Yaw collector ring temperature intelligent control system, method, equipment, medium and product

Abstract

This application discloses a yaw collector ring temperature intelligent control system, method, device, medium, and product, relating to the field of wind power generation. The method includes a temperature acquisition unit for real-time acquisition of temperature information from the collector ring surface; a temperature feature analysis unit for processing the temperature information to obtain multi-dimensional temperature feature values; a data management unit for collecting multi-dimensional temperature feature values ​​to construct a historical database; a temperature prediction and anomaly diagnosis unit for obtaining temperature prediction results and anomaly judgment results based on historical data in the historical database and real-time multi-dimensional temperature feature values; and a temperature control unit for adjusting the operating temperature of the collector ring based on real-time temperature information when the temperature prediction result exceeds a preset safety threshold or the anomaly judgment result indicates an abnormal situation. This application enables a leap from passive control to active prediction, effectively solving the problem that traditional technologies struggle to achieve predictive maintenance.

Description

Technical Field

[0001] This application relates to the field of wind power generation, and in particular to a yaw collector ring temperature intelligent control system, method, equipment, medium and product. Background Technology

[0002] With the rapid development of wind power technology in my country, wind turbines are moving towards larger and more intelligent designs, leading to increasingly higher requirements for the reliability of key components. The yaw collector ring, as a crucial rotating connection component in wind turbines for transmitting power and signals, is directly affected by its temperature environment, impacting its conductivity, mechanical lifespan, and the safe and stable operation of the entire power generation system. Improper temperature control, such as excessively high or abnormal temperatures, can directly damage its conductivity, resulting in decreased power transmission efficiency and compromised signal transmission stability. More seriously, abnormal temperatures may trigger collector ring failures, further affecting the safe and stable operation of the entire power generation system. Therefore, effective temperature control of the yaw collector ring is essential for ensuring the reliable operation of key components in wind turbines and maintaining the continuous and stable operation of the power generation system.

[0003] Currently, the industry generally adopts passive control logic based on fixed temperature thresholds for temperature monitoring and protection of yaw collector rings. This passive approach cannot intervene in the early stages of temperature anomalies, meaning that this traditional strategy is difficult to support the needs of predictive maintenance, affecting the lifespan and reliability of yaw collector rings. Summary of the Invention

[0004] The purpose of this application is to provide a yaw collector ring temperature intelligent control system, method, device, medium and product that can solve the problem that "traditional solutions cannot intervene in the early stage of temperature anomalies, that is, the traditional strategy is difficult to support the needs of predictive maintenance, affecting the life and reliability of the yaw collector ring".

[0005] To achieve the above objectives, this application provides the following solution:

[0006] Firstly, this application provides a yaw collector ring temperature intelligent control system, comprising:

[0007] Temperature acquisition unit is used to acquire temperature information of the slip ring surface in real time;

[0008] A temperature feature analysis unit, connected to the temperature acquisition unit, is used to process the temperature information to obtain multidimensional temperature feature values;

[0009] A data management unit, connected to the temperature feature analysis unit, is used to collect the multidimensional temperature feature values ​​to build a historical database.

[0010] The temperature prediction and anomaly diagnosis unit is connected to the data management unit and the temperature feature analysis unit, and is used to obtain temperature prediction results and anomaly judgment results based on historical data in the historical database and the real-time multidimensional temperature feature values.

[0011] A temperature control unit is connected to the temperature prediction and anomaly diagnosis unit and the temperature acquisition unit. The temperature control unit is used to adjust the working temperature of the collector ring based on the real-time temperature information when the temperature prediction result exceeds a preset safety threshold or the anomaly judgment result shows an abnormal situation.

[0012] The temperature prediction and anomaly diagnosis unit includes:

[0013] The temperature prediction module is connected to the data management unit and the temperature feature analysis unit, and is used to obtain temperature prediction results based on historical data in the historical database and the real-time multidimensional temperature feature values ​​using a time-series prediction algorithm.

[0014] An anomaly diagnosis module, connected to the data management unit and the temperature feature analysis unit, is used to obtain anomaly judgment results based on historical data in the historical database and real-time multidimensional temperature feature values ​​using an anomaly detection algorithm.

[0015] The temperature prediction module and the anomaly diagnosis module are respectively connected to the temperature control unit.

