Fungus and mushroom strain diameter online detection linkage environment regulation method and system

Abstract

The present application relates to the technical field of mushroom cultivation environment regulation, and discloses a mushroom stem diameter online detection linkage environment regulation method and system fusing machine vision, which comprises the following steps: synchronously collecting multiple initial mushroom images at different spatial points by multiple image collection terminals and performing time alignment; performing stem diameter feature extraction on each image to obtain geometric size data; performing time sequence fitting on multiple continuous images at each spatial point to obtain stem diameter growth rate and extrapolate predicted stem diameter value; comparing the predicted stem diameter value with a preset stem diameter reference value to generate a predicted growth stage identifier; matching an environment regulation strategy according to the predicted growth stage identifier and generating a regulation correction amount; generating an environment regulation instruction according to the regulation correction amount and driving an environment regulation terminal to perform a regulation action; and the present application also provides a corresponding system. The present application can respond to the growth trend of mushrooms in advance, and realize fine linkage regulation in multiple points and multiple environmental dimensions.

Description

Technical Field

[0001] This invention relates to the field of mushroom cultivation environment control technology, specifically to a method and system for online detection of mushroom plant diameter linked to environmental control using machine vision. Background Technology

[0002] In the industrialized cultivation of mushrooms, environmental parameters such as temperature, humidity, and carbon dioxide concentration need to be dynamically adjusted according to changes in the mushroom growth stage. Existing technologies include methods that acquire mushroom growth status through image acquisition and combine it with environmental control. For example, CN105867495B discloses a method for remotely controlling industrialized edible mushroom fruiting system and its method based on time parameters. This method acquires video or images of edible mushroom growth through an image acquisition camera and formulates control instructions for ventilation time, atomization time, and light exposure time based on the observation results. However, this approach has significant shortcomings in practical applications: Firstly, this method relies on manual observation of images to formulate control instructions, which is highly subjective and results in delayed control, making it impossible to achieve adaptive adjustment of environmental parameters according to changes in growth status. Secondly, this method only uses time as a control parameter and does not establish a real-time linkage mechanism between mushroom diameter growth status and environmental control, making it difficult to adapt to the refined control requirements imposed by different spatial location growth differences in industrialized cultivation. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for online detection of mushroom plant diameter and linkage with environmental control that integrates machine vision, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for online detection of mushroom plant diameter linked to environmental control using machine vision, comprising the following steps: S1: Multiple initial mushroom images are simultaneously acquired by multiple image acquisition terminals at different spatial locations in the mushroom growth area. The fields of view of the image acquisition terminals at adjacent spatial locations overlap. Pixel-level matching is performed on the images acquired synchronously by adjacent image acquisition terminals within the overlapping area. Synchronization deviation correction parameters are generated for each spatial location image acquisition terminal based on the matching results. The initial mushroom images are then time-aligned based on the synchronization deviation correction parameters. Corresponding initial environmental parameters are simultaneously acquired at each spatial location. S2: Perform plant diameter feature extraction on each initial mushroom image corresponding to each spatial point to obtain the geometric size data of the mushroom plant diameter in each frame of the image corresponding to each spatial point; statistically analyze the geometric size data of multiple mushroom individuals within the same spatial point to generate the plant diameter size distribution interval of that spatial point; determine the plant diameter size dispersion of that spatial point by the difference between the maximum and minimum values ​​of the plant diameter size distribution interval; when the plant diameter size dispersion is greater than the preset dispersion threshold, mark the spatial point as a non-uniform size point, and correct the effective collection quantity of subsequent plant diameter size data of that spatial point according to the ratio of plant diameter size dispersion to the preset dispersion threshold. S3: Perform time series fitting on the time series and geometric dimension data corresponding to multiple consecutive frames of images at each spatial point to obtain the plant diameter growth rate of each spatial point; extrapolate the predicted plant diameter value at the preset time point based on the plant diameter growth rate of each spatial point and the geometric dimension data of the latest frame. S4: Compare the predicted plant diameter value of each spatial point with the preset plant diameter benchmark value, and generate the predicted growth stage identifier corresponding to each spatial point based on the comparison results. After obtaining the predicted growth stage identifier of each spatial point, perform spatial consistency verification on the predicted growth stage identifiers between adjacent spatial points. When the predicted growth stage identifiers of adjacent spatial points are inconsistent, retrieve the time-series change curves of the geometric dimensions of the two inconsistent spatial points for a second comparison, and re-determine the predicted growth stage identifier based on the relative magnitude of the plant diameter growth rate in the second comparison results. S5: Based on the predicted growth stage identifiers of each spatial location, corresponding environmental control strategies are matched. The environmental control strategies include temperature control range, humidity control range, and carbon dioxide concentration control range. S6: Compare the initial environmental parameters of each spatial point with the environmental control strategy matched for each spatial point to generate the control correction amount for each environmental dimension of each spatial point. S7: Generate environmental control instructions for each spatial location based on the control correction values ​​of each environmental dimension. Before the predicted plant diameter value reaches the preset plant diameter benchmark value, the environmental control instructions for each spatial location drive the corresponding environmental control terminal to perform control actions.

[0005] As a preferred embodiment of the present invention, the plant diameter feature extraction operation in S2 includes the following processing: The initial mushroom images of each frame are converted to grayscale to obtain grayscale mushroom images; Background segmentation is performed on grayscale mushroom images, and non-mushroom regions are removed to obtain the mushroom target region image; Segmentation lines are generated at the concave points of connected components in the mushroom target region image, separating multiple adhered mushroom individuals into independent mushroom single-unit images; Edge detection was performed on the individual mushroom images to extract the edge contours of each mushroom cap. Perform circle fitting on the edge contour of each mushroom cap to determine the parameters of the fitted circle, which include the center coordinates and the fitted diameter. Determine the geometric dimensions based on the fitted diameter.

[0006] As a preferred technical solution of the present invention, when constructing the growth trend curve of each spatial point in S3, the geometric size data corresponding to multiple consecutive frames of images are first arranged in time sequence, and then the arranged data is fitted to obtain the plant diameter growth rate of each spatial point. The predicted plant diameter value at the preset time point is extrapolated based on the plant diameter growth rate at each spatial point and the geometric dimension data of the latest frame. After obtaining the plant diameter growth rate at each spatial location, the plant diameter growth rate of multiple mushroom individuals within the same spatial location is statistically analyzed to generate the plant diameter growth rate distribution interval for that spatial location. The growth uniformity index of that spatial location is determined by the difference between the upper and lower limits of the plant diameter growth rate distribution interval. When the growth uniformity index is less than the preset uniformity threshold, the spatial point is marked as a point of uneven growth, and the duration of extrapolation of the preset time point for the spatial point is adjusted according to the ratio of the growth uniformity index to the preset uniformity threshold.

