Marine photovoltaic support displacement monitoring method and system, and medium

Abstract

The application discloses a marine photovoltaic support displacement monitoring method and system, and a medium. The marine photovoltaic support is provided with a plurality of to-be-monitored areas in advance, and each to-be-monitored area is provided with a sensor group comprising at least a stress sensor, a corrosion sensor and a vibration sensor. For each to-be-monitored area, stress data, corrosion data and vibration data collected by the sensor group are acquired; the stress data, the corrosion data and the vibration data are generated into a sensor data group corresponding to the to-be-monitored area, and based on the sensor data group of each to-be-monitored area, it is determined whether the marine photovoltaic support has a displacement risk; if yes, a risk warning is given to the marine photovoltaic support based on a preset Beidou positioning module. The application divides the marine photovoltaic support into a plurality of to-be-monitored areas, sets a plurality of sensors to collect multiple types of data, and fuses the multiple types of data to monitor the overall displacement of the marine photovoltaic support, thereby achieving comprehensive and accurate multi-dimensional monitoring.

Description

Technical Field

[0001] This invention relates to the field of data analysis and processing technology, and in particular to a method, system and medium for monitoring the displacement of offshore photovoltaic support structures. Background Technology

[0002] With the country's vigorous development of new energy power generation, the construction of photovoltaic power stations has continued to expand, gradually extending from land to sea. In offshore photovoltaic power stations, photovoltaic support structures are an important component. However, due to their long-term exposure to the complex marine environment, including high humidity, high salt spray, strong winds, and wave impacts, they are prone to corrosion, loosening, and strain, which can lead to displacement of the photovoltaic support structures. This, in turn, affects the power generation effect and even directly impacts the safe operation of the offshore photovoltaic power station.

[0003] Therefore, monitoring the displacement of offshore photovoltaic (PV) support structures can help detect potential damage risks in a timely manner and facilitate maintenance. However, current monitoring methods are relatively limited and cannot accurately detect the displacement of offshore PV support structures. Summary of the Invention

[0004] The main objective of this invention is to provide a method, system, and medium for monitoring the displacement of offshore photovoltaic (PV) supports, aiming to solve the technical problem of how to accurately monitor the displacement of offshore PV supports in order to ensure the safe operation of offshore PV power plants.

[0005] To achieve the above objectives, the present invention provides a method for monitoring the displacement of a marine photovoltaic support structure. The marine photovoltaic support structure is pre-set with multiple areas to be monitored, and each area to be monitored is equipped with a corresponding sensor group. The sensor group includes at least a stress sensor, a corrosion sensor, and a vibration sensor. The method for monitoring the displacement of offshore photovoltaic support includes: For each area to be monitored, stress data, corrosion data, and vibration data are acquired by the stress sensor, corrosion sensor, and vibration sensor in the corresponding sensor group, respectively. The stress data, corrosion data, and vibration data are generated into a set of sensor data corresponding to the area to be monitored, and based on the set of sensor data corresponding to each area to be monitored, it is determined whether there is a risk of displacement of the offshore photovoltaic support. If there is a risk of displacement, the marine photovoltaic support will be given a risk warning based on the preset Beidou positioning module.

[0006] Preferably, the step of determining whether the offshore photovoltaic support structure has a displacement risk based on the sensor data set corresponding to each of the monitored areas includes: For each monitored area, a reference data group corresponding to the monitored area is found, and a similarity fusion operation is performed between the reference data group and the sensor data group to obtain a similarity value between the reference data group and the sensor data group. After obtaining the similarity value corresponding to each of the monitored areas, it is determined whether the offshore photovoltaic support has a displacement risk based on each of the similarity values.

[0007] Preferably, the step of performing a similarity fusion operation between the reference data group and the sensing data group to obtain a similarity value between the reference data group and the sensing data group includes: Each reference element in the reference data group is multiplied by each sensing element in the sensing data group to obtain a reference array corresponding to the reference element, and the values ​​in the reference array are summed to generate a reference summation result. After performing a product operation on each of the reference elements to obtain a corresponding reference array, and performing a summation operation on each of the reference arrays to generate a corresponding reference summation result, the summation operation on each of the reference summation results is performed to obtain a total summation result as the similarity value between the reference data group and the sensing data group.

[0008] Preferably, the step of determining whether the offshore photovoltaic support structure has a displacement risk based on the similarity values ​​includes: Obtain the preset weights corresponding to each of the monitored areas, and perform weight fusion calculation on each of the similarity values ​​according to each preset weight to obtain the risk coefficient of the marine photovoltaic support. The risk coefficient is compared with a preset threshold to determine whether the risk coefficient is greater than the preset threshold. If it is greater than the preset threshold, it is determined that the offshore photovoltaic support has a displacement risk.

[0009] Preferably, the step of providing risk warning for the offshore photovoltaic support based on a preset BeiDou positioning module if displacement risk exists includes: If there is a displacement risk, the risk coefficient is compared with multiple pre-set risk reference intervals to determine the target risk reference interval corresponding to the risk coefficient. Find the risk level corresponding to the target risk reference interval, and the early warning information corresponding to the risk level, and locate the risk location information based on the Beidou positioning module; Based on the risk level, the early warning information, and the risk location information, a risk warning is issued for the offshore photovoltaic support structure.

