Intelligent edible mushroom planting system, control method and terminal

Abstract

The application provides an intelligent edible mushroom planting system, a control method and a terminal, wherein the system comprises: a sensor group module comprising various types of sensors for collecting environmental parameters of edible mushrooms at various growth stages; a central control module in communication connection with the sensor group module to receive the environmental parameters of edible mushrooms at various growth stages from various sensors; the central control module is provided with a growth model based on deep learning, and the growth model is used to generate optimal environmental parameters of edible mushrooms at various growth stages; and an environmental control module in communication connection with the central control module for receiving the optimal environmental parameters from the central control module to adjust the environmental parameters of edible mushrooms at various growth stages in real time. The application can automatically adjust and optimize the environmental conditions according to the growth requirements of different types of edible mushrooms at different growth stages.

Description

Technical Field

[0001] This application relates to the field of modern agricultural technology, and in particular to an intelligent edible fungus cultivation system, control method, and terminal. Background Technology

[0002] With the increasing global population and the rising demand for high-quality, healthy food, edible fungi, as an agricultural product rich in nutrients and possessing multiple health benefits, are gaining increasing market favor. Traditional edible fungi cultivation methods are mostly small-scale, manual operations, resulting in low production efficiency and imprecise environmental control, making it difficult to guarantee yield and quality. Furthermore, traditional cultivation methods are highly dependent on the environment and easily affected by seasonal changes and climatic conditions.

[0003] To meet the growing market demand, the factory-style, large-scale cultivation model of edible fungi is gradually becoming a trend. While the existing factory-style cultivation model has improved yield and quality to some extent, it is difficult to precisely control the growth environment parameters of edible fungi according to different growth stages, nor can it adaptively control the growth parameters required by different types of edible fungi.

[0004] Therefore, it is necessary to provide an intelligent edible fungus cultivation system, control method, and terminal to solve the aforementioned problems in the existing technology. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this application is to provide an intelligent edible fungus cultivation system, control method and terminal to solve the technical problem that the prior art is unable to intelligently adjust the required growth environment parameters according to different stages of edible fungus growth.

[0006] To achieve the above and other related objectives, the first aspect of this application provides an intelligent edible mushroom cultivation system, comprising:

[0007] The sensor module includes various types of sensors used to collect environmental parameters at different growth stages of edible fungi.

[0008] The central control module is communicatively connected to the sensor group module to receive environmental parameters of the edible fungi at each growth stage from each sensor; the central control module is equipped with a growth model based on deep learning, which is used to generate the optimal environmental parameters of the edible fungi at each growth stage.

[0009] An environmental control module is communicatively connected to the central control module and is used to receive the optimal environmental parameters from the central control module, so as to adjust the environmental parameters of edible fungi in real time at each growth stage.

[0010] In some embodiments of the first aspect of this application, a growth model construction module is also included, which is used to input environmental parameters of various growth stages of edible fungi collected by various types of sensors into a deep learning model for training, testing and verification, so as to construct the growth model.

[0011] In some embodiments of the first aspect of this application, the environmental control module includes: a temperature and humidity control unit for controlling the temperature and humidity parameters required for the growth of edible fungi; a light intensity control unit for controlling the light intensity parameters required for the growth of edible fungi; and a carbon dioxide concentration control unit for controlling the carbon dioxide concentration parameters required for the growth of edible fungi.

[0012] In some embodiments of the first aspect of this application, a data management module is also included, which is connected to the sensor group module and is used to store and analyze environmental parameters of edible fungi at various growth stages collected by the sensor group module.

[0013] In some embodiments of the first aspect of this application, a planting rack module is also included for providing a substrate for edible fungi cultivation. The planting rack module includes a hollow frame, a support frame, and a plurality of planting trays. The support frame is disposed in the frame, and the plurality of planting trays are sequentially disposed on the support frame.

[0014] In some embodiments of the first aspect of this application, a plurality of the planting rack modules are arranged sequentially along the height direction of the frame.

[0015] To achieve the above and other related objectives, a second aspect of this application provides an intelligent edible mushroom cultivation control method, applied to a central control module; the central control module is located in the aforementioned intelligent edible mushroom cultivation system; the method includes:

[0016] The sensor group module receives environmental parameter data collected from each sensor at each growth stage of edible fungi.

[0017] The environmental parameter data is input into a deep learning model for training, testing, and validation to obtain a growth model;

[0018] The growth model is used to output the optimal environmental parameters for the edible fungi at each growth stage.

[0019] In some embodiments of the second aspect of this application, the method further includes: increasing environmental parameter data for each growth stage of edible fungi by incremental data processing; using L1 or L2 regularization to limit the complexity of the deep learning model; and stopping model training when the performance of the validation set no longer improves during model training.

[0020] In some embodiments of the second aspect of this application, the method of increasing environmental parameter data for each growth stage of edible fungi by data increment includes any of the following methods: data augmentation based on interpretation methods, noise methods, or sampling methods to increment environmental parameter data for each growth stage of edible fungi; obtaining synthetic data using generative adversarial networks or diffusion models to increment environmental parameter data for each growth stage of edible fungi; or collecting environmental parameters under different environments by installing environmental sensors in different regions and under different climatic conditions to increment environmental parameter data for each growth stage of edible fungi.

