CN117218424B - Material management method and system in photovoltaic power station construction - Google Patents
Material management method and system in photovoltaic power station construction Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention provides a material management method and a material management system in the construction of a photovoltaic power station, and relates to the technical field of photovoltaic power stations. Based on the category information, analyzing the importance level of the depreciated materials to the construction of the photovoltaic power station; analyzing basic value parameters of the depreciated materials according to the depreciated grades and the importance grades; according to the category information, calculating and obtaining recovery value parameters of the depreciated materials; based on the recovery value parameter, the basic value parameter and the recovery time, a recovery function is constructed, the recovery scheme of the old materials is adjusted, optimized and evaluated, an optimal management scheme is obtained through optimization, and the old materials are managed. The invention solves the technical problem of unreasonable management of depreciated materials in construction of the photovoltaic power station in the prior art.
Description
Technical Field
The invention relates to the technical field of photovoltaic power stations, in particular to a material management system in construction of a photovoltaic power station.
Background
The construction of photovoltaic power plant needs to use multiple materials, and including steel, concrete, timber etc. that building foundation needs to and photovoltaic equipment construction needs battery piece, tin-coated copper strips, EVA, backplate, toughened glass, aluminium frame, silica gel, terminal box, monocrystalline silicon, polycrystalline silicon etc. when the depreciation circumstances such as slight damage take place for the material, need manage the material, specifically include direct abandonment or carry out recycle.
The recycling of materials can save a part of cost but needs a certain time, and the recycling of materials with different importance degrees can possibly influence the construction quality of the photovoltaic power station, so that a strategy for material recycling management needs to be carefully formulated, no corresponding technical scheme exists in the prior art, and the photovoltaic power station is poor in material management effect and high in construction cost.
Disclosure of Invention
The application provides a material management method in photovoltaic power station construction, which is used for solving the technical problems of poor material management effect and high construction cost of a photovoltaic power station in the prior art.
In a first aspect, the present application provides a method for material management in photovoltaic power plant construction, the method comprising:
collecting a material image of a depreciated material to be managed, identifying the material image, acquiring category information, and identifying and acquiring depreciated grades;
Based on the category information, analyzing the importance level of the depreciated materials to the construction of the photovoltaic power station;
analyzing basic value parameters of the depreciated materials according to the depreciated grades and the importance grades;
extracting purchasing cost of the depreciated materials according to the category information, and analyzing recovery cost and recovery time of the depreciated materials according to the depreciated grade;
Obtaining recovery value parameters of the depreciated materials according to purchase cost and recovery cost;
Constructing a recovery function for carrying out recovery scheme evaluation on the depreciated materials based on the recovery value parameter, the basic value parameter and the recovery time;
and adopting the recovery function to adjust, optimize and evaluate the recovery scheme of the depreciated materials, optimizing to obtain an optimal management scheme, and managing the depreciated materials, wherein the optimal management scheme comprises recovery proportion information and waste proportion information, and the adjustment and optimization are performed through iteration head optimization and iteration tail optimization.
In a second aspect, the present application provides a material management system in the construction of a photovoltaic power plant, the system comprising:
the material identification module is used for acquiring material images of depreciated materials to be managed, identifying the material images, acquiring category information and identifying and acquiring depreciated grades;
the importance level analysis module is used for analyzing the importance level of the depreciated materials to the construction of the photovoltaic power station based on the category information;
the basic value identification module is used for analyzing basic value parameters of the depreciated materials according to the depreciated grades and the important grades;
The cost parameter acquisition module is used for extracting the purchasing cost of the depreciated materials according to the category information and analyzing the recovery cost and the recovery time of the depreciated materials according to the depreciated grade;
the recovery value calculation module is used for calculating and obtaining recovery value parameters of the depreciated materials according to the purchasing cost and the recovery cost;
The recovery function construction module is used for constructing a recovery function for carrying out recovery scheme evaluation on the depreciated materials based on the recovery value parameter, the basic value parameter and the recovery time;
And the recovery scheme optimizing module is used for adjusting, optimizing and evaluating the recovery scheme of the depreciated material by adopting the recovery function, optimizing to obtain an optimal management scheme and managing the depreciated material, wherein the optimal management scheme comprises recovery proportion information and abandon proportion information, and the adjustment and optimization are performed through iteration head optimization and iteration tail optimization.
In a third aspect, a computer device comprises a memory storing a computer program and a processor implementing the steps of the method of the first aspect when the computer program is executed.
In a fourth aspect, a computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the method in the first aspect.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
According to the technical scheme provided by the application, in the construction of the photovoltaic power station, the material image is acquired in the material stacking area, the class and the grade of material depreciation are identified, the basic value parameter of the material is analyzed according to the important grade of the class in the construction of the photovoltaic power station, then the recovery value parameter is calculated according to the purchase cost, the recovery cost and the recovery time of the material, a recovery function is constructed, the material recovery scheme is optimized through iteration head optimization and iteration tail optimization, and the optimal management scheme is obtained for management. According to the application, the value of the depreciated materials is analyzed in a multi-dimensional manner, and an adaptive recovery function is constructed, so that the rationality and the qualification of the material management analysis can be improved, the optimization of the management scheme is performed in an iterative head optimization and iterative tail optimization mode, the accuracy of the optimization is improved through a specific optimization strategy, the reliability and the effect of recovery management of the depreciated materials are improved, and the construction cost of the photovoltaic power station can be reduced.