[0016] In one embodiment, the temperature acquisition unit is a matrix infrared sensor, which is disposed inside the yaw collector ring housing and faces the collector ring.

[0017] In one embodiment, the temperature acquisition unit further includes an ambient temperature acquisition sensor, which is disposed on the outside of the yaw collector ring housing and is used to acquire ambient temperature.

[0018] The temperature control unit is also used to adjust the operating temperature of the slip ring based on the real-time temperature information when the difference between the ambient temperature and the temperature information exceeds a preset numerical threshold.

[0019] In one embodiment, the temperature control unit includes:

[0020] Heating module, heat dissipation module;

[0021] The controller, connected to the temperature prediction module, the anomaly diagnosis module, and the temperature acquisition unit, is used to adjust the working state of the heating module and / or the heat dissipation module based on the real-time temperature information when the temperature prediction result exceeds a preset safety threshold or the anomaly judgment result shows an abnormal situation.

[0022] In one embodiment, the multidimensional temperature feature values ​​include, but are not limited to, temperature gradient, temperature extreme values, temperature mean, temperature standard deviation, and regional temperature difference of a preset key area.

[0023] Secondly, this application provides a method for intelligent control of yaw collector ring temperature, including:

[0024] Real-time acquisition of temperature information on the surface of the slip ring;

[0025] The temperature information is processed to obtain multidimensional temperature feature values;

[0026] Collect the multidimensional temperature feature values ​​to construct a historical database;

[0027] Based on historical data in the historical database and real-time multidimensional temperature feature values, temperature prediction results and anomaly judgment results are obtained.

[0028] When the temperature prediction result exceeds the preset safety threshold, or when the anomaly judgment result shows an abnormal situation, the operating temperature of the slip ring is adjusted based on the real-time temperature information.

[0029] The steps for obtaining temperature prediction results and anomaly judgment results based on historical data in the historical database and real-time multidimensional temperature feature values ​​specifically include:

[0030] Temperature prediction results are obtained by using a time-series prediction algorithm based on historical data in the historical database and real-time multidimensional temperature feature values.

[0031] An anomaly detection algorithm is used to obtain an anomaly judgment result based on historical data in the historical database and the real-time multidimensional temperature feature value.

[0032] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method.

[0033] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0034] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0035] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0036] This application provides a yaw slip ring temperature intelligent control system, method, device, medium, and product. The temperature control unit can adjust the slip ring's operating temperature based on real-time temperature information when the predicted temperature exceeds a preset safety threshold. This early intervention effectively avoids equipment damage caused by overheating, ensuring the stability of the slip ring's critical functions. Furthermore, when the anomaly detection result indicates an abnormal situation, the temperature control unit can adjust the slip ring's operating temperature based on real-time temperature information. This timely adjustment in the early stages of an anomaly effectively prevents local anomalies from worsening into global failures and prevents them from developing into serious malfunctions. Therefore, this application achieves a leap from passive control to active prediction, effectively solving the problem of predictive maintenance that is difficult to achieve with traditional technologies. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a connection block diagram of a yaw collector ring temperature intelligent control system according to an embodiment of this application;

[0039] Figure 2 This is a flowchart of a method for intelligent temperature control of the yaw collector ring according to an embodiment of this application;

[0040] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0041] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0042] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] See Figure 1 This application provides a yaw collector ring temperature intelligent control system, including a temperature acquisition unit, a temperature feature analysis unit, a data management unit, a temperature prediction and anomaly diagnosis unit, and a temperature control unit.

[0044] In this embodiment, the temperature acquisition unit is used to acquire temperature information of the slip ring surface in real time. Preferably, the temperature acquisition unit is a matrix infrared sensor, which is disposed inside the yaw slip ring housing and faces the slip ring, and is used to acquire two-dimensional infrared temperature image data of the slip ring surface. The two-dimensional infrared temperature image data is acquired by the matrix infrared sensor and is image-based data that reflects the relationship between the spatial position of the yaw slip ring surface and the corresponding temperature value.

[0045] For example, the resolution of the matrix infrared sensor is no less than 32×24 pixels, and its data output interface can be connected to the temperature feature analysis unit via SPI or I2C protocol.

