Photovoltaic power generation project design cost prediction method, device, equipment and medium

Abstract

The invention relates to a photovoltaic power generation project design cost prediction method, device and equipment and a medium, and belongs to the technical field of project design cost prediction.The method comprises the steps that multiple pieces of target project information of a target photovoltaic power generation project are obtained; according to a preset mapping relation between each piece of project information and an influence coefficient, determining a target influence coefficient of the influence of each piece of target project information on the total design cost of the photovoltaic power generation project; performing weighted summation on the target influence coefficient according to the target weight distribution mode to obtain a comprehensive adjustment coefficient; wherein the mapping relation and the target weight distribution mode are obtained by learning project information and design cost information of the sample photovoltaic power generation project through a regression model or a machine learning model; and multiplying the comprehensive adjustment coefficient by a preset basic design cost to obtain a predicted total design cost. The prediction process is easier to operate, the prediction efficiency can be improved, and prediction is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of project design fee prediction, and in particular to a method, device, equipment and medium for predicting design fees for a photovoltaic power generation project. Background Art

[0002] Currently, the total design fee for photovoltaic power generation projects is often estimated based on NB T32030-2016, "Calculation Standard for Survey and Design Fees for Photovoltaic Power Generation Projects." However, this method is complex, and some indicators are based on subjective judgment. Consequently, this method relies solely on manual calculations, resulting in low efficiency. Furthermore, the total design fee derived from this method often differs significantly from the actual total design fee. Summary of the Invention

[0003] In view of this, it is necessary to provide a photovoltaic power generation project design fee prediction method, device, equipment and medium to solve the problem of low efficiency and inaccurate estimation of the total design fee of photovoltaic power generation projects in the existing technology.

[0004] In order to solve the above problems, in a first aspect, the present invention provides a method for predicting design fees for photovoltaic power generation projects, comprising: Acquire multiple target project information of target photovoltaic power generation projects; Determining the target impact coefficient of each target project information on the total design fee of the photovoltaic power generation project according to a preset mapping relationship between each project information and the impact coefficient; The target impact coefficients are weighted and summed according to the target weight distribution method to obtain a comprehensive adjustment coefficient; wherein the mapping relationship and the target weight distribution method are obtained by learning the project information and design fee information of the sample photovoltaic power generation project through a regression model or a machine learning model; The comprehensive adjustment coefficient is multiplied by the preset basic design fee to obtain the predicted total design fee.

[0005] In one possible implementation, the target project information includes installed capacity, and determining a target impact coefficient of the target project information on the total design fee of the target photovoltaic power generation project based on a preset mapping relationship between the project information and the impact coefficient includes: The target impact coefficient of the installed capacity is determined by the following formula:

[0006] in, is the target impact coefficient of the installed capacity, is the installed capacity of the target photovoltaic power generation project, is the benchmark installed capacity, is the attenuation factor.

[0007] In a possible implementation, when the target photovoltaic power generation project is to establish a centralized photovoltaic power station, 0.05≤ ≤0.07, when the target photovoltaic power generation project is to establish a distributed photovoltaic power station, 0.08≤ ≤0.1.

[0008] In one possible implementation, the target project information includes a project duration. Determining a target impact coefficient of the target project information on the total design fee of the photovoltaic power generation project based on a preset mapping relationship between the project information and the impact coefficient includes: The project duration is input into a linear regression model to obtain a target impact coefficient of the project duration on the total design fee of the photovoltaic power generation project; wherein the linear regression model is fitted based on the project duration of the sample photovoltaic power generation project, the design fee associated with the project duration, and the total design fee.

[0009] In one possible implementation, the target project information includes the city level of the project implementation city. Determining a target impact coefficient of the target project information on the total design fee of the target photovoltaic power generation project based on a preset mapping relationship between project information and impact coefficients includes: According to the mapping relationship between different city grades and impact coefficients, the target impact coefficient of the city grade of the project implementation city of the target photovoltaic power generation project is determined.

