CN114879786A - Method, system, device and medium for acquiring edible fungus decision scheme - Google Patents
Method, system, device and medium for acquiring edible fungus decision scheme Download PDFInfo
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Abstract
The embodiment of the specification provides a method, a system, a device and a medium for acquiring an edible fungus decision scheme, wherein the method comprises the following steps: the method comprises the steps of obtaining illumination and temperature information of a growing period, determining a first sequence and a second sequence through a decision model based on target quality, a target period and a target output rate as well as the illumination and temperature information, wherein the first sequence corresponds to humidity change of the growing period, the second sequence corresponds to carbon dioxide concentration change of the growing period, and the illumination and temperature information is determined based on natural environment information and energy consumption control information.
Description
Technical Field
The present disclosure relates to the field of agricultural informatization, and in particular, to a method, system, apparatus, and medium for obtaining a decision scheme for edible fungi.
Background
The edible fungi are very sensitive to the growth environment, and small changes of the growth environment can have great influence on the quality of the edible fungi. Generally, by combining with expert research results and actual cultivation experiences, an edible fungus cultivation technical scheme database is established, so that optimal management of edible fungus production is realized, although automatic adjustment of an edible fungus growth environment system to a certain degree can be realized, for a complex and variable actual growth environment, the adjustment function is limited, and batch production of edible fungi with stable quality is difficult to realize.
Therefore, it is necessary to provide a more intelligent edible fungus production decision method to provide a suitable edible fungus growth environment in real time and reduce the difficulty of mass production of edible fungus products with stable quality.
Disclosure of Invention
One or more embodiments of the present disclosure provide a method for obtaining a decision scheme for an edible fungus. The method for acquiring the edible fungus decision scheme comprises the following steps: acquiring first parameter information of the growing period of the edible fungi, wherein the first parameter information comprises illumination information and temperature information; inputting preset target output result information of the edible fungi and the first parameter information into a decision model, and outputting a first sequence and a second sequence, wherein the first sequence reflects humidity change information of the edible fungi in the growth period, and the second sequence reflects carbon dioxide concentration change information of the edible fungi in the growth period; controlling a growth environment parameter of the edible fungus based on the first sequence and the second sequence.
One or more embodiments of the present disclosure provide a system for acquiring a decision scheme of an edible fungus, the system including an acquisition module, an output module, and a control module; the acquisition module is used for acquiring first parameter information of the edible fungus in a growth period, wherein the first parameter information comprises illumination information and temperature information; the output module is used for inputting preset target output result information of the edible fungi and the first parameter information into a decision model and outputting a first sequence and a second sequence, wherein the first sequence reflects humidity change information of the edible fungi in a growth period, and the second sequence reflects carbon dioxide concentration change information of the edible fungi in the growth period; the control module is used for controlling the growth environment parameters of the edible fungi based on the first sequence and the second sequence.
One or more embodiments of the present disclosure provide an apparatus for obtaining a decision-making scheme for an edible fungus, the apparatus comprising at least one processor and at least one memory; the at least one memory is for storing computer instructions; the at least one processor is configured to execute a method for obtaining an edible fungus decision scheme.
One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions, and when the computer instructions in the storage medium are read by a computer, the computer executes a method for acquiring a decision scheme of edible fungi.
Drawings
The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
FIG. 1 is a schematic diagram of an application scenario of a system for obtaining a decision scheme for edible fungi according to some embodiments of the present disclosure;
FIG. 2 is an exemplary block diagram of a system for obtaining a decision scheme for a domestic fungus, according to some embodiments of the present disclosure;
FIG. 3 is an exemplary flow diagram of a method of obtaining a decision scheme for an edible fungus according to some embodiments of the present disclosure;
FIG. 4 is an exemplary flow diagram of a method of updating a first sequence and a second sequence in accordance with some embodiments of the present description;
FIG. 5 is a block diagram of an exemplary model for obtaining a model of an edible fungus decision making plan in accordance with certain embodiments of the present disclosure.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
Fig. 1 is a schematic view of an application scenario of a system for acquiring a decision scheme of an edible fungus according to some embodiments of the present disclosure.
As shown in fig. 1, in some embodiments, the application scenario 100 of the system for obtaining an edible fungus decision-making scheme may include an edible fungus cultivation room 110, a data acquisition apparatus 120, a network 130, a processor 140, and a storage device 150. The system for obtaining the edible fungus decision scheme can be used for determining the appropriate edible fungus growth environment parameters.
The edible fungus cultivation room 110 may be used for cultivation of edible fungi. The edible fungus cultivation room 110 further includes, but is not limited to, edible fungi 111, an environmental conditioning device 112, and the like. In some embodiments, the environmental conditioning devices may include, but are not limited to, temperature raising/lowering devices, light shading/supplementing devices, gas conditioning devices, and the like. For example, the edible fungus growing room 110 may receive a parameter adjusting command from the processor 140 to adjust the parameters of the environment adjusting device 112. For example, the edible fungus growing room 110 may receive data output by the environmental conditioning device 112 and send the data to the processor 140 and the storage device 150. In some embodiments, the edible mushroom growing room 110 is also referred to as a mushroom greenhouse, an edible mushroom production greenhouse, and the like.
