CN117158218A - Intelligent agricultural photovoltaic lighting system and method - Google Patents
Intelligent agricultural photovoltaic lighting system and method Download PDFInfo
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Abstract
The application relates to the technical field of agricultural illumination, in particular to an intelligent agricultural photovoltaic illumination system and method, comprising a server; the server comprises a first acquisition module, a second acquisition module, a judging module, an image acquisition module, a recognition module, a statistics module, a processing module and an execution module, wherein the recognition module is used for recognizing and judging crop image information according to crop image information and recognizing crop basic information of the crop; the statistical module is used for acquiring all environmental data before the current time information in one day, analyzing all the environmental data one by one to obtain corresponding environmental change data, and the processing module is used for predicting the illumination strategy information corresponding to the crops in illumination processing based on the prediction module according to the basic information of the crops and the environmental change data. The application can solve the problems of poor pertinence and poor light supplementing effect when crops are supplemented with light in the prior art.
Description
Technical Field
The application relates to the technical field of agricultural illumination, in particular to an intelligent agricultural photovoltaic illumination system and method.
Background
The illumination is the source of energy and signals required by the growth and development of various organisms on the earth, and the morphological formation of animals and plants in the metabolic process has close relation with the illumination. Natural illumination is different along with geographic position, weather condition and seasonal variation, weather such as even yin, rain, snow, fog can appear in winter and spring in higher latitude areas, and the atmosphere transparency is low, and the phenomenon that facility internal illumination is insufficient is caused by facility covering materials and filtering effect in addition, so that artificial intelligence light filling becomes an important guarantee means for high-efficiency production of modern agriculture. Thus, artificial light sources are playing an increasingly important role in the current agricultural industry.
At present, photovoltaic illumination is a relatively common artificial light source applied to agricultural production, and the existing agricultural photovoltaic illumination system is mainly used for agricultural illumination, so that the illumination lamp can run at night through the photovoltaic panel, and electric resources are saved. However, some problems still exist in the existing agricultural photovoltaic lighting system: for example, the degree to which the corresponding illumination information can be adjusted during illumination is limited, that is, the corresponding conditions are performed by preset parameters, which results in poor light supplementing pertinence to crops during illumination of the crops, and poor light supplementing effect.
Disclosure of Invention
The application aims to provide an intelligent agricultural photovoltaic lighting system and method, which can solve the problems of poor pertinence and poor light supplementing effect in the prior art when crops are supplemented with light.
In order to achieve the above purpose, an intelligent agricultural photovoltaic lighting system is provided, which comprises a server side; the server side comprises:
the first acquisition module is used for acquiring current time information of the crop locus in real time to obtain the current time information;
the second acquisition module is used for acquiring environmental data corresponding to the places where the crops are located in real time; the environment data comprises illumination information, temperature information and humidity information;
the judging module is used for judging whether the current illumination of the place where the crops are located can not meet the requirements of the crops according to the obtained current time information and the environment data, if so, judging that the crops are required to be illuminated at present, otherwise, the crops are not required to be illuminated;
the image acquisition module is used for acquiring images of all crops at the moment when the judgment result shows that the crops are required to be subjected to illumination treatment at present, and generating crop image information;
the identification module is used for identifying and judging crop image information according to the generated crop image information, and identifying crop basic information corresponding to the crop, wherein the crop basic information comprises crop type, crop growth information and crop planting time information;
the statistics module is used for acquiring all environmental data before the current time information in one day, and analyzing all the environmental data one by one to obtain corresponding environmental change data, wherein the environmental change data comprises illumination change information, temperature change information and humidity change information;
the processing module is used for predicting illumination strategy information corresponding to the crop when illumination processing is performed based on the prediction module according to the basic information of the crop and the environmental change data;
and the execution module is used for controlling each lighting device according to the predicted lighting strategy information.
