Smart Wind Power Hybrid Tower Safety Monitoring Online Management Platform

Abstract

The present invention proposes an intelligent wind power hybrid tower safety monitoring online management platform, which relates to the field of safety monitoring technology, obtains preset collection strategy information, obtains node collection data and node positioning information of each collection node; calculates weight data of the collection node, calculates the collection frequency coefficient according to the weight data combined with the node collection data, adjusts the collection frequency according to the collection frequency coefficient, and obtains the frequency adjustment result; calculates the node abnormality coefficient, performs risk assessment on the node, obtains the risk abnormal node and the positioning data of the abnormal node; generates a node abnormality chain according to the abnormal node, calculates the abnormality cumulative coefficient of each node on the node abnormality chain, and performs risk warning for different nodes according to the abnormality cumulative coefficient. The present invention improves the monitoring accuracy and timeliness of the operating status of the wind power hybrid tower, and provides a strong guarantee for the stable operation of the wind farm.

Description

Technical Field

[0001] The present invention proposes an online management platform for safety monitoring of smart wind power hybrid towers, which relates to the field of safety monitoring technology, and specifically to the field of safety monitoring technology of smart wind power hybrid towers. Background Art

[0002] With the continuous development and widespread application of wind power technology, wind turbine hybrid towers, as key components of wind power generation systems, are crucial for ensuring the stable operation of the entire wind farm. However, wind turbine hybrid towers are affected by a variety of factors during operation, such as natural environmental conditions, equipment aging, improper maintenance, etc. These may cause various abnormal conditions in the wind turbine hybrid tower, thereby affecting its normal operation and safety performance. In order to effectively monitor the operating status of wind turbine hybrid towers and promptly discover and deal with potential safety hazards, it is usually difficult to accurately and reasonably collect data from multiple nodes in existing technologies, and it is difficult to ensure that the operating data of wind turbine hybrid towers can be fully and accurately obtained. Traditional data collection and monitoring methods often have fixed data collection frequencies and cannot be dynamically adjusted according to actual conditions, resulting in insufficiently timely data collection, inability to promptly reflect the actual operating status of wind turbine hybrid towers, and difficulty in providing quantitative warnings based on dangerous situations. Summary of the Invention

[0003] The present invention provides an intelligent online management platform for wind turbine hybrid tower safety monitoring, which addresses the difficulties in existing technologies in accurately and rationally collecting data from multiple nodes and ensuring comprehensive and accurate acquisition of wind turbine hybrid tower operating data. Traditional data collection and monitoring methods often suffer from fixed data collection frequencies and the inability to dynamically adjust according to actual conditions, resulting in untimely data collection, an inability to promptly reflect the actual operating status of wind turbine hybrid towers, and difficulty in providing quantitative early warnings based on dangerous situations.

[0004] The present invention proposes an online management platform for smart wind power hybrid tower safety monitoring, which includes:

[0005] A node data acquisition module is used to obtain preset acquisition strategy information, determine the preset acquisition type, preset acquisition range and acquisition nodes of the wind power hybrid tower according to the preset acquisition strategy information, and obtain node acquisition data and node positioning information of each acquisition node;

[0006] An acquisition frequency adjustment module is used to calculate a weight coefficient of an acquisition node, calculate an acquisition frequency coefficient based on the weight coefficient combined with the node acquisition data, and adjust the acquisition frequency based on the acquisition frequency coefficient to obtain a frequency adjustment result;

[0007] The node anomaly positioning module is used to calculate the node risk coefficient, perform risk assessment on the node, and obtain the risk anomaly node and the positioning data of the anomaly node;

[0008] The node anomaly warning module is used to generate a node anomaly chain based on the abnormal node, calculate the abnormal cumulative coefficient of each node on the node anomaly chain, and perform risk warnings for different nodes based on the abnormal cumulative coefficient.

[0009] Furthermore, the node data acquisition module includes:

[0010] A node determination module is used to obtain preset collection strategy information, wherein the preset collection strategy information includes preset collection types, each preset collection type includes a preset collection range, and each preset collection range includes a collection node;

[0011] The preset acquisition range is the maximum data acquisition range of the sensor group;

[0012] The acquisition node is the center point of the preset acquisition range;

[0013] The sensor group is arranged at the central point;

[0014] The node collection module is used to collect data from each collection node of the wind power hybrid tower and obtain node collection data;

[0015] The corresponding node of each node collecting data is located and the node positioning information is obtained.

[0016] Furthermore, the acquisition frequency adjustment module includes:

[0017] A weight determination module is used to calculate the positioning node weight coefficient based on the node collection data combined with the preset positioning monitoring level of the node positioning information;

[0018] Setting a positioning weight coefficient of a corresponding node according to the positioning node weight coefficient;

[0019] A frequency adjustment module, configured to calculate an acquisition frequency coefficient of an acquisition node based on the weight coefficient and the node acquisition data;

[0020] Comparing the acquisition frequency coefficient with a preset frequency coefficient threshold to obtain a frequency coefficient comparison result;

[0021] The acquisition frequency of the acquisition node is adjusted according to the frequency coefficient comparison result to obtain a frequency adjustment result.

[0022] Furthermore, the node abnormality locating module includes:

[0023] The risk calculation module is used to calculate the node risk coefficient based on the node's positioning collection data, weight coefficient and current collection frequency;

[0024] A risk comparison module is used to compare the node risk coefficient with a preset risk coefficient threshold to obtain a risk comparison result;

[0025] Perform risk assessment on the node according to the risk comparison result to obtain a risk assessment result;

[0026] The abnormality acquisition module is used to obtain risk abnormal nodes and positioning data of the abnormal nodes according to the risk determination result.

[0027] Furthermore, the node abnormality warning module includes:

[0028] A cumulative calculation module is used to generate a node abnormality chain according to the positioning data of the abnormal node, and calculate the abnormal cumulative coefficient of the corresponding node according to the weight coefficient and acquisition frequency data of each node in the node abnormality chain combined with the positioning acquisition data;

[0029] A cumulative comparison module is used to compare the abnormal cumulative coefficient with a preset cumulative coefficient threshold to obtain a cumulative coefficient comparison result;

[0030] Perform risk warning on the node according to the cumulative coefficient comparison result to obtain risk warning information;

[0031] The cumulative warning module is used to issue a risk warning to the node when the abnormal cumulative coefficient is greater than the preset cumulative coefficient threshold. The warning level is a multiple of the abnormal cumulative coefficient relative to the preset cumulative coefficient threshold; otherwise, no risk warning is issued.