Drawings
FIG. 1 is a schematic flow diagram of a material management method in the construction of a photovoltaic power station;
FIG. 2 is a schematic flow chart of obtaining basic value parameters in a material management method in the construction of a photovoltaic power station;
Fig. 3 is a schematic structural diagram of a material management system in the construction of a photovoltaic power station.
Fig. 4 is an internal structural diagram of a computer device in one embodiment.
Reference numerals illustrate: the system comprises a material identification module 201, an importance level analysis module 202, a basic value identification module 203, a cost parameter acquisition module 204, a recovery value calculation module 205, a recovery function construction module 206 and a recovery scheme optimizing module 207.
Detailed Description
The application provides a material management method and a material management system in photovoltaic power station construction, which are used for solving the technical problems of poor material management effect and high construction cost of a photovoltaic power station in the prior art.
Example 1
As shown in fig. 1, an embodiment of the present application provides a material management method in photovoltaic power station construction, where the method is applied to a power generation loss analysis device for photovoltaic power station construction, the device includes an environmental analysis station, a weather analysis station, a dust analysis station, and a comprehensive loss calculation module, and the method includes:
s101: collecting a material image of a depreciated material to be managed, identifying the material image, acquiring category information, and identifying and acquiring depreciated grades;
The method provided by the embodiment of the application is used for managing the materials which are damaged but not invalid in the construction of the photovoltaic power station and have depreciation, so that the depreciation materials are determined to be recycled or discarded, and the quality of depreciation material management is improved.
In the embodiment of the application, the material images of the depreciated materials to be managed are collected, wherein the materials with slight damage and other depreciated conditions can be piled up to a designated area in the construction of the photovoltaic power station to be managed and analyzed.
Preferably, when the depreciated materials are stacked, the materials can be stacked in a partition mode according to the category, and management efficiency is improved.
For example, an image acquisition device may be provided in advance in the stacking area to acquire an image of the depreciated material to be subjected to the management processing.
And (3) carrying out material image identification on the acquired material images based on the acquired material images so as to automatically acquire category information and depreciation level of the materials as basic data for carrying out management analysis on the materials subsequently.
Step S101 in the method provided by the embodiment of the present application includes:
According to historical material management data of photovoltaic power station construction, extracting and obtaining material images of various depreciated materials, and extracting and obtaining a plurality of sample category information and a plurality of sample depreciated grades;
constructing a material identifier through a convolutional neural network, wherein the material identifier comprises a category identification path and a depreciation identification channel, and the depreciation identification channel comprises a plurality of depreciation identification paths corresponding to a plurality of sample category information;
Training the category identification path to be converged by adopting a plurality of sample material images and a plurality of sample category information;
training a plurality of depreciation recognition paths to be converged by adopting a plurality of sample material images and a plurality of sample depreciation levels;
and adopting the material identifier to perform type identification on the material image to obtain category information, inputting the material image into a depreciation identification path corresponding to the category information to perform image processing identification, and obtaining the depreciation grade.
According to the material management data of the photovoltaic power station built in the historical time, for example, material images of various depreciated materials can be extracted and obtained through the material management data in the construction process of a plurality of photovoltaic power stations, and the types and depreciated grades of the various depreciated materials are obtained based on manual identification and used as the type information and the depreciated grades of the plurality of samples. Illustratively, the plurality of sample depreciation levels may include 1-10 ten levels, with higher levels being higher depreciation levels.
The classification and depreciation level identification of the materials can be performed based on technicians of photovoltaic power station construction, wherein the identification is performed based on the same standard, and the accuracy of the data is guaranteed.
Based on a convolutional neural network in deep learning, a material identifier for identifying the material type of the material is constructed to identify the material type and depreciation level of the old material. The material identifier comprises a category identification path and an depreciation identification channel, wherein the depreciation identification channel comprises a plurality of depreciation identification paths corresponding to a plurality of sample category information, the category identification path can identify the type of materials in an input material image, and the depreciation identification channel can input the material image into the corresponding depreciation identification path according to the type of the materials to identify the depreciation level of the materials.
The input of the category identification path and the depreciation identification channel are material images, and the output is type information of materials and depreciation levels of different types of materials, wherein the types of materials comprise materials such as steel materials, wood materials, battery pieces, tin-coated copper strips, toughened glass, aluminum frames and the like in the construction of the photovoltaic power station. The category recognition paths and the depreciation recognition paths in the depreciation recognition channels comprise a convolution layer, a pooling layer and a full connection layer, and after the image features in the material images are extracted, the materials are classified according to the image features to obtain corresponding material categories, or the depreciation grades of the materials are obtained according to the image feature classification.
And performing supervision training on the category identification path by adopting the plurality of sample material images and the plurality of sample category information, and adjusting network parameters in the category identification path in the training process, so as to gradually improve the accuracy of the category identification path until convergence. Illustratively, the criteria for convergence may be, for example, convergence with respect to identification of the plurality of sample material images and the plurality of sample category information, or accuracy up to 90%.
Further, a plurality of sample material images and a plurality of sample depreciation grades are adopted respectively, the sample material images are divided according to the types of materials in the sample material images, a plurality of groups of training data of various material types are formed, and a plurality of depreciation recognition paths are trained to be converged respectively.
Based on the multiple depreciated recognition paths after training, a constructed depreciated recognition channel is obtained.
