracts the difference corresponding to the first adjustment factor from the parameter difference result as the first adjustment value. The parameter difference result is the difference between the current environmental data and the target standard environmental parameters. Taking juvenile bearded dragons as an example again, since its first adjustment factor is the temperature factor, the first adjustment value is the current temperature difference value ΔT corresponding to the temperature. w . For example, if the parameter difference result is [temperature difference 2°C, humidity difference 5%, light intensity difference -300 lux], since the first adjustment factor is temperature, the first adjustment value is 2°C.

[0104] Step 5022: Based on the natural conditions of the outdoor environment, conduct a strategy analysis on the first adjustment value to obtain the first adjustment strategy.

[0105] Optionally, the adjustment control system collects the natural condition data such as temperature, humidity and light intensity of the outdoor environment in real time according to various sensors installed outdoors, analyzes and judges the natural conditions of the outdoor environment and the indoor environmental parameters, and first obtains the indoor-outdoor parameter difference result, that is, the indoor-outdoor temperature difference result ΔT room-out , the indoor-outdoor humidity difference result ΔH room-out and the indoor-outdoor light intensity difference result ΔG room-out , and then correspond the first adjustment value with the indoor-outdoor parameter difference result. For example, if the first adjustment value is 2°C, it corresponds to the indoor-outdoor temperature difference result ΔT room-out , and compare the first adjustment value with the indoor-outdoor temperature difference result ΔTroom-out Perform an analysis, determine the adjustment strategy based on the analysis results. Taking the first adjustment value as 2°C as an example, when the detected outdoor temperature is 25°C and the indoor temperature is 35°C at this time, then ΔT room-out = 25 - 35 = -10°C, that is, the outdoor temperature is lower. At the same time, the first adjustment value is 2°C, which means that the indoor temperature needs to be lowered to meet the requirements of the target standard environmental parameters. Therefore, at this time, according to the situation that the outdoor temperature is lower than the indoor temperature, the ventilation strategy is activated, that is, the parameters of the ventilation equipment such as power and the size of the ventilation opening are adjusted, etc.

[0106] Furthermore, when the outdoor temperature is higher than the indoor temperature and the first adjustment value is still 2°C (i.e., cooling is required), at this time, directly use the cooling equipment indoors to cool the indoor temperature, and adjust the parameters of the cooling equipment to obtain the target environmental standard parameters finally.

[0107] Step 5023, based on the influence of the first adjustment strategy on the second adjustment factor and the third adjustment factor respectively, obtain the first influence coefficient for the second adjustment factor and the second influence coefficient for the third adjustment factor; the second adjustment factor and the third adjustment factor are any environmental factors other than the first adjustment factor in the current environmental data, and the second adjustment factor and the third adjustment factor are different.

[0108] Optionally, after the adjustment control system determines the first adjustment factor (such as temperature), select two other different environmental factors from the current environmental data as the second adjustment factor (such as humidity) and the third adjustment factor (such as light intensity). And the adjustment control system, according to the first adjustment strategy (such as enhancing ventilation or indoor cooling, etc.) corresponding to the first adjustment factor (such as temperature), first analyzes the influence of the first adjustment strategy (such as ventilation to increase temperature) on the second adjustment factor and the third adjustment factor. For example, for humidity, heating will cause the evaporation of air moisture to accelerate, resulting in a decrease in humidity; for light intensity, the heating equipment itself may generate a certain amount of thermal radiation, which has a weak impact on light intensity.

[0109] Furthermore, when determining the first influence coefficient and the second influence coefficient, analyze the influence of the first adjustment strategy (such as ventilation to increase temperature) on the second and third adjustment factors through experiments or based on past experience data. For example, through a series of ventilation experiments, measure the changes in humidity and light intensity under different ventilation volumes. Record the humidity before ventilation as H1, the humidity after ventilation as H2, the ventilation volume as V, and the light intensity before and after ventilation as I1 and I2 respectively. Calculate the influence coefficient according to the experimental data. For the second adjustment factor (humidity), the first influence coefficient γ can be obtained through the formula indicating the change in humidity caused by a unit ventilation volume. For the third adjustment factor (light intensity), the second influence coefficient δ can be obtained through the formula Calculation represents the change in light intensity caused by the unit ventilation volume.

