CN118216379A - Edible fungus growth environment control method and system - Google Patents
Edible fungus growth environment control method and system Download PDFInfo
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- 230000012010 growth Effects 0.000 title claims abstract description 563
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- 238000005070 sampling Methods 0.000 claims abstract description 11
- 230000021332 multicellular organism growth Effects 0.000 claims description 72
- 230000003698 anagen phase Effects 0.000 claims description 11
- 235000013399 edible fruits Nutrition 0.000 description 5
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Abstract
The invention provides a method and a system for controlling the growth environment of edible fungi, which relate to the technical field of edible fungus planting, and the method comprises the following steps: setting the growth environment of the edible fungi according to the growth environment parameters determined by the growth environment parameter model of the ith-1 control period in the ith control period; sampling all edible fungi to obtain a plurality of sample edible fungi, and determining growth condition data of each sample edible fungi; determining a growth condition vector and a growth stage discrimination parameter of the edible fungi; judging whether a growth environment parameter model of the ith-1 control period needs training or not; determining a loss function according to the ith control period, the edible fungus growth condition vector of the ith-1 control period and the growth stage discrimination parameters; obtaining a growth environment parameter model of the ith control period according to the loss function; the growth environment parameters for the (i+1) th control period are determined. According to the invention, the rationality of the control of the growth environment of the edible fungi can be improved.
Description
Technical Field
The invention relates to the technical field of edible fungus planting, in particular to a method and a system for controlling an edible fungus growth environment.
Background
In the related art, CN117148902B discloses an intelligent bacteria stick growth environment self-adaptive control system and method; collecting the carbon dioxide concentration of n areas in the cultivation room at the same moment; analyzing the carbon dioxide concentration, judging whether the current edible fungus growth stage is in a mycelium growth stage or generating a preliminary fruiting body growth stage switching instruction, and if the preliminary fruiting body growth stage switching instruction is generated, regulating the illumination of the culture chamber to be the illumination requirement of the fruiting body growth stage; collecting edible fungus pictures of n areas in a cultivation room at the same time according to a primary fruiting body growth stage switching instruction; analyzing the edible fungi photo, and judging whether the current edible fungi growth stage is in the fruiting body growth stage or not; controlling an environment regulating device to enable growth environment data of the culture chamber to be matched with different growth stages of the edible fungi; is suitable for the growth of edible fungi in different growth stages and types, and improves the quality and yield of the edible fungi.
CN116661530B discloses an intelligent control system and method in the industrial cultivation of edible fungi, which relates to the field of fungus intelligent control; acquiring a growth state image of edible fungi in a preset area, wherein the growth state image is acquired by a camera arranged in the preset area; determining a growth state label of the edible fungi based on the growth state image; scheduling optimal recommended environmental parameters from a database based on the growth state label of the edible fungi; and generating an environment parameter adjustment instruction based on the optimal recommended environment parameter, and transmitting the environment parameter adjustment instruction to the executor network. The scheme can improve the yield and quality of the edible fungi and reduce the labor cost and the resource consumption.
Based on the above related technology, the technical problem that the control of the growth environment of the edible fungi is unreasonable can be solved, however, the related technology does not consider the problem that the requirements of the edible fungi on the environment are different under different growth conditions, namely, the growth environment of the edible fungi cannot be accurately regulated according to the growth conditions of the edible fungi.
The information disclosed in the background section of the application is only for enhancement of understanding of the general background of the application and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a method and a system for controlling the growth environment of edible fungi, which can solve the technical problem that the growth environment of the edible fungi cannot be accurately controlled according to the growth condition of the edible fungi in the related technology.
According to a first aspect of an embodiment of the present invention, there is provided a method for controlling a growth environment of edible fungi, including: setting the growth environment of the edible fungi according to the growth environment parameters determined by the growth environment parameter model of the ith-1 control period in the ith control period; sampling all edible fungi to obtain a plurality of sample edible fungi, and determining growth condition data of each sample edible fungi, wherein the growth condition data comprises: mycelium extension range, mycelium density, number of fruiting bodies and size of fruiting bodies; determining edible fungus growth condition vectors and growth stage discrimination parameters of the ith control period according to the growth condition data of the sample edible fungus in the ith control period; judging whether a growth environment parameter model of the ith-1 th control period needs training according to the edible fungus growth condition vector and the growth stage judging parameter of the ith control period sample edible fungus and the edible fungus growth condition vector and the growth stage judging parameter of the ith-1 th control period sample edible fungus; under the condition that the growth environment parameter model of the ith control period and the 1 st control period need to be trained, determining a loss function of the growth environment parameter model of the ith control period and the 1 st control period according to the edible fungus growth condition vector and the growth period distinguishing parameter of the sample edible fungus of the ith control period and the edible fungus growth condition vector and the growth period distinguishing parameter of the sample edible fungus of the ith control period; training the growth environment parameter model of the ith control period to obtain the growth environment parameter model of the ith control period according to the loss function of the growth environment parameter model of the ith control period to the (1) th control period; and determining the growth environment parameters of the (i+1) th control period through the growth environment parameter model of the (i) th control period.
According to the invention, according to the growth condition data of the sample edible fungi in the ith control period, the edible fungi growth condition vector and the growth stage discrimination parameters of the ith control period are determined, and the method comprises the following steps: determining the growth stage discrimination parameters of the edible fungi according to the number and the size of the fruiting bodies of the sample edible fungi; determining fruiting body growth condition parameters of the edible fungi in the ith control period according to the fruiting body number and the fruiting body size of the edible fungi in the sample of the ith control period; determining mycelium growth condition parameters of the edible fungi in the ith control period according to the mycelium expansion range and mycelium density of the edible fungi in the sample of the ith control period; and determining the edible fungus growth condition vector of the ith control period according to the mycelium growth condition parameter and the fruiting body growth condition parameter of the edible fungus of the ith control period.
According to the invention, according to the number and size of fruiting bodies of the sample edible fungi, determining the growth stage discrimination parameters of the edible fungi comprises the following steps: according to the formulaDetermining the growth stage discrimination parameter/>, of the kth strain of sample edible fungi in the ith control periodWherein if is a conditional function,/>And/>For preset weight value,/>For the number of fruiting bodies of the kth strain of sample edible fungi at the end time of the ith control period,/>For the size of fruiting body of edible fungus of the kth strain sample at the end time of the ith control period,/>Is a preset fruiting body growth condition threshold value.
According to the invention, according to the fruiting body number and the fruiting body size of the sample edible fungi in the ith control period, the fruiting body growth condition parameters of the edible fungi in the ith control period are determined, and the method comprises the following steps: determining a fruiting body growth vector of the ith control period according to the fruiting body number and the fruiting body size of the sample edible fungi at the ending time of the ith control period; determining a standard fruiting body growth vector according to the number of preset fruiting bodies and the size of the preset fruiting bodies; according to the formulaDetermining fruiting body growth condition parameter/>, of the kth strain of sample edible fungi in the ith control periodWherein/>For the number of fruiting bodies of the kth sample edible fungi at the ending time of the ith control period,For the size of fruiting body of edible fungus of the kth strain sample at the end time of the ith control period,/>For presetting the number of sub-entities,/>To preset the size of the fruiting body,/>For the fruiting body growth vector of the kth strain of sample edible fungi in the ith control period,/>Is the standard fruiting body growth vector.
