A high-risk operation intelligent monitoring management system based on multi-element linkage

Abstract

The present application relates to the technical field of monitoring high-risk operations, and particularly relates to a high-risk operation intelligent monitoring management system based on multi-element linkage. The present application constructs the mutual linkage of key elements of high-risk operations to achieve the technical standard of risk perception and control in the operation process at the safety level. At the same time, through an electronic fence automatic drawing algorithm and an electronic fence automatic validation algorithm, the operation risk state is determined in a limited space. Through micro-network data acquisition, deep learning and continuous training, an operation risk high-accuracy AI model is obtained to maximize the elimination of safety hazards in the work of operation personnel on the fan platform working surface and the fan tower working surface. The working state of the crane is determined by the proportion of time nodes of the crane outside the electronic fence within the preset time length, and corresponding adjustment is made when the working state is unqualified, thereby improving the working accuracy of the crane. The present application effectively monitors the operation risk and improves the work efficiency.

Description

Technical Field

[0001] This invention relates to the technical field of monitoring high-risk operations, and in particular to an intelligent monitoring and management system for high-risk operations based on multi-element linkage. Background Technology

[0002] The core technologies for monitoring high-risk industries in the power sector include sensor integration, fault diagnosis methods that combine data-driven approaches with physical models, and IoT-based remote monitoring systems.

[0003] In the traditional power industry, workers rely mainly on manual experience to judge the safety environment of the production site. Without access to comprehensive information, they often miss fault points. There are many overlapping operations at the production site, and accidents often occur due to a lack of communication. Relying solely on workers' intuition to grasp the surrounding safety situation inevitably leads to accidents. Operation management lacks intelligence, with task planning and execution methods being set manually, and analysis, prediction, and operation steps relying on the experience of production personnel.

[0004] Chinese Patent Application Publication No. CN118154941A discloses a method for safety management of high-risk operations. The method involves acquiring surveillance video of workers at heights, dividing the video into at least two sub-videos, and then dividing all sub-videos into video sets to obtain a first video set and a second video set. Sub-videos in the first video set are allocated to a temporary storage space. Target frames are extracted from the first video set, and several initial detection images are obtained based on the extracted target frames. The initial detection images are then converted to grayscale to obtain initial grayscale images. The similarity between the initial grayscale images and a preset initial grayscale image is calculated. High-risk operation safety management is then performed based on the relationship between the similarity and the preset similarity.

[0005] It is evident that the existing technology has the following problems: it cannot effectively monitor whether there are risks in the operation process, and it has low work efficiency. Summary of the Invention

[0006] Therefore, the present invention provides an intelligent monitoring and management system for high-risk operations based on multi-element linkage, in order to overcome the problems of existing technologies that cannot effectively monitor whether there are risks in the operation and have low work efficiency.

[0007] To achieve the above objectives, the present invention provides an intelligent monitoring and management system for high-risk operations based on multi-factor linkage, comprising:

[0008] The data acquisition unit is used to periodically acquire the operating data of the wind turbine equipment using sensors, including at least start / stop switch quantities, equipment vibration frequency, and equipment operating temperature.

[0009] The monitoring unit, which is connected to the data acquisition unit, is used to train the monitoring network based on the operating data of the wind turbine equipment acquired at historical times, and input the current operating data of the wind turbine equipment into the trained monitoring network to obtain the abnormal location of the wind turbine equipment.

[0010] The signal transmission unit is used to relay crane positioning data calculated by several UWB base stations to the wind turbine platform working face monitoring system via a surface buoy device.

[0011] A position determination unit, which is connected to the signal transmission unit, is used to determine whether the crane is within the electronic fence based on the working surface of the wind turbine platform, so as to obtain the position status of the crane.

[0012] The data acquisition unit is connected to the location determination unit and is used to acquire the location status of the crane at each time node within a preset time period, and calculate the proportion of marked nodes based on the location status. The marked node is the time node corresponding to when the crane's location exceeds the electronic fence.

[0013] An analysis unit, which is connected to the monitoring unit and the acquisition unit respectively, is used to determine the crane's operating status based on the proportion of the marked nodes, and to determine the frequency of adjusting the geographic coordinates of the electronic fence when the operating status is unqualified, and to adjust the spacing of the UWB base stations when the number of adjustments is equal to the preset number and the operating status is still unqualified.