[0032] Furthermore, the management method includes:

[0033] S1. Obtain preset collection strategy information, determine the preset collection type, preset collection range and collection nodes of the wind turbine hybrid tower according to the preset collection strategy information, and obtain node collection data and node positioning information of each collection node;

[0034] S2. Calculate the weight coefficient of the collection node, calculate the collection frequency coefficient based on the weight coefficient and the node collection data, adjust the collection frequency based on the collection frequency coefficient, and obtain a frequency adjustment result;

[0035] S3. Calculate the node risk coefficient, perform risk assessment on the node, and obtain the abnormal risk nodes and the location data of the abnormal nodes;

[0036] S4. Generate a node abnormality chain based on the abnormal node, calculate the abnormality cumulative coefficient of each node on the node abnormality chain, and issue risk warnings for different nodes based on the abnormality cumulative coefficient.

[0037] Furthermore, the S1 includes:

[0038] Acquire preset collection strategy information, where the preset collection strategy information includes preset collection types, each preset collection type includes a preset collection range, and each preset collection range includes a collection node;

[0039] The preset acquisition range is the maximum data acquisition range of the sensor group;

[0040] The acquisition node is the center point of the preset acquisition range;

[0041] The sensor group is arranged at the central point;

[0042] Collect data from each collection node of the wind power hybrid tower to obtain node collection data;

[0043] The corresponding node of each node collecting data is located and the node positioning information is obtained.

[0044] Furthermore, the S2 includes:

[0045] Calculate the positioning node weight coefficient based on the node collection data and the preset positioning monitoring level of the node positioning information;

[0046] Setting a positioning weight coefficient of a corresponding node according to the positioning node weight coefficient;

[0047] Calculating the acquisition frequency coefficient of the acquisition node according to the weight coefficient and the node acquisition data;

[0048] Comparing the acquisition frequency coefficient with a preset frequency coefficient threshold to obtain a frequency coefficient comparison result;

[0049] The acquisition frequency of the acquisition node is adjusted according to the frequency coefficient comparison result to obtain a frequency adjustment result.

[0050] Furthermore, the S3 includes:

[0051] Calculate the node risk coefficient based on the node's positioning collection data, weight coefficient and current collection frequency;

[0052] Comparing the node risk coefficient with a preset risk coefficient threshold to obtain a risk comparison result;

[0053] Perform risk assessment on the node according to the risk comparison result to obtain a risk assessment result;

[0054] Obtain risk abnormal nodes and location data of the abnormal nodes according to the risk determination result.

[0055] Furthermore, the S4 includes:

[0056] Generate a node abnormality chain based on the positioning data of the abnormal node, and calculate the abnormality cumulative coefficient of the corresponding node based on the weight coefficient and acquisition frequency data of each node in the node abnormality chain combined with the positioning acquisition data;

[0057] Comparing the abnormal cumulative coefficient with a preset cumulative coefficient threshold to obtain a cumulative coefficient comparison result;

[0058] Perform risk warning on the node according to the cumulative coefficient comparison result to obtain risk warning information;

[0059] When the abnormal cumulative coefficient is greater than the preset cumulative coefficient threshold, a risk warning is issued for the node, and the warning level is a multiple of the abnormal cumulative coefficient relative to the preset cumulative coefficient threshold; otherwise, no risk warning is issued.

[0060] Beneficial effects of the present invention: The present invention proposes an online monitoring platform for the health of a wind turbine hybrid tower. The platform can determine the preset collection type, preset collection range, and collection nodes of the wind turbine hybrid tower based on preset collection strategy information, and obtain the node collection data and node positioning information of each collection node in real time. On this basis, the system further calculates the weight coefficient of the collection node, and calculates the collection frequency coefficient in combination with the node collection data, thereby realizing dynamic adjustment of the collection frequency. This dynamic adjustment mechanism can ensure that when the operating status of the wind turbine hybrid tower changes, the data collection frequency can respond in a timely manner and make corresponding adjustments, thereby improving the pertinence and real-time nature of data collection.

[0061] In addition, the present invention also proposes a method for calculating the node risk coefficient. Through a comprehensive analysis of the node collection data and the node positioning information, the abnormality coefficient of each node is calculated, and the risk of the node is determined based on the coefficient. When the node risk coefficient exceeds the preset threshold, the system will determine that the node is a risk abnormal node and obtain its positioning data. Furthermore, the system can also generate a node abnormality chain based on the abnormal node, calculate the abnormal cumulative coefficient of each node on the node abnormality chain, and perform risk warnings for different nodes based on the coefficient. This risk warning mechanism can detect potential safety hazards of wind power hybrid towers in advance, provide timely and accurate risk information to operation and maintenance personnel, and help ensure the safe operation of wind power hybrid towers.

[0062] In summary, the wind turbine hybrid tower health online monitoring system proposed in this patent improves the monitoring accuracy and timeliness of the wind turbine hybrid tower operating status by dynamically adjusting the collection frequency and real-time risk warning mechanism, providing a strong guarantee for the stable operation of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a schematic diagram of the online management method for smart wind power hybrid tower safety monitoring;

[0064] Figure 2 Schematic diagram of the collection node. DETAILED DESCRIPTION

[0065] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0066] In one embodiment of the present invention, a smart wind power hybrid tower safety monitoring online management platform is provided, the platform comprising:

[0067] A node data acquisition module is used to obtain preset acquisition strategy information, determine the preset acquisition type, preset acquisition range and acquisition nodes of the wind power hybrid tower according to the preset acquisition strategy information, and obtain node acquisition data and node positioning information of each acquisition node;

[0068] An acquisition frequency adjustment module is used to calculate a weight coefficient of an acquisition node, calculate an acquisition frequency coefficient based on the weight coefficient combined with the node acquisition data, and adjust the acquisition frequency based on the acquisition frequency coefficient to obtain a frequency adjustment result;

[0069] The node anomaly positioning module is used to calculate the node risk coefficient, perform risk assessment on the node, and obtain the risk anomaly node and the positioning data of the anomaly node;

[0070] The node anomaly warning module is used to generate a node anomaly chain based on the abnormal node, calculate the abnormal cumulative coefficient of each node on the node anomaly chain, and perform risk warnings for different nodes based on the abnormal cumulative coefficient.