According to the invention, according to the mycelium extension range and mycelium density of the sample edible fungi in the ith control period, the mycelium growth condition parameters of the edible fungi in the ith control period are determined, and the method comprises the following steps: fitting the mycelium expansion range and the moment in the ith control period to determine an expansion range function in the ith control period; determining an extension range guide function according to the extension range function; according to the expansion range derivative function, determining the change rate of the expansion range of the mycelium at a plurality of moments in the ith control period; fitting the mycelium density and the moment in the ith control period to determine a mycelium density function in the ith control period; determining a mycelium density derivative function according to the mycelium density function; determining the change rate of the mycelium density at a plurality of moments in an ith control period according to the mycelium density derivative function; and determining the mycelium growth condition parameters of the edible fungi in the ith control period according to the mycelium expansion range change rate and the mycelium density change rate in the ith control period.
According to the invention, according to the mycelium extension range change rate and mycelium density change rate of the ith control period, the mycelium growth condition parameters of the edible fungi of the ith control period are determined, and the mycelium growth condition parameters comprise: according to the formulaDetermining mycelium growth condition parameter/>, of the kth strain of sample edible fungi in the ith control periodWherein/>And/>For preset weight value,/>At the j-th moment of the i-th control cycle,/>For the mycelium expansion range change rate of the kth strain sample edible fungi at the jth moment of the ith control period,/>For the mycelium density change rate of the kth strain of sample edible fungi at the jth moment of the ith control period,/>And j is a positive integer for presetting a mycelium growth speed threshold value.
According to the invention, according to the edible fungus growth condition vector and the growth stage discrimination parameter of the i-th control period sample edible fungus and the edible fungus growth condition vector and the growth stage discrimination parameter of the i-1 th control period sample edible fungus, whether the i-1 th control period growth environment parameter model needs training is judged, comprising: according to the formula: Determining growth phase condition G, wherein/( Judging parameters of the growth stage of the kth strain of sample edible fungi in the ith control period,/>, forDistinguishing parameters for the growth stage of the kth strain of sample edible fungi in the (i-1) th control period; in case the growth stage condition G is not satisfied, according to the formula/>Obtaining a growth condition P and an equilibrium condition B, wherein max is a maximum function, min is a minimum function,/>Mycelium growth condition parameters of the kth strain of sample edible fungi in the ith control period,/>, are obtainedMycelium growth condition parameters of the kth strain of sample edible fungi in the ith-1 control period,/>, are obtainedFor the fruiting body growth condition parameters of the kth strain of sample edible fungi in the ith control period,/>For the fruiting body growth condition parameters of the kth strain sample edible fungi in the ith-1 control period,/>The edible fungi growth condition vector of the kth sample edible fungi in the ith control period is used,The edible fungi growth condition vector of the kth sample edible fungi in the ith-1 control period is used,K is the number of the edible fungi in the sample for presetting a balanced growth condition threshold; judging that the growth environment parameter model of the ith-1 control period needs to be trained under the condition that the growth stage condition G is met; and under the condition that the growth stage condition G is not met, judging that the growth environment parameter model of the i-1 th control period needs to be trained when at least one of the growth condition P and the balance condition B is met.
According to the invention, under the condition that the growth environment parameter model of the ith-1 control period needs to be trained, according to the edible fungus growth condition vector and the growth stage discrimination parameter of the sample edible fungus of the ith-1 control period and the edible fungus growth condition vector and the growth stage discrimination parameter of the sample edible fungus of the ith-1 control period, the loss function of the growth environment parameter model of the ith-1 control period is determined, and the method comprises the following steps: according to the formula，Determining the loss function/>, of the growth environment parameter model of the i-1 th control periodWherein if is a conditional function,/>And/>For preset weight value,/>In order to lose the function growth parameters,And K is less than or equal to K and is a positive integer for a preset growth condition parameter threshold value.
According to a second aspect of embodiments of the present invention, there is provided an edible fungi growth environment control system, comprising: the setting module is used for setting the growth environment of the edible fungi according to the growth environment parameters determined by the growth environment parameter model of the ith-1 control period in the ith control period; the growth data module is used for sampling all edible fungi to obtain a plurality of sample edible fungi and determining growth condition data of each sample edible fungi, wherein the growth condition data comprises: mycelium extension range, mycelium density, number of fruiting bodies and size of fruiting bodies; the growth condition module is used for determining an edible fungus growth condition vector and a growth stage discrimination parameter of the ith control period according to the growth condition data of the sample edible fungus in the ith control period; the judging module is used for judging whether the growth environment parameter model of the ith-1 control period needs training according to the edible fungus growth condition vector and the growth stage judging parameter of the sample edible fungus of the ith control period and the edible fungus growth condition vector and the growth stage judging parameter of the sample edible fungus of the ith-1 control period; the loss function module is used for determining a loss function of the growth environment parameter model of the ith-1 control period according to the edible fungus growth condition vector and the growth stage discrimination parameter of the sample edible fungus of the ith-1 control period and the edible fungus growth condition vector and the growth stage discrimination parameter of the sample edible fungus of the ith-1 control period under the condition that the growth environment parameter model of the ith-1 control period needs to be trained; the training module is used for training the growth environment parameter model of the ith control period and the ith control period according to the loss function of the growth environment parameter model of the ith control period and the ith control period to obtain the growth environment parameter model of the ith control period; and the determining module is used for determining the growth environment parameters of the (i+1) th control period through the growth environment parameter model of the (i) th control period.
