Wind power control method and system based on intelligent robot

Abstract

The invention discloses a wind power control method and system based on an intelligent robot. The method comprises the following steps: acquiring historical wind power data in the operation process of wind power equipment; performing feature extraction operation according to the historical wind power data to obtain data items corresponding to the historical wind power data; according to the data item, performing a data unit vectorization operation to obtain a data vector; performing correlation analysis operation according to the data vector to obtain an abnormal data set of the corresponding wind power equipment; performing feature vector extraction operation according to the abnormal data set to obtain potential feature vectors; according to the potential feature vector, performing a state change measurement index calculation operation to obtain a state change measurement index; carrying out risk value calculation operation according to the state change measurement index to obtain a state change risk value; and according to the state change risk value, carrying out alarm reminding operation so as to monitor the wind power equipment. The accuracy of evaluating the risk condition of the wind power equipment can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a wind power control method and system based on an intelligent robot. Background Art

[0002] At present, with the development of new energy, the demand for wind power equipment continues to rise. During the operation of wind power equipment, its operating state changes due to the influence of climate, environment and mechanical stress. Therefore, it is necessary to conduct comprehensive monitoring of wind turbines to ensure the safe operation of wind power equipment. Traditional wind power monitoring systems collect status data of wind turbines through multiple sensors to achieve real-time monitoring and fault detection of different components and different times of wind power equipment. Wind power monitoring systems collect and analyze various information through sensors to monitor the operating status of wind turbines. It can monitor various parameters, including temperature, pressure, speed, vibration and sound, to help detect potential problems and faults. However, traditional wind power monitoring systems have the following defects: Traditional wind power monitoring systems mainly rely on hardware devices, which may malfunction or fail, resulting in unreliable data or inability to process data in a timely manner. In addition, traditional methods require a large number of hardware devices and complex software systems, which increases the cost and complexity of the system. Traditional wind power monitoring systems mainly rely on pre-set thresholds and rules and cannot adaptively adjust monitoring strategies. When the operating environment and conditions of wind power equipment change, these fixed rules are no longer applicable, resulting in false alarms or missed alarms. At the same time, with the development of artificial intelligence technology, intelligent robot technology has been widely used in the field of wind power. By establishing an intelligent wind power monitoring platform, the performance status of wind power equipment can be monitored in real time, and predictive maintenance of wind power equipment can be performed. The intelligent wind power monitoring platform can receive data from various sensors in real time, analyze and warn the performance of wind power equipment. Through the application of historical data and artificial intelligence algorithms, the platform can not only identify and process common wind power equipment alarm information in real time, but also predict and warn potential wind power equipment performance degradation in advance, thereby greatly reducing the downtime and maintenance costs of wind power equipment.

[0003] Because the existing technology has many significant defects. First, it completely ignores the differences in potential risks at different times. In reality, the environment, personnel and equipment conditions at different times are constantly changing, and the characteristics, degree and probability of risks also change accordingly. However, the existing technology adopts a unified model for evaluation, which causes the results to deviate from reality. Second, it is extremely unreasonable to use the same indicator to define the importance of the collected data. The data sources are diverse and of different natures. Data such as key equipment parameters are crucial, while ordinary environmental data are relatively minor. However, the existing technology treats them equally, resulting in the key data not being prominent and the minor data interfering with the judgment, which ultimately makes the risk assessment results unsatisfactory and difficult to apply effectively.

[0004] Therefore, although the existing technology can monitor the performance degradation of wind power equipment in real time, the existing defects greatly reduce the accuracy of the assessment of the risk situation of wind power equipment, making it difficult to meet the needs of precise risk control in practical applications. Summary of the invention

[0005] The present invention provides a wind power control method and system based on an intelligent robot, so as to realize real-time monitoring of the performance degradation of wind power equipment and improve the accuracy of assessing the risk situation of wind power equipment.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a wind power control method based on an intelligent robot, comprising: During the operation of the wind power equipment, historical wind power data is obtained, wherein the historical wind power data is obtained by an intelligent robot; Performing a feature extraction operation according to the historical wind power data to obtain data items corresponding to the historical wind power data; According to the data item, a data unit vectorization operation is performed to obtain a data vector; Performing a correlation analysis operation according to the data vector to obtain an abnormal data set corresponding to the wind power equipment; Performing a feature vector extraction operation according to the abnormal data set to obtain a potential feature vector; Performing a state change metric calculation operation according to the potential feature vector to obtain a state change metric; Performing a risk value calculation operation according to the state change measurement indicator to obtain a state change risk value; According to the state change risk value, an alarm reminder operation is performed to monitor the wind power equipment.