[0010] In a possible implementation, the method further includes: The target weight allocation method is determined according to the project type of the target photovoltaic power generation project; the project type includes establishing a centralized photovoltaic power station or establishing a distributed photovoltaic power station.

[0011] In a possible implementation, the target project information includes installed capacity; when the project type of the target photovoltaic power generation project is to establish a centralized photovoltaic power station, the weight of the impact coefficient of the installed capacity in the target weight distribution method is the largest.

[0012] In a second aspect, the present invention further provides a photovoltaic power generation project design fee prediction device, comprising: An information acquisition module, used to acquire multiple target project information of a target photovoltaic power generation project; An influence coefficient determination module is used to determine a target influence coefficient of each target project information on the total design fee of the photovoltaic power generation project based on a preset mapping relationship between each project information and the influence coefficient; a comprehensive adjustment coefficient determination module, configured to obtain a comprehensive adjustment coefficient by weighted summing the target impact coefficients according to a target weight distribution method; wherein the mapping relationship and the target weight distribution method are obtained by learning project information and design fee information of a sample photovoltaic power generation project using a regression model or a machine learning model; The total design fee determination module is used to multiply the comprehensive adjustment coefficient by the preset basic design fee to obtain the predicted total design fee.

[0013] In a third aspect, the present invention also provides an electronic device comprising a memory and a processor, wherein the memory is used to store programs; and the processor is coupled to the memory and is used to execute the programs stored in the memory to implement the steps in any one of the above-mentioned methods for predicting design fees for photovoltaic power generation projects.

[0014] In a fourth aspect, the present invention further provides a computer-readable storage medium for storing a computer-readable program, wherein the program or instruction, when executed by a processor, can implement the steps in any one of the above-mentioned methods for predicting design costs of photovoltaic power generation projects.

[0015] The beneficial effects of the present invention are: The present invention presets a mapping relationship between each project information and the influence coefficient, so that after inputting multiple target project information of the target photovoltaic power generation project, the target influence coefficient of each target project information on the total design fee of the photovoltaic power generation project can be quickly obtained, and then the total design fee can be calculated according to the target influence coefficient and the preset subsequent calculation steps. Compared with manual calculation, the calculation efficiency is higher.

[0016] Moreover, the present invention studies the project information and design fee information of the sample photovoltaic power generation projects based on the model to obtain the mapping relationship between each project information and the influence coefficient, and then dynamically generates a comprehensive adjustment coefficient according to the mapping relationship and each target project information of the target photovoltaic power generation project, so that the comprehensive adjustment coefficient is more accurate and has a higher degree of matching with the actual project, and the total design fee calculated according to the comprehensive adjustment coefficient is also more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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 those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic flow chart of an embodiment of a method for predicting design fees for photovoltaic power generation projects provided by the present invention; Figure 2A weight distribution diagram provided by the present invention; Figure 3 A relationship diagram between influence coefficient and weight distribution provided by the present invention; Figure 4 A schematic flow chart of another embodiment of a photovoltaic power generation project design fee prediction method provided by the present invention; Figure 5 A schematic structural diagram of an embodiment of a photovoltaic power generation project design fee prediction device provided by the present invention; Figure 6 This is a schematic structural diagram of an embodiment of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise specified, the meaning of "plurality" is two or more. The "first", "second", etc. involved in the embodiments of the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence, nor are they used to indicate or imply their relative importance or implicitly indicate the number of technical features indicated. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more.

[0021] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0022] Reference Figure 1 , which shows a flow chart of an embodiment of a method for predicting design fees for photovoltaic power generation projects provided by the present invention, the method comprising: S101, obtaining multiple target project information of target photovoltaic power generation projects.

[0023] The target photovoltaic power generation project can be a photovoltaic power generation project to be forecasted. Multiple target project information can include installed capacity, project duration, design complexity, city rank of the project implementation city, and climate conditions at the project implementation site. Installed capacity refers to the maximum continuous output power of all installed power generation equipment. Project duration refers to the time required to complete the project. Design complexity can be determined based on information such as technology type, in accordance with NB T32030-2016, "Standard for Calculation of Survey and Design Fees for Photovoltaic Power Generation Projects." The city rank of the project implementation city can also refer to the city rank definition in that document.