The technical effects are as follows: according to the invention, the growth condition and the growth stage of the edible fungi can be accurately analyzed in the edible fungi planting process, the loss function is determined based on the growth condition and the growth stage, and the growth environment parameter model is trained according to the loss function, so that the growth environment of the edible fungi is optimized, and the growth speed and the quality of the edible fungi are improved. When determining the growth stage discrimination parameters, the growth stage discrimination parameters of the edible fungi can be determined according to the number and the size of fruiting bodies of the sample edible fungi, and in the calculation process, the growth conditions of the fruiting bodies of the sample edible fungi are fully considered, so that the growth stage of the edible fungi is determined according to the growth conditions of the fruiting bodies of the sample edible fungi, and the accuracy of judging the growth stage of the edible fungi is improved. When the fruiting body growth condition parameters are determined, cosine similarity calculation can be carried out according to the fruiting body growth vector of the kth strain sample edible fungi and the standard fruiting body growth vector, and the fruiting body growth condition parameters are determined according to the cosine similarity, so that in the operation process, the difference between the fruiting body growth condition and the standard growth condition of the kth strain sample edible fungi is determined, and the accuracy, the scientificity and the objectivity of the fruiting body growth condition parameters are improved. When the mycelium growth condition parameters are determined, the mycelium growth condition parameters can be determined according to the mycelium expansion range change rate and the mycelium density change rate, and in the operation process, the mycelium density growth speed and the mycelium expansion range growth speed of the edible fungi can be respectively considered, and the mycelium growth speed is determined based on the mycelium density growth speed and the mycelium expansion range growth speed, so that the accuracy, the scientificity and the objectivity of the mycelium growth condition parameters are improved. When determining whether the growth environment parameter model needs training, judging whether the growth environment parameter model needs training according to the edible fungus growth condition vector and the growth stage distinguishing parameter of the sample edible fungus in the ith control period and the edible fungus growth condition vector and the growth stage distinguishing parameter of the sample edible fungus in the ith-1 control period, and when judging whether the growth environment parameter model needs training, referencing the relation between the edible fungus growth condition vector and the growth stage distinguishing parameter in two adjacent periods, so as to judge whether the growth environment parameter model reaches a stable state or not, and whether the growth environment parameter generated by the growth environment parameter model indicates edible fungus growth or not, and if the load needs training, continuously optimizing the growth environment parameter model, and improving the accuracy and the effectiveness of the growth environment parameter model. When the loss function is determined, the loss function of the growth environment parameter model of the ith-1 control period can be determined through the edible fungus growth condition vector and the growth stage distinguishing parameter of the sample edible fungus and the edible fungus growth condition vector and the growth stage distinguishing parameter of the adjacent control periods, so that the growth environment parameter model can quickly reach a stable state, the growth conditions of the sample edible fungus are optimized, the overall growth conditions of a plurality of sample edible fungi are homogenized, the training efficiency is improved, and the accuracy of the growth environment parameter model is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed. Other features and aspects of the present invention will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other embodiments may be obtained according to these drawings without inventive effort to a person skilled in the art;
FIG. 1 schematically illustrates a flow chart of an edible fungus growth environment control method according to an embodiment of the present invention;
fig. 2 exemplarily shows a block diagram of an edible fungi growth environment control system according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Fig. 1 schematically shows a flow chart of an edible fungus growth environment control method according to an embodiment of the present invention, the method includes: step S101, in the ith control period, setting the growth environment of the edible fungi according to the growth environment parameters determined by the growth environment parameter model of the ith-1 control period; step S102, sampling is carried out in all edible fungi to obtain a plurality of sample edible fungi, and growth condition data of each sample edible fungi are determined, wherein the growth condition data comprise: mycelium extension range, mycelium density, number of fruiting bodies and size of fruiting bodies; step S103, determining edible fungus growth condition vectors and growth stage discrimination parameters of the ith control period according to the growth condition data of the sample edible fungus in the ith control period; step S104, judging whether a growth environment parameter model of the ith-1 control period needs training according to the edible fungus growth condition vector and the growth stage judging parameter of the sample edible fungus of the ith control period and the edible fungus growth condition vector and the growth stage judging parameter of the sample edible fungus of the ith-1 control period; step S105, under the condition that the growth environment parameter model of the ith control period is required to be trained, determining a loss function of the growth environment parameter model of the ith control period according to the edible fungus growth condition vector and the growth stage discrimination parameter of the sample edible fungus of the ith control period and the edible fungus growth condition vector and the growth stage discrimination parameter of the sample edible fungus of the ith control period; step S106, training the growth environment parameter model of the ith control period to obtain the growth environment parameter model of the ith control period according to the loss function of the growth environment parameter model of the ith control period to the (1) th control period; step S107, determining the growth environment parameters of the (i+1) th control period through the growth environment parameter model of the (i) th control period.
According to the edible fungus growth environment control method provided by the embodiment of the invention, the growth condition and the growth stage of the edible fungus can be accurately analyzed in the edible fungus planting process, the loss function is determined based on the growth condition and the growth stage, and the growth environment parameter model is trained according to the loss function, so that the growth environment of the edible fungus is optimized, and the growth speed and the quality of the edible fungus are improved.
According to one embodiment of the present invention, in step S101, in the ith control period, the growth environment of the edible fungi is set according to the growth environment parameters determined by the growth environment parameter model of the ith-1 th control period, wherein the growth environment parameters include: ventilation, illumination, temperature, humidity, etc.
For example, one control period is set to 2 days, and in the 2 nd control period, the growth environment of the edible fungi is set according to the growth environment parameters determined by the growth environment parameter model of the 1 st control period, and in the 1 st control period, the growth environment of the edible fungi is set according to the preset growth environment parameters.
According to one embodiment of the present invention, in step S102, sampling is performed on all edible fungi to obtain a plurality of sample edible fungi, and growth condition data of each sample edible fungi is determined, where the growth condition data includes: mycelium extension range, mycelium density, number of fruiting bodies and fruiting body size.
For example, edible fungi planted in the planting area are the same strain, the growth period is the same, the planting area of the edible fungi is divided into a plurality of sampling areas, 2 edible fungi are randomly extracted from each sampling area to serve as sample edible fungi, and the growth condition of the sample edible fungi in the sampling areas is determined by observing the growth condition of the sample edible fungi. Placing some markers in a culture medium of the sample edible fungi, tracking the expansion of mycelia as reference points, and measuring the expansion distance of the mycelia to the markers at intervals; in the control period, collecting mycelium samples of the edible fungi at intervals, and measuring the mycelium density of the edible fungi by a mycelium suction filtration method; in the control period, determining the number of fruiting bodies of the edible fungi by observing and counting at intervals; in the control period, the fruiting body size of the edible fungi is determined by measuring the diameter of the fruiting body of the edible fungi at intervals.
According to one embodiment of the present invention, in step S103, according to the growth status data of the sample edible fungi in the ith control period, the edible fungi growth status vector and the growth stage discrimination parameters of the ith control period are determined.
According to one embodiment of the present invention, step S103 includes: determining the growth stage discrimination parameters of the edible fungi according to the number and the size of the fruiting bodies of the sample edible fungi; determining fruiting body growth condition parameters of the edible fungi in the ith control period according to the fruiting body number and the fruiting body size of the edible fungi in the sample of the ith control period; determining mycelium growth condition parameters of the edible fungi in the ith control period according to the mycelium expansion range and mycelium density of the edible fungi in the sample of the ith control period; and determining the edible fungus growth condition vector of the ith control period according to the mycelium growth condition parameter and the fruiting body growth condition parameter of the edible fungus of the ith control period.
According to one embodiment of the present invention, determining the growth stage discrimination parameters of the edible fungi according to the number and the size of the fruiting bodies of the sample edible fungi includes: determining the growth stage discrimination parameters of the kth strain of sample edible fungi in the ith control period according to the formula (1)，/>(1) Wherein if is a conditional function,/>And/>For preset weight value,/>For the number of fruiting bodies of the kth strain of sample edible fungi at the end time of the ith control period,/>For the size of fruiting body of edible fungus of the kth strain sample at the end time of the ith control period,/>Is a preset fruiting body growth condition threshold value.