The principle and effect of this scheme: in the scheme, firstly, the time information and the environment data of the place where the crops are located are collected in real time, so that the time and the environment situation corresponding to the place where the crops are located can be known, artificial illumination treatment is not required to be carried out on the crops in the place where the crops are located in one day, and most of the time can be naturally illuminated through sunlight.
Once the crops are determined to be subjected to illumination treatment, the corresponding points are determined according to the illumination condition information corresponding to the illumination treatment, so that the crops can be positively promoted to grow by illumination, the crops are promoted to grow and are restrained to grow in various ways due to different illumination condition information, and therefore, the crops are subjected to image information acquisition at the first time, basic information of the crops is identified through image information acquisition, for example, the types corresponding to the crops, the planting time of the crops and the current growth conditions of the crops are identified.
Of course, all environmental data before the current time information in one day is acquired, so that the environmental change condition of the previous crops can be performed according to the previous environmental data, and the environmental change data, such as the illumination change information, the temperature change information and the humidity change information, are known.
And then, according to basic information of crops and environmental change data, the illumination strategy information of the current crops is predicted through a prediction model, so that the corresponding illumination strategy information is predicted when the crops are artificially illuminated, the subsequent artificial illumination treatment can play a good role in promoting the growth of the crops, and the problems of poor pertinence and poor light supplementing effect in the process of supplementing the crops in the prior art can be solved.
Further, the server side further includes:
the determining module is used for determining all crops in the illumination area according to the illumination area corresponding to each illumination device;
the image acquisition module is used for acquiring images of all crops in the illumination area to obtain image information of the illumination area;
the judging module is used for judging each crop in the image information according to the image information of the illumination area, judging the growth state corresponding to each crop and calculating the corresponding advantage ratio; the advantage ratio is the ratio of the number of crops in normal growth state to the number of all crops in the illumination area, and is calculated as follows:
wherein F is n The advantage ratio corresponding to the illumination equipment N is that M is the number of crops with normal growth conditions in the illumination area corresponding to the illumination equipment N, and N is the number of all crops in the illumination area corresponding to the illumination equipment N;
the processing module is also used for determining the illumination type in the illumination area of each illumination device according to the corresponding advantage ratio of each illumination device;
and the adjusting module is used for dynamically adjusting the lighting strategy information corresponding to each lighting device according to the lighting type corresponding to each lighting device.
The beneficial effects are that: in the scheme, the situation that the growth situation of crops in an illumination area corresponding to each illumination device is different is fully considered, the overall growth situation of the crops cannot represent that the growth situation of all the crops is the same, and the quality score is required to exist, when the illumination device performs illumination treatment, all the crops in the illumination area of the illumination device are subjected to image information acquisition, then the growth situation of all the crops in the illumination area is analyzed one by one, so that the growth situation of the crops in the illumination area is clear, the ratio of the number of the crops in the normal growth situation in each illumination area to the number of the crops in the illumination area can be calculated, and then the illumination strategy information of each illumination area of each illumination device is dynamically adjusted one by one according to the ratio, so that the illumination strategy of each illumination device can be better adapted to the growth of the crops in the illumination area.
Further, the specific steps of determining the illumination type in the illumination area of each illumination device according to the corresponding dominance ratio of each illumination device are as follows:
according to the advantage ratio of each lighting device, a preset type interval threshold value is called from a database;
according to the illumination areas of the corresponding illumination equipment and the growth state of each crop in the illumination areas, performing first adjustment on the type interval threshold;
obtaining a corresponding calculated value based on a first calculation formula according to the dominance ratio of each lighting device, and performing second adjustment on the type interval threshold after the first adjustment based on the calculated value;
the first calculation formula is as follows:
wherein M is n For the calculated value corresponding to the lighting device n,the dominance ratio of the lighting device n corresponding to t days;
and according to the second adjusted type zone threshold, determining the illumination type corresponding to the corresponding illumination equipment by comparing the dominance ratio of the corresponding illumination equipment with the type zone threshold.
