Photovoltaic power generation assembly safety monitoring system based on Internet of Things data transmission

Abstract

The invention belongs to the technical field of photovoltaic power generation assembly supervision, and particularly relates to a photovoltaic power generation assembly safety monitoring system based on Internet of Things data transmission, which comprises a bracket bearing risk analysis module, a bracket impact monitoring analysis module, a photovoltaic panel surface monitoring module, an assembly operation abnormity identification module and a visual monitoring center, according to the invention, the support bearing risk of the photovoltaic power generation assembly is analyzed through the support bearing risk analysis module, and the support impact damage analysis is carried out through the support impact monitoring analysis module when the support bearing low-risk signal is generated, so that the support stability of the photovoltaic power generation assembly is ensured; the photovoltaic panel surface monitoring module accurately analyzes the surface risk of the photovoltaic panel based on the surface image of the photovoltaic panel, and the module operation abnormity identification module detects and identifies the operation abnormity of the photovoltaic power generation module, thereby achieving the comprehensive monitoring and timely early warning of the photovoltaic power generation module. And the operation efficiency and the operation safety of the photovoltaic power generation assembly are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation assembly supervision, and in particular to a photovoltaic power generation assembly safety monitoring system based on Internet of Things data transmission. Background Art

[0002] Photovoltaic power generation components are devices that use the photovoltaic effect to convert sunlight into electrical energy. With the increasing global demand for renewable energy, photovoltaic power generation has been widely used as a clean and efficient energy form. A distributed photovoltaic power generation intelligent monitoring system and a monitoring method thereof are disclosed in a Chinese invention patent with publication number CN108880468A. The system improves the reliability of the entire photovoltaic power station through remote intelligent real-time monitoring and realizes the automated management of the photovoltaic power station.

[0003] However, in actual use, the above-mentioned technical solution cannot reasonably analyze the support status of photovoltaic power generation components, and it is difficult to accurately judge the surface cleanliness of photovoltaic panels and identify abnormal operation of photovoltaic power generation components. It cannot achieve comprehensive monitoring of photovoltaic power generation components and timely warning, which is not conducive to improving the operating efficiency and safety of photovoltaic power generation components. It is difficult to supervise the operation of photovoltaic power generation components.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a photovoltaic power generation component safety monitoring system based on Internet of Things data transmission, which solves the problems that the prior art cannot reasonably analyze the support status of photovoltaic power generation components, and it is difficult to accurately judge the surface cleanliness status of photovoltaic panels and identify abnormal operation of photovoltaic power generation components, and cannot achieve comprehensive monitoring of photovoltaic power generation components and timely warnings, and the operation supervision of photovoltaic power generation components is difficult.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] Photovoltaic power generation component safety monitoring system based on IoT data transmission, including support load risk analysis module, support impact monitoring and analysis module, photovoltaic panel surface monitoring module, component operation abnormality identification module and visual monitoring center;

[0008] The support bearing risk analysis module obtains the support supporting the photovoltaic power generation assembly, analyzes the support bearing risk to generate a support bearing high risk signal or a support bearing low risk signal, and sends the support bearing high risk signal to the visual monitoring center, and sends the support bearing low risk signal to the support impact monitoring and analysis module;

[0009] When the support impact monitoring and analysis module receives a support load low risk signal, it performs support impact damage analysis based on the support impact information, generates a support impact monitoring warning signal or a support impact monitoring safety signal through analysis, and sends the support impact monitoring warning signal to the visual monitoring center;

[0010] The photovoltaic panel surface monitoring module collects the surface image of the photovoltaic panel in the photovoltaic power generation group component, analyzes the surface risk of the photovoltaic panel based on the surface image, generates a photovoltaic panel surface risk warning signal or a photovoltaic panel surface risk safety signal through analysis, and sends the photovoltaic panel surface risk warning signal to the visual monitoring center;

[0011] The component operation abnormality identification module monitors the relevant parameters of the photovoltaic panels, inverters and energy storage devices in the photovoltaic power generation components in real time, and inputs the monitoring data of various parameters into the abnormality identification model of the photovoltaic power generation components. The abnormality identification model generates a power generation abnormality warning signal when the abnormal parameters are identified, and sends the power generation abnormality warning signal to the visual monitoring center; the visual monitoring center issues a warning when it receives a high-risk signal of the bracket load, a bracket impact monitoring warning signal, a photovoltaic panel surface risk warning signal or a power generation abnormality warning signal.