The data acquisition device 120 may be used to acquire data. The data collection device 120 may include, but is not limited to, a camera 120-1, a video camera 120-2, a temperature collection device 120-3, a lighting collection device 120-4, a gas detection device, a humidity detection device, and the like. In some embodiments, the camera 120-1 and the video camera 120-2 may acquire images containing the edible fungus 111. In some embodiments, camera 120-1 and camera 120-2 may acquire images in a variety of possible ways, including but not limited to continuous acquisition, timed acquisition, and the like. In some embodiments, there may be multiple cameras 120-1 and 120-2, and may be placed at different locations around the same target object to simultaneously obtain information from different angles of the target object. In some embodiments, temperature acquisition device 120-3 may include a thermometer, a temperature sensor, a thermal resistor, a thermocouple, or the like. In some embodiments, the illumination collection device 120-4 may include a light sensor, an illumination detector, a light detector, a photodiode, and the like. In some embodiments, the data collection device 120 may collect data such as images, temperature, light, etc. within the edible mushroom growing room 110.
The network 130 may connect various components of the system and/or connect the system with external resource components. The network 130 allows communication between the various components and with other components outside the system. For example, processor 140 obtains information and/or instructions from storage device 150 and edible fungus growing room 110 via network 130.
It should be noted that the above description of the system and its components is merely for convenience of description and should not be construed as limiting the present disclosure to the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of components or sub-systems may be combined with other components without departing from such teachings. For example, the employment service platform and the employment management platform may be integrated into one component. For another example, the components may share one storage device, and each component may have a storage device. Such variations are within the scope of the present disclosure.
FIG. 2 is a block diagram of a system 200 for obtaining a decision scheme for a fungus, according to some embodiments of the present disclosure.
In some embodiments, as shown in fig. 2, a system 200 for obtaining a decision scheme for an edible fungus may include an obtaining module 210, an output module 220, and a determining module 230.
The obtaining module 210 is configured to obtain first parameter information of the growing period of the edible fungus, where the first parameter information includes illumination information and temperature information. For the definition and description of the related terms, and the acquisition method, see fig. 3 and 4 and the related description thereof.
The output module 220 is configured to input the preset target output result information of the edible fungus and the first parameter information into the decision model, and output a first sequence and a second sequence, where the first sequence reflects humidity change information of the edible fungus in the growth period, and the second sequence reflects carbon dioxide concentration change information of the edible fungus in the growth period. Wherein the first sequence and the second sequence are updated by: acquiring image information of the growing period of the edible fungi; determining process quality information of the edible fungi based on the image information; acquiring actual illumination information and actual temperature information; determining updated first parameter information based on the actual illumination information and the actual temperature information; and inputting preset target output result information, process quality information, updated first parameter information and second parameter information into the decision model, and outputting an updated first sequence and an updated second sequence, wherein the second parameter information comprises planting information of the edible fungi. The update frequency of the first sequence and the second sequence is determined by: inputting the third parameter information into the quality model, and outputting the predicted quality information and the predicted easy picking information of the edible fungi, wherein the third parameter information at least comprises the following information: the updated first sequence, the second sequence and the second parameter information; performing weighted fusion based on the predicted phase information and the predicted easy picking information to determine a quality factor; the frequency of updates of the first and second sequences is determined based on the quality factor. See fig. 3 and its associated description for further description of related terms, and fig. 5 and its associated description for further description of decision models. See fig. 4 and its associated description for a method of updating the first sequence and the second sequence and an update frequency thereof.
The determining module 230 is used for controlling the growth environment parameter of the edible fungi based on the first sequence and the second sequence. For more explanation of the control method, refer to fig. 3 and its related description.
It should be noted that the above description of the system and the modules thereof for obtaining a decision scheme of edible fungi is only for convenience of description, and the description should not be limited to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. In some embodiments, the obtaining module, the outputting module and the determining module disclosed in fig. 2 may be different modules in a system, or may be a module that implements the functions of two or more modules described above. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present description.
FIG. 3 is an exemplary flow chart of a method of obtaining a decision scheme for an edible fungus according to some embodiments of the present disclosure. In some embodiments, the process 300 may be performed by the processor 140. As shown in fig. 3, the process 300 includes the following steps.
And 310, acquiring first parameter information of the growing period of the edible fungi, wherein the first parameter information comprises illumination information and temperature information, and the illumination information and the temperature information are determined based on natural environment information and energy consumption control information of the edible fungi.
The growth period refers to the time taken by the edible fungi from germination to maturity during cultivation. The first parameter information refers to parameter information required by the growth of the edible fungi in the growth period. The required parameter information may include one or more of temperature, humidity, light, carbon dioxide concentration, etc.
