A mountain wind power generation platform based on the Internet of Things

Abstract

The invention relates to the technical field of wind energy power generation, and specifically discloses a mountain wind power generation platform based on the Internet of Things, which comprises: a platform body, an Internet of Things integration and an intelligent control system; the platform body is equipped with a communication module and a sensor module; the platform body comprises a wind power tower arranged at the bottom and a wind turbine generator set installed at the top; the Internet of Things integration is used to ensure that the platform body can realize remote monitoring and management through the Internet of Things technology; the intelligent control system is used to: analyze the operating status of the wind turbine generator set according to the real-time monitoring data of the sensor module and transmit it to a remote server; the remote server responds to generate optimization adjustment information and transmits it to the communication module, the communication module receives the optimization adjustment information and generates a control signal; the wind turbine generator set receives the control signal to control and adjust the wind power generation; the communication module is also used to receive the alarm signal of the remote server and generate the alarm information; and improve the safe operation efficiency of the equipment of the mountain wind power generation platform.

Description

Technical Field

[0001] The present invention relates to the technical field of wind energy power generation, and in particular to a mountain wind power generation platform based on the Internet of Things. Background Art

[0002] As a clean and renewable energy, wind power generation has broad development prospects. As a low-carbon, clean and renewable resource, wind power generation can provide clean energy, improve energy supply, drive rural economic development, improve farmers' income levels, increase employment opportunities, and promote rural industrial upgrading and transformation. Wind power generation has become an important choice in agricultural development and is of great significance for vigorously promoting the sustainable development of agriculture and ecological protection.

[0003] However, traditional wind power generation facilities are mainly installed in plains or coastal areas, while the application of wind power generation in mountainous areas is subject to certain restrictions due to complex terrain and large changes in wind speed. In addition, the existing wireless transmission technology of power station signals is subject to inaccurate data due to interference from different mountain terrains, long transmission distances, and high power consumption of required equipment. Therefore, the development of a wind power generation platform suitable for mountainous environments is of great significance to improving the utilization rate of wind energy resources.

[0004] The existing platform for wind power generation equipment is developed through the Internet of Things technology to achieve intelligent control of the platform; however, the intelligent control in the existing technology is affected by the mountainous terrain and environment where the platform is located, and cannot timely transmit information in real time according to the terrain conditions at the time, resulting in a time delay between the data transmitted to the main service desk and the real-time platform data, which can easily lead to malfunctions of wind power generation equipment and the inability to timely troubleshoot, process and issue early warnings. Summary of the invention

[0005] The purpose of the present invention is to provide a mountain wind power generation platform based on the Internet of Things to solve the following technical problems:

[0006] How to improve the equipment safety of mountain wind power generation platforms and the timely and accurate acquisition of operating status data, and improve the utilization rate of mountain wind energy resources.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A mountain wind power generation platform based on the Internet of Things, comprising:

[0009] Platform entity, IoT integration and intelligent control system;

[0010] The platform body is made of lightweight materials to facilitate installation in mountainous terrain. The platform body is equipped with a communication module and a sensor module. The communication module is used to establish a connection with a remote server to receive and feedback signal data. The sensor module is used to monitor the operating status of the platform body and transmit real-time monitoring data to the remote server.

[0011] The platform body includes a wind turbine tower at the bottom and a wind turbine generator set installed on the top; the wind turbine tower is used to support the wind turbine generator set and absorb the vibration of the wind turbine generator set, and obtain the real-time vibration frequency according to the sensor module; the wind turbine generator set is used to generate wind power on the mountain;

[0012] IoT integration is used to ensure that the platform entity can be remotely monitored and managed through IoT technology;

[0013] Intelligent control systems are used to:

[0014] Analyze the operating status of the wind turbine generator set based on the real-time monitoring data of the sensor module, and transmit the analysis results to the remote server;

[0015] The remote server generates optimization adjustment information in response and transmits it to the communication module, and the communication module receives the optimization adjustment information and generates a control signal;

[0016] The wind turbine generator set receives the control signal to control and adjust the wind power generation;

[0017] The communication module is also used to receive an alarm signal from a remote server and generate alarm information.