[0007] As an optional implementation manner, during the operation of the wind power equipment, after acquiring the historical wind power data, the method further includes: Performing a data cleaning operation according to the historical wind power data to obtain cleaned data; According to the cleaned data, a preprocessing operation is performed to obtain preprocessed data, wherein the preprocessing operation formula is: , is the preprocessed data, is the cleaned data, is the mean of the cleaned data, is the standard deviation of the cleaned data.

[0008] As an optional implementation manner, performing a feature extraction operation according to the historical wind power data to obtain data items of the historical wind power data includes: The feature extraction operation formula is:

[0009] in, is the data item at time t, is the total number of data features extracted from the preprocessed data, For the The weight coefficient of each data feature, For at the moment When the preprocessed data The value of the data feature, For the After the preprocessing, For a specific moment .

[0010] As an optional implementation manner, performing a data unit vectorization operation according to the data item to obtain a data vector includes: The data unit vectorization operation is to input the obtained data items into a clustering model, wherein the clustering model adopts a K-means algorithm and includes normal features, fluctuation features and abnormal features; The data vectors include abnormal feature vectors, fluctuation feature vectors and normal feature vectors.

[0011] As an optional implementation manner, performing a correlation analysis operation according to the data vector to obtain an abnormal data set corresponding to the wind power equipment includes: According to the data vector, a time distance calculation operation is performed to obtain the time distance corresponding to the data vector, wherein the time distance calculation formula is: , In the formula, and For two different moments, For the data vector and The time distance, and is the dimension number of the time attribute, For two in The data vector of dimension According to the time distance, a similarity calculation operation is performed to obtain the correlation of the data vector, wherein the similarity calculation formula is: , In the formula, To control the preset parameters of the decay speed, For the correlation, and For two different moments; According to the correlation, a correlation threshold comparison operation is performed to obtain the abnormal data set, wherein the correlation threshold comparison includes if >v, it belongs to an abnormal data set, v is the preset correlation threshold, For the said correlation.

[0012] As an optional implementation manner, performing a state change metric index calculation operation according to the potential feature vector to obtain the state change metric index includes: The state change measurement formula is: , in, , , is the state change metric, is the average state change index, For the moment The latent feature vector of For the moment The latent feature vector of is the preset coefficient, and is the time variable, for The state change metric at each moment.

[0013] As an optional implementation manner, performing a risk value calculation operation according to the state change measurement indicator to obtain a state change risk value includes: The risk value calculation formula is:

[0014] in, is the state change risk value, z is the preset control coefficient, is the preset control factor, , for The state change metric at the time, for The state change metric at that moment.

[0015] In a second aspect, the present invention provides a wind power control method based on an intelligent robot, comprising: A data acquisition module, which acquires historical wind power data during the operation of the wind power equipment, wherein the historical wind power data is acquired by an intelligent robot; A feature extraction module, used to perform a feature extraction operation based on the historical wind power data to obtain data items corresponding to the historical wind power data; A vector conversion module, used for performing a data unit vectorization operation according to the data item to obtain a data vector; A correlation analysis module, used to perform a correlation analysis operation according to the data vector to obtain an abnormal data set corresponding to the wind power equipment; A feature vector extraction module, used to perform a feature vector extraction operation according to the abnormal data set to obtain a potential feature vector; A state change metric calculation module, used to perform a state change metric index calculation operation according to the potential feature vector to obtain a state change metric index; A risk value calculation module, used to perform a risk value calculation operation according to the state change measurement index to obtain a state change risk value; The user reminder module is used to perform an alarm reminder operation according to the state change risk value to monitor the wind power equipment.

[0016] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a wind power control method based on an intelligent robot as described above.

[0017] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a wind power control method based on an intelligent robot as described above.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a wind power control method based on an intelligent robot, comprising: acquiring historical wind power data during the operation of wind power equipment, wherein the historical wind power data is obtained by an intelligent robot; performing a feature extraction operation based on the historical wind power data to obtain data items of the historical wind power data; performing a data unit vectorization operation based on the data items to obtain vectors; performing a correlation analysis operation based on the data vectors to obtain an abnormal data set of the wind power equipment; performing a feature vector extraction operation based on the abnormal data set to obtain a potential feature vector; performing a state change measurement index calculation operation based on the potential feature vector to obtain a state change measurement index; performing a risk value calculation operation based on the state change measurement index to obtain a state change risk value; performing an alarm reminder operation based on the state change risk value to monitor the wind power equipment.

