A method and device for optimizing the layout of photovoltaic modules in highway service areas

Abstract

The present invention provides a layout optimization method and device for photovoltaic modules in highway service areas, the method comprising: obtaining the power Qt required by the target highway service area within a preset time period; obtaining the grid unit corresponding to the service area based on Qt; obtaining the photovoltaic module installation area corresponding to the service area as the target installation area, and dividing the target installation area by using the grid unit to obtain multiple grid areas; obtaining the sunshine amount of any grid area within a preset time period and the distance between the grid area and the target highway service area; obtaining the corresponding shaded area and the shaded area change speed; based on the sunshine amount of the grid area corresponding to the target installation area, the distance between the grid area and the service area, the shaded area of ​​the grid area within the preset time period and the shaded area change speed, obtaining the grid for installing photovoltaic modules. The present invention can obtain a reasonable layout of photovoltaic modules.

Description

Technical Field

[0001] The present invention relates to the technical field of transportation and energy integration technology, and in particular to a layout optimization method and device for photovoltaic components in a highway service area. Background Art

[0002] With the continuous improvement of battery life, electric vehicles have been widely popularized. The penetration rate and ownership of electric vehicles have increased rapidly in recent years, which has put forward higher requirements on the power supply capacity of highway service areas. As an important renewable energy source, solar energy has become a key area for the development and utilization of clean energy in highway service areas. Photovoltaic modules, as the core part of solar power generation systems, have been widely used. However, the current layout of photovoltaic modules is basically based on experience, resulting in unreasonable layout of some photovoltaic modules, and the output is difficult to meet the power demand of highway service areas, especially electric vehicles. Summary of the invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is:

[0004] According to a first aspect of the present invention, a method for optimizing the layout of photovoltaic modules in a highway service area is provided, the method comprising the following steps:

[0005] S100, obtaining the power Qt required by the target highway service area within a preset time period.

[0006] S110, obtaining a grid unit corresponding to a target expressway service area based on Qt.

[0007] S120, obtaining a photovoltaic module installable area corresponding to a target highway service area as a target installation area, and dividing the target installation area by using the grid units to obtain a plurality of grid areas.

[0008] S130, obtaining the sunshine amount of any grid area in the preset time period and the distance between any grid area and the target highway service area.

[0009] S140, based on the normalized vegetation index and growth rate of the green plants in the corresponding area of ​​any grid area, obtain the blocked area and the rate of change of the blocked area of ​​any grid area within a preset time period; the blocked area is the projection area of ​​the green plants in the corresponding area of ​​the grid area in the corresponding grid area.

[0010] S150, based on the sunshine amount of the grid area corresponding to the target installation area, the distance between the grid area and the target high-speed service area, the shaded area of ​​the grid area within a preset time period and the speed of change of the shaded area, obtain the target grid as the grid for installing the photovoltaic components.

[0011] According to a second aspect of the present invention, a layout optimization device for photovoltaic modules in a highway service area is provided, comprising:

[0012] The first acquisition module is used to acquire the power Qt required by the target highway service area within a preset time period.

[0013] The second acquisition module is used to acquire the grid unit corresponding to the target highway service area based on Qt.

[0014] The third acquisition module is used to acquire the photovoltaic module installable area corresponding to the target highway service area as the target installation area, and divide the target installation area by using the grid units to obtain multiple grid areas.

[0015] The fourth acquisition module is used to obtain the sunshine amount of any grid area within the preset time period and the distance between any grid area and the target highway service area.

[0016] The fifth acquisition module is used to obtain the blocked area and the rate of change of the blocked area of ​​any grid area within a preset time period based on the normalized vegetation index and growth rate of the green plants in the corresponding area of ​​any grid area; the blocked area is the projection area of ​​the green plants in the corresponding area of ​​the grid area in the corresponding grid area.

[0017] The sixth acquisition module is used to acquire the target grid as the grid for installing photovoltaic components based on the sunshine amount of the grid area corresponding to the target installation area, the distance between the grid area and the target high-speed service area, the shaded area of ​​the grid area within a preset time period, and the speed of change of the shaded area.