[0071] The working principle of the above technical solution is as follows: the platform first obtains preset collection strategy information, which defines the monitoring requirements of the wind turbine hybrid tower, including the types of data to be collected, the collection scope, and the specific collection nodes. Based on this information, the platform determines the specific tasks of each collection node and obtains the collected data and location information of each node. The platform calculates the importance weight of each collection node. Combining the node weight coefficient and the actual collected data, the platform calculates a collection frequency coefficient, which is used to dynamically adjust the data collection frequency of each node. In this way, the platform can optimize the data collection process, ensuring that data from important nodes is collected more frequently, thereby improving monitoring accuracy and efficiency. The platform calculates an anomaly coefficient for each collection node, which reflects the degree of anomaly in the node data. Based on the anomaly coefficient, the platform conducts risk assessment on the node, identifying risky anomaly nodes and their specific locations. For each identified anomaly node, the platform generates a node anomaly chain, which reflects the correlation between the anomaly nodes. The platform calculates an anomaly cumulative coefficient for each node in the anomaly chain, which takes into account the degree of anomaly of the node itself and its position in the anomaly chain. Based on the abnormal accumulation coefficient, the platform issues risk warnings for different nodes, reminding operation and maintenance personnel to pay attention to and deal with potential security issues in a timely manner.

[0072] The technical effect of the above technical solution is as follows: by dynamically adjusting the collection frequency, the platform can ensure that data from important nodes is collected more frequently, thereby improving the accuracy and efficiency of monitoring. By calculating the node risk coefficient and the cumulative coefficient of anomalies on the anomaly chain, the platform can more accurately identify potential and cumulative safety risks, and improve the sensitivity and accuracy of risk identification. The risk warning information provided by the platform can help operation and maintenance personnel understand the safety status of wind turbine hybrid towers more promptly, so as to make more reasonable operation and maintenance decisions and reduce losses caused by equipment failures. Through real-time monitoring and early warning, the platform helps to improve the overall safety of wind turbine hybrid towers, reduce the probability of safety accidents, and ensure the stable operation of wind farms.

[0073] In one embodiment of the present invention, the node data acquisition module includes:

[0074] The node determination module is used to obtain preset collection strategy information, wherein the preset collection strategy information includes preset collection types, each preset collection type includes a preset collection range, and each preset collection range includes a collection node; the preset collection types include current, voltage, temperature, humidity, vibration and speed, etc.

[0075] The preset acquisition range is the maximum data acquisition range of the sensor group;

[0076] The acquisition node is the center point of the preset acquisition range;

[0077] The sensor group is arranged at the central point;

[0078] The node collection module is used to collect data from each collection node of the wind power hybrid tower and obtain node collection data;

[0079] The corresponding node of each node collecting data is located and the node positioning information is obtained.

[0080] The working principle of the above technical solution is as follows: The smart wind turbine hybrid tower safety monitoring online management platform first obtains preset collection strategy information. This information details the types of data to be monitored, namely the preset collection types, including key parameters such as current, voltage, temperature, humidity, vibration, and speed. These parameters are important indicators for evaluating the operating status of the wind turbine hybrid tower. For each preset collection type, the platform sets a preset collection range, which represents the maximum coverage area of ​​the sensor group's data collection. Within this range, the platform selects a central point as the collection node, and the sensor group is installed at this central point to accurately collect data from this area. During actual operation, the platform regularly collects data from each collection node on the wind turbine hybrid tower, obtaining real-time node data. The platform also locates each collection node and records its accurate node location information. This allows the collected data to be associated with the specific collection node, providing a basis for subsequent data analysis and processing.

[0081] The technical effect of the above technical solution is: by pre-setting the collection strategy information, the platform can ensure comprehensive and accurate data collection of the key parameters of the wind power hybrid tower, without missing any important monitoring points. Each collection node has corresponding node positioning information, which makes it easier for the platform to match the data with the specific collection location when processing and analyzing the data, thereby improving the efficiency of data processing. The platform can collect the operating data of the wind power hybrid tower in real time and accurately record the collection location and collection time of the data, which provides real-time and accurate data support for the safety monitoring of the wind power hybrid tower. By collecting and analyzing data in real time, the platform can promptly detect anomalies and potential risks in the operation of the wind power hybrid tower, and issue early warning signals to provide timely decision-making support for operation and maintenance personnel and ensure the safe operation of the wind power hybrid tower.

[0082] In one embodiment of the present invention, the acquisition frequency adjustment module includes:

[0083] A weight determination module is used to calculate the positioning node weight coefficient based on the node collection data combined with the preset positioning monitoring level of the node positioning information;

[0084] The calculation formula of the positioning node weight coefficient is:

[0085]

[0086] Among them, Q jd is the weight coefficient of the positioning node, z is the number of preset collection types, C maxi is the maximum data collected so far for the i-th preset collection type, C mini is the minimum data collected so far for the i-th preset collection type, C bzi is the standard data of the i-th preset collection type, D j It is the preset positioning monitoring level value, ranging from 1 to 10;

[0087] Setting a positioning weight coefficient of a corresponding node according to the positioning node weight coefficient;

[0088] A frequency adjustment module, configured to calculate an acquisition frequency coefficient of an acquisition node based on the weight coefficient and the node acquisition data;

[0089] The calculation formula of the acquisition frequency coefficient is:

[0090]

[0091] Among them, P cx is the acquisition frequency coefficient, s is the total number of times the node acquires data, A c is the collected data of the cth data collection, A c-1 The data collected from the c-1th data collection, B y Collect fluctuation thresholds for preset data;

[0092] Comparing the acquisition frequency coefficient with a preset frequency coefficient threshold to obtain a frequency coefficient comparison result;

[0093] When the acquisition frequency coefficient is greater than the preset frequency coefficient threshold, the acquisition frequency is adjusted, otherwise, no adjustment is performed;

[0094] The acquisition frequency of the acquisition node is adjusted according to the frequency coefficient comparison result to obtain a frequency adjustment result.