The beneficial effects are that: when the type interval threshold value corresponding to the dominance ratio is determined, the determination of the corresponding type interval threshold value is more in line with the requirements of corresponding lighting equipment through multiple times of adjustment of the first adjustment and the second adjustment, and the accuracy of dominance ratio judgment is greatly improved.
Further, the server side further includes:
the computing module is used for acquiring the dominance ratios corresponding to the day, the previous day and the previous two days of each lighting device according to the dominance ratio of each lighting device, judging the growth condition of crops in the lighting area corresponding to each lighting device based on a second computing formula, and judging whether the growth condition of the crops in the lighting area corresponding to each lighting device is abnormal;
the second calculation formula is as follows:
the positioning alarm module is used for determining the position information corresponding to the lighting equipment and sending an alarm signal to the server when the judging result shows that the growth of crops in the lighting area corresponding to the lighting equipment is abnormal;
and the path planning module is used for acquiring all the position information of the crops in the place after sending out the alarm signal to obtain a corresponding position information set, and obtaining a corresponding abnormal check optimal path based on an improved genetic algorithm according to the position information set.
The beneficial effects are that: in the scheme, according to the dominance ratios of the lighting equipment on the same day as the previous day and the previous two days, the ratio of the increment of the dominance ratio of the current day from the previous day to the increment of the dominance ratio of the previous day to the previous two days is calculated based on a first calculation formula, so as to judge the growth condition of crops on the current day, judging whether the abnormal situation exists or not, and then when the abnormal situation exists, acquiring position information corresponding to all the abnormal situation of the current day, and planning an optimal path by utilizing the position information, so that the optimal path corresponding to the abnormal points is realized when an operator checks, and the checking efficiency of the operator is greatly improved.
Further, the path planning module includes:
the constraint module is used for randomly generating an initial population with the scale of M according to the departure point position information and the position information set of the operator, wherein the individuals of the initial population are paths corresponding to the paths of the operator passing through each position information; screening the initial population through constraint conditions, wherein the constraint conditions comprise a constraint condition of the number of the passed position information and the maximum repetition number of a single path; if the constraint condition is met, the corresponding path is a feasible solution, otherwise, the path is an infeasible solution;
the fitness calculation module is used for calculating fitness of the screened population; the fitness is calculated as follows:
f1 is the sum of paths in the corresponding population, X i For the path corresponding to the position i to the next position, F2 is the sum of walking difficulty corresponding to all paths in the corresponding population, m d For the walking difficulty coefficient corresponding to each meter from the position j to the next position, h1 is the first fitness, and h2 is the second fitness;
the selection module is used for selecting a population with the first fitness being greater than or equal to a preset first fitness threshold value in the preset first iteration times, reserving the population with the second fitness being greater than or equal to a second fitness first threshold value at the moment, and rejecting all other populations which do not meet the conditions;
after the first iteration times are exceeded, selecting a population with the second fitness being greater than or equal to a second threshold value of the second fitness, and rejecting all other populations which do not meet the conditions;
the hybridization mutation module is used for carrying out hybridization and mutation of a genetic algorithm on the selected population according to a preset hybridization mutation strategy to obtain a corresponding offspring population;
the circulation module is used for continuously executing the fitness calculation module after the offspring population is obtained until the iteration number of the population meets the preset iteration number;
the output module is used for outputting the child population as an optimal solution set after multi-objective optimization;
and the optimization module is used for determining an abnormally checked optimal route according to the multi-objective optimization result.
The beneficial effects are that: in the scheme, the constraint conditions of the population are determined, so that the preliminary screening of the population is realized, namely, the situation that the corresponding paths in the corresponding population do not pass through all points is eliminated, the limitation is also carried out by setting the repetition number of the points, excessive repeated routes are avoided, and the difficulty of subsequent population screening can be reduced.
And then, further screening of the population is realized through calculation of the fitness, and further screening is performed through the sum of the paths and the sum of the walking difficulty of the paths, so that the determination of the optimal abnormal viewing route is realized, and the determined optimal abnormal viewing route is more accurate and real.