[0021] To achieve the above and other related objectives, a third aspect of this application provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the method.

[0022] As described above, the intelligent edible fungus cultivation system, control method, and terminal of this application have the following beneficial effects:

[0023] Environmental parameters at different growth stages of edible fungi are collected by various types of sensors and transmitted to the central control module. The central control module, which is equipped with a growth model, outputs the optimal environmental parameters for edible fungi at different growth stages. The environmental control module adjusts the environmental parameters of edible fungi at different growth stages in real time according to the optimal environmental parameters. It can automatically adjust and optimize environmental conditions according to the growth requirements of different types of edible fungi at different growth stages, so as to achieve efficient and high-quality production of edible fungi. Attached Figure Description

[0024] Figure 1 The diagram shown is a schematic of an intelligent edible fungus cultivation system according to an embodiment of this application.

[0025] Figure 2 The diagram shown is a schematic representation of the operation of the environment and equipment monitoring module in one embodiment of this application.

[0026] Figure 3 The diagram shown is a schematic of a data management module and a remote monitoring system in one embodiment of this application.

[0027] Figure 4 The diagram shown is a schematic diagram of a planting rack module in one embodiment of this application.

[0028] Figure 5 The diagram shown is a schematic diagram of a multi-layer planting rack module in one embodiment of this application.

[0029] Figure 6 The diagram shown is a schematic representation of an automated logistics process in one embodiment of this application.

[0030] Figure 7The diagram shown is a schematic representation of the water and gas treatment process in one embodiment of this application.

[0031] Figure 8 The diagram shown is a flowchart of an intelligent edible fungus cultivation control method according to an embodiment of this application.

[0032] Figure 9 The diagram shown is a structural schematic of an electronic terminal according to an embodiment of this application. Detailed Implementation

[0033] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0034] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" do not necessarily imply that they are different.

[0035] It should be noted that, in the embodiments of this application, the words "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0036] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0037] Before providing a further detailed description of the present invention, the nouns and terms used in the embodiments of the present invention are explained, and the nouns and terms used in the embodiments of the present invention are subject to the following interpretations:

[0038] <1> L1 regularization: Regularization is achieved by adding the L1 norm of the weights (i.e., the sum of the absolute values ​​of the weight vectors) to the model's loss function. L1 regularization tends to produce a sparse weight matrix, pushing some weights towards zero, thereby achieving the effect of feature selection.

[0039] <2> L2 regularization: Regularization is achieved by adding the L2 norm of the weights (i.e., the sum of squares of the weight vector) to the model's loss function. L2 regularization makes the weight values ​​smaller, which can effectively control the complexity of the model, reduce the variance of the model parameters, and thus improve the stability of the model.

[0040] To facilitate understanding of the embodiments of this application, firstly, in conjunction with Figure 1 Detailed explanation. Figure 1 An intelligent edible fungus cultivation system according to an embodiment of the present invention is shown, comprising:

[0041] Sensor module 1 includes various types of sensors for collecting environmental parameters at different growth stages of edible fungi. Central control module 2 is communicatively connected to sensor module 1 to receive these environmental parameters from the sensors. Central control module 2 is equipped with a deep learning-based growth model, which generates optimal environmental parameters for the edible fungi at each growth stage. Environmental control module 3 is communicatively connected to central control module 2 to receive these optimal environmental parameters and adjust them in real time at each growth stage.

[0042] In edible mushroom cultivation, different species of edible mushrooms require different growing environments at different growth stages. The intelligent edible mushroom cultivation system provided in this application collects environmental parameters for various edible mushroom species at different growth stages through multiple types of sensors in sensor module 1; the collected environmental parameters are transmitted to central control module 2, which is equipped with a growth model; through processing and analysis by the growth model, the optimal environmental parameters for each growth stage of the edible mushrooms are generated; and environmental control module 3 adjusts the environmental parameters of the edible mushrooms in real time according to the received optimal environmental parameters, thereby achieving efficient production and quality assurance of edible mushrooms.

[0043] It should be understood that the optimal environmental parameters are one or more sets of environmental parameters that correspond to the good growth status of edible fungi at different growth stages.

[0044] In some embodiments of this application, the intelligent edible fungus cultivation system further includes a growth model construction module 21, which is used to input environmental parameters of various growth stages of edible fungi collected by various types of sensors into a deep learning model for training, testing and verification, so as to construct the growth model.

[0045] In some specific embodiments, various types of sensors include a temperature and humidity sensor 11, a light intensity sensor 12, and a carbon dioxide concentration sensor 13. The temperature and humidity sensor 11 is used to collect temperature and humidity data during the growth of edible fungi. The light intensity sensor 12 is used to collect light intensity data during the growth of edible fungi. The carbon dioxide concentration sensor 13 is used to collect carbon dioxide concentration data during the growth of edible fungi.