Based on the category identification path and the depreciation identification channel, a constructed material identifier is obtained.
And (3) adopting the constructed material identifier to identify the type of the material in the currently acquired material image, and specifically adopting a category identification path in the material identifier to identify the material image so as to obtain category information of the material.
After the category information is obtained, the material image is continuously input into a depreciation recognition path corresponding to the category information, and depreciation grade recognition is carried out, so that the depreciation grade of the material is obtained.
According to the embodiment of the application, the material images are identified through a deep learning image identification technology, and the category and the depreciation grade of the depreciated materials are obtained, so that the depreciated materials are used as a data base for subsequent material management.
S102: based on the category information, analyzing the importance level of the depreciated materials to the construction of the photovoltaic power station;
In the embodiment of the application, the importance of different types of materials for the construction of the photovoltaic power station is different, for example, the influence of building materials such as steel, wood and the like on the photovoltaic power generation after the construction of the photovoltaic power station is smaller, the influence of photovoltaic materials such as battery pieces, silicon and the like on the photovoltaic power generation is larger, and the importance level of different types of materials for the construction of the photovoltaic power station is analyzed according to the influence degree of the photovoltaic power generation.
Specifically, based on the category information, the importance level of the depreciated material is analyzed.
Step S102 in the method provided by the embodiment of the present application includes:
acquiring a plurality of sample category information of a plurality of materials in a photovoltaic power station;
Obtaining the loss of the power generation loss of the photovoltaic power station when the materials of the sample category information are damaged, and obtaining the loss of the sample;
According to the loss amount of the plurality of samples, distributing and calculating to obtain a plurality of sample importance levels;
and matching based on the category information to obtain the importance level of the depreciated material.
In the embodiment of the application, all types of various materials used in the photovoltaic power station are obtained and used as the information of a plurality of sample types.
Further, according to the loss amount of the power generation loss of the photovoltaic power station when different materials are damaged or depreciated after other photovoltaic power stations are built and operated, a plurality of sample loss amounts are obtained through statistics. The average value of the data of a plurality of photovoltaic power stations can be calculated, and the data of a single photovoltaic power station can also be calculated.
And obtaining a plurality of sample importance levels by distribution calculation according to the plurality of sample loss amounts. Illustratively, the plurality of sample importance levels may be obtained by sorting the plurality of sample loss amounts in order from large to small and assigning the importance levels, with the smallest being one level.
Alternatively, the ratio of each sample loss to the sum of the sample losses may be calculated, and the total importance level multiplied by the ratio may be assigned, for example, the total importance level is 100, to obtain a plurality of sample importance levels.
Further, a mapping relation between a plurality of sample category information and a plurality of sample importance levels is constructed, mapping matching is carried out according to the current category information, and the importance level of the current depreciated material is obtained.
According to the embodiment of the application, the importance level of the materials is analyzed based on the influence degree of different materials on photovoltaic power generation, so that the accuracy and rationality of material management analysis can be improved.
S103: analyzing basic value parameters of the depreciated materials according to the depreciated grades and the importance grades;
In the embodiment of the application, the basic value parameters of the recovery of the current depreciated materials are analyzed based on the depreciated grades and the importance grades.
As shown in fig. 2, step S103 in the method provided in the embodiment of the present application includes:
acquiring a sample depreciation grade set and a sample importance grade set according to historical material management data of photovoltaic power station construction;
according to the sample depreciation grade set and the sample importance grade set, evaluating and acquiring basic value parameters of different types of materials with different depreciation grades to acquire a sample basic value parameter set;
Based on a decision tree, adopting depreciation grades and importance grades as decision input, adopting basic value parameters as decision output, and constructing a multi-layer depreciation decision node and a multi-layer importance decision node according to a sample depreciation grade set and a sample importance grade set;
Connecting a plurality of layers of depreciated decision nodes and a plurality of layers of important decision nodes, and obtaining a basic value classifier according to a sample basic value parameter set as a plurality of decision results of the plurality of layers of decision nodes;
And adopting a basic value classifier to carry out decision classification on the depreciation grade and the importance grade, and obtaining the basic value parameter.
According to the material management data of the photovoltaic power station built in the historical time, the sample depreciation grade set and the sample importance grade set of the recorded multiple sample depreciation materials are obtained through the data processing method in the embodiment of the application.
Further, based on the sample depreciation level set and the plurality of sample depreciation levels and the plurality of sample importance levels in the sample importance level set, the basic value evaluation of different types of materials with different depreciation levels is performed, wherein the higher the depreciation level is, the higher the difficulty of recycling depreciation materials is, the easier the power generation quality of the photovoltaic power station is affected, and the lower the basic value parameter is. Alternatively, the base value parameter may range from [0,1].
Alternatively, the sample depreciation level and the sample importance level may be weighted, and the reciprocal of the weighted calculation result may be used as a sample basic value parameter, where the size of the sample basic value parameter and the size of the sample depreciation level are inversely related to the size of the sample importance level.
Thus, a sample base value parameter set is obtained.
Further, based on a decision tree algorithm, depreciation grade and importance grade are adopted as decision input, basic value parameters are adopted as decision output, and multi-layer depreciation decision nodes and multi-layer importance decision nodes are constructed according to a sample depreciation grade set and a sample importance grade set.