In accordance with one embodiment of the present invention, in equation (1), the following two cases can be expressed by the form of a conditional function whenWhen the value of the conditional function is/>Indicating that the k-th sample edible fungus is in the mycelium growth phase, when/>When the value of the conditional function is/>Indicating that the k-th sample edible fungi is in the fruiting body growth period.
In accordance with one embodiment of the present invention,The method comprises the steps of carrying out weighted summation according to the number of fruiting bodies and the size of the fruiting bodies of the kth sample edible fungi at the ending time of the ith control period, and representing the growth condition of the fruiting bodies of the kth sample edible fungi at the ith control period; when/>In the case that the fruiting body growth condition of the kth strain of sample edible fungi in the ith control period is worse than the preset fruiting body growth condition, the edible fungi may not form obvious fruiting body structure when in the mycelium growth period, and the value of the condition function is/>; When/>When the growth condition of the fruiting body of the kth strain of sample edible fungi in the ith control period is better than that of the preset fruiting body, the edible fungi are in the growth period of the fruiting body, hyphae are twisted into mycelium clusters to form a rudiment of the fruiting body, and the value of the condition function is/>。
According to the method, the judging parameters of the growth stages of the edible fungi can be determined according to the number and the size of the fruiting bodies of the sample edible fungi, and the growth conditions of the fruiting bodies of the sample edible fungi are fully considered in the calculation process, so that the growth stages of the edible fungi are determined according to the growth conditions of the fruiting bodies of the sample edible fungi, and the judging accuracy of the growth stages of the edible fungi is improved.
According to one embodiment of the present invention, determining the fruiting body growth condition parameter of the edible fungi in the ith control period according to the fruiting body number and the fruiting body size of the sample edible fungi in the ith control period includes: determining a fruiting body growth vector of the ith control period according to the fruiting body number and the fruiting body size of the sample edible fungi at the ending time of the ith control period; determining a standard fruiting body growth vector according to the number of preset fruiting bodies and the size of the preset fruiting bodies; determining fruiting body growth condition parameters of the kth strain of sample edible fungus in the ith control period according to formula (2)，(2) Wherein/>For the number of fruiting bodies of the kth strain of sample edible fungi at the end time of the ith control period,/>The size of fruiting body of the kth sample edible fungus at the ending time of the ith control period,For presetting the number of sub-entities,/>To preset the size of the fruiting body,/>For the fruiting body growth vector of the kth strain of sample edible fungi in the ith control period,/>Is the standard fruiting body growth vector.
For example, the number of the preset fruit bodies and the size of the preset fruit bodies are determined according to the date of the ith control period (for example, the date of the ith control period is the 10 th day after edible fungi are planted) and the theoretical growth date of the edible fungi, and the standard fruit body growth vector is determined according to the number of the preset fruit bodies and the size of the preset fruit bodies.
In accordance with one embodiment of the present invention,The cosine similarity between the fruiting body growth vector of the kth sample edible fungus in the ith control period and the standard fruiting body growth vector is 1, the closer the similarity is, the closer the growth condition of the kth sample edible fungus is to the standard growth condition, the fruiting body growth condition parameter is determined according to the similarity, and the quality of the edible fungus is reduced, the nutritive value is reduced and the like due to the fact that the growth speed of the edible fungus is too high or too low, so that the closer the fruiting body growth condition parameter is to 0, the better the growth condition of the fruiting body of the edible fungus is.
In this way, cosine similarity calculation can be performed according to the fruiting body growth vector of the kth sample edible fungus and the standard fruiting body growth vector, and the fruiting body growth condition parameters are determined according to the cosine similarity, so that in the operation process, the difference between the fruiting body growth condition and the standard growth condition of the kth sample edible fungus is determined, and the accuracy, the scientificity and the objectivity of the fruiting body growth condition parameters are improved.
According to one embodiment of the present invention, determining the mycelium growth condition parameter of the edible fungi in the ith control period according to the mycelium extension range and mycelium density of the edible fungi in the sample of the ith control period includes: fitting the mycelium expansion range and the moment in the ith control period to determine an expansion range function in the ith control period; determining an extension range guide function according to the extension range function; according to the expansion range derivative function, determining the change rate of the expansion range of the mycelium at a plurality of moments in the ith control period; fitting the mycelium density and the moment in the ith control period to determine a mycelium density function in the ith control period; determining a mycelium density derivative function according to the mycelium density function; determining the change rate of the mycelium density at a plurality of moments in an ith control period according to the mycelium density derivative function; and determining the mycelium growth condition parameters of the edible fungi in the ith control period according to the mycelium expansion range change rate and the mycelium density change rate in the ith control period.
For example, fitting is performed on the mycelium extension range and a plurality of moments in the ith control period to obtain an extension range function for describing a rule that the mycelium extension range in the ith control period expands with the passage of time, derivative is performed on the extension range function, an extension range derivative function is determined, the plurality of moments in the ith control period are substituted into the extension range derivative function, and the mycelium extension range change rate of the plurality of moments in the ith control period is determined; fitting the mycelium density and a plurality of moments in the ith control period to obtain a mycelium density function for describing a rule that the mycelium density in the ith control period increases with time, deriving the mycelium density function, determining a mycelium density derivative function, substituting the moments in the ith control period into the mycelium density derivative function, and determining the mycelium density change rate of the moments in the ith control period; and determining the mycelium growth condition parameters of the edible fungi in the ith control period according to the mycelium expansion range change rate and the mycelium density change rate in the ith control period.
According to one embodiment of the present invention, determining the mycelium growth condition parameter of the edible fungus in the ith control period according to the mycelium extension range change rate and the mycelium density change rate of the ith control period includes: determining mycelium growth condition parameters of the kth strain of sample edible fungi in the ith control period according to a formula (3)，(3) Wherein/>And/>For preset weight value,/>At the j-th moment of the i-th control cycle,/>For the mycelium expansion range change rate of the kth strain sample edible fungi at the jth moment of the ith control period,/>For the mycelium density change rate of the kth strain of sample edible fungi at the jth moment of the ith control period,/>And j is a positive integer for presetting a mycelium growth speed threshold value.
In accordance with one embodiment of the present invention,The average mycelium expansion range change rate of the kth sample edible fungi in the ith control period is represented as the mycelium expansion range growth speed of the kth sample edible fungi in the ith control period,/>The average mycelium density change rate of the kth sample edible fungi in the ith control period is represented as the mycelium density growth rate of the kth sample edible fungi in the ith control period,In order to carry out weighted summation according to the growth speed of the mycelium extension range of the kth sample edible fungi in the ith control period and the mycelium density growth speed of the kth sample edible fungi in the ith control period, to represent the growth speed of the mycelium of the kth sample edible fungi in the ith control period,The ratio of the difference value of the mycelium growth speed of the kth sample edible fungus in the ith control period to the preset mycelium growth speed threshold value is shown, and the quality and the nutritive value of the edible fungus are reduced due to the fact that the growth speed of the edible fungus is too high or too low, so that the smaller the ratio is, the smaller the difference value of the mycelium growth speed and the preset mycelium growth speed threshold value is, and the better the mycelium growth condition is.