[0046] A matrix infrared sensor is a signal acquisition device composed of multiple sensors arranged geometrically. The number of sensors in a matrix infrared sensor can be flexibly determined according to the size of different slip rings to adapt to different application scenarios. A matrix infrared sensor can simultaneously acquire temperature information from all temperature points within its field of view, facilitating subsequent feature extraction and predictive analysis. Furthermore, it features non-contact measurement characteristics, requiring no direct contact with the slip ring surface; it only needs to be fixed inside the yaw slip ring housing and facing the slip ring body to operate, making installation convenient.

[0047] This application introduces a matrix infrared sensor to achieve non-contact and full-coverage monitoring of the two-dimensional temperature field on the surface of the yaw collector ring. It can promptly detect anomalies that point sensors cannot detect, such as local overheating and poor contact, thereby improving the comprehensiveness and accuracy of state perception.

[0048] In this embodiment, the temperature feature analysis unit is connected to the temperature acquisition unit and is used to process temperature information to obtain multidimensional temperature feature values. These multidimensional temperature feature values ​​include, but are not limited to, temperature gradient, temperature extreme values, temperature mean, temperature standard deviation, and regional temperature differences in preset key areas. The preset key areas are specific areas of interest on the collector ring. This application can select multiple key areas of interest based on actual conditions and analyze the temperature differences in the corresponding areas.

[0049] In this embodiment, the data management unit is connected to the temperature feature analysis unit to collect multi-dimensional temperature feature values ​​to construct a historical database. The historical data in the database includes temperature-related values ​​and their corresponding timestamps. By setting up the data management unit, this application can systematically record the operational data throughout the entire lifecycle of the equipment, providing a rich data foundation for subsequent anomaly detection and temperature prediction.

[0050] For example, the data management unit includes a non-volatile storage unit, such as an embedded multimedia card (eMMC) or a micro SD card (TF card), whose file system supports CSV file format or custom binary format storage, facilitating the tracing, export, and analysis of historical data.

[0051] In this embodiment, the temperature prediction and anomaly diagnosis unit is connected to the data management unit and the temperature feature analysis unit, and is used to obtain temperature prediction results and anomaly judgment results based on historical data in the historical database and real-time multidimensional temperature feature values.

[0052] Specifically, the temperature prediction and anomaly diagnosis unit includes a temperature prediction module and an anomaly diagnosis module. The temperature prediction module is connected to the data management unit and the temperature feature analysis unit, and is used to obtain temperature prediction results based on historical data in the historical database and real-time multidimensional temperature feature values ​​using a time-series prediction algorithm. The anomaly diagnosis module is connected to the data management unit and the temperature feature analysis unit, and is used to obtain anomaly judgment results based on historical data in the historical database and real-time multidimensional temperature feature values ​​using an anomaly detection algorithm. The temperature prediction module and the anomaly diagnosis module are respectively connected to the temperature control unit.

[0053] Time-series prediction algorithms can include Autoregressive Integrated Moving Average (ARIMA) models or Long Short-Term Memory (LSTM) networks. For example, using LSTM networks, this model learns and memorizes dependencies in long-term time series through its internal gating mechanism. The specific input data consists of time-series features extracted by the temperature feature analysis unit, including historical and current temperature gradients, temperature maxima, minimum temperatures, and average temperatures. LSTM learns the dynamic patterns of collector ring temperature changes by analyzing the changing patterns of these features over time. Through training, the model can predict the highest temperature within a preset time period (e.g., the next 30 minutes) based on continuous feature sequences from the past period (e.g., the past hour). When the predicted highest temperature exceeds a preset safety threshold, intervention can be initiated in advance, thereby achieving proactive and predictive control of the collector ring's operating temperature.

[0054] Anomaly detection algorithms can include Isolation Forest or One-Class Support Vector Machine (SVM). For example, the Isolation Forest algorithm identifies anomalous data points by constructing multiple "isolated trees." The specific inputs include historical data collected by all sensors in a matrix infrared sensor under normal conditions, and the multi-dimensional temperature feature value obtained from real-time analysis of the current i-th sensor. Isolation Forest constructs its tree structure by randomly selecting features and segmentation values. Since anomalous data often differs significantly from normal data distribution, they are more quickly isolated to shallower nodes within the tree. The algorithm determines the degree of anomalousness by calculating the path length required to isolate a data point: the shorter the path, the higher the probability of an anomaly. When the feature value of the current i-th sensor is determined to be anomalous, an abnormal state can be located in the corresponding collector ring region of that sensor, enabling early warning and intervention to prevent further deterioration of the temperature anomaly.

