Power grid material demand prediction method and device, electronic equipment and storage medium

Abstract

The invention discloses a power grid material demand prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: extracting demand data of power grid materials in a historical time period from a database; based on the demand data of the power grid materials in the historical time period, predicting the demand data of the power grid materials in the future time period by using a plurality of prediction algorithms to obtain prediction results corresponding to the prediction algorithms; and selecting the prediction result corresponding to one prediction algorithm as the final prediction result of the power grid materials in the future time period according to the prediction results corresponding to various prediction algorithms. According to the embodiment of the invention, the optimal prediction algorithm can be dynamically selected, so that the accuracy of material demand prediction can be improved, and effective supply of power grid materials is guaranteed.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of smart grid technology, and in particular to a method, device, electronic device and storage medium for predicting grid material demand. Background Art

[0002] The forecast of power material demand is the core link of power material supply chain management, which directly affects the efficiency and cost of each link such as procurement, distribution, storage and use of power materials, and is also related to the safe operation of the power system and the stable development of the power market. However, due to the uncertainty, diversity and dynamics of power material demand, as well as the complexity and opacity of the power material supply chain, the forecast of power material demand is very difficult and challenging. The application scenarios of power grid materials can be roughly divided into two project categories according to the demand, one is infrastructure projects, and the other is repair and technical transformation projects. For the material demand forecast link, there is a lot of information related to engineering projects in power material infrastructure projects, such as voltage level, wiring method, equipment selection, construction scale, etc., and the materials of infrastructure projects can be predicted more accurately through typical design modeling. However, for repair and technical transformation projects, this type of project lacks detailed information related to engineering projects, and the forecast effect of power material demand is relatively poor, with low accuracy, which affects the procurement, distribution, and inventory of power materials, and cannot guarantee the effective supply of materials for repair and technical transformation projects, affecting the normal development of power projects. Summary of the invention

[0003] The present application provides a method, device, electronic device and storage medium for predicting power grid material demand, which can dynamically select the optimal prediction algorithm, thereby improving the accuracy of material demand prediction and ensuring the effective supply of power grid materials.

[0004] In a first aspect, an embodiment of the present application provides a method for predicting power grid material demand, the method comprising:

[0005] Extract the demand data of power grid materials in the historical period from the database;

[0006] Based on the demand data of the power grid materials in the historical period, using multiple prediction algorithms to respectively predict the demand data of the power grid materials in the future period, and obtaining prediction results corresponding to various prediction algorithms;

[0007] According to the prediction results corresponding to various prediction algorithms, a prediction result corresponding to a prediction algorithm is selected as the final prediction result of the power grid material in the future period.

[0008] In a second aspect, the embodiment of the present application further provides a power grid material demand forecasting device, the device comprising: an extraction module, a forecasting module and a selection module; wherein:

[0009] The extraction module is used to extract the demand data of power grid materials in the historical period from the database;

[0010] The prediction module is used to use a plurality of prediction algorithms to respectively predict the demand data of the power grid materials in the future time period based on the demand data of the power grid materials in the historical time period, and obtain prediction results corresponding to the various prediction algorithms;

[0011] The selection module is used to select the prediction result corresponding to a prediction algorithm as the final prediction result of the power grid material in the future time period according to the prediction results corresponding to various prediction algorithms.

[0012] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0013] one or more processors;

[0014] a memory for storing one or more programs,

[0015] When the one or more programs are executed by the one or more processors, the one or more processors implement the power grid material demand forecasting method described in any embodiment of the present application.

[0016] In a fourth aspect, an embodiment of the present application provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the method for predicting power grid material demand as described in any embodiment of the present application is implemented.

[0017] The embodiment of the present application proposes a method, device, electronic device and storage medium for predicting the demand for power grid materials. First, the demand data for power grid materials in the historical period is extracted from the database; then, based on the demand data for power grid materials in the historical period, multiple prediction algorithms are used to predict the demand data for power grid materials in the future period respectively, and the prediction results corresponding to various prediction algorithms are obtained; then, according to the prediction results corresponding to various prediction algorithms, the prediction results corresponding to a prediction algorithm are selected as the final prediction results of power grid materials in the future period. That is to say, in the technical scheme of the present application, a prediction algorithm can be dynamically selected from multiple prediction algorithms as the optimal prediction algorithm for the historical period, and different historical periods can correspond to different optimal prediction algorithms. In the prior art, due to the uncertainty, diversity and dynamics of the demand for power materials, as well as the complexity and opacity of the power material supply chain, the prediction of power material demand is very difficult and challenging. Therefore, compared with the prior art, the method, device, electronic device and storage medium for predicting the demand for power grid materials proposed in the embodiment of the present application can dynamically select the optimal prediction algorithm, thereby improving the accuracy of material demand prediction and ensuring the effective supply of power grid materials; and the technical scheme of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of a method for predicting power grid material demand provided in one embodiment of the present application;