The input depreciation grade is classified into a class which is larger than or not larger than the threshold, the input depreciation grade is classified into a corresponding subclass by classifying the multi-layer depreciation decision nodes, and the corresponding basic value parameter can be obtained by combining the subclass corresponding to the importance grade obtained by classifying the multi-layer important decision nodes.
Connecting a multi-layer depreciation decision node and a multi-layer important decision node, for example, connecting the topmost node of the multi-layer depreciation decision node and the bottommost node of the multi-layer important decision node, carrying out multiple classification on the input depreciation grade and importance grade to obtain a final classification result of a plurality of samples, and matching the final classification result of the samples according to a sample basic value parameter set to obtain a plurality of decision results of the multi-layer decision node as a constructed basic value classifier.
By adopting the basic value classifier, decision classification can be carried out on the depreciation grade and the importance grade obtained by the identification processing of the current depreciation material, and the corresponding basic value parameters can be obtained.
According to the embodiment of the application, the basic value classifier is constructed, so that the basic value analysis and evaluation of the depreciated materials can be carried out according to the depreciated grades and the important grades of the depreciated materials, the basic value parameters are obtained, and the basic value parameters are used as the basis for recycling management analysis of the depreciated materials, so that the accuracy of management analysis can be improved.
S104: extracting purchasing cost of the depreciated materials according to the category information, and analyzing recovery cost and recovery time of the depreciated materials according to the depreciated grade;
In the embodiment of the application, according to the category information, the purchase cost of the depreciated material if a brand new material is purchased is obtained. And obtaining the recovery cost and recovery time for recovering the depreciated material according to the recovery treatment and reutilization process of the depreciated material. The recovery cost is generally less than the depreciation cost.
S105: obtaining recovery value parameters of the depreciated materials according to purchase cost and recovery cost;
in the embodiment of the application, the difference between the purchase cost and the recovery cost is calculated and used as the recovery value parameter of the depreciated material, namely the purchase cost data which can be saved only in terms of recovery.
S106: constructing a recovery function for carrying out recovery scheme evaluation on the depreciated materials based on the recovery value parameter, the basic value parameter and the recovery time;
In the embodiment of the application, a recovery function for carrying out recovery scheme evaluation on the depreciated materials is constructed based on the recovery value parameter, the basic value parameter and the recovery time, and the recovery function is represented by the following formula:
Wherein fit is fitness, w 1、w2 and w 3 are weights, ρ is a basic value parameter, K c is a purchasing proportion of materials which are not recycled and purchase the category information, K h is a proportion of recycling the depreciated materials, M c is purchasing cost, M h is recycling cost, S h is a recycling value parameter, and T h is recycling time for recycling the depreciated materials according to K h.
In an embodiment of the present application, w 1、w2 and w 3 may be set based on one skilled in the art, illustratively, 0.3 and 0.4, respectively.
In the embodiment of the application, the recycling ratio and the abandoned purchasing ratio of the depreciated materials are optimized based on the recycling function, and when the depreciated materials are processed in the follow-up process, namely, the processing is performed according to the corresponding recycling ratio and abandoned purchasing ratio, so that the construction quality of the photovoltaic power station is ensured, and meanwhile, the construction cost is reduced.
S107: and adopting the recovery function to adjust, optimize and evaluate the recovery scheme of the depreciated materials, optimizing to obtain an optimal management scheme, and managing the depreciated materials, wherein the optimal management scheme comprises recovery proportion information and waste proportion information, and the adjustment and optimization are performed through iteration head optimization and iteration tail optimization.
In the embodiment of the application, the recovery function is adopted to adjust, optimize and evaluate the recovery scheme of the depreciated materials, and particularly optimize the recovery proportion information and the abandoned purchase proportion information of the depreciated materials. The optimization purpose based on the recovery function is to reduce the cost of material purchase and recovery treatment, improve the cost saving and reduce the time for recovering the depreciated materials.
The optimization of the management scheme is performed by adopting an optimization mode of iterative head optimization and iterative tail optimization, so that the efficiency and accuracy of optimizing are improved.
Step S107 in the method provided by the embodiment of the present application includes:
Acquiring an adjustment feasible region of a management scheme;
randomly generating a plurality of first head solutions and a plurality of first tail solutions based on a plurality of head particles and a plurality of tail particles within the adjustment feasible region;
calculating the fitness of the plurality of first head solutions and the plurality of first tail solutions to obtain a plurality of first head fitness and a plurality of first tail fitness;
calculating the similarity of a plurality of first head solutions, and combining the plurality of first head solutions to obtain a plurality of combined first head solutions;
adopting an optimization step length to adjust a plurality of first head solutions to obtain a plurality of second head solutions, and calculating to obtain a plurality of second head fitness;
according to the reciprocal of the ratio of the plurality of second head fitness to the plurality of first head fitness, adjusting the optimization step length to obtain an adjustment step length;
adjusting the first tail solutions by adopting an adjusting step length to obtain a plurality of second tail solutions;
And continuing to perform iterative head optimization and iterative tail optimization until a preset algebra is reached, outputting a solution with the maximum fitness, and obtaining optimal recovery proportion information and discard proportion information as an optimal management scheme.
In the embodiment of the present application, the adjustment feasible region of the management scheme is first obtained, wherein all possible management schemes are included in the adjustment feasible region, each management scheme includes a recovery ratio information and a discard ratio information, and the sum of the recovery ratio information and the discard ratio information is 1, so that multiple management schemes can be obtained in combination, for example, the recovery ratio information is one, the discard ratio information is nine, for example, the recovery ratio information is 1%, and the discard ratio information is 99%.