In some embodiments, the first parameter information may include illumination information and temperature information. The illumination information may be an illumination data sequence in which illumination data at a plurality of continuous time points in the growth period of the edible fungi are combined. The temperature information may be a temperature data series in which temperature data of a plurality of consecutive times during the growth period of the edible fungus are combined. In some embodiments, the first parameter information may also include other parameter information.
In some embodiments, the first parameter information may be obtained empirically and practically by one skilled in the art. For example, when it is found from the planting experience that the temperature is 23 to 25 ℃, the humidity is 65 to 75%, the optimum light intensity is 300lx, and the carbon dioxide concentration is 0.1% or less, which are the most ideal conditions for the growth of a certain hypha, the relevant data in the first parameter information of the hypha growth are set in the corresponding sections.
In some embodiments, the first parameter information may also be updated first parameter information. Please refer to step 440 in fig. 4 and the description thereof below, specifically how to obtain the updated first parameter information.
And 320, inputting the preset target output result information of the edible fungi and the first parameter information into a decision model, and outputting a first sequence and a second sequence, wherein the first sequence reflects the humidity change information of the edible fungi in the growth period, and the second sequence reflects the carbon dioxide concentration change information of the edible fungi in the growth period.
The preset target output result information refers to preset target output result information which the edible fungi need to reach. In some embodiments, the preset target output result information may include one or more of a target quality of the edible fungus, a target period of the edible fungus, a target output rate of the edible fungus, and the like. The quality may include one or more of the size, height, color, presence or absence of rotten and moldy parts, etc. of the edible fungi. The period may include the time spent by the edible fungus in one or more of the processes from cultivation to harvesting. The yield rate may refer to the yield of edible fungi per unit area.
The first sequence can reflect the humidity change information of the edible fungi in the growth period. For example, the first sequence may be a time-based moisture data sequence during a growth period of the edible fungus.
The second sequence reflects the carbon dioxide concentration change information of the edible fungus in the growth period. For example, the second sequence may be a time-based carbon dioxide concentration data sequence during a growth phase of the edible fungus.
In some embodiments, the processor 140 may input the preset target output result information of the edible fungi and the first parameter information into the decision model, and output the first sequence and the second sequence. For the definition of the decision model and how to obtain the decision model, please refer to fig. 5 and its related description below.
And 330, controlling the growth environment parameters of the edible fungi based on the first sequence and the second sequence.
The growth environment parameters refer to various growth environment parameters required by the edible fungi in the cultivation process. For example, the growth environment parameters may include humidity, carbon dioxide concentration, and the like during the growth of the edible fungi.
In some embodiments, based on the first sequence and the second sequence, if the current actual humidity data is detected to be different from the humidity data at the corresponding time point in the first sequence and/or the current actual carbon dioxide concentration is detected to be different from the carbon dioxide concentration data at the corresponding time point in the second sequence, the processor 140 may adjust the current humidity and the carbon dioxide concentration to the values in the corresponding sequence using the adjusting device. The conditioning means may include a humidifier, a dryer, a ventilator, etc.
In some embodiments, if it is detected that the current actual humidity data is different from the humidity data at the corresponding time point in the first sequence and/or it is detected that the current actual carbon dioxide concentration is different from the carbon dioxide concentration data at the corresponding time point in the second sequence, the processor 140 may further send an early warning message to the user terminal to remind the user whether to adjust the growth environment parameters of the edible fungi.
In some embodiments of the present specification, the edible fungi meeting the requirement are obtained by controlling and adjusting the growth environment parameters of the growing period of the edible fungi; the management optimization of the edible fungus production is realized by intelligently monitoring the cultivation and production of the edible fungus.
FIG. 4 is an exemplary flow chart of a method of updating a first sequence and a second sequence in accordance with some embodiments of the present description. In some embodiments, the process 400 may be performed by the processor 140. As shown in fig. 4, the process 400 includes the following steps.
And step 410, acquiring image information of the growing period of the edible fungi.
The image information may refer to image information capable of reflecting the growth process of the edible fungi. For example, the image information may include images or videos taken during the growth period of the edible fungi.
In some embodiments, images or videos of the growing period of the edible fungi can be acquired by the data acquisition device 120.
And step 420, determining the process quality information of the edible fungi based on the image information.
The process quality information refers to information capable of reflecting the quality of the edible fungi in the growth process. For example, the process quality information may include the size, height, color, presence or absence of rotten and moldy parts of the edible fungi, and the like.
In some embodiments, the processor 140 may perform image recognition on the images of the growing period of the edible fungus to determine the edible fungus in the images. The method of image recognition may include, but is not limited to, one or more of a computer image recognition method, a structural image recognition method, a blurred image recognition method, and the like. In some embodiments, the processor 140 may perform image feature extraction on the partial image with the identification box of the edible fungus to obtain process quality information of the edible fungus. The image feature extraction method may include, but is not limited to, one or more of a grayscale feature extraction method, a texture feature extraction method, and the like.