[0019] Figure 2 A schematic diagram of a flow chart of a method for predicting power grid material demand provided by another embodiment of the present application;

[0020] Figure 3 A flowchart of a method for predicting power grid material demand provided in yet another embodiment of the present application;

[0021] Figure 4 A flowchart of a method for predicting power grid material demand provided by another embodiment of the present application;

[0022] Figure 5 A schematic diagram of the structure of a power grid material demand forecasting device provided in one embodiment of the present application;

[0023] Figure 6 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only the parts related to the present application, rather than all structures, are shown in the accompanying drawings.

[0025] Figure 1 This is a flow chart of a method for predicting power grid material demand provided by an embodiment of the present application. The method can be executed by a power grid material demand prediction device or electronic device. The device or electronic device can be implemented by software and / or hardware. The device or electronic device can be integrated in any intelligent device with network communication function. Figure 1 As shown, the method for predicting power grid material demand may include the following steps:

[0026] S101. Extract demand data of power grid materials in a historical period from a database.

[0027] In this step, the original data of power grid materials in the historical period can be first extracted from the database; then, multiple fields in the original data can be combined to obtain the demand data of power grid materials in the historical period. Specifically, after extracting the original data of power grid materials in the historical period, the original data can be cleaned first to check and clean up missing values ​​or outliers; the date format can also be standardized to ensure that all date fields use a consistent format. The historical time period in the embodiment of the present application can be selected according to day, week, month or quarter. The necessary fields for calculating demand data may include: material ID, material name, material category, purchase date, demand, etc. In addition, SQL query or data analysis software can be used to aggregate data according to the required time period and material classification, and sum or other statistical operations can be performed on the demand for each material in each time period.

[0028] S102. Based on the demand data of power grid materials in the historical period, use a variety of prediction algorithms to respectively predict the demand data of power grid materials in the future period, and obtain prediction results corresponding to the various prediction algorithms.

[0029] In this step, data features can be first extracted from the demand data of power grid materials in the historical period; then, the prediction results corresponding to each prediction algorithm can be obtained based on the data features. The data features in the embodiment of the present application may include but are not limited to: time series features, statistical features, and material features; wherein the time series features may include: time features, periodic features, and trend features; statistical features may include: mean, median, standard deviation / variance, maximum / minimum, and percentile; material features may include: material type, material attribute, material amount, and material supplier.

[0030] S103. According to the prediction results corresponding to various prediction algorithms, a prediction result corresponding to a prediction algorithm is selected as the final prediction result of the power grid materials in the future period.

[0031] The various prediction algorithms in the embodiments of the present application may include but are not limited to at least two or more of the following: a first exponential smoothing algorithm, a second exponential smoothing algorithm, a third exponential smoothing algorithm, a first grey prediction algorithm, and a second grey prediction algorithm; wherein, the first exponential smoothing algorithm is applicable to stationary time series without trend or seasonality, and the method performs smoothing by assigning a larger weight to the most recent observations; the second exponential smoothing algorithm is applicable to data with linear trend but no seasonality, and the method introduces an additional trend term to capture trend changes in the data. The third exponential smoothing algorithm is applicable to data with trend and seasonality, and it not only includes smoothing coefficients for horizontal and smooth trend, but also introduces seasonal factors. In addition, the first grey prediction algorithm in the embodiments of the present application may be GM(1,1); the second grey prediction algorithm may be GM(2,1); wherein, the GM(1,1) model is a typical model in grey system theory, and is mainly used to process predictions under small samples and incomplete information. The GM(2,1) model is another model in grey system theory, which is an extension of the GM(1,1) model and is applicable to systems with higher-order dynamic characteristics. The GM (2, 1) model takes into account the interaction between two variables, so it can better simulate and predict data with complex dynamic relationships. This model is usually used to describe the influence relationship between two variables and can be used to deal with prediction problems of nonlinear or multivariable systems.