[0012] Furthermore, the specific analysis process of the support load risk analysis module includes:

[0013] The amount of settlement of the bracket compared to the initial time is obtained and marked as the bracket settlement value, and the change in the inclination of the bracket compared to the initial time is marked as the bracket inclination value; and the bracket shape inspection value of the bracket is obtained by analysis, and the bracket bearing risk coefficient is obtained by numerically calculating the bracket settlement value, the bracket inclination value and the bracket shape inspection value, and the bracket bearing risk coefficient is numerically compared with the preset bracket bearing risk coefficient threshold value. If the bracket bearing risk coefficient exceeds the preset bracket bearing risk coefficient threshold value, a bracket bearing high risk signal is generated; if the bracket bearing risk coefficient does not exceed the preset bracket bearing risk coefficient threshold value, a bracket bearing low risk signal is generated.

[0014] Furthermore, the analysis and acquisition method of the stent shape inspection value is as follows:

[0015] A number of monitoring points are set on the bracket, and the actual distance values ​​between the corresponding two groups of monitoring points are collected. The deviation value of the actual distance value between the corresponding two groups of monitoring points compared with the corresponding initial distance value is marked as a point distance variation value, and the point distance variation value is numerically compared with the corresponding preset point distance variation threshold value. If the point distance variation value exceeds the corresponding preset point distance variation threshold value, the corresponding point distance variation value is marked as a point distance abnormal variation value;

[0016] The number of point distance variation values ​​is obtained and the ratio is calculated with the number of point distance variation values ​​to obtain the point distance variation risk value, and the ratio of the point distance variation value to the corresponding preset point distance variation threshold is marked as the point distance variation measurement value, the average of all point distance variation measurement values ​​is calculated to obtain the point distance variation table value, and the point distance variation measurement value with the largest value is marked as the point distance variation amplitude value; the bracket shape inspection value is obtained by numerically calculating the point distance variation risk value, the point distance variation table value and the point distance variation amplitude value.

[0017] Furthermore, the specific analysis process of the bracket impact damage analysis includes:

[0018] Obtain the swing amplitude curve of the bracket in unit time, establish a rectangular coordinate system in the first quadrant with time as the X-axis and the swing amplitude as the Y-axis, place the swing amplitude curve in the rectangular coordinate system, and the initial point of the swing amplitude curve is on the Y-axis;

[0019] In a rectangular coordinate system, a ray parallel to the X-axis with its endpoint on the Y-axis is drawn and marked as a shaking detection ray, and the portion of the shaking amplitude curve above the shaking detection ray and the area enclosed by the shaking detection ray are marked as a shaking risk area;

[0020] The area of ​​the corresponding swaying danger area is obtained and marked as the swaying danger detection value, and the swaying danger detection value is compared with the preset swaying danger detection threshold. If the swaying danger detection value exceeds the preset swaying danger detection threshold, the corresponding swaying danger area is marked as an alarm area; if an alarm area exists, a bracket impact monitoring early warning signal is generated.

[0021] Furthermore, if there is no alarm area, the sway detection values ​​of all sway areas are summed up to obtain a total sway detection value, and the number of sway areas is marked as a sway condition value, and the sway detection value with the largest value is marked as a sway detection amplitude value. The support impact warning coefficient is obtained by numerically calculating the total sway detection value, the sway condition value and the sway detection amplitude value;

[0022] The bracket impact warning coefficient is numerically compared with the preset bracket impact warning coefficient threshold. If the bracket impact warning coefficient exceeds the preset bracket impact warning coefficient threshold, a bracket impact monitoring warning signal is generated; if the bracket impact warning coefficient does not exceed the preset bracket impact warning coefficient threshold, a bracket impact monitoring safety signal is generated.