[0024] The above target project information are the main factors affecting the total design fee of the target photovoltaic power generation project.

[0025] S102 : determining a target impact coefficient of each target project information on the total design fee of the photovoltaic power generation project according to a preset mapping relationship between each project information and the impact coefficient.

[0026] Existing design fee forecasting methods typically rely on industry standards, such as NB T32030-2016, "Calculation Standard for Survey and Design Fees for Photovoltaic Power Generation Projects," to produce a standard design fee. This calculation incorporates project information such as installed capacity and technical complexity. However, during project implementation, actual design fees can deviate from the standard design fee based on actual project conditions. For example, the project duration may be extended or shortened, design adjustments may be made due to the specific environment of the project site, or poor procurement conditions in the project city.

[0027] Therefore, after clarifying the influence relationship between each project information and the final total design fee, we can use big data technology to model the project information and design fee information of sample photovoltaic power generation projects to obtain a mapping relationship between each project information and the influence coefficient (the influence coefficient represents the degree of influence on the total design fee). The sample photovoltaic power generation project can be a historical photovoltaic power generation project.

[0028] Specifically, for example, for installed capacity, project duration, and city level, linear and nonlinear regression models can be used to fit a large amount of sample data, respectively. A target model can then be selected from these models, with evaluation indicators that meet the requirements. This means a highly accurate mapping relationship can be selected. When fitting installed capacity, the sample data can include the installed capacity and installed capacity impact coefficient of the sample photovoltaic power generation project. The installed capacity impact coefficient is determined based on the design fee for the installed capacity portion (which can be determined based on the detailed charge of the total design fee) and the total design fee. When fitting project duration and city level, the sample data can be deduced similarly. If the target model cannot be obtained through linear and nonlinear regression models, the mapping relationship can be learned through models such as neural networks.

[0029] Technical complexity is influenced by at least one factor, and environmental information can also include multiple factors. Therefore, the relationship between these two types of project information and the impact coefficient can be determined through learning models such as neural networks. For example, the technical complexity and technical complexity impact coefficient of sample photovoltaic power generation projects can be used as training data to train a neural network.

[0030] The above is only an exemplary description of a method for determining a mapping relationship. Those skilled in the art may also adopt other methods. This embodiment does not impose any specific limitation on the process for determining a mapping relationship.

[0031] S103, performing weighted summation of the target impact coefficients according to the target weight distribution method to obtain a comprehensive adjustment coefficient; wherein the mapping relationship and the target weight distribution method are learned by the model based on the project information and design fee information of the sample photovoltaic power generation project.

[0032] Different influence coefficients can be assigned different weights. The comprehensive adjustment coefficient can be obtained by multiplying the influence coefficient with their respective weights and then adding them together.

[0033] In this embodiment, the weight of each item information can be determined by linear regression, machine learning, etc.

[0034] In this embodiment, the calculation formula of the comprehensive adjustment coefficient is as follows:

[0035] in, Indicates the i The influence coefficient of project information, Indicates the i The weight of the influence coefficient of each project information.

[0036] S104: Multiply the comprehensive adjustment coefficient by the preset basic design fee to obtain the predicted total design fee.

[0037] The total design fee is calculated as follows:

[0038] in, It represents the preset basic design fee, which can be a fixed unit price or a preset percentage of the total project investment.

[0039] The photovoltaic power generation project design fee prediction method provided in this embodiment can be applied to a photovoltaic power generation project design fee prediction system. The photovoltaic power generation project design fee prediction system can be a software system running on a terminal device. The terminal device can be a tablet computer, augmented reality (AR) / virtual reality (VR) device, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), mobile phone, or other terminal device. This embodiment does not impose any restrictions on the specific type of terminal device.