[0046] In some specific embodiments, the deep learning model used in this application is a backpropagation neural network model. It should be understood that a backpropagation neural network (BP) model is a multi-layer feedforward neural network trained by backpropagating errors. It consists of an input layer, one or more hidden layers, and an output layer, where the number of neurons in the hidden layers can be set as needed. The training process of a BP neural network includes two stages: forward propagation and backward propagation. In the forward propagation stage, input data is passed from the input layer, processed by weighted summation and activation functions at each layer, and then passed to the output layer to form the output. If there is an error between the output and the expected result, the backpropagation stage is required. Backpropagation is an optimization process aimed at adjusting the weights and bias parameters in the network to reduce the error between the output and the expected result. During this process, the error propagates forward layer by layer from the output layer, and the weights of each layer are adjusted according to the error. The adjustment of the weights relies on gradient descent, that is, adjusting the weights in the opposite direction of the gradient of the loss function to achieve the minimum value of the loss function. For ease of description, the backpropagation neural network model will be simplified to a BP model below.

[0047] Temperature and humidity data, light intensity data, and carbon dioxide concentration data collected by temperature and humidity sensor 11, light intensity sensor 12, and carbon dioxide concentration sensor 13, respectively, are preprocessed and then input into the BP model for training. The model parameters are optimized through multiple iterations. Independent test and validation sets are used to test and validate the model; the testing and validation procedures are based on existing technologies and will not be elaborated here. The trained growth model is deployed to the central control module 2. The growth model generates optimal environmental parameters for each growth stage of edible fungi. The environmental control module 3 adjusts temperature, humidity, and light intensity in real time based on these optimal environmental parameters, thereby providing the best growth environment for the edible fungi.

[0048] Because current production environmental parameter control is based on historical experience and lacks a single growth model for support—for example, some temperatures are ranges, and precise points are non-standard parameters influenced by various factors—introducing autonomous learning can process data in batches, narrowing down the ranges and establishing a large-scale growth model. Therefore, the growth model constructed by the growth model construction module 21 in this embodiment can continuously optimize environmental control strategies and production processes based on historical data and real-time feedback. That is, by analyzing production data from different batches, it can summarize the optimal environmental control parameters and cultivation strategies, thereby improving the yield and quality of edible fungi.

[0049] In some embodiments of this application, the intelligent edible fungus cultivation system further includes a generative adversarial network module 22, which is connected before the growth model construction module 21. This module generates unconventional growth parameter data during the growth stages of edible fungi, and this unconventional growth parameter data serves as part of the training set for the growth model construction module 21. In this embodiment, the generated unconventional data enables the trained growth model to better simulate the actual growth process of edible fungi.

[0050] It is worth noting that edible fungi may exhibit rare and unconventional growth parameters during their growth process. Due to their rarity and unconventionality, data collection can be challenging. These unconventional growth parameters include, but are not limited to:

[0051] (1) Abnormal temperature tolerance data refers to the fact that edible fungi can still grow outside their suitable growth temperature range due to genetic variation or changes in environmental adaptation; (2) Atypical growth rate data refers to the fact that the growth rate of edible fungi is much higher or lower than the normal level due to pathogen infection, over- or under-nutrition; (3) Morphological variation data refers to the fact that the morphological characteristics (such as cap shape, stipe length, etc.) of edible fungi are abnormal due to genetic factors, environmental stress or chemical effects; (4) Color change data refers to the fact that the color of edible fungi is significantly different from the color under normal growth conditions due to light conditions, nutrient deficiency or certain metabolic disorders; (5) Mycelial abnormality data refers to the fact that the mycelial growth of edible fungi is uneven, the branching is abnormal or the mycelium is abnormal due to unsuitable environmental conditions or infection. (6) Data on incomplete development of fruiting bodies, which refers to the incomplete development or deformity of the fruiting bodies (visible parts of edible fungi) due to environmental stress or genetic factors; (7) Data on early or late maturity, which refers to the significant advance or delay in the maturity period of edible fungi due to environmental conditions (such as temperature, humidity, etc.); (8) Data on abnormal disease resistance, which refers to the abnormal disease resistance of edible fungi due to genetic improvement, environmental adaptation or changes in pathogens; (9) Data on abnormal nutrient absorption, which refers to the abnormal absorption and utilization of nutrients by edible fungi due to changes in the composition of the culture medium or changes in the metabolic characteristics of the strain; (10) Data on abnormal metabolism, which refers to the abnormal metabolic products (such as enzymes, pigments, flavor compounds) of edible fungi due to changes in metabolic pathways or the influence of environmental factors.

[0052] Specifically, the process by which Generative Adversarial Networks (GANs) generate unconventional growth parameter data during the growth stages of edible fungi includes the following:

[0053] Step A: Based on experimental data, literature data, or historical data, collect and generate unconventional growth parameter data during the growth stages of edible fungi; and collect conventional growth parameter data of edible fungi under normal growth conditions; label the collected data to distinguish between conventional and unconventional growth parameters; and preprocess the collected data.

