Intelligent temperature control method for edible mushroom cultivation growth room

Abstract

The invention belongs to the technical field of temperature control, and particularly relates to an intelligent temperature control method for an edible mushroom cultivation growth room, which comprises the following steps of: judging temperature abnormity by combining a comprehensive evaluation function of an instantaneous overrun proportion and a standard deviation, and determining a regulation priority according to the temperature abnormity, so that the conversion from average temperature control to abnormal region priority regulation is realized; a shelf two-dimensional temperature distribution diagram is constructed, gradient calculation is carried out, a high-temperature area and the orientation thereof are accurately positioned, the deflection direction and angle of a guide plate are directly controlled through coordinate transformation and nonlinear mapping, cold air flow preferentially flows to the high-temperature area, the problem of uneven horizontal and vertical temperature caused by a three-dimensional cultivation shelf is effectively solved, and the quality of the cultivation shelf is improved. Temperature regulation and control effect verification is intelligently fused with the mycelial growth speed conformity and the fruiting uniformity of the edible mushrooms, the growth conformity is quantified through image analysis, graded early warning is performed based on a secondary verification result, and a complete intelligent closed-loop control system is formed.

Description

Technical Field

[0001] This invention belongs to the field of temperature control technology, specifically a method for intelligent temperature control in edible fungi cultivation and growth chambers. Background Technology

[0002] Factory cultivation of edible fungi is an important part of modern agricultural production. The uniformity and stability of the temperature in the growth environment are key factors that determine yield and quality.

[0003] Currently, most edible mushroom cultivation and growth rooms adopt three-dimensional layered cultivation racks to improve space utilization. However, the layered structure can easily lead to uneven temperature distribution in different levels and areas. Traditional temperature control methods mainly rely on the readings of a limited number of temperature sensors in the growth room and use PID algorithms to regulate the total output power of the air conditioning unit.

[0004] For example, a temperature control system and method for edible fungi fruiting by combining time information, temperature information and real-time growth status information in Chinese patent publication number CN115968718A can rationally control the fruiting temperature of edible fungi.

[0005] While the above-mentioned solutions offer more precise temperature control, they still have the following limitations: 1. For modern mushroom houses using three-dimensional tiered cultivation racks, due to the rising of hot air, the sinking of cold air, and the shading effect of the racks, there are complex temperature gradients at different vertical heights and different positions on the same horizontal level. Control strategies based on point or regional average temperature are difficult to detect and eliminate this three-dimensional temperature unevenness, leading to local temperature anomalies and affecting the uniformity of edible fungi growth.

[0006] 2. Existing methods typically only use the temperature setpoint as the control target, lacking the ability to accurately locate and prioritize abnormal temperature areas, and are unable to dynamically adjust the air supply strategy based on the two-dimensional distribution characteristics of the temperature field. This results in blind distribution of cooling airflow, low control efficiency, and high energy consumption.

[0007] 3. Traditional control methods form an open-loop system, lacking effective verification of the control effect. After the temperature returns to normal, whether the agronomic goal of promoting uniform mycelial growth and uniform fruiting has truly been achieved still relies on human experience for judgment. It is impossible to achieve reverse verification and closed-loop optimization based on intelligent analysis of growth images, making it difficult to detect and warn of latent growth anomalies in a timely manner.

[0008] Therefore, there is an urgent need for an intelligent temperature control method that can achieve precise three-dimensional layered temperature monitoring and anomaly diagnosis, directional flow control based on spatial temperature field analysis, and quantitative evaluation and early warning of edible fungi growth compliance by integrating image processing technology. Summary of the Invention

[0009] To overcome the shortcomings of the prior art, this invention provides an intelligent temperature control method for edible fungi cultivation and growth chambers, which can effectively solve the problems mentioned in the prior art.

[0010] The objective of this invention can be achieved through the following technical solution: an intelligent temperature control method for edible fungi cultivation and growth chamber, comprising: acquiring temperature data of each layer of a three-dimensional layered cultivation rack and images of edible fungi growth.

[0011] Analyze whether the temperature of each cultivation rack is abnormal, screen out the corresponding racks with abnormal temperatures, and determine the priority of temperature control.

[0012] Analyze the temperature differences in the vertical and horizontal directions and the front and back directions of the shelf corresponding to the temperature anomaly, and adjust the angle of the guide vane at the cold air inlet according to the preset temperature adjustment rules based on the temperature differences.

[0013] After temperature control is completed, the set temperature is maintained for a preset period. Within the preset period, images of edible fungi growth in each cultivation unit of the corresponding shelf are collected. From left to right, the mycelial growth rate and uniformity of mycelial emergence in each cultivation unit are analyzed based on the images. The growth compliance of edible fungi is calculated based on the mycelial growth rate and uniformity of mycelial emergence.

[0014] The cultivation units whose edible fungi growth compliance did not meet the preset compliance were screened out and their corresponding positions were recorded. The image acquisition device was then triggered to move to the corresponding position to perform secondary image acquisition and recalculate the edible fungi growth compliance.

[0015] If the recalculated growth compliance of edible fungi does not reach the preset compliance level, an early warning operation will be triggered.

[0016] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention constructs a two-dimensional temperature distribution map of the shelf and performs gradient calculation to accurately locate the high temperature area and its direction. Through coordinate transformation and nonlinear mapping, the deflection direction and angle of the guide plate are directly controlled so that the cold airflow flows preferentially to the high temperature area, which effectively solves the problem of uneven horizontal and vertical temperature caused by the three-dimensional cultivation rack and significantly improves the temperature uniformity.

[0017] (2) The present invention combines the comprehensive evaluation function of instantaneous over-limit ratio and standard deviation to determine whether the temperature is abnormal, and determines the control priority accordingly. This realizes the transformation from average temperature control to priority control of abnormal areas, avoids blind allocation of cooling capacity, improves control efficiency, and reduces energy consumption.

[0018] (3) This invention intelligently integrates the verification of temperature control effect with the conformity of mycelial growth rate and uniformity of mycelial emergence of edible fungi, quantifies the conformity of growth through image analysis, and performs graded early warning based on the secondary verification results, forming a complete intelligent closed-loop control system that can detect latent growth abnormalities in a timely manner, ensuring yield and quality. Attached Figure Description

[0019] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the overall implementation steps of the method of the present invention.