[0055] In this embodiment, the temperature control unit is connected to the temperature prediction and anomaly diagnosis unit and the temperature acquisition unit. The temperature control unit is used to adjust the working temperature of the collector ring based on real-time temperature information when the temperature prediction result exceeds the preset safety threshold or the anomaly judgment result shows an abnormal situation.

[0056] Specifically, the temperature control unit includes a heating module, a heat dissipation module, and a controller. The controller is connected to a temperature prediction module, an anomaly diagnosis module, and a temperature acquisition unit. It is used to adjust the working status of the heating module and / or the heat dissipation module based on real-time temperature information when the temperature prediction result exceeds a preset safety threshold or the anomaly judgment result shows an abnormal situation.

[0057] For example, the heating module can be a heater or an electric heating element, and the heat dissipation module can be an axial fan or a centrifugal fan, etc. This application does not impose specific limitations.

[0058] For example, if it is predicted that the temperature in a certain area will gradually rise to a preset safety threshold (while the current measured temperature is still normal), the controller can activate the heat dissipation module in advance to slowly cool down the area at low power until the target temperature is reached. This avoids the need for high-power cooling or frequent start-up and shutdown of the cooling equipment due to a subsequent sudden temperature rise. Simultaneously, by intervening in advance, it can effectively prevent equipment damage caused by excessive temperature and ensure the stability of critical functions of the slip ring.

[0059] For example, the anomaly judgment result is used to determine whether the current state is abnormal. When the anomaly judgment result is identified as an abnormal situation, such as excessively rapid local temperature rise or uneven temperature distribution, the temperature control unit will combine the abnormal signal to adjust the heating module or heat dissipation module in advance until the target temperature is reached, so as to avoid the abnormality from further expanding and causing equipment failure.

[0060] In this embodiment, the temperature acquisition unit further includes an ambient temperature acquisition sensor, which is disposed on the outside of the yaw slip ring housing and is used to acquire ambient temperature. The temperature acquisition unit is connected to the temperature control unit, which is also used to adjust the working temperature of the slip ring based on real-time temperature information when the difference between the ambient temperature and the temperature information exceeds a preset numerical threshold.

[0061] For example, when the temperature difference between the surface of the slip ring and the ambient temperature is too large, the difference can be actively reduced by adjusting the heating or heat dissipation module. Thus, this application can prevent serious problems caused by excessive temperature differences through early intervention and control, thereby maintaining the surface temperature of the slip ring within a reasonable range and stabilizing its mechanical lifespan and conductivity.

[0062] Based on the same inventive concept, this application also provides a method for intelligent control of yaw collector ring temperature. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the intelligent control method for yaw collector ring temperature provided below can be found in the limitations of the intelligent control system for yaw collector ring temperature described above, and will not be repeated here.

[0063] See Figure 2 This application also provides a method for intelligent control of yaw collector ring temperature, including:

[0064] S100: Real-time acquisition of temperature information on the surface of the slip ring;

[0065] S200: Process temperature information to obtain multidimensional temperature feature values;

[0066] S300: Collect multi-dimensional temperature feature values ​​to build a historical database;

[0067] S400: Based on historical data in the historical database and real-time multi-dimensional temperature feature values, temperature prediction results and anomaly judgment results are obtained;

[0068] S500: When the temperature prediction result exceeds the preset safety threshold, or when the abnormal judgment result shows an abnormal situation, the working temperature of the slip ring is adjusted based on the real-time temperature information.

[0069] The steps for obtaining temperature prediction results and anomaly judgment results based on historical data in the historical database and real-time multidimensional temperature feature values ​​specifically include:

[0070] Temperature prediction results are obtained by using a time-series prediction algorithm based on historical data in the historical database and real-time multidimensional temperature feature values.

[0071] An anomaly detection algorithm is used to obtain an anomaly judgment result based on historical data in the historical database and the real-time multidimensional temperature feature value.

[0072] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection.