[0023] Furthermore, the specific analysis process of the photovoltaic panel surface monitoring module includes:

[0024] A surface image of the photovoltaic panel is obtained, and grayscale processing is performed on the surface image of the photovoltaic panel to divide the surface image of the photovoltaic panel into a plurality of identification areas; an average grayscale value of the corresponding identification area is collected and marked as an area grayscale analysis value, and the area grayscale analysis value is numerically compared with a preset area grayscale analysis threshold value; if the area grayscale analysis value exceeds the preset area grayscale analysis threshold value, the corresponding identification area is marked as a contaminated area;

[0025] The number of corroded areas is obtained and the ratio is calculated with the total number of identified areas to obtain the corroded coefficient, and the zone gray analysis values ​​of all identified areas are averaged to obtain the surface gray coefficient; the corroded coefficient and the surface gray coefficient are numerically compared with the preset corroded coefficient threshold and the preset surface gray coefficient threshold respectively. If the corroded coefficient or the surface gray coefficient exceeds the corresponding preset threshold, a photovoltaic panel surface danger warning signal is generated.

[0026] Furthermore, if the pollution coefficient and the surface gray coefficient do not exceed the corresponding preset threshold, the aggregation area of ​​the pollution area is obtained, the number of pollution areas in the corresponding aggregation area is marked as the pollution aggregation value, and the pollution aggregation value with the largest value is marked as the pollution aggregation amplitude value;

[0027] The photovoltaic panel surface risk coefficient is obtained by numerically calculating the corrosion coefficient, surface ash coefficient and corrosion amplitude value, and the photovoltaic panel surface risk coefficient is numerically compared with the preset photovoltaic panel surface risk coefficient threshold. If the photovoltaic panel surface risk coefficient exceeds the preset photovoltaic panel surface risk coefficient threshold, a photovoltaic panel surface risk warning signal is generated; if the photovoltaic panel surface risk coefficient does not exceed the preset photovoltaic panel surface risk coefficient threshold, a photovoltaic panel surface risk safety signal is generated.

[0028] Furthermore, the visual monitoring center communicates with the management status analysis module. The management status analysis module is used to set a detection period, analyze the management status of the photovoltaic power generation components during the detection period, generate a background management qualified signal or a background management abnormal signal through analysis, and strengthen subsequent on-site supervision of the visual monitoring center when the background management abnormal signal is generated.

[0029] Furthermore, the specific analysis process of the management status analysis module is as follows:

[0030] Based on the real-time monitoring video of the visual monitoring center, it is judged whether there is a management personnel in the visual monitoring center. If there is no management personnel in the visual monitoring, it is judged that the visual monitoring center is in an unmanned state; the total duration of the visual monitoring center in the unmanned state during the detection period is obtained and marked as the total unmanned time value, and the number of occurrences of the single continuous duration of the visual monitoring center in the unmanned state during the detection period exceeding the corresponding preset single continuous duration threshold is marked as an excessively long unmanned detection value, and the total unmanned time value and the excessively long unmanned detection value are numerically calculated to obtain the monitoring center management anomaly value;

[0031] All photovoltaic power generation components that need to be managed are obtained, the number of warnings issued by the visual monitoring center for the corresponding photovoltaic power generation components during the detection period is collected and marked as the component warning frequency value, the component warning frequency values ​​of all photovoltaic power generation components are averaged to obtain the warning value, and the proportion of photovoltaic power generation components whose component warning frequency values ​​exceed the preset component warning frequency threshold is marked as the abnormal warning value;

[0032] Compare the alarm value and the abnormal value with the preset alarm threshold and the preset abnormal threshold respectively. If the alarm value or the abnormal value exceeds the corresponding preset threshold, the preset monitoring center abnormal threshold YM1 is allocated; if the alarm value and the abnormal value do not exceed the corresponding preset threshold, the preset monitoring center abnormal threshold YM2 is allocated, and YM2>YM1>0;