[0007] As a preferred technical solution of the present invention, after obtaining the growth uniformity index of each spatial point, the growth uniformity index of each spatial point is also spatially cross-compared with the growth uniformity index of adjacent spatial points to generate the growth uniformity difference value of each spatial point relative to the adjacent spatial points. When the difference in growth uniformity exceeds a preset difference threshold, the spatial location is marked as a spatial outlier. Based on the marking of spatial outliers, when matching environmental control strategies in S5, a control range that is narrower than that of adjacent spatial outliers is matched for each spatial outlier. The narrowing of the temperature control range is a first preset proportion of the original control range width, the narrowing of the humidity control range is a second preset proportion of the original control range width, and the narrowing of the carbon dioxide concentration control range is a third preset proportion of the original control range width. The values ​​of the first preset proportion, the second preset proportion, and the third preset proportion are all different.

[0008] As a preferred technical solution of the present invention, after matching the environmental control strategy in S5, the initial environmental parameters of each spatial point are compared with the environmental control strategy matched by each spatial point in a hierarchical manner. The hierarchical comparison includes a first-level comparison and a second-level comparison. The first-level comparison involves directly comparing the initial environmental parameters with the control range in the environmental control strategy. When the initial environmental parameters are within the control range, a maintenance command is executed. When the initial environmental parameters exceed the control range, a second-level comparison is triggered. The second-level comparison involves comparing the initial environmental parameters with the adjacent external extension range of the control range. The adjacent external extension range is located within the first preset amplitude range outside the boundary of the control range. When the initial environmental parameters are within the adjacent external extension range, a slow adjustment command is generated; when the initial environmental parameters exceed the adjacent external extension range, an emergency adjustment command is generated. The adjustment magnitude corresponding to the slow adjustment command is the second preset ratio of the adjustment magnitude corresponding to the rapid adjustment command.

[0009] As a preferred technical solution of the present invention, when comparing the initial environmental parameters of each spatial point with the environmental control strategy matched by each spatial point in S6, the interval width values ​​of the temperature control interval, humidity control interval and carbon dioxide concentration control interval matched by any target spatial point are first extracted, and the interval width value of the temperature control interval is compared with the preset temperature width benchmark value to generate the temperature interval width deviation. The humidity control range width value is compared with the preset humidity width benchmark value to generate the humidity range width deviation. The carbon dioxide concentration control range width value is compared with the preset carbon dioxide width benchmark value to generate the carbon dioxide range width deviation. The priority control dimension for the target spatial point at the current moment is determined by the largest value among the deviations in temperature range width, humidity range width, and carbon dioxide range width. Priority environmental control instructions for the target spatial point are generated based on priority control dimensions, and these instructions are issued before those for non-priority control dimensions.

[0010] As a preferred technical solution of the present invention, when generating environmental control commands for each spatial point in S7, the largest absolute value among the temperature control correction amount, humidity control correction amount and carbon dioxide concentration control correction amount of each spatial point is taken as the main control correction amount of each spatial point, and the other two are taken as secondary control correction amounts. The initial execution direction of the environmental control command at each spatial point is determined based on the direction of the main control correction. The secondary control corrections that are in the same direction as the main control corrections are reduced by the first reduction ratio and then added to the main control corrections to generate the comprehensive control correction. The secondary control corrections that are in the opposite direction to the main control corrections are reduced by the second reduction ratio and then deducted from the main control correction to generate the comprehensive control correction. Environmental control instructions for each spatial location are generated based on the comprehensive control and correction amount. After generating environmental control instructions for each spatial point, the execution order of environmental control instructions for adjacent spatial points is cross-sorted to generate a global control instruction queue. The environmental control terminal executes the environmental control actions of each spatial point in the order arranged in the global control instruction queue.

[0011] A system for online detection and environmental control of mushroom plant diameter integrating machine vision, used to execute the method for online detection and environmental control of mushroom plant diameter integrating machine vision described in this invention, includes: Multiple image acquisition terminals are dispersed at different spatial locations in the mushroom growth area. The acquisition fields of adjacent spatial locations overlap. Each image acquisition terminal synchronously acquires multiple initial mushroom images and the corresponding initial environmental parameters at its own spatial location. The image processing module connects to each image acquisition terminal and extracts the plant diameter features of each initial mushroom image corresponding to each spatial point, outputting the geometric size data of each frame of the image corresponding to each spatial point. The image processing module also statistically analyzes the geometric size data of multiple mushroom individuals within the same spatial point, generating the plant diameter size distribution interval for each spatial point. The plant diameter size dispersion of each spatial point is determined by the difference between the maximum and minimum values ​​of the plant diameter size distribution interval. When the plant diameter size dispersion is greater than a preset dispersion threshold, each spatial point is marked as a non-uniform size point, and the effective number of subsequent plant diameter size data acquisitions for each spatial point is corrected according to the ratio of the plant diameter size dispersion to the preset dispersion threshold. The trend prediction module is connected to the image processing module. It performs time series fitting on the time series and geometric size data corresponding to multiple consecutive frames of images at each spatial point to obtain the plant diameter growth rate of each spatial point. Based on the plant diameter growth rate of each spatial point and the geometric size data of the latest frame, it extrapolates the predicted plant diameter value at the preset time point. The growth stage determination module, connected to the trend prediction module, compares the predicted plant diameter value of each spatial point with the preset plant diameter benchmark value to generate the predicted growth stage identifier corresponding to each spatial point. The growth stage determination module also performs spatial consistency verification on the predicted growth stage identifiers between adjacent spatial points. When the predicted growth stage identifiers of adjacent spatial points are inconsistent, the time-series change curves of the geometric dimension data of the two inconsistent spatial points are retrieved for secondary comparison. Based on the relative magnitude of the plant diameter growth rate in the secondary comparison result, the predicted growth stage identifier is re-determined. The strategy matching module is connected to the growth stage determination module. Based on the predicted growth stage identifier of each spatial point, it matches the corresponding environmental control strategy for each spatial point. The deviation comparison module is connected to each image acquisition terminal and the strategy matching module respectively. It compares the initial environmental parameters of each spatial point with the environmental control strategy matched for each spatial point and generates the control correction amount for each environmental dimension of each spatial point. The instruction generation module, connected to the deviation comparison module, generates environmental control instructions for each spatial point based on the control correction amount of each environmental dimension. Before the predicted plant diameter value of each spatial point reaches the preset plant diameter benchmark value, the instruction generation module sends instructions to the environmental control terminal of the corresponding spatial point. The instruction generation module also cross-sorts the execution order of environmental control instructions of adjacent spatial points to generate a global control instruction queue. Multiple environmental control terminals are deployed at various spatial locations. Each environmental control terminal is connected to the instruction generation module and receives and executes the environmental control instructions for the corresponding spatial location in the order arranged in the global control instruction queue. The feedback control module is connected to each image acquisition terminal and the image processing module. After the environmental control terminal at each spatial point performs the control action, it controls the corresponding image acquisition terminal to re-acquire the current mushroom image and transmit it to the image processing module. The image processing module is triggered in a loop to the instruction generation module until the geometric size data of each spatial point reaches the harvest threshold.