[0021] Figure 2 This is a schematic diagram illustrating the process of analyzing whether the temperature of each layer of the cultivation rack is abnormal according to the present invention.

[0022] Figure 3 This is a flowchart illustrating the early warning operation of the present invention. Detailed Implementation

[0023] 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.

[0024] Please see Figure 1 As shown, the present invention provides an intelligent temperature control method for edible fungi cultivation and growth chamber, including: acquiring temperature data of each layer of a three-dimensional layered cultivation rack and images of edible fungi growth.

[0025] Analyze whether the temperature of each cultivation rack is abnormal, screen out the corresponding racks with abnormal temperatures, and determine the priority of temperature control.

[0026] Analyze the temperature differences in the vertical and horizontal directions and the front and back directions of the shelf corresponding to the temperature anomaly, and adjust the angle of the guide vane at the cold air inlet according to the preset temperature adjustment rules based on the temperature differences.

[0027] After temperature control is completed, the set temperature is maintained for a preset period. Within the preset period, images of edible fungi growth in each cultivation unit of the corresponding shelf are collected. From left to right, the mycelial growth rate and uniformity of mycelial emergence in each cultivation unit are analyzed based on the images. The growth compliance of edible fungi is calculated based on the mycelial growth rate and uniformity of mycelial emergence.

[0028] The cultivation units whose edible fungi growth compliance did not meet the preset compliance were screened out and their corresponding positions were recorded. The image acquisition device was then triggered to move to the corresponding position to perform secondary image acquisition and recalculate the edible fungi growth compliance.

[0029] If the recalculated growth compliance of edible fungi does not reach the preset compliance level, an early warning operation will be triggered.

[0030] This invention achieves a shift from average temperature control to priority control of abnormal areas by prioritizing temperature regulation. It intelligently integrates the verification of temperature regulation effect with the conformity of mycelial growth rate and uniformity of mycelial emergence of edible fungi. By quantifying the conformity of growth and conducting graded early warning based on the secondary verification results, a complete intelligent closed-loop control system is formed, which can promptly detect hidden growth abnormalities and ensure yield and quality.

[0031] In a preferred embodiment of the present invention, a temperature sensor is provided at each of the four corners of the upper and lower surfaces of each layer of the three-dimensional layered cultivation rack, and the temperature sensor collects the temperature data of the corresponding position in real time at a preset sampling frequency.

[0032] Each shelf is equipped with a reciprocating image acquisition device along the horizontal direction to acquire images of the edible fungi growth in each cultivation unit.

[0033] It should be noted that the temperature distribution within the shelf space of a three-dimensional tiered cultivation rack is easily affected by factors such as interlayer obstruction and airflow. Temperatures may vary in different locations. By installing temperature sensors at the four corners of the upper and lower ends of the rack, temperature data can be collected from different directions. Compared to single-point sampling, this method can capture temperature information within the rack more comprehensively, reduce monitoring distortion caused by local temperature deviations, and provide more reliable basic data for subsequent temperature anomaly analysis.

[0034] The three-dimensional tiered cultivation rack contains multiple cultivation units. The image acquisition device, which slides back and forth in the horizontal direction, can traverse all cultivation units on the rack, avoiding the omission of edge cultivation units due to fixed position acquisition. This ensures that complete growth images of each cultivation unit are obtained, providing a reliable basis for comprehensively evaluating the growth status of each cultivation unit. When cultivation units that do not meet the preset compliance are selected, the device can be precisely moved to the corresponding position for secondary image acquisition, reducing image acquisition errors through repeated verification.

[0035] Please see Figure 2 As shown, in a preferred embodiment of the present invention, the specific method for analyzing whether the temperature of each layer of the cultivation rack is abnormal includes: acquiring all temperature sampling data of the target layer rack within a preset time window to form a temperature time series data sequence of the layer rack.

[0036] It should be noted that the preset time window was obtained through experimental calibration. Specifically, multiple sets of temperature fluctuation feature datasets of the target strain at each cultivation stage were collected. Each dataset contained temperature data from multiple repeated cultivation cycles, with a sampling frequency of once every 10 minutes. Environmental variables such as humidity and light were kept consistent. During the mycelial growth stage, time windows of different lengths were used as algorithm inputs to monitor the accuracy of temperature anomaly identification within each window. Using manually marked temperature anomaly periods as a benchmark, when the overlap rate between the anomaly periods identified by the algorithm and the benchmark was ≥90% under a certain time window, the window was deemed valid. Finally, the window length that showed the highest stability and computational efficiency on most datasets was selected from all valid windows and determined as the preset time window.

[0037] The temperature time series data sequence is filtered and denoised, and the smoothed temperature change curve is calculated using the moving average method.

[0038] Calculate the average temperature value of the smoothed temperature change curve, and use this average temperature value as a reference to calculate the instantaneous temperature shift amplitude at each sampling point.

[0039] The number of sampling points whose instantaneous offset exceeds a preset amplitude threshold within the time window is counted, and the proportion of these points to the total number of sampling points is calculated and recorded as the instantaneous over-limit ratio.

[0040] It should be noted that the instantaneous offset amplitude reflects the degree of deviation of the temperature of a single sampling point from the average temperature, while the proportion of sampling points exceeding the preset amplitude threshold can quantify the frequency of short-term drastic temperature fluctuations within the preset time window. The higher the instantaneous over-limit ratio, the worse the short-term temperature stability. This instantaneous over-limit ratio, as one of the core inputs of the comprehensive evaluation function, complements the standard deviation, which reflects the overall dispersion of temperature. A single instantaneous offset amplitude can only reflect the anomaly of an individual sampling point, while its proportion can reflect the universality of the anomaly. This provides a quantitative basis for the high-frequency short-term fluctuation dimension for comprehensively judging whether the shelf temperature is abnormal, avoiding misjudgment due to isolated anomalies.

