A data processing method and system for wind farm monitoring data

Abstract

This invention relates to a data processing method and system for wind farm monitoring data. The method comprises: using a pre-built wind farm monitoring data processing model to process wind farm monitoring data collected by the wind farm monitoring system in real time; and using the pre-built wind farm monitoring data processing model to perform timed aggregation calculations on the parallel-processed wind farm monitoring data to obtain the timed aggregation calculation results of the wind farm. This invention can significantly reduce processing time, improve the real-time performance and response speed of the system, and significantly improve processing efficiency, and can be widely applied in the field of wind power technology.

Description

Technical Field

[0001] This invention relates to the field of wind power technology, and in particular to a data processing method and system for wind farm monitoring data. Background Technology

[0002] As the scale of wind power expands, wind power companies are exploring and implementing a new intensive and platform-based business model under the three-tier management system of group-region-wind farm.

[0003] However, wind farm monitoring data processing faces challenges such as large data volume and high processing complexity, making it difficult for traditional data processing methods to meet the needs of large-scale wind farm monitoring data processing. Therefore, an efficient and reliable data processing method and system for wind farm monitoring data is required. Summary of the Invention

[0004] To address the aforementioned problems, the purpose of this invention is to provide a data processing method and system for wind farm monitoring data, which can solve the problems of large data volume and high processing complexity faced by wind farm monitoring data processing.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, it provides a data processing method for wind farm monitoring data, comprising: A pre-built wind farm monitoring data processing model is used to process the wind farm monitoring data collected by the wind farm monitoring system in real time. A pre-built wind farm monitoring data processing model is used to perform timed aggregation calculations on the parallel-processed wind farm monitoring data to obtain the timed aggregation calculation results of the wind farm.

[0006] Furthermore, the wind farm monitoring data processing model includes a dimension determination module, an equipment type definition module, an equipment model template definition module, and a specific equipment determination module; The dimension determination module is used to define the view that users view when viewing the wind farm monitoring system, including the primary system dimension, monitoring system dimension, financial dimension, and operation monitoring dimension. The equipment type definition module is used to define the equipment types of the wind farm and to encode each equipment type; The equipment model template definition module is used to define equipment model templates for different equipment of the same type in a wind farm, so as to standardize the data format of the equipment. The specific equipment determination module is used to determine the equipment details and associated measurement points of specific equipment in the wind farm.

[0007] Furthermore, the method of using a pre-built wind farm monitoring data processing model to process the wind farm monitoring data collected by the wind farm monitoring system in real time includes: Data conversion is performed on the wind farm monitoring data collected by the wind farm monitoring system; Load the pre-built wind farm monitoring data processing model and preprocess the converted wind farm monitoring data; A loaded wind farm monitoring data processing model is used to process the wind farm monitoring data in parallel.

[0008] Furthermore, the method of using a pre-built wind farm monitoring data processing model to process the wind farm monitoring data collected by the wind farm monitoring system in real time also includes: A method to prevent cyclic conversion is adopted to avoid repeated conversion and processing of wind farm monitoring data.

[0009] Furthermore, the loading of the pre-built wind farm monitoring data processing model and the preprocessing of the converted wind farm monitoring data include: Load the pre-built wind farm monitoring data processing model; Based on the wind farm monitoring data processing model, the converted wind farm monitoring data is converted to standard measurement points. Based on the wind farm monitoring data processing model, alarm rules for wind turbines in the wind farm monitoring system are preloaded.

[0010] Furthermore, the pre-built wind farm monitoring data processing model is used to perform timed aggregation calculations on the parallel-processed wind farm monitoring data to obtain the timed aggregation calculation results of the wind farm, including: Perform scheduled tasks; Based on scheduled tasks, query data from sub-devices in the wind farm monitoring system. Use a fixed time window or a sliding time window to process the queried sub-device data; Timestamp alignment is performed on the processed sub-device data; A wind farm monitoring data processing model is adopted to aggregate the data of sub-devices after timestamp alignment, and obtain the timed aggregation calculation results of the wind farm.