[0012] As a preferred embodiment of the present invention, the image processing module includes: The grayscale processing submodule performs grayscale processing on the initial mushroom images of each frame corresponding to each spatial point. The background segmentation submodule is connected to the grayscale processing submodule. It performs background segmentation on the grayscale mushroom image, removes non-mushroom areas, and outputs the mushroom target area image. The adhesion segmentation submodule is connected to the background segmentation submodule. It generates segmentation lines at the concave points of the connected components in the mushroom target region image, separating multiple adhered mushroom individuals into independent mushroom single images. The edge detection submodule, connected to the adhesion segmentation submodule, performs edge detection on the images of each individual mushroom and extracts the edge contour of the mushroom cap. The circle fitting submodule, connected to the edge detection submodule, performs circle fitting on the mushroom cap edge contour and outputs the center coordinates and the fitted diameter. The size determination submodule is connected to the circle fitting submodule and determines the geometric dimension data based on the fitted diameter.

[0013] As a preferred embodiment of the present invention, the environmental control terminal includes a temperature control device, a humidity control device, and a ventilation control device; each spatial location is also equipped with an independent environmental parameter verification sensor group, which includes a temperature verification sensor, a humidity verification sensor, and a carbon dioxide verification sensor. After the environmental control terminal performs the control action, the environmental parameter verification sensor group collects the environmental parameters of the corresponding spatial points after the control and transmits them to the deviation comparison module; The deviation comparison module performs a secondary comparison between the regulated environmental parameters and the corresponding spatial location matching environmental regulation strategy. When the regulated environmental parameters exceed the regulation range in the environmental regulation strategy, the deviation comparison module regenerates the supplementary regulation correction amount and transmits it to the instruction generation module. The instruction generation module generates supplementary environmental control instructions based on the supplementary control correction amount and drives the environmental control terminal to execute them until the environmental parameters fall into the control range of the environmental control strategy after control.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention uses multiple image acquisition terminals to simultaneously acquire mushroom images at different spatial locations and extract plant diameter features. It combines time-series fitting and extrapolation to predict plant diameter values. Based on the predicted plant diameter values, it matches corresponding environmental control strategies, enabling environmental control to respond in advance to the growth and change trends of mushrooms and avoiding the adverse effects of delayed control on mushroom quality.

[0015] 2. This invention performs pixel-level matching and synchronization deviation correction by using overlapping areas of the fields of view of adjacent spatial point image acquisition terminals. This eliminates the timing deviation introduced by the difference in acquisition start time between multiple terminals, making the multi-point image data comparable in the time dimension, and providing an accurate data foundation for subsequent growth trend curve construction and spatial consistency verification.

[0016] 3. This invention generates a distribution range of plant diameter size by statistically analyzing the geometric dimensions of multiple mushroom individuals within the same spatial location and determines the dispersion of plant diameter size. When the dispersion exceeds a threshold, the effective number of samples is adjusted to avoid interference with the overall state determination caused by excessive differences in individual size within the same spatial location, thereby improving the reliability of growth stage determination.

[0017] 4. This invention performs spatial consistency verification on the predicted growth stage identifiers between adjacent spatial points. When inconsistencies occur, it retrieves the time-series change curves for secondary comparison and re-determines the stage based on the relative size of the plant diameter growth rate. This eliminates the stage misjudgment that may be caused by a single threshold comparison and improves the spatial consistency of growth stage determination for multiple spatial points.

[0018] 5. This invention determines the main control correction amount and execution direction based on the absolute value of the control correction amount, and then superimposes or subtracts the secondary control correction amounts in the same and opposite directions according to different proportions to generate a comprehensive control correction amount, thereby avoiding execution conflicts between multi-dimensional control commands and improving the coordination of environmental control commands. Attached Figure Description