[0040] In summary, this embodiment presets a mapping relationship between each project information and the impact coefficient, so that after inputting multiple target project information of the target photovoltaic power generation project, the target impact coefficient of each target project information on the total design fee of the photovoltaic power generation project can be quickly obtained, and then the total design fee can be calculated according to the target impact coefficient and the preset subsequent calculation steps. Compared with manual calculation, the calculation efficiency is higher.

[0041] In addition, this embodiment studies the project information and design fee information of the sample photovoltaic power generation projects based on the model to obtain the mapping relationship between each project information and the impact coefficient, and then dynamically generates a comprehensive adjustment coefficient based on the mapping relationship and each target project information of the target photovoltaic power generation project, so that the comprehensive adjustment coefficient is more accurate and has a higher actual matching degree with the target photovoltaic power generation project, and the total design fee calculated based on the comprehensive adjustment coefficient is also more accurate.

[0042] In one embodiment, the target impact coefficient and the calculation result after multiplying the target impact coefficient by the weight can be displayed in the form of a chart, so that the user can more intuitively understand the impact of multiple target project information of the target photovoltaic power generation project on the predicted total cost.

[0043] In some embodiments of the present invention, the target project information is installed capacity, and S102 includes: calculating the target impact coefficient of the installed capacity using the following formula:

[0044] in, is the influence coefficient of installed capacity, is the installed capacity of the target photovoltaic power generation project, is the benchmark installed capacity, is the attenuation factor.

[0045] In some embodiments of the present invention, Can be 1MW, 0.05≤ ≤0.1.

[0046] In some embodiments of the present invention, when the photovoltaic power generation project is to establish a centralized photovoltaic power station, 0.05≤ ≤0.07, when the photovoltaic power generation project is to build a distributed photovoltaic power station, 0.08≤ ≤0.1.

[0047] In some embodiments of the present invention, the target project information is the project duration, and S102 includes: inputting the project duration into a linear regression model to obtain a target impact coefficient of the project duration on the total design fee of the photovoltaic power generation project; wherein the linear regression model is fitted based on the project duration of the sample photovoltaic power generation project, the design fee associated with the project duration, and the total design fee.

[0048] The impact coefficient of the project duration on the total design fee of a photovoltaic power generation project is related to the project duration compression ratio. For example, if the construction period is shortened by 30%, the duration compression ratio is 30%. The greater the duration compression ratio, the greater the impact coefficient.

[0049] In some embodiments of the present invention, the project duration impact coefficient increases linearly with the duration compression ratio. For example, during a normal duration, the project duration impact coefficient is 1. When the duration compression ratio is 30%, the project duration impact coefficient is 1 + 0.3, that is, 1.3. When the duration compression ratio is 50%, the project duration impact coefficient is 1.5.

[0050] In some embodiments of the present invention, the design complexity can be determined based on the technology type, which includes: conventional ground-mounted photovoltaic power plants, rooftop distributed photovoltaic power plants (including load capacity assessment), and floating photovoltaic power plants (including hydrological analysis). The design complexity corresponding to these three technology types increases in order, with greater design complexity resulting in a greater impact coefficient.

[0051] For example, the impact coefficient of a conventional ground-mounted PV power station is 1.0, the impact coefficient of a rooftop distributed PV power station is 1.2-1.5, and the impact coefficient of a floating PV power station is 1.5-2.0. The specific impact coefficients of rooftop distributed PV power stations and floating PV power stations can be determined based on their lower-level mapping relationships. After obtaining the technology type of the target PV power generation project, the target impact coefficient can be determined by looking up the table (or mapping relationship).

[0052] In some embodiments of the present invention, the impact coefficient of the city level of the project implementation city can range from 0.9 to 1.3. Different city levels correspond to different impact coefficients within this range. In this embodiment, the project implementation city can be directly input, and the system will identify its city level and perform subsequent calculations.