[0011] Furthermore, the step of using a pre-built wind farm monitoring data processing model to perform timed aggregation calculations on the parallel-processed wind farm monitoring data to obtain the timed aggregation calculation results of the wind farm also includes: The intermediate and final results of the calculations are saved to the real-time database and time-series database of the wind farm monitoring system.

[0012] Secondly, a data processing system for wind farm monitoring data is provided, including: The real-time data processing module is used to process the wind farm monitoring data collected by the wind farm monitoring system in real time using a pre-built wind farm monitoring data processing model. The timed aggregation calculation module is used to perform timed aggregation calculations on the parallel-processed wind farm monitoring data using a pre-built wind farm monitoring data processing model, and obtain the timed aggregation calculation results of the wind farm.

[0013] Thirdly, a processing device is provided, including computer program instructions, wherein when the computer program instructions are executed by the processing device, they are used to implement the steps corresponding to the data processing method for the aforementioned wind farm monitoring data.

[0014] Fourthly, a computer-readable storage medium is provided, wherein computer program instructions are stored on the computer-readable storage medium, wherein the computer program instructions, when executed by a processor, are used to implement the steps corresponding to the above-mentioned data processing method for wind farm monitoring data.

[0015] The present invention has the following advantages due to the adoption of the above technical solutions: 1. The model structure in this invention is the basis for the parallel and aggregate processing of this invention. The model structure adopts advanced data structures and algorithms, which makes data storage and access more efficient, while supporting flexible expansion and customization.

[0016] 2. The model of this invention combines physical entities and virtual systems, and covers multiple dimensions, such as primary systems, monitoring systems, financial and operational monitoring, etc. It not only accurately describes the entity relationships in the wind power field, but also has scalability and can be applied to photovoltaic and other Internet of Things fields.

[0017] 3. In complex systems within the wind power sector, it is often necessary to process and analyze large amounts of data, such as real-time monitoring of wind farms, fault prediction, and performance optimization. This invention utilizes parallel processing technology to decompose these tasks into multiple sub-tasks, which are then executed simultaneously on multiple processors or computing cores. This significantly reduces processing time, improves system real-time performance and response speed, and substantially enhances processing efficiency.

[0018] 4. This invention relates to the process of integrating and comprehensively analyzing large amounts of data. In wind power systems, different equipment and different latitudes may generate a large amount of data, which needs to be effectively integrated and used for analysis and decision-making. This invention, through aggregation processing technology, can perform operations such as filtering, classifying, and aggregating these data to extract valuable information, providing decision support for the operation and management of wind farms. It can also help the system discover correlations and trends between data, providing strong support for the optimized operation and fault prediction of wind farms.

[0019] 5. The relevant parameters in the parallel processing of this invention can be adjusted and optimized according to actual needs to achieve the best processing performance; the specific parameters in the aggregation processing can be adjusted according to different application scenarios to meet the actual needs of users.

[0020] 6. Through reasonable parameter configuration and optimization, this invention can ensure system stability and reliability while guaranteeing processing efficiency.

[0021] In summary, this invention can be widely applied in the field of wind power technology. Attached Figure Description

[0022] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings: Figure 1 This is a schematic diagram of a model structure provided in an embodiment of the present invention; Figure 2 This is a schematic view of a wind farm monitoring system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a device model template definition module provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a specific device determination module provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of a timed aggregation process provided in an embodiment of the present invention. Detailed Implementation

[0023] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0024] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0025] Although terms such as first, second, third, etc., may be used in this document to describe multiple elements, components, regions, layers, and / or segments, these elements, components, regions, layers, and / or segments should not be limited by these terms. These terms may be used only to distinguish one element, component, region, layer, or segment from another. Unless the context clearly indicates otherwise, terms such as "first," "second," and other numerical terms used herein do not imply order or sequence. Therefore, the first element, component, region, layer, or segment discussed below may be referred to as the second element, component, region, layer, or segment without departing from the teachings of the exemplary embodiments.