[0010] Preferably, the step of locating the risk location information based on the BeiDou positioning module includes: The preset weight corresponding to each monitored region is multiplied with the similarity value corresponding to each monitored region to obtain the regional risk coefficient corresponding to each monitored region. Find the target area risk coefficient that is greater than the preset area threshold from the risk coefficients of each area, and find the target monitoring area that corresponds to the risk coefficient of the target area from each area to be monitored; The location information corresponding to each target monitoring area is determined based on the preset Beidou positioning module as the risk location information.

[0011] Preferably, the step of acquiring stress data, corrosion data, and vibration data collected by the stress sensor, corrosion sensor, and vibration sensor in the corresponding sensor group includes: The system sends a first ultrasonic wave to the area to be monitored based on a stress sensor, and receives a second ultrasonic wave after the first ultrasonic wave has propagated through the area to be monitored. The system then generates the stress data based on the first and second ultrasonic waves. The corrosion sensor detects the resistance value of the area to be monitored, and the corrosion data is generated based on the resistance value. The vibration sensor detects the capacitance value of the area to be monitored, and the vibration data is generated based on the capacitor.

[0012] Preferably, each of the monitored areas includes multiple stress sensors, multiple corrosion sensors, and multiple vibration sensors. The step of generating a sensor data set corresponding to the monitored area from the stress data, corrosion data, and vibration data includes the following prior steps: The stress data collected by the multiple stress sensors are subjected to median filtering, and the filtered stress data are then averaged. The corrosion data collected by the multiple corrosion sensors are each subjected to low-pass filtering, and the filtered corrosion data are then averaged. Gaussian filtering is performed on each of the vibration data collected by the multiple vibration sensors, and the filtered vibration data is then averaged.

[0013] Furthermore, to achieve the above objectives, the present invention also provides a displacement monitoring system for offshore photovoltaic (PV) supports. The offshore PV support is pre-configured with multiple monitoring areas, and each monitoring area is equipped with a corresponding sensor group. The sensor group includes at least a stress sensor, a corrosion sensor, and a vibration sensor. The offshore PV support displacement monitoring system includes a storage device, a processor, a communication bus, and a control program stored in the storage device. The communication bus is used to enable communication between the processor and the memory. The processor is used to execute the control program to implement the steps of the offshore photovoltaic support displacement monitoring method described above.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a medium, which is a readable storage medium storing a control program. When the control program is executed by a processor, it implements the steps of the offshore photovoltaic support displacement monitoring method described above.

[0015] This invention discloses a method, system, and medium for monitoring the displacement of offshore photovoltaic (PV) supports. First, multiple monitoring areas are pre-defined for the offshore PV support, and a corresponding sensor group is set up for each monitoring area. This sensor group includes at least a stress sensor, a corrosion sensor, and a vibration sensor. Based on this, for each monitoring area, stress data, corrosion data, and vibration data collected by the stress sensor, corrosion sensor, and vibration sensor are acquired. These data are then used to generate a sensor data set corresponding to that monitoring area. Based on the sensor data set for each monitoring area, it is determined whether there is a displacement risk to the offshore PV support. Once a displacement risk is determined, a risk warning is issued to the offshore PV support based on a pre-set BeiDou positioning module. In this way, by dividing the offshore PV support into multiple monitoring areas and setting up multiple sensors in each monitoring area to collect and monitor multiple types of data, and then integrating the multi-type data from each monitoring area, the overall displacement of the offshore PV support is monitored, achieving comprehensive and accurate multi-dimensional monitoring. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the first embodiment of the offshore photovoltaic support displacement monitoring method of the present invention; Figure 2 This is a flowchart illustrating the second embodiment of the offshore photovoltaic support displacement monitoring method of the present invention; Figure 3 This is a flowchart illustrating the third embodiment of the offshore photovoltaic support displacement monitoring method of the present invention; Figure 4 This is a schematic diagram of the hardware operating environment involved in an embodiment of the offshore photovoltaic support displacement monitoring system of the present invention.

[0017] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0019] This invention provides a method for monitoring the displacement of offshore photovoltaic (PV) supports. Please refer to [the relevant documentation]. Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for monitoring the displacement of marine photovoltaic supports according to the present invention.

[0020] This invention provides an embodiment of a method for monitoring the displacement of offshore photovoltaic (PV) supports. It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order. Specifically, the method for monitoring the displacement of offshore PV supports in this embodiment includes:

[0021] Step S10: For each area to be monitored, acquire stress data, corrosion data, and vibration data collected by the stress sensor, corrosion sensor, and vibration sensor in the corresponding sensor group, respectively.

[0022] This embodiment of the offshore photovoltaic (PV) support displacement monitoring method is applied to the monitoring server of a displacement monitoring system. The monitoring server monitors the displacement of the offshore PV supports to promptly detect displacement risks caused by corrosion, loosening, strain, etc. Furthermore, because offshore PV power plants have a large number of PV supports distributed over a wide area, and different areas are affected by seawater to varying degrees, to better monitor the PV supports in different areas, the offshore PV supports are pre-divided into multiple monitoring areas. A corresponding sensor group is set in each monitoring area, and each sensor group includes at least a stress sensor, a corrosion sensor, and a vibration sensor to monitor stress, corrosion, and vibration in the monitored area. Of course, more sensors can be set up according to the specific conditions of the monitored area for more precise monitoring.

