A wind vibration data management method and system based on a two-level architecture

Abstract

The present application provides a wind vibration data management method and system based on a two - level architecture. The method includes: an industrial control computer acquires detection data collected by different sensors in the wind farm and calculates the characteristic values corresponding to the detection data for each first preset time period; determines whether the characteristic values meet the corresponding threshold requirements; sends the second characteristic values that do not meet the threshold requirements and the original data of the sensors corresponding to the second characteristic values in the target first preset time period to the wind farm system; the group system receives the second characteristic values and the original data sent by each wind farm system; determines whether there is a target analysis case in the preset database that matches the original data of the target sensor corresponding to the second characteristic value in the target preset time period. If there is, the target analysis case is selected to analyze the original data of the target sensor corresponding to the second characteristic value in the target preset time period. This solution can ensure a relatively high target data analysis efficiency.

Description

Technical Field

[0001] The present application relates to a wind vibration data management method and system based on a two - level architecture, belonging to the technical field of data management. Background Art

[0002] Wind power generation is a very important part of today's green energy. Wind turbines are generally installed in places with large airflows such as mountain slopes, ridges, and at sea, and are also generally in places where few people go. Moreover, the deployment of wind turbines is highly related to the actual terrain. The actual terrain conditions in our country have led to the very scattered installation and deployment of wind turbines. Often, a large - scale wind power generation company will have wind turbine deployments in many provinces across the country. And with the strong support of national policies, the number of installed wind turbines is also increasing. How to uniformly manage such scattered and large - number of wind turbines is also a significant challenge.

[0003] To ensure the long - term, stable, and safe operation of wind turbines, it is necessary to effectively monitor their actual operating conditions and conduct real - time operating data analysis. The amount of data to be transmitted for real - time vibration data is very large. For wind power companies with a wide distribution area, directly transmitting this data to the group is very unrealistic and will result in huge network cost expenditures. Without centralized observation of these data, the overall fault characteristics cannot be well grasped globally and predicted, and it is also not conducive to continuously improving the relevant logics of fault prediction and life - cycle prediction. Only by solving these problems and continuously improving the degree of automation and intelligence of the monitoring system can efficient management and maintenance of wind turbines be achieved.

[0004] In the existing technologies, much research and exploration have been carried out on how to monitor the vibration data of a single wind turbine, how to store and analyze the data. However, there are no relevant methods and solutions for a large - scale management system that simultaneously manages dozens or even hundreds of wind farms, and each wind farm has dozens, hundreds, or even thousands of wind turbines. This leads to the inability to effectively integrate and manage the vibration data of multiple wind turbines, and is also not conducive to timely understanding the vibration data acquisition situation of multiple wind turbines in each wind farm. Summary of the Invention

[0005] The present application provides a wind vibration data management method and system based on a two - level architecture to solve the technical problems in the existing technical solutions of "being unable to effectively integrate and manage the vibration data of multiple wind turbines, and being not conducive to timely understanding the vibration data acquisition situation of multiple wind turbines in each wind farm".

[0006] In a first aspect, according to an embodiment of the present application, a wind vibration data management method based on a two - level architecture is provided, which is applied to a wind power generation system including a wind farm system, an industrial control computer belonging to the wind farm system, and a group system. The method includes:

[0007] The industrial control computer in each wind farm system acquires the detection data collected by different sensors in the wind farm and calculates the characteristic values corresponding to the detection data of each first preset time period collected by each sensor;

[0008] The industrial control computer in the wind farm system determines whether the characteristic values corresponding to the detection data of each first preset time period collected by each sensor meet the corresponding threshold requirements;

[0009] The industrial control computer in the wind farm system sends the second characteristic values that do not meet the threshold requirements and the original data of the sensors corresponding to the second characteristic values in the target first preset time period to the wind farm system;

[0010] The data management module of the group system receives the second characteristic values sent by each wind farm system and the original data of the target preset time period of the target sensors corresponding to the second characteristic values;

[0011] The data management module of the group system determines whether there is a target analysis case in the preset database that matches the original data of the target sensors corresponding to the second characteristic values in the target preset time period. If so, the target analysis case is selected to analyze the original data of the target sensors corresponding to the second characteristic values in the target preset time period.

[0012] Preferably, the method further includes:

[0013] The industrial control computer sends the original data of each sensor collected during the third preset time period to the wind farm system every second preset time period.

[0014] Preferably, the characteristic values include at least one of the following:

[0015] The mean value, absolute average value, variance, standard deviation, root mean square amplitude, root mean square value, peak value, maximum value, minimum value, waveform index, peak index, pulse index, margin index, skewness and kurtosis of the detection data in the first preset time period.