[0026] Due to the large volume and high complexity of wind farm monitoring data processing, traditional data processing methods are insufficient to meet the demands of large-scale wind farm monitoring data processing. Therefore, an efficient and reliable data processing method and system for wind farm monitoring data is needed. This invention provides a data processing method for wind farm monitoring data, comprising: using a pre-built wind farm monitoring data processing model to process wind farm monitoring data collected by the wind farm monitoring system in real time; and using the pre-built wind farm monitoring data processing model to perform timed aggregation calculations on the parallel-processed wind farm monitoring data to obtain the timed aggregation calculation results of the wind farm. This invention employs a unique model, improving configuration and debugging efficiency, and uses a parallel processing approach to enhance data processing performance.

[0027] Example 1 This embodiment provides a data processing method for wind farm monitoring data, including the following steps: 1) Construct a wind farm monitoring data processing model.

[0028] Specifically, such as Figure 1 As shown, the wind farm monitoring data processing model includes a dimension determination module, an equipment type definition module, an equipment model template definition module, and a specific equipment determination module.

[0029] The dimension determination module is used to define the view that users can view of the wind farm monitoring system (including components such as real-time operation monitoring, equipment control, historical data analysis, alarm handling, substation primary system monitoring, real-time database, and time-series database), such as... Figure 2 As shown, the dimensions include primary system dimensions, monitoring system dimensions, financial dimensions, and operation monitoring dimensions, depending on the needs of different users. The view of each dimension includes which devices in the wind farm, the associated measurement points for each device, and the relationships between the devices differ. Defining dimensions allows the same device information to provide different structures and information to different users.

[0030] The equipment type definition module is used to define the equipment types in a wind farm, such as wind turbines, electricity meters, gate meters, substation equipment, and transmission lines. Each equipment type is coded. Defining equipment types is to achieve unified management and data processing of different types of equipment in a wind farm. Equipment of the same type has similar characteristics. For example, each wind turbine has a transformer substation, and each wind turbine has similar measurement points such as wind speed and power.

[0031] The equipment model template definition module is used to define equipment model templates for different devices of the same equipment type in a wind farm, including measurement points, conversion rules, alarm conditions, and aggregation rules, to standardize the data format of the equipment and facilitate unified management and processing. For example... Figure 3 As shown, equipment models of the same type have a parent-child inheritance relationship. Generally, the parent model defines standardized information such as measurement points and conversion rules. The child model inherits all the defined measurement points and conversion rules of the parent model, and can cover, delete, or specialize some information definitions. This achieves the goal of both standardization and management of specialized data between different models of the same type of equipment.

[0032] The specific equipment identification module is used to determine the equipment details and associated monitoring points of specific equipment in a wind farm. Equipment details include detailed information on specific equipment such as wind turbines, substation equipment, and transmission lines as defined in the equipment type definition module. Determining the equipment details and associated monitoring points of specific equipment is essential for accurately monitoring and managing the status of each piece of equipment. For example... Figure 4 As shown, the specific device must belong to a certain device model. The specific device generally uses the definition of the measurement points and conversion rules of the template of the device model to which it belongs. This allows for convenient and quick modification of the measurement points, conversion rules, alarm conditions, etc. of all devices under a certain model.

[0033] Specifically, the equipment types in a wind farm include physical equipment such as wind turbines, electricity meters, gate meters, step-up substations, substation equipment, and transmission lines, as well as virtual or logical equipment such as wind farms, fire protection zones, wind turbine SCADA monitoring systems, and energy management systems (EMS).