An intelligent agricultural photovoltaic lighting method is applied to the intelligent agricultural photovoltaic lighting system.
Drawings
FIG. 1 is a logic block diagram of an intelligent agricultural photovoltaic lighting system according to an embodiment of the present application;
fig. 2 is a logic block diagram of a path planning module according to an embodiment of the application.
Detailed Description
The following is a further detailed description of the embodiments:
example 1
An intelligent agricultural photovoltaic lighting system, substantially as shown in figure 1, comprising a server; the server side comprises:
the first acquisition module is used for acquiring current time information of the crop locus in real time to obtain the current time information;
the second acquisition module is used for acquiring environmental data corresponding to the places where the crops are located in real time; the environment data comprises illumination information, temperature information and humidity information;
the judging module is used for judging whether the current illumination of the place where the crops are located can not meet the requirements of the crops according to the obtained current time information and the environment data, if so, judging that the crops are required to be illuminated at present, otherwise, the crops are not required to be illuminated;
the image acquisition module is used for acquiring images of all crops at the moment when the judgment result shows that the crops are required to be subjected to illumination treatment at present, and generating crop image information;
the identification module is used for identifying and judging crop image information according to the generated crop image information, and identifying crop basic information corresponding to the crop, wherein the crop basic information comprises crop type, crop growth information and crop planting time information;
the statistics module is used for acquiring all environmental data before the current time information in one day, and analyzing all the environmental data one by one to obtain corresponding environmental change data, wherein the environmental change data comprises illumination change information, temperature change information and humidity change information;
the processing module is used for predicting illumination strategy information corresponding to the crop when illumination processing is performed based on the prediction module according to the basic information of the crop and the environmental change data;
and the execution module is used for controlling each lighting device according to the predicted lighting strategy information.
The server side further comprises:
the determining module is used for determining all crops in the illumination area according to the illumination area corresponding to each illumination device;
the image acquisition module is used for acquiring images of all crops in the illumination area to obtain image information of the illumination area;
the judging module is used for judging each crop in the image information according to the image information of the illumination area, judging the growth state corresponding to each crop and calculating the corresponding advantage ratio; the advantage ratio is the ratio of the number of crops in normal growth state to the number of all crops in the illumination area, and is calculated as follows:
wherein f n The advantage ratio corresponding to the illumination equipment N is that M is the number of crops with normal growth conditions in the illumination area corresponding to the illumination equipment N, and N is the number of all crops in the illumination area corresponding to the illumination equipment N;
the processing module is also used for determining the illumination type in the illumination area of each illumination device according to the corresponding advantage ratio of each illumination device;
the specific steps of determining the illumination type in the illumination area of each illumination device according to the corresponding advantage ratio of each illumination device are as follows:
according to the advantage ratio of each lighting device, a preset type interval threshold value is called from a database;
according to the illumination areas of the corresponding illumination equipment and the growth state of each crop in the illumination areas, performing first adjustment on the type interval threshold;
obtaining a corresponding calculated value based on a first calculation formula according to the dominance ratio of each lighting device, and performing second adjustment on the type interval threshold after the first adjustment based on the calculated value;
the first calculation formula is as follows:
wherein M is n For the calculated value corresponding to the lighting device n,the dominance ratio of the lighting device n corresponding to t days;
and according to the second adjusted type zone threshold, determining the illumination type corresponding to the corresponding illumination equipment by comparing the dominance ratio of the corresponding illumination equipment with the type zone threshold.
And the adjusting module is used for dynamically adjusting the lighting strategy information corresponding to each lighting device according to the lighting type corresponding to each lighting device. In this embodiment, the type interval thresholds are x and y respectively; the specific numerical values of x and y are dynamically adjusted and are determined by two times of adjustment. The initial value is x=0.5, y=0.8.
When f n When x is less than or equal to, the health type of the battery is abnormal;
when y is>f n >x, then the health type of the cell is intermediate;
when f n >And y, the health type of the battery is normal.