[0018] Preferably, the sensor module acquires the vibration frequency in real time in the following manner:

[0019] The real-time vibration sound waves of the wind turbine tower at its current height position are obtained through MEMS acoustic sensors;

[0020] The real-time vibration sound waves are converted into electrical signals through high-frequency oscillators, modulators and detectors, and the vibration frequency is obtained by analysis;

[0021] The vibration frequency is subjected to data parameterization processing to obtain vibration frequency parameters.

[0022] Preferably, the sensor module further comprises:

[0023] An angle sensor is arranged at a vibration frequency detection position of the wind turbine tower and is used to measure the tilt angle of the wind turbine tower;

[0024] The distance sensor is arranged at the vibration frequency detection position of the wind turbine tower and is used to measure the vertical height from the ground to the detection position.

[0025] Preferably, the intelligent control system comprises:

[0026] A data acquisition module, used to collect a data set of real-time vibration frequency parameters of the sensor module;

[0027] A data analysis module is used to obtain the change state of vibration frequency parameters within a period of time to analyze the real-time operation risk factor of the wind turbine generator set;

[0028] A data control module is used to control the wind turbine generator set according to the real-time operation risk coefficient;

[0029] The alarm module is used to send out a first alarm signal and a second alarm signal according to the safety status of the wind turbine generator set.

[0030] Preferably, the method for the data analysis module to obtain the real-time operation risk coefficient is:

[0031] By formula:

[0032] Calculate the time period Operational risk factor within ;

[0033] in, , , is the preset weight coefficient, and , , All are greater than 0; is the vibration frequency parameter; The power generation of the wind turbine; is the power generation temperature value of the wind turbine generator set; is the standard value of vibration frequency parameter ； is the standard value of wind turbine power generation; is the standard temperature value of the wind turbine generator set; is the preset wind turbine generator set power generation deviation value; It is the preset temperature deviation value of the wind turbine generator set; A preset first conversion function corresponding to a change in the vibration frequency parameter; A preset second conversion function corresponding to the change in power generation of the wind turbine generator set; A preset third conversion function corresponding to the temperature change of the wind turbine generator set; The position coefficient for the current vibration frequency detection position.

[0034] Preferably, is the judgment function, judging when > ,but = ;

[0035] otherwise, ;

[0036] in, For the current time period The corresponding allowed range value; is the warning value;

[0037] Set time period Operational risk factor within With preset threshold To compare:

[0038] like ≥ , it is determined that there is a safety risk in the current operation state of the wind turbine generator set, and the intelligent control system controls the generator to stop generating electricity and generates a control strategy and a first alarm signal.

[0039] Preferably, it also includes:

[0040] when = When: Formula ;

[0041] Set time period Operational risk factor within With preset threshold To compare:

[0042] like < , it is determined that there is a safety risk in the current operation state of the wind turbine generator set, and the data control module generates a control strategy; otherwise, the alarm module generates a second alarm signal.

[0043] Preferably, the position coefficient The way to obtain is:

[0044] Get the tilt angle of the vibration frequency detection position of the wind turbine tower in the historical time period Parameters and vertical height parameters A collection of cumulative values ​​over time ;gather ∈ ,in, is the total number of time points; is the cumulative value parameter;

[0045] Analyze the collection of several consecutive time periods The size of the coefficient of change with the period size; ∈ ,in, For continuous cycles Total number of;

[0046] Will With the preset threshold coefficient To compare:

[0047] like > , it is judged that the current wind turbine tower position has changed significantly, and an early warning signal is generated and maintenance is carried out;

[0048] like ≤ , then it is judged that the current wind tower position change is normal, and the change curve of the product value of the inclination angle parameter and the vertical height parameter corresponding to all current time points is obtained as the standard curve ;

[0049] For standard curve Take the derivative The slope obtained is the position coefficient .

[0050] Preferably, the platform body also includes:

[0051] The platform body is also equipped with an energy storage device to store excess electrical energy;

[0052] The platform is also equipped with a power dispatching system to automatically adjust the output and storage strategies of electricity according to the needs of the power grid and the power generation situation, so as to achieve optimal utilization of energy.