[0034] Specifically, the equipment model template defined by the equipment model template definition module needs to define the standard name, standard code, and model inheritance tree; the measurement points of the equipment model include the measurement point code, measurement point name, and unit; the alarm rules include the alarm measurement point that issues the alarm, the alarm value, the alarm level, the alarm type, the alarm code, whether the alarm is audible, and whether the alarm needs to be acknowledged; the aggregation rules include the aggregation type (summation, average) and the aggregation conditions (how long the interval, from which data types and which measurement points to aggregate, and which dimension of the structure to use for aggregation).

[0035] Specifically, a child model inherits all definitions from its parent model, unless the child model defines a configuration with the same encoding. For example, if the parent model has a GEP power generation measurement point in kWh, the child model will also inherit this measurement point; if the child model defines a GEP power generation measurement point in MWh, then the child model will use its own definition (i.e., it overrides the parent model's definition).

[0036] 2) A pre-built wind farm monitoring data processing model is used to process the wind farm monitoring data collected by the wind farm monitoring system in real time, specifically as follows: 2.1) Perform data conversion on the wind farm monitoring data collected by the wind farm monitoring system (including SCADA data collected by wind turbines, as well as data collected by AGC, AVC, energy management system and booster station SCADA, etc.).

[0037] Specifically, the collected raw wind farm monitoring data is converted into a data format used for data processing. This mainly involves adding information such as the measurement points of the equipment (measurement point type, unit, etc.), conversion rules, alarm conditions, and the correlation information between the equipment in various dimensions to the raw wind farm monitoring data. The data conversion is to improve the efficiency of data processing in subsequent steps and avoid performance issues caused by repeatedly reading the model configuration.

[0038] 2.2) Load the pre-built wind farm monitoring data processing model and preprocess the converted wind farm monitoring data: 2.2.1) Load the pre-built wind farm monitoring data processing model.

[0039] 2.2.2) Based on the wind farm monitoring data processing model, standard measurement point conversion is performed on the converted wind farm monitoring data.

[0040] Specifically, standard measuring points refer to measuring points with unified naming and unified units. For example, the "wind speed" and "power" of a wind turbine can both be defined as standard measuring points. Original measuring points refer to data directly collected by the wind farm monitoring system. For example, "wind speed 1" and "wind speed 2" collected from the wind turbine SCADA system are original measuring points.

[0041] Specifically, since most of the time when wind farm monitoring data is collected, only the original measurement points are directly submitted, it is necessary to convert the original measurement points of the converted wind farm monitoring data into standard measurement points according to the conversion definition of standard measurement points in the wind farm monitoring processing model. For example, the original measurement point "wind speed 1" is converted into the standard measurement point "wind speed".

[0042] 2.2.3) Preload alarm rules for wind turbines based on the wind farm monitoring data processing model.

[0043] Specifically, wind turbine alarms are generally alarm lists composed of multiple measuring points. Adding or removing alarms for a wind turbine requires comprehensive data from all alarm measuring points to determine the alarms. This is different from simple measuring point alarm logic. Therefore, the alarm rules of the wind turbines need to be preloaded based on the wind farm monitoring and processing model.

[0044] 2.3) The wind farm monitoring data processing model after loading is used to process the wind farm monitoring data in parallel.

[0045] Specifically, parallel processing includes operations such as partial data transformation, wind turbine standard state calculation, raw alarm processing, custom alarm processing, real-time page refresh, saving to real-time database and saving to time series database.

[0046] More specifically, some data conversions include unit conversion between the original measuring points and the standard measuring points, such as calculating the theoretical power of a wind turbine based on wind speed.

[0047] More specifically, the standard state calculation of wind turbines is a comprehensive calculation that combines the AGC and AVC states within the wind farm, the original state of the wind turbine (original state code, wind speed, power), and the wind turbine environmental information (temperature, humidity, weather conditions).

[0048] More specifically, the original alarm processing is similar to the alarm information reported by the substation or wind turbine transformer using the 104 protocol. After reading the alarm configuration, a complete alarm or recovery alarm record is generated.