The actual illumination information refers to actual illumination data information of the edible fungi at each time point in the growth period. For example, the actual illumination information may be an actual illumination data sequence formed by combining actual illumination data at a plurality of continuous time points in the growth period of the edible fungi.
The actual temperature information refers to actual environmental temperature data information of the edible fungi at each time point in the growth period. For example, the actual temperature information may be an actual environmental temperature data sequence formed by combining actual environmental temperature data at a plurality of continuous time points in the growth period of the edible fungi.
In some embodiments, the actual illumination data may be acquired by a light sensor, and the actual ambient temperature data may be acquired by a temperature sensor.
In some embodiments, the first parameter information of the edible fungi in the whole growth period can be predicted according to experience and actual conditions before the cultivation of the edible fungi is started.
In some embodiments, after the cultivation of the edible fungi is started, when the actual illumination value and/or the actual temperature value measured by the sensor at a certain time point of the growth period of the edible fungi at every preset time period is different from the illumination value and/or the temperature value predicted at the corresponding time point in the first parameter information (for example, the illumination data sequence and the temperature data sequence), the processor 140 may replace the part of the predicted illumination value and/or the temperature value in the first parameter information with the corresponding actual illumination value and/or actual temperature value. For example, when the edible fungus cultivation starts, all the temperature data in the growth period are predicted data, if the actual temperature data is measured once a day and all the predicted temperature data in the growth period of the edible fungus are (2,2,3,3,3,5,5,5,8,8,12,12,12), and on the 5 th day, the actual temperature data obtained on the previous 5 th day is (3,3,4,4,4,5,5, 8,8,12,12,12), the temperature data sequence after the update is (3,3,4,4,4,5,5,5,8,8, 12). Because the illumination data and the environmental temperature data required by the edible fungi in the growth period generally have a reasonable interval, when the actual illumination data and the actual environmental temperature data of the edible fungi are not in the reasonable interval, the energy consumption can be controlled within a preset range, and meanwhile, the actual illumination data and the actual environmental temperature data of the edible fungi production and cultivation environment are controlled within the reasonable interval. In some embodiments, the first parameter information may be determined based on natural environment information and energy consumption control information of the edible fungi when cultivation of the edible fungi is started. The natural environment information refers to the information of the natural environment actually existing in the growing period of the edible fungi. In some embodiments the natural environment information may include actual lighting information and actual temperature information.
The energy consumption control information refers to extra energy consumption control information which needs to be consumed for changing the natural environment information of the edible fungi to a reasonable interval in the growth period. For example, the energy consumption control information may be a preset energy consumption threshold.
In some embodiments, when it is detected that the replaced actual illumination data and actual environment temperature data at a certain time point in the first parameter information of the edible fungus are not within a reasonable interval of the illumination data and the environment temperature data required in the growing period of the edible fungus, the processor 140 may control the light supplementing/shading device and the temperature raising/reducing device to adjust within a preset energy consumption threshold range, so that the reasonable light supplementing/shading illumination and the temperature raising/reducing temperature reach preset condition values, and thus the natural environment information in the growing period of the edible fungus is adjusted to the illumination data and the temperature data required in the growing period of the edible fungus and serves as the first parameter information at the time point. The preset condition value refers to a preset growth environment parameter value suitable for the growth period of the edible fungi, such as a suitable temperature value or a suitable illumination intensity value. For example, if the suitable illumination in a stage of the growth period of the edible fungi is weak light, the suitable temperature is 5-12 ℃, the current natural illumination is sub-strong light, and the temperature is 15 ℃, the light shielding device and the cooling device are required to be utilized to properly reduce the current natural illumination intensity and temperature on the premise of meeting the preset energy consumption threshold, and then the properly reduced natural illumination intensity and temperature are used as the first parameter information in the stage of the growth period of the edible fungi.
In some embodiments, if the information of various parameters (for example, temperature, humidity, illumination intensity, carbon dioxide concentration and the like) cannot be adjusted to the preset condition values of various parameters in the growth period of the edible fungi at the same time within the preset energy consumption threshold range, the actual illumination intensity data and the actual temperature data in the growth period of the edible fungi are preferably adjusted to the preset condition values.
And step 450, inputting the process quality information, the preset target output result information, the updated first parameter information and the second parameter information into the decision model, and outputting the updated first sequence and the updated second sequence, wherein the second parameter information comprises planting information of the edible fungi.
In some embodiments, the second parameter information may include planting information of the edible fungi. The planting information refers to information related to edible fungus planting. For example, the planting information may include weather factors, quality of the medium, water content of the medium, species of the edible fungus, quality of species of the edible fungus, and the like. For example, the weather factor refers to a weather-related factor such as air temperature, air pressure, humidity, wind, cloud, rain, and the like.