[0053] Beneficial effects of the present invention:

[0054] (1) The present invention realizes the safe and continuous operation process of the mountain wind power generation platform based on the Internet of Things by setting up a platform body, Internet of Things integration, and an intelligent control system. The intelligent control of the platform body is realized through the Internet of Things integration, ensuring the timely acquisition of the operation and safety status of the wind turbine tower and wind turbine generator set of the mountain wind power generation platform body, and the digital real-time acquisition of the equipment status is realized through the intelligent control system to ensure the normal operation of the equipment and the real-time acquisition of the local mountain environment status information. By operating the wind power generation platform in combination with the Internet of Things technology, interactive information transmission is realized to ensure the timely supply of electricity, while increasing the flexibility and interactivity of the operation between various systems, improving work efficiency and improving the communication efficiency between platforms.

[0055] (2) The present invention is designed to determine whether the result of the vibration frequency position detection in advance conforms to the objective law of equipment operation safety currently obtained. In the actual measurement process, the inclination angle and vertical height of the wind turbine tower are obtained by various sensors; and according to the length and height of the blades of the wind turbine on the mountain, the unfavorable environmental factors are determined in advance; when the inclination exceeds the set threshold, it may directly affect the life and operating performance of the wind turbine, and also cause the data reception and transmission speed to be smooth or interrupted; therefore, the intelligent control system will automatically adjust the working state of the generator set to avoid overload operation; when equipment failure is detected, the intelligent control system will promptly issue an alarm and take corresponding measures to deal with it.

[0056] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0058] Figure 1 This is a schematic diagram of the structure of a mountain wind power generation platform based on the Internet of Things in the present invention;

[0059] Figure 2 This is a module diagram of the intelligent control system of the present invention. DETAILED DESCRIPTION

[0060] 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.

[0061] See also Figure 1 As shown, the present invention is a mountain wind power generation platform based on the Internet of Things, which specifically includes: a platform body, an Internet of Things integration and an intelligent control system;

[0062] Since traditional wind power generation facilities are mostly built in plains or coastal areas, the application of wind power generation in mountainous areas is limited by factors such as complex terrain and variable wind speed; therefore, this design uses lightweight materials to manufacture the platform body to adapt to the installation requirements of mountainous terrain; since the process of wind power generation in the mountains requires a relatively long journey, it is not conducive to the smooth transportation and construction of equipment by humans for heavier structures; in addition, the use of lightweight materials not only contributes to the normal operation of wind power generation equipment, but also reduces the requirements for wind strength, ensuring that wind power generation can be carried out smoothly under conventional wind speeds.

[0063] In addition, the platform body of the present design is equipped with a communication module and a sensor module; the communication module is set up to establish a connection with a remote server to realize signal data reception and feedback. Since data and information need to be exchanged between wind power base station sites established on the mountain and between the sites and the headquarters, the communication module is used to ensure the exchange and timely transmission of information; the sensor module is set up to monitor the operating status of the platform body and transmit the real-time monitoring data to the remote server; the various sensors set up on the platform body are conducive to real-time information collection and acquisition of wind force obtained on site and the operating status of the platform, ensuring the accurate acquisition of on-site data signals and real-time processing of data signals.

[0064] The platform body of this design is mainly composed of the wind turbine tower at the bottom and the wind turbine generator set at the top; the wind turbine tower of this design also includes a base and a connected power supply system; the wind turbine generator set is set up for mountain wind power generation, and the wind turbine generator set includes blades and blade automatic correction devices, gear boxes, generators, distribution controllers, etc.; to ensure the normal wind power generation needs of the entire platform body. The wind turbine tower is used to support the wind turbine generator set and absorb the vibration and vibration information of the wind turbine generator set. The acquisition of vibration information is mainly based on the real-time vibration frequency obtained by the sensor module.

[0065] It should be further explained that since the terrain and landform structure on the mountain are different from those in the plains, it is difficult to directly measure the wind speed direction and wind speed changes. In order to normally detect the acquired wind speed characteristics, this design uses the monitoring changes of vibration frequency instead of directly detecting wind speed changes to ensure that the overall information collection of wind power generation is more reasonable and accurate.

[0066] In addition to the platform body, Internet of Things technology is also integrated to achieve remote monitoring and management of the platform.