[0023] Furthermore, each sensor in the sensor group is communicatively connected to the monitoring server. This communication can be achieved by placing a communication module in the monitored area, with each sensor transmitting its collected data to this module, which then transmits it to the monitoring server. Alternatively, each sensor can transmit data directly to the monitoring server. A preset monitoring cycle can be set, and at regular intervals, each sensor transmits its collected data to the monitoring server. In this way, the monitoring server obtains stress, corrosion, and vibration data collected by its respective sensor, corrosion sensor, and vibration sensor for each monitored area. This data reflects the stress, corrosion, and vibration conditions of the monitored area, and combined with these findings, a comprehensive assessment is made to determine whether there is a risk of displacement in the monitored area.

[0024] Furthermore, in this embodiment, stress data, corrosion data, and vibration data are monitored using ultrasonic waves, resistance, and capacitance, respectively. Specifically, the step of acquiring stress data, corrosion data, and vibration data collected by the corresponding stress sensor, corrosion sensor, and vibration sensor in the sensor group includes:

[0025] Step S11: Send a first ultrasonic wave to the area to be monitored based on the stress sensor, and receive a second ultrasonic wave after the first ultrasonic wave has propagated through the area to be monitored, and generate the stress data based on the first ultrasonic wave and the second ultrasonic wave. Step S12: Detect the resistance value of the area to be monitored based on the corrosion sensor, and generate the corrosion data based on the resistance value; Step S13: Detect the capacitance value of the area to be monitored based on the vibration sensor, and generate the vibration data based on the capacitor.

[0026] Furthermore, the stress sensor includes an ultrasonic transmitter and an ultrasonic receiver. The ultrasonic transmitter emits ultrasonic waves toward the area to be monitored, which propagate through the area and are received by the ultrasonic receiver. The emitted ultrasonic wave is taken as the first ultrasonic wave, and the received ultrasonic wave is taken as the second ultrasonic wave. When the area to be monitored is subjected to stress, its elastic modulus changes, which causes changes in the propagation speed, phase, or frequency of the ultrasonic waves. Therefore, by measuring the propagation time, speed, or phase difference of the first and second ultrasonic waves, the stress effect on the area to be monitored can be calculated, and stress data of the area to be monitored can be generated. In this embodiment, propagation time is preferred for calculation, and the specific calculation formula can be found in the following formula (1).

[0027] (1); Where F represents stress data, E represents the material elastic modulus of the area to be monitored, K represents the acoustic elastic coefficient of the area to be monitored, t represents the propagation time of the first and second ultrasonic waves, t0 represents the ultrasonic propagation time of the area to be monitored at zero stress and reference temperature, u1 represents the linear expansion coefficient of the area to be monitored, T represents the difference between the current temperature and the reference temperature, and u2 represents the second-order acoustic elastic coefficient of the area to be monitored.

[0028] Furthermore, in this embodiment, the corrosion sensor acquires corrosion data of the monitored area through resistance detection. For corroded metals, their cross-sectional area decreases due to corrosion, resulting in an increase in resistance. The corrosion sensor detects changes in the resistance of the monitored area to determine whether corrosion has occurred and the size of the corroded area. For vibration data, the vibration sensor detects changes in capacitance. Specifically, the vibration sensor detects the capacitance value of the monitored area and determines the vibration displacement from the capacitance value, generating vibration data for the monitored area.

[0029] Step S20: Generate the stress data, corrosion data, and vibration data into a sensor data set corresponding to the area to be monitored, and determine whether the offshore photovoltaic support has a displacement risk based on the sensor data set corresponding to each area to be monitored.

[0030] Furthermore, the stress, corrosion, and vibration data of each monitored area are compiled into a sensor data set corresponding to that monitored area to reflect whether the monitored area is affected by stress, corrosion, and vibration. Then, through overall analysis of each sensor data set, it is determined whether there is a risk of displacement of the offshore photovoltaic support structure. To ensure the accuracy of the stress, corrosion, and vibration data collected for each monitored area, multiple sensors of the same type can be set for each monitored area; for example, a sensor set for a monitored area may include 2 stress sensors, 3 corrosion sensors, and 4 vibration sensors. Therefore, before generating the sensor data set, the data collected by each sensor needs to be averaged. Specifically, the step of generating the stress, corrosion, and vibration data into a sensor data set corresponding to the monitored area includes:

[0031] Step a1: Perform median filtering on each of the stress data collected by the multiple stress sensors, and then perform mean processing on the filtered stress data. Step a2: Low-pass filtering is performed on each of the corrosion data collected by the multiple corrosion sensors, and the filtered corrosion data is then averaged. Step a3: Gaussian filtering is performed on each of the vibration data collected by the multiple vibration sensors, and the mean value is then applied to the filtered vibration data. Understandably, the detection data from stress sensors, corrosion sensors, and vibration sensors may be affected by environmental electromagnetic interference and their own thermal noise, causing fluctuations, spikes, or deviations in the monitored stress, corrosion, and vibration data. To eliminate such influences, this embodiment performs filtering processing on the collected stress, corrosion, and vibration data separately. Specifically, median filtering is applied to the stress data collected by each stress sensor, and after median filtering of all stress data, the filtered stress data are averaged to obtain stress data that accurately reflects the stress condition of the monitored area. Simultaneously, low-pass filtering is applied to the corrosion data collected by each corrosion sensor, and after low-pass filtering of all corrosion data, the filtered corrosion data are averaged to obtain corrosion data that accurately reflects the corrosion condition of the monitored area.