[0095] The working principle of the above technical solution is as follows: After obtaining node data and node location information, the smart wind turbine hybrid tower safety monitoring online management platform further calculates a weight coefficient for each location node. Once the weight coefficients are calculated, the platform sets a corresponding weight coefficient for each node. This data will be used to adjust the subsequent data collection frequency. Based on the weight coefficients and the real-time status of the node's collected data, the platform calculates a collection frequency coefficient. This coefficient reflects the urgency of node data collection, that is, how frequently data updates should be performed. The platform compares the calculated collection frequency coefficient with a preset frequency coefficient threshold. If the collection frequency coefficient is greater than the threshold, the node's data is changing rapidly or is more important, requiring more frequent data collection. Conversely, if the collection frequency coefficient is less than or equal to the threshold, the current data collection frequency is sufficient and no adjustment is required. Based on the frequency coefficient comparison results, the platform adjusts the collection frequency of the collection node accordingly to achieve the optimal frequency adjustment result. In this way, the platform can dynamically adjust the data collection frequency based on the actual operating status and monitoring needs of the wind turbine hybrid tower, ensuring real-time data accuracy while avoiding unnecessary resource waste.

[0096] The technical effect of the above technical solution is as follows: by calculating the weight coefficient of the positioning node, the platform can more accurately identify important monitoring nodes, making data collection more targeted. According to the calculation of the weight coefficient and the acquisition frequency coefficient, the platform can dynamically adjust the frequency of data acquisition, which not only ensures the real-time nature of the data, but also avoids waste of resources. Through real-time data collection and frequency adjustment, the platform can more efficiently monitor the operating status of the wind power hybrid tower and promptly discover and deal with potential safety hazards. The platform can perform adaptive adjustments based on the actual operating conditions and monitoring needs of the wind power hybrid tower, thereby improving the flexibility and reliability of the system.

[0097] In one embodiment of the present invention, the node abnormality locating module includes:

[0098] The risk calculation module is used to calculate the node risk coefficient based on the node's positioning collection data, weight coefficient and current collection frequency;

[0099] The calculation formula of the node risk coefficient is:

[0100]

[0101] Among them, F jx is the node risk coefficient, P dcx is the current data collection frequency, P ccx is the initial data collection frequency.

[0102] A risk comparison module is used to compare the node risk coefficient with a preset risk coefficient threshold to obtain a risk comparison result;

[0103] Perform risk assessment on the node according to the risk comparison result to obtain a risk assessment result;

[0104] When the node risk coefficient is greater than the preset risk coefficient threshold, the node is determined to be a risk abnormal node; otherwise, it is determined to be a risk normal node;

[0105] The abnormality acquisition module is used to obtain risk abnormal nodes and positioning data of the abnormal nodes according to the risk determination result.

[0106] The working principle of the above technical solution is as follows: After obtaining the node's location data, weight coefficient, and current data collection frequency, the smart wind turbine hybrid tower safety monitoring online management platform further calculates the risk factor for each node. This factor is determined based on the node's real-time data value, its historical trend (reflected by the weight coefficient), and the data collection frequency. The formula takes these three factors into account to derive a coefficient that reflects the node's risk level. After determining the risk factor, the platform compares it with a preset risk factor threshold. This threshold is determined based on wind turbine hybrid tower safety operation standards and historical data analysis and represents the acceptable level of node risk. If the node's risk factor exceeds this threshold, it indicates significant anomalies or fluctuations in the node's data, potentially posing a threat to the safe operation of the wind turbine hybrid tower. Based on the risk comparison results, the platform conducts a risk assessment for the node and outputs a risk assessment result. If the node's risk factor exceeds the preset risk factor threshold, the platform identifies it as an abnormally risky node; conversely, if the risk factor is less than or equal to the threshold, it is identified as a normal-risk node. For nodes that are identified as risky and abnormal, the platform will further obtain their positioning data so that operation and maintenance personnel can quickly and accurately find these nodes and conduct further inspection and processing.

[0107] The technical effect of the above technical solution is as follows: by comprehensively considering the node's positioning and collection data, weight coefficient, and collection frequency, the platform can more accurately identify potential risk nodes. The platform can calculate the node's risk coefficient in real time and compare it with the preset threshold to promptly detect and warn of potential safety hazards. By providing risk determination results and the positioning data of risky abnormal nodes, the platform can help operation and maintenance personnel more accurately understand the safety status of wind turbine hybrid towers, thereby making more reasonable operation and maintenance decisions. By real-time monitoring and warning of potential risks, the platform helps to improve the overall safety of wind turbine hybrid towers and reduce the probability of safety accidents.

[0108] In one embodiment of the present invention, the node abnormality warning module includes:

[0109] A cumulative calculation module is used to generate a node abnormality chain according to the positioning data of the abnormal node, and calculate the abnormal cumulative coefficient of the corresponding node according to the weight coefficient and acquisition frequency data of each node in the node abnormality chain combined with the positioning acquisition data;

[0110] The calculation formula of the abnormal accumulation coefficient is:

[0111]

[0112] Among them, Y lj is the abnormal cumulative coefficient, Q djd is the current weight coefficient of the corresponding node, F djx is the risk coefficient of the corresponding node, l is the total number of nodes up to the current node, Q djde is the weight coefficient of the e-th node within the current node, F djxe is the node risk coefficient of the e-th node within the current node;

[0113] A cumulative comparison module, configured to compare the abnormal cumulative coefficient with a preset cumulative coefficient threshold to obtain a cumulative coefficient comparison result;

[0114] Perform risk warning on the node according to the cumulative coefficient comparison result to obtain risk warning information;

[0115] The cumulative warning module is used to issue a risk warning to the node when the abnormal cumulative coefficient is greater than the preset cumulative coefficient threshold. The warning level is a multiple of the abnormal cumulative coefficient relative to the preset cumulative coefficient threshold; otherwise, no risk warning is issued.