In the adjustment feasible domain, optimization is performed by adopting the improvement of a particle swarm optimization algorithm, and a plurality of first head solutions and a plurality of first tail solutions are randomly generated based on a plurality of head particles and a plurality of tail particles, wherein each first head solution and each first tail solution comprise a management scheme.
The multiple head particles are used for analyzing the optimizing direction, so that optimizing accuracy and efficiency are improved, and the multiple tail particles are used for conducting finer optimizing, so that optimizing accuracy is improved.
And calculating and obtaining the first head fitness and the first tail fitness by adopting the recovery function according to the recovery ratio information and the abandon ratio information of the fitness internal management scheme of the first head solutions and the first tail solutions.
And calculating the similarity of the first head solutions in the plurality of first head solutions, combining head solutions with larger similarity to reduce the consumption of calculation power, improving the optimizing efficiency, amplifying the adaptability of the combined first head solutions, and considering that the solutions are relatively close to global optimum due to the similar solutions in the optimizing process so as to deviate to the optimizing direction of the solutions, thereby improving the optimizing efficiency.
After merging, several first head solutions are obtained.
And adjusting the proportion information of the management scheme in the plurality of first head solutions by adopting an optimization step length to obtain a plurality of second head solutions, and calculating and obtaining a plurality of second head fitness. The optimization step length is the step length for adjusting the proportion information in the management scheme, for example, the step length is 5%, the self-setting can be realized, the step length is large, the optimizing efficiency is high, the global optimization can be missed, the optimizing accuracy is high when the step length is small, and the efficiency is low.
Calculating a plurality of ratios of the plurality of second head fitness to the plurality of first head fitness, and adjusting the optimization step length by adopting a mean value of reciprocal values of the plurality of ratios to obtain an adjustment step length. And if the second head fitness is larger than the first head fitness, the current optimization direction is close to the global optimum, the step length is shortened, the optimizing accuracy is improved, otherwise, the current optimization direction is far from the global optimum, the step length is enlarged, and the inferior solution is jumped out.
And adopting the adjustment step length to adjust the plurality of first tail solutions to obtain a plurality of second tail solutions, and calculating the fitness.
Thus, a round of head optimization and tail optimization are completed, and the iterative head optimization and iterative tail optimization are continued based on the step length in the foregoing, until a preset algebra is reached, where the preset algebra can be set by itself, for example, based on adjusting the number of management schemes in the feasible domain, for example, 20. And outputting the solution with the maximum adaptability after the iterative optimization is finished, and obtaining the optimal recovery proportion information and the discard proportion information as an optimal management scheme.
Wherein calculating the similarity of the plurality of first head solutions includes:
Constructing a similarity calculation function:
Wherein sim is similarity, ω 1 and ω 2 are weights, K c2 and K c1 are waste proportion information in the two first solutions, K h2 and K h1 are recovery proportion information in the two first solutions, fit 2 and fit 1 are fitness of the two first solutions;
Calculating the similarity of any two first head solutions in the plurality of first head solutions by adopting a similarity calculation function;
If the similarity is larger than the similarity threshold, combining the two first head solutions, and expanding the adaptability of the combined first head solutions according to a preset proportion.
In the embodiment of the application, a similarity calculation function is constructed to calculate the similarity of the solutions at two ends, and the following formula is adopted:
Where sim is the similarity of the two first solutions, ω 1 and ω 2 are weights, for example 0.5 and 0.5, K c2 and K c1 are the discard proportion information in the two first solutions, K h2 and K h1 are the recycle proportion information in the two first solutions, and fit 2 and fit 1 are the first head fitness of the two first solutions, respectively. The similarity calculation function is also used to calculate the similarity of other head solutions in the subsequent iterative head optimization.
And calculating the similarity of any two first head solutions in the plurality of first head solutions according to the abandoned proportion information and the recovered proportion information in the plurality of first head solutions and the fitness by adopting the similarity calculation function, so as to obtain a plurality of similarities.
Further, determining whether the similarity is greater than a similarity threshold may be performed by one skilled in the art, and the similarity threshold may be set based on the average similarity of the similar first solutions, for example, 4.
Two first solutions with similarity greater than the similarity threshold are combined, and illustratively, the first solutions with greater similarity are reserved and used as combined first solutions, so that a plurality of combined first solutions can be obtained.
Further, the fitness of the combined first head solutions is enlarged according to a preset proportion, so that the precision of optimization nearby the similar first head solutions is improved. The predetermined ratio may be in the range of (1, 1.5), preferably 1.1, for example.
According to the embodiment of the application, the similarity of a plurality of first head solutions is calculated and combined, so that the optimization efficiency and precision can be improved.
According to the embodiment of the application, through the technical scheme, at least the following technical effects are achieved:
According to the technical scheme provided by the application, in the construction of the photovoltaic power station, the material image is acquired in the material stacking area, the class and the grade of material depreciation are identified, the basic value parameter of the material is analyzed according to the important grade of the class in the construction of the photovoltaic power station, then the recovery value parameter is calculated according to the purchase cost, the recovery cost and the recovery time of the material, a recovery function is constructed, the material recovery scheme is optimized through iteration head optimization and iteration tail optimization, and the optimal management scheme is obtained for management. According to the application, the value of the depreciated materials is analyzed in a multi-dimensional manner, and an adaptive recovery function is constructed, so that the rationality and the qualification of the material management analysis can be improved, the optimization of the management scheme is performed in an iterative head optimization and iterative tail optimization mode, the accuracy of the optimization is improved through a specific optimization strategy, the reliability and the effect of recovery management of the depreciated materials are improved, and the construction cost of the photovoltaic power station can be reduced.
