Visual configuration modeling method and system for new energy data analysis

Abstract

The invention provides a visual configuration modeling method and system for new energy data analysis, and the method comprises the steps: carrying out the recognition and conversion of multi-source heterogeneous data inputted into a data model through a data model; storing the identified and converted multi-source heterogeneous data into a data area in a data model to obtain an object file; and on the basis of a visual configuration tool, enabling the data model to have a visual window. According to the method, business personnel can quickly and effectively complete complex data analysis modeling work, the complex process of new energy data analysis is simplified, the operation difficulty is reduced, the accuracy and efficiency of data analysis are remarkably improved, and powerful data support is provided for decision makers.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy data analysis, and in particular to a visualization configuration modeling method and system for new energy data analysis. Background Art

[0002] In the new energy industry, data analysis and modeling are key technologies to improve wind farm operation efficiency and equipment reliability. By analyzing and modeling multi-source heterogeneous data such as wind turbine operation data and meteorological data, it is possible to achieve status monitoring, fault warning and performance optimization of wind turbines. This data analysis and modeling process requires the visualization of complex analysis algorithms and business logic, so that business personnel can easily build models and analyze data.

[0003] At present, industrial control logic configuration products are widely used in traditional power generation industries such as thermal power and hydropower. Mainstream products such as Emerson, ABB, Siemens, etc., although they have the advantages of strong real-time performance and fast processing speed, are generally strongly bound to DCS or PLC hardware, support points within 100,000 points, and do not support long-term massive data analysis and modeling. On the other hand, the data analysis and modeling platform of the Internet industry is mainly aimed at professional developers. Users need to have database knowledge and programming skills. The granularity of the operator model is too coarse and cannot meet the modeling needs brought about by the rapid development of the new energy industry.

[0004] Due to the above-mentioned deficiencies in existing technologies, business personnel in the new energy industry face great difficulties in data analysis and modeling, especially when dealing with massive multi-source heterogeneous data. Due to the lack of visual configuration tools suitable for the new energy industry, business personnel are unable to effectively build analysis models, resulting in low data analysis efficiency and failure to fully realize the value of data. Summary of the invention

[0005] In order to solve the problem that the existing new energy industry lacks visual configuration tools, which makes it impossible for business personnel to effectively build analysis models and leads to low data analysis efficiency, the present invention provides a visual configuration modeling method and system for new energy data analysis.

[0006] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a visualization configuration modeling method for new energy data analysis, comprising the following steps: Using the data model, identifying and converting multi-source heterogeneous data input into the data model; The identified and converted multi-source heterogeneous data are stored in the data area in the data model to obtain an object file; Based on a visualization configuration tool, the data model is provided with a visualization window.

[0007] Preferably, the multi-source heterogeneous data includes: Wind turbine operation data and meteorological data, the data model is used to identify the types of the wind turbine operation data and the meteorological data and convert the formats of the wind turbine operation data and the meteorological data to make the formats of the wind turbine operation data and the meteorological data consistent.

[0008] Preferably, the data area includes a relational database, a time series database and a third-party interface database, and the data model stores wind turbine operation data and meteorological data in a consistent format in the relational database, the time series database or the third-party interface database.

[0009] Preferably, the visualization window includes a toolbar window, an operator window, a canvas window and a property panel window; Wherein, the toolbar window is used to edit, display and run the object file; The operator window is used to perform fan fault warning and fan fault diagnosis according to the object file; The canvas window is used to input background data into the data model; The property panel window is used to define the name and configuration parameters of the operator window.

[0010] Preferably, the data model also includes an early warning processing library: The early warning processing library performs fan fault diagnosis and early warning based on the object file to obtain a control strategy file; The data model performs task classification and deployment based on the control strategy file.

[0011] Preferably, the data model classifies and allocates tasks based on the control strategy file, including: The data model divides the control strategy file into periodic execution tasks, periodic execution tasks and the offline triggering tasks; The data model performs task scheduling based on the divided periodic execution tasks, the regularly executed tasks and the offline triggered tasks, and obtains a periodic deployment task file, a regularly deployed task file and an offline triggered deployment task file respectively.