[0054] It should be understood that there are various ways to preprocess data, such as data cleaning (removing noise and outliers), data standardization to make it suitable for input into generative adversarial networks, and dividing the data into training, validation and test sets.

[0055] Step B: Input the preprocessed data into the generative adversarial network model for training; the generative adversarial network model includes a generator and a discriminator, the generator is used to generate unconventional growth parameter data, and the discriminator is used to distinguish between real data and generated data.

[0056] Specifically, the discriminator is trained using the training set data to recognize real data. The generator is then trained using the same training set data to produce data that can fool the discriminator. The generator and discriminator are trained alternately until the generator produces sufficiently realistic data that the discriminator cannot effectively distinguish between them. Furthermore, model training includes a model optimization process, which involves adjusting model parameters and structure, such as the learning rate and batch size, to improve the quality of the generated data; and evaluating model performance using validation set data to ensure that the generated data distribution is similar to that of real data.

[0057] Step C: Use the trained generative adversarial network to generate unconventional growth parameter data as part of the training set for the growth model.

[0058] Specifically, the quality and authenticity of the generated data are verified using test set data to ensure that the generated data is statistically similar to real unconventional growth parameter data. Furthermore, based on application results feedback, the generative adversarial network model is further adjusted and optimized; and new unconventional growth parameter data is continuously collected to update the training set, thereby improving the model's generalization ability.

[0059] In some embodiments of this application, the environmental control module 3 includes a temperature and humidity control unit 31, a light intensity control unit 32, and a carbon dioxide concentration control unit 33, which can automatically adjust and optimize environmental conditions according to the growth requirements of different edible fungi. The temperature and humidity control unit 31 controls the temperature and humidity parameters required for the growth of edible fungi. The light intensity control unit 32 controls the light intensity parameters required for the growth of edible fungi. The carbon dioxide concentration control unit 33 controls the carbon dioxide concentration parameters required for the growth of edible fungi. The environmental control module 3 can be equipped with or replaced with specific functional modules as needed, such as UV lamp disinfection and a CO2 compensation system, to adapt to special planting requirements.

[0060] It should be noted that the environmental control module 3 also includes control units for other environmental parameters required by edible fungi, which will not be described in detail here.

[0061] In some specific embodiments, such as Figure 2As shown, the intelligent edible mushroom cultivation system is also equipped with environmental and equipment monitoring modules. Environmental monitoring includes temperature and humidity monitoring, leakage monitoring, water level monitoring, noise monitoring, sulfur hexafluoride (SF6) gas monitoring, harmful gas monitoring, switch cabinet temperature monitoring, and wireless cable temperature monitoring. By monitoring these data, if any abnormalities are detected, the environmental control module 3 controls the operating status of equipment such as lighting, air conditioning, dehumidifiers, drainage pumps, fresh air units, fans, heaters, and rodent repellents to maintain a suitable growth environment for edible mushrooms in real time and ensure safety. Equipment status monitoring includes transformer over-temperature monitoring, switch cabinet contact temperature measurement, switch cabinet local monitoring, low-voltage feeder monitoring, and battery monitoring. By monitoring the status of the equipment, the operating status of the equipment can be monitored in real time to ensure safe operation. In addition, security and fire monitoring are also configured, including perimeter beam detectors, smoke detectors, intrusion alarms, open flame detectors, audible and visual alarms, fire monitoring, intelligent access control, and video surveillance to ensure system security. AI intelligent inspection is also configured to ensure the safety of the environment for edible mushroom growth.

[0062] In some embodiments of this application, the intelligent edible fungus cultivation system further includes a data management module 4. The data management module 4 is connected to the sensor group module 1 and is used to store and analyze environmental parameters collected by the sensor group module 1 at various growth stages of the edible fungus. Through data collection and analysis, the data management module 4 records, analyzes, and optimizes various data points during the cultivation process, providing data support for adjusting cultivation strategies.

[0063] In some embodiments of this application, the intelligent edible mushroom cultivation system further includes a communication module 6. The communication module 6 connects the intelligent edible mushroom cultivation system to an external management system, that is, it communicates with a cloud server, and the cloud server communicates with a client, supporting remote monitoring and operation, and realizing intelligent management of the entire process.

[0064] In some specific embodiments, such as Figure 3As shown, the data management module 4 and the monitoring system are used for real-time data processing and monitoring. Data from various sensors and control devices are uploaded to the cloud in real time via the communication module. Managers can view the operating status within the cultivation chamber at any time through the remote monitoring system and perform remote operations and adjustments as needed. Specifically, the real-time data collected by each sensor is preprocessed and then transmitted to the time window module to process time-related data. After processing, the data is transmitted to the rule executor via scene routing to execute the rules defined by the rule engine, outputting processed events. These events are then forwarded to the external management system via an external repeater or triggered periodically by the timed triggering module. Simultaneously, the monitoring system monitors the data, and if any abnormalities are detected, an alarm module is triggered. Other modules are also included, which will not be detailed here. This design enables the processing of large amounts of real-time data, supports complex event processing logic, and can be integrated with third-party services. Through real-time monitoring and rule configuration, it can respond quickly to various events, record and process environmental parameters for edible fungi, and continuously optimize cultivation strategies.