The server side further comprises:
the computing module is used for acquiring the dominance ratios corresponding to the day, the previous day and the previous two days of each lighting device according to the dominance ratio of each lighting device, judging the growth condition of crops in the lighting area corresponding to each lighting device based on a second computing formula, and judging whether the growth condition of the crops in the lighting area corresponding to each lighting device is abnormal;
the second calculation formula is as follows:
the positioning alarm module is used for determining the position information corresponding to the lighting equipment and sending an alarm signal to the server when the judging result shows that the growth of crops in the lighting area corresponding to the lighting equipment is abnormal;
and the path planning module is used for acquiring all the position information of the crops in the place after sending out the alarm signal to obtain a corresponding position information set, and obtaining a corresponding abnormal check optimal path based on an improved genetic algorithm according to the position information set. In this embodiment, the second calculation formula is to calculate the ratio of the dominance ratio from the previous day to the current day to the dominance ratio from the previous two days to the previous day along with the continuous growth of the crop, and determine whether the growth vigor is abnormal by presetting a determination threshold.
As shown in fig. 2, the path planning module includes:
the constraint module is used for randomly generating an initial population with the scale of M according to the departure point position information and the position information set of the operator, wherein the individuals of the initial population are paths corresponding to the paths of the operator passing through each position information; screening the initial population through constraint conditions, wherein the constraint conditions comprise a constraint condition of the number of the passed position information and the maximum repetition number of a single path; if the constraint condition is met, the corresponding path is a feasible solution, otherwise, the path is an infeasible solution;
the fitness calculation module is used for calculating fitness of the screened population; the fitness is calculated as follows:
f1 is the sum of paths in the corresponding population, X i For the path corresponding to the position i to the next position, F2 is the sum of walking difficulty corresponding to all paths in the corresponding population, m d For the walking difficulty coefficient corresponding to each meter from the position j to the next position, h1 is the first fitness, and h2 is the second fitness; in the present embodiment, m d And counting the walking difficulty of each meter corresponding to each road to form a corresponding table, and referring and calling the table when needed.
The selection module is used for selecting a population with the first fitness being greater than or equal to a preset first fitness threshold value in the preset first iteration times, reserving the population with the second fitness being greater than or equal to a second fitness first threshold value at the moment, and rejecting all other populations which do not meet the conditions;
after the first iteration times are exceeded, selecting a population with the second fitness being greater than or equal to a second threshold value of the second fitness, and rejecting all other populations which do not meet the conditions;
the hybridization mutation module is used for carrying out hybridization and mutation of a genetic algorithm on the selected population according to a preset hybridization mutation strategy to obtain a corresponding offspring population;
in this embodiment, the hybridization variation strategy is:
during hybridization, the selected populations are combined in pairs randomly, individuals corresponding to the populations at preset positions are selected, corresponding hybridization fragments a1 and a2 are formed, numbers b1 and b2 between [0 and N ] and a random number b0 of [0 and 1] are randomly generated for the hybridization fragments of the two populations, and if b0 is more than or equal to 0.5 and b1 is more than or equal to b2, the hybridization fragments a1 and a2 are exchanged to form two new populations; if b0 is more than or equal to 0.5 and b1 is less than b2, the hybridization fragment a1 is unchanged, and the hybridization fragment a2 is replaced by the hybridization fragment a1; for example, population 1 is A1, B1, C1, F1, E1, population 2 is A2, B2, C2, F2, E2; at this time, the corresponding hybridization fragment a1 is [ C1, F1, E1], a2 is [ C2, F2, E2], and then the numbers of b0, b1, b2 are randomly generated, for example, b0=0.6, b1=50, b2=6, respectively; at this time, according to the corresponding conditions, 0.6>0.5 and 50>6, the populations after hybridization at this time were A1, B1, C2, F2 and E2, respectively, and A2, B2, C1, F1 and E1, respectively.