[0012] Preferably, the early warning processing library includes: a SCADA early warning model package, a CMS diagnosis model package, a wind power curve model package and a wind turbine status model package; Wherein, the wind power curve model package constructs a wind turbine power curve based on the object file; The CMS diagnostic model package performs fan fault diagnosis based on the fan power curve to obtain fault diagnosis data; The SCADA early warning model package obtains warning data based on the fault diagnosis data and the object file; The wind turbine status model package updates the status of the wind turbine based on the control strategy file to obtain wind turbine status update data.

[0013] In a second aspect, an embodiment of the present invention provides a visualization configuration modeling system for new energy data analysis, which is used to implement the above-mentioned visualization configuration modeling method for new energy data analysis, including: The first processing module is configured to: for identifying and converting multi-source heterogeneous data input into the data model using the data model; The second processing module is configured to: Used to store the identified and converted multi-source heterogeneous data into the data area in the data model to obtain an object file; The third processing module is configured as follows: It is used for configuring tools based on visualization so that the data model has a visualization window.

[0014] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned visual configuration modeling method for new energy data analysis when executing the computer program.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned visual configuration modeling method for new energy data analysis.

[0016] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention proposes a visual configuration modeling method for new energy data analysis. The method uses a data model to identify and convert multi-source heterogeneous data, ensures the uniformity and accuracy of the data, realizes the unified management and analysis modeling of multi-source heterogeneous data, improves the efficiency of data analysis, and lays a solid foundation for subsequent analysis. The identified and converted data are stored in the data area in the data model to form an object file, which is convenient for centralized management and efficient use of data. By introducing a visual configuration tool, the data model is equipped with a visualization window, so that business personnel can quickly and effectively complete complex data analysis and modeling work, simplify the complex process of new energy data analysis, reduce the difficulty of operation, and significantly improve the accuracy and efficiency of data analysis, providing strong data support for decision makers.

[0017] Furthermore, this method ensures the accuracy and reliability of the data by accurately identifying different types of wind turbine operation data and meteorological data, providing a solid foundation for subsequent analysis. The model further converts the format of the identified data to achieve the unification of the formats of wind turbine operation data and meteorological data. The unification of data formats greatly facilitates subsequent in-depth analysis, prediction model construction and decision support, making the operation and maintenance management, performance optimization and energy scheduling of wind farms more intelligent and refined.

[0018] Furthermore, the visualization window in this method integrates the toolbar window, operator window, canvas window and property panel window, which greatly improves the efficiency and flexibility of new energy data analysis, simplifies the analysis process, and significantly improves the accuracy and practicality of the analysis.

[0019] Furthermore, the data model in this method realizes efficient diagnosis and early warning of wind turbine faults and accurate generation of control strategy files by integrating the functions of the early warning processing library and the data model. The early warning processing library is based on object files and uses advanced algorithms to deeply analyze wind turbine operation data, accurately identify fault modes, issue early warning signals in a timely manner, and generate control strategy files, which improves the fault response speed and effectively reduces the impact of faults on wind farm operations.

[0020] Furthermore, the data model in this method refines the control strategy file and divides it into periodic execution tasks, regular execution tasks and offline triggered tasks, thereby achieving accurate classification of tasks. The data model performs task scheduling and generates periodic deployment task files, regular deployment task files and offline triggered deployment task files respectively, thereby ensuring the orderly execution and efficient management of various tasks, optimizing the task processing process, and significantly improving the operation and maintenance efficiency and energy output of wind farms.