In some embodiments, the updated first parameter information may be input to an environmental feature extraction layer in the decision model, and the environmental feature extraction layer outputs a temperature, an illumination temperature, and an illumination feature vector. In some embodiments, the preset target output result information may be input into a target feature extraction layer in the decision model, and the target feature extraction layer outputs a target feature vector. In some embodiments, the process quality information and the second parameter information may be input to an embedding layer in the decision model, the embedding layer outputting the process feature vectors. In some embodiments, the temperature, the illumination feature vector, and the process feature vector may be input to a first output layer in the decision model, and the updated first sequence may be output. In some embodiments, the target feature vector and the process feature vector may be input to a second output layer in the decision model, and the updated second sequence may be output. For more description of the decision model, the embedding layer, the environmental feature extraction layer, the target feature extraction layer, the first output layer, the second output layer, the temperature, the illumination feature vector, the target feature vector, and the process feature vector, please refer to fig. 5 and its related description below.
In some embodiments, the first sequence and the second sequence may be updated one or more times, where the process of updating once is the process of repeating step 410 and step 450, and the update frequency of the specific first sequence and the second sequence may be determined by step 460 and step 480 as described below.
The predicted edible fungus condition information refers to predicted edible fungus condition information. For example, the predicted facies information may be a predicted grade of appearance of the edible fungus. For example, the grade of the appearance of the edible fungi can be special appearance grade, first grade appearance, second grade appearance and the like. The information for predicting easy picking performance is information for predicting whether the edible fungi are easy to pick. For example, the predicted easy-to-pick information may be easy-to-pick or not easy-to-pick, and the like. In some embodiments, the third parameter information includes at least: the updated first sequence, the second sequence and the second parameter information.
In some embodiments, the updated first and second sequences and second parameter information may be input into a quality model, and the quality model outputs predicted quality information and predicted ease-of-picking information of the edible fungi. For more description of the quality model, please refer to fig. 5 and its related description below.
And 470, performing weighted fusion based on the predicted item phase information and the predicted easy picking information, and determining a quality factor.
The quality factor can be a factor for evaluating the quality of the edible fungi.
In some embodiments, processor 140 may determine the quality factor based on a weighted fusion of the predicted facies information and the predicted ease of plucking information.
In some embodiments, the predicted facies information and the predicted picking ease information may be preset values empirically by one skilled in the art.
In some embodiments, the weight of the predicted facies information can be preset according to the appearance grade of the edible fungi. For example, the appearance grade of the edible fungus can be divided into a special grade and a first grade, and if the predicted item information indicates that the appearance grade of the edible fungus is the special grade, the weight of the predicted item information can be set to 0.8; if the predicted quality information indicates that the appearance grade of the edible fungi is one grade, the weight of the predicted quality information may be set to 0.7.
In some embodiments, the weights of the predicted facies information and the predicted picking propensity information are also related to the accuracy of the decision model. In some embodiments, the higher the accuracy of the prediction of a parameter (e.g., humidity, carbon dioxide concentration) by the decision model, the greater the weight of the predicted facies information or predicted picking ease information that the parameter corresponds to. In some embodiments, the prediction accuracy may be calculated by comparing a predicted certain parameter (e.g., humidity, carbon dioxide concentration) with an actual humidity or carbon dioxide concentration, with the higher the verification accuracy calculated for the certain parameter. For example, if the carbon dioxide concentration and humidity predicted by the decision model are compared with the actual carbon dioxide and humidity, and the verification accuracy of the carbon dioxide is higher than that of the humidity in the verification accuracy obtained by calculation, and the carbon dioxide concentration affects the predicted item phase information, the weight setting of the predicted item phase information affected by the carbon dioxide concentration is larger than the weight setting of the predicted easy-picking information.
In some embodiments of the present disclosure, the quality factor is determined by correlating the weight of the predicted facies information and the predicted picking tendency information with the accuracy of the decision model, and then the update frequency of the first sequence and the second sequence is determined by the quality factor, so that the determination of the update frequency of the first sequence and the second sequence is more practical and has higher accuracy.
The frequency of updating the first sequence and the second sequence is determined based on the quality factor, step 480.
The larger the quality factor is, the higher the requirement on the production quality of the edible fungi is, and whether the first parameter information of the edible fungi meets the condition needs to be paid attention to in real time in the production and cultivation process. In some embodiments, the greater the quality factor value, the more frequently the first and second sequences are updated. For example, if the quality factor value is 8.0, the frequency of updating the first sequence and the second sequence may be 3 times; if the quality factor value is 8.5, the frequency of updating the first sequence and the second sequence may be 4 times.
In some embodiments of the present description, the quality factor is determined by using the predicted quality information and the predicted easy picking information obtained by the quality model, and then the update frequency of the first sequence and the second sequence is determined according to the size of the quality factor, so that the first parameter information is continuously adjusted according to the actual illumination information and the actual temperature information according to the update frequency requirement, and then the decision model is input to perform the prediction of the first sequence and the second sequence, so that the decision model predicts more accurately and meets the actual growth environment parameter requirement of the edible fungi.
It should be noted that the above description related to the flow 400 is only for illustration and description, and does not limit the applicable scope of the present specification. Various modifications and changes to flow 400 will be apparent to those skilled in the art in light of this description. However, such modifications and variations are intended to be within the scope of the present description. For example, only step 410 and 450 of flow 400 may be performed.