[0019] Figure 1This is a schematic diagram of the overall process of the online detection and environmental control method for mushroom plant diameter integrated with machine vision according to the present invention. Figure 2 This is a block diagram of the overall structure of the mushroom plant diameter online detection and environmental control system integrating machine vision, as described in this invention. Figure 3 This is a block diagram of the internal structure of the image processing module in the online detection and environmental control system for mushroom plant diameter integrated with machine vision, as described in this invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1 like Figure 1 As shown, this invention provides a method for online detection of mushroom plant diameter combined with environmental control using machine vision, comprising the following steps: S1: Multiple initial mushroom images are simultaneously acquired by multiple image acquisition terminals at different spatial locations in the mushroom growth area. The fields of view of the image acquisition terminals at adjacent spatial locations overlap. Pixel-level matching is performed on the images acquired synchronously by adjacent image acquisition terminals within the overlapping area. Synchronization deviation correction parameters are generated for each spatial location image acquisition terminal based on the matching results. The initial mushroom images are then time-aligned based on the synchronization deviation correction parameters. Corresponding initial environmental parameters are simultaneously acquired at each spatial location. S2: Perform plant diameter feature extraction on each initial mushroom image corresponding to each spatial point to obtain the geometric size data of the mushroom plant diameter in each frame of the image corresponding to each spatial point; statistically analyze the geometric size data of multiple mushroom individuals within the same spatial point to generate the plant diameter size distribution interval of that spatial point; determine the plant diameter size dispersion of that spatial point by the difference between the maximum and minimum values ​​of the plant diameter size distribution interval; when the plant diameter size dispersion is greater than the preset dispersion threshold, mark the spatial point as a non-uniform size point, and correct the effective collection quantity of subsequent plant diameter size data of that spatial point according to the ratio of plant diameter size dispersion to the preset dispersion threshold. S3: Perform time series fitting on the time series and geometric dimension data corresponding to multiple consecutive frames of images at each spatial point to obtain the plant diameter growth rate of each spatial point; extrapolate the predicted plant diameter value at the preset time point based on the plant diameter growth rate of each spatial point and the geometric dimension data of the latest frame. S4: Compare the predicted plant diameter value of each spatial point with the preset plant diameter benchmark value, and generate the predicted growth stage identifier corresponding to each spatial point based on the comparison results. After obtaining the predicted growth stage identifier of each spatial point, perform spatial consistency verification on the predicted growth stage identifiers between adjacent spatial points. When the predicted growth stage identifiers of adjacent spatial points are inconsistent, retrieve the time-series change curves of the geometric dimensions of the two inconsistent spatial points for a second comparison, and re-determine the predicted growth stage identifier based on the relative magnitude of the plant diameter growth rate in the second comparison results. S5: Based on the predicted growth stage identifiers of each spatial location, corresponding environmental control strategies are matched. The environmental control strategies include temperature control range, humidity control range, and carbon dioxide concentration control range. S6: Compare the initial environmental parameters of each spatial point with the environmental control strategy matched for each spatial point to generate the control correction amount for each environmental dimension of each spatial point. S7: Generate environmental control instructions for each spatial location based on the control correction values ​​of each environmental dimension. Before the predicted plant diameter value reaches the preset plant diameter benchmark value, the environmental control instructions for each spatial location drive the corresponding environmental control terminal to perform control actions.

[0022] Furthermore, the plant diameter feature extraction operation in S2 includes the following processing: The initial mushroom images of each frame are converted to grayscale to obtain grayscale mushroom images; Background segmentation is performed on grayscale mushroom images, and non-mushroom regions are removed to obtain the mushroom target region image; Segmentation lines are generated at the concave points of connected components in the mushroom target region image, separating multiple adhered mushroom individuals into independent mushroom single-unit images; Edge detection was performed on the individual mushroom images to extract the edge contours of each mushroom cap. Perform circle fitting on the edge contour of each mushroom cap to determine the parameters of the fitted circle, which include the center coordinates and the fitted diameter. Determine the geometric dimensions based on the fitted diameter.

[0023] Furthermore, when constructing the growth trend curves of each spatial point in S3, the geometric size data corresponding to multiple consecutive frames of images are first arranged in time sequence, and then the arranged data is fitted to obtain the plant diameter growth rate of each spatial point. The predicted plant diameter value at the preset time point is extrapolated based on the plant diameter growth rate at each spatial point and the geometric dimension data of the latest frame. After obtaining the plant diameter growth rate at each spatial location, the plant diameter growth rate of multiple mushroom individuals within the same spatial location is statistically analyzed to generate the plant diameter growth rate distribution interval for that spatial location. The growth uniformity index of that spatial location is determined by the difference between the upper and lower limits of the plant diameter growth rate distribution interval. When the growth uniformity index is less than the preset uniformity threshold, the spatial point is marked as a point of uneven growth, and the duration of extrapolation of the preset time point for the spatial point is adjusted according to the ratio of the growth uniformity index to the preset uniformity threshold.

[0024] Furthermore, after obtaining the growth uniformity index of each spatial point, the growth uniformity index of each spatial point is also spatially cross-compared with the growth uniformity index of adjacent spatial points to generate the growth uniformity difference value of each spatial point relative to the adjacent spatial points. When the difference in growth uniformity exceeds a preset difference threshold, the spatial location is marked as a spatial outlier. Based on the marking of spatial outliers, when matching environmental control strategies in S5, a control range that is narrower than that of adjacent spatial outliers is matched for each spatial outlier. The narrowing of the temperature control range is a first preset proportion of the original control range width, the narrowing of the humidity control range is a second preset proportion of the original control range width, and the narrowing of the carbon dioxide concentration control range is a third preset proportion of the original control range width. The values ​​of the first preset proportion, the second preset proportion, and the third preset proportion are all different.

[0025] Furthermore, after matching the environmental control strategy in S5, the initial environmental parameters of each spatial point are compared with the environmental control strategy matched by each spatial point in a hierarchical manner. The hierarchical comparison includes first-level comparison and second-level comparison. The first-level comparison involves directly comparing the initial environmental parameters with the control range in the environmental control strategy. When the initial environmental parameters are within the control range, a maintenance command is executed. When the initial environmental parameters exceed the control range, a second-level comparison is triggered. The second-level comparison involves comparing the initial environmental parameters with the adjacent external extension range of the control range. The adjacent external extension range is located within the first preset amplitude range outside the boundary of the control range. When the initial environmental parameters are within the adjacent external extension range, a slow adjustment command is generated; when the initial environmental parameters exceed the adjacent external extension range, an emergency adjustment command is generated. The adjustment magnitude corresponding to the slow adjustment command is the second preset ratio of the adjustment magnitude corresponding to the rapid adjustment command.

[0026] Furthermore, in S6, when comparing the initial environmental parameters of each spatial point with the environmental control strategy matched for each spatial point, the width values ​​of the temperature control range, humidity control range and carbon dioxide concentration control range matched for any target spatial point are first extracted. The width value of the temperature control range is then compared with the preset temperature width benchmark value to generate the temperature range width deviation. The humidity control range width value is compared with the preset humidity width benchmark value to generate the humidity range width deviation. The carbon dioxide concentration control range width value is compared with the preset carbon dioxide width benchmark value to generate the carbon dioxide range width deviation. The priority control dimension for the target spatial point at the current moment is determined by the largest value among the deviations in temperature range width, humidity range width, and carbon dioxide range width. Priority environmental control instructions for the target spatial point are generated based on priority control dimensions, and these instructions are issued before those for non-priority control dimensions.