In this way, the mycelium growth condition parameters can be determined according to the mycelium expansion range change rate and the mycelium density change rate, and in the operation process, the mycelium density growth speed and the mycelium expansion range growth speed of the edible fungi can be respectively considered, and the mycelium growth speed is determined based on the mycelium density growth speed and the mycelium expansion range growth speed, so that the accuracy, the scientificity and the objectivity of the mycelium growth condition parameters are improved.
According to an embodiment of the present invention, in step S104, it is determined whether the growth environment parameter model of the i-1 th control period needs training according to the edible fungus growth condition vector and the growth stage discrimination parameter of the i-1 th control period sample edible fungus and the edible fungus growth condition vector and the growth stage discrimination parameter of the i-1 th control period sample edible fungus.
According to one embodiment of the present invention, step S104 includes: the growth stage condition G is determined according to equation (4),(4) Wherein/>Distinguishing parameters for the kth sample edible fungi in the growth stage of the ith control period,Distinguishing parameters for the growth stage of the kth strain of sample edible fungi in the (i-1) th control period; in the case where the growth stage condition G is not satisfied, the growth condition P and the equilibrium condition B are obtained according to the formula (5),(5) Wherein max is a maximum function, min is a minimum function, and/(m)The mycelium growth condition parameters of the kth sample edible fungi in the ith control period are obtained,Mycelium growth condition parameters of the kth strain of sample edible fungi in the ith-1 control period,/>, are obtainedFor the fruiting body growth condition parameters of the kth strain of sample edible fungi in the ith control period,/>For the fruiting body growth condition parameters of the kth strain sample edible fungi in the ith-1 control period,/>For the edible fungus growth condition vector of the kth strain sample edible fungus in the ith control period,/>For the edible fungus growth condition vector of the kth strain sample edible fungus in the ith-1 control period,/>K is the number of the edible fungi in the sample for presetting a balanced growth condition threshold; judging that the growth environment parameter model of the ith-1 control period needs to be trained under the condition that the growth stage condition G is met; and under the condition that the growth stage condition G is not met, judging that the growth environment parameter model of the i-1 th control period needs to be trained when at least one of the growth condition P and the balance condition B is met.
According to one embodiment of the present invention, in equation (4),The growth phase discrimination parameters of the kth sample edible fungi in the ith period are different from the growth phase discrimination parameters of the ith-1 period, namely, the kth sample edible fungi are in the mycelium growth phase in the ith-1 period and in the fruiting body growth phase in the ith period, the theoretical growth periods of the edible fungi planted in the planting area are the same, the duration of one control period is 2 days, and when the kth sample edible fungi are in the mycelium growth phase in the ith-1 period and in the fruiting body growth phase in the ith period, the other edible fungi in the planting area are basically in the same growth phase. When the edible fungi are in the fruiting body growth period, the requirements on temperature, humidity, illumination and ventilation are higher than those of the mycelium growth period, and the growth environment parameters determined by the growth environment parameter model of the ith-1 control period are not suitable for the growth of the edible fungi of the ith control period and the growth environment parameter model needs to be trained.
According to one embodiment of the present invention, in equation (5),The product of the edible fungus growth condition vector of the kth sample edible fungus in the ith control period and the growth stage discrimination parameter of the ith control period, namely the actual growth condition of the kth sample edible fungus in the ith control period (the smaller the product is, the better the growth condition) is represented that the growth condition of the mycelium of the edible fungus is focused and observed when the growth stage of the edible fungus is in the mycelium growth stage, the growth condition of the fruiting body of the edible fungus is focused and observed when the growth stage of the edible fungus is in the fruiting body growth stage,The actual growth condition of the kth sample edible fungi in the ith-1 control period is shown,The average actual growth condition of the k-strain sample edible fungi in the ith control period is worse than the average actual growth condition of the k-strain sample edible fungi in the ith control period, and the environment parameter setting error in the ith control period or the growth environment parameter determined by the growth environment parameter model in the ith control period to the 1 st control period is not suitable for the growth of the edible fungi in the ith control period under the condition that the growth period of the edible fungi is unchanged, so that the growth environment parameter model needs to be trained.
According to one embodiment of the present invention, in equation (5),The ratio of the difference between the actual growth optimal condition of the k-strain sample edible fungi and the actual growth worst condition of the k-strain sample edible fungi to the actual growth optimal condition of the k-strain sample edible fungi shows the growth non-uniformity degree of the k-strain sample edible fungi, the smaller the ratio is, the more uniform the growth condition of the k-strain sample edible fungi is, when the growth non-uniformity degree of the k-strain sample edible fungi is larger than a preset uniform growth condition threshold value, the growth non-uniformity degree of the k-strain sample edible fungi is overlarge, the situation that part of sample edible fungi are dysplasia is caused is shown, and the situation that the environmental parameter of the i-1 control period is set wrong or the growth environmental parameter determined by the growth environmental parameter model of the i-1 control period is not suitable for the growth of the edible fungi of the i-th control period is needed to train the growth environmental parameter model is shown.
According to one embodiment of the invention, when the growth stage condition G is satisfied, the growth stage of the edible fungi is changed, and the need of training the growth environment parameter model is judged; when the growth stage condition is not met, at least one of the growth condition P and the balance condition B is met, the growth condition of the sample edible fungi is poorer than the growth condition of the previous control period, or the growth and development conditions of the plurality of sample edible fungi are unbalanced, and the need of training the growth environment parameter model is judged.
In this way, according to the edible fungus growth condition vector and the growth stage discrimination parameter of the sample edible fungus in the ith control period and the edible fungus growth condition vector and the growth stage discrimination parameter of the sample edible fungus in the ith-1 control period, whether the growth environment parameter model in the ith-1 control period needs training or not can be judged, and the relation between the edible fungus growth condition vector and the growth stage discrimination parameter of two adjacent periods is referred to when judging whether the growth environment parameter model needs training or not, so that whether the growth environment parameter model has reached a stable state or not is judged, and whether the growth environment parameter generated by the growth environment parameter model indicates the growth of edible fungi or not is judged, if the conditions of the training are needed by the load, the growth environment parameter model is continuously optimized, and the accuracy and the effectiveness of the growth environment parameter model are improved.
According to one embodiment of the present invention, in step S105, in the case where the growth environment parameter model of the i-1 th control period needs to be trained, a loss function of the growth environment parameter model of the i-1 th control period is determined according to the edible fungus growth condition vector and the growth stage discrimination parameter of the i-1 th control period and the edible fungus growth condition vector and the growth stage discrimination parameter of the i-1 th control period.