[0049] More specifically, the custom alarm handling is to generate an alarm or restore an alarm record when the measured point value changes and meets the alarm definition, based on alarm definitions such as limit, jump and remote signaling change.

[0050] More specifically, real-time page refresh means pushing change data from equipment such as fans to the user's page in real time.

[0051] More specifically, saving to the real-time database means saving device data to a real-time database.

[0052] More specifically, saving the time series database involves writing timestamped data sequentially into the time series database.

[0053] Different types of data have different timeliness, accuracy, display units, and storage requirements. Parallel processing can improve the efficiency of data processing, and multiple data processing operations do not affect each other. By arranging system resources with different configurations according to different characteristics, CPU / memory / IO resources can be effectively utilized.

[0054] 2.4) Adopt methods to prevent cyclic conversion and avoid repeated conversion and processing of wind farm monitoring data.

[0055] Specifically, due to the complexity of the above processing and configuration, some data may undergo multiple rounds of processing. A mechanism must be provided to prevent infinite loops. Here, the method of checking key data values ​​is generally used.

[0056] 3) such as Figure 5 As shown, a pre-built wind farm monitoring data processing model is used to perform timed aggregation calculations on the parallel-processed wind farm monitoring data to obtain the timed aggregation calculation results of the wind farm, including the total power generation, average wind speed, real-time power, and the number of wind turbines in various states, etc., which are the indicators that need to be calculated. 3.1) Perform timed task scheduling.

[0057] Specifically, based on the time interval, scheduled tasks are categorized into six levels: second-level, minute-level, hour-level, day-level, month-level, and year-level. Second-level scheduling executes once per second; minute-level, hour-level, day-level, month-level, and year-level scheduling are generally defined as executing once per minute. Second-level scheduling requires high resources, so second-level scheduled tasks can be allocated to higher-configuration computing infrastructure. Differentiated scheduling can balance the pressure on aggregated computing.

[0058] 3.2) Based on timed task scheduling, query the sub-device data of the wind farm monitoring system.

[0059] Specifically, according to the aggregation rule definition, query the measurement point data of the lowest-level sub-equipment of the wind farm. For example, to calculate the real-time power generation index of the wind farm, it is necessary to query the power generation of all wind turbine equipment in the wind farm (from the perspective of system monitoring, wind turbine equipment is a sub-equipment of the wind farm).

[0060] 3.3) Use a fixed time window or a sliding time window to process the data of the queried sub-devices, such as calculating the average, maximum and difference values.

[0061] Specifically, a fixed time window refers to a time period for aggregation calculation that is fixed, such as a 5-minute average. A fixed time window calculation is performed every 5 minutes on the hour. A sliding time window refers to a calculation that is performed based on the time point at which the calculation is performed. For example, a 5-minute average is calculated once every minute, while a sliding time window calculation will perform 5 calculations over 5 minutes. The time series database will store all values: one value for 00:00-05:00, one value for 01:00-06:00, one value for 02:00-07:00, one value for 03:00-08:00, and one value for 04:00-09:00.

[0062] 3.4) Timestamp alignment is performed on the processed sub-device data.

[0063] Specifically, some devices may experience data loss due to various reasons (network interruption, underlying hardware / software system failure). It is necessary to supplement the data of all devices at each time stamp based on the calculated timestamp. The supplementation methods include supplementing previous values, supplementing average values, and supplementing linear values.

[0064] 3.5) Using the wind farm monitoring data processing model, the data of the sub-devices after the timestamps are aligned are aggregated with the data of the parent and child devices to obtain the timed aggregation calculation results of the wind farm.

[0065] Specifically, aggregation calculations are performed based on the dimensions defined in the dimension determination module of the wind farm monitoring data processing model, aggregating data from the lowest-level sub-device data of the wind farm all the way up to the highest-level parent device data. For example, in the monitoring system dimension, a wind farm includes multiple projects, each project includes multiple lines, and each line includes multiple wind turbines. When aggregating the average wind speed of the wind farm: first, the wind speeds of the turbines under each line are averaged to obtain the average wind speed of the line; then, the average wind speeds of the lines under each project are averaged to obtain the average wind speed of the project; finally, the average wind speeds of the projects under the wind farm are averaged to obtain the average wind speed of the wind farm.