[0053] In some embodiments of the present invention, the mapping relationship between the climate environment of the project implementation site and the impact coefficient can be expressed by the following formula:

[0054] in, is the impact coefficient of the climate environment in the project implementation area, It is an additional coefficient for a single special environment, and the single special environment includes at least one of salt spray corrosion environment, extreme temperature difference, high altitude, high temperature, low temperature, strong ultraviolet rays, and strong wind. The single special environment is determined according to the environmental conditions when the design is adjusted due to environmental factors in the sample photovoltaic power generation project. In this embodiment, a mapping relationship between the project implementation site and the single special environment can be established according to the sample photovoltaic power generation project. Then, the project implementation site of the target photovoltaic power generation project is input, and the system can automatically identify the corresponding single special environment and perform subsequent calculations. Alternatively, the project implementation site of the target photovoltaic power generation project can be input, and the system automatically imports the climate environment of the project implementation site and matches it with the sample single special environment information, thereby determining the single special environment matched by the project implementation site, and performing subsequent calculations. The sample single special environment information is extracted from the environmental information of the sample photovoltaic power generation project.

[0055] The additional coefficients for different special environments may not be exactly the same. For example, the additional coefficient for high altitude is 0.1, the additional coefficient for salt spray corrosion is 0.15, and the additional coefficient for extreme temperature difference is 0.1. The additional coefficients for each special environment are added together to calculate the impact coefficient of the environmental type of the project implementation area.

[0056] In some embodiments of the present invention, S103 may include: when the photovoltaic power generation project is to establish a centralized photovoltaic power station, the influence coefficients are weighted and summed according to the first weight distribution method to obtain a comprehensive adjustment coefficient; the weight of the influence coefficient of installed capacity in the first weight distribution method is the largest; when the photovoltaic power generation project is to establish a distributed photovoltaic power station, the influence coefficients are weighted and summed according to the second weight distribution method to obtain a comprehensive adjustment coefficient; the weight of the influence coefficient of climate environment in the second weight distribution method is the largest.

[0057] In terms of weight distribution, different project types have different weights for each impact coefficient. Centralized projects tend to assign a greater weight to the impact coefficient of installed capacity, while distributed projects tend to assign a greater weight to the impact coefficient of climate environment.

[0058] In some embodiments of the present invention, Figure 2 As shown in the figure, when the photovoltaic power generation project is to build a centralized photovoltaic power station, its weight distribution can be: installed capacity weight =40%, weight of technical complexity =30%, climate and environment weight =20%, other key weights =10%. When the photovoltaic power generation project is to establish a distributed photovoltaic power station, its weight distribution can be: installed capacity weight =20%, weight of technical complexity =25%, climate and environment weight =35%, other key weights =20%.

[0059] Reference Figure 3 , showing a relationship between influence coefficients and weights provided by the present invention. The comprehensive adjustment coefficient is equal to the product of the climate environment influence coefficient and a weight of 20%, the installed capacity influence coefficient and a weight of 40%, the technical complexity influence coefficient and a weight of 30%, and the other key influence coefficients and a weight of 10%.

[0060] Reference Figure 4 , which shows a flow chart of another embodiment of a photovoltaic power generation project design fee prediction method provided by the present invention. After obtaining information about each project, the impact coefficient of each project is calculated sequentially. The project type is then determined, and a weight is assigned based on the project type. A weighted sum is then taken based on the weights assigned and the impact coefficients of each project information to obtain the total design fee for the photovoltaic power generation project.

[0061] The present invention is described in detail below using a specific photovoltaic power generation project as an example. The photovoltaic power generation project is a 100 MW highland centralized photovoltaic project with a base design fee of RMB 500,000.

[0062] 1. First calculate the influence coefficient: =1+ ≈1.33; =1.2 (complex terrain); =1+0.1(high altitude)+0.1(strong ultraviolet rays)= 1.2; =1.1 (city level is lower); = 1.0 (normal duration).

[0063] 2. Determine the weight distribution method: This project is a centralized power station on the plateau, which adopts a centralized weight distribution strategy: =40%; = 30%; =20%; =10%.

[0064] 3. Determine the comprehensive adjustment coefficient: = × + × + × + × =1.33×40 %+1.2×30 %+1.2×20 %+1.1×1.0×10 %≈1.24.