[0014] Furthermore, the position determination unit is also used to draw an electronic fence based on the abnormal positioning of the wind turbine equipment. The electronic fence sets a safe operating area for the crane based on UWB positioning technology to monitor whether the crane is within the safe operating area. The position determination unit is also used to determine the position status of the crane based on the point-line judgment method set on the working surface of the wind turbine platform.

[0015] Furthermore, the position determination unit is also used to determine the position status of the crane based on the ray method set on the working surface of the wind turbine tower. When the point-line judgment method determines that the working status of the crane is unqualified, the position of the crane is adjusted and a second judgment is performed based on the ray method.

[0016] Furthermore, the signal transmission unit also includes several wireless signal probes, each of which is set at a preset distance from the corresponding sea surface buoy device to detect UWB signal strength in real time; the analysis unit is also used to calculate the variance of several UWB signal strengths, and, if the variance is greater than a preset variance, adjust the correction frequency of the geographic coordinates of the electronic fence based on the obtained variance; wherein, the above operation is performed when it is determined that the crane's operating status is unqualified.

[0017] Furthermore, the analysis unit is also used to increase the correction frequency of the electronic fence geographic coordinates based on the variance of several UWB signal strengths, and the increase in the correction frequency of the electronic fence geographic coordinates is proportional to the variance.

[0018] Furthermore, the analysis unit is also used to adjust the correction frequency of the geo coordinates of the electronic fence at least once when the working state of the crane is unqualified, after adjusting the correction frequency of the geo coordinates of the electronic fence. The adjustment is stopped when the number of adjustments is less than a preset number and the working state of the crane is qualified, or when the number of adjustments is equal to the preset number. The analysis unit is also used to redetermine the working state of the crane and, when the working state of the crane is unqualified, adjust the spacing of the UWB base station based on the measured distance between the crane and the electronic fence.

[0019] Furthermore, the analysis unit is also used to reduce the spacing between UWB base stations based on the difference between the distance and the preset distance, and the reduction in spacing is inversely proportional to the difference.

[0020] Furthermore, the analysis unit is also used to reduce the transmission power based on the spacing between UWB base stations, and the reduction in transmission power is inversely proportional to the spacing.

[0021] Furthermore, the analysis unit is also used to redetermine the crane's operating status after adjusting the spacing of the UWB base stations, and to plot the time node-crane position status curve within a preset time period when the crane's operating status is unqualified; the analysis unit is also used to increase the number of training iterations of the monitoring network based on the ratio of the curve integral to the preset curve integral when the curve integral is less than or equal to the preset curve integral, and the increase in the number of training iterations of the monitoring network is inversely proportional to the ratio; wherein, the value is set to 1 when the crane is within the electronic fence, and the value is set to 0 when the crane is not within the electronic fence.

[0022] Furthermore, the analysis unit is also used to redetermine the crane's operating status after adjusting the number of training iterations of the monitoring network, and, when the crane's operating status is unqualified, to reduce the cycle of the sensor acquiring the wind turbine's operating data based on the difference between the percentage of marked nodes within a preset time period and the preset percentage, and the reduction in the cycle of the sensor acquiring the wind turbine's operating data is proportional to the ratio.

[0023] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention constructs a system of interconnected key elements for high-risk operations, achieving a technical standard for risk perception and control during the operation process at the safety level; simultaneously, through automatic electronic fence drawing and automatic electronic fence activation algorithms, it enables the assessment of operational risk status within a limited space; furthermore, through microgrid data collection, deep learning, and continuous training, it derives a high-accuracy AI model for operational risk, minimizing safety hazards for workers operating on wind turbine platform and tower surfaces; and by determining the crane's working status based on the percentage of time the crane spends outside the electronic fence within a preset timeframe, and making corresponding adjustments when the working status is unqualified, it improves the crane's operational accuracy. This invention effectively monitors operational risks, thereby improving work efficiency.

[0024] Furthermore, the present invention determines the position of the crane based on the point-line judgment method set on the working surface of the wind turbine platform, and performs a second judgment on the position of the crane based on the ray method set on the working surface of the wind turbine tower after adjustment. This can more accurately determine the position of the crane, thereby enabling subsequent adjustments based on the position of the crane to be made more effectively.

[0025] Furthermore, this invention adjusts the correction frequency of the electronic fence's geographic coordinates when the variance of several UWB signal strengths is greater than a preset variance. This allows for timely adjustments when the preset fence coordinates become inaccurate due to the displacement caused by the impact of waves on the floating platform, thereby more accurately determining the crane's working status.