[0041] The preset amplitude threshold is the critical temperature range at which short-term temperature deviations significantly and adversely affect mycelial activity and fruiting body morphology of the target fungal species under suitable growth temperature, as determined by experiments. For example, when the target fungal species is enoki mushroom, short-term temperature deviations of 0.4℃, 0.6℃, and 0.8℃ are applied to enoki mushroom at a set temperature of 20℃ and last for 1 hour. Multiple sets of repeated experiments show that when the temperature deviation is 0.8℃, the daily growth rate of mycelium decreases by 25% from the standard rate. Therefore, the preset amplitude threshold can be set to 0.8℃.

[0042] Calculate the standard deviation of the temperature data within the time window, establish a comprehensive evaluation function with the instantaneous exceedance ratio and standard deviation as input, and output a comprehensive anomaly index value.

[0043] It should be noted that the comprehensive evaluation function is constructed using a weighted linear combination. The instantaneous exceedance ratio reflects the frequency of short-term temperature anomalies, while the standard deviation reflects the overall temperature dispersion. These two indicators characterize temperature anomalies from different dimensions, and the weighted combination avoids the bias of a single indicator. The weighting coefficients for the instantaneous exceedance ratio and standard deviation can be set according to the sensitivity of the edible fungi variety to short-term temperature fluctuations and long-term temperature stability. This sensitivity setting is further validated through experimental calibration, specifically by collecting data on the daily mycelial growth rate and uniformity of colony emergence for multiple groups of target fungi strains under short-term temperature fluctuations and long-term temperature dispersion. The decay rate is determined by the fact that when the decay rate caused by short-term fluctuations is greater than the decay rate caused by long-term dispersion, it is considered to be sensitive to short-term sudden changes. Conversely, it is considered to have higher requirements for long-term stability. For example, if enoki mushrooms are sensitive to short-term sudden temperature changes, the weighting coefficient of the instantaneous out-of-limit ratio is larger than the weighting coefficient of the standard deviation. For example, the weighting coefficient of the instantaneous out-of-limit ratio is 0.6 and the weighting coefficient of the standard deviation is 0.4. Oyster mushrooms have higher requirements for long-term stability, and the weighting coefficient of the standard deviation is larger than the weighting coefficient of the instantaneous out-of-limit ratio. For example, the weighting coefficient of the instantaneous out-of-limit ratio is 0.3 and the weighting coefficient of the standard deviation is 0.7.

[0044] The comprehensive abnormality index value is compared with a preset threshold. If it exceeds the preset threshold, the shelf temperature is determined to be abnormal.

[0045] It should be noted that the preset threshold is derived by combining the biological characteristics of edible fungi cultivation, historical temperature data, and actual control needs. Specifically, the temperature fluctuation limit of the target fungus at the suitable growth temperature is determined by experiment. That is, when the degree of temperature abnormality exceeds a certain value, it will significantly affect the mycelial growth rate or the uniformity of mycelial emergence. The significant impact is defined as when the mycelial growth rate decreases by ≥20% or the dispersion coefficient of mycelial emergence uniformity increases by ≥30%. This serves as the biological basis for setting the threshold. Multiple sets of shelf temperature data within the normal cultivation cycle are collected, and the distribution range of the comprehensive abnormal index value is calculated. The maximum comprehensive abnormal index value is used as the preset threshold.

[0046] A comprehensive evaluation function combining instantaneous over-limit ratio and standard deviation is used to determine temperature anomalies and determine control priorities accordingly. This realizes the transformation from average temperature control to priority control of abnormal areas, avoids blind allocation of cooling capacity, improves control efficiency, and reduces energy consumption.

[0047] All shelves identified as having abnormal temperatures were screened out and sorted according to their comprehensive abnormality index values ​​to determine the priority of temperature control.

[0048] In a preferred embodiment of the present invention, the method for determining the temperature control priority includes: sorting all the selected temperature abnormality shelves in descending order according to their comprehensive temperature abnormality index values ​​to generate a first priority sequence.

[0049] In the first priority sequence, if there are shelves with the same comprehensive abnormal index value, then the shelves are sorted again according to their height, and the shelves with higher height in the three-dimensional layered cultivation rack are given priority in regulation.

[0050] It should be noted that the comprehensive temperature anomaly index value is the core quantitative indicator reflecting the severity of temperature anomalies on the shelves. The higher the value, the more significant the temperature deviation from the normal state, and the greater the potential impact on the growth of edible fungi. Therefore, it should be prioritized for regulation. In three-dimensional tiered cultivation racks, shelves of different heights are affected differently by environmental factors such as airflow and heat distribution. Generally, higher shelves may have a higher risk of temperature anomaly spread due to weaker airflow circulation, which may indirectly affect the temperature stability of lower shelves. Therefore, when the comprehensive anomaly index value is the same, prioritizing the regulation of higher shelves can reduce the chain reaction of anomalies and meet the regulation requirements of three-dimensional cultivation environment.

[0051] In a preferred embodiment of the present invention, the preset temperature adjustment rule includes: obtaining the real-time temperature values ​​of monitoring points in the vertical and horizontal directions and the front and back directions within the plane of the temperature-abnormal shelf, and constructing a two-dimensional temperature distribution map of the shelf.

[0052] Gradient calculation is performed on the two-dimensional temperature distribution map to obtain the maximum direction and maximum amplitude of the temperature spatial change rate. The maximum direction indicates the direction of the high-temperature region within the shelf plane.

[0053] The maximum direction is transformed from the shelf coordinate system to the guide vane deflection coordinate system, and the target guide vane deflection direction used to eliminate the temperature unevenness is calculated.

[0054] It should be noted that the shelf coordinate system is based on the shelf plane, and the horizontal front-back direction can be set as the X-axis, the vertical up-down direction as the Y-axis, and the origin is a fixed vertex of the shelf, such as the lower left corner, which is used to quantify the direction of the maximum temperature gradient within the shelf plane.

[0055] The deflection coordinate system of the guide vane is based on the cold air inlet. The initial vertical state of the guide vane is set to 0°, the left deflection angle is a positive value and the right deflection angle is a negative value, which is used to define the mechanical movement direction of the guide vane.