FIG. 5 is a block diagram of an exemplary model for obtaining a model of an edible fungus decision making plan in accordance with certain embodiments of the present disclosure.
In some embodiments, obtaining the edible fungus decision plan model 500 may include a decision model and a quality model.
A decision model may be used to obtain the first sequence and the second sequence. In some embodiments, the decision model may include at least an environmental feature extraction layer, a target feature extraction layer, and an output layer. For example, the decision model, the environmental feature extraction layer, the target feature extraction layer, and the output layer may include a Convolutional Neural Network (CNN) or a Deep Neural Network (DNN) or a combination thereof.
In some embodiments, the input of the environmental feature extraction layer may include first parameter information, and the output thereof may include a temperature feature vector and an illumination feature vector. In some embodiments, the temperature characteristic vector refers to a characteristic vector capable of reflecting the temperature of each time point in the growth period of the edible fungi, and the illumination characteristic vector refers to a characteristic vector capable of reflecting the illumination intensity of each time point in the growth period of the edible fungi. In some embodiments, the input of the target feature extraction layer may include preset target outcome information, and the output thereof may include a target feature vector. In some embodiments, the target feature vector refers to a feature vector capable of reflecting preset target output result information of the edible fungi. For example, the target feature vector may include one or more of a target quality feature vector, a target period feature vector, a target yield rate feature vector, and the like. In some embodiments, the target quality feature vector refers to a feature vector capable of reflecting the target quality of the edible fungi. In some embodiments, the target period feature vector refers to a feature vector capable of reflecting the target period of the edible fungi. In some embodiments, the target output rate feature vector is a feature vector capable of reflecting the target quality of the edible fungi. In some embodiments, the decision model may also include an embedding layer. In some embodiments, the input to the embedding layer may include process quality information and second parameter information, and the output thereof may include a process feature vector. In some embodiments, the process feature vector may represent a relationship between process quality information of the edible fungi and preset target output result information. For example, if the process quality information of the edible fungi includes rotten and moldy parts of the edible fungi, and the preset target parameter result information includes no rotten and moldy parts of the edible fungi, the process feature vector is that the process quality information of the edible fungi does not match the preset target output result information.
In some embodiments, the output layer may include a first output layer and a second output layer. In some embodiments, the inputs to the first output layer and the second output layer each include a temperature feature vector, an illumination feature vector, a target feature vector, and a process feature vector. In some embodiments, the output of the first output layer may be a first sequence. In some embodiments, the output of the first output layer may also be the updated first sequence. In some embodiments, the output of the second output layer may be a second sequence. In some embodiments, the output of the second output layer may also be the updated second sequence.
In some embodiments, the decision model may be obtained based on training, which may be performed by a processing device. For example, the training of the decision model may be based on separate training implementations of an environmental feature extraction layer, a target feature extraction layer, an output layer, and an embedding layer. In some embodiments, the decision model may be obtained by obtaining a plurality of training samples and training based on the plurality of training samples, where the training samples include sample first parameter information and labels thereof, and the labels represent a first sequence and a second sequence corresponding to the sample first parameter information.
In some embodiments, the environmental feature extraction layer, the target feature extraction layer, the output layer, and the embedding layer may be obtained based on training, and the training of the environmental feature extraction layer, the target feature extraction layer, the output layer, and the embedding layer may be performed by a processing device, and the training may be implemented based on the following method.
In some embodiments, at least one training sample is obtained, and the initial environmental feature extraction layer, the initial target feature extraction layer, the initial output layer and the initial embedding layer, where the training sample corresponding to the initial environmental feature extraction layer may include sample first parameter information and its corresponding illumination feature vector and temperature feature vector, respectively, where the sample first parameter information may include sample illumination information and sample temperature information; the training sample corresponding to the initial target feature extraction layer may include preset target output result information and a target feature vector corresponding thereto; the training samples corresponding to the initial embedding layer can comprise sample process quality information, sample second parameter information and corresponding process characteristic vectors; the training samples corresponding to the initial output layer may include a sample temperature feature vector, a sample illumination feature vector, a sample target feature vector, and a sample process feature vector, and a corresponding sample first sequence and a sample second sequence, or a corresponding updated first sequence and second sequence.
In some embodiments, the illumination information and the temperature information may be obtained using a data acquisition device.
In some embodiments, the related information in the training sample can be obtained through manual labeling, and also can be obtained through obtaining data of existing labeled information as the training sample, so as to save a manual labeling link.
In some embodiments, parameters of the initial environmental feature extraction layer, the initial target feature extraction layer, the initial output layer, and the initial embedding layer are iteratively updated based on at least one training sample to yield an environmental feature extraction layer, a target feature extraction layer, an output layer, and an embedding layer.