[0032] The method for predicting the demand for power grid materials proposed in the embodiment of the present application first extracts the demand data for power grid materials in the historical period from the database; then, based on the demand data for power grid materials in the historical period, multiple prediction algorithms are used to predict the demand data for power grid materials in the future period respectively, and the prediction results corresponding to various prediction algorithms are obtained; then, according to the prediction results corresponding to various prediction algorithms, a prediction result corresponding to a prediction algorithm is selected as the final prediction result of power grid materials in the future period. That is to say, in the technical scheme of the present application, a prediction algorithm can be dynamically selected from multiple prediction algorithms as the optimal prediction algorithm for the historical period, and different historical periods can correspond to different optimal prediction algorithms. In the prior art, due to the uncertainty, diversity and dynamics of the demand for power materials, as well as the complexity and opacity of the power material supply chain, the prediction of the demand for power materials is very difficult and challenging. Therefore, compared with the prior art, the method for predicting the demand for power grid materials proposed in the embodiment of the present application can dynamically select the optimal prediction algorithm, thereby improving the accuracy of the material demand prediction and ensuring the effective supply of power grid materials; and the technical scheme of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.

[0033] Figure 2 A flowchart of a method for predicting power grid material demand provided by another embodiment of the present application. The above technical solution is further optimized and expanded, and can be combined with the above optional implementation methods. Figure 2 As shown, the method for predicting power grid material demand may include the following steps:

[0034] S201. Extracting original data of power grid materials in a historical period from a database.

[0035] S202. Combine multiple fields in the original data to obtain demand data for power grid materials in a historical period.

[0036] S203. Extract data features from the demand data of power grid materials in a historical period.

[0037] S204: Obtain prediction results corresponding to each prediction algorithm based on data features.

[0038] In this step, the data features can be directly input into the prediction models corresponding to each prediction algorithm, and the prediction results corresponding to each prediction algorithm can be output through each prediction model; or the data features can be statistically analyzed first to obtain the statistical analysis results of the data features; and then the statistical analysis results of the data features can be input into the prediction models corresponding to each prediction algorithm, and the prediction results corresponding to each prediction algorithm can be output through each prediction model.

[0039] S205. According to the prediction results corresponding to various prediction algorithms, a prediction result corresponding to a prediction algorithm is selected as the final prediction result of the power grid materials in the future period.

[0040] In this step, appropriate evaluation indicators can be selected according to the problem type, and the evaluation indicators can be: mean square error, root mean square error, mean absolute error, etc.; then the performance of each prediction algorithm on each evaluation indicator is recorded, so that a prediction algorithm can be selected as the optimal prediction algorithm, and the prediction result corresponding to the algorithm is used as the final prediction result of the power grid materials in the future period.

[0041] The method for predicting the demand for power grid materials proposed in the embodiment of the present application first extracts the demand data for power grid materials in the historical period from the database; then, based on the demand data for power grid materials in the historical period, multiple prediction algorithms are used to predict the demand data for power grid materials in the future period respectively, and the prediction results corresponding to various prediction algorithms are obtained; then, according to the prediction results corresponding to various prediction algorithms, a prediction result corresponding to a prediction algorithm is selected as the final prediction result of power grid materials in the future period. That is to say, in the technical scheme of the present application, a prediction algorithm can be dynamically selected from multiple prediction algorithms as the optimal prediction algorithm for the historical period, and different historical periods can correspond to different optimal prediction algorithms. In the prior art, due to the uncertainty, diversity and dynamics of the demand for power materials, as well as the complexity and opacity of the power material supply chain, the prediction of the demand for power materials is very difficult and challenging. Therefore, compared with the prior art, the method for predicting the demand for power grid materials proposed in the embodiment of the present application can dynamically select the optimal prediction algorithm, thereby improving the accuracy of the material demand prediction and ensuring the effective supply of power grid materials; and the technical scheme of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.

[0042] Figure 3 A flowchart of a method for predicting power grid material demand provided by another embodiment of the present application is provided. The above technical solution is further optimized and expanded, and can be combined with the above optional implementation methods. Figure 3 As shown, the method for predicting power grid material demand may include the following steps:

[0043] S301. Extract the demand data of power grid materials in the historical period from the database.

[0044] S302. Extract data features from the demand data of power grid materials in a historical period.

[0045] S303: Input the data features into the prediction models corresponding to the prediction algorithms, and output the prediction results corresponding to the prediction algorithms through the prediction models.

[0046] In this step, the data features can be directly input into the prediction models corresponding to the prediction algorithms, and the prediction results corresponding to the prediction algorithms can be output through the prediction models. The data features in the embodiments of the present application may include, but are not limited to, time series features, statistical features, and material features; wherein, time series features may include, time features, periodic features, and trend features; statistical features may include, mean, median, standard deviation / variance, maximum / minimum, and percentile; material features may include, material types, material attributes, material amounts, and material suppliers.