[0018] The present invention has at least the following beneficial effects:

[0019] The layout optimization scheme for photovoltaic modules in highway service areas provided by the embodiment of the present invention can select the optimal photovoltaic module installation area as much as possible and achieve the optimal layout of photovoltaic modules because it comprehensively considers the amount of sunlight in the installation area, the distance between the installation area and the service area, and the impact of green plants on the installation area.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A flow chart of a method for optimizing the layout of photovoltaic modules in a highway service area provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0025] It should be noted that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the steps as sequential processes, many of the steps can be implemented in parallel, concurrently or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0026] The embodiment of the present invention provides a method for optimizing the layout of photovoltaic modules in a highway service area. Figure 1 As shown, the method may include the following steps:

[0027] S100, obtaining the power Qt required by the target highway service area within a preset time period.

[0028] S110, obtaining a grid unit corresponding to a target expressway service area based on Qt.

[0029] S120, obtaining a photovoltaic module installable area corresponding to a target highway service area as a target installation area, and dividing the target installation area by using the grid units to obtain a plurality of grid areas;

[0030] S130, obtaining the sunshine amount of any grid area in the preset time period and the distance between any grid area and the target highway service area.

[0031] S140, based on the normalized vegetation index and growth rate of the green plants in the corresponding area of ​​any grid area, obtain the blocked area and the rate of change of the blocked area of ​​any grid area within a preset time period; the blocked area is the projection area of ​​the green plants in the corresponding area of ​​the grid area in the corresponding grid area.

[0032] S150, based on the sunshine amount of the grid area corresponding to the target installation area, the distance between the grid area and the target high-speed service area, the shaded area of ​​the grid area within a preset time period and the speed of change of the shaded area, obtain the target grid as the grid for installing the photovoltaic components.

[0033] The layout optimization method of photovoltaic modules in highway service areas provided by the embodiment of the present invention can select the optimal photovoltaic module installation area as much as possible and achieve the optimal layout of photovoltaic modules because it comprehensively considers the amount of sunlight in the installation area, the distance between the installation area and the service area, and the impact of green plants on the installation area.

[0034] In the embodiment of the present invention, the photovoltaic assembly may be an existing photovoltaic assembly, for example, including components such as a combiner box, an electric energy converter, and a bridge.

[0035] Further, in the embodiment of the present invention, the target high-speed service area may be a high-speed service area specified by the user. The target high-speed service area may be one service area, two service areas, or more than two service areas.

[0036] In the embodiment of the present invention, the preset time period may be a time period specified by the user, for example, may be 1 year or more than 1 year, for example, 10 years.

[0037] Furthermore, in an embodiment of the present invention, Qt can be obtained based on a trained power prediction model. The power prediction model can be an existing AI model, such as a deep neural network model.

[0038] Furthermore, in the embodiment of the present invention, the trained power prediction model can be obtained through the following steps:

[0039] S101, obtaining a sample data set, wherein the sample data set includes a plurality of sample data, each sample data includes U and D, wherein U is the ID of the corresponding high-speed service area, such as the name or name code of the service area, and D is the power consumption time series data corresponding to the corresponding high-speed service area in a set historical time period, D = {D1, D2, ..., D i , ..., D n}, Di is the electricity consumption data corresponding to the i-th unit time period, i ranges from 1 to n, n is the number of unit time periods corresponding to the set historical segment, D i =(DC i ,DE i , Q i ), DC i is the traffic volume entering the corresponding highway service area in the i-th unit time period, that is, the number of all vehicles entering the service area, including fuel vehicles and electric vehicles, DE i is the number of electric vehicles entering the corresponding highway service area in the i-th unit time period, Q i is the power consumption of the corresponding high-speed service area in the i-th unit time period.

[0040] In an embodiment of the present invention, the duration of the historical time period may be a time period specified by the user, for example, several years. The duration of the unit time period may be a time period specified by the user, for example, one day or one month, etc., preferably one day. The power consumption data in the sample data may be obtained based on the historical data of the corresponding service area.

[0041] S102, inputting the training sample data of the current batch into the current electricity prediction model for training to obtain corresponding prediction results, wherein the prediction results include electricity consumption data corresponding to the next unit time period of the unit time period corresponding to the current batch.

[0042] S103, based on the prediction results of the current batch and the corresponding actual results, obtain the current loss function value of the current electricity prediction model, and determine whether the current loss function value meets the preset model training end condition. If yes, execute step S105, otherwise, execute step S104.