[0027] Furthermore, when generating environmental control commands for each spatial location in S7, the one with the largest absolute value among the temperature control correction, humidity control correction, and carbon dioxide concentration control correction for each spatial location is taken as the main control correction for each spatial location, and the other two are taken as secondary control corrections. The initial execution direction of the environmental control command at each spatial point is determined based on the direction of the main control correction. The secondary control corrections that are in the same direction as the main control corrections are reduced by the first reduction ratio and then added to the main control corrections to generate the comprehensive control correction. The secondary control corrections that are in the opposite direction to the main control corrections are reduced by the second reduction ratio and then deducted from the main control correction to generate the comprehensive control correction. Environmental control instructions for each spatial location are generated based on the comprehensive control and correction amount. After generating environmental control instructions for each spatial point, the execution order of environmental control instructions for adjacent spatial points is cross-sorted to generate a global control instruction queue. The environmental control terminal executes the environmental control actions of each spatial point in the order arranged in the global control instruction queue.

[0028] like Figure 2 and Figure 3 As shown, the present invention also provides a mushroom plant diameter online detection and environmental control system integrating machine vision, used to execute the mushroom plant diameter online detection and environmental control method integrating machine vision of the present invention, including: Multiple image acquisition terminals are distributed at different spatial locations in the mushroom growth area. The fields of view of adjacent spatial location image acquisition terminals overlap. This overlap area is used to perform pixel-level matching of images synchronously acquired by adjacent image acquisition terminals. Based on the matching results, synchronization deviation correction parameters are generated for each spatial location image acquisition terminal. After each image acquisition terminal performs time alignment at its respective spatial location according to the synchronization deviation correction parameters, it synchronously acquires multiple frames of initial mushroom images and the corresponding initial environmental parameters for each frame. The image acquisition terminals can be implemented using industrial cameras with optical lenses. The overlapping area of ​​the fields of view of adjacent image acquisition terminals is used to eliminate the timing deviation introduced by the difference in acquisition start time between terminals.

[0029] The image processing module, connected to each image acquisition terminal, is used to extract the plant diameter features of each initial mushroom image corresponding to each spatial point, and output the geometric size data of each frame of the image corresponding to each spatial point. The image processing module statistically analyzes the geometric size data of multiple mushroom individuals within the same spatial point to generate the plant diameter size distribution interval for each spatial point. The plant diameter size dispersion of each spatial point is determined by the difference between the maximum and minimum values ​​of the plant diameter size distribution interval. When the plant diameter size dispersion is greater than a preset dispersion threshold, the image processing module marks each spatial point as a non-uniform size point and corrects the effective acquisition quantity of subsequent plant diameter size data for each spatial point according to the ratio of plant diameter size dispersion to the preset dispersion threshold. The image processing module can be implemented using an embedded image processor or an industrial control computer. Its built-in image processing unit is used to complete image processing tasks such as grayscale conversion, segmentation, edge detection, and circle fitting.

[0030] The trend prediction module, connected to the image processing module, performs time-series fitting on the time series and geometric dimension data corresponding to multiple consecutive frames of images at each spatial point to obtain the plant diameter growth rate of each spatial point. Based on the plant diameter growth rate of each spatial point and the geometric dimension data of the latest frame, it extrapolates the predicted plant diameter value at a preset time point. The trend prediction module can be implemented using a microcontroller in conjunction with a time-series data storage unit. Its internal time-series fitting unit is used to perform linear or polynomial fitting on multiple consecutive frames of geometric dimension data to obtain the plant diameter growth rate and the extrapolated predicted value.

[0031] The growth stage determination module, connected to the trend prediction module, compares the predicted plant diameter value at each spatial point with the preset plant diameter benchmark value to generate a predicted growth stage identifier for each spatial point. The growth stage determination module also performs spatial consistency verification on the predicted growth stage identifiers between adjacent spatial points. When the predicted growth stage identifiers of adjacent spatial points are inconsistent, the time-series change curves of the geometric dimension data of the two inconsistent spatial points are retrieved for secondary comparison. Based on the relative magnitude of the plant diameter growth rate in the secondary comparison result, the predicted growth stage identifier is re-determined. The growth stage determination module can be implemented using a comparator circuit in conjunction with a register group, or by using the comparison unit in a microcontroller to compare different predicted plant diameter values ​​with the preset benchmark value and generate stage identifiers.

[0032] The strategy matching module is connected to the growth stage determination module. The strategy matching module is used to match the corresponding environmental control strategy for each spatial point according to the predicted growth stage identifier. The environmental control strategy includes temperature control range, humidity control range and carbon dioxide concentration control range. The strategy matching module can be implemented by using a lookup table memory that stores the correspondence between different growth stages and environmental control strategies. After the predicted growth stage identifier is input, the lookup table memory outputs the corresponding temperature control range, humidity control range and carbon dioxide concentration control range.

[0033] The deviation comparison module is connected to each image acquisition terminal and the strategy matching module. The deviation comparison module is used to compare the initial environmental parameters of each spatial point with the environmental control strategy matched for each spatial point to generate the control correction amount for each environmental dimension of each spatial point. The deviation comparison module compares the measured temperature value of each spatial point with the median of the matched temperature control range to generate the temperature deviation value. The humidity deviation value and carbon dioxide concentration deviation value are generated in the same way. The deviation comparison module can be composed of a comparator array, with an independent comparison channel configured for each environmental dimension, and each comparison channel outputs the corresponding deviation signal.

[0034] The instruction generation module, connected to the deviation comparison module, generates environmental control instructions for each spatial point based on the control correction amounts for each environmental dimension. Before the predicted plant diameter value at each spatial point reaches the preset plant diameter baseline value, the instruction generation module sends instructions to the environmental control terminal of the corresponding spatial point. The instruction generation module also cross-sorts the execution order of environmental control instructions from adjacent spatial points to generate a global control instruction queue. The instruction generation module can be implemented using a programmable logic controller or a microcontroller, and it integrates an instruction encoding unit to convert the control correction amount into the corresponding adjustment instruction format.

[0035] Multiple environmental control terminals are deployed at various spatial locations. Each environmental control terminal is connected to the instruction generation module and receives and executes the environmental control instructions for the corresponding spatial location in the order arranged in the global control instruction queue. The environmental control terminal includes a temperature control device, a humidity control device, and a ventilation control device, which are used to adjust the temperature, humidity, and carbon dioxide concentration of the corresponding spatial location, respectively.

