CN115127617A - Intelligent management and control system for edible fungus bionic planting - Google Patents
Intelligent management and control system for edible fungus bionic planting Download PDFInfo
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Abstract
The invention discloses an intelligent management and control system for edible fungus bionic planting, relates to the technical field of edible fungus bionic planting management and control, and solves the technical problem that an abnormal condition in a hypha culture process cannot be found accurately and timely by manpower; the method comprises the steps of collecting environmental data and hypha images through a data collection module; storing the environmental data to a cloud platform, and sending the hypha image to a data processing module; the data processing module acquires a corresponding state label according to the hypha image and the growth monitoring model, identifies the state label, acquires a corresponding environment correlation value when the hypha is in an abnormal state, and generates different early warning signals according to the environment correlation value; sending the early warning signal to an intelligent early warning module; the intelligent early warning module informs workers to process according to the early warning signal; when the hyphae are abnormal, the abnormal conditions caused by environmental reasons or other reasons are analyzed in time, and the growth condition of the hyphae is monitored.
Description
Technical Field
The invention belongs to the field of edible fungi, relates to a control technology for edible fungi bionic planting, and particularly relates to an intelligent control system for edible fungi bionic planting.
Background
The forest fungus development is based on forest health as a premise and development conditions, the product is cultivated in a wild-simulated environment under the condition of a natural ecosystem, no medicament or fertilizer is applied, the product is far away from urban areas and is prevented from being polluted by industrial waste gas and sewage, the nutrient components or the effective components of medicaments are higher than the products under the common cultivation conditions of fields, and the forest fungus development method has bright natural attributes and ecological advantages and is the most characteristic forest economic development mode of forestry. The problem of resource waste of large-area idle land under the forest is solved while the microclimate under the forest is fully utilized, the market prospect of planting the edible fungi is quite good, the income-increasing benefit is quite remarkable, and the method is a cultivation mode which is worth popularizing.
The wild-simulated cultivation edible fungi in forests adopts a simulated ecological cultivation technology, primary strains are obtained by separating and domesticating wild resources, and part or most of the hypha cultivation stage in cultivation is completed by adopting artificial conditions, but the hypha cultivation fails because the abnormal conditions in the hypha cultivation process cannot be accurately and timely found by manpower.
Therefore, an intelligent management and control system for edible fungus bionic planting is provided.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides an intelligent control system for edible fungus bionic planting, which solves the problem that an artificial person cannot accurately and timely find abnormal conditions in a hypha culture process.
In order to achieve the above object, an embodiment according to a first aspect of the present invention provides an intelligent management and control system for edible fungus bionic planting, including a data acquisition module, a data processing module, an intelligent early warning module, and a cloud platform; information interaction is carried out among all modules based on a data signal mode;
the data acquisition module is used for acquiring environmental data and hypha images; wherein the environmental data comprises a temperature value and a humidity value;
storing the environmental data to a cloud platform, and sending the hypha image to a data processing module;
the data processing module is used for receiving the hypha image, acquiring a corresponding state label according to the hypha image and the growth monitoring model, identifying the state label, acquiring a corresponding environment correlation value when the hypha is in an abnormal state, and generating different early warning signals according to the environment correlation value; wherein the growth monitoring model is established based on an artificial intelligence model;
sending the early warning signal to the intelligent early warning module;
the intelligent early warning module is used for receiving the early warning signal and informing a worker to process according to the early warning signal.
Preferably, the data acquisition module comprises a temperature sensor, a humidity sensor and an image acquisition device; the image acquisition device comprises a high-definition camera.
Preferably, the data acquisition module acquires environmental data and hypha images, and the specific process comprises the following steps:
setting an acquisition period, wherein the acquisition period is marked as T; wherein T is a real number greater than 0 and has a unit of s;
the temperature sensor collects temperature values in the culture room once every Ts, and the temperature values are marked as W i In units of; wherein i is the number of the acquisition period, and the value of i is 1,2,3 … … n;
the humidity sensor collects humidity values in the culture room once every Ts, and the humidity value is marked as S i In units of RH;
the data acquisition module stores the temperature value and the humidity value to a cloud platform;
the image acquisition device acquires hypha images in the culture room once every Ts;
the hypha images are numbered according to the collected time sequence marks;
and the data acquisition module sends the hypha image to the data processing module.