[0110] Step 5024: Update the second adjustment factor and the third adjustment factor respectively based on the first influence coefficient and the second influence coefficient to obtain the first target factor adjustment value and the second target factor adjustment value.

[0111] Optionally, after the adjustment control system obtains the first influence coefficient and the second influence coefficient through step 5023, update the second adjustment factor with the first influence coefficient to obtain the first target factor adjustment value, and update the third adjustment factor with the second influence coefficient to obtain the second target factor adjustment value.

[0112] In one embodiment, let the original difference of the second adjustment factor (humidity) be ΔH w , the first influence coefficient be γ, and the ventilation volume in the first adjustment strategy be V. Then the updated first target factor adjustment value (for humidity) ΔH target can be calculated by the formula ΔH target = ΔH w + γ * V. For the third adjustment factor (light intensity), let the original difference be ΔG w , the second influence coefficient be δ, the ventilation volume be V, and the updated second target factor adjustment value (for light intensity) ΔG target can be calculated by the formula ΔG target = ΔG w + δ * V.

[0113] Step 5025: Determine the second adjustment strategy based on the combined influence of the adjustment devices corresponding to the first target factor adjustment value and the adjustment devices corresponding to the second target factor adjustment value, and adjust the device parameters of the temperature device, humidity device, lighting device, and ventilation device based on the first adjustment strategy and the second adjustment strategy.

[0114] Optionally, the adjustment control system analyzes the combined influence when the adjustment devices corresponding to the first target factor adjustment value (such as the updated humidity adjustment value) (such as a humidifier or a dehumidifier) and the adjustment devices corresponding to the second target factor adjustment value (such as the updated light intensity adjustment value) (such as a supplementary light or a shading device) operate simultaneously. For example, when the humidifier increases the humidity, it may slightly decrease the indoor temperature, and when the supplementary light is turned on, it will increase the indoor temperature. Therefore, in order to achieve the target humidity and target light intensity, after considering the combined influence of the devices, determine the second adjustment strategy. For example, using a humidifier will cause the temperature to drop. If the original plan was to turn on a heating pad to raise the temperature by 3°C, now it is necessary to appropriately increase the heating amount to offset the impact of the humidifier on the temperature. And when planning to increase the supplementary light, since it will increase the indoor temperature, it is necessary to consider the increase in temperature after the supplementary light is added, determine the power of the temperature increase according to the influence amplitude, and then determine the second adjustment strategy.

[0115] Further, after determining the first adjustment strategy and the second adjustment strategy, the adjustment control system adjusts the equipment parameters of the temperature equipment (such as heating lamps or air conditioners), humidity equipment (humidifiers or dehumidifiers), lighting equipment (supplementary lighting lamps or shading equipment), and ventilation equipment according to the first adjustment strategy (such as the ventilation volume and operation time of the ventilation equipment) and the second adjustment strategy (such as the operation time and power of the humidifier and supplementary lighting lamp), so as to make the final temperature, humidity, and light intensity meet the requirements of the target standard environmental parameters.

[0116] The embodiments of the present invention always determine the adjustment factors and strategies according to the needs of the target animals and plants at the current growth stage, and can accurately meet their growth needs; and considering the mutual influence between different environmental factors and the chain reaction of the adjustment strategy on each factor, through calculating the influence coefficient, updating the adjustment value, and analyzing the influence of collaborative equipment, the whole regulation process is comprehensive and scientific, avoiding the situation of attending to one thing and losing sight of another.