[0036] The feedback control module is connected to each image acquisition terminal and the image processing module. After the environmental control terminal at each spatial point performs a control action, the feedback control module controls the corresponding image acquisition terminal to re-acquire the current mushroom image and transmit it to the image processing module. The image processing module is triggered cyclically to the instruction generation module until the geometric dimension data of each spatial point reaches the harvest threshold. The feedback control module can be implemented using a timing controller, which integrates a timing unit to trigger the image acquisition terminal to re-acquire the image after a preset time interval.

[0037] Furthermore, the image processing module includes: The grayscale processing submodule performs grayscale processing on the initial mushroom images of each frame corresponding to each spatial point. The background segmentation submodule is connected to the grayscale processing submodule. It performs background segmentation on the grayscale mushroom image, removes non-mushroom areas, and outputs the mushroom target area image. The adhesion segmentation submodule is connected to the background segmentation submodule. It generates segmentation lines at the concave points of the connected components in the mushroom target region image, separating multiple adhered mushroom individuals into independent mushroom single images. The edge detection submodule, connected to the adhesion segmentation submodule, performs edge detection on the images of each individual mushroom and extracts the edge contour of the mushroom cap. The circle fitting submodule, connected to the edge detection submodule, performs circle fitting on the mushroom cap edge contour and outputs the center coordinates and the fitted diameter. The size determination submodule is connected to the circle fitting submodule and determines the geometric dimension data based on the fitted diameter.

[0038] Furthermore, the environmental control terminal includes a temperature control device, a humidity control device, and a ventilation control device; each spatial location is also equipped with an independent environmental parameter verification sensor group, which includes a temperature verification sensor, a humidity verification sensor, and a carbon dioxide verification sensor. After the environmental control terminal performs the control action, the environmental parameter verification sensor group collects the environmental parameters of the corresponding spatial points after the control and transmits them to the deviation comparison module; The deviation comparison module performs a secondary comparison between the regulated environmental parameters and the corresponding spatial location matching environmental regulation strategy. When the regulated environmental parameters exceed the regulation range in the environmental regulation strategy, the deviation comparison module regenerates the supplementary regulation correction amount and transmits it to the instruction generation module. The instruction generation module generates supplementary environmental control instructions based on the supplementary control correction amount and drives the environmental control terminal to execute them until the environmental parameters fall into the control range of the environmental control strategy after control.

[0039] Example 2 In the industrialized cultivation area of ​​king oyster mushrooms, each of the 12 spatial locations is equipped with a set of sensors. Each set includes a temperature sensor, a humidity sensor, and a carbon dioxide concentration sensor. After matching the environmental control strategy to each spatial location, taking one spatial location as an example, the temperature control range of this location is 15℃-18℃, the humidity control range is 85%-90%, and the carbon dioxide concentration control range is 1500ppm-2500ppm. The width of the above temperature control range is 3℃, the width of the humidity control range is 5%, and the width of the carbon dioxide concentration control range is 1000ppm. The preset temperature width benchmark value, preset humidity width benchmark value, and preset carbon dioxide width benchmark value are 4℃, 8%, and 1500ppm, respectively.

[0040] Comparing the temperature control range width of 3℃ at this location with the preset temperature width benchmark value of 4℃, the ratio of the difference between the two to the preset temperature width benchmark value is 25%. Comparing the humidity control range width of 5% with the preset humidity width benchmark value of 8%, the ratio of the difference between the two to the preset humidity width benchmark value is 37.5%. Comparing the carbon dioxide concentration control range width of 1000ppm with the preset carbon dioxide width benchmark value of 1500ppm, the ratio of the difference between the two to the preset carbon dioxide width benchmark value is approximately 33.3%.

[0041] Among the three ratios mentioned above, the ratio corresponding to humidity is the largest. Based on this, the priority control dimension of this spatial point at the current moment is determined to be humidity. Priority environmental control instructions for this point are generated according to this priority control dimension. These instructions are executed before temperature control instructions and carbon dioxide concentration control instructions. Other spatial points determine their respective priority control dimensions and generate corresponding priority environmental control instructions in the same way.

[0042] Example 3 The temperature control correction for a certain spatial location is +2℃, the humidity control correction is -3%, and the carbon dioxide concentration control correction is +500ppm. Comparing the absolute values ​​of the three corrections, the humidity control correction has the largest absolute value. Therefore, the humidity control correction is taken as the primary control correction, while the temperature control correction and the carbon dioxide concentration control correction are taken as secondary control corrections. The direction of the primary control correction -3% is negative, indicating that the humidity output of this spatial location needs to be reduced.

[0043] The two secondary control corrections are opposite in direction to the primary control correction. They are reduced by 50% and then deducted from the primary control correction. The deduction for the temperature secondary control correction is 2% × 50% = 1%, and the deduction for the carbon dioxide secondary control correction is 500ppm × 50% = 250ppm. The primary control correction is then reduced by the above two items to obtain the comprehensive control correction, which is -3% - 1% - 250ppm = -4% - 250ppm. The environmental control command for this spatial location is generated based on this comprehensive control correction.

[0044] The humidity control correction for adjacent spatial point A is -3%, and the humidity control correction for adjacent spatial point B is +2%. The two corrections are in opposite directions and the absolute values ​​of the corrections are both greater than 1%. The environmental control commands for the two points are cross-sorted, with negative commands placed before positive commands. The negative control commands for spatial point A are placed before the positive control commands for spatial point B, forming a global control command queue. The environmental control terminal executes the control actions of each point according to the order of this queue.