Preferably, the value of the status label is 0 or 1, and when the status label is 0, the corresponding hyphae are in a safe state, and when the status label is 1, the corresponding hyphae are in an abnormal state.
Preferably, the data processing module receives the hypha image, and acquires a corresponding state label according to the hypha image and the growth monitoring model, and the specific process includes:
the data processing module receives the hypha image;
acquiring a growth monitoring model from a data processing module;
and inputting the hypha image into a growth monitoring model to obtain a corresponding state label.
Preferably, the growth monitoring model is obtained based on an artificial intelligence model, and the specific process includes:
acquiring standard training data from a data processing module;
training the artificial intelligence model through standard training data, and marking the trained artificial intelligence model as a growth monitoring model;
the standard training comprises a plurality of groups of input images and corresponding state labels, and the content attributes of the input images and the hypha images are consistent;
the artificial intelligence model comprises a deep convolution neural network model or an RBF neural network model.
Preferably, the status label is identified, when hyphae are in an abnormal status, a corresponding environment correlation value is obtained, and different early warning signals are generated according to the environment correlation value, and the specific process includes:
identifying the state label, and acquiring a temperature value and a humidity value in the cloud platform when the hyphae are in an abnormal state;
obtaining an environment-associated value according to the temperature value and the humidity value, wherein the environment-associated value is marked as HJ i ；
The calculation formula of the environment correlation value is as follows:
wherein alpha and beta are temperature correction coefficient and humidity correction coefficient respectively, and alpha and beta are real numbers larger than zero;
the data processing module sets an environment correlation threshold value, wherein the environment correlation threshold value is marked as HJ min ，
Judging the magnitude relation between the environment correlation value and the environment correlation threshold value;
when HJ i ＞HJ min Then, sending an environment early warning signal to the intelligent early warning module;
when HJ i ≤HJ min And then sending a non-environment early warning signal to the intelligent early warning module.
Preferably, the intelligent early warning module is used for receiving the early warning signal and informing a worker to process the early warning signal, and the specific process comprises the following steps:
the intelligent early warning module receives an environment early warning signal and acquires a temperature value and a humidity value in the cloud platform;
generating a corresponding temperature change curve graph and a corresponding humidity change curve graph according to the temperature value and the humidity value;
sending the temperature change curve graph and the humidity change curve graph to an intelligent terminal of a worker, and sending an environment early warning short message to the intelligent terminal of the worker;
and the intelligent early warning module receives the non-environment early warning signal and sends a non-environment early warning short message to an intelligent terminal of a worker.
Preferably, the intelligent terminal comprises an intelligent mobile phone and a computer.
Preferably, the data acquisition module is in communication and/or electrical connection with the data processing module;
the data acquisition module is in communication and/or electrical connection with the cloud platform;
the cloud platform is in communication and/or electrical connection with the data processing module;
the cloud platform is in communication and/or electrical connection with the intelligent early warning module;
the data processing module is in communication and/or electrical connection with the intelligent early warning module.
Compared with the prior art, the invention has the beneficial effects that:
the method comprises the steps of collecting environmental data and hypha images through a data collection module; storing the environmental data to a cloud platform, and sending the hypha image to a data processing module; the data processing module receives the hypha image, acquires a corresponding state label according to the hypha image and the growth monitoring model, identifies the state label, acquires a corresponding environment correlation value when the hypha is in an abnormal state, and generates different early warning signals according to the environment correlation value; sending the early warning signal to an intelligent early warning module; the intelligent early warning module receives the early warning signal and informs workers to process according to the early warning signal; when the hyphae are abnormal, the abnormal conditions caused by environmental reasons or other reasons are analyzed in time, and the growth condition of the hyphae is monitored.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
As shown in fig. 1, the intelligent management and control system for edible fungus bionic planting comprises a data acquisition module, a data processing module, an intelligent early warning module and a cloud platform; information interaction is carried out among all modules based on a data signal mode;
the data acquisition module is used for acquiring environmental data and hypha images; wherein the environmental data comprises a temperature value and a humidity value; it should be further explained that the color of the normal growth of hyphae should be white, and if the color is dark and yellow, the reason should be found in time; if the edge of the hyphae has a brown 'head blocking edge', firstly, whether sundry fungi exist in a culture room or not is checked, the sundry fungi and the hyphae possibly form an antagonistic phenomenon, the hyphae are weakened due to overhigh temperature of fungus culture, and the color of the hyphae is yellow and dark, so that the adjustment and management are timely carried out according to specific conditions;
storing the environmental data to a cloud platform, and sending the hypha image to a data processing module;
the data processing module is used for receiving the hypha image, acquiring a corresponding state label according to the hypha image and the growth monitoring model, identifying the state label, acquiring a corresponding environment correlation value when the hypha is in an abnormal state, and generating different early warning signals according to the environment correlation value; wherein the growth monitoring model is established based on an artificial intelligence model;
sending the early warning signal to the intelligent early warning module;
the intelligent early warning module is used for receiving the early warning signal and informing a worker to process according to the early warning signal.