[0033] The monitoring center management anomaly value is numerically compared with the corresponding preset monitoring center management anomaly threshold. If the monitoring center management anomaly value exceeds the corresponding preset monitoring center management anomaly threshold, a background management abnormal signal is generated; if the monitoring center management anomaly value does not exceed the corresponding preset monitoring center management anomaly threshold, a background management qualified signal is generated.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. In the present invention, the support load risk analysis module is used to analyze the support load risk of the photovoltaic power generation component. When the support load low risk signal is generated, the support impact damage analysis is performed through the support impact monitoring and analysis module, which is conducive to ensuring the support stability of the photovoltaic power generation component. The photovoltaic panel surface monitoring module accurately analyzes the surface risk of the photovoltaic panel, and the component operation abnormality identification module detects and identifies the operation abnormality of the photovoltaic power generation component, so as to realize comprehensive monitoring of the photovoltaic power generation component and timely early warning, and significantly improve the operation efficiency and operation safety of the photovoltaic power generation component.

[0036] 2. In the present invention, the background management status of the photovoltaic power generation components during the detection period is analyzed by the management status analysis module to generate a background management qualification signal or a background management abnormality signal. When the background management abnormality signal is generated, the subsequent on-site supervision of the visual monitoring center is strengthened, which is conducive to ensuring the supervision effect and timeliness of response for all photovoltaic power generation components, and further improving the operating safety of all photovoltaic power generation components. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0038] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0039] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Embodiment 1: Figure 1 As shown, the photovoltaic power generation component safety monitoring system based on Internet of Things data transmission proposed by the present invention includes a bracket load risk analysis module, a bracket impact monitoring and analysis module, a photovoltaic panel surface monitoring module, a component operation abnormality identification module and a visual monitoring center;

[0042] The support load risk analysis module obtains the support supporting the photovoltaic power generation component, analyzes the support load risk to generate a support load high risk signal or a support load low risk signal, and sends the support load high risk signal to the visual monitoring center through the Internet of Things;

[0043] When the visual monitoring center receives a high-risk signal of the bracket load, it issues an early warning to remind the back-end management personnel to repair or replace the bracket in time, so as to ensure the support stability of the photovoltaic power generation components, which is conducive to improving the operation safety of the photovoltaic power generation components; the specific analysis process of the bracket load risk analysis module is as follows:

[0044] The settlement amount of the bracket compared to the initial time is obtained and marked as the bracket settlement value, and the change amount of the bracket inclination compared to the initial time is marked as the bracket inclination value; wherein, the larger the values ​​of the bracket settlement value and the bracket inclination value are, the more unfavorable it is to ensure the support stability of the bracket;

[0045] And the stent shape inspection value of the stent is obtained through analysis, specifically: a number of monitoring points are set on the stent, the actual distance value between the corresponding two groups of monitoring points is collected, and the deviation value of the actual distance value between the corresponding two groups of monitoring points compared with the corresponding initial distance value is marked as the point distance variation value; wherein, the larger the value of the point distance variation value is, the more serious the deformation displacement between the corresponding two groups of monitoring points is;

[0046] Compare the point distance variation value with the corresponding preset point distance variation threshold, if the point distance variation value exceeds the corresponding preset point distance variation threshold, mark the corresponding point distance variation value as the point distance variation value;

[0047] The number of point distance variation values ​​is obtained and the ratio thereof is calculated with the number of point distance variation values ​​to obtain the point distance variation value, and the ratio of the point distance variation value to the corresponding preset point distance variation threshold is marked as the point distance variation value, the average of all point distance variation values ​​is calculated to obtain the point distance variation value, and the point distance variation value with the largest value is marked as the point distance variation value;

[0048] The bracket shape inspection value XL is obtained by numerically calculating the point distance variation value XF, the point distance variation value XN and the point distance variation value XR through the formula XL=b1*XF+(b2*XN+b3*XR) / 2; wherein b1, b2 and b3 are preset proportional coefficients, b1>b2>b3>0; and the larger the value of the bracket shape inspection value XL is, the more serious the overall deformation of the bracket is;