[0032] Furthermore, Gaussian filtering is applied to each vibration data collected by each vibration sensor, and after Gaussian filtering of each vibration data, the mean value of each filtered vibration data is processed to obtain vibration data that accurately reflects the vibration status of the monitored area. The formula for Gaussian filtering can be found in the following formula (2).

[0033] (2); Where g(x) represents the filtered vibration data, s represents the preset filter sampling offset, q represents the preset filter kernel half-width, h represents the standard deviation of the number of filtered sampling points, and f(x+s) represents the vibration data before filtering.

[0034] Furthermore, the mean-processed stress data, corrosion data, and vibration data are used to generate sensor data sets corresponding to the areas to be monitored. By using the sensor data sets of each area to be monitored, it is possible to accurately determine whether there is a risk of displacement of the offshore photovoltaic support structure.

[0035] Step S30: If there is a risk of displacement, a risk warning is issued for the offshore photovoltaic support based on the preset Beidou positioning module.

[0036] Furthermore, the displacement monitoring system is pre-installed with a BeiDou positioning module, which can communicate with the monitoring server via either wireless or short message communication. Once the sensor data from each monitored area determines that there is a displacement risk to the offshore photovoltaic support structure, the BeiDou positioning module locates the areas subjected to stress, corrosion, or vibration, determines the level of displacement risk, and issues a risk warning to the offshore photovoltaic support structure based on the area and risk level. The warning information is then output to the displacement monitoring system for display, facilitating the rapid identification and investigation of potential displacement risks to the offshore photovoltaic support structure.

[0037] The proposed method for monitoring the displacement of offshore photovoltaic (PV) supports involves first pre-setting multiple monitoring areas for the PV support system, and then setting up a corresponding sensor group for each area. This sensor group includes at least stress sensors, corrosion sensors, and vibration sensors. Based on this, for each monitoring area, stress, corrosion, and vibration data collected by the stress, corrosion, and vibration sensors are acquired. These data are then used to generate a sensor data set corresponding to that monitoring area. Based on this sensor data set for each monitoring area, the method determines whether there is a displacement risk to the offshore PV support system. If a displacement risk is detected, a risk warning is issued to the offshore PV support system based on a pre-set BeiDou positioning module. In this way, by dividing the offshore PV support system into multiple monitoring areas and setting up multiple sensors in each area to collect and monitor various types of data, and then integrating the multi-type data from each monitoring area, the overall displacement of the offshore PV support system can be monitored, achieving comprehensive and accurate multi-dimensional monitoring.

[0038] Further, please refer to Figure 2 Based on the first embodiment of the offshore photovoltaic support displacement monitoring method of the present invention, a second embodiment of the offshore photovoltaic support displacement monitoring method of the present invention is proposed.

[0039] The difference between the second embodiment of the offshore photovoltaic support displacement monitoring method and the first embodiment of the offshore photovoltaic support displacement monitoring method is that the step of determining whether there is a displacement risk of the offshore photovoltaic support based on the sensor data group corresponding to each monitored area includes: Step S21: For each area to be monitored, find the reference data group corresponding to the area to be monitored, and perform a similarity fusion operation between the reference data group and the sensing data group to obtain the similarity value between the reference data group and the sensing data group. Furthermore, for each area to be monitored, a reference data set representing the presence of abnormal applications, corrosion, and vibration is pre-set. Corresponding to the stress data, corrosion data, and vibration data included in the sensing data set, the reference data set also includes reference stress data, reference corrosion data, and reference vibration data. After generating the sensing data set for each area to be monitored, the corresponding reference data set for each area is found. Then, a similarity fusion operation is performed between the reference data set and the sensing data set to obtain a similarity value representing the degree of similarity between them. Specifically, the step of performing a similarity fusion operation between the reference data set and the sensing data set to obtain a similarity value between them includes:

[0040] Step S211: Perform a product operation between each reference element in the reference data group and each sensing element in the sensing data group to obtain a reference array corresponding to the reference element, and perform a summation operation on each value in the reference array to generate a reference summation result. Step S212: After performing a product operation on each of the reference elements to obtain a corresponding reference array, and performing a summation operation on each of the reference arrays to generate a corresponding reference summation result, the summation operation on each of the reference summation results is performed to obtain a total summation result as the similarity value between the reference data group and the sensing data group.

[0041] Furthermore, to facilitate similarity fusion calculations between the reference data set and the sensor data set, they can be generated into a data matrix for similarity fusion calculations. For example, the reference elements in the reference data set form the column elements of the data matrix, and the sensor elements in the sensor data set form the row elements of the data matrix. That is, stress data, corrosion data, and vibration data form the row elements of the first, second, and third rows of the data matrix, respectively, and reference stress data, reference corrosion data, and reference vibration data form the column elements of the first, second, and third columns of the data matrix, respectively.