[0067] The functions of the intelligent control system include:

[0068] First, the operating status of the wind turbine is analyzed based on the real-time monitoring data of the sensor module, and the analysis results are transmitted to the remote server;

[0069] Second, the remote server generates optimization adjustment information in response and transmits it to the communication module, and the communication module receives the optimization adjustment information and generates a control signal;

[0070] Thirdly, the wind turbine generator set receives the control signal to control and adjust the wind power generation;

[0071] Fourthly, the communication module is also used to receive an alarm signal from a remote server and generate alarm information.

[0072] In the above technical scheme, in this embodiment, the safe and continuous operation process of the mountain wind power generation platform based on the Internet of Things is realized by setting up a platform body, Internet of Things integration, and an intelligent control system. The intelligent control of the platform body is realized through the Internet of Things integration, ensuring the timely acquisition of the operation and safety status of the wind turbine tower and wind turbine generator set of the mountain wind power generation platform body, and the real-time acquisition of the equipment status is realized through the intelligent control system, ensuring the normal operation of the equipment and the real-time acquisition of the local mountain environment status information. By combining the Internet of Things technology to operate the wind power generation platform, interactive information transmission is realized to ensure the timely supply of electricity, while increasing the flexibility and interactivity of the operation between various systems, improving work efficiency and improving the communication efficiency between platforms.

[0073] As an implementation mode of the present invention, the sensor module acquires the vibration frequency in real time in the following manner:

[0074] The real-time vibration sound waves of the wind turbine tower at its current height position are obtained through MEMS acoustic sensors;

[0075] The real-time vibration sound waves are converted into electrical signals through high-frequency oscillators, modulators and detectors, and the vibration frequency is obtained by analysis;

[0076] The vibration frequency is subjected to data parameterization processing to obtain vibration frequency parameters.

[0077] In the above technical scheme, the specific method for the sensor module in this embodiment to obtain the data of the vibration frequency is: first, the real-time vibration sound wave of the current height position of the wind turbine tower is obtained through the MEMS acoustic sensor; the MEMS acoustic sensor is a MEMS microsystem for acoustic signal acquisition, including sound power and sound intensity, and can detect up to 24 different frequencies, then, the real-time vibration sound wave is converted into an electrical signal through a high-frequency oscillator, a modulator and a detector, and the data is classified and calculated according to the data and output, and the output data is the vibration frequency; finally, the vibration frequency is parameterized and represented in the form of vibration frequency parameters. The MEMS acoustic sensor of the sensor module can classify and calculate the sound waves emitted between two discrete small transistors; then the data is output to the sensor control unit for processing and outputting the results, and the real-time and accurate acquisition of the vibration frequency parameters is ensured by acquiring and processing the sound waves.

[0078] As an embodiment of the present invention, the sensor module further includes:

[0079] An angle sensor is arranged at a vibration frequency detection position of the wind turbine tower and is used to measure the tilt angle of the wind turbine tower;

[0080] The distance sensor is arranged at the vibration frequency detection position of the wind turbine tower and is used to measure the vertical height from the ground to the detection position.

[0081] In the above technical scheme, the sensor module in this embodiment also includes various types of sensor modules that cooperate with each other, mainly including an angle sensor for measuring the inclination angle of the wind turbine tower, and the angle sensor is arranged at the vibration frequency detection position of the wind turbine tower, and also includes a distance sensor for measuring the vertical height from the ground to the detection position, and the distance sensor is arranged at the vibration frequency detection position of the wind turbine tower.

[0082] It should be noted that the MEMS acoustic sensor in this embodiment needs to be connected to multiple sensors to realize data monitoring, wireless communication and automatic detection functions. Therefore, it needs to be specifically set according to the main vibration sound wave information obtained from the specific mountain wind power generation platform. Therefore, the MEMS acoustic sensor in this embodiment needs to have the characteristics of intelligence, integration and miniaturization.

[0083] As an embodiment of the present invention, please refer to Figure 2 As shown, the intelligent control system includes:

[0084] A data acquisition module, used to collect a data set of real-time vibration frequency parameters of the sensor module;

[0085] A data analysis module is used to obtain the change state of vibration frequency parameters within a period of time to analyze the real-time operation risk factor of the wind turbine generator set;

[0086] A data control module is used to control the wind turbine generator set according to the real-time operation risk coefficient;

[0087] The alarm module is used to send out a first alarm signal and a second alarm signal according to the safety status of the wind turbine generator set.