In some embodiments, the training samples may be input into the environmental feature extraction layer, the target feature extraction layer, the output layer, and the embedding layer, and parameters thereof may be updated through training iteration until the trained environmental feature extraction layer, the target feature extraction layer, the output layer, and the embedding layer satisfy a preset condition, and the trained environmental feature extraction layer, the target feature extraction layer, the output layer, and the embedding layer are obtained, where the preset condition may be that a loss function is less than a threshold, convergence, or a training period reaches a threshold. In some embodiments, the method of iteratively updating the model parameters may include a conventional model training method such as stochastic gradient descent.
The quality model can be used for acquiring the predicted quality information and the predicted easy picking information of the edible fungi. For example, the quality model may include a model derived from a convolutional neural network, a deep neural network, or a combination thereof, and the like.
In some embodiments, the input to the quality model may include third parameter information, and the output may include predicted facies information and predicted ease of picking information for the edible fungus. For the description of the information on the predicted product phase and the information on the predicted easy picking property of the edible fungi, refer to fig. 4 and the related description thereof, which are not repeated herein.
In some embodiments, the quality model may be obtained based on training. The training of the quality model may be performed by a processing device. The training of the quality model may be achieved based on the following method.
In some embodiments, at least one training sample and an initial quality model are obtained, wherein the training sample comprises sample third parameter information labeled with predicted facies information and predicted picking susceptibility information. The initial quality model may be a quality model for which the model parameters have not been adjusted or for which training requirements have not been met. The number of training samples can be determined according to factors such as the precision requirement of the quality model and the actual application scene.
In some embodiments, the third parameter information may be obtained based on data of the edible fungus cultivation process.
In some embodiments, the label of the third parameter information about the predicted item phase information and the predicted easy-picking information can be obtained through manual labeling, and the third parameter information with the predicted item phase information and the predicted easy-picking information labeled can also be obtained as a sample, so as to omit a manual labeling link.
In some embodiments, parameters of the initial quality model are iteratively updated based on the at least one training sample to obtain the quality model.
In some embodiments, the training samples may be input into the quality model, and the parameters of the initial quality model are updated through training iterations until the trained quality model satisfies the preset conditions, so as to obtain the trained quality model. For more details on the preset condition, reference is made to the decision model and the related description thereof, which are not repeated herein.
In some embodiments, the parameters of the decision model and the quality model may be obtained by joint training. The joint training may be performed by a processing device. The joint training may be implemented based on the following method.
In some embodiments, the output of the decision model may be the input of the quality model, and the decision model and the quality model may be obtained by joint training. For example, training sample data, namely sample first parameter information and sample preset target output result information, is input into the decision model to obtain an updated first sequence and an updated second sequence output by the decision model; then, inputting the updated first sequence and second sequence as training sample data and second parameter information into a quality model to obtain predicted phase information and predicted easy-picking information output by the quality model, and verifying the output of the quality model by using the sample phase information and the sample easy-picking information; and obtaining the updated verification data of the first sequence and the second sequence output by the decision model by utilizing the back propagation characteristic of the neural network model, and training the decision model by using the updated verification data of the first sequence and the second sequence as tags.
For another example, the first parameter information and a preset target output result information sample are input into an initial decision model, the third parameter information sample is input into an initial quality model, a loss function is constructed based on the label and the result predicted by the initial quality model, and the parameters of the initial decision model and the initial quality model are updated simultaneously until the trained decision model and the trained quality model meet a preset condition, so as to obtain the trained decision model and the trained quality model, wherein the preset condition may be that the loss function is smaller than a threshold value, convergence is achieved, or a training period reaches a threshold value. In some embodiments, the method of iteratively updating the model parameters may include a conventional model training method such as stochastic gradient descent.
When the training of the decision model and the quality model is joint training, cost functions related to the predicted item phase information and the predicted easy-picking information have different weights, the weights are related to the weights used in the process of determining the quality factors, and the model can meet the requirements of a certain aspect more emphatically by setting different weights. For example, by setting a larger weight value for the cost function associated with the predicted facies information, the model can be made to focus more on producing edible fungi with a higher facies grade. For more description of the weights, refer to fig. 4 and its related description, which are not repeated herein.
In some embodiments, a loss function F may be constructed＝α*A 1 +β*B 1 + C, wherein alpha and beta are weighted values; a. the 1 The loss items are corresponding to the item phases and are determined based on the prediction results and the labels of the item phases; b is 1 Determining a loss item corresponding to the picking performance based on the predicted result of the picking performance and the label; c is a regularization term. When the product phase in the target quality is more important or higher than the product phase grade, alpha is larger than beta, so that the function F can be converged or equal to 0 only when the predicted product phase value is closer to the actual value than the predicted easy picking value, thereby realizing the aim of producing edible fungi with higher product phase grade.
In some embodiments of the present description, the decision model has a multilayer structure, and can simultaneously input and output various parameters and perform joint training, so as to improve the prediction efficiency, and by using the decision model and the quality model, the production parameters of the edible fungi can be determined based on a large amount of extensive data, so as to meet the requirements of a complex and variable actual production process, improve the acquisition efficiency of the production data of the edible fungi, and reduce the data processing amount. And the prediction is carried out based on the machine learning technology, more and richer data can be analyzed, and the predicted edible fungus production parameters have higher accuracy.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.