[0117] In one embodiment, the descriptions of steps 5031 - 5035 are as follows:

[0118] In step 5031, analyze the degree of factor deviation of the parameter difference result to obtain the deviation degree values corresponding to each environmental factor; the environmental factors include temperature factor, humidity factor, and light intensity factor.

[0119] Optionally, when the adjustment control system starts the third mode, first analyze according to the degree of deviation of each environmental factor in the parameter difference result, that is, for the temperature factor, humidity factor, and light intensity factor, respectively determine and calculate their deviation degree values. Among them, for the temperature factor, let the target temperature be T mu , and the current temperature difference value be ΔT w , then the temperature deviation degree value D T can be calculated by the formula . Similarly, for the humidity factor, let the target humidity be H mu , and the current temperature difference value be ΔH w , then the temperature deviation degree value D H can be calculated by the formula . For the light intensity factor, let the target light intensity be G mu , and the current light intensity value be ΔG w , then the temperature deviation degree value D G can be calculated by the formula .

[0120] In step 5032, based on the deviation degree values of the target animal and plant species and the corresponding environmental factors, conduct an adjustment sequence analysis to determine the strategy adjustment order.

[0121] Optionally, after the adjustment control system determines the deviation degree values of various environmental factors, due to the different sensitivities of different types of target animals and plants to environmental factors, the sensitivities of different types of target animals and plants at different growth stages are first assigned values. For example, the temperature has the greatest impact on the juvenile bearded dragon, so the juvenile bearded dragon is given a relatively large weight for temperature. During the breeding period, the suitability of the light intensity has an important impact on its breeding behavior. Therefore, during the breeding period, the bearded dragon is given a relatively large weight for light intensity. Therefore, according to the database of target animals and plants, the influence degree, that is, the weight value, of different environmental factors at each stage is determined. Then, based on the calculated deviation degree value of the current environmental factors and combined with the weight values of various environmental factors at the current stage in the database, a total priority value is comprehensively obtained, and the adjustment order is determined according to the size of the priority value. For example, when the bearded dragon is in the juvenile stage, the temperature deviation degree value is 12.5%, the humidity deviation degree value is 20%, and the light intensity deviation degree value is 33.3%. Since the juvenile stage is sensitive to temperature, although the deviation degrees of humidity and light intensity are relatively high, the temperature weight of the juvenile bearded dragon is 60%, the humidity weight is 30%, and the light intensity weight is 10%. After comprehensive calculation, the priority value of temperature is still the highest, and then the adjustment order is determined as temperature, humidity, and light intensity.

[0122] Step 5033: Based on the strategy adjustment sequence, match it with the preset adjustment devices to determine the target strategy adjustment device.

[0123] Optionally, after the adjustment control system determines the adjustment order, it clarifies the preset adjustment devices for environmental factors such as temperature, humidity, and light intensity and their corresponding relationships. For example, temperature adjustment devices include heating lamps, heating pads, refrigeration equipment, etc.; humidity adjustment devices include humidifiers and dehumidifiers; light intensity adjustment devices include supplementary lighting lamps and sunshade nets. Therefore, if the adjustment order is temperature, humidity, and light intensity, for temperature adjustment, a heating lamp is selected (assuming the current temperature is low); for humidity adjustment, if the humidity is high, a dehumidifier is selected; for light intensity adjustment, if the light intensity is insufficient, a supplementary lighting lamp is selected.

[0124] Step 5034: Analyze the magnitudes of the deviation degree values corresponding to various environmental factors to obtain the adjustment durations of the corresponding adjustment devices respectively.