[0088] In the above technical scheme, the basis for collecting vibration frequency parameters and analyzing the real-time operation risk coefficient of the wind turbine generator set through the change state of the vibration frequency parameters in this embodiment is that due to the uncertainty of the topography and topography, the wind speed on the mountain cannot be directly measured by the rotation speed of the wind turbine blades to determine its impact on the operation risk of the entire equipment. For the entire platform body, the overall operation safety can be analyzed through the detection of the operating status of the wind turbine tower and the wind turbine generator set. Therefore, the stability of the wind turbine tower and its impact on the safety of the wind turbine generator set can be directly determined by the vibration that mainly affects the normal fixed state of the wind turbine tower.

[0089] As an implementation of the present invention, the method for the data analysis module to obtain the real-time operation risk coefficient is:

[0090] By formula:

[0091] Calculate the time period Operational risk factor within ;

[0092] in, , , is the preset weight coefficient, and , , All are greater than 0; is the vibration frequency parameter; The power generation of the wind turbine; is the power generation temperature value of the wind turbine generator set; is the standard value of vibration frequency parameter ； is the standard value of wind turbine power generation; is the standard temperature value of the wind turbine generator set; is the preset wind turbine generator set power generation deviation value; It is the preset temperature deviation value of the wind turbine generator set; A preset first conversion function corresponding to a change in the vibration frequency parameter; A preset second conversion function corresponding to the change in power generation of the wind turbine generator set; A preset third conversion function corresponding to the temperature change of the wind turbine generator set; The position coefficient for the current vibration frequency detection position.

[0093] In the above technical solution, the data analysis module in this embodiment obtains the real-time operation risk coefficient by calculating the time period obtained by the formula Operational risk factor within The operation risk coefficient can be used to predict and judge the current operation safety risk status of the entire platform, specifically by obtaining the vibration frequency parameters of the set position of the wind turbine tower. The power generation corresponding to the operation of the wind turbine and the power generation temperature during the operation of the wind turbine The safety of the operating status is judged by comparison, and then the operating risk coefficient within the measured time period is determined. The size of the operation risk is judged based on the size of the operation risk.

[0094] It should be noted that the preset weight coefficient , , They are respectively determined by fitting according to the historical vibration frequency, power generation and the power generation temperature during the operation of the wind turbine generator set, and the importance of the safety risk of the entire wind power generation platform operation, and are obtained based on empirical data, which will not be detailed here; conversion function , , All of them are obtained based on the data fitting of the normal operation of the wind power platform equipment. Therefore, by substituting the current vibration frequency parameters , the power generation corresponding to the operation of wind turbines , Power generation temperature during operation of wind turbines The corresponding real-time parameter change value is used to determine the current function and issue an early warning for the existing safety risks in the operation of the power generation platform.

[0095] In addition, the time period The settings are also selected based on historical inspection data, and can be adaptively adjusted according to the current operation cycle of wind power generation equipment and the current wind force changes in the mountain environment; , , All are standard data obtained from a machine simulation analyzer under normal wind power generation operation conditions according to the magnitude of the values ​​corresponding to the current power generation operation equipment; , They are all obtained through real-time analysis of the network training based on conventional data, and are set in advance based on the historical database, which will not be described in detail here.

[0096] As an embodiment of the present invention, is the judgment function, judging when > ,but = ;

[0097] otherwise, ;

[0098] in, For the current time period The corresponding allowed range value; is the warning value;

[0099] Set time period Operational risk factor within With preset threshold To compare:

[0100] like ≥ , it is determined that there is a safety risk in the current operation state of the wind turbine generator set, and the intelligent control system controls the generator to stop generating electricity and generates a control strategy and a first alarm signal.

[0101] As an embodiment of the present invention, it also includes:

[0102] when = When: Formula ;

[0103] Will Operational risk factor within the time period With preset threshold To compare:

[0104] like < , it is determined that there is a safety risk in the current operation state of the wind turbine generator set, and the data control module generates a control strategy; otherwise, the alarm module generates a second alarm signal.

[0105] In the above technical solution, in this embodiment, when the judgment function With warning value When they are equal, the warning value The size of must ensure the corresponding calculated operation risk coefficient Generally not less than the preset threshold ; If it is less than, further analysis is carried out to determine whether there is a safety risk in the current operation status of the wind turbine, and the data control module generates a control strategy.