[0056] The direction of maximum temperature gradient in the shelf coordinate system is converted to the corresponding angle in the guide vane coordinate system using a geometric transformation matrix. The transformation relationship between the shelf coordinate system and the guide vane deflection coordinate system is determined based on the relative position of the cold air inlet and the shelf. The angle from the origin to the direction of maximum gradient in the shelf coordinate system is defined as follows: The deflection angle of the deflector target is The conversion relationship is as follows: ,in, This is the scaling factor. The specific value of the offset compensation angle is determined through simulation experiments of the relative installation position of the cold air inlet and the shelf, as well as the airflow field.

[0057] The simulation experiment specifically involves: simulating the diffusion path, temperature field changes, and interaction with high-temperature regions of the cold airflow within the shelf plane, considering different relative installation positions of the cold air inlet and the shelf. Using ANSYS Fluent software, a three-dimensional model of the shelf space is constructed, setting parameters such as cold air velocity, initial temperature, and the location of the high-temperature region. Numerical calculations simulate the airflow distribution under different deflection angles of the guide vanes, and iterative tests are conducted to determine the optimal airflow path. Value and The corresponding deflector action is monitored to see if the cold airflow can accurately cover the high-temperature area within the shelf, ultimately selecting the optimal method that reduces the temperature gradient to the target range. Value and value.

[0058] For example, when the cold air inlet is directly facing the center of the shelf, , ,like ,but .

[0059] The shelf coordinate system is used to accurately describe the temperature distribution characteristics within the shelf plane, while the deflection of the baffle depends on its own mechanical coordinate system. The two belong to different spatial dimensions. Through coordinate system transformation, the deflection direction of the baffle is made to strictly correspond to the spatial position of the high-temperature area within the shelf, avoiding the flow of cold air to meaningless areas, ensuring that the cooling capacity is accurately applied to the temperature anomaly points, and quickly balancing the shelf temperature.

[0060] The maximum amplitude of the temperature space change rate is input into a predefined nonlinear function, which defines a mapping relationship between the gradient amplitude and the deflection angle of the guide vane, and its output is the target deflection angle of the guide vane.

[0061] The predefined nonlinear function was obtained through experimental calibration. Specifically, for the target strain cultivation rack structure and cold air input system, under different maximum temperature spatial change rates, the effect of different deflection angles of the guide vanes on improving temperature unevenness was tested. The optimal deflection angle that reduced the temperature gradient amplitude to the target range was recorded. For example, the test range of the guide vane deflection angle was -40° to +40°, with each 5° interval. Each set of parameters was tested three times. After removing outliers exceeding ±10% of the average value, the average was taken, and the minimum deflection angle that reduced the temperature gradient amplitude to the target range was recorded as the optimal angle.

[0062] Using the maximum rate of change of temperature space in the experiment as input and the corresponding optimal deflection angle as output, a mapping relationship between the two is established through polynomial fitting to form a predefined nonlinear function. For example, when the gradient amplitude is small, the function outputs a small deflection angle, and when the gradient amplitude increases significantly, the function outputs an angle with a faster growth rate to enhance the cold air guiding effect.

[0063] The maximum amplitude of the temperature spatial change rate directly reflects the severity of temperature unevenness, and the corresponding optimal deflection angle is the key parameter for eliminating this unevenness. The deflection angle output by the function has been experimentally verified to effectively guide the cold airflow to the high temperature region, weaken the temperature gradient by enhancing the local cooling supply, and ultimately achieve uniform temperature distribution.

[0064] Based on the target deflection direction and target deflection angle, a deflector control command is generated to drive the deflector to perform corresponding actions so that the cold airflow preferentially flows to the high temperature area.

[0065] It should be noted that the target deflection direction clarifies the spatial orientation of the high-temperature area to which the cold airflow needs to be directed, while the target deflection angle quantifies the specific magnitude of the deflection required by the guide plate. The combination of the two can precisely control the flow direction and intensity of the cold airflow, directly addressing the issue of uneven temperature within the shelves. By generating control commands that integrate the target deflection direction and angle, the guide plate can precisely guide the cold airflow directly to the high-temperature area, avoiding the waste of cold energy in non-high-temperature areas, quickly balancing the shelf temperature, shortening the duration of temperature anomalies, and reducing adverse effects on the growth of edible fungi.

[0066] By constructing a two-dimensional temperature distribution map of the shelves and performing gradient calculations, the high-temperature areas and their directions can be accurately located. Through coordinate transformation and nonlinear mapping, the deflection direction and angle of the guide plates can be directly controlled, so that the cold airflow preferentially flows to the high-temperature area. This effectively solves the problem of uneven horizontal and vertical temperatures caused by the three-dimensional cultivation rack and significantly improves temperature uniformity.

[0067] In a preferred embodiment of the present invention, the mycelial growth rate conformity includes: acquiring multiple edible fungi growth images of the same cultivation unit collected in chronological order to form an image time sequence of the cultivation unit.

[0068] Each image in the image time sequence is preprocessed, including grayscale conversion, noise reduction, and image enhancement, to improve the image's feature recognition.

[0069] Edge detection is used to accurately extract the hyphal region from the preprocessed image, distinguishing hyphae from non-target regions, and obtaining a binary contour image of the hyphal region.

[0070] Feature reference points are selected in the binarized contour image. By comparing images at different time points using image registration technology, the displacement distance of the feature reference points is calculated. Combined with the acquisition time interval, the growth rate of mycelium is obtained.

[0071] It should be noted that the feature reference point is a stable and recognizable point in the binary contour image of the mycelial region, such as the inflection point of the mycelial edge. Through the feature registration algorithm, images of the same cultivation unit collected at different time points are aligned to eliminate background offset interference caused by slight displacement or vibration of the equipment during the image acquisition process.

[0072] In the image after interference is eliminated, the pixel coordinates of the same feature reference point are obtained in the images at two different time points. The pixel displacement distance of the point is calculated using the Euclidean distance formula. Then, combined with the physical scale of the image, the pixel distance is converted into the actual physical displacement distance. The Euclidean distance formula is an existing calculation formula and will not be elaborated here.

[0073] Dividing the calculated actual physical displacement distance of the feature reference point by the time interval between the two image acquisitions yields the mycelial growth rate. For example, if the displacement distance of a certain feature reference point within 24 hours is... The mycelial growth rate is .