[0066] 3.6) Save the intermediate and final results calculated in the above steps to the real-time database and time-series database of the wind farm monitoring system.

[0067] Example 2 This embodiment provides a data processing system for wind farm monitoring data, including: The real-time data processing module is used to process the wind farm monitoring data collected by the wind farm monitoring system in real time using a pre-built wind farm monitoring data processing model.

[0068] The timed aggregation calculation module is used to perform timed aggregation calculations on the parallel-processed wind farm monitoring data using a pre-built wind farm monitoring data processing model, and obtain the timed aggregation calculation results of the wind farm.

[0069] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0070] Example 3 This embodiment provides a processing device corresponding to the data processing method for wind farm monitoring data provided in Embodiment 1. The processing device can be applied to client processing devices, such as mobile phones, laptops, tablets, desktop computers, etc., to execute the method of Embodiment 1.

[0071] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores computer programs that can run on the processing device. When the processing device runs the computer programs, it executes the data processing method for wind farm monitoring data provided in Embodiment 1.

[0072] In some implementations, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0073] In other implementations, the processor can be any type of general-purpose processor, such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation here.

[0074] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0075] Those skilled in the art will understand that the structure of the above-described computing device is only a partial structure related to the present invention and does not constitute a limitation on the computing device to which the present invention is applied. A specific computing device may include more or fewer components, or combine certain components, or have different component arrangements.

[0076] Example 4 This embodiment provides a computer program product corresponding to the data processing method for wind farm monitoring data provided in Embodiment 1. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the data processing method for wind farm monitoring data described in Embodiment 1 are loaded.

[0077] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0078] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0079] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0080] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0081] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0082] The above embodiments are only used to illustrate the present invention. The structure, connection method and manufacturing process of each component can be varied. All equivalent transformations and improvements made on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.

Claims

1. A data processing method for wind farm monitoring data, characterized in that, include: A pre-built wind farm monitoring data processing model is used to process the wind farm monitoring data collected by the wind farm monitoring system in real time. A pre-built wind farm monitoring data processing model is used to perform timed aggregation calculations on the parallel-processed wind farm monitoring data to obtain the timed aggregation calculation results of the wind farm. The wind farm monitoring data processing model includes a dimension determination module, an equipment type definition module, an equipment model template definition module, and a specific equipment determination module. The dimension determination module is used to define the view that users view when viewing the wind farm monitoring system, including the primary system dimension, monitoring system dimension, financial dimension, and operation monitoring dimension. The equipment type definition module is used to define the equipment types of the wind farm and to encode each equipment type; The equipment model template definition module is used to define equipment model templates for different equipment of the same equipment type in a wind farm, so as to standardize the data format of the equipment. Equipment models of the same equipment type have a parent-child inheritance relationship. The child model will inherit all the definitions of the parent model, unless the child model defines the same code configuration. The specific equipment determination module is used to determine the equipment details and associated measuring points of specific equipment in the wind farm; The aforementioned method employs a pre-built wind farm monitoring data processing model to process wind farm monitoring data collected by the wind farm monitoring system in real time, including: Data conversion is performed on the wind farm monitoring data collected by the wind farm monitoring system; Load the pre-built wind farm monitoring data processing model and preprocess the converted wind farm monitoring data; The wind farm monitoring data processing model after loading is adopted to process the wind farm monitoring data in parallel. The aforementioned pre-built wind farm monitoring data processing model performs timed aggregation calculations on the parallel-processed wind farm monitoring data to obtain the timed aggregation calculation results of the wind farm, including: Perform scheduled tasks; Based on scheduled tasks, query data from sub-devices in the wind farm monitoring system. Use a fixed time window or a sliding time window to process the queried sub-device data; Timestamp alignment is performed on the processed sub-device data; A wind farm monitoring data processing model is adopted to aggregate the data of sub-devices after timestamp alignment, and obtain the timed aggregation calculation results of the wind farm.