[0043] S104, updating the parameters of the current power prediction model based on the current loss function value, and using the next batch of sample data as the training sample data of the current batch, and executing S102.

[0044] S105: Using the current power prediction model as the trained power prediction model.

[0045] In an embodiment of the present invention, the loss function value can be calculated based on an existing loss function. The preset model training end condition can be set based on actual needs. For example, the loss is less than or equal to a set loss threshold and remains unchanged within a set time period.

[0046] Furthermore, Qt can be obtained through the following steps:

[0047] S10, based on the vehicle flow rate and the number of electric vehicles in the target highway service area in a set historical time period, determine the vehicle flow rate and the number of electric vehicles in the target highway service area in a unit time period as initial electricity consumption data.

[0048] S11, set counter variable j=1.

[0049] S12, if j≤k, execute S13, otherwise, execute S16; k is the number of unit time periods corresponding to the preset time period, k=roundup(t1 / t2), t1 is the duration corresponding to the preset time period, t2 is the duration corresponding to the unit time period, and roundup() means rounding up.

[0050] S13, using the current prediction result based on the trained electricity prediction model as the current electricity consumption data corresponding to the target highway service area, and adding the current prediction result to the current prediction result set; the current prediction result includes the corresponding predicted traffic flow, predicted number of electric vehicles and predicted electricity consumption; the initial value of the current prediction result set is empty.

[0051] S14, inputting the current electricity consumption data corresponding to the target highway service area into the trained electricity consumption prediction model to obtain the corresponding current prediction result.

[0052] S15, set j=j+1, and execute S12;

[0053] S16, obtaining Qt based on the predicted power consumption corresponding to the current prediction result set.

[0054] Furthermore, in an embodiment of the present invention, the grid unit corresponding to the target highway service area is obtained by consulting a preset lookup table, wherein each row of data in the preset lookup table includes the highway service area, the power consumption of the service area within a preset time period, and the corresponding grid unit.

[0055] The preset query table can be obtained based on the following steps:

[0056] S201, obtaining the power required by any reference highway service area within a preset time period.

[0057] In the embodiment of the present invention, the reference high-speed service area may be a high-speed service area specified by a user.

[0058] S202, obtaining a photovoltaic module installable area corresponding to any reference highway service area as a corresponding target installation area.

[0059] In an embodiment of the present invention, the photovoltaic module installation area corresponding to the highway service area can be determined based on actual needs, for example, it can be the two sides of the highway between the reference highway service area and the highway service area adjacent to the reference highway service area. If there is a green belt on the highway, it can also include the top of the green belt, etc. S203, using preset initial grid units to divide the target installation area corresponding to any reference highway service area to obtain a corresponding plurality of grid areas; the preset initial grid unit is a grid area suitable for installing a photovoltaic module, that is, the size of the preset initial grid unit is set to a size that can install a photovoltaic module. The grid unit can be rectangular or square.

[0060] S204: Obtain the sunshine amount of any grid area corresponding to any reference highway service area within the preset time period.

[0061] S205, based on the amount of electricity required by any reference highway service area within a preset time period and the amount of sunshine corresponding to each grid area, obtain a target grid area from multiple grid areas as a grid unit corresponding to the reference highway service area; wherein the target grid area is a square area formed by at least one initial grid unit, and the amount of electricity that can be generated by the target grid area is greater than or equal to the amount of electricity required by the corresponding reference highway service area within the preset time period.

[0062] S206: Based on the power required by any reference highway service area within a preset time period and the corresponding grid unit, the preset query table is formed.

[0063] In an embodiment of the present invention, the sunshine amount of any grid area in the preset time period is obtained based on a trained sunshine amount prediction model. The sunshine amount prediction model may be an AI model, such as a deep neural network model.

[0064] Furthermore, the trained sunshine amount prediction model can be obtained through the following steps:

[0065] S1, obtain a training sample data set, each sample data includes the sunshine amount time series data SA of the corresponding sampling point of the corresponding sample data in the set historical time period, SA = {SA1, SA2, ..., SA r , ..., SA m}, SA r is the sunshine data corresponding to the rth unit time period, r ranges from 1 to m, m is the number of unit time periods corresponding to the set historical period, SA r =(L r , SA1 r , SA2 r ), L r is the position of the sampling point corresponding to the sample data, SA1r is the weather characteristic information corresponding to the sampling point in the rth unit time period, SA2 r is the sunshine amount corresponding to the sampling point in the rth unit time period.