[0021] Furthermore, the data model in this method achieves full coverage of wind turbine fault warning and diagnosis by integrating the SCADA early warning model package, CMS diagnostic model package, wind power curve model package and wind turbine status model package in the early warning processing library. The wind power curve model package accurately constructs the wind turbine power curve based on the object file, providing a reliable basis for fault diagnosis. The CMS diagnostic model package uses the wind turbine power curve for in-depth analysis, accurately identifies the fault mode, and generates fault diagnosis data. The SCADA early warning model package further combines the fault diagnosis data and object files to issue early warning signals in a timely manner, effectively avoiding the impact of potential faults on the operation of the wind farm. The wind turbine status model package dynamically updates the wind turbine status based on the control strategy file to ensure that the wind turbine is always in the best operating state, improving the accuracy and efficiency of fault warning and diagnosis, and realizing real-time monitoring and optimization of the wind turbine status, providing a strong guarantee for the stable operation and efficient output of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of a visualization configuration modeling method for new energy data analysis proposed by the present invention; Figure 2 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 3 The block diagram is a chip provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0024] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0025] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0026] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or a communication; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0027] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0028] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] In one embodiment of the present invention, a visualization configuration modeling method and system for new energy data analysis are provided, such as Figure 1 As shown, the following steps are included: S101. Using a data model, identify and convert multi-source heterogeneous data input into the data model; illustratively, obtain historical operating data of the wind farm and historical meteorological data corresponding to the historical wind turbine operating data, perform preprocessing such as denoising, interpolation, and missing value supplementation on the historical wind turbine operating data and historical meteorological data, construct an initial data model through the preprocessed historical wind turbine operating data and historical meteorological data, collect current wind turbine operating data and current meteorological data in the wind farm, divide the collected current wind turbine operating data and current meteorological data into multiple verification groups, input the multiple verification groups into the initial data model respectively, iterate, and converge the model through multiple iterations to obtain a data model. Among them, multi-source heterogeneous data include: wind turbine operation data and meteorological data. The data model is used to identify the types of wind turbine operation data and meteorological data and convert the formats of wind turbine operation data and meteorological data to make the formats of wind turbine operation data and meteorological data consistent, thereby realizing standardized data management, so that the data model can efficiently process multi-source heterogeneous data such as wind turbine operation data and meteorological data, thereby improving the efficiency and accuracy of data processing, ensuring the reliability and efficiency of data processing, and providing an efficient data processing method for business personnel in the new energy industry, providing strong support for the operation and maintenance management, performance analysis and optimization decision-making of wind farms, and significantly improving the overall efficiency and reliability of wind power operations.

[0030] S102. Store the identified and converted multi-source heterogeneous data in the data area in the data model to obtain an object file; illustratively, the data area includes a relational database, a time series database, and a third-party interface database, and the data model stores the wind turbine operation data and meteorological data with consistent formats in the relational database, the time series database, or the third-party interface database to obtain an object file.

[0031] S103, based on a visualization configuration tool, so that the data model has a visualization window; exemplarily, the visualization window includes a toolbar window, an operator window, a canvas window and a property panel window; wherein the toolbar window is used to perform functions such as file editing, file display and file operation on the object file; the operator window is used to perform fan fault warning and fan fault diagnosis according to the object file; the canvas window is used to input background data into the data model, so that users can accurately place and connect components; the property panel window is used to define the name and configuration parameters of the operator window; the toolbar window, the operator window, the canvas window and the property panel window are integrated on a display window, and a four-area layout is adopted, wherein the top of the display window is the toolbar window, the left side of the display window is the operator window, the middle of the display window is the canvas window, and the right side of the display window is the property panel window; the operator window includes various predefined components such as SCADA warning operator, CMS diagnosis operator, wind power curve operator and data quality operator; the visualization configuration tool enables the data model to perform human-computer interaction during use, so that business personnel can quickly and effectively complete complex data analysis and modeling work.

[0032] Furthermore, the data model proposed in the present invention also includes an early warning processing sub-library: the early warning processing library performs wind turbine fault diagnosis and early warning based on the object file to obtain a control strategy file; the data model performs task classification and deployment based on the control strategy file, and the data model performs task classification and deployment based on the control strategy file, including: the data model performs task identification and division on the control strategy file, that is, identifies the periodic tasks, regular tasks and offline tasks in the control strategy file, and divides the control strategy file into periodic execution tasks, regular execution tasks and offline triggered tasks according to the periodic tasks, regular tasks and offline tasks; the data model performs task scheduling based on the divided periodic execution tasks, regular execution tasks and offline triggered tasks, and obtains periodic deployment task files, regular deployment task files and offline triggered deployment task files respectively, thereby realizing intelligent management of tasks and greatly improving the operating efficiency of wind farms.