[0065] In some embodiments of this application, such as Figure 4 As shown, the intelligent edible mushroom cultivation system also includes a cultivation rack module 5, which provides a cultivation carrier for edible mushrooms. The cultivation rack module 5 is located inside the cultivation chamber. For example, the cultivation rack module 5 can be designed to be mobile, giving it both mobility and modularity, facilitating adaptive adjustments according to production needs. The cultivation rack module 5 includes a hollow frame 51, a support frame 52, and multiple cultivation trays 53. The support frame 52 is located within the frame 51, and the multiple cultivation trays 53 are sequentially arranged on the support frame 52. The cultivation trays 53 are used for cultivating edible mushrooms, and the specific number of cultivation trays 53 can be designed according to actual conditions.

[0066] In some embodiments of this application, such as Figure 5 As shown, multiple planting rack modules 5 are arranged sequentially along the height direction of the frame 51. Using multi-layer planting rack modules 5 stacked vertically allows for efficient use of vertical space and enables high-density planting. For example, six layers of planting rack modules 5 are stacked along the height direction of the frame 51. It should be noted that the height direction of the frame 51 is... Figure 5 The vertical height is shown.

[0067] In some embodiments of this application, the intelligent edible mushroom cultivation system also includes an automated logistics module. For example, an automated guided vehicle (AGV), conveyor belts, and other logistics systems are installed inside the cultivation warehouse to handle, sort, and release cultivation pallets. This system can dynamically adjust the transport routes and times of the pallets according to production plans and actual conditions. Furthermore, by optimizing logistics scheduling, it minimizes the waiting time and unnecessary movement of cultivation pallets, greatly improving production efficiency and reducing manual operations. Exemplarily, the automated logistics and warehousing process is as follows: Figure 6 As shown, the information management system, as the core of the entire process, includes a logistics management information system, a warehouse management system, a warehouse control system, and a sorting system. Orders and production information from the enterprise resource planning system or production management system are sent to the logistics management information system. The logistics management information system is responsible for decomposing and integrating logistics information before sending it to the warehouse management system, and can also provide feedback on logistics information. The warehouse management system transmits inbound and outbound information to the warehouse control system and sorting information to the sorting system. The warehouse control system transmits both inbound / outbound information and sorting information from the sorting system to the electrical control system, thereby controlling the warehouse equipment, handling and transportation equipment, and sorting and picking equipment in the logistics equipment to perform storage, handling, and sorting tasks for various materials, realizing the transportation of various materials to the production line or inbound / outbound processes. This achieves an automated and information-based logistics and warehousing process.

[0068] In some embodiments of this application, the intelligent edible mushroom cultivation system further includes a resource recycling module. The resource recycling module integrates a water resource recycling system and a waste treatment system, enabling the recycling of water resources and the resource-based treatment of waste mushroom residue, thereby reducing environmental impact. Exemplarily, the process for treating water and gas resources is as follows: Figure 7 As shown, gases generated during the production process, such as NH3, are collected using a suction hood and transported through an intake pipe to a primary spray tower. Water is sprayed onto the exhaust gas, causing water-soluble components to settle. The remaining gas is then transported through an intake pipe to a secondary spray tower, where water is sprayed onto the exhaust gas again, causing water-soluble components to settle. The remaining gas is then transported through an intake pipe to a demister to remove mist. Finally, the remaining gas is discharged through a chimney, completing the gas treatment process. Water generated in each process is treated through a water circulation system, and the return pipe transports the water. Water containing NH3 is reused in a soaking tank, ensuring no water is discharged externally. Rainwater is treated using a separate pipe network connected to the municipal stormwater drainage system.

[0069] In some embodiments of this application, the intelligent edible mushroom cultivation system also includes an energy management module. The energy management module is responsible for monitoring and optimizing energy consumption within the cultivation chamber, and is equipped with renewable energy devices such as solar power to improve energy efficiency and reduce production costs. For example, solar photovoltaic panels are installed on the roof of the cultivation chamber, and energy storage facilities are configured to store excess energy during off-peak hours for use during peak hours.

[0070] The intelligent edible mushroom cultivation system provided in this application adopts a modular design, which can flexibly adjust the configuration of each functional module according to the growth requirements of different edible mushroom varieties at various growth stages. It is highly adaptable and easy to maintain and upgrade. The following will provide specific examples of adjusting the configuration of each functional module to adapt to different types of edible mushrooms.

[0071] Example 1: Intelligent edible mushroom cultivation system for button mushrooms.