[0016] Preferably,

[0017] The calculation model for the mean value of the detection data in the first preset time period is:

[0018]

[0019] The calculation model for the absolute average value of the detection data in the first preset time period is:

[0020]

[0021] The calculation model for the variance of the detection data in the first preset time period is:

[0022]

[0023] The calculation model for the standard deviation of the detection data in the first preset time period is as follows:

[0024]

[0025] The calculation model for the root mean amplitude of the detection data in the first preset time period is as follows:

[0026]

[0027] The calculation model for the root mean square of the detection data in the first preset time period is as follows:

[0028]

[0029] The mathematical model adopted for the peak value of the detection data in the first preset time period is as follows:

[0030] x p = max|x(n)|

[0031] The calculation model for the maximum value of the detection data in the first preset time period is as follows:

[0032] x max = max(x n )

[0033] The calculation model for the minimum value of the detection data in the first preset time period is as follows:

[0034] x min = min(x n )

[0035] The calculation model for the waveform index of the detection data in the first preset time period is as follows:

[0036]

[0037] The calculation model for the peak index of the detection data in the first preset time period is as follows:

[0038]

[0039] The calculation model for the pulse index of the detection data in the first preset time period is as follows:

[0040]

[0041] The calculation model for the margin index of the detection data in the first preset time period is as follows:

[0042]

[0043] The skewness calculation model for the detection data in the first preset time period is:

[0044]

[0045] The kurtosis calculation model for the detection data in the first preset time period is:

[0046]

[0047] Preferably, the method further includes:

[0048] If there is no target analysis case in the preset database that matches the original data of the target sensor corresponding to the second eigenvalue in the target preset time period, then send the original data of the target sensor corresponding to the second eigenvalue in the target preset time period, receive and store the second analysis case corresponding to the target analysis case that matches the original data of the target sensor corresponding to the second eigenvalue in the target preset time period, and perform analysis using the second analysis case and the original data of the target sensor corresponding to the second eigenvalue in the target preset time period.

[0049] Preferably, determining whether there is a target analysis case in the preset database that matches the original data of the target sensor corresponding to the second eigenvalue in the target preset time period, and if so, selecting the target analysis case to perform analysis on the original data of the target sensor corresponding to the second eigenvalue in the target preset time period, includes:

[0050] Determine whether there is a target analysis case in the preset database that matches the original data of the target sensor corresponding to the second eigenvalue in the target preset time period;

[0051] If so, display the target analysis case;

[0052] When a usage command for the target analysis scheme is received within the fourth preset time period, then perform analysis on the original data of the target sensor corresponding to the second eigenvalue in the target preset time period using the target analysis scheme and generate a processing scheme.

[0053] Preferably, the method further includes:

[0054] When a usage command for the target analysis scheme is not received within the fourth preset time period, then send the original data of the target sensor corresponding to the second eigenvalue in the target preset time period, receive and store the third analysis case corresponding to the target analysis case that matches the original data of the target sensor corresponding to the second eigenvalue in the target preset time period, and perform analysis using the third analysis case and the original data of the target sensor corresponding to the second eigenvalue in the target preset time period.

[0055] In a second aspect, according to an embodiment of the present application, a wind vibration data management system based on a two-level architecture is provided, which is applied to a wind power generation system including a wind farm system, an industrial control computer belonging to the wind farm system, and a group system. The system includes:

[0056] The industrial control computer in each wind farm system is used to

[0057] A data acquisition module, which is used to acquire the detection data collected by different sensors in the wind farm and calculate the characteristic values corresponding to the detection data of each first preset time period collected by each sensor;

[0058] A first judgment module, which is used to judge whether the characteristic values corresponding to the detection data of each first preset time period collected by each sensor meet the corresponding threshold requirements;

[0059] A sending module, which is used to send the second characteristic values that do not meet the threshold requirements and the original data of the sensors corresponding to the second characteristic values in the target first preset time period to the group system;

[0060] The group system includes:

[0061] A data receiving module, which is used to receive the second characteristic values sent by each wind farm system and the original data of the target sensors corresponding to the second characteristic values in the target preset time period;

[0062] A second judgment module, which is used to determine whether there is a target analysis case in the preset database that matches the original data of the target sensor corresponding to the second characteristic value in the target preset time period;

[0063] An analysis module, which is used to, if there is a target analysis case that matches the original data of the target sensor corresponding to the second characteristic value in the target preset time period, select the target analysis case to analyze the original data of the target sensor corresponding to the second characteristic value in the target preset time period.