[0125] Optionally, after the adjustment control system determines the adjustment order and the adjustment devices corresponding to the adjustment order, for different adjustment devices, a model for calculating the adjustment duration based on the environmental factor deviation degree value is established. Taking the adjustment of temperature by a heating lamp as an example, let the heating power of the heating lamp be P (unit: watt), the heat capacity of the ecological environment space be C (unit: joule per degree Celsius), and the current temperature difference be ΔT w , according to the thermodynamic formula Q r =C*ΔT w , Qr is the amount of heat change, and the heat Q provided by the heating lamp within time T jia is Q r = P * T jia , then the adjustment time For the dehumidifier to adjust the humidity, let the dehumidification capacity of the dehumidifier be R cu (unit: grams per hour), the air volume of the ecological environment space be V se (unit: cubic meters), the humidity holding capacity of the air be k sidu (unit: grams per cubic meter), and the current humidity difference be ΔH w , the adjustment time T si can be obtained through the formula For the supplementary light to adjust the light intensity, let the light intensity adjustment capacity of the supplementary light be I bu (unit: lux per hour), the light-receiving area of the ecological environment space be A se (unit: square meters), and the current light intensity difference be ΔG w , then the adjustment time T guang can be obtained through the formula Substitute the differences of each environmental factor, the performance parameters of the equipment, the parameters of the environmental space, etc. into the above formula to calculate the adjustment duration of the corresponding adjustment equipment.

[0126] Step 5035, fuse the policy adjustment order, the adjustment duration of the target policy adjustment equipment and the corresponding adjustment equipment to determine the target adjustment policy, and adjust the equipment parameters of the preset adjustment equipment based on the target adjustment policy.

[0127] Optionally, after the adjustment control system determines the policy adjustment order, the target policy adjustment equipment and the adjustment duration of the corresponding adjustment equipment, establish a set of rules for fusing the policy adjustment order, the target policy adjustment equipment and the adjustment duration. For example, according to the running duration and performance of the equipment, determine specific parameters such as the power and wind speed of the equipment. For the heating lamp, according to the calculated adjustment duration and the target temperature change, determine the continuous running time with a power of 100 watts; for the dehumidifier, according to the humidity adjustment requirement and duration, determine its dehumidification power and running mode; for the supplementary light, according to the light intensity adjustment duration and the target light change, determine its light intensity output and running time arrangement, and then form a detailed target adjustment policy. After that, adjust the equipment parameters of the preset adjustment equipment according to the target adjustment policy. For example, within the next 40 minutes, set the power of the heating lamp to 100 watts and run continuously; after the heating lamp runs for 10 minutes, start the dehumidifier, set the dehumidification power to [specific dehumidification power value], and run for 20 minutes; after the dehumidifier runs for 10 minutes, start the supplementary light, set the light intensity to [specific light intensity value], and run for 60 minutes.

[0128] Embodiments of the present invention start from the degree of deviation of environmental factors, and combine the sensitivity of target animals and plants to environmental factors to formulate highly targeted adjustment strategies to meet the specific growth needs of target animals and plants.

[0129] Further, the artificial intelligence-based control system for regulating the ecological environment of animals and plants provided by the present invention will be described below. The artificial intelligence-based control system for regulating the ecological environment of animals and plants described below can be correspondingly referred to the artificial intelligence-based method for regulating the ecological environment of animals and plants described above.

[0130] Optionally, referring to Figure 2 , Figure 2 is a schematic structural diagram of the artificial intelligence-based control system for regulating the ecological environment of animals and plants provided by the present invention. The artificial intelligence-based control system for regulating the ecological environment of animals and plants includes: an acquisition unit 210, configured to acquire current environmental data, a current time point, and growth image data in the season in which the target animals and plants are located;

[0131] An analysis unit 220, configured to analyze the growth stage of the target animals and plants based on the growth image data to determine the current growth stage of the target;

[0132] A model prediction unit 230, configured to input the current growth stage of the target and the current time point into an environmental parameter model to obtain target standard environmental parameters output by the environmental parameter model; the target standard environmental parameters are standard environmental parameters at a preset time interval immediately after the current time point; the environmental parameter model is trained based on sample target growth stage and time data and their corresponding standard environmental parameter label results;

[0133] A comparison unit 240, configured to compare the difference between the current environmental data and the target standard environmental parameters to obtain a parameter difference result;

[0134] A regulation unit 250, configured to perform comparative analysis based on the comparison between the parameter difference result and a preset threshold, and perform parameter regulation on the ecological environment based on the comparative analysis result.