[0065] 4. Total design fee: 50×1.24=620,000 yuan.

[0066] In summary, the present invention has the following beneficial effects: 1. Simply input the target photovoltaic power generation project information to automatically calculate the predicted total design fee, which is highly efficient and saves time and labor costs.

[0067] 2. Multi-dimensional quantification: Cover the main factors affecting design costs through the combination of influence coefficients.

[0068] 3. Dynamic adaptation: The calculation logic can be automatically adjusted according to the project characteristics, and the design fee can be calculated more reasonably and transparently, making the calculation results more accurate and more referenceable.

[0069] Reference Figure 5 , shows a schematic structural diagram of an embodiment of a photovoltaic power generation project design fee prediction device provided by the present invention, wherein the device 500 includes: An information acquisition module 501 is used to acquire multiple target project information of a target photovoltaic power generation project; The influence coefficient determination module 502 is used to determine the target influence coefficient of each target project information on the total design fee of the photovoltaic power generation project based on the preset mapping relationship between each project information and the influence coefficient; Comprehensive adjustment coefficient determination module 503 is configured to perform weighted summation of the target impact coefficients according to the target weight distribution method to obtain the comprehensive adjustment coefficient; wherein the mapping relationship and the target weight distribution method are obtained by learning the project information and design fee information of the sample photovoltaic power generation project through a regression model or a machine learning model; The total design fee determination module 504 is configured to multiply the comprehensive adjustment coefficient by the preset basic design fee to obtain the predicted total design fee.

[0070] It should be noted that the implementation principles or implementation processes of the above modules can refer to the embodiment of the above-mentioned photovoltaic power generation project design fee prediction method, and will not be described in detail here.

[0071] Reference Figure 6 , shows an electronic device 600 provided by the present invention. The electronic device 600 includes a processor 601, a memory 602 and a display 603. Figure 6 Only some of the components of the electronic device 600 are shown, but it should be understood that implementation of all of the shown components is not required, and more or fewer components may be implemented instead.

[0072] In some embodiments, the processor 601 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to execute program codes or process data stored in the memory 602 , such as the magnetic resonance image optimization method of the present invention.

[0073] In some embodiments, processor 601 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, processor 601 may be local or remote. In some embodiments, processor 601 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, multiple clouds, or any combination thereof.

[0074] In some embodiments, the memory 602 may be an internal storage unit of the electronic device 600, such as a hard disk or memory of the electronic device 600. In other embodiments, the memory 602 may also be an external storage device of the electronic device 600, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 600.

[0075] Furthermore, the memory 602 may include both an internal storage unit of the electronic device 600 and an external storage device. The memory 602 is used to store application software installed in the electronic device 600 and various data.

[0076] In some embodiments, the display 603 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 603 is used to display information about the electronic device 600 and to display a visual user interface. Components 601-603 of the electronic device 600 communicate with each other via a system bus.

[0077] In one embodiment, when the processor 601 executes the photovoltaic power generation project design fee prediction program in the memory 602, the following steps may be implemented: Acquire multiple target project information of target photovoltaic power generation projects; Determine the target impact coefficient of each target project information on the total design fee of the photovoltaic power generation project based on the preset mapping relationship between each project information and the impact coefficient; The target impact coefficients are weighted and summed according to the target weight distribution method to obtain a comprehensive adjustment coefficient; wherein the mapping relationship and the target weight distribution method are obtained by learning the project information and design fee information of the sample photovoltaic power generation project through a regression model or a machine learning model; Multiply the comprehensive adjustment coefficient by the preset basic design fee to obtain the predicted total design fee.

[0078] It should be understood that, when the processor 601 executes the photovoltaic power generation project design fee prediction program in the memory 602 , in addition to the above functions, it can also implement other functions. For details, please refer to the description of the corresponding method embodiment above.

[0079] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 600 mentioned. The electronic device 600 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, or the like. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft, or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 600 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0080] In one embodiment, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by the processor, the steps of any one of the above-mentioned photovoltaic power generation project design fee prediction methods are implemented.