[0106] As an embodiment of the present invention, the position coefficient The way to obtain is:

[0107] Get the tilt angle of the vibration frequency detection position of the wind turbine tower in the historical time period Parameters and vertical height parameters A collection of cumulative values ​​over time ;gather ∈ ,in, is the total number of time points; is the cumulative value parameter;

[0108] Analyze the collection of several consecutive time periods The size of the coefficient of change with the period size; ∈ ,in, For continuous cycles Total number of;

[0109] Will With the preset threshold coefficient To compare:

[0110] like > , it is judged that the current wind turbine tower position has changed significantly, and an early warning signal is generated and maintenance is carried out;

[0111] like ≤ , then it is judged that the current wind tower position change is normal, and the change curve of the product value of the inclination angle parameter and the vertical height parameter corresponding to all current time points is obtained as the standard curve ;

[0112] For standard curve Take the derivative The slope obtained is the position coefficient .

[0113] In the above technical solution, the position coefficient is The specific method of obtaining is as follows: First, obtain the inclination angle of the vibration frequency detection position of the wind turbine tower in the historical time period Parameters and vertical height parameters A collection of cumulative values ​​over time ; Among them, the set ∈ ,in, is the total number of time points, which is a positive integer; is the cumulative value parameter, The cumulative value parameter after weighting the total amount of cumulative changes and the importance of the cumulative changes on the current equipment; then, analyze the collection within several consecutive time periods The size of the coefficient of change with the period size; ∈ ,in, For continuous cycles The total number of With the preset threshold coefficient Compare the sizes, if > , it is judged that the current wind turbine tower position has changed significantly, and an early warning signal is generated and maintenance is carried out; if ≤ , then it is judged that the current wind tower position change is normal, and the change curve of the product value of the inclination angle parameter and the vertical height parameter corresponding to all current time points is obtained as the standard curve ; Finally, the standard curve Take the derivative The slope obtained is the position coefficient .

[0114] It should be noted that the present invention is designed to perform vibration frequency position detection in advance to determine whether the result conforms to the objective law of equipment operation safety currently obtained. It is necessary to use a variety of sensors to obtain data such as the inclination angle and vertical height of the wind power tower in actual measurement; and according to the blade length and height of the wind turbine on the mountain, the unfavorable environmental factors are determined in advance, such as the influence of natural earthquakes, storms, etc. on the change of the tower inclination angle, and it is determined that when the inclination exceeds the set threshold, it may directly affect the life of the wind turbine and reduce its operating performance, and may also cause data reception and transmission speed or cause communication interruption; the monitoring parameters are generally such as wind speed (embodied by vibration in this embodiment), power generation, equipment temperature, etc. For example, when it is detected that the wind speed exceeds the rated wind speed of the generator, the intelligent control system will automatically adjust the working state of the generator to avoid overload operation; when an equipment failure is detected, the intelligent control system will promptly issue an alarm and take corresponding measures to deal with it.

[0115] As an implementation mode of the present invention, the platform body further includes:

[0116] The platform body is also equipped with an energy storage device to store excess electrical energy;

[0117] The platform is also equipped with a power dispatching system to automatically adjust the output and storage strategies of electricity according to the needs of the power grid and the power generation situation, so as to achieve optimal utilization of energy.