[0064] Preferably,

[0065] The industrial control computer in each wind farm system is further used to:

[0066] Every second preset time period, send the original data of each sensor collected during the third preset time period to the wind farm system.

[0067] Preferably,

[0068] The industrial control computer in each wind farm system calculates the characteristic values corresponding to the detection data of each first preset time period collected by each sensor, including at least one of the following:

[0069] The mean, absolute mean, variance, standard deviation, root mean square amplitude, root mean square, peak value, maximum value, minimum value, waveform index, peak index, pulse index, margin index, skewness, and kurtosis of the detection data in the first preset time period.

[0070] The beneficial effects of this application are as follows:

[0071] For the wind vibration data management method based on a two - level architecture provided in the embodiments of this application, regardless of whether the characteristic data corresponding to the original data in the preset first time period collected meets the preset threshold requirements, the industrial control computer in the fan system needs to send the most recently collected original data to the group system at intervals of the second preset time period. This facilitates the group system to store and manage the original data collected by the industrial control computer. When it is necessary to call the data later, it can be directly called to analyze the industrial control computer or the data acquisition module. At the same time, in the embodiments of this application, the characteristic data calculated from the original data in the preset first time period includes the mean, absolute mean, variance, standard deviation, root mean square amplitude, root mean square, peak value, maximum value, minimum value, waveform index, peak index, pulse index, margin index, skewness, and kurtosis of the detection data. These characteristic data are comprehensive and multi - dimensional, thus comprehensively reflecting the operating conditions of the original data or the data acquisition module. In addition, in the group system of the embodiments of this application, after receiving the original data in the target first preset time period, it sends it to the administrator or the front - end, then receives the second analysis case sent by the administrator or the front - end and stores the second analysis case, and then uses the second analysis case to analyze the target original data. At the same time, after storing the second analysis case, when the target data is received again, the stored second analysis case can be directly retrieved, thus ensuring a high efficiency of target data analysis.

[0072] The above description is only an overview of the technical solution of this application. In order to understand the technical means of this application more clearly and implement it according to the content of the specification, the following uses the preferred embodiments of this application and combines the accompanying drawings to describe in detail as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figures 1 - 5 It is a flowchart of the wind vibration data management method based on a two - level architecture provided in the embodiments of this application;

[0074] Figure 6 It is a schematic diagram of the wind vibration data management system based on a two - level architecture provided in the embodiments of this application; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] The following embodiments are used to illustrate this application, but do not limit the scope of this application.

[0076] An embodiment of the present application provides a wind vibration data management method based on a two - level architecture, which is applied to a wind power generation system including a wind farm system, an industrial control computer belonging to the wind farm system, and a group system. Refer to Figure 1 As shown, the method includes:

[0077] Step S21: The industrial control computer in each wind farm system acquires the detection data collected by different sensors in the wind farm, and calculates the characteristic values corresponding to the detection data of each first preset time period collected by each sensor;

[0078] In the embodiment of the present application, the group system is connected to multiple wind farm systems, receives and manages the data collected by the multiple wind farm systems. Each wind farm system includes an industrial control computer, which is connected to the data acquisition modules at various positions of the corresponding wind farm system. Among them, the data acquisition module can be a sensor, that is, the industrial control computer in each wind farm system is connected to the sensors at each position, and then acquires the data collected by each sensor.

[0079] In the embodiment of the present application, the sensors are mainly vibration sensors and speed sensors. Among them, the speed sensor is used to collect the speed of the wind turbine, and the vibration sensor is used to collect the vibration data at the corresponding position of the wind turbine. It should be noted that at different positions of each wind turbine, the installation position and quantity of the vibration sensor are set according to the shape of the current area.

[0080] As a specific embodiment, the data acquisition module collects data for a time period of 15 - 25 s each time, and the specific data acquisition frequency is set according to actual requirements.

[0081] In the embodiment of the present application, after the industrial control computer acquires the detection data collected by different sensors, it calculates the characteristic values corresponding to the detection data of each first preset time period collected by each sensor, so as to understand the correctness of the data collected by each sensor during the current time period.

[0082] Step S23: The industrial control computer in the wind farm system determines whether the characteristic values corresponding to the detection data of each first preset time period collected by each sensor meet the corresponding threshold requirements;

[0083] In the embodiment of the present application, a judgment module is set in the industrial control computer. After the industrial control computer receives the detection data collected by the sensors in the corresponding wind farm within the preset first time period and calculates the corresponding characteristic values, it continues to determine whether the characteristic values corresponding to the detection data within the first preset time period meet the corresponding threshold requirements. If they meet, they can be directly transmitted to the group system. If the second characteristic value does not meet the threshold requirements, then the group system needs to obtain not only the second characteristic data, but also the original data corresponding to the second characteristic data, so as to facilitate the group system to analyze the operating conditions of the wind turbine.