[0081] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0082] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for predicting design costs of photovoltaic power generation projects, characterized in that: include: Acquire multiple target project information of target photovoltaic power generation projects; Determining the target impact coefficient of each target project information on the total design fee of the photovoltaic power generation project according to a preset mapping relationship between each project information and the impact coefficient; The target impact coefficients are weighted and summed according to the target weight distribution method to obtain a comprehensive adjustment coefficient; wherein the mapping relationship and the target weight distribution method are obtained by learning the project information and design fee information of the sample photovoltaic power generation project through a regression model or a machine learning model; The comprehensive adjustment coefficient is multiplied by the preset basic design fee to obtain the predicted total design fee.

2. The photovoltaic power generation project design fee prediction method according to claim 1, characterized in that: The target project information includes installed capacity. According to a preset mapping relationship between project information and impact coefficients, a target impact coefficient of the target project information on the total design fee of the target photovoltaic power generation project is determined, including: The target impact coefficient of the installed capacity is determined by the following formula: in, is the target impact coefficient of the installed capacity, is the installed capacity of the target photovoltaic power generation project, is the benchmark installed capacity, is the attenuation factor.

3. The photovoltaic power generation project design fee prediction method according to claim 2, characterized in that: When the target photovoltaic power generation project is to establish a centralized photovoltaic power station, 0.05≤ ≤0.07, when the target photovoltaic power generation project is to establish a distributed photovoltaic power station, 0.08≤ ≤0.

1.

4. The photovoltaic power generation project design fee prediction method according to claim 1, characterized in that: The target project information includes a project duration. Determining a target impact coefficient of the target project information on the total design fee of the photovoltaic power generation project based on a preset mapping relationship between the project information and the impact coefficient includes: The project duration is input into a linear regression model to obtain a target impact coefficient of the project duration on the total design fee of the photovoltaic power generation project; wherein the linear regression model is fitted based on the project duration of the sample photovoltaic power generation project, the design fee associated with the project duration, and the total design fee.

5. The photovoltaic power generation project design fee assessment method according to claim 1, characterized in that: The target project information includes the city level of the project implementation city. According to a preset mapping relationship between project information and impact coefficients, a target impact coefficient of the target project information on the total design fee of the target photovoltaic power generation project is determined, including: According to the mapping relationship between different city grades and impact coefficients, the target impact coefficient of the city grade of the project implementation city of the target photovoltaic power generation project is determined.

6. The photovoltaic power generation project design fee prediction method according to claim 1, characterized in that: The method further comprises: The target weight allocation method is determined according to the project type of the target photovoltaic power generation project; the project type includes establishing a centralized photovoltaic power station or establishing a distributed photovoltaic power station.

7. The photovoltaic power generation project design fee prediction method according to claim 6, characterized in that: The target project information includes installed capacity; when the project type of the target photovoltaic power generation project is to establish a centralized photovoltaic power station, the weight of the impact coefficient of the installed capacity in the target weight distribution method is the largest.

8. A photovoltaic power generation project design fee prediction device, characterized in that: include: An information acquisition module, used to acquire multiple target project information of a target photovoltaic power generation project; An influence coefficient determination module is used to determine a target influence coefficient of each target project information on the total design fee of the photovoltaic power generation project based on a preset mapping relationship between each project information and the influence coefficient; a comprehensive adjustment coefficient determination module, configured to obtain a comprehensive adjustment coefficient by weighted summing the target impact coefficients according to a target weight distribution method; wherein the mapping relationship and the target weight distribution method are obtained by learning project information and design fee information of a sample photovoltaic power generation project using a regression model or a machine learning model; The total design fee determination module is used to multiply the comprehensive adjustment coefficient by the preset basic design fee to obtain the predicted total design fee.

9. An electronic device, characterized in that: comprising a memory and a processor, wherein, The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the photovoltaic power generation project design fee prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Used to store computer-readable programs, which, when executed by a processor, can implement the steps of the photovoltaic power generation project design fee prediction method described in any one of claims 1 to 7.

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