[0049] The bracket settlement value ZW, the bracket tilt value ZN and the bracket shape inspection value XL are numerically calculated by the formula ZF=(up1*ZW+up2*ZN+up3*XL) / 3 to obtain the bracket bearing risk coefficient ZF; wherein up1, up2, up3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the bracket bearing risk coefficient ZF, the higher the overall support risk of the bracket;

[0050] The bracket load-bearing risk coefficient ZF is numerically compared with the preset bracket load-bearing risk coefficient threshold. If the bracket load-bearing risk coefficient exceeds the preset bracket load-bearing risk coefficient threshold, it indicates that the support risk of the bracket is generally high, and a bracket load-bearing high-risk signal is generated; if the bracket load-bearing risk coefficient ZF does not exceed the preset bracket load-bearing risk coefficient threshold, it indicates that the support risk of the bracket is generally low, and a bracket load-bearing low-risk signal is generated.

[0051] The support bearing risk analysis module sends the support bearing low risk signal to the support impact monitoring and analysis module. When the support impact monitoring and analysis module receives the support bearing low risk signal, it performs support impact damage analysis based on the impact information received by the support, and generates a support impact monitoring early warning signal or a support impact monitoring safety signal through analysis;

[0052] The support impact monitoring and early warning signal is sent to the visual monitoring center through the Internet of Things. When the visual monitoring center receives the support impact monitoring and early warning signal, it issues an early warning to achieve effective monitoring of the impact on the support and timely early warning, so as to remind the back-end management personnel to check the corresponding support in time and take corresponding treatment measures for the support as needed to ensure the support safety of the support. The specific analysis process of the support impact damage analysis is as follows:

[0053] Obtain the swing amplitude curve of the bracket in unit time, establish a rectangular coordinate system in the first quadrant with time as the X-axis and the swing amplitude as the Y-axis, place the swing amplitude curve in the rectangular coordinate system, and the initial point of the swing amplitude curve is on the Y-axis;

[0054] In a rectangular coordinate system, a ray parallel to the X-axis and with its endpoint on the Y-axis is drawn and marked as a shake detection ray, wherein the Y-axis coordinate value corresponding to the shake detection ray represents a preset shake amplitude threshold, and the value of the preset shake amplitude threshold is a positive number; the portion of the shake amplitude curve above the shake detection ray and the area enclosed by the shake detection ray are marked as a shake risk area;

[0055] The area of ​​the corresponding swaying danger zone is obtained and marked as the swaying danger detection value, and the swaying danger detection value is compared with the preset swaying danger detection threshold. If the swaying danger detection value exceeds the preset swaying danger detection threshold, it indicates that the support may be subjected to a large impact during the time period corresponding to the corresponding swaying danger zone, and the corresponding swaying danger zone is marked as an alarm area; if there is an alarm area, it indicates that the support is damaged by the impact, and a support impact monitoring warning signal is generated.

[0056] Furthermore, if there is no alarm area, the sway detection values ​​of all sway areas are summed up to obtain a total sway detection value, and the number of sway areas is marked as a sway value, and the sway detection value with the largest value is marked as a sway detection amplitude value;

[0057] By formula The total sway inspection value HM, the sway condition value HW and the sway inspection amplitude value HF are numerically calculated to obtain the support impact warning coefficient HP; wherein er1, er2 and er3 are preset proportional coefficients, er2>er3>er1>0.352; and the larger the value of the support impact warning coefficient HP, the more serious the impact damage to the support in unit time is.

[0058] The bracket impact warning coefficient HP is numerically compared with the preset bracket impact warning coefficient threshold. If the bracket impact warning coefficient HP exceeds the preset bracket impact warning coefficient threshold, it indicates that the impact damage to the bracket per unit time is generally serious, and a bracket impact monitoring warning signal is generated; if the bracket impact warning coefficient HP does not exceed the preset bracket impact warning coefficient threshold, it indicates that the impact damage to the bracket per unit time is generally light, and a bracket impact monitoring safety signal is generated.