[0066] In the embodiment of the present invention, the sampling point may be an open area without any obstruction. The position of the sampling point may be the longitude and latitude coordinates of the sampling point, which may be obtained through the corresponding remote sensing image.

[0067] In the embodiment of the present invention, the weather characteristic information may include sunrise time, sunset time, ultraviolet intensity, temperature, humidity, visibility, etc. The sunshine amount at the sampling point may be obtained by testing an installed sunshine meter, sunshine radiation sensor, etc.

[0068] S2, input the training sample data of the current batch into the current sunshine prediction model for training to obtain corresponding prediction results, which include weather characteristic information and sunshine amount corresponding to the next unit time period of the unit time period corresponding to the current batch.

[0069] S3, based on the prediction results of the current batch and the corresponding actual results, obtain the current loss function value of the current sunshine prediction model, and determine whether the current loss function value meets the preset model training end condition. If yes, execute step S5, otherwise, execute step S4.

[0070] S4, updating the parameters of the current sunshine prediction model based on the current loss function value, and using the next batch of sample data as the training sample data of the current batch, and executing S2.

[0071] S5: Using the current sunshine amount prediction model as the trained sunshine amount prediction model.

[0072] Furthermore, the sunshine amount of any grid area in the preset time period is obtained based on the following steps:

[0073] S20, taking the location corresponding to any grid area and the weather characteristic information corresponding to the current unit time period as initial input data.

[0074] S21, set the counter variable j1=1.

[0075] S22, if j1≤k1, execute S23, otherwise, execute S26; k1 is the number of unit time periods corresponding to the preset time period, k1=roundup(t1 / t2), t1 is the duration corresponding to the preset time period, t2 is the duration corresponding to the unit time period, and roundup() means rounding up.

[0076] S23, using the current prediction result based on the trained sunshine amount prediction model as the current input data corresponding to any grid area, and adding the current prediction result to the current prediction result set; the current prediction result includes the corresponding weather characteristic information and sunshine amount; the initial value of the current prediction result set is empty.

[0077] S24, inputting the current input data corresponding to any grid area into the trained sunshine amount prediction model to obtain the corresponding current prediction result.

[0078] S25, set j1=j1+1, and execute S22.

[0079] S26, obtaining the sunshine amount of any grid area within a preset time period based on the sunshine amount corresponding to the current prediction result set corresponding to the grid area.

[0080] Further, in an embodiment of the present invention, if there is one target highway service area, the photovoltaic component installable area corresponding to the target highway service area may be located on both sides of the highway between the target highway service area and the highway service area adjacent to the target highway service area. If there is a green belt on the highway, it may also include the top of the green belt, etc. If there are two target highway service areas, the photovoltaic component installable area corresponding to the target highway service area may be located on both sides of the highway between the corresponding two highway service areas. If there is a green belt on the highway, it may also include the top of the green belt, etc. If there are multiple target highway service areas, the photovoltaic component installable area corresponding to the target highway service area may be located on both sides of all highways within the enclosing box of the corresponding multiple highway service areas. If there is a green belt on the corresponding highway, it may also include the top of the green belt, etc.

[0081] Further, in the embodiment of the present invention, in S130, if there is one target highway service area, the distance between each grid area and the target highway service area may be the distance between the center of the grid area and the center of the corresponding highway service area. If there are multiple target highway service areas, the distance between each grid area and the target highway service area may be the average of the distances between the center of the grid area and the centers of all corresponding highway service areas.

[0082] Those skilled in the art know that the position of each location point in each grid area can be obtained based on a corresponding map.

[0083] Further, in S140, the area corresponding to any grid area may be the green plant planting area closest to the grid area. The blocked area of ​​any grid area within a preset time period may be predicted based on the current normalized vegetation index and environmental variables of the green plants in the area corresponding to the grid area.

[0084] In the embodiment of the present invention, the environmental variables may be temperature, precipitation, etc.

[0085] Specifically, based on the current normalized vegetation index and environmental variables, the growth range of green plants in the corresponding area after a preset time period can be predicted, and then a regression model between the blocked area and the normalized vegetation index and environmental variables can be obtained.