[0135] In the embodiments of the present invention, the current growth stage of the target animals and plants is determined through precise analysis of the growth image data of the target animals and plants, enabling the precise identification of changes in the growth stage of animals and plants. Furthermore, it can accurately address the problem of environmental changes caused by changes in the growth stage, reducing the difficulty of environmental regulation and providing a reliable basis for subsequent adjustment of the ecological environment. Further, in the environmental parameter model, based on the precisely identified growth stage of animals and plants, the target standard environmental parameters for the next stage of the target animals and plants can be obtained more accurately. During the subsequent automatic regulation process of the ecological environment, based on the comparison between the more accurate target standard environmental parameters and the current environmental data, it can accurately identify and predict whether the current environment requires automatic regulation, and perform automatic regulation according to the identification and prediction results. Therefore, in the embodiments of the present invention, when adjusting the ecological environment, it is not necessary for ordinary users to deeply understand the complex environmental requirement knowledge of animals and plants, and the automatic change of the animal and plant ecology can be realized, reducing the professional threshold and the difficulty of environmental regulation and facilitating the use of users.

[0136] Please refer to Figure 3 , Figure 3 which is the embodiment diagram of the electronic device provided by the embodiments of the present invention. As Figure 3 shown, the embodiments of the present invention provide an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored on the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, the following steps are implemented:

[0137] Obtain the current environmental data, the current time point, and the growth image data of the target animals and plants in the current season;

[0138] Perform a growth stage analysis on the target animals and plants based on the growth image data to determine the current growth stage of the target;

[0139] Input the current growth stage of the target and the current time point into the environmental parameter model to obtain the target standard environmental parameters output by the environmental parameter model; the target standard environmental parameters are the standard environmental parameters within a preset time interval immediately after the current time point; the environmental parameter model is trained based on the sample target growth stage and time data and their corresponding standard environmental parameter label results;

[0140] Perform a difference comparison between the current environmental data and the target standard environmental parameters to obtain a parameter difference result;

[0141] Based on the comparison and analysis of the parameter difference result with a preset threshold, perform parameter regulation on the ecological environment.

[0142] Please refer to Figure 4 , Figure 4 which is the embodiment diagram of the computer-readable storage medium provided by the embodiments of the present invention. As Figure 4As shown, this embodiment provides a computer-readable storage medium 400, on which a computer program 311 is stored. When the computer program 311 is executed by a processor, the following steps are implemented:

[0143] Obtain the current environmental data, the current time point, and the growth image data in the season in which the target animals and plants are located;

[0144] Based on the growth image data, perform a growth stage analysis on the target animals and plants to determine the current growth stage of the target;

[0145] Input the current growth stage of the target and the current time point into the environmental parameter model to obtain the target standard environmental parameters output by the environmental parameter model; the target standard environmental parameters are the standard environmental parameters within a preset time interval immediately after the current time point; the environmental parameter model is trained based on the sample target growth stage and time data and their corresponding standard environmental parameter label results;

[0146] Perform a difference comparison between the current environmental data and the target standard environmental parameters to obtain a parameter difference result;

[0147] Based on the comparison and analysis of the parameter difference result and a preset threshold, perform parameter regulation on the ecological environment.

[0148] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence provided by the above various methods. The method includes:

[0149] Obtain the current environmental data, the current time point, and the growth image data in the season in which the target animals and plants are located;

[0150] Based on the growth image data, perform a growth stage analysis on the target animals and plants to determine the current growth stage of the target;

[0151] Input the current growth stage of the target and the current time point into the environmental parameter model to obtain the target standard environmental parameters output by the environmental parameter model; the target standard environmental parameters are the standard environmental parameters within a preset time interval immediately after the current time point; the environmental parameter model is trained based on the sample target growth stage and time data and their corresponding standard environmental parameter label results;

[0152] Perform a difference comparison between the current environmental data and the target standard environmental parameters to obtain a parameter difference result;

[0153] Based on the comparison and analysis of the parameter difference result and a preset threshold, perform parameter regulation on the ecological environment.