[0072] Button mushrooms are one of the most in-demand edible mushroom varieties. To meet the demands of large-scale production, a multi-layered planting rack module is used. The environmental control module can precisely adjust parameters such as temperature, humidity, light, and CO2 concentration to adapt to the different growth stages of button mushrooms. The central control module monitors various environmental data in real time through the data management module and dynamically adjusts the planting environment according to the growth curve of the button mushrooms. The automated logistics module is responsible for automatically transporting the harvested button mushroom trays to the warehouse for preliminary processing and packaging. For example, the environmental parameters for each growth stage of Agaricus bisporus are as follows: (1) Mycelial growth stage: the temperature is controlled at 22-24℃; the substrate humidity is maintained at about 65% and the relative humidity of the air is maintained at about 65%; no light is required at this stage, and it can be carried out in the dark; the carbon dioxide concentration has little effect on mycelial growth and can be maintained at the normal atmospheric level; (2) Primordial formation stage: the temperature is maintained at 15-18℃; the air humidity is controlled at about 90%; the light intensity is controlled at 500-1000 lux; the carbon dioxide concentration is controlled at 0.1%-0.5%; (3) Fruiting body growth stage: the temperature is controlled at 15℃-16℃; the relative humidity of the air should be maintained at 90%-95% to maintain the moisture of the fruiting body; the light intensity is 2000-3000 lux to promote the formation of the cap and the synthesis of pigments; the CO2 concentration should be controlled at 0.1%-0.3% to ensure the normal development of the fruiting body.

[0073] Example 2: Intelligent edible fungi cultivation system for Ganoderma lucidum.

[0074] As a precious medicinal fungus, Ganoderma lucidum has stricter production requirements. The system adopts a closed high-rise planting rack module and a highly precise environmental control module, which can finely regulate the temperature, humidity, light intensity, CO2 concentration and other factors in the planting environment. It is also equipped with a spectrometer to detect the growth status and effective ingredient content of Ganoderma lucidum. The automated logistics module ensures that the planting, harvesting and transportation of Ganoderma lucidum are fully automated, avoiding pollution and damage caused by human factors. For example, the environmental parameters of Ganoderma lucidum at each stage are as follows: (1) Mycelial growth stage: temperature is controlled between 24℃ and 28℃; relative humidity should be maintained between 70% and 80%; CO2 concentration is controlled between 0.03% and 0.05%; (2) Fruiting body stage: temperature is controlled between 20℃ and 25℃; relative humidity needs to be increased to 85% to 95%; light intensity is between 500 and 1000 lux; CO2 concentration is appropriately increased, but usually not exceeding 0.1%.

[0075] Example 3: Intelligent edible fungus cultivation system for Agaricus blazei.

[0076] Agaricus blazei is a high-value edible fungus, and its production has stringent environmental requirements. The system employs a specialized air filtration system and humidity control unit to ensure a pure growing environment and precise humidity control. The planting rack modules within the cultivation chamber can adjust the position and height of the trays according to the Agaricus blazei's growth cycle, ensuring that each layer of Agaricus blazei receives optimal growing conditions. After harvesting, the logistics system automatically transports the trays to the initial processing area, minimizing human contact and maintaining the quality of the Agaricus blazei. For example, the environmental parameters for each stage of Agaricus blazei are as follows: (1) Mycelial growth stage: the temperature is controlled at 22℃ to 25℃; the humidity of the culture medium should be maintained at about 65%, and the relative humidity of the air should be maintained at 70%-80%; no light is required at this stage, and it can be carried out in the dark; the CO2 concentration should be controlled at 0.03% to 0.05%; (2) Fruiting body stage: the temperature is controlled at 18℃ to 22℃; the relative humidity of the air needs to be increased to 85% to 95%; the light intensity range is 500 to 1000 lux; the CO2 concentration should be maintained at 0.05% to 0.1%, and ventilation should be strengthened.

[0077] Example 4: Intelligent edible fungi cultivation system for shiitake mushrooms.

[0078] The cultivation of shiitake mushrooms requires specific temperature, humidity, and light conditions. The system adopts a multi-layer planting rack module design, which can achieve high-density planting in a limited space. The environmental control module automatically adjusts the planting environment by monitoring the temperature, humidity, and light intensity in real time to ensure the optimal growth state of shiitake mushrooms. The data management module records and analyzes the production data of each batch to help managers optimize production plans and increase yield. For example, the environmental parameters of shiitake mushrooms at each stage are as follows: (1) Mycelial growth stage: temperature is controlled at 22℃ to 25℃; relative humidity is maintained at 70% to 80%; no light is required at this stage, and it can be carried out in the dark; CO2 concentration should be controlled at 0.03% to 0.05%; (2) Fruiting body stage: temperature is controlled at 12℃ to 18℃; relative humidity should be maintained at 85% to 95%; light intensity is controlled at 2000 to 3000 lux; CO2 concentration should be maintained at 0.05% to 0.1%, and ventilation should be strengthened.

[0079] This application also provides an intelligent method for controlling edible fungi cultivation, the flowchart of which is shown below. Figure 8 As shown, the method is applied to a central control module; the central control module is located in the intelligent edible fungus cultivation system described above; the method includes:

[0080] Step S81: Receive environmental parameter data of each growth stage of edible fungi from each sensor in the sensor group module;

[0081] Step S82: Input the environmental parameter data into the deep learning model for training, testing, and validation to obtain the growth model;

[0082] Step S83: Use the growth model to output the optimal environmental parameters for the edible fungi at each growth stage.