[0086] Those skilled in the art know that, since the blocked area of ​​each grid region in each unit time period can be known, the changing speed of the blocked area of ​​each grid region in a preset time period can be obtained.

[0087] Furthermore, in the embodiment of the present invention, the target grid area can be obtained by the following steps:

[0088] S161, based on the sunshine amount, distance, shaded area and shaded area change rate corresponding to any grid area, determine the scores corresponding to the sunshine amount, distance, shaded area and shaded area change rate corresponding to the grid area.

[0089] In the embodiment of the present invention, the score determination method of the sunshine amount, distance, shaded area and shaded area change speed can be set based on actual needs. Generally, the scores of sunshine amount, distance, shaded area and shaded area change speed are positively correlated with the size of sunshine amount, distance, shaded area and shaded area change speed. For example, the greater the sunshine amount, the greater the corresponding score, and vice versa.

[0090] S162, multiplying the scores corresponding to the sunshine amount, distance, shaded area and shaded area change speed corresponding to any grid area by the corresponding weights respectively and adding the respective multiplication results as the installation weight of the grid area.

[0091] In an embodiment of the present invention, the weights of the sunlight amount, distance, shaded area, and shaded area change speed can be set based on actual needs. In an exemplary embodiment, the weights of the sunlight amount, distance, shaded area, and shaded area change speed can be the same, for example, all 0.25. In another exemplary embodiment, the weights of the sunlight amount, distance, shaded area, and shaded area change speed can be different, for example, the weights of the sunlight amount, distance, shaded area, and shaded area change speed can be set in a descending order.

[0092] S163: The grid area corresponding to the largest of all installation weights is used as the target grid area.

[0093] Based on the same inventive concept, an embodiment of the present invention provides a layout optimization device for photovoltaic modules in a highway service area, comprising:

[0094] The first acquisition module is used to acquire the power Qt required by the target highway service area within a preset time period.

[0095] The second acquisition module is used to acquire the grid unit corresponding to the target highway service area based on Qt.

[0096] The third acquisition module is used to acquire the photovoltaic module installable area corresponding to the target highway service area as the target installation area, and divide the target installation area by using the grid units to obtain multiple grid areas.

[0097] The fourth acquisition module is used to obtain the sunshine amount of any grid area within the preset time period and the distance between any grid area and the target highway service area.

[0098] The fifth acquisition module is used to obtain the blocked area and the rate of change of the blocked area of ​​any grid area within a preset time period based on the normalized vegetation index and growth rate of the green plants in the corresponding area of ​​any grid area; the blocked area is the projection area of ​​the green plants in the corresponding area of ​​the grid area in the corresponding grid area.

[0099] The sixth acquisition module is used to acquire the target grid as the grid for installing photovoltaic components based on the sunshine amount of the grid area corresponding to the target installation area, the distance between the grid area and the target high-speed service area, the shaded area of ​​the grid area within a preset time period, and the speed of change of the shaded area.

[0100] The device can be used to perform Figure 1 Therefore, for the functions that can be realized by each functional module of the device, please refer to Figure 1 The description of the illustrated embodiment is not repeated in detail.

[0101] An embodiment of the present invention also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the method described in the embodiment of the present invention.

[0102] The embodiment of the present invention further provides a non-transitory computer-readable storage medium storing computer-executable instructions, wherein the computer instructions are used to execute the method described in the embodiment of the present invention.

[0103] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0104] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the layout of photovoltaic modules in a highway service area, characterized in that: The method comprises the following steps: S100, obtaining the power Qt required by the target highway service area within a preset time period; S110, obtaining a grid unit corresponding to a target expressway service area based on Qt; S120, obtaining a photovoltaic module installable area corresponding to a target highway service area as a target installation area, and dividing the target installation area by using the grid units to obtain a plurality of grid areas; S130, obtaining the sunshine amount of any grid area in the preset time period and the distance between any grid area and the target highway service area; S140, based on the normalized vegetation index and environmental variables of the green plants in the area corresponding to any grid area, obtaining the blocked area and the speed of change of the blocked area of ​​any grid area within a preset time period; the blocked area is the projection area of ​​the green plants in the area corresponding to the grid area in the corresponding grid area; S150, based on the sunshine amount of the grid area corresponding to the target installation area, the distance between the grid area and the target high-speed service area, the shaded area of ​​the grid area within a preset time period and the speed of change of the shaded area, obtain the target grid as the grid for installing the photovoltaic components.