Example two
Based on the same inventive concept as the material management method in the construction of a photovoltaic power plant in the foregoing embodiment, as shown in fig. 3, the present application provides a material management system in the construction of a photovoltaic power plant, the system comprising:
the material identification module 201 is configured to collect a material image of a depreciated material to be managed, identify the material image, obtain category information, and identify and obtain a depreciated level;
an importance level analysis module 202, configured to analyze an importance level of the depreciated material for the photovoltaic power plant construction based on the category information;
a basic value identifying module 203, configured to analyze basic value parameters of the depreciated material according to the depreciated level and the importance level;
The cost parameter collection module 204 is configured to extract a purchase cost of the depreciated material according to the category information, and analyze a recovery cost and a recovery time of the depreciated material according to the depreciated level;
the recovery value calculation module 205 is configured to calculate and obtain a recovery value parameter of the depreciated material according to the purchase cost and the recovery cost;
A recovery function construction module 206, configured to construct a recovery function for performing recovery scheme evaluation on the depreciated material based on the recovery value parameter, the basic value parameter, and the recovery time;
And the recovery scheme optimizing module 207 is configured to adjust, optimize and evaluate the recovery scheme of the depreciated material by using the recovery function, and optimize to obtain an optimal management scheme, and manage the depreciated material, where the optimal management scheme includes recovery proportion information and discard proportion information, and the adjustment and optimization are performed by iterative head optimization and iterative tail optimization.
Further, the material identification module 201 is further configured to perform the following steps:
According to historical material management data of photovoltaic power station construction, extracting and obtaining material images of various depreciated materials, and extracting and obtaining a plurality of sample category information and a plurality of sample depreciated grades;
constructing a material identifier through a convolutional neural network, wherein the material identifier comprises a category identification path and a depreciation identification channel, and the depreciation identification channel comprises a plurality of depreciation identification paths corresponding to a plurality of sample category information;
Training the category identification path to be converged by adopting a plurality of sample material images and a plurality of sample category information;
training a plurality of depreciation recognition paths to be converged by adopting a plurality of sample material images and a plurality of sample depreciation levels;
and adopting the material identifier to perform type identification on the material image to obtain category information, inputting the material image into a depreciation identification path corresponding to the category information to perform image processing identification, and obtaining the depreciation grade.
Further, the importance level analysis module 202 is further configured to perform the following steps:
acquiring a plurality of sample category information of a plurality of materials in a photovoltaic power station;
Obtaining the loss of the power generation loss of the photovoltaic power station when the materials of the sample category information are damaged, and obtaining the loss of the sample;
According to the loss amount of the plurality of samples, distributing and calculating to obtain a plurality of sample importance levels;
and matching based on the category information to obtain the importance level of the depreciated material.
Further, the basic value identifying module 203 is further configured to perform the following steps:
acquiring a sample depreciation grade set and a sample importance grade set according to historical material management data of photovoltaic power station construction;
according to the sample depreciation grade set and the sample importance grade set, evaluating and acquiring basic value parameters of different types of materials with different depreciation grades to acquire a sample basic value parameter set;
Based on a decision tree, adopting depreciation grades and importance grades as decision input, adopting basic value parameters as decision output, and constructing a multi-layer depreciation decision node and a multi-layer importance decision node according to a sample depreciation grade set and a sample importance grade set;
Connecting a plurality of layers of depreciated decision nodes and a plurality of layers of important decision nodes, and obtaining a basic value classifier according to a sample basic value parameter set as a plurality of decision results of the plurality of layers of decision nodes;
And adopting a basic value classifier to carry out decision classification on the depreciation grade and the importance grade, and obtaining the basic value parameter.
Further, the reclamation function construction module 206 is further configured to perform the following steps:
Based on the recovery value parameter, the basic value parameter and the recovery time, constructing a recovery function for carrying out recovery scheme evaluation on the depreciated material, wherein the recovery function comprises the following formula:
Wherein fit is fitness, w 1、w2 and w 3 are weights, ρ is a basic value parameter, K c is a purchasing proportion of materials which are not recycled and purchase the category information, K h is a proportion of recycling the depreciated materials, M c is purchasing cost, M h is recycling cost, S h is a recycling value parameter, and T h is recycling time for recycling the depreciated materials according to K h.
Further, the recovery scheme optimizing module 207 is further configured to perform the following steps:
Acquiring an adjustment feasible region of a management scheme;
randomly generating a plurality of first head solutions and a plurality of first tail solutions based on a plurality of head particles and a plurality of tail particles within the adjustment feasible region;
calculating the fitness of the plurality of first head solutions and the plurality of first tail solutions to obtain a plurality of first head fitness and a plurality of first tail fitness;
calculating the similarity of a plurality of first head solutions, and combining the plurality of first head solutions to obtain a plurality of combined first head solutions;
adopting an optimization step length to adjust a plurality of first head solutions to obtain a plurality of second head solutions, and calculating to obtain a plurality of second head fitness;
according to the reciprocal of the ratio of the plurality of second head fitness to the plurality of first head fitness, adjusting the optimization step length to obtain an adjustment step length;
adjusting the first tail solutions by adopting an adjusting step length to obtain a plurality of second tail solutions;
And continuing to perform iterative head optimization and iterative tail optimization until a preset algebra is reached, outputting a solution with the maximum fitness, and obtaining optimal recovery proportion information and discard proportion information as an optimal management scheme.