[0083] It should be noted that the intelligent edible fungus cultivation control method in this embodiment can achieve the functions of the above-mentioned intelligent edible fungus cultivation system, which will not be elaborated here.

[0084] In some embodiments of this application, the method further includes: increasing environmental parameter data for each growth stage of edible fungi by incremental data processing; using L1 or L2 regularization to limit the complexity of the deep learning model; and stopping model training when the performance of the validation set no longer improves during model training.

[0085] Because the growth process of crops and their industrial applications differ, the growth data of edible fungi is highly complex and variable, leading to a shortage of data. Therefore, when machine learning models are applied to the field of edible fungi growth, the models learn noise and details from the training data but fail to capture the general patterns, resulting in overfitting and underfitting. To overcome these issues, this embodiment employs an incremental data approach to increase the amount of training data, reducing the model's sensitivity to noise. L1 or L2 regularization (see the glossary above) is used to limit the model's complexity, preventing overfitting and improving its generalization ability. An early stopping strategy is employed to stop training when the model stops improving, preventing overfitting.

[0086] In some embodiments of this application, the method of increasing environmental parameter data for each growth stage of edible fungi by data increment includes any of the following methods: data augmentation based on interpretation methods, noise methods, or sampling methods to increment environmental parameter data for each growth stage of edible fungi; obtaining synthetic data using generative adversarial networks or diffusion models to increment environmental parameter data for each growth stage of edible fungi; or collecting environmental parameters under different environments by installing environmental sensors in different regions and under different climatic conditions to increment environmental parameter data for each growth stage of edible fungi.

[0087] It should be understood that the interpretation method refers to using the interpretation of the original data as augmented data, such as replacing the environmental parameter data during the growth of edible fungi with synonyms to generate new data points; the noise method refers to adding noise to the original data while ensuring the validity of the results to improve the robustness of the model, such as adding some random noise to the environmental parameter data of edible fungi growth to simulate the impact of environmental changes; the sampling method refers to sampling data based on the distribution of the original data as augmented data, such as generating new growth cycle data through statistical methods based on existing growth cycle data in the growth of edible fungi.

[0088] It should also be understood that Generative Adversarial Networks (GANs) consist of a generator and a discriminator. The generator is responsible for generating data, while the discriminator is responsible for distinguishing between generated and real data. During training, the generator and discriminator compete with each other. The generator tries to generate increasingly realistic data, while the discriminator tries to distinguish between real and fake data more accurately. In this way, synthetic data with a distribution similar to real data can be generated, which can be used to generate environmental parameter data for the incremental growth stage of edible fungi. Diffusion models generate data by gradually adding noise to the data and then learning how to reverse this process. This can be used to generate synthetic environmental parameter data for the growth of edible fungi.

[0089] Figure 9This is a schematic block diagram of the electronic terminal provided in an embodiment of this application. Figure 9 As shown, the electronic terminal 900 includes at least one processor 901, a memory 902, at least one network interface 903, and a user interface 905. The various components in the electronic terminal 900 are coupled together via a bus system 904. It is understood that the bus system 904 is used to implement communication between these components. In addition to a data bus, the bus system 904 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 9 The general will label all buses as bus systems.

[0090] The user interface 905 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0091] It is understood that memory 902 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0092] In this embodiment of the invention, the memory 902 is used to store various types of data to support the operation of the electronic terminal 900. Examples of this data include: any executable program for operation on the electronic terminal 900, such as the operating system 9021 and application program 9022; the operating system 9021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 9022 may contain various applications, such as a media player, browser, etc., for implementing various application services. The intelligent edible fungus cultivation control method provided in this embodiment of the invention can be included in the application program 9022.

[0093] The methods disclosed in the above embodiments of the present invention can be applied to or implemented by processor 901. Processor 901 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 901 or by instructions in software form. The processor 901 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 901 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 901 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0094] In an exemplary embodiment, the electronic terminal 900 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0095] 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 technical scope 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.

[0096] In summary, existing factory-style cultivation models often employ a single-layer rack structure, resulting in limited planting density and low space utilization. Furthermore, the environmental control system lacks intelligence, making it difficult to precisely adjust temperature, humidity, light, and CO2 concentration parameters according to different growth stages of edible fungi. Simultaneously, existing management systems largely rely on manual operation, exhibiting low automation levels and hindering refined management. Therefore, this invention provides an intelligent edible fungi cultivation system, control method, and terminal. During operation, a central control module sets a production plan, determining key elements such as the cultivation cycle and environmental parameters, outputting optimal growth parameters for the edible fungi. The environmental control module automatically adjusts environmental conditions such as temperature, humidity, light, and CO2 concentration according to the set growth stages to meet the growth needs of the edible fungi. Simultaneously, during cultivation, the planting rack module stacks and moves planting trays according to a preset arrangement to maximize space utilization. The automated logistics module is responsible for transporting the planting trays… The system employs a multi-layered architecture and modular design, combining advanced environmental control technology and an automated management system. This results in automated environmental control, intelligent production processes, and highly efficient resource utilization. The system enables high-density, high-efficiency cultivation of various edible fungi under large-scale production conditions. It can monitor and manage environmental parameters in real time, optimize water resource recycling, and treat waste. The communication module uploads collected environmental and production data to the data management module for storage and analysis. Managers can monitor production status and adjust parameters in real time via remote terminals. The energy management module optimizes energy allocation based on actual electricity consumption, ensuring stable system operation. Therefore, this application effectively overcomes the shortcomings of existing technologies and possesses high industrial application value.