[0026] Furthermore, this invention adjusts the correction frequency of the electronic fence's geographic coordinates based on the variance of several UWB signal strengths. This allows for more accurate adjustment of the correction frequency, making the electronic fence's geographic coordinates more closely match the current state. Consequently, the working state of the crane can be determined more accurately, and parameters can be adjusted more precisely to ensure the crane's working state is up to standard, thereby further improving work efficiency.

[0027] Furthermore, this invention repeatedly adjusts the correction frequency of the geographic coordinates of the electronic fence. If the working state of the crane is still unqualified after the adjustment is stopped, the distance between the UWB base station is adjusted based on the distance between the crane and the electronic fence. This avoids the problem of large ranging error caused by the reflection of UWB signals by the tower steel structure, thereby making the path of UWB signal detection more accurate and further improving work efficiency.

[0028] Furthermore, the present invention adjusts the transmission power based on the spacing of UWB base stations, which can avoid co-channel interference when base stations are densely deployed, thereby making the UWB signal monitoring results more accurate, that is, more accurate in determining the working status of the crane, and thus further improving work efficiency.

[0029] Furthermore, the present invention adjusts the number of training iterations of the monitoring network based on the ratio of the integral of the time node-crane position state curve within a preset time period to the integral of the preset curve, which can make the results obtained by the monitoring network more accurate, thus making the crane position more accurate and further improving work efficiency.

[0030] Furthermore, if the crane's working status is still unqualified after adjusting the number of training iterations of the monitoring network, the present invention adjusts the cycle of the sensor acquiring the wind turbine's operating data, which can acquire the wind turbine's operating data more effectively and timely, making the results output by the monitoring network more accurate, and thus further improving work efficiency. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the structure of the intelligent monitoring and management system for high-risk operations based on multi-element linkage, according to an embodiment of the present invention.

[0032] Figure 2 This is a flowchart illustrating the steps of the intelligent monitoring and management method for high-risk operations based on multi-element linkage, as described in an embodiment of the present invention.

[0033] Figure 3 This is a diagram showing the area where the lifting equipment on the working face of the wind turbine platform exceeds its capacity, according to an embodiment of the present invention.

[0034] Figure 4 A ray-based area diagram showing the working surface of the wind turbine tower in an embodiment of the present invention;

[0035] Figure 5 This is a flowchart illustrating the steps for determining the proportion of marked nodes based on a comparison with a preset proportion in an embodiment of the present invention. Detailed Implementation

[0036] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0037] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0038] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0039] Please see Figure 1 As shown, it is a structural schematic diagram of the intelligent monitoring and management system for high-risk operations based on multi-element linkage according to an embodiment of the present invention;

[0040] The system includes a data acquisition unit, a monitoring unit, a signal transmission unit, a location determination unit, a data acquisition unit, and an analysis unit.

[0041] The data acquisition unit is used to periodically acquire the operating data of the wind turbine equipment using sensors, including at least start / stop switch quantities, equipment vibration frequency, and equipment operating temperature;

[0042] The monitoring unit is connected to the data acquisition unit and is used to train the monitoring network based on the operating data of the wind turbine equipment acquired at historical times, and input the current operating data of the wind turbine equipment into the trained monitoring network to obtain the abnormal location of the wind turbine equipment.

[0043] The signal transmission unit is used to relay crane positioning data calculated by several UWB base stations to the wind turbine platform working face monitoring system via a sea surface buoy device.

[0044] The position determination unit is connected to the signal transmission unit to determine whether the crane is within the electronic fence based on the working surface of the wind turbine platform, so as to obtain the position status of the crane.

[0045] The acquisition unit is connected to the location determination unit and is used to acquire the position status of the crane at each time node within a preset time period, and calculate the proportion of marked nodes based on the position status. The marked node is the time node corresponding to when the position of the crane exceeds the electronic fence.

[0046] The analysis unit is connected to the monitoring unit and the acquisition unit respectively, and is used to determine the crane's operating status based on the proportion of the marked nodes, and determine the frequency of adjusting the geographic coordinates of the electronic fence when the operating status is unqualified, and adjust the spacing of the UWB base stations when the number of adjustments is equal to the preset number and the operating status is still unqualified.