[0154] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

[0155] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiments.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. An artificial intelligence-based method for regulating and controlling the ecological environment of animals and plants, characterized in that, Including: Obtain the current environmental data, the current time point, and the growth image data of the target animals and plants in the current season; Based on the growth image data, analyze the growth stage of the target animals and plants to determine the target current growth stage; Input the target current growth stage and the current time point into the environmental parameter model to obtain the target standard environmental parameters output by the environmental parameter model; the target standard environmental parameters are the standard environmental parameters within a preset time interval immediately after the current time point; The environmental parameter model is trained based on the sample target growth stage and time data and their corresponding standard environmental parameter label results; Compare the difference between the current environmental data and the target standard environmental parameters to obtain a parameter difference result; Based on the comparison and analysis of the parameter difference result and a preset threshold, and based on the comparison and analysis result, perform parameter regulation on the ecological environment.

2. The method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence according to claim 1, characterized in that The preset threshold includes a first threshold, a second threshold, and a third threshold; the performing parameter regulation on the ecological environment based on the comparison and analysis result includes: If the parameter difference result is greater than the first threshold and less than or equal to the second threshold, then within the preset time interval, perform regulation on the ecological environment based on the parameter difference result and the first mode; If the parameter difference result is greater than the second threshold and less than or equal to the third threshold, then within the preset time interval, perform regulation on the ecological environment based on the parameter difference result and the second mode; If the parameter difference result is greater than the third threshold, then within the preset time interval, perform regulation on the ecological environment based on the parameter difference result and the third mode.

3. The method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence according to claim 2, characterized in that, The first mode represents adjustment based on the energy conservation and environmental protection mode; the performing regulation on the ecological environment based on the parameter difference result and the first mode includes: Perform data decomposition on the parameter difference result to obtain the temperature difference, humidity difference, and light intensity difference corresponding to the current environmental data; Based on the analysis of the light intensity difference and the natural light intensity of the outdoor environment, determine a light adjustment strategy; Execute the light adjustment strategy, and analyze the execution result of the light adjustment strategy to determine the temperature compensation coefficient and the humidity compensation coefficient; Compensate the temperature difference based on the temperature compensation coefficient to obtain a target temperature difference, and compensate the humidity difference based on the humidity compensation coefficient to obtain a target humidity difference; Based on the temperature and humidity data of the outdoor environment, the target temperature difference, and the target humidity difference, analyze the ventilation intensity to determine a ventilation adjustment strategy, and adjust the equipment parameters of the ventilation equipment based on the ventilation adjustment strategy.

4. The method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence according to claim 2, characterized in that, The second mode represents adjustment based on the needs of the target animals and plants; The performing regulation on the ecological environment based on the parameter difference result and the second mode includes: Based on the needs of the target animals and plants in the target current growth stage, determine a first adjustment factor, and match the first adjustment factor with the parameter difference result to obtain a first adjustment value; the first adjustment factor is any environmental factor in the current environmental data; Based on the natural conditions of the outdoor environment, perform strategy analysis on the first adjustment value to obtain a first adjustment strategy; Based on the influences of the first adjustment strategy on the second adjustment factor and the third adjustment factor respectively, obtain the first influence coefficient for the second adjustment factor and the second influence coefficient for the third adjustment factor; the second adjustment factor and the third adjustment factor are any environmental factors other than the first adjustment factor in the current environmental data respectively, and the second adjustment factor and the third adjustment factor are different; Based on the first influence coefficient and the second influence coefficient, update the second adjustment factor and the third adjustment factor respectively to obtain the first target factor adjustment value and the second target factor adjustment value; Based on the collaborative influence of the adjustment device corresponding to the first target factor adjustment value and the adjustment device corresponding to the second target factor adjustment value, determine the second adjustment strategy, and based on the first adjustment strategy and the second adjustment strategy, adjust the device parameters of the temperature device, the humidity device, the lighting device and the ventilation device.