[0042] Furthermore, each column element, i.e., each reference element, is multiplied one by one with each row element to obtain a reference array corresponding to that reference element. The data in this reference array constitutes the values ​​of the data column corresponding to that column element in the data matrix. For example, for the data matrix constructed above, with stress data, corrosion data, and vibration data as row elements and reference stress data, reference corrosion data, and reference vibration data as column elements, the reference stress data is multiplied by the stress data, corrosion data, and vibration data respectively, and the result is used as the values ​​of the first column of the data matrix in the first, second, and third rows. Similarly, the reference corrosion data is multiplied by the stress data, corrosion data, and vibration data respectively, and the result is used as the values ​​of the second column of the data matrix in the first, second, and third rows. And so on, the reference vibration data is multiplied by the stress data, corrosion data, and vibration data respectively, and the result is used as the values ​​of the third column of the data matrix in the first, second, and third rows. In this way, the reference array corresponding to each reference element, i.e., the values ​​of the entire data matrix, is obtained.

[0043] Furthermore, the values ​​in the reference array are summed, that is, the values ​​in the same data column of the data matrix are summed to obtain a column sum, which constitutes the reference sum. After each data column in the data matrix is ​​summed to generate its corresponding column sum, the reference sum for each reference array is obtained. Then, the reference sums are summed to obtain the total sum of the entire data matrix. This total sum is the similarity value, which reflects the degree of similarity between the reference data set and the sensor data set.

[0044] Step S22: After obtaining the similarity value corresponding to each monitored area, determine whether there is a displacement risk of the offshore photovoltaic support based on each similarity value.

[0045] Furthermore, after similarity fusion calculations are performed on the reference data set and the sensor data set for each monitored area to obtain their respective similarity values, the difference between the sensor data set and the reference data set, represented by the similarity values ​​for each monitored area, can be used to determine whether there is a displacement risk to the offshore photovoltaic support structure. Specifically, the step of determining whether there is a displacement risk to the offshore photovoltaic support structure based on the aforementioned similarity values ​​includes:

[0046] Step S221: Obtain the preset weights corresponding to each of the monitored areas, and perform weight fusion calculation on each similarity value according to each preset weight to obtain the risk coefficient of the marine photovoltaic support. Step S222: Compare the risk coefficient with a preset threshold to determine whether the risk coefficient is greater than the preset threshold. If it is greater than the preset threshold, it is determined that the offshore photovoltaic support has a displacement risk.

[0047] Understandably, the impact of stress, corrosion, and vibration on the overall displacement of offshore photovoltaic (PV) supports varies depending on the monitored areas. To better reflect the overall impact of each monitored area, different weight values ​​are pre-set for each monitored area. After generating the similarity value for each monitored area, the pre-set weights for each monitored area are found, and then the similarity values ​​are weighted and fused based on the pre-set weights of each monitored area to obtain the risk coefficient representing the overall displacement risk of the offshore PV support. The formula for the weight fusion calculation is shown in equation (3) below.

[0048] (3); Where S represents the risk coefficient, n represents the number of regions to be monitored, zi represents the similarity value of the i-th region to be monitored, wi represents the preset weight of the i-th region to be monitored, ri represents the coefficient of variation adjustment factor corresponding to the similarity value of the i-th region to be monitored, ai represents the standard deviation of the similarity value of the i-th region to be monitored over all similarity values, ui represents the mean of the similarity value of the i-th region to be monitored over all similarity values, d represents a very small positive number used to prevent division by zero, and z0i represents the standardized mean of the similarity value of the i-th region to be monitored over all similarity values.

[0049] Furthermore, to determine the overall displacement risk of the offshore photovoltaic (PV) support structure, a preset threshold is established. The risk coefficient obtained through weighted fusion calculation is compared with this preset threshold. If the risk coefficient is greater than the preset threshold, it indicates a high displacement risk for the offshore PV support structure, thus confirming the presence of displacement risk. Conversely, if the risk coefficient is less than or equal to the preset threshold, it indicates a low displacement risk between the PV units, thus confirming that there is no displacement risk for the offshore PV support structure.

[0050] This embodiment determines the degree of similarity between the sensor data set and the reference data set for each monitored area through similarity fusion calculation. Since both the sensor data set and the reference data set contain stress, corrosion, and vibration data, the calculated similarity value reflects the stress, corrosion, and vibration conditions of the monitored area. Simultaneously, by combining the preset weights of each monitored area with the respective similarity values ​​through weighted fusion calculation, the embodiment reflects the degree of influence of different monitored areas on the overall displacement risk of the offshore photovoltaic support structure, thus improving the accuracy of displacement risk assessment for offshore photovoltaic support structures.

[0051] Further, please refer to Figure 3 Based on the first and second embodiments of the offshore photovoltaic support displacement monitoring method of the present invention, a third embodiment of the offshore photovoltaic support displacement monitoring method of the present invention is proposed.

[0052] The difference between the third embodiment of the offshore photovoltaic support displacement monitoring method and the first and second embodiments of the offshore photovoltaic support displacement monitoring method is that the step of providing risk warning for the offshore photovoltaic support based on a preset Beidou positioning module if there is a displacement risk includes: Step S31: If there is a displacement risk, the risk coefficient is compared with a number of pre-set risk reference intervals to determine the target risk reference interval corresponding to the risk coefficient. Step S32: Find the risk level corresponding to the target risk reference interval and the early warning information corresponding to the risk level, and locate the risk location information based on the Beidou positioning module; Understandably, the degree of displacement of offshore photovoltaic (PV) supports affects the power generation of offshore PV power plants to varying degrees; the greater the displacement, the greater the impact on the offshore PV power plant. Therefore, after determining that there is a displacement risk between offshore PV supports, this embodiment pre-sets multiple risk reference intervals to further determine the impact of the displacement risk on the offshore PV power plant. Different risk reference intervals correspond to different risk levels of displacement, and the early warning measures are also different accordingly.