According to one embodiment of the present invention, step S105 includes: determining a loss function of the growth environment parameter model of the i-1 th control period according to formulas (6) and (7)，/>（6）(7) Wherein if is a conditional function,/>And/>For preset weight value,/>Growth parameters for loss function,/>And K is less than or equal to K and is a positive integer for a preset growth condition parameter threshold value.
According to one embodiment of the present invention, in the formula (6), the following two cases can be expressed by the form of a conditional function whose value is when the growth stage condition G is satisfiedThat is, the average fruiting body growth condition parameter of k strains of edible fungi is used as a loss function, and in the process of training the growth environment parameter model, the parameters related to the edible fungi fruiting body growth in the loss function are required to be adjusted so that the average fruiting body growth condition parameter is reduced (the smaller the fruiting body growth condition parameter is, the better the fruiting body growth condition is) to enable the growth environment parameter model to reach a stable state as soon as possible, and when the growth stage condition G is not satisfied, the value of the condition function is/>。
According to one embodiment of the present invention, in equation (7),
The following two cases are represented by the form of a conditional function whose value is when the growth condition P is satisfiedNamely, the difference between the preset growth condition parameter threshold and the difference between the average actual growth condition of the k-th sample edible fungi in the ith control period and the average actual growth condition of the k-th sample edible fungi in the ith-1 th control period is used as a loss function, the difference is reduced in the process of training the growth environment parameter model, the average growth condition difference of the sample edible fungi between two adjacent control periods is close to the preset growth condition parameter threshold, the growth condition standard is met, the growth environment parameter model reaches a stable state as soon as possible, when the growth condition P is not met, the value of the condition function is 0, and the parameters related to the growth of the edible fungi in the loss function do not need to be adjusted.
According to one embodiment of the present invention, in equation (7),The following two cases are represented by the form of a conditional function whose value is when the equalization condition B is satisfiedThat is, the average value of the difference between the actual growth condition of each sample edible fungus and the average growth condition of K sample edible fungi is reduced in the training process, so that the average value of the whole growth condition of the sample edible fungi is improved, when the balance condition B is not met, the value of the condition function is 0, and the parameters related to uniform growth of the edible fungi in the loss function do not need to be adjusted.
In accordance with one embodiment of the present invention, in equation (7), the two terms may be weighted and summed to determine。
By the mode, the loss function of the growth environment parameter model of the i-1 th control period can be determined through the edible fungus growth condition vector and the growth stage distinguishing parameter of the sample edible fungus and the edible fungus growth condition vector and the growth stage distinguishing parameter of the adjacent control periods, so that the growth environment parameter model can quickly reach a stable state, the growth conditions of the sample edible fungus are optimized, the overall growth conditions of a plurality of sample edible fungi are homogenized, the training efficiency is improved, and the accuracy of the growth environment parameter model is improved.
According to one embodiment of the present invention, in step S106, the growth environment parameter model of the i-1 th control period is trained according to the loss function of the growth environment parameter model of the i-1 th control period, and the growth environment parameter model of the i-1 th control period is obtained.
According to one embodiment of the present invention, in step S107, the growth environment parameters of the (i+1) th control period are determined by the growth environment parameter model of the (i) th control period.
For example, according to the growth environment parameter model of the ith control period after training, the growth environment parameters of the (i+1) th control period are determined, so that the growth condition of the edible fungi is optimal, and the growth condition of each strain of edible fungi is more uniform.
According to the edible fungus growth environment control method provided by the embodiment of the invention, the growth condition and the growth stage of the edible fungus can be accurately analyzed in the edible fungus planting process, the loss function is determined based on the growth condition and the growth stage, and the growth environment parameter model is trained according to the loss function, so that the growth environment of the edible fungus is optimized, and the growth speed and the quality of the edible fungus are improved. When determining the growth stage discrimination parameters, the growth stage discrimination parameters of the edible fungi can be determined according to the number and the size of fruiting bodies of the sample edible fungi, and in the calculation process, the growth conditions of the fruiting bodies of the sample edible fungi are fully considered, so that the growth stage of the edible fungi is determined according to the growth conditions of the fruiting bodies of the sample edible fungi, and the accuracy of judging the growth stage of the edible fungi is improved. When the fruiting body growth condition parameters are determined, cosine similarity calculation can be carried out according to the fruiting body growth vector of the kth strain sample edible fungi and the standard fruiting body growth vector, and the fruiting body growth condition parameters are determined according to the cosine similarity, so that in the operation process, the difference between the fruiting body growth condition and the standard growth condition of the kth strain sample edible fungi is determined, and the accuracy, the scientificity and the objectivity of the fruiting body growth condition parameters are improved. When the mycelium growth condition parameters are determined, the mycelium growth condition parameters can be determined according to the mycelium expansion range change rate and the mycelium density change rate, and in the operation process, the mycelium density growth speed and the mycelium expansion range growth speed of the edible fungi can be respectively considered, and the mycelium growth speed is determined based on the mycelium density growth speed and the mycelium expansion range growth speed, so that the accuracy, the scientificity and the objectivity of the mycelium growth condition parameters are improved. When determining whether the growth environment parameter model needs training, judging whether the growth environment parameter model needs training according to the edible fungus growth condition vector and the growth stage distinguishing parameter of the sample edible fungus in the ith control period and the edible fungus growth condition vector and the growth stage distinguishing parameter of the sample edible fungus in the ith-1 control period, and when judging whether the growth environment parameter model needs training, referencing the relation between the edible fungus growth condition vector and the growth stage distinguishing parameter in two adjacent periods, so as to judge whether the growth environment parameter model reaches a stable state or not, and whether the growth environment parameter generated by the growth environment parameter model indicates edible fungus growth or not, and if the load needs training, continuously optimizing the growth environment parameter model, and improving the accuracy and the effectiveness of the growth environment parameter model. When the loss function is determined, the loss function of the growth environment parameter model of the ith-1 control period can be determined through the edible fungus growth condition vector and the growth stage distinguishing parameter of the sample edible fungus and the edible fungus growth condition vector and the growth stage distinguishing parameter of the adjacent control periods, so that the growth environment parameter model can quickly reach a stable state, the growth conditions of the sample edible fungus are optimized, the overall growth conditions of a plurality of sample edible fungi are homogenized, the training efficiency is improved, and the accuracy of the growth environment parameter model is improved.