[0033] Among them, the early warning processing library includes: a SCADA early warning model package, a CMS diagnostic model package, a wind power curve model package and a wind turbine status model package; wherein the wind power curve model package constructs a wind turbine power curve based on the object file; the CMS diagnostic model package performs wind turbine fault diagnosis based on the wind turbine power curve to obtain fault diagnosis data; the SCADA early warning model package obtains warning data based on the fault diagnosis data and the object file; the wind turbine status model package updates the status of the wind turbine based on the control strategy file to obtain wind turbine status update data; through the fine-grained setting of the SCADA early warning model package, the CMS diagnostic model package, the wind power curve model package and the wind turbine status model package in the early warning processing library, the early warning processing library can better adapt to the wind turbine fault diagnosis and early warning of different types of wind turbines in the wind farm, thereby improving the adaptability of this model.

[0034] This data model also includes a platform service monitoring library, which obtains the real-time operating status of the data model. When it finds that the data module load is too high, it automatically redistributes tasks to ensure the balanced operation of the data model.

[0035] In yet another embodiment of the present invention, a visual configuration modeling system for new energy data analysis is provided, which is used to implement the visual configuration modeling method for new energy data analysis described above, comprising: The first processing module is configured to: for identifying and converting multi-source heterogeneous data input into the data model using the data model; It is further configured as follows: a module construction unit, used to obtain historical operation data of the wind farm and historical meteorological data corresponding to historical wind turbine operation data, perform preprocessing such as denoising, interpolation and missing value supplementation on the historical wind turbine operation data and historical meteorological data, and construct an initial data model through the preprocessed historical wind turbine operation data and historical meteorological data; an iterative processing unit, used to collect current operation data and current meteorological data of wind turbines in the wind farm, divide the collected current operation data and current meteorological data of wind turbines into multiple verification groups, input the multiple verification groups into the initial data model respectively, perform iteration, and make the model converge through multiple iterations to obtain a data model; The second processing module is configured to: Used to store the identified and converted multi-source heterogeneous data into the data area in the data model to obtain an object file; The third processing module is configured as follows: It is used for configuring tools based on visualization so that the data model has a visualization window.

[0036] It is further configured as: a visualization window unit, used for performing functions such as file editing, file display and file operation on the object file; used for performing fan fault warning and fan fault diagnosis according to the object file; used for inputting background data into the data model to facilitate users to perform accurate component placement and connection operations; used for defining the name and configuration parameters of the operator window, so that business personnel can quickly and effectively complete complex data analysis and modeling work; Furthermore, the system is also configured with an early warning processing unit, which is used to perform fan fault diagnosis and early warning based on the object file, obtain the control strategy file, classify and deploy tasks based on the control strategy file, and the data model classifies and deploys tasks based on the control strategy file.

[0037] The system is also equipped with a platform service monitoring unit, which obtains the real-time operating status of the data model. When it finds that the data module load is too high, it automatically redistributes tasks to ensure the balanced operation of the data model.

[0038] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a visual configuration modeling method for new energy data analysis, including: The data model is used to identify and convert the multi-source heterogeneous data input into the data model; the identified and converted multi-source heterogeneous data is stored in the data area of ​​the data model to obtain an object file; based on a visual configuration tool, the data model is provided with a visual window.

[0039] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (Non-Volatile Memory), such as at least one disk memory.

[0040] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the visual configuration modeling method for new energy data analysis in the above embodiment; the processor may load and execute the following steps: The data model is used to identify and convert the multi-source heterogeneous data input into the data model; the identified and converted multi-source heterogeneous data is stored in the data area of ​​the data model to obtain an object file; based on a visual configuration tool, the data model is provided with a visual window.

[0041] See also Figure 2 The terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the composition of fluid in the reservoir transformation wellbore in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the calculation system for the composition of fluid in the reservoir transformation wellbore in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0042] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 2 It is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0043] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0044] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0045] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0046] Any reference to a memory, database or other medium used in the embodiments provided in the present application may include at least one of a non-volatile and a volatile memory. Non-volatile memory may include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical memory, a high-density embedded non-volatile memory, a resistive random access memory (ReRAM), a magnetic random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. Volatile memory may include a random access memory (RAM) or an external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc.