[0047] Specifically, the data acquisition unit periodically acquires the start / stop switching quantities, tension, vibration, and temperature of the wind turbine equipment using photoelectric sensors, strain gauge load cells, piezoelectric accelerometers, and temperature monitoring sensors. Then, a monitoring network is trained based on the historical operating data of the wind turbine equipment acquired by the data acquisition unit at historical moments. The monitoring network structure is built based on a deep learning neural network structure, specifically consistent with the structure of a Long Short-Term Memory (LSTM) network, to detect abnormal wind turbine equipment locations. That is, the current operating data of the wind turbine equipment is input to the monitoring network, and the abnormal conditions of the wind turbine equipment are output. The specific location of the abnormal conditions is determined through visualization equipment (details omitted). Subsequently, the crane positioning data calculated by several UWB base stations is relayed to the wind turbine platform working face monitoring system via a surface buoy device. Based on the wind turbine platform working face, an electronic fence algorithm is used to detect whether the crane is within the electronic fence, thereby determining the crane's position status.

[0048] Specifically, the process of UWB base stations calculating crane positioning data involves UWB tags at key points of the crane periodically emitting nanosecond-level pulse signals. Fixed base stations transmit and receive signals via nanosecond-level narrow pulses, and the surrounding base station array (usually ≥4) synchronously records the signal arrival time using a high-precision clock. The TDOA (Time Difference of Arrival) algorithm is used to calculate the distance difference between the tags and each base station, forming a hyperbolic positioning model. Then, the three-dimensional coordinates of the crane are calculated by combining trilateration with least squares optimization. This process is existing technology and will not be elaborated further.

[0049] Please see Figure 2 The diagram shown is a flowchart illustrating the steps of an intelligent monitoring and management method for high-risk operations based on multi-element linkage, according to an embodiment of the present invention.

[0050] S1, through the data acquisition unit, periodically acquires the operating data of the wind turbine equipment using sensors, including at least start / stop switch quantities, equipment vibration frequency and equipment operating temperature;

[0051] S2, the monitoring network is trained by the monitoring unit connected to the data acquisition unit based on the operation data of the wind turbine equipment acquired at historical times, and the current operation data of the wind turbine equipment is input into the trained monitoring network to obtain the abnormal location of the wind turbine equipment;

[0052] S3 transmits crane positioning data calculated by several UWB base stations to the wind turbine platform working face monitoring system via a signal transmission unit and a sea surface buoy device.

[0053] S4, the position determination unit connected to the signal transmission unit determines whether the crane is in the electronic fence based on the working surface of the wind turbine platform, so as to obtain the position status of the crane;

[0054] S5, the position status of the crane at each time node within a preset time period is collected by the acquisition unit connected to the position determination unit, and the proportion of marked nodes is calculated based on the position status. The marked node is the time node corresponding to when the position of the crane exceeds the electronic fence.

[0055] S6, the analysis unit, which is connected to the monitoring unit and the acquisition unit respectively, determines the crane's operating status based on the proportion of the marked nodes, and determines the frequency of adjusting the geographic coordinates of the electronic fence when the operating status is unqualified, and adjusts the spacing of the UWB base stations when the number of adjustments is equal to the preset number and the operating status is still unqualified.

[0056] Specifically, the location determination unit described in this embodiment of the invention is also used to draw an electronic fence based on the abnormal positioning of the wind turbine equipment. The electronic fence sets a safe operating area for the crane based on UWB positioning technology to monitor whether the crane is within the safe operating area. The location determination unit is also used to determine the position status of the crane based on the point-line judgment method set on the working surface of the wind turbine platform.

[0057] Specifically, the process of drawing an electronic fence based on the abnormal positioning of the wind turbine equipment involves obtaining the real-time centimeter-level coordinates of the crane through a UWB base station, pre-setting the boundary coordinates of the dangerous area in the monitoring system, and connecting the boundary coordinates to draw the electronic fence. The electronic fence is a technical solution for achieving spatial safety management through virtual boundaries. Its core principle is to use positioning technology or sensor networks to delineate an invisible digital boundary in a specific area (such as the equipment operation area), and trigger automatic early warning or linkage control by monitoring the location information of the target in real time. This is existing technology and will not be elaborated further.

[0058] Specifically, UWB positioning technology is a high-precision positioning scheme that uses nanosecond-level non-sinusoidal narrow pulse signals (bandwidth ≥ 500MHz) for wireless communication. Its core principle is to achieve centimeter-level spatial positioning by measuring the time difference of arrival (TDOA) or time of flight (TOF) of the signal and combining it with multi-base station collaborative calculation. This technology is existing technology and will not be elaborated here.