[0116] The working principle of the above technical solution is as follows: After identifying risky abnormal nodes, the smart wind turbine hybrid tower safety monitoring online management platform further generates a node abnormality chain based on the location data of these nodes. The node abnormality chain is an ordered list that connects abnormal nodes according to the control logic sequence between nodes, allowing for better analysis and handling of abnormal conditions at these nodes. The platform calculates the abnormality accumulation coefficient for each node in the node abnormality chain. This coefficient is determined based on the node weight coefficient, acquisition frequency data, and location acquisition data. The formula takes these three factors into account to derive a cumulative coefficient that reflects the severity of the node abnormality. This coefficient not only considers the abnormal condition of the node itself, but also its position in the abnormality chain and its relationship with other abnormal nodes. After obtaining the abnormality accumulation coefficient, the platform compares it with a preset cumulative coefficient threshold. This threshold is determined based on wind turbine hybrid tower safety operation standards and historical data analysis, and represents the acceptable level of node abnormality accumulation. If the node abnormality accumulation coefficient exceeds this threshold, it indicates that the abnormal condition of the node is severe and may pose a significant threat to the safe operation of the wind turbine hybrid tower. Based on the results of the cumulative coefficient comparison, the platform will issue a risk warning for the node and generate risk warning information. If the node's abnormal cumulative coefficient exceeds the preset cumulative coefficient threshold, the platform will issue a risk warning. The warning level is determined by the multiple of the abnormal cumulative coefficient relative to the preset cumulative coefficient threshold. The higher the warning level, the more serious the node's abnormality, requiring more attention and action from operations and maintenance personnel. Conversely, if the abnormal cumulative coefficient is less than or equal to the threshold, no risk warning is issued.

[0117] The technical effect of the above technical solution is as follows: by comprehensively considering the node weight coefficient, collection frequency data and positioning collection data, the platform can more accurately calculate the node's abnormal cumulative coefficient and issue risk warnings based on the comparison results with the preset threshold, thereby improving the accuracy and pertinence of the warning. By generating a node abnormality chain and calculating the abnormal cumulative coefficient, the platform can associate multiple abnormal nodes for analysis and processing, optimize the risk warning processing flow, and improve operation and maintenance efficiency. The platform can adaptively adjust and issue warnings based on the actual operating conditions and monitoring needs of the wind power hybrid tower, improving the system's adaptability and robustness. By real-time monitoring, warning and processing of potential risks, the platform helps to improve the overall safety of the wind power hybrid tower, reduce the probability of safety accidents, and ensure the stable operation of the wind farm.

[0118] In one embodiment of the present invention, the management method includes:

[0119] S1. Obtain preset collection strategy information, determine the preset collection type, preset collection range and collection nodes of the wind turbine hybrid tower according to the preset collection strategy information, and obtain node collection data and node positioning information of each collection node;

[0120] S2. Calculate the weight coefficient of the collection node, calculate the collection frequency coefficient based on the weight coefficient and the node collection data, adjust the collection frequency based on the collection frequency coefficient, and obtain a frequency adjustment result;

[0121] S3. Calculate the node risk coefficient, perform risk assessment on the node, and obtain the abnormal risk nodes and the location data of the abnormal nodes;

[0122] S4. Generate a node abnormality chain based on the abnormal node, calculate the abnormality cumulative coefficient of each node on the node abnormality chain, and issue risk warnings for different nodes based on the abnormality cumulative coefficient.

[0123] The working principle of the above technical solution is as follows: the platform first obtains preset collection strategy information, which defines the monitoring requirements of the wind turbine hybrid tower, including the types of data to be collected, the collection scope, and the specific collection nodes. Based on this information, the platform determines the specific tasks of each collection node and obtains the collected data and location information of each node. The platform calculates the importance weight of each collection node. Combining the node weight coefficient and the actual collected data, the platform calculates a collection frequency coefficient, which is used to dynamically adjust the data collection frequency of each node. In this way, the platform can optimize the data collection process, ensuring that data from important nodes is collected more frequently, thereby improving monitoring accuracy and efficiency. The platform calculates an anomaly coefficient for each collection node, which reflects the degree of anomaly in the node data. Based on the anomaly coefficient, the platform conducts risk assessment on the node, identifying risky anomaly nodes and their specific locations. For each identified anomaly node, the platform generates a node anomaly chain, which reflects the correlation between the anomaly nodes. The platform calculates an anomaly cumulative coefficient for each node in the anomaly chain, which takes into account the degree of anomaly of the node itself and its position in the anomaly chain. Based on the abnormal accumulation coefficient, the platform issues risk warnings for different nodes, reminding operation and maintenance personnel to pay attention to and deal with potential security issues in a timely manner.

[0124] The technical effect of the above technical solution is as follows: by dynamically adjusting the collection frequency, the platform can ensure that data from important nodes is collected more frequently, thereby improving the accuracy and efficiency of monitoring. By calculating the node risk coefficient and the cumulative coefficient of anomalies on the anomaly chain, the platform can more accurately identify potential and cumulative safety risks, and improve the sensitivity and accuracy of risk identification. The risk warning information provided by the platform can help operation and maintenance personnel understand the safety status of wind turbine hybrid towers more promptly, so as to make more reasonable operation and maintenance decisions and reduce losses caused by equipment failures. Through real-time monitoring and early warning, the platform helps to improve the overall safety of wind turbine hybrid towers, reduce the probability of safety accidents, and ensure the stable operation of wind farms.

[0125] In one embodiment of the present invention, the S1 includes:

[0126] Acquire preset acquisition strategy information, wherein the preset acquisition strategy information includes preset acquisition types, each preset acquisition type includes a preset acquisition range, and each preset acquisition range includes a collection node; the preset acquisition types include current, voltage, temperature, humidity, vibration, and speed, etc.

[0127] The preset acquisition range is the maximum data acquisition range of the sensor group;

[0128] The acquisition node is the center point of the preset acquisition range;

[0129] The sensor group is arranged at the central point;

[0130] Collect data from each collection node of the wind power hybrid tower to obtain node collection data;

[0131] The corresponding node of each node collecting data is located and the node positioning information is obtained.

[0132] The working principle of the above technical solution is as follows: The smart wind turbine hybrid tower safety monitoring online management platform first obtains preset collection strategy information. This information details the types of data to be monitored, namely the preset collection types, including key parameters such as current, voltage, temperature, humidity, vibration, and speed. These parameters are important indicators for evaluating the operating status of the wind turbine hybrid tower. For each preset collection type, the platform sets a preset collection range, which represents the maximum coverage area of ​​the sensor group's data collection. Within this range, the platform selects a central point as the collection node, and the sensor group is installed at this central point to accurately collect data from this area. During actual operation, the platform regularly collects data from each collection node on the wind turbine hybrid tower, obtaining real-time node data. The platform also locates each collection node and records its accurate node location information. This allows the collected data to be associated with the specific collection node, providing a basis for subsequent data analysis and processing.