2. The data processing method for wind farm monitoring data as described in claim 1, characterized in that, The method of using a pre-built wind farm monitoring data processing model to process wind farm monitoring data collected by the wind farm monitoring system in real time also includes: A method to prevent cyclic conversion is adopted to avoid repeated conversion and processing of wind farm monitoring data.

3. The data processing method for wind farm monitoring data as described in claim 1, characterized in that, The loading of the pre-built wind farm monitoring data processing model and the preprocessing of the converted wind farm monitoring data include: Load the pre-built wind farm monitoring data processing model; Based on the wind farm monitoring data processing model, the converted wind farm monitoring data is converted to standard measurement points. Based on the wind farm monitoring data processing model, alarm rules for wind turbines in the wind farm monitoring system are preloaded.

4. The data processing method for wind farm monitoring data as described in claim 1, characterized in that, The method of using a pre-built wind farm monitoring data processing model to perform timed aggregation calculations on the parallel-processed wind farm monitoring data to obtain the timed aggregation calculation results of the wind farm also includes: The intermediate and final results of the calculations are saved to the real-time database and time-series database of the wind farm monitoring system.

5. A data processing system for wind farm monitoring data, characterized in that, include: The real-time data processing module is used to process the wind farm monitoring data collected by the wind farm monitoring system in real time using a pre-built wind farm monitoring data processing model. The timed aggregation calculation module is used to perform timed aggregation calculations on the parallel-processed wind farm monitoring data using a pre-built wind farm monitoring data processing model, and obtain the timed aggregation calculation results of the wind farm. The wind farm monitoring data processing model includes a dimension determination module, an equipment type definition module, an equipment model template definition module, and a specific equipment determination module. The dimension determination module is used to define the view that users view when viewing the wind farm monitoring system, including the primary system dimension, monitoring system dimension, financial dimension, and operation monitoring dimension. The equipment type definition module is used to define the equipment types of the wind farm and to encode each equipment type; The equipment model template definition module is used to define equipment model templates for different equipment of the same equipment type in a wind farm, so as to standardize the data format of the equipment. Equipment models of the same equipment type have a parent-child inheritance relationship. The child model will inherit all the definitions of the parent model, unless the child model defines the same code configuration. The specific equipment determination module is used to determine the equipment details and associated measuring points of specific equipment in the wind farm; The aforementioned method employs a pre-built wind farm monitoring data processing model to process wind farm monitoring data collected by the wind farm monitoring system in real time, including: Data conversion is performed on the wind farm monitoring data collected by the wind farm monitoring system; Load the pre-built wind farm monitoring data processing model and preprocess the converted wind farm monitoring data; The wind farm monitoring data processing model after loading is adopted to process the wind farm monitoring data in parallel. The aforementioned pre-built wind farm monitoring data processing model performs timed aggregation calculations on the parallel-processed wind farm monitoring data to obtain the timed aggregation calculation results of the wind farm, including: Perform scheduled tasks; Based on scheduled tasks, query data from sub-devices in the wind farm monitoring system. Use a fixed time window or a sliding time window to process the queried sub-device data; Timestamp alignment is performed on the processed sub-device data; A wind farm monitoring data processing model is adopted to aggregate the data of sub-devices after timestamp alignment, and obtain the timed aggregation calculation results of the wind farm.

6. A processing apparatus, characterized in that, It includes computer program instructions, wherein when the computer program instructions are executed by the processing device, they are used to implement the steps corresponding to the data processing method for wind farm monitoring data according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, wherein when the computer program instructions are executed by a processor, they are used to implement the steps corresponding to the data processing method for wind farm monitoring data according to any one of claims 1-4.

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