[0059] Specifically, such as Figure 3 As shown, this is a diagram of the over-limit capacity area of ​​the lifting equipment on the working face of the wind turbine platform according to an embodiment of the present invention. In the figure, 1 is the lifting capacity determined by the bearing capacity of the mechanism, 2 is the lifting capacity determined by the bearing capacity of the boom structure, and 3 is the lifting capacity determined by the overall stability. In the figure, the vertical axis Q represents the rated lifting capacity in tons, and the horizontal axis R represents the slewing distance from the center of the hook to the center of rotation of the crane in meters.

[0060] Specifically, the electronic fence algorithm includes the point-line judgment method and the ray method. The point-line judgment method for the working surface of the wind turbine platform treats each edge of the polygon as a directed line segment connected end to end. If a point's direction (left or right) relative to each edge (directed line segment) of the polygon is the same, then the point is inside the polygon. This method only applies to convex polygons, not concave polygons. Define a point on one side of a directed line segment, and define directed line segments (x1, y1) and (x2, y2). For the point (x, y), calculate: v = (x2 - x1) * (y - y1) - (y2 - y1) * (x - x1), where v = 0 indicates the point is on the line segment, v > 0 indicates the point is to the left of the line segment, and v < 0 indicates the point is to the right of the line segment. This method can be used to determine whether the crane's position is within the electronic fence.

[0061] Specifically, the position determination unit described in this embodiment of the invention is also used to determine the position status of the crane based on the ray method set on the working surface of the wind turbine tower. When the point-line judgment method determines that the working status of the crane is unqualified, the position of the crane is adjusted and a second judgment is performed based on the ray method.

[0062] Specifically, such as Figure 4 As shown, it is a ray-based area diagram of the working surface of the wind turbine tower in an embodiment of the present invention. When the position of the red dot in the diagram, i.e. the position of the crane, is inside the electronic fence, each ray extending from that point in any horizontal direction has an odd number of intersections with the boundary of the electronic fence. The idea of ​​this algorithm is to draw a ray horizontally from the point and calculate the number of intersections between the ray and the edge of the polygon. If the number of intersections is odd, then the point must be inside the polygon; otherwise, it is outside.

[0063] Please see Figure 5 The diagram shows a flowchart illustrating the steps of determining the proportion of marked nodes based on a comparison with a preset proportion in an embodiment of the present invention. The signal transmission unit in this embodiment further includes several wireless signal probes, each positioned at a preset distance from the corresponding sea surface buoy device to detect UWB signal strength in real time. The analysis unit is also used to calculate the variance of several UWB signal strengths, and, if the variance is greater than a preset variance, adjust the correction frequency of the electronic fence's geographic coordinates based on the calculated variance. The above operations are performed when the crane's operating status is determined to be unqualified.

[0064] Specifically, if the preset proportion P0 = 0.2, the comparison process between the proportion P based on the marked nodes and the preset proportion P0 is as follows:

[0065] If the percentage P is less than or equal to the preset percentage P0, it indicates that the crane's operating status is qualified.

[0066] If the percentage P is greater than the preset percentage P0, it indicates that the crane's operating status is unqualified. In this case, several wireless signal probes are set at preset distances between the sea surface buoy devices to detect the UWB signal strength in real time.

[0067] Specifically, if the preset variance Q0 of several UWB signal strengths is 9.4, then the comparison process based on the variance Q and the preset variance Q0 is as follows:

[0068] If the variance Q is greater than the preset variance Q0, it indicates that the floating platform is displaced by the impact of waves, causing the preset fence coordinates to be inaccurate. Therefore, based on the variance of several UWB signal strengths, the correction frequency of the electronic fence geographic coordinates is increased. By correcting the preset fence coordinates more promptly, the problem of the preset fence coordinates being inaccurate due to the impact of waves can be avoided.

[0069] Specifically, if the maximum preset variance Q1 of several UWB signal strengths is 11.3, then the comparison process based on the variance Q and the maximum preset variance Q1 is as follows:

[0070] If the variance Q is less than or equal to the maximum preset variance Q1, the correction frequency of the geographic coordinates of the electronic fence will be adjusted to 1.4 times the original correction frequency.

[0071] If the variance Q is greater than the maximum preset variance Q1, the correction frequency of the geographic coordinates of the electronic fence will be adjusted to 2.9 times the original correction frequency.