[0053] Furthermore, the generated risk coefficients are compared with each risk reference interval to determine the target risk reference interval in which the risk coefficients lie, and then the risk level and corresponding early warning information for that target risk reference interval are found. Simultaneously, since the displacement risk of offshore photovoltaic supports is jointly manifested by stress, corrosion, and vibration in different monitored areas, the risk area most severely affected by stress, corrosion, and vibration can be identified from each monitored area, and its location can be pinpointed using the BeiDou positioning module for more accurate early warning. The step of locating the risk location information based on the BeiDou positioning module includes:

[0054] Step S321: Multiply the preset weight corresponding to each region to be monitored with the similarity value corresponding to each region to be monitored to obtain the regional risk coefficient corresponding to each region to be monitored. Step S322: Find the target area risk coefficient that is greater than the preset area threshold from each of the area risk coefficients, and find the target monitoring area that corresponds to the target area risk coefficient from each of the areas to be monitored; Step S322: Based on the preset BeiDou positioning module, determine the location information corresponding to each target monitoring area as the risk location information.

[0055] Furthermore, for each monitored area, its corresponding preset weight is multiplied by its similarity value. The result is the regional risk coefficient, reflecting the degree of influence of stress, corrosion, and vibration on that monitored area. After obtaining the regional risk coefficient for each monitored area through multiplication, each regional risk coefficient is compared with a preset regional threshold. Regional risk coefficients exceeding the preset threshold are identified as target regional risk coefficients. The preset regional threshold represents the magnitude of the influence of stress, corrosion, and vibration that may affect the displacement of the offshore photovoltaic support structure. All target regional risk coefficients exceeding the preset regional threshold constitute all values ​​that affect the overall displacement of the offshore photovoltaic support structure.

[0056] Furthermore, from each monitored area, the monitored area corresponding to the risk coefficient of the target area is identified as the target monitoring area. This target monitoring area is all the areas that cause the overall displacement of the offshore photovoltaic support structure. The location of each target monitoring area is determined by a preset BeiDou positioning module, obtaining the location information corresponding to each target monitoring area. Based on this location information, the specific location of the area causing the displacement of the offshore photovoltaic support structure is revealed.

[0057] Step S33: Based on the risk level, the early warning information, and the risk location information, a risk warning is issued for the offshore photovoltaic support structure.

[0058] Furthermore, the risk level and early warning information found are combined with the risk location information to generate early warning output information, which is then displayed on the displacement monitoring system to indicate the severity of the current displacement risk between offshore photovoltaic systems and the specific area causing the displacement risk. This facilitates maintenance personnel in taking appropriate measures to maintain the offshore photovoltaic support system.

[0059] This embodiment addresses the risk level and early warning information for offshore photovoltaic (PV) supports exhibiting displacement risk. It determines the severity of the risk and provides warnings through a risk reference range, while simultaneously using a BeiDou positioning module to accurately pinpoint the location causing the displacement risk, thus improving the accuracy of risk warnings for offshore PV supports.

[0060] Furthermore, this invention also provides a displacement monitoring system for offshore photovoltaic (PV) supports. Please refer to... Figure 4 , Figure 4 This is a schematic diagram of the hardware operating environment of the equipment involved in the embodiment of the offshore photovoltaic support displacement monitoring system of the present invention.

[0061] like Figure 4 As shown, the offshore photovoltaic support displacement monitoring system may include: a processor 1001, such as a CPU; a communication bus 1002; a user interface 1003; a network interface 1004; and a storage device 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The storage device 1005 may be a high-speed RAM or a stable, non-volatile memory, such as a disk storage device. Optionally, the storage device 1005 may also be a storage device independent of the aforementioned processor 1001.

[0062] Those skilled in the art will understand that Figure 4The hardware structure of the offshore photovoltaic support displacement monitoring system shown in the figure does not constitute a limitation on the offshore photovoltaic support displacement monitoring system. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0063] like Figure 4 As shown, the storage device 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a control program. The operating system is a program that manages and controls the offshore photovoltaic support displacement monitoring system and its software resources, supporting the operation of the network communication module, the user interface module, the control program, and other programs or software. The network communication module manages and controls the network interface 1004; the user interface module manages and controls the user interface 1003.

[0064] exist Figure 4 In the hardware structure of the offshore photovoltaic support displacement monitoring system shown, the network interface 1004 is mainly used to connect to other system servers and communicate data with them; the user interface 1003 is mainly used to connect to the client (user end) and communicate data with it; the processor 1001 can call the control program stored in the memory 1005 and perform the following operations: For each area to be monitored, stress data, corrosion data, and vibration data are acquired by the stress sensor, corrosion sensor, and vibration sensor in the corresponding sensor group, respectively. The stress data, corrosion data, and vibration data are generated into a set of sensor data corresponding to the area to be monitored, and based on the set of sensor data corresponding to each area to be monitored, it is determined whether there is a risk of displacement of the offshore photovoltaic support. If there is a risk of displacement, the marine photovoltaic support will be given a risk warning based on the preset Beidou positioning module.