[0084] Step S25: The industrial control computer in the wind farm system sends the target eigenvalue that does not meet the threshold requirement and the original data corresponding to the target eigenvalue to the wind farm system;

[0085] In the embodiment of the present application, the original data corresponding to the target eigenvalue is the data collected by the sensor in the target first preset time period, and the target first preset time period is the time period corresponding to the target eigenvalue. When the industrial control computer determines that the eigenvalue corresponding to the original data collected by a sensor in the target first preset time period does not meet the threshold requirement, it determines that the eigenvalue that does not meet the threshold requirement is the target first eigenvalue, and at the same time sends the original data corresponding to the target first eigenvalue to the wind farm system for the wind farm system to analyze the original data, and further determine the reason why the eigenvalue does not meet the threshold requirement.

[0086] Step S27: The data management module of the group system receives the target eigenvalue sent by each wind farm system and the original data corresponding to the target eigenvalue;

[0087] Step S29: Determine whether there is a target analysis case corresponding to the target eigenvalue in the preset database. If so, select the target analysis case to analyze the original data corresponding to the target eigenvalue.

[0088] In the embodiment of the present application, after the data management module of the group system receives the target eigenvalue sent by the target wind farm system and the original data corresponding to the target eigenvalue, it starts to detect whether there is a target analysis case corresponding to the target eigenvalue in the preset database. If so, it uses the target analysis case to analyze the original data corresponding to the target eigenvalue, and further determines the sensor with a data acquisition failure.

[0089] The wind vibration data management method based on a two - level architecture provided by the embodiments of the present application installs one or more data acquisition modules at positions where data needs to be collected in the wind farm system. Among them, the data acquisition module can be a sensor; the industrial control computer corresponding to the fan system receives the data collected by the corresponding data acquisition module, and determines the corresponding characteristic value through a preset algorithm. When the determined characteristic value meets the requirements of the preset threshold, the characteristic value that meets the requirements of the preset threshold is directly sent to the group system to store the characteristic data that meets the requirements; when the determined characteristic value does not meet the requirements of the preset threshold, the characteristic value that does not meet the requirements of the preset threshold and its corresponding original data are sent to the group system. After receiving the characteristic value that does not meet the requirements of the preset threshold and the corresponding original data, the group system detects whether there is a corresponding target analysis case in the preset database. If there is, the original data of the target sensor corresponding to the second characteristic value that does not meet the requirements of the preset threshold for a target preset time period is analyzed using the target analysis case. In this solution, the industrial control computer performs a preliminary processing process on the data collected by the corresponding data acquisition module. When the characteristic data corresponding to the initial data of the preset time period collected meets the requirements of the preset threshold, only the characteristic data that meets the requirements of the preset threshold is sent for the group system to back up the data collection situation of the data acquisition module. This solution only backs up the characteristic data corresponding to the original data under normal circumstances, effectively saving the storage space of the group system and enabling the group system to back up the data of the fan for a longer time period; when the characteristic data corresponding to the initial data of the preset time period collected does not meet the requirements of the preset threshold, while sending the characteristic data that does not meet the requirements, the corresponding original data is also sent to the group system. When there is a target analysis case in the group system that matches the received original data, the original data within the preset time period is analyzed using the target analysis case. Therefore, for the originally collected data with abnormal operation, the group system determines whether there is a matching target analysis case stored. If there is, the target analysis case is automatically used to analyze it, effectively ensuring the comprehensiveness of the data stored in the group system and the data analysis efficiency. Therefore, this solution effectively ensures the management efficiency of the group system for the wind farm systems under its jurisdiction.

[0090] In the embodiments of the present application, as shown in Figure 2 it is shown that the method further includes:

[0091] Step S26: The industrial control computer sends the original data of each sensor collected for the third preset time period to the wind farm system at intervals of a second preset time period;

[0092] The second preset time period is greater than the first preset time period.

[0093] In the embodiment of the present application, a second preset time period with a relatively long duration is set. Every time the second preset time period is reached, the industrial control computer of the wind farm system sends the original data of the most recent first preset time period to the group system, thereby ensuring that the group system effectively supervises the data collected by the data acquisition module in each wind farm system, and further effectively manages the wind farm system.

[0094] In the embodiment of the present application, the eigenvalue includes at least one of the following:

[0095] The mean value, absolute average value, variance, standard deviation, root mean square amplitude, root mean square, peak value, maximum value, minimum value, waveform index, peak index, pulse index, margin index, skewness, and kurtosis of the detection data in the first preset time period.