5. The method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence according to claim 2, wherein, The third mode represents adjustment based on the deviation degree of environmental factors; The regulation of the ecological environment based on the parameter difference result and the third mode includes: Conduct a factor deviation degree analysis on the parameter difference result to obtain the deviation degree values corresponding to each environmental factor; the environmental factors include temperature factor, humidity factor and light intensity factor; Based on the deviation degree values of the target animal and plant species and the corresponding environmental factors, conduct a smoothness analysis of the adjustment to determine the strategy adjustment sequence; Based on the strategy adjustment smoothness, match with the preset adjustment device to determine the target strategy adjustment device; Analyze the magnitudes of the deviation degree values of the corresponding environmental factors respectively to obtain the adjustment durations of the corresponding adjustment devices; Fuse the strategy adjustment sequence, the target strategy adjustment device and the adjustment durations of the corresponding adjustment devices to determine the target adjustment strategy, and based on the target adjustment strategy, adjust the device parameters of the preset adjustment device.

6. The method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence according to claim 1, wherein, The analysis of the growth stage of the target animals and plants based on the growth image data to determine the target current growth stage includes: Based on the position of the target animals and plants in the growth image data, determine the growth scenario where the target animals and plants are located; Based on the growth scenario, conduct a comprehensive analysis of the growth image data to obtain the species to which the target animals and plants belong; Extract morphological features from multiple pieces of the generated image data to obtain feature nodes; Using the feature nodes as endpoints and the distances between the feature nodes as edges, construct a topological skeleton structure; Based on the species to which the target animals and plants belong and the topological skeleton structure, conduct a growth stage analysis of the target animals and plants to determine the target current growth stage.

7. The method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence according to claim 6, characterized in that, The analysis of the growth stage of the target animals and plants based on the species to which the target animals and plants belong and the topological skeleton structure to determine the target current growth stage includes: Based on the species to which the target animals and plants belong, divide the topological skeleton structure to obtain multiple layers of skeletons; Based on the multiple layers of skeletons, analyze and process the connection relationship features between adjacent levels to obtain level features; Analyze the variation rules of the level features of the species to which the target animals and plants belong at different growth stages, and construct a level feature model for each growth stage; Compare the hierarchical features with the hierarchical feature models at each growth stage to determine the target current growth stage.

8. An artificial intelligence-based control system for regulating the ecological environment of animals and plants, characterized in that, Applied to the method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence according to any one of claims 1 to 7; the system for regulating and controlling the ecological environment of animals and plants based on artificial intelligence includes: An acquisition unit for acquiring the current environmental data, the current time point, and the growth image data of the target animals and plants in the season they are in; An analysis unit for analyzing the growth stage of the target animals and plants based on the growth image data to determine the target current growth stage; A model prediction unit for inputting the target current growth stage and the current time point into the environmental parameter model to obtain the target standard environmental parameters output by the environmental parameter model; the target standard environmental parameters are the standard environmental parameters at a preset time interval immediately after the current time point; the environmental parameter model is trained based on the sample target growth stage and time data and their corresponding standard environmental parameter label results; A comparison unit for comparing the difference between the current environmental data and the target standard environmental parameters to obtain a parameter difference result; A regulation unit for comparing and analyzing the parameter difference result with a preset threshold, and regulating the parameters of the ecological environment based on the comparison and analysis result.

9. An electronic device, comprising: A memory for storing computer software programs; A processor for reading and executing the computer software programs, characterized in that when the processor executes the computer software programs, it implements the method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing a computer software program, characterized in that, When the computer software programs are executed by the processor, it implements the method for regulating and controlling the ecological environment of animals and plants based on artificial intelligence according to any one of claims 1 to 7.

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