[0065] Furthermore, the step of determining whether the offshore photovoltaic support structure faces displacement risk based on the sensor data set corresponding to each of the monitored areas includes: For each monitored area, a reference data group corresponding to the monitored area is found, and a similarity fusion operation is performed between the reference data group and the sensor data group to obtain a similarity value between the reference data group and the sensor data group. After obtaining the similarity value corresponding to each of the monitored areas, it is determined whether the offshore photovoltaic support has a displacement risk based on each of the similarity values.

[0066] Further, the step of performing a similarity fusion operation between the reference data group and the sensing data group to obtain a similarity value between the reference data group and the sensing data group includes: Each reference element in the reference data group is multiplied by each sensing element in the sensing data group to obtain a reference array corresponding to the reference element, and the values ​​in the reference array are summed to generate a reference summation result. After performing a product operation on each of the reference elements to obtain a corresponding reference array, and performing a summation operation on each of the reference arrays to generate a corresponding reference summation result, the summation operation on each of the reference summation results is performed to obtain a total summation result as the similarity value between the reference data group and the sensing data group.

[0067] Furthermore, the step of determining whether the offshore photovoltaic support structure faces displacement risk based on the similarity values ​​includes: Obtain the preset weights corresponding to each of the monitored areas, and perform weight fusion calculation on each of the similarity values ​​according to each preset weight to obtain the risk coefficient of the marine photovoltaic support. The risk coefficient is compared with a preset threshold to determine whether the risk coefficient is greater than the preset threshold. If it is greater than the preset threshold, it is determined that the offshore photovoltaic support has a displacement risk.

[0068] Furthermore, the step of providing risk warnings for the offshore photovoltaic support based on a preset BeiDou positioning module if displacement risk exists includes: If there is a displacement risk, the risk coefficient is compared with multiple pre-set risk reference intervals to determine the target risk reference interval corresponding to the risk coefficient. Find the risk level corresponding to the target risk reference interval, and the early warning information corresponding to the risk level, and locate the risk location information based on the Beidou positioning module; Based on the risk level, the early warning information, and the risk location information, a risk warning is issued for the offshore photovoltaic support structure.

[0069] Furthermore, the step of locating risk location information based on the BeiDou positioning module includes: The preset weight corresponding to each monitored region is multiplied with the similarity value corresponding to each monitored region to obtain the regional risk coefficient corresponding to each monitored region. Find the target area risk coefficient that is greater than the preset area threshold from the risk coefficients of each area, and find the target monitoring area that corresponds to the risk coefficient of the target area from each area to be monitored; The location information corresponding to each target monitoring area is determined based on the preset Beidou positioning module as the risk location information. Further, the step of acquiring stress data, corrosion data, and vibration data respectively collected by the stress sensor, corrosion sensor, and vibration sensor in the corresponding sensor group includes: The system sends a first ultrasonic wave to the area to be monitored based on a stress sensor, and receives a second ultrasonic wave after the first ultrasonic wave has propagated through the area to be monitored. The system then generates the stress data based on the first and second ultrasonic waves. The corrosion sensor detects the resistance value of the area to be monitored, and the corrosion data is generated based on the resistance value. The vibration sensor detects the capacitance value of the area to be monitored, and the vibration data is generated based on the capacitor.

[0070] Furthermore, each of the monitored areas includes multiple stress sensors, multiple corrosion sensors, and multiple vibration sensors. Before the step of generating a sensor data set corresponding to the monitored area from the stress data, corrosion data, and vibration data, the processor 1001 can call the control program stored in the storage 1005 and perform the following operations: The stress data collected by the multiple stress sensors are subjected to median filtering, and the filtered stress data are then averaged. The corrosion data collected by the multiple corrosion sensors are each subjected to low-pass filtering, and the filtered corrosion data are then averaged. Gaussian filtering is performed on each of the vibration data collected by the multiple vibration sensors, and the filtered vibration data is then averaged.

[0071] The specific implementation of the offshore photovoltaic support displacement monitoring system of the present invention is basically the same as the embodiments of the above-mentioned offshore photovoltaic support displacement monitoring method, and will not be repeated here.

[0072] This invention also proposes a medium. The medium is a readable storage medium storing a control program. When executed by a processor, the control program implements the steps of the offshore photovoltaic support displacement monitoring method described above.

[0073] The storage medium of the present invention can be a computer-readable storage medium, and its implementation is basically the same as the embodiments of the above-described offshore photovoltaic support displacement monitoring method, and will not be repeated here.

[0074] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many modifications under the guidance of the present invention without departing from the spirit and scope of the claims. All equivalent structural or procedural transformations made using the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are within the protection scope of the present invention.

Claims

1. A method for monitoring the displacement of offshore photovoltaic support structures, characterized in that, The offshore photovoltaic support structure is pre-set with multiple monitoring areas, and each monitoring area is equipped with a corresponding sensor group, which includes at least a stress sensor, a corrosion sensor, and a vibration sensor. The method for monitoring the displacement of offshore photovoltaic support structures includes: For each area to be monitored, stress data, corrosion data, and vibration data are acquired by the stress sensor, corrosion sensor, and vibration sensor in the corresponding sensor group, respectively. The stress data, corrosion data, and vibration data are generated into a set of sensor data corresponding to the area to be monitored, and based on the set of sensor data corresponding to each area to be monitored, it is determined whether there is a risk of displacement of the offshore photovoltaic support. If there is a risk of displacement, the marine photovoltaic support will be given a risk warning based on the preset Beidou positioning module.