[0045] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for online detection of mushroom plant diameter combined with environmental control using machine vision, characterized in that, Includes the following steps: S1: Multiple initial mushroom images are simultaneously acquired by multiple image acquisition terminals at different spatial locations in the mushroom growth area. The fields of view of the image acquisition terminals at adjacent spatial locations overlap. Pixel-level matching is performed on the images acquired synchronously by adjacent image acquisition terminals within the overlapping area. Synchronization deviation correction parameters are generated for each spatial location image acquisition terminal based on the matching results. The initial mushroom images are then time-aligned based on the synchronization deviation correction parameters. Corresponding initial environmental parameters are simultaneously acquired at each spatial location. S2: Perform plant diameter feature extraction on each initial mushroom image corresponding to each spatial point to obtain the geometric size data of the mushroom plant diameter in each frame of the image corresponding to each spatial point; statistically analyze the geometric size data of multiple mushroom individuals within the same spatial point to generate the plant diameter size distribution interval of that spatial point; determine the plant diameter size dispersion of that spatial point by the difference between the maximum and minimum values ​​of the plant diameter size distribution interval; when the plant diameter size dispersion is greater than the preset dispersion threshold, mark the spatial point as a non-uniform size point, and correct the effective collection quantity of subsequent plant diameter size data of that spatial point according to the ratio of plant diameter size dispersion to the preset dispersion threshold. S3: Perform time series fitting on the time series and geometric dimension data corresponding to multiple consecutive frames of images at each spatial point to obtain the plant diameter growth rate of each spatial point; extrapolate the predicted plant diameter value at the preset time point based on the plant diameter growth rate of each spatial point and the geometric dimension data of the latest frame. S4: Compare the predicted plant diameter value of each spatial point with the preset plant diameter benchmark value, and generate the predicted growth stage identifier corresponding to each spatial point based on the comparison results. After obtaining the predicted growth stage identifier of each spatial point, perform spatial consistency verification on the predicted growth stage identifiers between adjacent spatial points. When the predicted growth stage identifiers of adjacent spatial points are inconsistent, retrieve the time-series change curves of the geometric dimensions of the two inconsistent spatial points for a second comparison, and re-determine the predicted growth stage identifier based on the relative magnitude of the plant diameter growth rate in the second comparison results. S5: Based on the predicted growth stage identifiers of each spatial location, corresponding environmental control strategies are matched. The environmental control strategies include temperature control range, humidity control range, and carbon dioxide concentration control range. S6: Compare the initial environmental parameters of each spatial point with the environmental control strategy matched for each spatial point to generate the control correction amount for each environmental dimension of each spatial point. S7: Generate environmental control instructions for each spatial location based on the control correction values ​​of each environmental dimension. Before the predicted plant diameter value reaches the preset plant diameter benchmark value, the environmental control instructions for each spatial location drive the corresponding environmental control terminal to perform control actions.

2. The method according to claim 1, characterized in that, The plant diameter feature extraction operation in S2 includes the following processing: The initial mushroom images of each frame are converted to grayscale to obtain grayscale mushroom images; Background segmentation is performed on grayscale mushroom images, and non-mushroom regions are removed to obtain the mushroom target region image; Segmentation lines are generated at the concave points of connected components in the mushroom target region image, separating multiple adhered mushroom individuals into independent mushroom single-unit images; Edge detection was performed on the individual mushroom images to extract the edge contours of each mushroom cap. Perform circle fitting on the edge contour of each mushroom cap to determine the parameters of the fitted circle, which include the center coordinates and the fitted diameter. Determine the geometric dimensions based on the fitted diameter.

3. The method according to claim 1, characterized in that, When constructing the growth trend curves of each spatial point in S3, the geometric size data corresponding to multiple consecutive frames of images are first arranged in time sequence, and then the arranged data is fitted to obtain the plant diameter growth rate of each spatial point. The predicted plant diameter value at the preset time point is extrapolated based on the plant diameter growth rate at each spatial point and the geometric dimension data of the latest frame. After obtaining the plant diameter growth rate at each spatial location, the plant diameter growth rate of multiple mushroom individuals within the same spatial location is statistically analyzed to generate the plant diameter growth rate distribution interval for that spatial location. The growth uniformity index of that spatial location is determined by the difference between the upper and lower limits of the plant diameter growth rate distribution interval. When the growth uniformity index is less than the preset uniformity threshold, the spatial point is marked as a point of uneven growth, and the duration of extrapolation of the preset time point for the spatial point is adjusted according to the ratio of the growth uniformity index to the preset uniformity threshold.

4. The method according to claim 3, characterized in that, After obtaining the growth uniformity index of each spatial point, the growth uniformity index of each spatial point is also spatially cross-compared with the growth uniformity index of adjacent spatial points to generate the growth uniformity difference value of each spatial point relative to the adjacent spatial points. When the difference in growth uniformity exceeds a preset difference threshold, the spatial location is marked as a spatial outlier. Based on the marking of spatial outliers, when matching environmental control strategies in S5, a control range that is narrower than that of adjacent spatial outliers is matched for each spatial outlier. The narrowing of the temperature control range is a first preset proportion of the original control range width, the narrowing of the humidity control range is a second preset proportion of the original control range width, and the narrowing of the carbon dioxide concentration control range is a third preset proportion of the original control range width. The values ​​of the first preset proportion, the second preset proportion, and the third preset proportion are all different.

5. The method according to claim 1, characterized in that, After matching the environmental control strategy in S5, the initial environmental parameters of each spatial point are compared with the environmental control strategy matched by each spatial point in a hierarchical manner. The hierarchical comparison includes first-level comparison and second-level comparison. The first-level comparison involves directly comparing the initial environmental parameters with the control range in the environmental control strategy. When the initial environmental parameters are within the control range, a maintenance command is executed. When the initial environmental parameters exceed the control range, a second-level comparison is triggered. The second-level comparison involves comparing the initial environmental parameters with the adjacent external extension range of the control range. The adjacent external extension range is located within the first preset amplitude range outside the boundary of the control range. When the initial environmental parameters are within the adjacent external extension range, a slow adjustment command is generated; when the initial environmental parameters exceed the adjacent external extension range, an emergency adjustment command is generated. The adjustment magnitude corresponding to the slow adjustment command is the second preset ratio of the adjustment magnitude corresponding to the rapid adjustment command.

6. The method according to claim 1, characterized in that, In step S6, when comparing the initial environmental parameters of each spatial point with the environmental control strategy matched by each spatial point, the width values ​​of the temperature control range, humidity control range and carbon dioxide concentration control range matched by any target spatial point are first extracted. The width value of the temperature control range is then compared with the preset temperature width benchmark value to generate the temperature range width deviation. The humidity control range width value is compared with the preset humidity width benchmark value to generate the humidity range width deviation. The carbon dioxide concentration control range width value is compared with the preset carbon dioxide width benchmark value to generate the carbon dioxide range width deviation. The priority control dimension for the target spatial point at the current moment is determined by the largest value among the deviations in temperature range width, humidity range width, and carbon dioxide range width. Priority environmental control instructions for the target spatial point are generated based on the priority control dimension, and these instructions are issued before those for non-priority control dimensions.