[0059] The photovoltaic panel surface monitoring module collects the surface image of the photovoltaic panel in the photovoltaic power generation group component, analyzes the surface risk of the photovoltaic panel based on the surface image, generates a photovoltaic panel surface risk warning signal or a photovoltaic panel surface risk safety signal through analysis, and sends the photovoltaic panel surface risk warning signal to the visual monitoring center through the Internet of Things;

[0060] When the visual monitoring center receives the photovoltaic panel surface risk warning signal, it will issue a warning to remind the back-end management personnel to clean the photovoltaic panel surface in time to avoid the photovoltaic panel surface being covered with rust and dirt, which will affect the power generation efficiency and safety of photovoltaic power generation components, significantly reduce the difficulty of supervision of photovoltaic power generation components, and further improve the operation safety of photovoltaic power generation components; the specific analysis process of the photovoltaic panel surface monitoring module is as follows:

[0061] A surface image of the photovoltaic panel is obtained, and grayscale processing is performed on the surface image of the photovoltaic panel to divide the surface image of the photovoltaic panel into a number of identification areas; an average grayscale value of the corresponding identification area is collected and marked as an area grayscale analysis value, and the area grayscale analysis value is numerically compared with a preset area grayscale analysis threshold. If the area grayscale analysis value exceeds the preset area grayscale analysis threshold, it indicates that the rust and dirt conditions of the corresponding identification area are serious, and the corresponding identification area is marked as a rusted area;

[0062] The number of contaminated areas is obtained and the ratio is calculated with the total number of identified areas to obtain the contamination coefficient, and the area gray analysis values ​​of all identified areas are averaged to obtain the surface gray coefficient; the contamination coefficient and the surface gray coefficient are numerically compared with the preset contamination coefficient threshold and the preset surface gray coefficient threshold, respectively. If the contamination coefficient or the surface gray coefficient exceeds the corresponding preset threshold, indicating that the surface cleanliness of the photovoltaic panel is poor, a photovoltaic panel surface risk warning signal is generated.

[0063] Furthermore, if the pollution coefficient and the surface gray coefficient do not exceed the corresponding preset threshold, the clustering area of ​​the pollution area is obtained, the number of pollution areas in the corresponding clustering area is marked as the pollution clustering value, and the pollution clustering value with the largest value is marked as the pollution clustering amplitude value;

[0064] By formula The surface risk coefficient GY of the photovoltaic panel is obtained by numerically calculating the corrosion coefficient GS, the surface gray coefficient GK and the corrosion amplitude GP; wherein wq1, wq2 and wq3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the surface risk coefficient GY of the photovoltaic panel is, the worse the surface cleanliness of the photovoltaic panel is in general;

[0065] The photovoltaic panel surface risk coefficient GY is numerically compared with the preset photovoltaic panel surface risk coefficient threshold. If the photovoltaic panel surface risk coefficient GY exceeds the preset photovoltaic panel surface risk coefficient threshold, indicating that the surface cleanliness condition of the photovoltaic panel is generally poor, then a photovoltaic panel surface risk warning signal is generated; if the photovoltaic panel surface risk coefficient GY does not exceed the preset photovoltaic panel surface risk coefficient threshold, indicating that the surface cleanliness condition of the photovoltaic panel is generally good, then a photovoltaic panel surface risk safety signal is generated.

[0066] The component operation abnormality identification module monitors the relevant parameters (including voltage, current, temperature and other parameters) of the photovoltaic panels, inverters and energy storage devices in the photovoltaic power generation components in real time, collects the monitoring data of various relevant parameters, and inputs the monitoring data of various parameters into the abnormality identification model of the photovoltaic power generation components (the abnormality identification model is a mathematical model based on algorithms or machine learning technology, which is specially designed and trained to analyze various parameter data generated by photovoltaic power generation components during operation to identify their operating faults). The abnormality identification model generates a power generation abnormality warning signal when abnormal parameters are identified;

[0067] In addition, the abnormal power generation warning signal is sent to the visual monitoring center through the Internet of Things. When the visual monitoring center receives the abnormal power generation warning signal, it will issue a warning. It can realize real-time monitoring, data analysis and safety warning of the operation process of photovoltaic power generation components, and facilitate back-end management personnel to remotely control and timely maintain photovoltaic power generation components, which significantly improves the operation efficiency and safety of photovoltaic power generation components.