[0047] S304. According to the prediction results corresponding to various prediction algorithms, a prediction result corresponding to a prediction algorithm is selected as the final prediction result of the power grid materials in the future period.

[0048] The method for predicting the demand for power grid materials proposed in the embodiment of the present application first extracts the demand data for power grid materials in the historical period from the database; then, based on the demand data for power grid materials in the historical period, multiple prediction algorithms are used to predict the demand data for power grid materials in the future period respectively, and the prediction results corresponding to various prediction algorithms are obtained; then, according to the prediction results corresponding to various prediction algorithms, a prediction result corresponding to a prediction algorithm is selected as the final prediction result of power grid materials in the future period. That is to say, in the technical scheme of the present application, a prediction algorithm can be dynamically selected from multiple prediction algorithms as the optimal prediction algorithm for the historical period, and different historical periods can correspond to different optimal prediction algorithms. In the prior art, due to the uncertainty, diversity and dynamics of the demand for power materials, as well as the complexity and opacity of the power material supply chain, the prediction of the demand for power materials is very difficult and challenging. Therefore, compared with the prior art, the method for predicting the demand for power grid materials proposed in the embodiment of the present application can dynamically select the optimal prediction algorithm, thereby improving the accuracy of the material demand prediction and ensuring the effective supply of power grid materials; and the technical scheme of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.

[0049] Figure 4 A flowchart of a method for predicting power grid material demand provided by another embodiment of the present application is provided. The above technical solution is further optimized and expanded, and can be combined with the above optional implementation methods. Figure 4 As shown, the method for predicting power grid material demand may include the following steps:

[0050] S401. Extract the demand data of power grid materials in the historical period from the database.

[0051] S402. Extract data features from the demand data of power grid materials in a historical period.

[0052] S403: Perform statistical analysis on the data features to obtain statistical analysis results of the data features.

[0053] In this step, statistical analysis of data features may include: procurement data analysis, procurement progress monitoring, procurement unit price analysis, and investment plan analysis; procurement data analysis refers to the statistical analysis, analysis, and mining of a large amount of data generated during the procurement process to discover patterns, problems, and opportunities for improvement in the procurement process. Procurement progress monitoring refers to tracking and managing each stage of the procurement order from order placement to receipt to ensure that procurement activities are completed on time and that possible problems are resolved in a timely manner. Procurement unit price analysis refers to an in-depth study of the price of the purchased items to determine the rationality of the price, market competitiveness, and the possibility of cost savings. Investment plan analysis refers to the planning and evaluation of the company's capital expenditures over a period of time in the future to ensure the effective use of funds and support the company's strategic goals.

[0054] S404: Input the statistical analysis results of the data features into the prediction models corresponding to the prediction algorithms, and output the prediction results corresponding to the prediction algorithms through the prediction models.

[0055] S405. According to the prediction results corresponding to various prediction algorithms, a prediction result corresponding to a prediction algorithm is selected as the final prediction result of the power grid materials in the future period.

[0056] The method for predicting the demand for power grid materials proposed in the embodiment of the present application first extracts the demand data for power grid materials in the historical period from the database; then, based on the demand data for power grid materials in the historical period, multiple prediction algorithms are used to predict the demand data for power grid materials in the future period respectively, and the prediction results corresponding to various prediction algorithms are obtained; then, according to the prediction results corresponding to various prediction algorithms, a prediction result corresponding to a prediction algorithm is selected as the final prediction result of power grid materials in the future period. That is to say, in the technical scheme of the present application, a prediction algorithm can be dynamically selected from multiple prediction algorithms as the optimal prediction algorithm for the historical period, and different historical periods can correspond to different optimal prediction algorithms. In the prior art, due to the uncertainty, diversity and dynamics of the demand for power materials, as well as the complexity and opacity of the power material supply chain, the prediction of the demand for power materials is very difficult and challenging. Therefore, compared with the prior art, the method for predicting the demand for power grid materials proposed in the embodiment of the present application can dynamically select the optimal prediction algorithm, thereby improving the accuracy of the material demand prediction and ensuring the effective supply of power grid materials; and the technical scheme of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.