7. The method according to claim 1, characterized in that, When generating environmental control commands for each spatial point in S7, the largest absolute value among the temperature control correction, humidity control correction, and carbon dioxide concentration control correction for each spatial point is taken as the main control correction for each spatial point, and the other two are taken as secondary control corrections. The initial execution direction of the environmental control command at each spatial point is determined based on the direction of the main control correction. The secondary control corrections that are in the same direction as the main control corrections are reduced by the first reduction ratio and then added to the main control corrections to generate the comprehensive control correction. The secondary control corrections that are in the opposite direction to the main control corrections are reduced by the second reduction ratio and then deducted from the main control correction to generate the comprehensive control correction. Environmental control instructions for each spatial location are generated based on the comprehensive control and correction amount. After generating environmental control instructions for each spatial point, the execution order of environmental control instructions for adjacent spatial points is cross-sorted to generate a global control instruction queue. The environmental control terminal executes the environmental control actions of each spatial point in the order arranged in the global control instruction queue.

8. A mushroom plant diameter online detection and environmental control system integrating machine vision, used to execute the method of any one of claims 1-7, characterized in that, include: Multiple image acquisition terminals are dispersed at different spatial locations in the mushroom growth area. The acquisition fields of adjacent spatial locations overlap. Each image acquisition terminal synchronously acquires multiple initial mushroom images and the corresponding initial environmental parameters at its own spatial location. The image processing module is connected to each image acquisition terminal, extracts the plant diameter features of each initial mushroom image corresponding to each spatial point, and outputs the geometric dimension data of each frame of the image corresponding to each spatial point. The image processing module also performs statistical analysis on the geometric dimensions of multiple mushroom cells within the same spatial location, generating a distribution range of plant diameter dimensions for each spatial location. The difference between the maximum and minimum values ​​of the plant diameter dimension distribution range determines the plant diameter dimension dispersion of each spatial location. When the plant diameter dimension dispersion is greater than a preset dispersion threshold, each spatial location is marked as a non-uniform dimension location. The effective number of subsequent plant diameter dimension data collected at each spatial location is adjusted based on the ratio of the plant diameter dimension dispersion to the preset dispersion threshold. The trend prediction module is connected to the image processing module. It performs time series fitting on the time series and geometric size data corresponding to multiple consecutive frames of images at each spatial point to obtain the plant diameter growth rate of each spatial point. Based on the plant diameter growth rate of each spatial point and the geometric size data of the latest frame, it extrapolates the predicted plant diameter value at the preset time point. The growth stage determination module, connected to the trend prediction module, compares the predicted plant diameter value of each spatial point with the preset plant diameter benchmark value to generate the predicted growth stage identifier corresponding to each spatial point. The growth stage determination module also performs spatial consistency verification on the predicted growth stage identifiers between adjacent spatial points. When the predicted growth stage identifiers of adjacent spatial points are inconsistent, the time-series change curves of the geometric dimension data of the two inconsistent spatial points are retrieved for secondary comparison. Based on the relative magnitude of the plant diameter growth rate in the secondary comparison result, the predicted growth stage identifier is re-determined. The strategy matching module is connected to the growth stage determination module. Based on the predicted growth stage identifier of each spatial point, it matches the corresponding environmental control strategy for each spatial point. The deviation comparison module is connected to each image acquisition terminal and the strategy matching module respectively. It compares the initial environmental parameters of each spatial point with the environmental control strategy matched for each spatial point and generates the control correction amount for each environmental dimension of each spatial point. The instruction generation module, connected to the deviation comparison module, generates environmental control instructions for each spatial point based on the control correction amount of each environmental dimension. Before the predicted plant diameter value of each spatial point reaches the preset plant diameter benchmark value, the instruction generation module sends instructions to the environmental control terminal of the corresponding spatial point. The instruction generation module also cross-sorts the execution order of environmental control instructions of adjacent spatial points to generate a global control instruction queue. Multiple environmental control terminals are deployed at various spatial locations. Each environmental control terminal is connected to the instruction generation module and receives and executes the environmental control instructions for the corresponding spatial location in the order arranged in the global control instruction queue. The feedback control module is connected to each image acquisition terminal and the image processing module. After the environmental control terminal at each spatial point performs the control action, it controls the corresponding image acquisition terminal to re-acquire the current mushroom image and transmit it to the image processing module. The image processing module is triggered in a loop to the instruction generation module until the geometric size data of each spatial point reaches the harvest threshold.

9. The system according to claim 8, characterized in that, The image processing module includes: The grayscale processing submodule performs grayscale processing on the initial mushroom images of each frame corresponding to each spatial point. The background segmentation submodule is connected to the grayscale processing submodule. It performs background segmentation on the grayscale mushroom image, removes non-mushroom areas, and outputs the mushroom target area image. The adhesion segmentation submodule is connected to the background segmentation submodule. It generates segmentation lines at the concave points of the connected components in the mushroom target region image, separating multiple adhered mushroom individuals into independent mushroom single images. The edge detection submodule, connected to the adhesion segmentation submodule, performs edge detection on the images of each individual mushroom and extracts the edge contour of the mushroom cap. The circle fitting submodule, connected to the edge detection submodule, performs circle fitting on the mushroom cap edge contour and outputs the center coordinates and the fitted diameter. The size determination submodule is connected to the circle fitting submodule and determines the geometric dimension data based on the fitted diameter.

10. The system according to claim 8, characterized in that, The environmental control terminal includes a temperature control device, a humidity control device, and a ventilation control device; each spatial location is also equipped with an independent environmental parameter verification sensor group, which includes a temperature verification sensor, a humidity verification sensor, and a carbon dioxide verification sensor. After the environmental control terminal performs the control action, the environmental parameter verification sensor group collects the environmental parameters of the corresponding spatial points after the control and transmits them to the deviation comparison module; The deviation comparison module performs a secondary comparison between the regulated environmental parameters and the corresponding spatial location matching environmental regulation strategy. When the regulated environmental parameters exceed the regulation range in the environmental regulation strategy, the deviation comparison module regenerates the supplementary regulation correction amount and transmits it to the instruction generation module. The instruction generation module generates supplementary environmental control instructions based on the supplementary control correction amount and drives the environmental control terminal to execute them until the environmental parameters fall into the control range of the environmental control strategy after control.

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