[0019] In the present invention, the wind power control method based on the intelligent robot has the following advantages: First, the integrity and accuracy of the wind power equipment data are improved through data preprocessing. Second, key information is effectively extracted to provide a basis for monitoring. Third, cluster analysis and neural networks are used to accurately identify abnormal equipment states and related information, thereby improving operational safety and reliability. Fourth, real-time monitoring is achieved to promptly handle potential problems and avoid losses caused by equipment failures. The specific invention process of the present invention is as follows: During the operation of the wind power equipment, the relevant historical wind power data must first be obtained. Next, feature extraction is performed on the acquired historical wind power data to obtain data items. Subsequently, the obtained data items are vectorized to convert them into vector form. Afterwards, a correlation analysis operation is performed based on this vector to obtain an abnormal data set of the wind power equipment. Feature vectors are further extracted from the abnormal data set to obtain potential feature vectors. Subsequently, a state change metric is calculated based on the potential feature vector. Then, a risk value is calculated based on the calculated state change metric. Finally, alarm reminders are performed based on the risk value to achieve comprehensive and effective monitoring of wind power equipment, ensure that wind power equipment can operate safely, promptly discover and handle problems, and ensure the normal operation of the entire wind power system. According to the above method, wind power control based on intelligent robots can not only achieve real-time monitoring of wind power equipment performance degradation, but also improve the accuracy of assessing the risk of wind power equipment, ensure that wind power equipment can operate stably and safely, ensure that staff can promptly discover and handle problems, and ensure the normal operation of the entire wind power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a wind power control method based on an intelligent robot provided by an embodiment of the present invention; Figure 2It is a structural schematic diagram of a wind power control system based on an intelligent robot provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] It should be noted that with the development of new energy, the demand for wind power equipment continues to rise. During the operation of wind power equipment, the operating state changes due to the influence of climate, environment and mechanical stress. Therefore, it is necessary to conduct comprehensive monitoring of wind turbines to ensure the safe operation of wind power equipment. Traditional wind power monitoring systems collect status data of wind turbines through multiple sensors to achieve real-time monitoring and fault detection of different components and different times of wind power equipment. Wind power monitoring systems collect and analyze various information through sensors to monitor the operating status of wind turbines. It can monitor various parameters, including temperature, pressure, speed, vibration and sound, to help detect potential problems and faults. However, traditional wind power monitoring systems have the following defects: Traditional wind power monitoring systems mainly rely on hardware devices, which may malfunction or fail, resulting in unreliable data or inability to process data in a timely manner. In addition, traditional methods require a large number of hardware devices and complex software systems, which increases the cost and complexity of the system. Traditional wind power monitoring systems mainly rely on pre-set thresholds and rules and cannot adaptively adjust monitoring strategies. When the operating environment and conditions of wind power equipment change, these fixed rules are no longer applicable, resulting in false alarms or missed alarms. At the same time, with the development of artificial intelligence technology, intelligent robot technology has been widely used in the field of wind power. By establishing an intelligent wind power monitoring platform, the performance status of wind power equipment can be monitored in real time, and predictive maintenance of wind power equipment can be carried out. The intelligent wind power monitoring platform can receive data from various sensors in real time, analyze and warn the performance of wind power equipment. Through the application of historical data and artificial intelligence algorithms, the platform can not only identify and process common wind power equipment alarm information in real time, but also predict and warn potential wind power equipment performance degradation in advance, thereby greatly reducing the downtime and maintenance costs of wind power equipment. Because, the existing technology has many significant defects. First, it completely ignores the differences in potential risks at different times. In practice, the environment, personnel and equipment conditions at different times are constantly changing, and the characteristics, degree and probability of risks are also changing accordingly. However, the existing technology adopts a unified model evaluation to deal with all changes, resulting in results that deviate from reality. Second, it is extremely unreasonable to use the same indicator to define the importance of the collected data. Data sources are diverse and of different natures. Data such as key equipment parameters are crucial, while general environmental data is relatively unimportant. However, existing technologies treat them equally, resulting in the lack of prominent role of key data and interference of secondary data in judgment, which ultimately makes the risk assessment results unsatisfactory and difficult to apply effectively. Therefore, although existing technologies can monitor the performance degradation of wind power equipment in real time, the existing defects greatly reduce the accuracy of the assessment of wind power equipment risk conditions, making it difficult to meet the needs of accurate risk control in actual applications.