[0074] By selecting stable feature points and combining them with image registration technology, the spatial position changes of mycelia in the time dimension can be accurately captured, eliminating the interference of non-growth factors. The growth rate can be calculated by correlating the displacement distance with the time interval, directly quantifying the dynamic growth state of mycelia. This provides an objective basis for subsequent comparison with standard growth rates and calculation of the growth rate conformity, ensuring that the assessment of the growth status of edible fungi is accurate and reliable.

[0075] The actual measured mycelial growth rate is compared with the standard growth rate of the edible fungus variety at a set temperature, and the growth rate conformity is calculated using a preset formula.

[0076] It should be noted that the preset formula is: ,in For growth rate compliance, This represents the actual growth rate of the mycelium. The formula is the standard growth rate at a set temperature. It is directly related to the actual growth rate of the mycelium and the standard growth rate at the set temperature. The core logic is that the closer the growth rate is to the standard growth rate at the set temperature, the higher the degree of conformity.

[0077] The standard growth rate at the set temperature is obtained by referring to the growth rate data of the target strain published in academic research literature and industry technical specifications at the set temperature, combined with small-scale cultivation experiments under ideal conditions, comparing and calibrating the measured rate with the literature values, and finally obtaining the standard growth rate at the set temperature.

[0078] In a preferred embodiment of the present invention, the uniformity of mushroom growth includes: acquiring multiple images of edible fungi growth from the same cultivation unit in chronological order, with the acquisition range covering the entire cultivation unit to avoid missing edge areas.

[0079] The collected images of edible fungi growth were processed by denoising, background separation, and image enhancement.

[0080] A pre-trained target detection neural network model is used to identify the processed edible fungus growth image, locate each sub-entity in the image, and generate its minimum bounding rectangle bounding box.

[0081] It should be noted that the target detection neural network model adopts the YOLOv5 model. The processed edible fungus growth image is input into the YOLOv5 model. Through network structures such as convolutional layers and pooling layers, the feature information of sub-entities, such as edges, textures, and shapes, is extracted from the input image layer by layer, and high-order features that can represent sub-entities are gradually abstracted.

[0082] Based on the extracted features, the model outputs the regions where sub-entities may exist as candidate boxes through the prediction layer, and calculates the confidence score of each candidate box belonging to the sub-entity category. At the same time, the coordinates of the candidate boxes are optimized through the bounding box regression algorithm to make them more accurately surround the sub-entities.

[0083] Candidate boxes with high confidence are selected based on the confidence threshold, while low-confidence candidate boxes that are false positives are removed. For each selected sub-entity, the model generates the smallest bounding rectangle that can completely enclose the sub-entity. The bounding box is output in the form of coordinates, so as to achieve accurate positioning of each sub-entity.

[0084] An instance segmentation algorithm is used to segment the pixel region of each sub-entity from the processed edible fungus growth image.

[0085] For each detected sub-entity, its morphological features are calculated, including: calculating its pixel area, calculating the aspect ratio of its minimum bounding rectangle, fitting the minimum circumcircle of its cap, and calculating its diameter.

[0086] It should be noted that the pixel area mentioned is a pixel-level mask of the sub-entity output by the instance segmentation algorithm. The pixel area of ​​the sub-entity is obtained by directly counting the total number of pixels within the mask. Then, combined with the physical scale of the image, the pixel area is converted into the actual physical area.

[0087] The minimum circumcircle diameter of the cap is obtained by first extracting the cap's outline through edge detection, and then fitting the cap outline using the minimum circumcircle algorithm. This diameter can be converted into the actual physical length through the conversion relationship between pixels and actual scale. Using the minimum circumcircle algorithm to fit the cap outline can more stably and accurately reflect the actual size of the cap compared to directly using the side length of the minimum circumcircle rectangle, avoiding measurement deviations caused by random cap orientation, thus making the evaluation results of the uniformity of the mushroom growth more objective and reliable.

[0088] Calculate the statistical dispersion of all fruiting bodies in terms of the above morphological features, and obtain the area dispersion coefficient, aspect ratio dispersion coefficient and cap diameter dispersion coefficient, respectively.

[0089] It should be noted that the coefficient of variation is a standardized metric that measures the degree of dispersion of data. The formula for calculating the coefficient of variation is as follows: ,in For discrete coefficients, The standard deviation of the morphological feature data. If the average value of the morphological characteristic data is taken as the average value, then the area dispersion coefficient, aspect ratio dispersion coefficient, and cap diameter dispersion coefficient mentioned above can all be calculated using the dispersion coefficient calculation formula.

[0090] For example, if five fruiting bodies are detected in a cultivation unit, with cap diameters of 5.0 cm, 5.2 cm, 4.7 cm, 5.3 cm, and 4.8 cm respectively, the average value can be calculated. Standard deviation The coefficient of variation of cap diameter , The smaller the value, the higher the neatness.

[0091] The coefficient of variation eliminates the influence of the dimensions and magnitude of the feature data itself, and can objectively reflect the consistency of the morphological characteristics of the same batch of fruiting bodies. For example, the smaller the area coefficient of variation, the more uniform the size of the fruiting bodies. If the aspect ratio coefficient of variation is too large, it indicates that the morphological differences of the fruiting bodies are significant, and there may be problems such as uneven growth environment. These coefficients provide a quantitative basis for assessing the uniformity of the growth of edible fungi populations and are important indicators for subsequent comprehensive judgment of growth status.

[0092] To calculate the spatial distribution uniformity of all child entities in the cultivation unit image, the image is divided into several grids. The number of child entities in each grid is counted, and the standard deviation of the number of child entities in all grids is calculated. This standard deviation is used to characterize the spatial distribution uniformity.

[0093] The area dispersion coefficient, aspect ratio dispersion coefficient, cap diameter dispersion coefficient, and spatial distribution standard deviation were normalized.

[0094] It should be noted that the normalization process uses linear normalization, mapping the calculated area dispersion coefficient, aspect ratio dispersion coefficient, cap diameter dispersion coefficient, and spatial distribution standard deviation to the 0-1 interval. The specific calculation formula is as follows: ,in: The eigenvalues ​​of the normalized result, These are the original eigenvalues ​​to be normalized. To be the minimum value, Maximum value.