The method comprises the steps of calculating the similarity of a plurality of first head solutions, combining the plurality of first head solutions, and further comprising:
Constructing a similarity calculation function:
Wherein sim is similarity, ω 1 and ω 2 are weights, K c2 and K c1 are waste proportion information in the two first solutions, K h2 and K h1 are recovery proportion information in the two first solutions, fit 2 and fit 1 are fitness of the two first solutions;
Calculating the similarity of any two first head solutions in the plurality of first head solutions by adopting a similarity calculation function;
If the similarity is larger than the similarity threshold, combining the two first head solutions, and expanding the adaptability of the combined first head solutions according to a preset proportion.
The foregoing detailed description of a method for managing materials in construction of a photovoltaic power station will be clear to those skilled in the art, and the device disclosed in this embodiment is relatively simple in description and relevant places refer to the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
For a specific embodiment of a material management system in photovoltaic power plant construction, reference may be made to the above embodiment of a material management method in photovoltaic power plant construction, which is not described herein. All or part of each module in the material management device in the construction of the photovoltaic power station can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Example III
As shown in fig. 4, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method in embodiment one when the computer program is executed.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing news data, time attenuation factors and other data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method of material management in the construction of a photovoltaic power plant.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
Example IV
The present embodiment provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method of the first embodiment.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (8)
1. A method of material management in the construction of a photovoltaic power plant, the method comprising:
collecting a material image of a depreciated material to be managed, identifying the material image, acquiring category information, and identifying and acquiring depreciated grades;
Based on the category information, analyzing the importance level of the depreciated materials to the construction of the photovoltaic power station;
analyzing basic value parameters of the depreciated materials according to the depreciated grades and the importance grades;
extracting purchasing cost of the depreciated materials according to the category information, and analyzing recovery cost and recovery time of the depreciated materials according to the depreciated grade;
Obtaining recovery value parameters of the depreciated materials according to purchase cost and recovery cost;
Constructing a recovery function for carrying out recovery scheme evaluation on the depreciated materials based on the recovery value parameter, the basic value parameter and the recovery time;
adopting the recovery function to adjust, optimize and evaluate the recovery scheme of the depreciated materials, and optimize to obtain an optimal management scheme, and manage the depreciated materials, wherein the optimal management scheme comprises recovery proportion information and waste proportion information, and the adjustment optimization is performed through iteration head optimization and iteration tail optimization;
the recovery function is adopted to adjust, optimize and evaluate the recovery scheme of the depreciated materials, and optimize to obtain an optimal management scheme, and the method comprises the following steps:
Acquiring an adjustment feasible region of a management scheme;
randomly generating a plurality of first head solutions and a plurality of first tail solutions based on a plurality of head particles and a plurality of tail particles within the adjustment feasible region;
calculating the fitness of the plurality of first head solutions and the plurality of first tail solutions to obtain a plurality of first head fitness and a plurality of first tail fitness;
calculating the similarity of a plurality of first head solutions, and combining the plurality of first head solutions to obtain a plurality of combined first head solutions;
adopting an optimization step length to adjust a plurality of first head solutions to obtain a plurality of second head solutions, and calculating to obtain a plurality of second head fitness;
according to the reciprocal of the ratio of the plurality of second head fitness to the plurality of first head fitness, adjusting the optimization step length to obtain an adjustment step length;
adjusting the first tail solutions by adopting an adjusting step length to obtain a plurality of second tail solutions;
Continuing to perform iterative head optimization and iterative tail optimization until reaching a preset algebra, outputting a solution with the maximum fitness to obtain optimal recovery proportion information and discard proportion information, and taking the optimal recovery proportion information and discard proportion information as an optimal management scheme;
the method for calculating the similarity of the plurality of first head solutions, combining the plurality of first head solutions comprises the following steps:
Constructing a similarity calculation function:
Wherein sim is similarity, ω 1 and ω 2 are weights, K c2 and K c1 are waste proportion information in the two first solutions, K h2 and K h1 are recovery proportion information in the two first solutions, fit 2 and fit 1 are fitness of the two first solutions;
Calculating the similarity of any two first head solutions in the plurality of first head solutions by adopting a similarity calculation function;
If the similarity is larger than the similarity threshold, combining the two first head solutions, and expanding the adaptability of the combined first head solutions according to a preset proportion.
2. The method according to claim 1, characterized in that the method comprises:
According to historical material management data of photovoltaic power station construction, extracting and obtaining material images of various depreciated materials, and extracting and obtaining a plurality of sample category information and a plurality of sample depreciated grades;
constructing a material identifier through a convolutional neural network, wherein the material identifier comprises a category identification path and a depreciation identification channel, and the depreciation identification channel comprises a plurality of depreciation identification paths corresponding to a plurality of sample category information;
Training the category identification path to be converged by adopting a plurality of sample material images and a plurality of sample category information;
training a plurality of depreciation recognition paths to be converged by adopting a plurality of sample material images and a plurality of sample depreciation levels;
and adopting the material identifier to perform type identification on the material image to obtain category information, inputting the material image into a depreciation identification path corresponding to the category information to perform image processing identification, and obtaining the depreciation grade.