Fig. 2 exemplarily shows a block diagram of an edible fungi growth environment control system according to an embodiment of the present invention, the system including: the setting module is used for setting the growth environment of the edible fungi according to the growth environment parameters determined by the growth environment parameter model of the ith-1 control period in the ith control period; the growth data module is used for sampling all edible fungi to obtain a plurality of sample edible fungi and determining growth condition data of each sample edible fungi, wherein the growth condition data comprises: mycelium extension range, mycelium density, number of fruiting bodies and size of fruiting bodies; the growth condition module is used for determining an edible fungus growth condition vector and a growth stage discrimination parameter of the ith control period according to the growth condition data of the sample edible fungus in the ith control period; the judging module is used for judging whether the growth environment parameter model of the ith-1 control period needs training according to the edible fungus growth condition vector and the growth stage judging parameter of the sample edible fungus of the ith control period and the edible fungus growth condition vector and the growth stage judging parameter of the sample edible fungus of the ith-1 control period; the loss function module is used for determining a loss function of the growth environment parameter model of the ith-1 control period according to the edible fungus growth condition vector and the growth stage discrimination parameter of the sample edible fungus of the ith-1 control period and the edible fungus growth condition vector and the growth stage discrimination parameter of the sample edible fungus of the ith-1 control period under the condition that the growth environment parameter model of the ith-1 control period needs to be trained; the training module is used for training the growth environment parameter model of the ith control period and the ith control period according to the loss function of the growth environment parameter model of the ith control period and the ith control period to obtain the growth environment parameter model of the ith control period; and the determining module is used for determining the growth environment parameters of the (i+1) th control period through the growth environment parameter model of the (i) th control period.
It will be appreciated by persons skilled in the art that the embodiments of the invention described above and shown in the drawings are by way of example only and are not limiting. The objects of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been shown and described in the examples and embodiments of the invention may be modified or practiced without departing from the principles described.
Claims (9)
1. The edible fungus growth environment control method is characterized by comprising the following steps: setting the growth environment of the edible fungi according to the growth environment parameters determined by the growth environment parameter model of the ith-1 control period in the ith control period; sampling all edible fungi to obtain a plurality of sample edible fungi, and determining growth condition data of each sample edible fungi, wherein the growth condition data comprises: mycelium extension range, mycelium density, number of fruiting bodies and size of fruiting bodies; determining edible fungus growth condition vectors and growth stage discrimination parameters of the ith control period according to the growth condition data of the sample edible fungus in the ith control period; judging whether a growth environment parameter model of the ith-1 th control period needs training according to the edible fungus growth condition vector and the growth stage judging parameter of the ith control period sample edible fungus and the edible fungus growth condition vector and the growth stage judging parameter of the ith-1 th control period sample edible fungus; under the condition that the growth environment parameter model of the ith control period and the 1 st control period need to be trained, determining a loss function of the growth environment parameter model of the ith control period and the 1 st control period according to the edible fungus growth condition vector and the growth period distinguishing parameter of the sample edible fungus of the ith control period and the edible fungus growth condition vector and the growth period distinguishing parameter of the sample edible fungus of the ith control period; training the growth environment parameter model of the ith control period to obtain the growth environment parameter model of the ith control period according to the loss function of the growth environment parameter model of the ith control period to the (1) th control period; and determining the growth environment parameters of the (i+1) th control period through the growth environment parameter model of the (i) th control period.
2. The method according to claim 1, wherein determining the edible fungi growth condition vector and the growth phase discrimination parameters of the ith control period according to the growth condition data of the sample edible fungi in the ith control period comprises: determining the growth stage discrimination parameters of the edible fungi according to the number and the size of the fruiting bodies of the sample edible fungi; determining fruiting body growth condition parameters of the edible fungi in the ith control period according to the fruiting body number and the fruiting body size of the edible fungi in the sample of the ith control period; determining mycelium growth condition parameters of the edible fungi in the ith control period according to the mycelium expansion range and mycelium density of the edible fungi in the sample of the ith control period; and determining the edible fungus growth condition vector of the ith control period according to the mycelium growth condition parameter and the fruiting body growth condition parameter of the edible fungus of the ith control period.
3. The method according to claim 2, wherein determining the growth stage discrimination parameters of the edible fungi based on the number of fruiting bodies and the size of fruiting bodies of the sample edible fungi comprises: according to the formulaDetermining the growth stage discrimination parameter/>, of the kth strain of sample edible fungi in the ith control periodWherein if is a conditional function,/>And/>For preset weight value,/>For the number of fruiting bodies of the kth strain of sample edible fungi at the end time of the ith control period,/>For the size of fruiting body of edible fungus of the kth strain sample at the end time of the ith control period,/>Is a preset fruiting body growth condition threshold value.
4. The method according to claim 2, wherein determining the fruiting body growth condition parameters of the edible fungi of the ith control period according to the fruiting body number and the fruiting body size of the sample edible fungi of the ith control period comprises: determining a fruiting body growth vector of the ith control period according to the fruiting body number and the fruiting body size of the sample edible fungi at the ending time of the ith control period; determining a standard fruiting body growth vector according to the number of preset fruiting bodies and the size of the preset fruiting bodies; according to the formulaDetermining fruiting body growth condition parameter/>, of the kth strain of sample edible fungi in the ith control periodWherein/>For the number of fruiting bodies of the kth strain of sample edible fungi at the end time of the ith control period,/>For the size of fruiting body of edible fungus of the kth strain sample at the end time of the ith control period,/>For presetting the number of sub-entities,/>To preset the size of the fruiting body,/>For the fruiting body growth vector of the kth strain of sample edible fungi in the ith control period,/>Is the standard fruiting body growth vector.
5. The method according to claim 2, wherein determining the mycelium growth condition parameters of the edible fungi of the i-th control period based on the mycelium extension range and mycelium density of the sample edible fungi of the i-th control period comprises: fitting the mycelium expansion range and the moment in the ith control period to determine an expansion range function in the ith control period; determining an extension range guide function according to the extension range function; according to the expansion range derivative function, determining the change rate of the expansion range of the mycelium at a plurality of moments in the ith control period; fitting the mycelium density and the moment in the ith control period to determine a mycelium density function in the ith control period; determining a mycelium density derivative function according to the mycelium density function; determining the change rate of the mycelium density at a plurality of moments in an ith control period according to the mycelium density derivative function; and determining the mycelium growth condition parameters of the edible fungi in the ith control period according to the mycelium expansion range change rate and the mycelium density change rate in the ith control period.
6. The method according to claim 5, wherein determining the mycelium growth condition parameters of the edible fungi of the i-th control period based on the mycelium extension range change rate and the mycelium density change rate of the i-th control period comprises: according to the formulaDetermining mycelium growth condition parameter/>, of the kth strain of sample edible fungi in the ith control periodWherein/>And/>For preset weight value,/>At the j-th moment of the i-th control cycle,/>For the mycelium expansion range change rate of the kth strain sample edible fungi at the jth moment of the ith control period,/>For the mycelium density change rate of the kth strain of sample edible fungi at the jth moment of the ith control period,/>And j is a positive integer for presetting a mycelium growth speed threshold value.