[0095] The minimum and maximum values ​​can be determined based on the normal range of the target strain and the same growth stage in historical cultivation data. The original value characteristic value is the dispersion coefficient of the morphological characteristics calculated above.

[0096] The four normalized feature values ​​are combined into a comprehensive score using a weighted fusion algorithm. This comprehensive score is the uniformity of bacterial growth.

[0097] It should be noted that the weighted fusion algorithm refers to merging multiple normalized data... A comprehensive scoring algorithm is obtained by linearly weighting and summing the results according to preset weights. The preset weights can be determined based on historical data and expert experience. Specifically, multiple sets of cultivation data are collected, including the original data of the above four feature values ​​in normal batches and their corresponding final cultivation quality scores. Experts, based on experience, combine the above four feature values ​​in pairs to compare and judge the importance of each feature to uniformity, construct a judgment matrix, and calculate a set of subjective weights. Based on the original data of the four feature values, the information entropy of each feature value is calculated, and an objective weight is calculated according to its degree of variation. The subjective weights and objective weights are calculated using a linear combination method to obtain the final weights. The final weights are applied to new cultivation batches for verification and fine-tuning, and finally fixed as the preset weights used by the system.

[0098] Different features have different weights in terms of their impact on neatness, and the weighting method can flexibly reflect these differences.

[0099] The core of mycelial uniformity is the consistency of morphological characteristics and spatial distribution of fruiting bodies in the same batch. The more uniform the morphological characteristics and the more even the distribution, the higher the uniformity. The smaller the coefficient of variation, the smaller the differences in the area, shape and other dimensions of the fruiting bodies. The smaller the standard deviation of spatial distribution, the more even the distribution of the fruiting bodies in the growth area. The weighted and integrated comprehensive score integrates these scattered consistency indicators into a single value. The smaller the value, the higher the overall uniformity, and vice versa. Therefore, it can directly characterize mycelial uniformity.

[0100] In a preferred embodiment of the present invention, the calculation of the edible fungus growth conformity based on the conformity of mycelial growth rate and the uniformity of mycelial emergence includes: normalizing the calculated conformity of mycelial growth rate and the uniformity of mycelial emergence to make them fall within the same numerical dimension range, and assigning weight values ​​to them respectively.

[0101] The weighted geometric mean algorithm is used to integrate the normalized mycelial growth rate conformity, mycelial uniformity, and their corresponding weight values ​​to calculate the final edible fungus growth conformity.

[0102] It should be noted that the conformity of mycelial growth rate and the uniformity of mycelial emergence are the core indicators for measuring the growth status of edible fungi. The two together determine the overall growth conformity. The conformity of mycelial growth rate is the foundation, while the uniformity of mycelial emergence is a direct reflection of the growth quality. Both need to be evaluated comprehensively.

[0103] The original values ​​of the calculated mycelial growth rate conformity and mycelial emergence uniformity are processed using the same linear normalization method as the above morphological characteristics to ensure they are within the same numerical dimension range, and weight values ​​are assigned to them respectively. The weight values ​​can be determined based on historical data and expert experience, and their determination logic is consistent with the method for determining the internal weight of mycelial emergence uniformity.

[0104] The weighted geometric mean algorithm is used to fuse the normalized mycelial growth rate conformity and mycelial emergence uniformity with their corresponding weight values. The formula for calculating the edible fungus growth conformity is as follows: ,in , For weight values, To achieve uniformity of bacterial growth after normalization, To assess the conformity of mycelial growth rate after normalization, the geometric mean algorithm better reflects the correlation between indicators than the simple arithmetic mean, and is more robust to extreme values, making the final growth conformity result more reliable. By integrating two key indicators, the algorithm avoids the one-sidedness of single indicator evaluation and more comprehensively reflects the growth status of edible fungi.

[0105] In a preferred embodiment of the present invention, gradient calculation is performed on the two-dimensional temperature distribution map to obtain the maximum direction and maximum amplitude of the temperature spatial change rate, including: discretizing the shelf plane into a plane composed of... A two-dimensional temperature matrix composed of grid points.

[0106] Calculate the temperature of each grid point in the two-dimensional temperature matrix. direction and Temperature gradient component in the direction.

[0107] Based on the gradient components, calculate the gradient magnitude and gradient direction for each grid point.

[0108] Iterate through the gradient magnitudes of all grid points to find the maximum gradient magnitude and the coordinates of the grid point where it is located.

[0109] Extract the gradient direction corresponding to the coordinate point, and use it as the direction of the maximum rate of change of the temperature space.

[0110] It should be noted that the temperature distribution on the shelf plane is a continuous spatial field, but in actual measurement and calculation, it is impossible to obtain the temperature of every point. It is necessary to discretize the continuous space into a set of finite grid points, i.e., a two-dimensional temperature matrix. This process is consistent with the computer's sampling and storage logic for spatial data, and it is convenient to approximate the overall temperature distribution through the temperature values ​​of discrete points.

[0111] Gradient is a mathematical concept describing the local rate of change of a multivariate function. In two-dimensional space, the temperature gradient... direction and The directions reflect the temperature changes in the horizontal and vertical directions, respectively. The central difference method is used to calculate the gradient components. The gradient magnitude is obtained by taking the square root of the sum of the squares of the two gradient components. The calculation of the gradient magnitude is based on the mathematical definition of the magnitude and direction of the vector, which can quantify the strength and direction of temperature changes. By traversing all grid points and filtering the maximum gradient magnitude and the grid point coordinates where it is located, the gradient direction corresponding to the coordinate point is extracted. This can accurately pinpoint the direction of the fastest temperature rise in the region. This direction directly reflects the dominant distribution trend of temperature spatial differences, providing a clear direction for analyzing the influencing factors such as the location of heat sources and ventilation paths in the environment.

[0112] Please see Figure 3 As shown, in a preferred embodiment of the present invention, the triggering of the early warning operation includes: comparing the recalculated edible fungus growth compliance with a preset compliance threshold.

[0113] If the recalculated edible fungus growth compliance is much lower than the preset compliance threshold, an advanced warning operation is triggered.

[0114] If the recalculated edible fungus growth compliance is close to but does not reach the preset compliance threshold, a low-level warning operation is triggered.