[0072] Specifically, if the crane's operating status remains unsatisfactory after repeatedly adjusting the correction frequency of the electronic fence's geographic coordinates, it indicates that the reflection of UWB signals from the tower steel structure is causing path deviation, leading to increased ranging errors. Therefore, the distance between the crane and the electronic fence is measured, and the spacing of the UWB base stations is adjusted based on the difference between this distance and a preset distance. The preset difference R0 is 1.2m. The comparison process between the difference R and the preset difference R0 is as follows:

[0073] If the difference R is less than or equal to the preset difference R0, the spacing between UWB base stations will be adjusted to 0.5 times the original spacing.

[0074] If the difference R is greater than the preset difference R0, the spacing between UWB base stations will be adjusted to 0.71 times the original spacing.

[0075] Specifically, after adjusting the spacing between UWB base stations, co-channel interference may occur due to the dense deployment of base stations. Therefore, it is necessary to reduce the transmission power to avoid this situation. The preset spacing T0 of UWB base stations is 40m. The comparison process between the UWB base station spacing T and the preset spacing T0 is as follows:

[0076] If the spacing T between UWB base stations is less than or equal to the preset spacing T0, the transmission power will be adjusted to 0.57 times the original transmission power.

[0077] If the spacing T between UWB base stations is greater than the preset spacing T0, the transmission power will be adjusted to 0.83 times the original transmission power.

[0078] Specifically, the analysis unit described in this embodiment of the invention is further used to redetermine the crane's operating status after adjusting the spacing of the UWB base stations, and to draw a time node-crane position status curve within a preset time period when the crane's operating status is unqualified; the analysis unit is further used to increase the number of training iterations of the monitoring network based on the ratio of the curve integral to the preset curve integral when the curve integral is less than or equal to the preset curve integral, and the increase in the number of training iterations of the monitoring network is inversely proportional to the ratio; wherein, the value is set to 1 when the crane is within the electronic fence, and the value is set to 0 when the crane is not within the electronic fence.

[0079] Specifically, if the preset ratio U0 of the line integral and the preset line integral is 0.4, then the comparison process based on the ratio U and the preset ratio U0 is as follows:

[0080] If the ratio U is less than or equal to the preset ratio U0, the number of training iterations of the monitoring network will be adjusted to 2.7 times the original number of training iterations;

[0081] If the ratio U is greater than the preset ratio U0, the number of training iterations of the monitoring network will be adjusted to 1.9 times the original number of training iterations.

[0082] Specifically, the analysis unit described in this embodiment of the invention is also used to redetermine the operating status of the crane after adjusting the number of training iterations of the monitoring network, and, when the operating status of the crane is unqualified, to reduce the cycle of the sensor acquiring the operating data of the wind turbine equipment based on the difference between the proportion of the marked nodes within a preset time period and the preset proportion, and the reduction in the cycle of the sensor acquiring the operating data of the wind turbine equipment is proportional to the ratio.

[0083] Specifically, if the percentage of marked nodes within a preset time period has a preset difference V0 = 0.1 compared to the preset percentage, then the comparison process based on the difference V and the preset difference V0 is as follows:

[0084] If the difference V is less than or equal to the preset difference V0, the period for the sensor to acquire the operating data of the wind turbine equipment will be adjusted to 0.87 times the original period for the sensor to acquire the operating data of the wind turbine equipment.

[0085] If the difference V is greater than the preset difference V0, the period for the sensor to acquire the operating data of the wind turbine equipment will be adjusted to 0.61 times the original period for the sensor to acquire the operating data of the wind turbine equipment.