7. The method according to claim 6, wherein determining whether the growth environment parameter model of the i-1 th control period requires training based on the edible fungus growth condition vector and the growth phase discrimination parameter of the i-1 th control period sample edible fungus and the edible fungus growth condition vector and the growth phase discrimination parameter of the i-1 th control period sample edible fungus, comprises: according to the formula: Determining growth phase condition G, wherein/( Judging parameters of the growth stage of the kth strain of sample edible fungi in the ith control period,/>, forDistinguishing parameters for the growth stage of the kth strain of sample edible fungi in the (i-1) th control period; in the case where the growth stage condition G is not satisfied, the formula is usedObtaining a growth condition P and an equilibrium condition B, wherein max is a maximum function, min is a minimum function,/>Mycelium growth condition parameters of the kth strain of sample edible fungi in the ith control period,/>, are obtainedMycelium growth condition parameters of the kth strain of sample edible fungi in the ith-1 control period,/>, are obtainedFor the fruiting body growth condition parameters of the kth strain of sample edible fungi in the ith control period,/>For the fruiting body growth condition parameters of the kth strain sample edible fungi in the ith-1 control period,/>For the edible fungus growth condition vector of the kth strain sample edible fungus in the ith control period,/>For the edible fungus growth condition vector of the kth strain sample edible fungus in the ith-1 control period,/>K is the number of the edible fungi in the sample for presetting a balanced growth condition threshold; judging that the growth environment parameter model of the ith-1 control period needs to be trained under the condition that the growth stage condition G is met; and under the condition that the growth stage condition G is not met, judging that the growth environment parameter model of the i-1 th control period needs to be trained when at least one of the growth condition P and the balance condition B is met.
8. The method according to claim 7, wherein, in the case where the growth environment parameter model of the i-1 th control cycle needs to be trained, determining a loss function of the growth environment parameter model of the i-1 th control cycle based on the edible fungus growth condition vector and the growth stage discrimination parameter of the i-1 th control cycle and the edible fungus growth condition vector and the growth stage discrimination parameter of the i-1 th control cycle, comprises: according to the formula，/>Determining the loss function/>, of the growth environment parameter model of the i-1 th control periodWherein if is a conditional function,/>And/>For preset weight value,/>Growth parameters for loss function,/>And K is less than or equal to K and is a positive integer for a preset growth condition parameter threshold value.
9. An edible fungi growth environment control system, comprising: the setting module is used for setting the growth environment of the edible fungi according to the growth environment parameters determined by the growth environment parameter model of the ith-1 control period in the ith control period; the growth data module is used for sampling all edible fungi to obtain a plurality of sample edible fungi and determining growth condition data of each sample edible fungi, wherein the growth condition data comprises: mycelium extension range, mycelium density, number of fruiting bodies and size of fruiting bodies; the growth condition module is used for determining an edible fungus growth condition vector and a growth stage discrimination parameter of the ith control period according to the growth condition data of the sample edible fungus in the ith control period; the judging module is used for judging whether the growth environment parameter model of the ith-1 control period needs training according to the edible fungus growth condition vector and the growth stage judging parameter of the sample edible fungus of the ith control period and the edible fungus growth condition vector and the growth stage judging parameter of the sample edible fungus of the ith-1 control period; the loss function module is used for determining a loss function of the growth environment parameter model of the ith-1 control period according to the edible fungus growth condition vector and the growth stage discrimination parameter of the sample edible fungus of the ith-1 control period and the edible fungus growth condition vector and the growth stage discrimination parameter of the sample edible fungus of the ith-1 control period under the condition that the growth environment parameter model of the ith-1 control period needs to be trained; the training module is used for training the growth environment parameter model of the ith control period and the ith control period according to the loss function of the growth environment parameter model of the ith control period and the ith control period to obtain the growth environment parameter model of the ith control period; and the determining module is used for determining the growth environment parameters of the (i+1) th control period through the growth environment parameter model of the (i) th control period.
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| TWI707215B (en) * | 2019-11-08 | 2020-10-11 | 國立虎尾科技大學 | Method and system for monitoring growth environment of bag cultivated mushrooms |
| CN111831040A (en) * | 2020-07-31 | 2020-10-27 | 河南科技大学 | Agaricus bisporus mushroom house environment control system and control method |
| WO2022127943A1 (en) * | 2020-12-15 | 2022-06-23 | 广东省科学院微生物研究所(广东省微生物分析检测中心) | Low-spore variety of ganoderma lucidum having high polysaccharide yield and artificial cultivation method therefor |
| CN114879786A (en) * | 2022-05-23 | 2022-08-09 | 连云港银丰食用菌科技有限公司 | Method, system, device and medium for acquiring edible fungus decision scheme |
| CN116649160A (en) * | 2023-08-01 | 2023-08-29 | 南京康之春生物科技有限公司 | Edible fungus strain production monitoring system and monitoring method |
| CN116649159A (en) * | 2023-08-01 | 2023-08-29 | 江苏慧岸信息科技有限公司 | Edible fungus growth parameter optimizing system and method |
| CN117148902A (en) * | 2023-10-31 | 2023-12-01 | 安徽农业大学 | Intelligent fungus stick growth environment self-adaptive control system and method |
| CN117475429A (en) * | 2023-10-08 | 2024-01-30 | 广州奥坤科技有限公司 | Control method and device based on key environmental parameters of straw mushroom biological characteristics |
| CN118053154A (en) * | 2024-03-14 | 2024-05-17 | 北京市农林科学院智能装备技术研究中心 | Oyster mushroom growth monitoring method and device, electronic equipment and storage medium |
-
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- 2024-05-24 CN CN202410656951.8A patent/CN118216379B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI707215B (en) * | 2019-11-08 | 2020-10-11 | 國立虎尾科技大學 | Method and system for monitoring growth environment of bag cultivated mushrooms |
| CN111831040A (en) * | 2020-07-31 | 2020-10-27 | 河南科技大学 | Agaricus bisporus mushroom house environment control system and control method |
| WO2022127943A1 (en) * | 2020-12-15 | 2022-06-23 | 广东省科学院微生物研究所(广东省微生物分析检测中心) | Low-spore variety of ganoderma lucidum having high polysaccharide yield and artificial cultivation method therefor |
| CN114879786A (en) * | 2022-05-23 | 2022-08-09 | 连云港银丰食用菌科技有限公司 | Method, system, device and medium for acquiring edible fungus decision scheme |
| CN116649160A (en) * | 2023-08-01 | 2023-08-29 | 南京康之春生物科技有限公司 | Edible fungus strain production monitoring system and monitoring method |
| CN116649159A (en) * | 2023-08-01 | 2023-08-29 | 江苏慧岸信息科技有限公司 | Edible fungus growth parameter optimizing system and method |
| CN117475429A (en) * | 2023-10-08 | 2024-01-30 | 广州奥坤科技有限公司 | Control method and device based on key environmental parameters of straw mushroom biological characteristics |
| CN117148902A (en) * | 2023-10-31 | 2023-12-01 | 安徽农业大学 | Intelligent fungus stick growth environment self-adaptive control system and method |
| CN118053154A (en) * | 2024-03-14 | 2024-05-17 | 北京市农林科学院智能装备技术研究中心 | Oyster mushroom growth monitoring method and device, electronic equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 万华等: "基于ScSPM算法的蛹虫草长势识别研究", 现代计算机(专业版), no. 27, 25 September 2018 (2018-09-25), pages 48 - 51 * |
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