[0115] The early warning operation includes an early warning report containing the location number of the abnormal cultivation unit, image data, and the calculation results of the growth compliance.

[0116] The warning report is sent to the remote monitoring terminal.

[0117] It should be noted that the preset compliance threshold is determined through historical data experiments. Specifically, multiple sets of growth compliance samples of known target bacterial species at different growth stages are collected, the optimal growth range that can guarantee their normal growth and development is statistically determined, and the minimum compliance value corresponding to the growth state within this range is used as the preset compliance threshold.

[0118] The specific method for determining that the growth compliance rate of edible fungi is far less than the preset compliance threshold is as follows: If the recalculated compliance value is significantly lower than the preset threshold and exceeds the lower limit that edible fungi can tolerate under normal growth conditions, the lower limit is set based on experimental data. For example, the lower limit is 70% lower than the preset threshold. For instance, when the target fungus is enoki mushroom and the preset compliance threshold is 80 points, simulated growth states with compliance rates of 75, 65, and 55 points are applied to enoki mushrooms respectively. Each group is repeated 3 times and monitored continuously for 3 days. The results show that when the compliance rate is 55 points, the mycelial growth stagnation rate reaches 30% and the fruiting body deformity rate rises to 40%, which is significantly lower than the normal growth state. Therefore, a compliance rate of 55 points or below is far less than the preset threshold, which means that the current environmental conditions have significantly deviated from the suitable range and may lead to serious consequences such as growth stagnation, severe quality decline, or even death. At this time, it is determined to be far less than triggering a high-level warning for emergency intervention.

[0119] The method for determining whether the growth compliance of edible fungi is close to but does not reach the preset compliance threshold is as follows: the recalculated compliance value is close to but does not reach the preset compliance threshold, but the compliance value is within the critical range of the preset threshold. The critical range is set based on experimental data, that is, by measuring the degree of influence of the target fungus on mycelial activity and fruiting body development when the growth compliance is near the threshold under suitable growth conditions, the range of values ​​that will not cause serious growth problems but have potential risks is determined.

[0120] The critical range is set based on experimental data. An exemplary critical range is 80%-99% of the preset threshold. When the target fungus is *Flammulina velutipes*, and the preset compliance threshold is 80 points, simulated growth states with compliance scores of 78, 70, and 65 points are applied to *Flammulina velutipes*. Each group is repeated 5 times, and monitoring is conducted continuously for 2 days. The results show that when the compliance score is 78 points, the mycelial growth rate decreases by 5% compared to the normal state of 80 points, and the uniformity of fruiting bodies decreases slightly, but the deformity rate remains below 8%. When the compliance score is 70 points, the growth rate decreases by 10%, and the deformity rate increases to 12%. When the compliance score is 65 points, the growth rate decreases by 15%, and the deformity rate is 15%. No growth stagnation or severe quality decline occurs, but there are significant differences compared to the normal state. Therefore, a growth compliance score of 65-78 points, i.e., 81.25%-97.5% of the preset threshold critical range, can be determined as a critical range close to but not reaching the preset compliance threshold.

[0121] By intelligently integrating the verification of temperature control effects with the conformity of mycelial growth rate and uniformity of mushroom emergence, the growth conformity is quantified through image analysis, and graded early warnings are issued based on the secondary verification results, forming a complete intelligent closed-loop control system that can promptly detect hidden growth anomalies and ensure yield and quality.

[0122] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0123] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0124] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0125] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0126] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0127] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent temperature control in an edible fungus cultivation and growth chamber, characterized in that, include: Acquire temperature data for each layer of the three-dimensional tiered cultivation rack and images of edible fungi growth; Analyze whether the temperature of each cultivation rack is abnormal, screen out the corresponding racks with abnormal temperatures, and determine the priority of temperature control. Analyze the temperature differences in the vertical and horizontal directions and the front and back directions of the shelf corresponding to the temperature anomaly, and adjust the angle of the guide vane at the cold air inlet according to the preset temperature adjustment rules based on the temperature differences. After the temperature is controlled, the set temperature is maintained for a preset period. Within the preset period, the growth images of edible fungi in each cultivation unit of the corresponding shelf are collected. From left to right, the mycelial growth rate conformity and the uniformity of mycelial emergence of edible fungi in each cultivation unit are analyzed based on the images. The growth conformity of edible fungi is calculated based on the mycelial growth rate conformity and the uniformity of mycelial emergence. The cultivation units whose edible fungi growth compliance did not reach the preset compliance were screened out and their corresponding positions were recorded. The image acquisition device was then triggered to move to the corresponding position to perform secondary image acquisition and recalculate the edible fungi growth compliance. If the recalculated growth compliance of edible fungi does not reach the preset compliance level, an early warning operation will be triggered.

2. The intelligent temperature control method for an edible fungus cultivation and growth chamber according to claim 1, characterized in that: Temperature sensors are installed at the four corners of the upper and lower surfaces of each layer of the three-dimensional tiered cultivation rack. The temperature sensors collect temperature data at the corresponding positions in real time at a preset sampling frequency. Each shelf is equipped with a reciprocating image acquisition device along the horizontal direction to acquire images of the edible fungi growth in each cultivation unit.

3. The intelligent temperature control method for an edible fungus cultivation and growth chamber according to claim 1, characterized in that: The specific methods for analyzing whether the temperature of each layer of the cultivation rack is abnormal include: Acquire all temperature sampling data of the target shelf within a preset time window to form a temperature time series data sequence of the shelf. The temperature time series data sequence is filtered and denoised, and the smoothed temperature change curve is calculated using the moving average method; Calculate the average temperature value of the smoothed temperature change curve, and use this average temperature value as a reference to calculate the instantaneous temperature shift amplitude at each sampling point; The number of sampling points whose instantaneous offset amplitude exceeds the preset amplitude threshold within the time window is counted, and the proportion of the instantaneous offset amplitude to the total number of sampling points is calculated and recorded as the instantaneous over-limit ratio. Calculate the standard deviation of the temperature data within the time window, establish a comprehensive evaluation function with the instantaneous exceedance ratio and standard deviation as input, and output a comprehensive anomaly index value; The comprehensive abnormality index value is compared with a preset threshold. If it exceeds the preset threshold, the shelf temperature is determined to be abnormal. All shelves identified as having abnormal temperatures were screened out and sorted according to their comprehensive abnormality index values ​​to determine the priority of temperature control.