In this embodiment, the data acquisition module includes a temperature sensor, a humidity sensor, and an image acquisition device;
the temperature sensor and the humidity sensor are both arranged in the culture chamber;
the image acquisition device comprises a high-definition camera; it should be further explained that a high-definition camera is used for shooting images of hyphae in the culture chamber, so that the images are clear, and the subsequent analysis of the growth condition of the hyphae is guaranteed.
The data acquisition module acquires environmental data and hypha images, and the specific process comprises the following steps:
setting an acquisition period, wherein the acquisition period is marked as T; wherein T is a real number greater than 0 and has a unit of s;
the temperature sensor collects temperature values in the culture room once every Ts, and the temperature values are marked as W i In units of; wherein i is the number of the acquisition cycle, and the value of i is 1,2,3 … … n;
the humidity sensor collects humidity values in the culture room once every Ts, and the humidity value is marked as S i In units of RH;
the data acquisition module stores the temperature value and the humidity value to a cloud platform;
the image acquisition device acquires hypha images in the culture room once every Ts;
the hypha images are numbered according to the collected time sequence marks;
and the data acquisition module sends the hypha image to the data processing module.
In this embodiment, the value of the status label is 0 or 1, when the status label is 0, it indicates that the corresponding hyphae are in a safe state, and when the status label is 1, it indicates that the corresponding hyphae are in an abnormal state; in other preferred embodiments, the status label may be further differentiated by other markers, for example, the value of the status label is a or B, when the status label is a, it indicates that the corresponding hyphae is in a safe state, and when the status label is B, it indicates that the corresponding user is in an abnormal state.
The data processing module receives the hypha image, and acquires a corresponding state label according to the hypha image and the growth monitoring model, and the specific process comprises the following steps:
the data processing module receives the hypha image;
acquiring a growth monitoring model from a data processing module;
and inputting the hypha image into a growth monitoring model to obtain a corresponding state label.
In this embodiment, the growth monitoring model is obtained based on an artificial intelligence model, and the specific process includes:
acquiring standard training data from a data processing module;
and training the artificial intelligence model through standard training data, and marking the trained artificial intelligence model as a growth monitoring model.
In this embodiment, the standard training includes a plurality of sets of input images and corresponding status labels, and the input images and the hypha images have consistent content attributes; it is understood that the input image and the hypha image are both images during the hypha growth process, and only the hypha has different morphologies.
In this embodiment, the artificial intelligence model includes a model with strong nonlinear fitting capability, such as a deep convolutional neural network model or an RBF neural network model.
Identifying the state label, acquiring a corresponding environment correlation value when the hyphae are in an abnormal state, and generating different early warning signals according to the environment correlation value, wherein the specific process comprises the following steps:
identifying the state label, and acquiring a temperature value and a humidity value in the cloud platform when the hyphae are in an abnormal state;
obtaining an environment-associated value according to the temperature value and the humidity value, wherein the environment-associated value is marked as HJ i ；
The calculation formula of the environment correlation value is as follows:
wherein, alpha and beta are temperature correction coefficient and humidity correction coefficient respectively, and alpha, beta, and beta, and beta, respectively,Beta is a real number greater than zero;
the data processing module sets an environment correlation threshold value, wherein the environment correlation threshold value is marked as HJ min ，
Judging the magnitude relation between the environment correlation value and the environment correlation threshold value;
when HJ i ＞HJ min When the abnormal conditions of the hyphae are related to the environmental data, an environmental early warning signal is sent to the intelligent early warning module;
when HJ i ≤HJ min And in time, the abnormal conditions of the hyphae are shown to be irrelevant to the environmental data, namely the hyphae are abnormal due to other reasons, and non-environmental early warning signals are sent to the intelligent early warning module.