[0118] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A mountain wind power generation platform based on the Internet of Things, characterized in that: include: Platform entity, IoT integration and intelligent control system; The platform body is equipped with a communication module and a sensor module; The communication module is used to establish a connection with a remote server to realize signal data reception and feedback; the sensor module is used to monitor the operating status of the platform body and transmit real-time monitoring data to the remote server; The platform body includes a wind turbine tower arranged at the bottom and a wind turbine generator set installed at the top; the wind turbine tower is used to support the wind turbine generator set and absorb the vibration of the wind turbine generator set, and obtain the real-time vibration frequency according to the sensor module; the wind turbine generator set is used to generate wind power on the mountain; The IoT integration is used to ensure that the platform entity can achieve remote monitoring and management through IoT technology; The intelligent control system is used for: Analyze the operating status of the wind turbine generator set based on the real-time monitoring data of the sensor module, and transmit the analysis results to the remote server; The remote server generates optimization adjustment information in response and transmits it to the communication module, and the communication module receives the optimization adjustment information and generates a control signal; The wind turbine generator set receives the control signal to control and adjust the wind power generation; The communication module is also used to receive an alarm signal from a remote server and generate alarm information; The intelligent control system comprises: A data acquisition module, used to collect a data set of real-time vibration frequency parameters of the sensor module; A data analysis module is used to obtain the change state of vibration frequency parameters within a period of time to analyze the real-time operation risk factor of the wind turbine generator set; A data control module is used to control the wind turbine generator set according to the real-time operation risk coefficient; An alarm module, used for issuing a first alarm signal and a second alarm signal according to the safety status of the wind turbine generator set; The method for the data analysis module to obtain the real-time operation risk coefficient is: By formula: Calculate the time period Operational risk factor within ; in, , , is the preset weight coefficient, and , , All are greater than 0; is the vibration frequency parameter; The power generation of the wind turbine; is the power generation temperature value of the wind turbine generator set; is the standard value of vibration frequency parameter ； is the standard value of wind turbine power generation; is the standard temperature value of the wind turbine generator set; is the preset wind turbine generator set power generation deviation value; It is the preset temperature deviation value of the wind turbine generator set; A preset first conversion function corresponding to a change in the vibration frequency parameter; A preset second conversion function corresponding to the change in power generation of the wind turbine generator set; A preset third conversion function corresponding to the temperature change of the wind turbine generator set; The position coefficient for the current vibration frequency detection position; in, is the judgment function, judging when > ,but = ;otherwise, ; in, For the current time period The corresponding allowed range value; is the warning value; Set time period Operational risk factor within With preset threshold To compare: like ≥ , it is determined that there is a safety risk in the current operation state of the wind turbine generator set, and the intelligent control system controls the generator to stop generating electricity and generates a control strategy and a first alarm signal; Also includes: when = When: Formula ; Set time period Operational risk factor within With preset threshold To compare: like < , it is determined that there is a safety risk in the current operation state of the wind turbine generator set, and the data control module generates a control strategy; otherwise, the alarm module generates a second alarm signal.

2. The mountain wind power generation platform based on the Internet of Things according to claim 1 is characterized in that: The sensor module acquires the vibration frequency in real time in the following manner: The real-time vibration sound waves of the wind turbine tower at its current height position are obtained through MEMS acoustic sensors; The real-time vibration sound waves are converted into electrical signals through high-frequency oscillators, modulators and detectors, and the vibration frequency is obtained by analysis; The vibration frequency is subjected to data parameterization processing to obtain vibration frequency parameters.

3. The mountain wind power generation platform based on the Internet of Things according to claim 2 is characterized in that: The sensor module further comprises: An angle sensor is arranged at a vibration frequency detection position of the wind turbine tower and is used to measure the tilt angle of the wind turbine tower; The distance sensor is arranged at the vibration frequency detection position of the wind turbine tower and is used to measure the vertical height from the ground to the detection position.

4. The mountain wind power generation platform based on the Internet of Things according to claim 1 is characterized in that: The position coefficient The way to obtain is: Get the tilt angle of the vibration frequency detection position of the wind turbine tower in the historical time period Parameters and vertical height parameters A collection of cumulative values ​​over time ;gather ∈ ,in, is the total number of time points; is the cumulative value parameter; Analyze the collection of several consecutive time periods The size of the coefficient of change with the period size; ∈ ,in, For continuous cycles Total number of; Will With the preset threshold coefficient To compare: like > , it is judged that the current wind turbine tower position has changed significantly, and an early warning signal is generated and maintenance is carried out; like ≤ , then it is judged that the current wind tower position change is normal, and the change curve of the product value of the inclination angle parameter and the vertical height parameter corresponding to all current time points is obtained as the standard curve ; For standard curve Take the derivative The slope obtained is the position coefficient .

5. The mountain wind power generation platform based on the Internet of Things according to claim 1 is characterized in that: The platform body also includes: The platform body is also equipped with an energy storage device for storing excess electrical energy; The platform body is also equipped with a power dispatching system for automatically adjusting the output strategy and storage strategy of electric energy according to the demand of the power grid and the power generation situation, so as to achieve optimal utilization of energy.

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