4. The intelligent temperature control method for an edible fungus cultivation and growth chamber according to claim 3, characterized in that: The methods for determining the temperature control priority include: All the selected temperature anomaly shelves are sorted in descending order according to their comprehensive temperature anomaly index values ​​to generate a first priority sequence. In the first priority sequence, if there are shelves with the same comprehensive abnormal index value, then the shelves are sorted again according to their height, and the shelves with higher height in the three-dimensional layered cultivation rack are given priority in regulation.

5. The intelligent temperature control method for an edible fungus cultivation and growth chamber according to claim 1, characterized in that: The preset temperature adjustment rules include: Obtain real-time temperature values ​​at monitoring points in the vertical and horizontal directions and the front and back directions within the plane of the temperature-abnormal shelf, and construct a two-dimensional temperature distribution map of the shelf. Gradient calculation is performed on the two-dimensional temperature distribution map to obtain the maximum direction and maximum amplitude of the temperature spatial change rate. The maximum direction indicates the direction of the high-temperature region within the shelf plane. The maximum direction is transformed from the shelf coordinate system to the guide vane deflection coordinate system, and the target guide vane deflection direction used to eliminate the temperature unevenness is calculated. The maximum amplitude of the temperature space change rate is input into a predefined nonlinear function, which defines a mapping relationship between the gradient amplitude and the deflection angle of the guide vane, and its output is the target deflection angle of the guide vane. Based on the target deflection direction and target deflection angle, a deflector control command is generated to drive the deflector to perform corresponding actions so that the cold airflow preferentially flows to the high temperature area.

6. The intelligent temperature control method for an edible fungus cultivation and growth chamber according to claim 1, characterized in that: The conformity of the mycelial growth rate includes: Multiple images of edible fungi growth from the same cultivation unit, collected in chronological order, are acquired to form a temporal sequence of images for that cultivation unit. Each image in the image time sequence is preprocessed, including grayscale conversion, noise reduction, and image enhancement, to improve the feature recognition of the image; Edge detection is used to accurately extract the hyphal region from the preprocessed image, distinguishing hyphae from non-target regions, and obtaining a binary contour image of the hyphal region; Feature reference points are selected in the binarized contour image. Images at different time points are compared using image registration technology to calculate the displacement distance of the feature reference points. Combined with the acquisition time interval, the growth rate of mycelium is obtained. The actual measured mycelial growth rate is compared with the standard growth rate of the edible fungus variety at a set temperature, and the growth rate conformity is calculated using a preset formula.

7. The intelligent temperature control method for an edible fungus cultivation and growth chamber according to claim 6, characterized in that: The uniformity of bacterial growth includes: Acquire multiple images of edible fungi growth from the same cultivation unit in chronological order. The acquisition range should cover the entire cultivation unit to avoid missing edge areas. A pre-trained target detection neural network model is used to identify the processed edible fungus growth image, locate each sub-entity in the image, and generate its minimum bounding rectangle bounding box. An instance segmentation algorithm is used to segment the pixel region of each sub-entity from the processed edible fungus growth image; For each detected sub-entity, its morphological features are calculated, including: calculating its pixel area, calculating the aspect ratio of its minimum bounding rectangle, fitting the minimum bounding circle of its cap and calculating its diameter. Calculate the statistical dispersion of all fruiting bodies on the above morphological features to obtain the area dispersion coefficient, aspect ratio dispersion coefficient and cap diameter dispersion coefficient, respectively. To calculate the spatial distribution uniformity of all child entities in the cultivation unit image, the image is divided into several grids, the number of child entities in each grid is counted, and the standard deviation of the number of child entities in all grids is calculated. This standard deviation is used to characterize the spatial distribution uniformity. The area dispersion coefficient, aspect ratio dispersion coefficient, cap diameter dispersion coefficient, and spatial distribution standard deviation were normalized. The four normalized feature values ​​are combined into a comprehensive score using a weighted fusion algorithm. This comprehensive score is the uniformity of bacterial growth.

8. The intelligent temperature control method for an edible fungus cultivation and growth chamber according to claim 7, characterized in that: The calculation of edible fungus growth conformity based on mycelial growth rate conformity and mycelial uniformity includes: The calculated mycelial growth rate conformity and mycelial emergence uniformity were normalized to ensure they were within the same numerical dimension, and weight values ​​were assigned to them respectively. The weighted geometric mean algorithm is used to integrate the normalized mycelial growth rate conformity, mycelial uniformity, and their corresponding weight values ​​to calculate the final edible fungus growth conformity.

9. The intelligent temperature control method for an edible fungus cultivation and growth chamber according to claim 5, characterized in that: Gradient calculation is performed on the two-dimensional temperature distribution map to obtain the maximum direction and maximum amplitude of the temperature spatial change rate, including: Discretize the shelf plane into... A two-dimensional temperature matrix composed of grid points; Calculate the temperature of each grid point in the two-dimensional temperature matrix. direction and Temperature gradient component in the direction; Based on the gradient components, calculate the gradient magnitude and gradient direction for each grid point; Iterate through the gradient magnitudes of all grid points to find the maximum gradient magnitude and the coordinates of the grid point where it is located. Extract the gradient direction corresponding to the coordinate point, and use it as the direction of the maximum rate of change of the temperature space.

10. The intelligent temperature control method for an edible fungus cultivation and growth chamber according to claim 1, characterized in that: The triggering of the early warning operation includes: The recalculated edible fungus growth compliance is compared with a preset compliance threshold. If the recalculated compliance value is lower than the preset compliance threshold and exceeds the lower limit that edible fungi can tolerate for normal growth, an advanced warning operation will be triggered. If the recalculated edible fungus growth compliance does not reach the preset compliance threshold, but the compliance value is within the critical range of the preset compliance threshold, a low-level warning operation is triggered. The early warning operation includes an early warning report containing the location number of the abnormal cultivation unit, image data, and growth compliance calculation results; The warning report is sent to the remote monitoring terminal.

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