[0068] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the visual monitoring center is connected to the management status analysis module, and the management status analysis module is used to set the detection period, preferably, the detection period is twenty-five days; the management status of the photovoltaic power generation components during the detection period is analyzed, and a background management qualified signal or a background management abnormal signal is generated through the analysis;

[0069] When abnormal background management signals are generated, the subsequent on-site supervision of the visual monitoring center is strengthened to ensure the supervision effect and timely response of all photovoltaic power generation components, which is conducive to further improving the operational safety of all photovoltaic power generation components. The specific analysis process of the management status analysis module is as follows:

[0070] Based on the real-time monitoring video of the visual monitoring center, it is determined whether there is a manager in the visual monitoring center. If there is no manager in the visual monitoring, it is determined that the visual monitoring center is in an unmanned state; the total length of time that the visual monitoring center is in an unmanned state during the detection period is obtained and marked as the total unmanned time value;

[0071] The single duration of each time when the visual monitoring center is in an unmanned state during the detection period is collected, and the number of occurrences in which the single duration of the visual monitoring center in an unmanned state during the detection period exceeds the corresponding preset single duration threshold is marked as an excessively long unmanned detection value;

[0072] The total unmanned time value YS and the overlong unmanned detection value YL are numerically calculated by the formula YM=k1*YS+k2*YL to obtain the monitoring center management abnormality value YM; wherein k1 and k2 are preset weight coefficients, k2>k1>0.15; and the larger the value of the monitoring center management abnormality value YM, the worse the overall performance of personnel management for the visual monitoring center during the detection period;

[0073] All photovoltaic power generation components that need to be managed are obtained, the number of warnings issued by the visual monitoring center for the corresponding photovoltaic power generation components during the detection period is collected and marked as the component warning frequency value, the component warning frequency values ​​of all photovoltaic power generation components are averaged to obtain the warning value, and the component warning frequency value is numerically compared with the preset component warning frequency threshold, and the number of photovoltaic power generation components whose component warning frequency value exceeds the preset component warning frequency threshold is marked as the abnormal warning value;

[0074] The warning value and the abnormal value are numerically compared with the preset warning threshold and the preset abnormal threshold, respectively. If the warning value or the abnormal value exceeds the corresponding preset threshold, it indicates that the operating performance of all photovoltaic power generation components that need to be managed during the detection period is generally poor, and the preset monitoring center abnormal threshold YM1 is allocated; if the warning value and the abnormal value do not exceed the corresponding preset threshold, it indicates that the operating performance of all photovoltaic power generation components that need to be managed during the detection period is generally good, and the preset monitoring center abnormal threshold YM2 is allocated, and YM2>YM1>0;

[0075] Compare the monitoring center abnormal value with the corresponding preset monitoring center abnormal threshold, and assign a matching preset monitoring center abnormal threshold based on the operating performance of all photovoltaic power generation components, which can significantly improve the accuracy of management status analysis results;

[0076] If the monitoring center management anomaly value exceeds the corresponding preset monitoring center management anomaly threshold, indicating that the personnel management performance of the visual monitoring center during the detection period is generally poor, then a background management abnormality signal is generated; if the monitoring center management anomaly value does not exceed the corresponding preset monitoring center management anomaly threshold, indicating that the personnel management performance of the visual monitoring center during the detection period is generally good, then a background management qualified signal is generated.