[0047] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0048] See also Figure 3 , the terminal device is a chip, and the chip 600 of this embodiment includes a processor 622, which may be one or more, and a memory 632 for storing a computer program executable by the processor 622. The computer program stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. In addition, the processor 622 may be configured to execute the computer program to execute the above-mentioned generalizable monocular absolute depth map estimation method.

[0049] In addition, the chip 600 may further include a power supply component 626 and a communication component 650, wherein the power supply component 626 may be configured to perform power management of the chip 600, and the communication component 650 may be configured to implement communication, for example, wired or wireless communication, of the chip 600. In addition, the chip 600 may further include an input / output interface 658. The chip 600 may operate based on an operating system stored in the memory 632.

[0050] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0051] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0052] In addition, it should be understood that although this specification is described in accordance with the implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. The above content is only to illustrate the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A visual configuration modeling method for new energy data analysis, characterized in that: The following steps are involved: Using the data model, identifying and converting multi-source heterogeneous data input into the data model; The identified and converted multi-source heterogeneous data are stored in the data area in the data model to obtain an object file; Based on a visualization configuration tool, the data model is provided with a visualization window.

2. The visualization configuration modeling method for new energy data analysis according to claim 1 is characterized in that: The multi-source heterogeneous data includes: Wind turbine operation data and meteorological data, the data model is used to identify the types of the wind turbine operation data and the meteorological data and convert the formats of the wind turbine operation data and the meteorological data to make the formats of the wind turbine operation data and the meteorological data consistent.

3. The visualization configuration modeling method for new energy data analysis according to claim 2 is characterized in that: The data area includes a relational database, a time series database and a third-party interface database, and the data model stores wind turbine operation data and meteorological data with consistent formats in the relational database, the time series database or the third-party interface database.

4. The visualization configuration modeling method for new energy data analysis according to claim 1 is characterized in that: The visualization window includes a toolbar window, an operator window, a canvas window and a property panel window; Wherein, the toolbar window is used to edit, display and run the object file; The operator window is used to perform fan fault warning and fan fault diagnosis according to the object file; The canvas window is used to input background data into the data model; The property panel window is used to define the name and configuration parameters of the operator window.

5. The visualization configuration modeling method for new energy data analysis according to claim 1 is characterized in that: The data model also includes an early warning processing library: The early warning processing library performs fan fault diagnosis and early warning based on the object file to obtain a control strategy file; The data model performs task classification and deployment based on the control strategy file.

6. The visual configuration modeling method for new energy data analysis according to claim 5 is characterized in that: The data model classifies and allocates tasks based on the control strategy file, including: The data model divides the control strategy file into periodic execution tasks, periodic execution tasks and the offline triggering tasks; The data model performs task scheduling based on the divided periodic execution tasks, the regularly executed tasks and the offline triggered tasks, and obtains a periodic allocation task file, a regularly allocated task file and an offline triggered allocation task file respectively.

7. The visual configuration modeling method for new energy data analysis according to claim 5 is characterized in that: The early warning processing library includes: a SCADA early warning model package, a CMS diagnosis model package, a wind power curve model package and a wind turbine status model package; Wherein, the wind power curve model package constructs a wind turbine power curve based on the object file; The CMS diagnostic model package performs fan fault diagnosis based on the fan power curve to obtain fault diagnosis data; The SCADA early warning model package obtains warning data based on the fault diagnosis data and the object file; The wind turbine status model package updates the status of the wind turbine based on the control strategy file to obtain wind turbine status update data.

8. A visualization configuration modeling system for new energy data analysis, used to implement a visualization configuration modeling method for new energy data analysis as claimed in any one of claims 1 to 7, characterized in that: include: The first processing module is configured to: for identifying and converting multi-source heterogeneous data input into the data model using the data model; The second processing module is configured to: Used to store the identified and converted multi-source heterogeneous data into the data area in the data model to obtain an object file; The third processing module is configured as follows: It is used for configuring tools based on visualization so that the data model has a visualization window.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the visual configuration modeling method for new energy data analysis described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium comprising a computer program, characterized in that: The computer program, when executed by a processor, is the steps of a visual configuration modeling method for new energy data analysis as described in any one of claims 1 to 7.

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