The intelligent early warning module is used for receiving the early warning signal and notifying a worker to process according to the early warning signal, and the specific process comprises the following steps:
the intelligent early warning module receives an environment early warning signal and acquires a temperature value and a humidity value in the cloud platform;
generating a corresponding temperature change curve graph and a corresponding humidity change curve graph according to the temperature value and the humidity value;
sending the temperature change curve graph and the humidity change curve graph to an intelligent terminal of a worker, and sending an environment early warning short message to the intelligent terminal of the worker; after receiving the early warning short message, the worker adjusts the environmental data in the culture room;
the intelligent early warning module receives the non-environment early warning signal and sends a non-environment early warning short message to an intelligent terminal of a worker; and after receiving the early warning short message, the worker checks hyphae and searches for abnormal reasons.
In this embodiment, the intelligent terminal includes intelligent devices such as a smart phone and a computer.
In this embodiment, the data acquisition module is in communication and/or electrical connection with the data processing module;
the data acquisition module is in communication and/or electrical connection with the cloud platform;
the cloud platform is in communication and/or electrical connection with the data processing module;
the cloud platform is in communication and/or electrical connection with the intelligent early warning module;
the data processing module is in communication and/or electrical connection with the intelligent early warning module.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows:
setting an acquisition period T; the temperature sensor collects temperature values in the cultivation room every Ts, the humidity sensor collects humidity values in the cultivation room every Ts, and the data acquisition module stores the temperature values and the humidity values to the cloud platform;
the image acquisition device acquires hypha images in the culture room once every Ts; the hypha images are numbered according to the collected time sequence marks; and the data acquisition module sends the hypha image to the data processing module.
Inputting the hypha image into a growth monitoring model to obtain a corresponding state label; identifying the state label, and acquiring a temperature value and a humidity value in the cloud platform when the hyphae are in an abnormal state; acquiring an environment correlation value according to the temperature value and the humidity value;
the data processing module sets an environment correlation threshold value and judges the magnitude relation between the environment correlation value and the environment correlation threshold value; when HJ i ＞HJ min When the hyphae are abnormal, the hyphae are related to environmental data, and an environmental early warning signal is sent to the intelligent early warning module; when HJ i ≤HJ min When the abnormal conditions of the hyphae are expressed to be irrelevant to the environmental data, namely the hyphae are abnormal due to other reasons, a non-environmental early warning signal is sent to the intelligent early warning module;
the intelligent early warning module receives an environment early warning signal and acquires a temperature value and a humidity value in the cloud platform; generating a corresponding temperature change curve graph and a corresponding humidity change curve graph according to the temperature value and the humidity value; sending the temperature change curve graph and the humidity change curve graph to an intelligent terminal of a worker, and sending an environment early warning short message to the intelligent terminal of the worker; after receiving the early warning short message, the worker adjusts the environmental data in the culture room;
the intelligent early warning module receives the non-environment early warning signal and sends a non-environment early warning short message to an intelligent terminal of a worker; and after receiving the early warning short message, the worker checks hyphae and searches for abnormal reasons.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (10)
1. The intelligent management and control system for the bionic planting of the edible fungi is characterized by comprising a data acquisition module, a data processing module, an intelligent early warning module and a cloud platform; information interaction is carried out among all modules based on a data signal mode;
the data acquisition module is used for acquiring environmental data and hypha images; wherein the environmental data comprises a temperature value and a humidity value;
storing the environmental data to a cloud platform, and sending the hypha image to a data processing module;
the data processing module is used for receiving the hypha image, acquiring a corresponding state label according to the hypha image and the growth monitoring model, identifying the state label, acquiring a corresponding environment correlation value when the hypha is in an abnormal state, and generating different early warning signals according to the environment correlation value; wherein the growth monitoring model is established based on an artificial intelligence model;
sending the early warning signal to the intelligent early warning module;
the intelligent early warning module is used for receiving the early warning signal and informing a worker to process according to the early warning signal.
2. The intelligent management and control system for edible fungus bionic planting according to claim 1, wherein the data acquisition module comprises a temperature sensor, a humidity sensor and an image acquisition device; the image acquisition device comprises a high-definition camera.