[0096] In the embodiment of the present application, the mean value of the detection data in the first time period is expressed as:

[0097]

[0098] The absolute average value is:

[0099]

[0100] The mathematical model used to calculate the variance is:

[0101]

[0102] The mathematical model used to calculate the standard deviation is:

[0103]

[0104] The mathematical model used to calculate the root mean square amplitude is:

[0105]

[0106] The mathematical model used to calculate the root mean square is:

[0107]

[0108] The mathematical model used to calculate the peak value is:

[0109] x p =max|x(n)

[0110] The mathematical model used to calculate the maximum value is:

[0111] x max =max[x(n)]

[0112] The mathematical model used to calculate the minimum value is:

[0113] xmin = min[x(n)]

[0114] The mathematical model for obtaining the waveform index is:

[0115]

[0116] The mathematical model for obtaining the peak index is:

[0117]

[0118] The mathematical model for obtaining the pulse index is:

[0119]

[0120] The mathematical model for obtaining the margin index is:

[0121]

[0122] The mathematical model for obtaining the skewness is:

[0123]

[0124] The mathematical model for obtaining the kurtosis is:

[0125]

[0126] x(n) is the nth detected data, and N is the number of detected data.

[0127] In the application embodiment, refer to Figure 3 As shown, the method further includes:

[0128] Step S210: If there is no target analysis case in the preset database that matches the original data of the target preset time period of the target sensor corresponding to the second eigenvalue, then send the original data of the target preset time period of the target sensor corresponding to the second eigenvalue, receive and store the second analysis case corresponding to the original data of the target preset time period of the target sensor corresponding to the second eigenvalue, and perform analysis using the second analysis case and the original data of the target preset time period of the target sensor corresponding to the second eigenvalue.

[0129] In the application embodiment, if there is no target analysis case in the preset database of the group system that matches the original data of the target preset time period of the target sensor corresponding to the second eigenvalue, the original data of the target preset time period of the target sensor corresponding to the second eigenvalue can be sent to the front end or the administrator. After receiving the original data, the administrator or the front end gives a suitable second analysis case based on the original data. The group system receives the second analysis case and uses it to analyze the corresponding original data.

[0130] As a specific embodiment, after receiving the second analysis case, the group system also stores the second analysis case, the corresponding original data, and the mapping relationship between the second analysis case and the corresponding original data. Then, when the original data is received again, it can directly use the stored second analysis case to analyze the corresponding original data without sending it to the administrator or the front end again, effectively ensuring a high management efficiency of the group system for the wind farm system.

[0131] Furthermore, in the application embodiment, refer to Figure 4 As shown, in step S29, determining whether there is a target analysis case in the preset database that matches the original data of the target preset time period of the target sensor corresponding to the second eigenvalue. If so, selecting the target analysis case to analyze the original data of the target preset time period of the target sensor corresponding to the second eigenvalue includes:

[0132] S291. Determine whether there is a target analysis case in the preset database that matches the original data of the target first preset time period of the target sensor corresponding to the second eigenvalue. If so, display the target analysis case. When a usage command for the target analysis scheme is received within the fourth preset time period, use the target analysis scheme to analyze the original data of the target preset time period of the target sensor corresponding to the second eigenvalue and generate a processing scheme.

[0133] In the application embodiment, when the group system receives the original data within the target first preset time from the wind turbine system, it determines whether there is a target analysis case in the preset database that matches the received original data based on the received original data this time. Here, it should be noted that the original data, the target analysis case, and the corresponding relationship between the original data and the target analysis case can be stored in the preset database. The target analysis case is to analyze the original data using a target analysis model, and present the obtained analysis result in a preset form.

[0134] In the application embodiment, if the judgment result of the group system is that there is a target analysis case in the preset database that corresponds to the original data received this time, it is displayed on the display interface.

[0135] In an embodiment of the present application, after the target analysis case is displayed on the display interface, a button for receiving a user's touch operation is simultaneously displayed on the display interface. When the specified button receives the user's touch operation within the fourth preset time period, the target analysis solution is used to analyze the original data, and a processing solution is generated.

[0136] In an embodiment of the present application, as shown in Figure 5 the method further includes:

[0137] Step S292: When a usage command for the target analysis solution is not received within the fourth preset time period, the original data of the target preset time period of the target sensor corresponding to the second eigenvalue is sent, the third analysis case corresponding to the target analysis case matching the original data of the target preset time period of the target sensor corresponding to the second eigenvalue is received and stored, and the original data of the target preset time period of the target sensor corresponding to the third analysis case and the second eigenvalue is analyzed.