[0073] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0074] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0075] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0076] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0077] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0078] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0079] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0080] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A yaw slip ring temperature intelligent control system, characterized in that, The application relates to a temperature monitoring and control system for a collector ring, comprising: a temperature acquisition unit for acquiring temperature information of a surface of the collector ring in real time; a temperature feature analysis unit connected with the temperature acquisition unit, for processing the temperature information to obtain multi-dimensional temperature feature values; a data management unit connected with the temperature feature analysis unit, for collecting the multi-dimensional temperature feature values to construct a historical database; a temperature prediction and abnormality diagnosis unit connected with the data management unit and the temperature feature analysis unit, for obtaining a temperature prediction result and an abnormality judgment result based on historical data in the historical database and the multi-dimensional temperature feature values in real time; a temperature control unit connected with the temperature prediction and abnormality diagnosis unit and the temperature acquisition unit, the temperature control unit being used for adjusting a working temperature of the collector ring based on the temperature information in real time when the temperature prediction result exceeds a preset safety threshold or the abnormality judgment result shows an abnormal condition; the temperature prediction and abnormality diagnosis unit comprises: a temperature prediction module connected with the data management unit and the temperature feature analysis unit, for obtaining a temperature prediction result by using a time series prediction algorithm according to historical data in the historical database and the multi-dimensional temperature feature values in real time; an abnormality diagnosis module connected with the data management unit and the temperature feature analysis unit, for obtaining an abnormality judgment result by using an abnormality detection algorithm according to historical data in the historical database and the multi-dimensional temperature feature values in real time; wherein the temperature prediction module and the abnormality diagnosis module are connected with the temperature control unit respectively.

2. The yaw busbar temperature intelligent control system of claim 1, wherein, The temperature acquisition unit is a matrix infrared sensor, the matrix infrared sensor is arranged in a yaw collector ring box and faces the collector ring.

3. The yaw slip ring temperature intelligent control system of claim 2, wherein, The temperature acquisition unit further comprises an ambient temperature acquisition sensor arranged outside the yaw collector ring box and used for acquiring an ambient temperature; the temperature control unit is further used for adjusting the working temperature of the collector ring based on the temperature information in real time when a difference between the ambient temperature and the temperature information exceeds a preset numerical threshold.

4. The yaw busbar temperature intelligent control system of claim 1, wherein, The temperature control unit comprises: a heating module and a heat dissipation module; a controller connected with the temperature prediction module, the abnormality diagnosis module and the temperature acquisition unit, used for adjusting a working state of the heating module and / or the heat dissipation module based on the temperature information in real time when the temperature prediction result exceeds a preset safety threshold or the abnormality judgment result shows an abnormal condition.

5. The yaw slip ring temperature intelligent control system of claim 1, wherein, The multi-dimensional temperature feature values include but are not limited to a temperature gradient, a temperature extreme value, a temperature mean value, a temperature standard deviation and a regional temperature difference of a preset key region.

6. A yaw slip ring temperature intelligent control method, characterized in that, The application relates to a temperature monitoring and control system for a collector ring, comprising: acquiring temperature information of a surface of the collector ring in real time; processing the temperature information to obtain multi-dimensional temperature feature values; collecting the multi-dimensional temperature feature values to construct a historical database; obtaining a temperature prediction result and an abnormality judgment result based on historical data in the historical database and the multi-dimensional temperature feature values in real time; adjusting a working temperature of the collector ring based on the temperature information in real time when the temperature prediction result exceeds a preset safety threshold or the abnormality judgment result shows an abnormal condition; The step of obtaining a temperature prediction result and an abnormality judgment result based on the historical data in the historical database and the real-time multi-dimensional temperature characteristic value specifically comprises: A time series prediction algorithm is used to obtain a temperature prediction result according to the historical data in the historical database and the real-time multi-dimensional temperature characteristic value. An abnormality detection algorithm is used to obtain an abnormality judgment result according to the historical data in the historical database and the real-time multi-dimensional temperature characteristic value.

7. A computer device comprising: The memory, the processor, and a computer program stored in the memory and running on the processor are characterized in that the processor executes the computer program to implement the yaw bus collector ring temperature intelligent control method in claim 6.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the yaw bus collector ring temperature intelligent control method in claim 6.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the yaw bus collector ring temperature intelligent control method in claim 6.

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