[0133] The technical effect of the above technical solution is: by pre-setting the collection strategy information, the platform can ensure comprehensive and accurate data collection of the key parameters of the wind power hybrid tower, without missing any important monitoring points. Each collection node has corresponding node positioning information, which makes it easier for the platform to match the data with the specific collection location when processing and analyzing the data, thereby improving the efficiency of data processing. The platform can collect the operating data of the wind power hybrid tower in real time and accurately record the collection location and collection time of the data, which provides real-time and accurate data support for the safety monitoring of the wind power hybrid tower. By collecting and analyzing data in real time, the platform can promptly detect anomalies and potential risks in the operation of the wind power hybrid tower, and issue early warning signals to provide timely decision-making support for operation and maintenance personnel and ensure the safe operation of the wind power hybrid tower.

[0134] In one embodiment of the present invention, the S2 includes:

[0135] Calculate the positioning node weight coefficient based on the node collection data and the preset positioning monitoring level of the node positioning information;

[0136] The calculation formula of the positioning node weight coefficient is:

[0137]

[0138] Among them, Q jd is the weight coefficient of the positioning node, z is the number of preset collection types, C maxi is the maximum data collected so far for the i-th preset collection type, C mini is the minimum data collected so far for the i-th preset collection type, C bzi is the standard data of the i-th preset collection type, D j It is the preset positioning monitoring level value, ranging from 1 to 10;

[0139] Setting a positioning weight coefficient of a corresponding node according to the positioning node weight coefficient;

[0140] Calculating the acquisition frequency coefficient of the acquisition node according to the weight coefficient and the node acquisition data;

[0141] The calculation formula of the acquisition frequency coefficient is:

[0142]

[0143] Among them, P cx is the acquisition frequency coefficient, s is the total number of times the node acquires data, A c is the collected data of the cth data collection, A c-1 The data collected for the c-1th data collection, B y Collect fluctuation thresholds for preset data;

[0144] Comparing the acquisition frequency coefficient with a preset frequency coefficient threshold to obtain a frequency coefficient comparison result;

[0145] When the acquisition frequency coefficient is greater than the preset frequency coefficient threshold, the acquisition frequency is adjusted, otherwise, no adjustment is performed;

[0146] The acquisition frequency of the acquisition node is adjusted according to the frequency coefficient comparison result to obtain a frequency adjustment result.

[0147] The working principle of the above technical solution is as follows: After obtaining node data and node location information, the smart wind turbine hybrid tower safety monitoring online management platform further calculates a weight coefficient for each location node. Once the weight coefficients are calculated, the platform sets a corresponding weight coefficient for each node. This data will be used to adjust the subsequent data collection frequency. Based on the weight coefficients and the real-time status of the node's collected data, the platform calculates a collection frequency coefficient. This coefficient reflects the urgency of node data collection, that is, how frequently data updates should be performed. The platform compares the calculated collection frequency coefficient with a preset frequency coefficient threshold. If the collection frequency coefficient is greater than the threshold, the node's data is changing rapidly or is more important, requiring more frequent data collection. Conversely, if the collection frequency coefficient is less than or equal to the threshold, the current data collection frequency is sufficient and no adjustment is required. Based on the frequency coefficient comparison results, the platform adjusts the collection frequency of the collection node accordingly to achieve the optimal frequency adjustment result. In this way, the platform can dynamically adjust the data collection frequency based on the actual operating status and monitoring needs of the wind turbine hybrid tower, ensuring real-time data accuracy while avoiding unnecessary resource waste.

[0148] The technical effect of the above technical solution is as follows: by calculating the weight coefficient of the positioning node, the platform can more accurately identify important monitoring nodes, making data collection more targeted. According to the calculation of the weight coefficient and the acquisition frequency coefficient, the platform can dynamically adjust the frequency of data acquisition, which not only ensures the real-time nature of the data, but also avoids waste of resources. Through real-time data collection and frequency adjustment, the platform can more efficiently monitor the operating status of the wind power hybrid tower and promptly discover and deal with potential safety hazards. The platform can perform adaptive adjustments based on the actual operating conditions and monitoring needs of the wind power hybrid tower, thereby improving the flexibility and reliability of the system.

[0149] In one embodiment of the present invention, S3 includes:

[0150] Calculate the node risk coefficient based on the node's positioning collection data, weight coefficient and current collection frequency;

[0151] The calculation formula of the node risk coefficient is:

[0152]

[0153] Among them, F jx is the node risk coefficient, P dcx is the current data collection frequency, P ccx is the initial data collection frequency.

[0154] Comparing the node risk coefficient with a preset risk coefficient threshold to obtain a risk comparison result;

[0155] Perform risk assessment on the node according to the risk comparison result to obtain a risk assessment result;

[0156] When the node risk coefficient is greater than the preset risk coefficient threshold, the node is determined to be a risk abnormal node; otherwise, it is determined to be a risk normal node;

[0157] Obtain risk abnormal nodes and location data of the abnormal nodes according to the risk determination result.

[0158] The working principle of the above technical solution is as follows: After obtaining the node's location data, weight coefficient, and current data collection frequency, the smart wind turbine hybrid tower safety monitoring online management platform further calculates the risk factor for each node. This factor is determined based on the node's real-time data value, its historical trend (reflected by the weight coefficient), and the data collection frequency. The formula takes these three factors into account to derive a coefficient that reflects the node's risk level. After determining the risk factor, the platform compares it with a preset risk factor threshold. This threshold is determined based on wind turbine hybrid tower safety operation standards and historical data analysis and represents the acceptable level of node risk. If the node's risk factor exceeds this threshold, it indicates significant anomalies or fluctuations in the node's data, potentially posing a threat to the safe operation of the wind turbine hybrid tower. Based on the risk comparison results, the platform conducts a risk assessment for the node and outputs a risk assessment result. If the node's risk factor exceeds the preset risk factor threshold, the platform identifies it as an abnormally risky node; conversely, if the risk factor is less than or equal to the threshold, it is identified as a normal-risk node. For nodes that are identified as risky and abnormal, the platform will further obtain their positioning data so that operation and maintenance personnel can quickly and accurately find these nodes and conduct further inspection and processing.