[0023] For the above problems, refer to Figure 1 The first embodiment of the present invention provides a wind power control method based on an intelligent robot, comprising the following steps: S11, during the operation of the wind power equipment, obtaining historical wind power data, wherein the historical wind power data is obtained by an intelligent robot; S12, performing a feature extraction operation according to the historical wind power data to obtain data items corresponding to the historical wind power data; S13, performing a data unit vectorization operation according to the data item to obtain a data vector; S14, performing a correlation analysis operation according to the data vector to obtain an abnormal data set corresponding to the wind power equipment; S15, performing a feature vector extraction operation according to the abnormal data set to obtain a potential feature vector; S16, performing a state change metric calculation operation according to the potential feature vector to obtain a state change metric; S17, performing a risk value calculation operation according to the state change measurement indicator to obtain a state change risk value; S18, performing an alarm reminder operation according to the state change risk value to monitor the wind power equipment.

[0024] In step S11, historical wind power data is acquired during the operation of the wind power equipment, wherein the historical wind power data is acquired by an intelligent robot.

[0025] In one embodiment, after acquiring historical wind power data during the operation of the wind power equipment, the method further includes: performing a data cleaning operation based on the historical wind power data to obtain cleaned data; performing a preprocessing operation based on the cleaned data to obtain preprocessed data, wherein the preprocessing operation formula is: , is the preprocessed data, is the cleaned data, is the mean of the cleaned data, is the standard deviation of the cleaned data.

[0026] In the above embodiment, the historical wind power data such as wind speed, wind direction and power generation are collected in real time by intelligent robots and stored in the central control system. After processing, these data are used to analyze the operating efficiency and health status of the equipment, thereby optimizing the operating strategy, improving power generation efficiency, reducing maintenance costs, and enhancing the reliability of the wind farm. In this way, the data of wind power equipment can be effectively monitored, providing a data basis for assessing the risk status of wind power equipment.

[0027] It should be specifically stated that based on the wind power equipment working status monitoring platform, data collection work on wind power equipment is carried out, and the collected data covers multiple key dimensions such as temperature, voltage, current and vibration data of wind power equipment. The collected data are screened using statistical methods in order to identify and retain valid data samples. Subsequently, the collected data is preprocessed, specifically using the principal component analysis (PCA) method to normalize the data in order to reduce the data correlation, thereby providing a more reasonable data basis for subsequent in-depth data analysis and application, and ensuring the accuracy and reliability of wind power equipment working status evaluation and monitoring.

[0028] It should also be specified that during the operation of wind power equipment, the acquisition of historical wind power data mainly depends on the cooperation of intelligent robots and sensors. Intelligent robots can automatically patrol in wind farms and are equipped with multiple sensors to monitor key parameters of equipment, such as temperature, voltage, current and vibration data. For example, a certain type of intelligent robot can automatically go to each wind turbine at a fixed time set by humans, use the built-in temperature sensor to monitor the operating temperature of the equipment, and use voltage and current sensors to record the power output. On this basis, the vibration sensor configured by the intelligent robot itself can also monitor the vibration level of the wind turbine in real time, thereby discovering potential signs of failure. The collected data will be transmitted to the central control system via a wireless network for analysis and storage. These historical data obtained by the intelligent robot not only help users evaluate the operating status of the equipment, but also improve the accuracy of risk assessment of wind power equipment through data trend analysis.

[0029] In step S12, a feature extraction operation is performed based on the historical wind power data to obtain data items corresponding to the historical wind power data.

[0030] In one implementation, the feature extraction operation formula is:

[0031] in, is the data item at time t, is the total number of data features extracted from the preprocessed data, For the The weight coefficient of each data feature, For at the moment When the preprocessed data The value of the data feature, For the After the preprocessing, For a specific moment .

[0032] In the above embodiment, feature extraction is performed based on historical wind power data, aiming to extract key information from the raw data for subsequent analysis. This process first cleans and standardizes the data through data preprocessing, then selects features related to wind power generation, such as wind speed, wind direction, and air pressure, and then performs feature conversion and combination to generate new data items. These extracted data items can more accurately reflect the operating laws of wind power and provide high-quality input for subsequent prediction models, thereby improving the prediction accuracy and reliability of wind power generation and optimizing power dispatch.