2. The method according to claim 1, characterized in that: Qt is obtained based on a trained power prediction model; the trained power prediction model is obtained through the following steps: S101, obtaining a sample data set, wherein the sample data set includes a plurality of sample data, each sample data includes U and D, wherein U is the ID of the corresponding high-speed service area, and D is the time series data of the power load corresponding to the corresponding high-speed service area in a set historical time period, D = {D1, D2, ..., D i , ..., D n }, D i is the power load data corresponding to the i-th unit time period, i ranges from 1 to n, n is the number of unit time periods corresponding to the set historical segment, D i =(DC i ,DE i , Q i ), DC i is the traffic volume entering the corresponding highway service area in the i-th unit time period, DE i is the number of electric vehicles entering the corresponding highway service area in the i-th unit time period, Q i is the power consumption of the corresponding high-speed service area in the i-th unit time period; S102, inputting the training sample data of the current batch into the current power prediction model for training to obtain corresponding prediction results, wherein the prediction results include power consumption data corresponding to the next unit time period of the unit time period corresponding to the current batch; S103, based on the prediction results of the current batch and the corresponding actual results, the current loss function value of the current electricity prediction model is obtained, and it is determined whether the current loss function value meets the preset model training end condition. If so, step S105 is executed, otherwise, step S104 is executed; S104, updating the parameters of the current power prediction model based on the current loss function value, and using the next batch of sample data as the training sample data of the current batch, and executing S102; S105: Using the current power prediction model as the trained power prediction model.

3. The method according to claim 2, characterized in that Qt is obtained through the following steps: S10, based on the traffic volume and the number of electric vehicles in the target highway service area in a set historical time period, determining the traffic volume and the number of electric vehicles in the target highway service area in a unit time period as initial electricity consumption data; S11, set the counter variable j=1; S12, if j≤k, execute S13, otherwise execute S16; k is the number of unit time periods corresponding to the preset time period, k=roundup(t1 / t2), t1 is the duration corresponding to the preset time period, t2 is the duration corresponding to the unit time period, and roundup() means rounding up; S13, using the current prediction result based on the trained power prediction model as the current power consumption data corresponding to the target highway service area, and adding the current prediction result to the current prediction result set; the current prediction result includes the corresponding predicted vehicle flow, predicted number of electric vehicles and predicted power consumption; the initial value of the current prediction result set is empty; S14, inputting the current power consumption data corresponding to the target highway service area into the trained power consumption prediction model to obtain the corresponding current prediction result; S15, set j=j+1, and execute S12; S16, obtaining Qt based on the predicted power consumption corresponding to the current prediction result set.

4. The method according to claim 1, characterized in that: The grid unit corresponding to the target highway service area is obtained by consulting a preset lookup table, wherein each row of data in the preset lookup table includes the highway service area, the power consumption of the service area within a preset time period, and the corresponding grid unit.

5. The method according to claim 4, characterized in that The preset query table is obtained based on the following steps: S201, obtaining the power required by any reference highway service area within a preset time period; S202, obtaining a photovoltaic module installable area corresponding to any reference highway service area as a corresponding target installation area; S203, using preset initial grid units to divide the target installation area corresponding to any reference highway service area to obtain a plurality of corresponding grid areas; the preset initial grid unit is a grid area suitable for installing a group of photovoltaic modules; S204, obtaining the sunshine amount of any grid area corresponding to any reference highway service area within the preset time period; S205, based on the amount of electricity required by any reference highway service area within a preset time period and the amount of sunshine corresponding to each grid area, a target grid area is obtained from multiple grid areas as a grid unit corresponding to the reference highway service area; wherein the target grid area is a square area formed by at least one initial grid unit, and the amount of photovoltaic power generated by the target grid area is greater than or equal to the amount of electricity required by the corresponding reference highway service area within the preset time period; S206: Based on the power required by any reference highway service area within a preset time period and the corresponding grid unit, the preset query table is formed.

6. The method according to claim 1, characterized in that The sunshine amount of any grid area within the preset time period is obtained based on the trained sunshine amount prediction model.