[0138] In an embodiment of the present application, if the specified button does not receive the user's touch operation within the fourth preset time period, a prompt message may be sent to alert the user to send an operation command in a timely manner, so as to analyze the original data.

[0139] In summary, for the wind vibration data management method based on a two - level architecture provided by the embodiments of the present application, regardless of whether the characteristic data corresponding to the original data within the preset first time period meets the preset threshold requirements, the industrial control computer in the fan system needs to send the most recently collected original data to the group system at intervals of a second preset time period. This facilitates the group system to store and manage the original data collected by the industrial control computer. When it is necessary to call the data later, it can be directly called to analyze the industrial control computer or the data acquisition module. At the same time, in the embodiments of the present application, the characteristic data calculated from the original data in the preset first time period includes the mean value, absolute mean value, variance, standard deviation, root - mean - square amplitude, root - mean - square value, peak value, maximum value, minimum value, waveform index, peak index, impulse index, margin index, skewness, and kurtosis of the detection data. These characteristic data are comprehensive and multi - dimensional, thus comprehensively reflecting the operating conditions of the original data or the data acquisition module. In addition, in the group system of the embodiments of the present application, after receiving the original data within the target first preset time period, it sends the data to the administrator or the front - end, then receives the second analysis case sent by the administrator or the front - end and stores the second analysis case, and then uses the second analysis case to analyze the target original data. At the same time, after storing the second analysis case, when the target data is received again, the stored second analysis case can be directly retrieved, thus ensuring a high target data analysis efficiency. In the embodiments of the present application, each analysis case uses a corresponding mathematical analysis model to perform mathematical analysis on the corresponding original data received, and then obtains the corresponding analysis result. The analysis result can be in the form of a numerical value or an analysis curve, etc.

[0140] Embodiment 2

[0141] Figure 6 FIG. is a block diagram of a wind vibration data management system based on a two - level architecture provided by an embodiment of the present application. As shown in the figure, the wind vibration data management system based on a two - level architecture also includes a wind farm system, an industrial control computer in the wind farm system, and a group system. Among them, each wind farm system includes its corresponding industrial control computer, and in the group system, a data management module is provided. Specifically, the functions of each module at least include:

[0142] The industrial control computer 71 in each wind farm system is used for

[0143] The data acquisition module 711 is used to acquire the detection data collected by different sensors in the wind farm and calculate the characteristic values corresponding to the detection data of each first preset time period collected by each sensor;

[0144] The first judgment module 712 is used to judge whether the characteristic values corresponding to the detection data of each first preset time period collected by each sensor meet the corresponding threshold requirements;

[0145] A sending module 713, configured to send a second eigenvalue that does not meet the threshold requirement and the original data of the sensor corresponding to the second eigenvalue in a target first preset time period to a group system;

[0146] The group system 72 includes:

[0147] A data receiving module 721, configured to receive the second eigenvalue sent by each wind farm system and the original data of the target sensor corresponding to the second eigenvalue in a target preset time period;

[0148] A second determination module 722, configured to determine whether there is a target analysis case in a preset database that matches the original data of the target sensor corresponding to the second eigenvalue in a target preset time period;

[0149] An analysis module 723, configured to, if there is a target analysis case that matches the original data of the target sensor corresponding to the second eigenvalue in a target preset time period, select the target analysis case to analyze the original data of the target sensor corresponding to the second eigenvalue in a target preset time period.

[0150] In an embodiment of the present application, the industrial control computer in each wind farm system is further configured to:

[0151] Every second preset time period, send the original data of each sensor collected in a third preset time period to the wind farm system.

[0152] In an embodiment of the present application, the industrial control computer in each wind farm system calculates the eigenvalue corresponding to the detection data of each first preset time period collected by each sensor, including at least one of the following:

[0153] The mean value, absolute average value, variance, standard deviation, root mean square amplitude, root mean square value, peak value, maximum value, minimum value, waveform index, peak index, pulse index, margin index, skewness, and kurtosis of the detection data in the first preset time period.