[0159] The technical effect of the above technical solution is as follows: by comprehensively considering the node's positioning and collection data, weight coefficient, and collection frequency, the platform can more accurately identify potential risk nodes. The platform can calculate the node's risk coefficient in real time and compare it with the preset threshold to promptly detect and warn of potential safety hazards. By providing risk determination results and the positioning data of risky abnormal nodes, the platform can help operation and maintenance personnel more accurately understand the safety status of wind turbine hybrid towers, thereby making more reasonable operation and maintenance decisions. By real-time monitoring and warning of potential risks, the platform helps to improve the overall safety of wind turbine hybrid towers and reduce the probability of safety accidents.

[0160] In one embodiment of the present invention, the S4 includes:

[0161] Generate a node abnormality chain based on the positioning data of the abnormal node, and calculate the abnormality cumulative coefficient of the corresponding node based on the weight coefficient and acquisition frequency data of each node in the node abnormality chain combined with the positioning acquisition data;

[0162] The calculation formula of the abnormal accumulation coefficient is:

[0163]

[0164] Among them, Y lj is the abnormal cumulative coefficient, Q djd is the current weight coefficient of the corresponding node, F djx is the risk coefficient of the corresponding node, l is the total number of nodes up to the current node, Q djde is the weight coefficient of the e-th node within the current node, F djxe is the node risk coefficient of the e-th node within the current node;

[0165] Comparing the abnormal cumulative coefficient with a preset cumulative coefficient threshold to obtain a cumulative coefficient comparison result;

[0166] Perform risk warning on the node according to the cumulative coefficient comparison result to obtain risk warning information;

[0167] When the abnormal cumulative coefficient is greater than the preset cumulative coefficient threshold, a risk warning is issued for the node, and the warning level is a multiple of the abnormal cumulative coefficient relative to the preset cumulative coefficient threshold; otherwise, no risk warning is issued.

[0168] The working principle of the above technical solution is as follows: After identifying risky abnormal nodes, the smart wind turbine hybrid tower safety monitoring online management platform further generates a node abnormality chain based on the location data of these nodes. The node abnormality chain is an ordered list that connects abnormal nodes according to the control logic sequence between nodes, allowing for better analysis and handling of abnormal conditions at these nodes. The platform calculates the abnormality accumulation coefficient for each node in the node abnormality chain. This coefficient is determined based on the node weight coefficient, acquisition frequency data, and location acquisition data. The formula takes these three factors into account to derive a cumulative coefficient that reflects the severity of the node abnormality. This coefficient not only considers the abnormal condition of the node itself, but also its position in the abnormality chain and its relationship with other abnormal nodes. After obtaining the abnormality accumulation coefficient, the platform compares it with a preset cumulative coefficient threshold. This threshold is determined based on wind turbine hybrid tower safety operation standards and historical data analysis, and represents the acceptable level of node abnormality accumulation. If the node abnormality accumulation coefficient exceeds this threshold, it indicates that the abnormal condition of the node is severe and may pose a significant threat to the safe operation of the wind turbine hybrid tower. Based on the results of the cumulative coefficient comparison, the platform will issue a risk warning for the node and generate risk warning information. If the node's abnormal cumulative coefficient exceeds the preset cumulative coefficient threshold, the platform will issue a risk warning. The warning level is determined by the multiple of the abnormal cumulative coefficient relative to the preset cumulative coefficient threshold. The higher the warning level, the more serious the node's abnormality, requiring more attention and action from operations and maintenance personnel. Conversely, if the abnormal cumulative coefficient is less than or equal to the threshold, no risk warning is issued.

[0169] The technical effect of the above technical solution is as follows: by comprehensively considering the node weight coefficient, collection frequency data and positioning collection data, the platform can more accurately calculate the node's abnormal cumulative coefficient and issue risk warnings based on the comparison results with the preset threshold, thereby improving the accuracy and pertinence of the warning. By generating a node abnormality chain and calculating the abnormal cumulative coefficient, the platform can associate multiple abnormal nodes for analysis and processing, optimize the risk warning processing flow, and improve operation and maintenance efficiency. The platform can adaptively adjust and issue warnings based on the actual operating conditions and monitoring needs of the wind power hybrid tower, improving the system's adaptability and robustness. By real-time monitoring, warning and processing of potential risks, the platform helps to improve the overall safety of the wind power hybrid tower, reduce the probability of safety accidents, and ensure the stable operation of the wind farm.

[0170] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. Smart wind power hybrid tower safety monitoring online management platform, characterized by: The platform includes: A node data acquisition module is used to obtain preset acquisition strategy information, determine the preset acquisition type, preset acquisition range and acquisition nodes of the wind power hybrid tower according to the preset acquisition strategy information, and obtain node acquisition data and node positioning information of each acquisition node; Wherein, the node data acquisition module includes: A node determination module is used to obtain preset collection strategy information, wherein the preset collection strategy information includes preset collection types, each preset collection type includes a preset collection range, and each preset collection range includes a collection node; The preset acquisition range is the maximum data acquisition range of the sensor group; The acquisition node is the center point of the preset acquisition range; The sensor group is arranged at the central point; The node collection module is used to collect data from each collection node of the wind power hybrid tower and obtain node collection data; Locate the corresponding node of each node collecting data and obtain node positioning information; An acquisition frequency adjustment module is used to calculate a weight coefficient of an acquisition node, calculate an acquisition frequency coefficient based on the weight coefficient combined with the node acquisition data, and adjust the acquisition frequency based on the acquisition frequency coefficient to obtain a frequency adjustment result; The calculation formula of the positioning node weight coefficient is: Among them, Q jd is the weight coefficient of the positioning node, z is the number of preset collection types, C maxi is the maximum data collected so far for the i-th preset collection type, C mini is the minimum data collected so far for the i-th preset collection type, C bzi is the standard data of the i-th preset collection type, D j It is the preset positioning monitoring level value, ranging from 1 to 10; The calculation formula of the acquisition frequency coefficient is: Wherein, Pcx is the acquisition frequency coefficient, s is the total number of data acquisition times of the node, Ac is the collected data of the cth data acquisition, Ac-1 is the collected data of the c-1th data acquisition, and By is the preset data acquisition fluctuation threshold; The node anomaly positioning module is used to calculate the node risk coefficient, perform risk assessment on the node, and obtain the risk anomaly node and the positioning data of the anomaly node; The calculation formula of the node risk coefficient is: Among them, F jx is the node risk coefficient, P dcx is the current data collection frequency, P ccx is the initial data collection frequency; A node anomaly warning module is used to generate a node anomaly chain based on the abnormal node, calculate the abnormal cumulative coefficient of each node on the node anomaly chain, and issue risk warnings for different nodes based on the abnormal cumulative coefficient; The calculation formula of the abnormal accumulation coefficient is: Among them, Y lj is the abnormal cumulative coefficient, Q djd is the current weight coefficient of the corresponding node, F djx is the risk coefficient of the corresponding node, l is the total number of nodes up to the current node, Q djde is the weight coefficient of the e-th node within the current node, F djxe is the node risk coefficient of the e-th node within the current node.