[0086] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0087] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A high-risk operation intelligent monitoring and management system based on multi-element linkage, characterized in that, include: The data acquisition unit is used to periodically acquire the operating data of the wind turbine equipment using sensors. The operating data of the wind turbine equipment includes at least the start / stop switch quantity, equipment vibration frequency, and equipment operating temperature. The monitoring unit, which is connected to the data acquisition unit, is used to train the monitoring network based on the operating data of the wind turbine equipment acquired at historical times, and input the current operating data of the wind turbine equipment into the trained monitoring network to obtain the abnormal location of the wind turbine equipment. The signal transmission unit is used to relay crane positioning data calculated by several UWB base stations to the wind turbine platform working face monitoring system via a surface buoy device. A position determination unit, which is connected to the signal transmission unit, is used to determine whether the crane is within the electronic fence based on the working surface of the wind turbine platform, so as to obtain the position status of the crane. The data acquisition unit is connected to the location determination unit and is used to acquire the location status of the crane at each time node within a preset time period, and calculate the proportion of marked nodes based on the location status. The marked node is the time node corresponding to when the crane's location exceeds the electronic fence. An analysis unit, which is connected to the monitoring unit and the acquisition unit respectively, is used to determine the crane's operating status based on the proportion of the marked nodes, and determine the frequency of adjusting the geographic coordinates of the electronic fence when the operating status is unqualified, and adjust the spacing of the UWB base stations when the number of adjustments is equal to the preset number and the operating status is still unqualified. The signal transmission unit also includes several wireless signal probes, each of which is set at a preset distance from the corresponding sea surface buoy device to detect the UWB signal strength in real time. When the crane's operating status is determined to be unqualified, the analysis unit is also used to calculate the variance of several UWB signal strengths, and, if the variance is greater than the preset variance, adjust the correction frequency of the electronic fence's geographic coordinates based on the obtained variance. The location determination unit is also used to draw an electronic fence based on the abnormal positioning of the wind turbine equipment. The electronic fence is based on UWB positioning technology to set a safe operating area for the crane in order to monitor whether the crane is within the safe operating area. The position determination unit is also used to determine the position status of the crane based on the point-line judgment method set on the working surface of the wind turbine platform.

2. The intelligent monitoring and management system for high-risk operations based on multi-element linkage as described in claim 1, characterized in that, The position determination unit is also used to determine the position status of the crane based on the ray method set on the working surface of the wind turbine tower. When the point-line judgment method determines that the working status of the crane is unqualified, the position of the crane is adjusted and a second judgment is made based on the ray method.

3. The intelligent monitoring and management system for high-risk operations based on multi-element linkage as described in claim 1, characterized in that, The analysis unit is also used to increase the correction frequency of the electronic fence geographic coordinates based on the variance of several UWB signal strengths, and the increase in the correction frequency of the electronic fence geographic coordinates is proportional to the variance.

4. The intelligent monitoring and management system for high-risk operations based on multi-element linkage according to claim 3, characterized in that, The analysis unit is also used to adjust the correction frequency of the geo coordinates of the electronic fence at least once when the working condition of the crane is not qualified, after adjusting the correction frequency of the geo coordinates of the electronic fence. The adjustment is stopped when the number of adjustments is less than the preset number and the working condition of the crane is qualified or the number of adjustments is equal to the preset number. The analysis unit is also used to redetermine the working status of the crane, and, if the working status of the crane is unqualified, to adjust the spacing of the UWB base station based on the measured distance between the crane and the electronic fence.

5. The intelligent monitoring and management system for high-risk operations based on multi-element linkage according to claim 4, characterized in that, The analysis unit is also used to reduce the spacing between UWB base stations based on the difference between the distance and the preset distance, and the reduction in spacing is inversely proportional to the difference.

6. The intelligent monitoring and management system for high-risk operations based on multi-element linkage according to claim 5, characterized in that, The analysis unit is also used to reduce the transmission power based on the spacing between UWB base stations, and the reduction in transmission power is inversely proportional to the spacing.

7. The intelligent monitoring and management system for high-risk operations based on multi-element linkage according to claim 6, characterized in that, The analysis unit is also used to redetermine the crane's operating status after adjusting the spacing of the UWB base stations, and to draw a time node-crane position status curve within a preset time period when the crane's operating status is unqualified. The analysis unit is also used to increase the number of training iterations of the monitoring network based on the ratio of the curve integral to the preset curve integral when the curve integral is less than or equal to the preset curve integral, and the increase in the number of training iterations of the monitoring network is inversely proportional to the ratio. The value is set to 1 when the crane is inside the electronic fence and 0 when the crane is not inside the electronic fence.

8. The intelligent monitoring and management system for high-risk operations based on multi-element linkage according to claim 7, characterized in that, The analysis unit is also used to redetermine the crane's operating status after adjusting the number of training iterations of the monitoring network, and, when the crane's operating status is unqualified, reduce the cycle of the sensor acquiring the wind turbine's operating data based on the difference between the percentage of marked nodes within a preset time period and the preset percentage, and the reduction in the cycle of the sensor acquiring the wind turbine's operating data is proportional to the ratio.

Images