[0033] In step S13, a data unit vectorization operation is performed according to the data item to obtain a data vector.

[0034] In one embodiment, the data unit vectorization operation is to input the obtained data item into a clustering model, wherein the clustering model adopts a K-means algorithm, including normal features, fluctuation features and abnormal features; the data vector includes an abnormal feature vector, a fluctuation feature vector and a normal feature vector.

[0035] In the above embodiment, key information is extracted from the original data for subsequent analysis. This process first performs pre-processing, cleaning and standardization operations on the collected data, selects features related to wind power generation, such as wind speed, wind direction and air pressure, and then performs feature conversion and combination to generate new data items. These extracted data items can more accurately reflect the operation rules of wind power and provide high-quality input for subsequent prediction models, thereby improving the accuracy and reliability of risk prediction of wind power equipment.

[0036] Exemplarily, the clustering model adopts the K-means algorithm, and the K of the preset clustering algorithm is 3, which is divided into normal features, fluctuation features and abnormal features. The clustering model calculates the similarity of the features of the data items of each unit, and takes the M items of data at the same time as a unit to obtain N vectors S, which include abnormal feature vectors, fluctuation feature vectors and normal feature vectors.

[0037] In step S14, a correlation analysis operation is performed according to the data vector to obtain an abnormal data set corresponding to the wind power equipment.

[0038] In the above embodiment, the correlation analysis operation is performed according to the data vectors, with the purpose of identifying abnormal data sets related to the operating status of the wind power equipment. By calculating the correlation between the data vectors, the potential relationship between different data points can be found. This analysis can help identify specific data patterns related to equipment performance degradation or failure, and then extract abnormal data sets. This process helps to timely discover potential equipment problems, optimize maintenance strategies, improve equipment operating efficiency and reliability, and thus reduce downtime and maintenance costs.

[0039] In step S15, a feature vector extraction operation is performed based on the abnormal data set to obtain a potential feature vector.

[0040] In the above embodiment, these abnormal data sets are derived from the abnormal data sets obtained after correlation analysis operations are performed on the real-time monitoring data and historical operation data collected during the operation of the wind power equipment. By extracting feature vectors, real-time monitoring of equipment performance changes can be achieved, and abnormal situations can be discovered in time, thereby improving the accuracy of risk assessment of wind power equipment.

[0041] In step S16, a state change metric index calculation operation is performed according to the potential feature vector to obtain a state change metric index.

[0042] In one implementation, the state change measurement formula is: , in, , , is the state change metric, is the average state change index, For the moment The latent feature vector of For the moment The latent feature vector of is the preset coefficient, and is the time variable, for The state change metric at each moment.

[0043] In the above embodiment, the state change metric can reflect the degree of change of the system state, specifically including quantitative analysis of the changes in each dimension in the feature vector, and using mathematical model methods to evaluate the stability and change trend of the system in time series. By comparing the metric indicators of the current state with the historical state, potential abnormal states or trend changes can be identified, thereby providing data support and basis for subsequent decision-making.

[0044] It should be specifically explained that the state change measurement formula accurately inputs the data set at a specific moment into a fully trained and stable model. Subsequently, the data set is deeply analyzed and processed by the model to extract the potential feature vectors corresponding to the wind power equipment at each moment. Finally, based on these extracted potential feature vectors, algorithms and calculation rules are used to further calculate the measurement indicators that can quantify the state changes of wind power equipment. It can not only reflect the changes in the operating state of wind power equipment at different times in real time, but also provide important data support and decision-making basis for subsequent equipment status monitoring, fault diagnosis and operation optimization, thereby ensuring the stable operation and efficient power generation of wind power equipment, and improving the accuracy of risk assessment of wind power equipment.

[0045] It should also be specifically stated that the neural network model introduced is a multi-layer neural network, which includes various types such as BP neural network, RNN neural network and CNN neural network. For such trained neural network models, feature extraction operations are required. Specifically, the wind power equipment data at each moment is input into the model one by one, and then the feature vector corresponding to each moment is obtained. On this basis, the state difference index and state risk value between moment n and moment n+1 are further obtained. In this way, the operating status and changes of wind power equipment at different times can be analyzed more deeply, providing strong data support and analysis basis for subsequent related research and applications.