3. The method according to claim 1, characterized in that the method comprises:
acquiring a plurality of sample category information of a plurality of materials in a photovoltaic power station;
Obtaining the loss of the power generation loss of the photovoltaic power station when the materials of the sample category information are damaged, and obtaining the loss of the sample;
According to the loss amount of the plurality of samples, distributing and calculating to obtain a plurality of sample importance levels;
and matching based on the category information to obtain the importance level of the depreciated material.
4. The method according to claim 1, characterized in that the method comprises:
acquiring a sample depreciation grade set and a sample importance grade set according to historical material management data of photovoltaic power station construction;
according to the sample depreciation grade set and the sample importance grade set, evaluating and acquiring basic value parameters of different types of materials with different depreciation grades to acquire a sample basic value parameter set;
Based on a decision tree, adopting depreciation grades and importance grades as decision input, adopting basic value parameters as decision output, and constructing a multi-layer depreciation decision node and a multi-layer importance decision node according to a sample depreciation grade set and a sample importance grade set;
Connecting a plurality of layers of depreciated decision nodes and a plurality of layers of important decision nodes, and obtaining a basic value classifier according to a sample basic value parameter set as a plurality of decision results of the plurality of layers of decision nodes;
And adopting a basic value classifier to carry out decision classification on the depreciation grade and the importance grade, and obtaining the basic value parameter.
5. The method according to claim 1, characterized in that the method comprises:
Based on the recovery value parameter, the basic value parameter and the recovery time, constructing a recovery function for carrying out recovery scheme evaluation on the depreciated material, wherein the recovery function comprises the following formula:
Wherein fit is fitness, w 1、w2 and w 3 are weights, ρ is a basic value parameter, K c is a purchasing proportion of materials which are not recycled and purchase the category information, K h is a proportion of recycling the depreciated materials, M c is purchasing cost, M h is recycling cost, S h is a recycling value parameter, and T h is recycling time for recycling the depreciated materials according to K h.
6. A material management system in the construction of a photovoltaic power plant, the system comprising:
the material identification module is used for acquiring material images of depreciated materials to be managed, identifying the material images, acquiring category information and identifying and acquiring depreciated grades;
the importance level analysis module is used for analyzing the importance level of the depreciated materials to the construction of the photovoltaic power station based on the category information;
the basic value identification module is used for analyzing basic value parameters of the depreciated materials according to the depreciated grades and the important grades;
The cost parameter acquisition module is used for extracting the purchasing cost of the depreciated materials according to the category information and analyzing the recovery cost and the recovery time of the depreciated materials according to the depreciated grade;
the recovery value calculation module is used for calculating and obtaining recovery value parameters of the depreciated materials according to the purchasing cost and the recovery cost;
The recovery function construction module is used for constructing a recovery function for carrying out recovery scheme evaluation on the depreciated materials based on the recovery value parameter, the basic value parameter and the recovery time;
The recovery scheme optimizing module is used for adjusting, optimizing and evaluating the recovery scheme of the depreciated materials by adopting the recovery function, optimizing to obtain an optimal management scheme and managing the depreciated materials, wherein the optimal management scheme comprises recovery proportion information and abandon proportion information, and the adjustment and optimization are performed through iteration head optimization and iteration tail optimization;
the recovery function is adopted to adjust, optimize and evaluate the recovery scheme of the depreciated materials, and optimize to obtain an optimal management scheme, and the method comprises the following steps:
Acquiring an adjustment feasible region of a management scheme;
randomly generating a plurality of first head solutions and a plurality of first tail solutions based on a plurality of head particles and a plurality of tail particles within the adjustment feasible region;
calculating the fitness of the plurality of first head solutions and the plurality of first tail solutions to obtain a plurality of first head fitness and a plurality of first tail fitness;
calculating the similarity of a plurality of first head solutions, and combining the plurality of first head solutions to obtain a plurality of combined first head solutions;
adopting an optimization step length to adjust a plurality of first head solutions to obtain a plurality of second head solutions, and calculating to obtain a plurality of second head fitness;
according to the reciprocal of the ratio of the plurality of second head fitness to the plurality of first head fitness, adjusting the optimization step length to obtain an adjustment step length;
adjusting the first tail solutions by adopting an adjusting step length to obtain a plurality of second tail solutions;
Continuing to perform iterative head optimization and iterative tail optimization until reaching a preset algebra, outputting a solution with the maximum fitness to obtain optimal recovery proportion information and discard proportion information, and taking the optimal recovery proportion information and discard proportion information as an optimal management scheme;
the method for calculating the similarity of the plurality of first head solutions, combining the plurality of first head solutions comprises the following steps:
Constructing a similarity calculation function:
Wherein sim is similarity, ω 1 and ω 2 are weights, K c2 and K c1 are waste proportion information in the two first solutions, K h2 and K h1 are recovery proportion information in the two first solutions, fit 2 and fit 1 are fitness of the two first solutions;
Calculating the similarity of any two first head solutions in the plurality of first head solutions by adopting a similarity calculation function;
If the similarity is larger than the similarity threshold, combining the two first head solutions, and expanding the adaptability of the combined first head solutions according to a preset proportion.
7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 5 when the computer program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5.
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