3. The intelligent management and control system for edible fungus bionic planting according to claim 2, wherein the data acquisition module acquires environmental data and hypha images, and the specific process comprises the following steps:
setting an acquisition period, wherein the acquisition period is marked as T; wherein T is a real number greater than 0 and has a unit of s;
the temperature sensor collects temperature values in the culture room once every Ts, and the temperature values are marked as W i In units of; wherein i is the number of the acquisition period, and the value of i is 1,2,3 … … n;
the humidity sensor collects humidity values in the culture room once every Ts, and the humidity value is marked as S i In units of RH;
the data acquisition module stores the temperature value and the humidity value to a cloud platform;
the image acquisition device acquires hypha images in the culture room once every Ts;
the hypha images are numbered according to the collected time sequence marks;
and the data acquisition module sends the hypha image to the data processing module.
4. The intelligent management and control system for edible fungus bionic planting according to claim 3, wherein the value of the state label is 0 or 1, when the state label is 0, the corresponding hyphae are in a safe state, and when the state label is 1, the corresponding hyphae are in an abnormal state.
5. The intelligent management and control system for edible fungus bionic planting according to claim 4, wherein the data processing module receives the hypha image, and acquires a corresponding state label according to the hypha image and the growth monitoring model, and the specific process comprises the following steps:
the data processing module receives the hypha image;
acquiring a growth monitoring model from a data processing module;
and inputting the hypha image into a growth monitoring model to obtain a corresponding state label.
6. The intelligent management and control system for edible fungus bionic planting according to claim 5, wherein the growth monitoring model is obtained based on an artificial intelligence model, and the specific process comprises the following steps:
acquiring standard training data from a data processing module;
training the artificial intelligence model through standard training data, and marking the trained artificial intelligence model as a growth monitoring model;
the standard training comprises a plurality of groups of input images and corresponding state labels, and the content attributes of the input images are consistent with those of hypha images;
the artificial intelligence model comprises a deep convolutional neural network model or an RBF neural network model.
7. The intelligent management and control system for edible fungus bionic planting according to claim 6, wherein the state tags are identified, when hyphae are in abnormal states, corresponding environment correlation values are obtained, different early warning signals are generated according to the environment correlation values, and the specific process comprises the following steps:
identifying the state label, and acquiring a temperature value and a humidity value in the cloud platform when the hyphae are in an abnormal state;
obtaining an environment-associated value according to the temperature value and the humidity value, wherein the environment-associated value is marked as HJ i ；
The calculation formula of the environment correlation value is as follows:
wherein, alpha and beta are respectively a temperature correction coefficient and a humidity correction coefficient, and both alpha and beta are real numbers larger than zero;
the data processing module sets an environment association threshold, the environment association threshold is marked as HJ min ，
Judging the magnitude relation between the environment correlation value and the environment correlation threshold value;
when HJ i ＞HJ min Then, sending an environment early warning signal to the intelligent early warning module;
when HJ i ≤HJ min And then sending a non-environment early warning signal to the intelligent early warning module.
8. The intelligent management and control system for the bionic planting of edible fungi according to claim 7, wherein the intelligent early warning module is used for receiving the early warning signal and informing a worker to process the early warning signal, and the specific process comprises the following steps:
the intelligent early warning module receives an environment early warning signal and acquires a temperature value and a humidity value in the cloud platform;
generating a corresponding temperature change curve graph and a corresponding humidity change curve graph according to the temperature value and the humidity value;
sending the temperature change curve graph and the humidity change curve graph to an intelligent terminal of a worker, and sending an environment early warning short message to the intelligent terminal of the worker;
and the intelligent early warning module receives the non-environment early warning signal and sends a non-environment early warning short message to an intelligent terminal of a worker.
9. The intelligent management and control system for edible fungus bionic planting according to claim 8, wherein the intelligent terminal comprises a smart phone and a computer.
10. The intelligent management and control system for edible fungus bionic planting according to claim 9, wherein the data acquisition module is in communication and/or electrical connection with the data processing module;
the data acquisition module is in communication and/or electrical connection with the cloud platform;
the cloud platform is in communication and/or electrical connection with the data processing module;
the cloud platform is in communication and/or electrical connection with the intelligent early warning module;
the data processing module is in communication and/or electrical connection with the intelligent early warning module.
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| CN116661530A (en) * | 2023-07-31 | 2023-08-29 | 山西聚源生物科技有限公司 | Intelligent control system and method in edible fungus industrial cultivation |
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| CN117215351A (en) * | 2023-09-16 | 2023-12-12 | 张家港泽诺科技有限公司 | Intelligent monitoring and early warning system for edible fungus planting environment |
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