[0046] In step S17, a risk value calculation operation is performed according to the state change measurement index to obtain a state change risk value.

[0047] In one embodiment, the risk value calculation formula is:

[0048] in, is the state change risk value, z is the preset control coefficient, is the preset control factor, , for The state change metric at the time, for The state change metric at that moment.

[0049] In the above embodiment, the calculation of the risk value is based on a comprehensive analysis of historical data and real-time monitoring data. The state change risk value finally obtained is collected in real time by sensors and monitoring systems, and then obtained through operations such as feature extraction, vector conversion, state change metric calculation, and risk value calculation. Through this method, the downward trend of equipment performance can be identified in a timely manner, thereby evaluating its risk situation. Real-time monitoring of the performance degradation of wind power equipment can effectively reduce the occurrence of sudden failures, reduce maintenance costs, and at the same time improve the accuracy of assessing the risk situation of wind power equipment, ensuring the safe and efficient operation of the wind power system.

[0050] In step S18, an alarm reminder operation is performed according to the state change risk value to monitor the wind power equipment.

[0051] In the above embodiment, the state change risk value is obtained by real-time collection of wind power equipment operating data, such as power generation, rotation speed, vibration frequency and temperature, combined with historical data and machine learning algorithms for analysis. These data are used to evaluate the performance degradation of the equipment and judge the health status of the equipment in real time. Once it is detected that the risk value exceeds the preset threshold, the system will automatically issue an alarm reminder to prompt maintenance personnel to check and maintain the equipment in time, thereby effectively reducing the probability of failure, extending the service life of the equipment, and improving the overall operation efficiency and reliability of the wind power system. The purpose of this step is to achieve real-time monitoring and early warning of the performance degradation of wind power equipment. By setting a precise risk value threshold, the accuracy of risk assessment of wind power equipment can be improved.

[0052] In summary, the present invention provides a wind power control method based on an intelligent robot, comprising: obtaining historical wind power data during the operation of wind power equipment, wherein the historical wind power data is obtained by an intelligent robot; performing a feature extraction operation based on the historical wind power data to obtain data items of the historical wind power data; performing a data unit vectorization operation based on the data items to obtain a vector; performing a correlation analysis operation based on the data vector to obtain an abnormal data set of the wind power equipment; performing a feature vector extraction operation based on the abnormal data set to obtain a potential feature vector; performing a state change measurement indicator calculation operation based on the potential feature vector to obtain a state change measurement indicator; performing a risk value calculation operation based on the state change measurement indicator to obtain a state change risk value; performing an alarm reminder operation based on the state change risk value to monitor the wind power equipment.

[0053] The embodiment of the present invention also provides a wind power control system based on an intelligent robot, such as Figure 2 As shown, it is a structural block diagram of a wind power control system based on an intelligent robot provided by an embodiment of the present invention, including: Data acquisition module 1, acquiring historical wind power data during the operation of the wind power equipment, wherein the historical wind power data is acquired by an intelligent robot; A feature extraction module 2 is used to perform a feature extraction operation based on the historical wind power data to obtain data items corresponding to the historical wind power data; A vector conversion module 3, used for performing a data unit vectorization operation according to the data item to obtain a data vector; A correlation analysis module 4 is used to perform a correlation analysis operation according to the data vector to obtain an abnormal data set corresponding to the wind power equipment; A feature vector extraction module 5 is used to perform a feature vector extraction operation according to the abnormal data set to obtain a potential feature vector; A state change metric calculation module 6 is used to perform a state change metric index calculation operation according to the potential feature vector to obtain a state change metric index; A risk value calculation module 7 is used to perform a risk value calculation operation according to the state change measurement index to obtain a state change risk value; The user reminder module 8 is used to perform an alarm reminder operation according to the state change risk value to monitor the wind power equipment.

[0054] In an optional implementation, the data acquisition module 1 is further used for: After obtaining historical wind power data during the operation of the wind power equipment, the method further includes: performing a data cleaning operation according to the historical wind power data to obtain cleaned data; performing a preprocessing operation according to the cleaned data to obtain preprocessed data, wherein the preprocessing operation formula is: , is the preprocessed data, is the cleaned data, is the mean of the cleaned data, is the standard deviation of the cleaned data.