2. The smart wind power hybrid tower safety monitoring online management platform according to claim 1 is characterized in that: The acquisition frequency adjustment module includes: Setting a positioning weight coefficient of a corresponding node according to the positioning node weight coefficient; Comparing the acquisition frequency coefficient with a preset frequency coefficient threshold to obtain a frequency coefficient comparison result; The acquisition frequency of the acquisition node is adjusted according to the frequency coefficient comparison result to obtain a frequency adjustment result.

3. The smart wind power hybrid tower safety monitoring online management platform according to claim 2 is characterized in that: The node abnormality positioning module includes: A risk comparison module is used to compare the node risk coefficient with a preset risk coefficient threshold to obtain a risk comparison result; Perform risk assessment on the node according to the risk comparison result to obtain a risk assessment result; The abnormality acquisition module is used to obtain risk abnormal nodes and positioning data of the abnormal nodes according to the risk determination result.

4. The smart wind power hybrid tower safety monitoring online management platform according to claim 3 is characterized in that: The node abnormality warning module includes: A cumulative comparison module is used to compare the abnormal cumulative coefficient with a preset cumulative coefficient threshold to obtain a cumulative coefficient comparison result; Perform risk warning on the node according to the cumulative coefficient comparison result to obtain risk warning information; The cumulative warning module is used to issue a risk warning to the node when the abnormal cumulative coefficient is greater than the preset cumulative coefficient threshold. The warning level is a multiple of the abnormal cumulative coefficient relative to the preset cumulative coefficient threshold; otherwise, no risk warning is issued.

5. A management method for implementing the smart wind power hybrid tower safety monitoring online management platform as claimed in claim 1, characterized in that: The management method includes: S1. Obtain preset collection strategy information, determine the preset collection type, preset collection range and collection nodes of the wind turbine hybrid tower according to the preset collection strategy information, and obtain node collection data and node positioning information of each collection node; Wherein, the S1 includes: Acquire preset collection strategy information, where the preset collection strategy information includes preset collection types, each preset collection type includes a preset collection range, and each preset collection range includes a collection node; The preset acquisition range is the maximum data acquisition range of the sensor group; The acquisition node is the center point of the preset acquisition range; The sensor group is arranged at the central point; Collect data from each collection node of the wind power hybrid tower to obtain node collection data; Locate the corresponding node of each node collecting data and obtain node positioning information; S2. Calculate the weight coefficient of the collection node, calculate the collection frequency coefficient based on the weight coefficient and the node collection data, adjust the collection frequency based on the collection frequency coefficient, and obtain a frequency adjustment result; The calculation formula of the positioning node weight coefficient is: Among them, Q jd is the weight coefficient of the positioning node, z is the number of preset collection types, C maxi is the maximum data collected so far for the i-th preset collection type, C mini is the minimum data collected so far for the i-th preset collection type, C bzi is the standard data of the i-th preset collection type, D j It is the preset positioning monitoring level value, ranging from 1 to 10; The calculation formula of the acquisition frequency coefficient is: Wherein, Pcx is the acquisition frequency coefficient, s is the total number of data acquisition times of the node, Ac is the collected data of the cth data acquisition, Ac-1 is the collected data of the c-1th data acquisition, and By is the preset data acquisition fluctuation threshold; S3. Calculate the node risk coefficient, perform risk assessment on the node, and obtain the abnormal risk nodes and the location data of the abnormal nodes; The calculation formula of the node risk coefficient is: Among them, F jx is the node risk coefficient, P dcx is the current data collection frequency, P ccx is the initial data collection frequency; S4. Generate a node abnormality chain based on the abnormal node, calculate the abnormality cumulative coefficient of each node on the node abnormality chain, and issue risk warnings for different nodes based on the abnormality cumulative coefficient; The calculation formula of the abnormal accumulation coefficient is: Among them, Y lj is the abnormal cumulative coefficient, Q djd is the current weight coefficient of the corresponding node, F djx is the risk coefficient of the corresponding node, l is the total number of nodes up to the current node, Q djde is the weight coefficient of the e-th node within the current node, F djxe is the node risk coefficient of the e-th node within the current node.

6. The management method of the smart wind power hybrid tower safety monitoring online management platform according to claim 5 is characterized in that: The S2 includes: Setting a positioning weight coefficient of a corresponding node according to the positioning node weight coefficient; Comparing the acquisition frequency coefficient with a preset frequency coefficient threshold to obtain a frequency coefficient comparison result; The acquisition frequency of the acquisition node is adjusted according to the frequency coefficient comparison result to obtain a frequency adjustment result.

7. The management method of the smart wind power hybrid tower safety monitoring online management platform according to claim 6 is characterized in that: The S3 includes: Comparing the node risk coefficient with a preset risk coefficient threshold to obtain a risk comparison result; Perform risk assessment on the node according to the risk comparison result to obtain a risk assessment result; Obtain risk abnormal nodes and location data of the abnormal nodes according to the risk determination result.

8. The management method of the smart wind power hybrid tower safety monitoring online management platform according to claim 7 is characterized in that: The S4 includes: Comparing the abnormal cumulative coefficient with a preset cumulative coefficient threshold to obtain a cumulative coefficient comparison result; Perform risk warning on the node according to the cumulative coefficient comparison result to obtain risk warning information; When the abnormal cumulative coefficient is greater than the preset cumulative coefficient threshold, a risk warning is issued for the node, and the warning level is a multiple of the abnormal cumulative coefficient relative to the preset cumulative coefficient threshold; otherwise, no risk warning is issued.

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