In this embodiment, in order to make mutation better, a random number of [0,1] is randomly generated for each individual in the population during mutation, and if the random number is smaller than the mutation probability according to the preset mutation probability, the mutation of the individual is performed.
Through formulating mutation and hybridization strategies, mutation and hybridization are more random, more practical and more true, the comprehensiveness and the integrity of mutation and hybridization are greatly improved, and individuals in a population can be better and more comprehensively reserved.
The circulation module is used for continuously executing the fitness calculation module after the offspring population is obtained until the iteration number of the population meets the preset iteration number;
the output module is used for outputting the child population as an optimal solution set after multi-objective optimization;
and the optimization module is used for determining an abnormally checked optimal route according to the multi-objective optimization result.
The embodiment also provides an intelligent agricultural photovoltaic lighting method, and the intelligent agricultural photovoltaic lighting system is applied.
The foregoing is merely exemplary of the present application, and the specific structures and features well known in the art will be described in detail herein so that those skilled in the art will be able to ascertain the general knowledge of the technical field of the application, whether it is the application date or the priority date, and to ascertain all of the prior art in this field, with the ability to apply the conventional experimental means before this date, without the ability of those skilled in the art to make various embodiments with the benefit of this disclosure. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present application, and these should also be considered as the scope of the present application, which does not affect the effect of the implementation of the present application and the utility of the patent. The protection scope of the present application is subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (6)
1. An intelligent agricultural photovoltaic lighting system, characterized in that: the method comprises a server; the server side comprises:
the first acquisition module is used for acquiring current time information of the crop locus in real time to obtain the current time information;
the second acquisition module is used for acquiring environmental data corresponding to the places where the crops are located in real time; the environment data comprises illumination information, temperature information and humidity information;
the judging module is used for judging whether the current illumination of the place where the crops are located can not meet the requirements of the crops according to the obtained current time information and the environment data, if so, judging that the crops are required to be illuminated at present, otherwise, the crops are not required to be illuminated;
the image acquisition module is used for acquiring images of all crops at the moment when the judgment result shows that the crops are required to be subjected to illumination treatment at present, and generating crop image information;
the identification module is used for identifying and judging crop image information according to the generated crop image information, and identifying crop basic information corresponding to the crop, wherein the crop basic information comprises crop type, crop growth information and crop planting time information;
the statistics module is used for acquiring all environmental data before the current time information in one day, and analyzing all the environmental data one by one to obtain corresponding environmental change data, wherein the environmental change data comprises illumination change information, temperature change information and humidity change information;
the processing module is used for predicting illumination strategy information corresponding to the crop when illumination processing is performed based on the prediction module according to the basic information of the crop and the environmental change data;
and the execution module is used for controlling each lighting device according to the predicted lighting strategy information.
2. An intelligent agricultural photovoltaic lighting system according to claim 1, characterized in that: the server side further comprises:
the determining module is used for determining all crops in the illumination area according to the illumination area corresponding to each illumination device;
the image acquisition module is used for acquiring images of all crops in the illumination area to obtain image information of the illumination area;
the judging module is used for judging each crop in the image information according to the image information of the illumination area, judging the growth state corresponding to each crop and calculating the corresponding advantage ratio; the advantage ratio is the ratio of the number of crops in normal growth state to the number of all crops in the illumination area, and is calculated as follows:
wherein F is n The advantage ratio corresponding to the illumination equipment N is that M is the number of crops with normal growth conditions in the illumination area corresponding to the illumination equipment N, and N is the number of all crops in the illumination area corresponding to the illumination equipment N;
the processing module is also used for determining the illumination type in the illumination area of each illumination device according to the corresponding advantage ratio of each illumination device;
and the adjusting module is used for dynamically adjusting the lighting strategy information corresponding to each lighting device according to the lighting type corresponding to each lighting device.