[0154] The wind vibration data management method based on a two - level architecture provided by the embodiments of the present application requires the industrial control computer in the fan system to send the most recently collected original data to the group system at intervals of a second preset time period regardless of whether the characteristic data corresponding to the original data within a preset first time period meets the preset threshold requirements. This facilitates the storage and management of the original data collected by the industrial control computer in the group system. When it is necessary to call the data later, it can be directly called for analysis of the industrial control computer or the data acquisition module. At the same time, in the embodiments of the present application, the characteristic data calculated from the original data within the preset first time period includes the mean value, absolute mean value, variance, standard deviation, root - mean - square amplitude, root - mean - square value, peak value, maximum value, minimum value, waveform index, peak index, impulse index, margin index, skewness, and kurtosis of the detection data. These characteristic data are comprehensive and multi - dimensional, thus comprehensively reflecting the operating conditions of the original data or the data acquisition module. In addition, in the group system of the embodiments of the present application, after receiving the original data within a target first preset time period, it sends the data to the administrator or the front - end, then receives the second analysis case sent by the administrator or the front - end and stores the second analysis case, and then analyzes the target original data using the second analysis case. At the same time, after storing the second analysis case, when the target data is received again, the stored second analysis case can be directly retrieved, thus ensuring a high efficiency of target data analysis.

[0155] The wind vibration data management system based on a two - level architecture provided by the embodiments of the present application can be used for the method executed by the business application system in the above - mentioned embodiments. For relevant details, refer to the above - mentioned method embodiments. Their implementation principles and technical effects are similar and will not be elaborated here.

[0156] It should be noted that: The wind vibration data management method, device, and storage medium embodiments based on a two - level architecture provided in the above - mentioned embodiments belong to the same concept. For the specific implementation process, refer to the method embodiments and will not be elaborated here.

[0157] Those skilled in the art can understand that the device described in this embodiment is only an example of the wind vibration data management device based on a two - level architecture, and does not constitute a limitation on the wind vibration data management device based on a two - level architecture. In other implementation manners, it may also include more or fewer components, or combine certain components, or different components. For example, the wind vibration data management device based on a two - level architecture may also include input - output devices, network access devices, buses, etc. The processor, memory, and peripheral device interface can be connected through a bus or signal line. Each peripheral device can be connected to the peripheral device interface through a bus, signal line, or circuit board. Schematically, the peripheral devices include, but are not limited to: radio frequency circuits, touch display screens, audio circuits, and power supplies, etc.

[0158] Of course, the wind vibration data management device based on the two-level architecture may also include fewer or more components, which is not limited in this embodiment.

[0159] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0160] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A wind vibration data management method based on a two - level architecture, which is applied to a wind power generation system including a wind farm system, an industrial control computer belonging to the wind farm system, and a group system. Characterized in that, The method includes: The industrial control computer in each wind farm system acquires the detection data collected by different sensors in the wind farm, and calculates the characteristic values corresponding to the detection data of each first preset time period collected by each sensor. The industrial control computer in the wind farm system determines whether the characteristic values corresponding to the detection data of each first preset time period collected by each sensor meet the corresponding threshold requirements. The industrial control computer in the wind farm system sends the second characteristic values that do not meet the threshold requirements and the original data of the sensors corresponding to the second characteristic values in the target first preset time period to the wind farm system. The data management module of the group system receives the second characteristic values sent by each wind farm system and the original data of the target preset time period of the target sensors corresponding to the second characteristic values. The data management module of the group system determines whether there is a target analysis case in the preset database that matches the original data of the target sensors corresponding to the second characteristic values in the target preset time period. If so, the target analysis case is selected to analyze the original data of the target sensors corresponding to the second characteristic values in the target preset time period, including: The data management module of the group system determines whether there is a target analysis case in the preset database that matches the original data of the target sensors corresponding to the second characteristic values in the target preset time period. If so, the target analysis case is displayed through the display module, and when a usage command for the target analysis scheme is received within the fourth preset time period, the target analysis scheme is used to analyze the original data of the target sensors corresponding to the second characteristic values and a processing scheme is generated. If there is no target analysis case in the preset database that matches the original data of the target sensors corresponding to the second characteristic values in the target preset time period, then: the data processing module of the group system sends the original data of the target sensors corresponding to the second characteristic values in the target preset time period, receives and stores the second analysis case that matches the original data of the target sensors corresponding to the second characteristic values in the target preset time period, and uses the second analysis case to analyze the original data of the target sensors corresponding to the second characteristic values. When the data management module of the group system does not receive a usage command for the target analysis scheme within the fourth preset time period, it sends the original data of the target sensors corresponding to the second characteristic values in the target preset time period, receives and stores the third analysis case corresponding to the target analysis case that matches the original data of the target sensors corresponding to the second characteristic values in the target preset time period, and uses the third analysis case to analyze the original data of the target sensors corresponding to the second characteristic values.

2. The method according to claim 1, Characterized in that, The method further includes: Every second preset time period, the industrial control computer sends the original data collected by each sensor regarding the third preset time period to the wind farm system.