7. The method according to claim 6, characterized in that The trained sunshine amount prediction model is obtained through the following steps: S1, obtain a training sample data set, each sample data includes the sunshine amount time series data SA of the corresponding sampling point of the corresponding sample data in the set historical time period, SA = {SA1, SA2, ..., SA r , ..., SA m }, SA r is the sunshine data corresponding to the rth unit time period, r ranges from 1 to m, m is the number of unit time periods corresponding to the set historical period, SA r =(L r , SA1 r , SA2 r ), L r is the position of the sampling point corresponding to the sample data, SA1 r is the weather characteristic information corresponding to the sampling point in the rth unit time period, SA2 r is the sunshine amount corresponding to the sampling point in the rth unit time period; S2, inputting the training sample data of the current batch into the current sunshine prediction model for training to obtain the corresponding prediction result, wherein the prediction result includes the weather characteristic information and sunshine amount corresponding to the next unit time period of the unit time period corresponding to the current batch; S3, based on the prediction results of the current batch and the corresponding actual results, the current loss function value of the current sunshine prediction model is obtained, and it is determined whether the current loss function value meets the preset model training end condition. If so, step S5 is executed, otherwise, step S4 is executed; S4, updating the parameters of the current sunshine amount prediction model based on the current loss function value, and using the next batch of sample data as the training sample data of the current batch, and executing S2; S5: Using the current sunshine amount prediction model as the trained sunshine amount prediction model.

8. The method according to claim 7, characterized in that The sunshine amount of any grid area in the preset time period is obtained based on the following steps: S20, taking the location corresponding to any grid area and the weather characteristic information corresponding to the current unit time period as initial input data; S21, set the counter variable j1=1; S22, if j1≤k1, execute S23, otherwise, execute S26; k1 is the number of unit time periods corresponding to the preset time period, k1=roundup(t1 / t2), t1 is the duration corresponding to the preset time period, t2 is the duration corresponding to the unit time period, and roundup() means rounding up; S23, using the current prediction result based on the trained sunshine amount prediction model as the current input data corresponding to the any grid area, and adding the current prediction result to the current prediction result set; the current prediction result includes the corresponding weather characteristic information and sunshine amount; the initial value of the current prediction result set is empty; S24, inputting the current input data corresponding to any grid area into the trained sunshine amount prediction model to obtain the corresponding current prediction result; S25, set j1=j1+1, and execute S22; S26, obtaining the sunshine amount of any grid area within a preset time period based on the sunshine amount corresponding to the current prediction result set corresponding to the grid area.

9. The method according to claim 1, characterized in that: The target grid area is obtained by the following steps: S161, based on the sunshine amount, distance, shaded area and shaded area change rate corresponding to any grid area, determining the scores corresponding to the sunshine amount, distance, shaded area and shaded area change rate corresponding to the grid area; S162, multiplying the scores corresponding to the sunshine amount, distance, shaded area and shaded area change speed corresponding to any grid area by the corresponding weights respectively and adding the respective multiplication results as the installation weight of the grid area; S163: The grid area corresponding to the largest of all installation weights is used as the target grid area.

10. A layout optimization device for photovoltaic modules in a highway service area, characterized in that: include: The first acquisition module is used to acquire the power Qt required by the target highway service area within a preset time period; The second acquisition module is used to acquire the grid unit corresponding to the target highway service area based on Qt; A third acquisition module is used to acquire a photovoltaic module installable area corresponding to a target highway service area as a target installation area, and divide the target installation area by using the grid units to obtain a plurality of grid areas; A fourth acquisition module, used to acquire the sunshine amount of any grid area within the preset time period and the distance between any grid area and the target highway service area; A fifth acquisition module is used to acquire the shaded area and the shaded area change rate of any grid area within a preset time period based on the normalized vegetation index and growth rate of the green plants in the area corresponding to any grid area; the shaded area is the projection area of ​​the green plants in the area corresponding to the grid area in the corresponding grid area; The sixth acquisition module is used to acquire the target grid as the grid for installing photovoltaic components based on the sunshine amount of the grid area corresponding to the target installation area, the distance between the grid area and the target high-speed service area, the shaded area of ​​the grid area within a preset time period, and the speed of change of the shaded area.

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