[0097] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. An intelligent edible mushroom cultivation system, characterized in that, include: The sensor module includes various types of sensors used to collect environmental parameters at different growth stages of edible fungi. A growth model construction module is used to input environmental parameters collected by various types of sensors at different growth stages of edible fungi into a deep learning model for training, testing, and validation to construct a growth model. The module increases the environmental parameter data at each growth stage of edible fungi through data increment methods, including any of the following: data augmentation based on interpretation, noise, or sampling methods to increment the environmental parameter data at each growth stage; using generative adversarial networks or diffusion models to obtain synthetic data to increment the environmental parameter data at each growth stage; collecting environmental parameters under different environments by installing environmental sensors in different regions and climates to increment the environmental parameter data at each growth stage; using L1 or L2 regularization to limit the complexity of the deep learning model; stopping model training when the performance on the validation set no longer improves during training; the deep learning model is a backpropagation neural network model. A generative adversarial network (GAN) module, connected before the growth model construction module, is used to generate unconventional growth parameter data during the growth stages of edible fungi. This unconventional growth parameter data includes: abnormal temperature tolerance data, atypical growth rate data, morphological variation data, color change data, mycelial abnormality data, incomplete fruiting body development data, early or late maturity data, abnormal disease resistance data, abnormal nutrient absorption data, and abnormal metabolism data. The process by which the GAN module generates this unconventional growth parameter data includes: collecting data based on experimental data, literature data, or historical data. The process involves generating unconventional growth parameter data during the growth stages of edible fungi; collecting conventional growth parameter data of edible fungi under normal growth conditions; labeling the collected data to distinguish between conventional and unconventional growth parameters; preprocessing the collected data; and inputting the preprocessed data into a generative adversarial network (GAN) model for training. The GAN model includes a generator and a discriminator, where the generator generates unconventional growth parameter data, and the discriminator distinguishes between real and generated data. The trained GAN is then used to generate unconventional growth parameter data as part of the training set for the growth model. The central control module is communicatively connected to the sensor group module to receive environmental parameters of the edible fungi at each growth stage from each sensor; the central control module is equipped with a growth model based on deep learning, which is used to generate the optimal environmental parameters of the edible fungi at each growth stage. An environmental control module, which is communicatively connected to the central control module, is used to receive the optimal environmental parameters from the central control module and adjust the environmental parameters of edible fungi at each growth stage in real time. The planting rack module is used to provide a carrier for the cultivation of edible fungi.

2. The intelligent edible fungus cultivation system according to claim 1, characterized in that, The environmental control module includes: Temperature and humidity control unit, used to control the temperature and humidity parameters required for the growth of edible fungi; The light intensity control unit is used to control the light intensity parameters required for the growth of edible fungi. The carbon dioxide concentration control unit is used to control the carbon dioxide concentration parameter required for the growth of edible fungi.

3. The intelligent edible fungus cultivation system according to claim 1, characterized in that, It also includes a data management module, which is connected to the sensor group module and is used to store and analyze environmental parameters of edible fungi at each growth stage collected by the sensor group module.

4. The intelligent edible fungus cultivation system according to claim 1, characterized in that, The planting rack module includes a hollow frame, a support frame, and multiple planting trays. The support frame is disposed in the frame, and the multiple planting trays are sequentially disposed on the support frame.

5. The intelligent edible fungus cultivation system according to claim 4, characterized in that, Multiple planting rack modules are arranged sequentially along the height direction of the frame.

6. An intelligent method for controlling edible mushroom cultivation, characterized in that, The method is applied to a central control module; the central control module is located in the intelligent edible fungus cultivation system as described in claim 1; the method includes: The sensor group module receives environmental parameter data collected from each sensor at each growth stage of edible fungi. The environmental parameter data is input into a deep learning model for training, testing, and validation to obtain a growth model. The environmental parameter data for each growth stage of edible fungi is increased using incremental data methods, including any of the following: data augmentation based on interpretation, noise, or sampling methods to increase the environmental parameter data for each growth stage; using generative adversarial networks or diffusion models to obtain synthetic data to increase the environmental parameter data for each growth stage; collecting environmental parameters under different environments by installing environmental sensors in different regions and climates to increase the environmental parameter data for each growth stage; using L1 or L2 regularization to limit the complexity of the deep learning model; and stopping model training when the performance on the validation set no longer improves during training. The growth model is used to output the optimal environmental parameters for the edible fungi at each growth stage.

7. An electronic terminal, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method of claim 6.

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