3. The method according to claim 1 or 2, characterized in that the characteristic value includes at least one of the following: the mean value, absolute average value, variance, standard deviation, root mean square amplitude, root mean square, peak value, maximum value, minimum value, waveform index, peak index, pulse index, margin index, skewness and kurtosis of the detection data in the first preset time period.

4. The method according to claim 3, characterized in that the calculation model for the mean value of the detection data in the first preset time period is: the calculation model for the absolute average value of the detection data in the first preset time period is: the calculation model for the variance of the detection data in the first preset time period is: the calculation model for the standard deviation of the detection data in the first preset time period is: the calculation model for the root mean square amplitude of the detection data in the first preset time period is: the calculation model for the root mean square of the detection data in the first preset time period is: the mathematical model adopted for the peak value of the detection data in the first preset time period is: x p = max|x(n)| the calculation model for the maximum value of the detection data in the first preset time period is: x max = max(x n ) the calculation model for the minimum value of the detection data in the first preset time period is: x min = min(x n ) the calculation model for the waveform index of the detection data in the first preset time period is: the calculation model for the peak index of the detection data in the first preset time period is: the calculation model for the pulse index of the detection data in the first preset time period is: the calculation model for the margin index of the detection data in the first preset time period is: the calculation model for the skewness of the detection data in the first preset time period is: the calculation model for the kurtosis of the detection data in the first preset time period is: x(n) is the nth detection data, and N is the number of detection data.

5. A wind vibration data management system based on a two-level architecture, applied to a wind power generation system including a wind farm system, an industrial control computer belonging to the wind farm system, and a group system, characterized in that the system includes: the industrial control computer in each wind farm system, used for a data acquisition module, used to acquire the detection data collected by different sensors in this wind farm, and calculate the characteristic values corresponding to the detection data of each first preset time period collected by each sensor; a first judgment module, used to judge whether the characteristic values corresponding to the detection data of each first preset time period collected by each sensor meet the corresponding threshold requirements; a sending module, used to send the second characteristic values that do not meet the threshold requirements and the original data of the sensors corresponding to the second characteristic values in the target first preset time period to the group system; the group system includes: a data receiving module, used to receive the second characteristic values sent by each wind farm system, and the original data of the target sensors corresponding to the second characteristic values in the target preset time period; a second judgment module, used to determine whether there is a target analysis case in the preset database that matches the original data of the target sensors corresponding to the second characteristic values in the target preset time period; An analysis module, configured to, if there is a target analysis case that matches the original data of the target preset time period of the target sensor corresponding to the second eigenvalue, select the target analysis case to analyze the original data of the target preset time period of the target sensor corresponding to the second eigenvalue, including: The data management module of the group system determines whether there is a target analysis case in the preset database that matches the original data of the target preset time period of the target sensor corresponding to the second eigenvalue; if so, the target analysis case is displayed through the display module, and when a usage command for the target analysis solution is received within the fourth preset time period, the original data of the target preset time period of the target sensor corresponding to the second eigenvalue is analyzed using the target analysis solution, and a processing solution is generated; If there is no target analysis case in the preset database that matches the original data of the target preset time period of the target sensor corresponding to the second eigenvalue, then: the data processing module of the group system sends the original data of the target preset time period of the target sensor corresponding to the second eigenvalue, receives and stores a second analysis case that matches the original data of the target preset time period of the target sensor corresponding to the second eigenvalue, and analyzes the original data of the target preset time period of the target sensor corresponding to the second eigenvalue using the second analysis case; When the data management module of the group system does not receive a usage command for the target analysis solution within the fourth preset time period, it sends the original data of the target preset time period of the target sensor corresponding to the second eigenvalue, receives and stores a third analysis case corresponding to the target analysis case that matches the original data of the target preset time period of the target sensor corresponding to the second eigenvalue, and analyzes the original data of the target preset time period of the target sensor corresponding to the second eigenvalue using the third analysis case.

6. The wind vibration data management system based on a two-level architecture according to claim 5, wherein, The sending module of the industrial control computer in each wind farm system is further configured to: Every second preset time period, send the original data collected by each sensor about the third preset time period to the wind farm system.

7. The wind vibration data management system based on a two-level architecture according to claim 5, wherein, The industrial control computer in each wind farm system calculates the eigenvalue corresponding to the detection data of each first preset time period collected by each sensor, including at least one of the following: The mean value, absolute average value, variance, standard deviation, root mean square amplitude, root mean square value, peak value, maximum value, minimum value, waveform index, peak index, pulse index, margin index, skewness, and kurtosis of the detection data of the first preset time period.

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