Claims (10)
1. A method for obtaining a decision scheme of an edible fungus is characterized by comprising the following steps:
acquiring first parameter information of the growing period of the edible fungi, wherein the first parameter information comprises illumination information and temperature information;
inputting preset target output result information of the edible fungi and the first parameter information into a decision model, and outputting a first sequence and a second sequence, wherein the first sequence reflects humidity change information of the edible fungi in the growth period, and the second sequence reflects carbon dioxide concentration change information of the edible fungi in the growth period;
controlling a growth environment parameter of the edible fungus based on the first sequence and the second sequence.
2. The method of deriving an edible fungus decision scheme as claimed in claim 1 wherein the first and second sequences are updated by:
acquiring image information of the growing period of the edible fungi;
determining process quality information of the edible fungi based on the image information;
acquiring actual illumination information and actual temperature information;
determining the updated first parameter information based on the actual illumination information and the actual temperature information;
and inputting the preset target output result information, the process quality information, the updated first parameter information and the updated second parameter information into the decision model, and outputting the updated first sequence and the updated second sequence, wherein the second parameter information comprises the planting information of the edible fungi.
3. The method of deriving an edible fungus decision scheme as claimed in claim 2 wherein the update frequency of the first and second sequences is determined by:
inputting third parameter information into a quality model, and outputting predicted quality information and predicted easy picking information of the edible fungi, wherein the third parameter information at least comprises: the updated first sequence, second sequence and second parameter information;
performing weighted fusion based on the predicted item phase information and the predicted easy picking information to determine a quality factor;
determining the update frequency of the first sequence and the second sequence based on the quality factor.
4. The method for obtaining a decision scheme for edible fungi according to claim 1, wherein the decision model comprises at least an environmental feature extraction layer, a target feature extraction layer and an output layer, wherein the output layer comprises a first output layer and a second output layer;
processing the first parameter information through the environmental feature extraction layer, wherein the output temperature feature vector and the output illumination feature vector are respectively used as the input of the first output layer and the second output layer;
processing the preset target output result information through the target feature extraction layer, wherein the output target feature vector is used as the input of the first output layer and the second output layer;
outputting the first sequence and the second sequence through the first output layer and the second output layer, respectively.
5. The method for obtaining edible fungus decision-making scheme as claimed in claim 4, wherein the decision model is obtained by the following method:
obtaining a plurality of training samples, wherein the training samples comprise sample first parameter information and labels thereof, and the labels represent the first sequence and the second sequence corresponding to the sample first parameter information;
and training based on the plurality of training samples to obtain the decision model.
6. A system for acquiring a decision scheme of edible fungi is characterized by comprising an acquisition module, an output module and a control module;
the acquisition module is used for acquiring first parameter information of the edible fungus in a growth period, wherein the first parameter information comprises illumination information and temperature information;
the output module is used for inputting preset target output result information of the edible fungi and the first parameter information into a decision model and outputting a first sequence and a second sequence, wherein the first sequence reflects humidity change information of the edible fungi in a growth period, and the second sequence reflects carbon dioxide concentration change information of the edible fungi in the growth period;
the control module is used for controlling the growth environment parameters of the edible fungi based on the first sequence and the second sequence.
7. The system for obtaining a decision scheme for edible fungi of claim 6 wherein the first and second sequences are updated by:
acquiring image information of the growing period of the edible fungi;
determining process quality information of the edible fungi based on the image information;
acquiring actual illumination information and actual temperature information;
determining the updated first parameter information based on the actual illumination information and the actual temperature information;
and inputting the preset target output result information, the process quality information, the updated first parameter information and the updated second parameter information into the decision model, and outputting the updated first sequence and the updated second sequence, wherein the second parameter information comprises the planting information of the edible fungi.
8. The system for obtaining a decision scheme for edible fungi according to claim 7, wherein the update frequency of the first sequence and the second sequence is determined by the following method:
inputting third parameter information into a quality model, and outputting predicted quality information and predicted easy picking information of the edible fungi, wherein the third parameter information at least comprises: the updated first sequence, second sequence and second parameter information;
performing weighted fusion based on the predicted item phase information and the predicted easy picking information to determine a quality factor;
determining the update frequency of the first and second sequences based on the quality factor.
9. An apparatus for obtaining a decision scheme for an edible fungus, the apparatus comprising at least one processor and at least one memory; the at least one memory is for storing computer instructions;
the at least one processor is configured to execute at least a portion of the computer instructions to implement the method of obtaining an edible fungus decision making plan as claimed in any one of claims 1-5.
10. A computer-readable storage medium, wherein the storage medium stores computer instructions, and when the computer instructions in the storage medium are read by a computer, the computer executes the method for obtaining a decision scheme of edible fungi according to any one of claims 1 to 5.
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