[0055] In an optional implementation, the feature extraction module 2 is further used for: The feature extraction operation is performed according to the historical wind power data to obtain the data items of the historical wind power data, including: the feature extraction operation formula is:

[0056] in, is the data item at time t, is the total number of data features extracted from the preprocessed data, For the The weight coefficient of each data feature, For at the moment When the preprocessed data The value of the data feature, For the After the preprocessing, For a specific moment .

[0057] In an optional implementation, the vector conversion module 3 is further used for: The step of performing a data unit vectorization operation according to the data item to obtain a data vector includes: The data unit vectorization operation is to input the obtained data item into a clustering model, wherein the clustering model adopts a K-means algorithm, including normal features, fluctuation features and abnormal features; the data vector includes an abnormal feature vector, a fluctuation feature vector and a normal feature vector.

[0058] In an optional implementation, the correlation analysis module 4 is further used for: The performing a correlation analysis operation according to the data vector to obtain an abnormal data set corresponding to the wind power equipment includes: performing a time distance calculation operation according to the data vector to obtain a time distance corresponding to the data vector, wherein the time distance calculation formula is: , In the formula, and For two different moments, For the data vector and The time distance, and is the dimension number of the time attribute, For two in The data vector of dimension; performing a similarity calculation operation according to the time distance to obtain the correlation of the data vector, wherein the similarity calculation formula is: , In the formula, To control the preset parameters of the decay speed, For the correlation, and For two different moments; According to the correlation, a correlation threshold comparison operation is performed to obtain the abnormal data set, wherein the correlation threshold comparison includes if >v, it belongs to an abnormal data set, v is the preset correlation threshold, For the said correlation.

[0059] In an optional implementation, the state change metric calculation module 6 is further used for: The state change measurement index calculation operation is performed according to the potential feature vector to obtain the state change measurement index, including: the state change measurement formula is: , in, , , is the state change metric, is the average state change index, For the moment The latent feature vector of For the moment The latent feature vector of is the preset coefficient, and is the time variable, for The state change metric at each moment.

[0060] In an optional implementation, the risk value calculation module 7 is further used for: The step of performing a risk value calculation operation according to the state change measurement indicator to obtain a state change risk value includes: the risk value calculation formula is:

[0061] in, is the state change risk value, z is the preset control coefficient, is the preset control factor, , for The state change metric at the time, for The state change metric at that moment.

[0062] An embodiment of the present invention further provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the wind power control method based on the intelligent robot as described in the above embodiment when executing the computer program.

[0063] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0064] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0065] The processor may be a central processing unit (CPU), other general-purpose 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, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0066] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0067] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0068] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0069] Compared with the prior art, in the present invention, the wind power control method based on intelligent robots has the following advantages: First, the integrity and accuracy of wind power equipment data are improved through data preprocessing. Second, key information is effectively extracted to provide a basis for monitoring. Third, cluster analysis and neural networks are used to accurately identify abnormal equipment states and related information, thereby improving operational safety and reliability. Fourth, real-time monitoring is achieved to promptly handle potential problems and avoid losses caused by equipment failure. The specific invention process of the present invention is as follows: During the operation of wind power equipment, relevant historical wind power data must first be obtained. Next, feature extraction is performed on the acquired historical wind power data to obtain data items. Subsequently, the obtained data items are vectorized to convert them into vector form. Afterwards, a correlation analysis operation is performed based on this vector to obtain an abnormal data set of the wind power equipment. Feature vectors are further extracted from the abnormal data set to obtain potential feature vectors. Subsequently, state change metrics are calculated based on the potential feature vectors. Next, the risk value is calculated based on the calculated state change metrics. Finally, alarm reminders are performed based on the risk value to achieve comprehensive and effective monitoring of wind power equipment, ensure that wind power equipment can operate safely, promptly discover and handle problems, and ensure the normal operation of the entire wind power system. According to the above method, wind power control based on intelligent robots can not only achieve real-time monitoring of wind power equipment performance degradation, but also improve the accuracy of assessing the risk of wind power equipment, ensure that wind power equipment can operate stably and safely, ensure that staff can promptly discover and handle problems, and ensure the normal operation of the entire wind power system.