[0057] Figure 5 This is a schematic diagram of the structure of a power grid material demand forecasting device provided in one embodiment of the present application. Figure 5 As shown, the power grid material demand forecasting device includes: an extraction module 501, a forecasting module 502 and a selection module 503; wherein,

[0058] The extraction module 501 is used to extract the demand data of power grid materials in the historical period from the database;

[0059] The prediction module 502 is used to predict the demand data of the power grid materials in the future time period using multiple prediction algorithms based on the demand data of the power grid materials in the historical time period, and obtain prediction results corresponding to various prediction algorithms;

[0060] The selection module 503 is used to select the prediction result corresponding to a prediction algorithm as the final prediction result of the power grid material in the future period according to the prediction results corresponding to various prediction algorithms.

[0061] The above-mentioned power grid material demand prediction device can execute the method provided by any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not fully described in this embodiment, please refer to the power grid material demand prediction method provided by any embodiment of the present application.

[0062] Figure 6 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. Figure 6 A block diagram of an exemplary electronic device suitable for implementing the embodiments of the present application is shown. Figure 6 The electronic device 12 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0063] like Figure 6 As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0064] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0065] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0066] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 6 not shown, usually called a "hard drive"). Although Figure 6 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0067] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0068] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although Figure 6Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0069] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the power grid material demand forecasting method provided in the embodiment of the present application.

[0070] The embodiment of the present application also provides a computer storage medium.

[0071] The computer-readable storage medium of the embodiment of the present application can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, - but not limited to - a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive lists) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0072] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0073] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0074] Computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0075] The embodiment of the present application also provides a computer program product.

[0076] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer program products, which can include one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor, which can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0077] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for predicting power grid material demand, characterized in that: The method comprises: Extract the demand data of power grid materials in the historical period from the database; Based on the demand data of the power grid materials in the historical period, using multiple prediction algorithms to respectively predict the demand data of the power grid materials in the future period, and obtaining prediction results corresponding to various prediction algorithms; According to the prediction results corresponding to various prediction algorithms, a prediction result corresponding to a prediction algorithm is selected as the final prediction result of the power grid material in the future period.

2. The method according to claim 1, characterized in that Extract the demand data of power grid materials in the historical period from the database, including: Extracting the original data of the power grid materials in the historical period from the database; Multiple fields in the original data are combined to obtain demand data of the power grid materials in the historical period.

3. The method according to claim 1, characterized in that Based on the demand data of the power grid materials in the historical period, multiple prediction algorithms are used to predict the demand data of the power grid materials in the future period, and prediction results corresponding to various prediction algorithms are obtained, including: Extracting data features from the demand data of the power grid materials in the historical period; The prediction results corresponding to each prediction algorithm are obtained based on the data features.

4. The method according to claim 3, characterized in that The prediction results corresponding to each prediction algorithm are obtained based on the data features, including: The data features are input into the prediction models corresponding to the prediction algorithms, and the prediction results corresponding to the prediction algorithms are output through the prediction models.

5. A method according to claim 4, characterized in that Before inputting the data features into the prediction models corresponding to the prediction algorithms, the method further includes: According to the demand data of the power grid materials in the historical period, the parameters in one or more prediction models are adjusted to obtain the target prediction models corresponding to each prediction algorithm; the data features are input into the target prediction models corresponding to each prediction algorithm, and the prediction results corresponding to each prediction algorithm are output through each target prediction model.

6. The method according to claim 3, characterized in that: The prediction results corresponding to each prediction algorithm are obtained based on the data features, including: Performing statistical analysis on the data features to obtain statistical analysis results of the data features; The statistical analysis results of the data features are input into the prediction models corresponding to the prediction algorithms, and the prediction results corresponding to the prediction algorithms are output through the prediction models.

7. The method according to claim 1, characterized in that The multiple prediction algorithms include but are not limited to at least two or more of the following: a first exponential smoothing algorithm, a second exponential smoothing algorithm, a third exponential smoothing algorithm, a first grey prediction algorithm, and a second grey prediction algorithm.

8. A power grid material demand forecasting device, characterized in that: The device comprises: an extraction module, a prediction module and a selection module; wherein, The extraction module is used to extract the demand data of power grid materials in the historical period from the database; The prediction module is used to use a plurality of prediction algorithms to respectively predict the demand data of the power grid materials in the future time period based on the demand data of the power grid materials in the historical time period, and obtain prediction results corresponding to the various prediction algorithms; The selection module is used to select the prediction result corresponding to a prediction algorithm as the final prediction result of the power grid material in the future time period according to the prediction results corresponding to various prediction algorithms.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the power grid material demand forecasting method as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for predicting power grid material demand as described in any one of claims 1 to 7 is implemented.

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