3. An intelligent agricultural photovoltaic lighting system according to claim 2, characterized in that: the specific steps of determining the illumination type in the illumination area of each illumination device according to the corresponding advantage ratio of each illumination device are as follows:
according to the advantage ratio of each lighting device, a preset type interval threshold value is called from a database;
according to the illumination areas of the corresponding illumination equipment and the growth state of each crop in the illumination areas, performing first adjustment on the type interval threshold;
obtaining a corresponding calculated value based on a first calculation formula according to the dominance ratio of each lighting device, and performing second adjustment on the type interval threshold after the first adjustment based on the calculated value;
the first calculation formula is as follows:
wherein M is n For the calculated value corresponding to the lighting device n,the dominance ratio of the lighting device n corresponding to t days;
and according to the second adjusted type zone threshold, determining the illumination type corresponding to the corresponding illumination equipment by comparing the dominance ratio of the corresponding illumination equipment with the type zone threshold.
4. A smart agricultural photovoltaic lighting system according to claim 3, characterized in that: the server side further comprises:
the computing module is used for acquiring the dominance ratios corresponding to the day, the previous day and the previous two days of each lighting device according to the dominance ratio of each lighting device, judging the growth condition of crops in the lighting area corresponding to each lighting device based on a second computing formula, and judging whether the growth condition of the crops in the lighting area corresponding to each lighting device is abnormal;
the second calculation formula is as follows:
the positioning alarm module is used for determining the position information corresponding to the lighting equipment and sending an alarm signal to the server when the judging result shows that the growth of crops in the lighting area corresponding to the lighting equipment is abnormal;
and the path planning module is used for acquiring all the position information of the crops in the place after sending out the alarm signal to obtain a corresponding position information set, and obtaining a corresponding abnormal check optimal path based on an improved genetic algorithm according to the position information set.
5. An intelligent agricultural photovoltaic lighting system according to claim 4, wherein: the path planning module comprises:
the constraint module is used for randomly generating an initial population with the scale of M according to the departure point position information and the position information set of the operator, wherein the individuals of the initial population are paths corresponding to the paths of the operator passing through each position information; screening the initial population through constraint conditions, wherein the constraint conditions comprise a constraint condition of the number of the passed position information and the maximum repetition number of a single path; if the constraint condition is met, the corresponding path is a feasible solution, otherwise, the path is an infeasible solution;
the fitness calculation module is used for calculating fitness of the screened population; the fitness is calculated as follows:
f1 is the sum of paths in the corresponding population, X i For the path corresponding to the position i to the next position, F2 is the sum of walking difficulty corresponding to all paths in the corresponding population, m d For the walking difficulty coefficient corresponding to each meter from the position j to the next position, h1 is the first fitness, and h2 is the second fitness;
the selection module is used for selecting a population with the first fitness being greater than or equal to a preset first fitness threshold value in the preset first iteration times, reserving the population with the second fitness being greater than or equal to a second fitness first threshold value at the moment, and rejecting all other populations which do not meet the conditions;
after the first iteration times are exceeded, selecting a population with the second fitness being greater than or equal to a second threshold value of the second fitness, and rejecting all other populations which do not meet the conditions;
the hybridization mutation module is used for carrying out hybridization and mutation of a genetic algorithm on the selected population according to a preset hybridization mutation strategy to obtain a corresponding offspring population;
the circulation module is used for continuously executing the fitness calculation module after the offspring population is obtained until the iteration number of the population meets the preset iteration number;
the output module is used for outputting the child population as an optimal solution set after multi-objective optimization;
and the optimization module is used for determining an abnormally checked optimal route according to the multi-objective optimization result.
6. An intelligent agricultural photovoltaic lighting method is characterized in that: an intelligent agricultural photovoltaic lighting system employing any one of the above 1-5.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311118336.3A CN117158218A (en) | 2023-08-31 | 2023-08-31 | Intelligent agricultural photovoltaic lighting system and method |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311118336.3A CN117158218A (en) | 2023-08-31 | 2023-08-31 | Intelligent agricultural photovoltaic lighting system and method |
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