[0070] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A wind power control method based on an intelligent robot, characterized in that: include: During the operation of the wind power equipment, historical wind power data is obtained, wherein the historical wind power data is obtained by an intelligent robot; Performing a feature extraction operation according to the historical wind power data to obtain data items corresponding to the historical wind power data; According to the data item, a data unit vectorization operation is performed to obtain a data vector; Performing a correlation analysis operation according to the data vector to obtain an abnormal data set corresponding to the wind power equipment; Performing a feature vector extraction operation according to the abnormal data set to obtain a potential feature vector; Performing a state change metric calculation operation according to the potential feature vector to obtain a state change metric; Performing a risk value calculation operation according to the state change measurement indicator to obtain a state change risk value; According to the state change risk value, an alarm reminder operation is performed to monitor the wind power equipment.

2. The wind power control method based on intelligent robots according to claim 1 is characterized in that: In the process of wind power equipment operation, after obtaining historical wind power data, the method further includes: Performing a data cleaning operation according to the historical wind power data to obtain cleaned data; According to the cleaned data, a preprocessing operation is performed to obtain preprocessed data, wherein the preprocessing operation formula is: ， is the preprocessed data, is the cleaned data, is the mean of the cleaned data, is the standard deviation of the cleaned data.

3. The wind power control method based on intelligent robots according to claim 2 is characterized in that: The performing of feature extraction operation according to the historical wind power data to obtain data items of the historical wind power data includes: The feature extraction operation formula is: in, is the data item at time t, is the total number of data features extracted from the preprocessed data, For the The weight coefficient of each data feature, For at the moment When the preprocessed data The value of the data feature, For the After the preprocessing, For a specific moment .

4. The wind power control method based on intelligent robots according to claim 1 is characterized in that: The step of performing a data unit vectorization operation according to the data item to obtain a data vector includes: The data unit vectorization operation is to input the obtained data items into a clustering model, wherein the clustering model adopts a K-means algorithm and includes normal features, fluctuation features and abnormal features; The data vectors include abnormal feature vectors, fluctuation feature vectors and normal feature vectors.

5. The wind power control method based on intelligent robots according to claim 1, characterized in that: The performing a correlation analysis operation according to the data vector to obtain an abnormal data set corresponding to the wind power equipment includes: According to the data vector, a time distance calculation operation is performed to obtain the time distance corresponding to the data vector, wherein the time distance calculation formula is: ， In the formula, and For two different moments, For the data vector and The time distance, and is the dimension number of the time attribute, For two in The data vector of dimension According to the time distance, a similarity calculation operation is performed to obtain the correlation of the data vector, wherein the similarity calculation formula is: ， In the formula, To control the preset parameters of the decay speed, For the correlation, and For two different moments; According to the correlation, a correlation threshold comparison operation is performed to obtain the abnormal data set, wherein the correlation threshold comparison includes if >v, it belongs to an abnormal data set, v is the preset correlation threshold, For the said correlation.

6. The wind power control method based on intelligent robots according to claim 1, characterized in that: The step of performing a state change metric index calculation operation according to the potential feature vector to obtain a state change metric index includes: The state change measurement formula is: ， in, , , is the state change metric, is the average state change index, For the moment The latent feature vector of For the moment The latent feature vector of is the preset coefficient, and is the time variable, for The state change metric at each moment.

7. The wind power control method based on intelligent robots according to claim 1, characterized in that: The step of performing a risk value calculation operation according to the state change measurement indicator to obtain a state change risk value includes: The risk value calculation formula is: in, is the state change risk value, z is the preset control coefficient, is the preset control factor, , for The state change metric at the time, for The state change metric at that moment.

8. A wind power control system based on an intelligent robot, characterized in that: include: A data acquisition module, which acquires historical wind power data during the operation of the wind power equipment, wherein the historical wind power data is acquired by an intelligent robot; A feature extraction module, used to perform a feature extraction operation based on the historical wind power data to obtain data items corresponding to the historical wind power data; A vector conversion module, used for performing a data unit vectorization operation according to the data item to obtain a data vector; A correlation analysis module, used to perform a correlation analysis operation according to the data vector to obtain an abnormal data set corresponding to the wind power equipment; A feature vector extraction module, used to perform a feature vector extraction operation according to the abnormal data set to obtain a potential feature vector; A state change metric calculation module, used to perform a state change metric index calculation operation according to the potential feature vector to obtain a state change metric index; A risk value calculation module, used to perform a risk value calculation operation according to the state change measurement index to obtain a state change risk value; The user reminder module is used to perform an alarm reminder operation according to the state change risk value to monitor the wind power equipment.

9. An electronic device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the wind power control method based on the intelligent robot as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the wind power control method based on the intelligent robot according to any one of claims 1 to 7.

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