2. The method for monitoring the displacement of offshore photovoltaic supports as described in claim 1, characterized in that, The step of determining whether the offshore photovoltaic support structure is at risk of displacement based on the sensor data set corresponding to each of the monitored areas includes: For each monitored area, a reference data group corresponding to the monitored area is found, and a similarity fusion operation is performed between the reference data group and the sensor data group to obtain a similarity value between the reference data group and the sensor data group. After obtaining the similarity value corresponding to each of the monitored areas, it is determined whether the offshore photovoltaic support has a displacement risk based on each of the similarity values.

3. The method for monitoring the displacement of offshore photovoltaic supports as described in claim 2, characterized in that, The step of performing a similarity fusion operation between the reference data group and the sensing data group to obtain a similarity value between the reference data group and the sensing data group includes: Each reference element in the reference data group is multiplied by each sensing element in the sensing data group to obtain a reference array corresponding to the reference element, and the values ​​in the reference array are summed to generate a reference summation result. After performing a product operation on each of the reference elements to obtain a corresponding reference array, and performing a summation operation on each of the reference arrays to generate a corresponding reference summation result, the summation operation on each of the reference summation results is performed to obtain a total summation result as the similarity value between the reference data group and the sensing data group.

4. The method for monitoring the displacement of offshore photovoltaic supports as described in claim 2, characterized in that, The step of determining whether the offshore photovoltaic support structure has a displacement risk based on the similarity values ​​includes: Obtain the preset weights corresponding to each of the monitored areas, and perform weight fusion calculation on each of the similarity values ​​according to each preset weight to obtain the risk coefficient of the marine photovoltaic support. The risk coefficient is compared with a preset threshold to determine whether the risk coefficient is greater than the preset threshold. If it is greater than the preset threshold, it is determined that the offshore photovoltaic support has a displacement risk.

5. The method for monitoring the displacement of offshore photovoltaic supports as described in claim 4, characterized in that, The step of providing risk warning for the offshore photovoltaic support based on a preset BeiDou positioning module if displacement risk exists includes: If there is a displacement risk, the risk coefficient is compared with multiple pre-set risk reference intervals to determine the target risk reference interval corresponding to the risk coefficient. Find the risk level corresponding to the target risk reference interval, and the early warning information corresponding to the risk level, and locate the risk location information based on the Beidou positioning module; Based on the risk level, the early warning information, and the risk location information, a risk warning is issued for the offshore photovoltaic support structure.

6. The method for monitoring the displacement of offshore photovoltaic support structures as described in claim 5, characterized in that, The step of locating risk location information based on the BeiDou positioning module includes: The preset weight corresponding to each monitored region is multiplied with the similarity value corresponding to each monitored region to obtain the regional risk coefficient corresponding to each monitored region. Find the target area risk coefficient that is greater than the preset area threshold from the risk coefficients of each area, and find the target monitoring area that corresponds to the risk coefficient of the target area from each area to be monitored; The location information corresponding to each target monitoring area is determined based on the preset Beidou positioning module as the risk location information.

7. The method for monitoring the displacement of offshore photovoltaic supports as described in any one of claims 1-6, characterized in that, The steps for acquiring stress data, corrosion data, and vibration data collected by the stress sensor, corrosion sensor, and vibration sensor in the corresponding sensor group include: The system sends a first ultrasonic wave to the area to be monitored based on a stress sensor, and receives a second ultrasonic wave after the first ultrasonic wave has propagated through the area to be monitored. The system then generates the stress data based on the first and second ultrasonic waves. The corrosion sensor detects the resistance value of the area to be monitored, and the corrosion data is generated based on the resistance value. The vibration sensor detects the capacitance value of the area to be monitored, and the vibration data is generated based on the capacitor.

8. The method for monitoring the displacement of offshore photovoltaic support structures as described in any one of claims 1-6, characterized in that, Each of the monitored areas includes multiple stress sensors, multiple corrosion sensors, and multiple vibration sensors. Prior to the step of generating a sensor data set corresponding to the monitored area from the stress data, corrosion data, and vibration data, the following steps are included: The stress data collected by the multiple stress sensors are subjected to median filtering, and the filtered stress data are then averaged. The corrosion data collected by the multiple corrosion sensors are each subjected to low-pass filtering, and the filtered corrosion data are then averaged. Gaussian filtering is performed on each of the vibration data collected by the multiple vibration sensors, and the filtered vibration data is then averaged.

9. A displacement monitoring system for offshore photovoltaic support structures, characterized in that, The offshore photovoltaic support structure is pre-set with multiple monitoring areas, and each monitoring area is equipped with a corresponding sensor group, which includes at least a stress sensor, a corrosion sensor, and a vibration sensor. The offshore photovoltaic support displacement monitoring system includes a storage device, a processor, a communication bus, and a control program stored in the storage device. The communication bus is used to enable communication between the processor and the memory. The processor is used to execute the control program to implement the steps of the offshore photovoltaic support displacement monitoring method as described in any one of claims 1-8.

10. A medium, characterized in that, The medium is a readable storage medium, on which a control program is stored. When the control program is executed by a processor, it implements the steps of the offshore photovoltaic support displacement monitoring method as described in any one of claims 1-8.

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