[0077] The working principle of the present invention is as follows: when in use, the support load risk of the photovoltaic power generation component is analyzed through the support load risk analysis module, and when a support load low risk signal is generated, the support impact damage analysis is performed through the support impact monitoring and analysis module, and when a support load high risk signal or a support impact monitoring and early warning signal is generated, corresponding processing measures are taken for the support to ensure the support stability of the photovoltaic power generation component, which is beneficial to improving the operation safety of the photovoltaic power generation component, and the surface risk of the photovoltaic panel is analyzed based on the surface image of the photovoltaic panel through the photovoltaic panel surface monitoring module, and the surface of the photovoltaic panel is cleaned when the photovoltaic panel surface risk early warning signal is generated to avoid the photovoltaic panel surface covering rust and dirt. The surface of the photovoltaic panel affects the power generation efficiency and safety of the photovoltaic power generation component, and the component operation abnormality identification module detects and identifies the operation abnormality of the photovoltaic power generation component, thereby realizing real-time monitoring, data analysis and safety early warning of the operation process of the photovoltaic power generation component, and further improving the operation efficiency and operation safety of the photovoltaic power generation component.

[0078] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A photovoltaic power generation component safety monitoring system based on Internet of Things data transmission, characterized in that: It includes a support bearing risk analysis module, a support impact monitoring and analysis module, a photovoltaic panel surface monitoring module, a component operation abnormality identification module and a visual monitoring center; the support bearing risk analysis module obtains the support supporting the photovoltaic power generation component, analyzes the support bearing risk to generate a support bearing high risk signal or a support bearing low risk signal; When the support impact monitoring and analysis module receives a support load low risk signal, it performs support impact damage analysis based on the support impact information, and generates a support impact monitoring early warning signal or a support impact monitoring safety signal through analysis; The photovoltaic panel surface monitoring module collects the surface image of the photovoltaic panel in the photovoltaic power generation group component, analyzes the surface risk of the photovoltaic panel based on the surface image, and generates a photovoltaic panel surface risk warning signal or a photovoltaic panel surface risk safety signal through analysis; The component operation abnormality identification module monitors the relevant parameters of the photovoltaic panels, inverters and energy storage devices in the photovoltaic power generation components in real time. The abnormality identification model generates a power generation abnormality warning signal when abnormal parameters are identified; the visual monitoring center issues a warning when it receives a high-risk signal of the bracket load, a bracket impact monitoring warning signal, a photovoltaic panel surface risk warning signal or a power generation abnormality warning signal.

2. The photovoltaic power generation component safety monitoring system based on Internet of Things data transmission according to claim 1 is characterized in that: The specific analysis process of the support load risk analysis module includes: The bracket shape inspection value of the bracket is obtained by analysis, and the bracket bearing risk coefficient is obtained by numerically calculating the bracket settlement value, bracket inclination value and bracket shape inspection value. If the bracket bearing risk coefficient exceeds the preset bracket bearing risk coefficient threshold, a bracket bearing high risk signal is generated; if the bracket bearing risk coefficient does not exceed the preset bracket bearing risk coefficient threshold, a bracket bearing low risk signal is generated.

3. The photovoltaic power generation component safety monitoring system based on Internet of Things data transmission according to claim 2 is characterized in that: The analysis and acquisition method of the stent shape inspection value is as follows: The number of point distance variation values ​​is obtained and the ratio is calculated with the number of point distance variation values ​​to obtain the point distance variation risk value, the average of all point distance variation values ​​is calculated to obtain the point distance variation table value, and the point distance variation value with the largest value is marked as the point distance variation amplitude value; the bracket shape inspection value is obtained by numerically calculating the point distance variation risk value, the point distance variation table value and the point distance variation amplitude value.

4. The photovoltaic power generation component safety monitoring system based on Internet of Things data transmission according to claim 1 is characterized in that: The specific analysis process of the bracket impact damage analysis includes: The sway amplitude curve of the bracket within unit time is obtained, and the part of the sway amplitude curve above the sway detection ray and the area enclosed by the sway detection ray are marked as sway risk areas; if the sway risk detection value exceeds the preset sway risk detection threshold, the corresponding sway risk area is marked as an alarm area; if an alarm area exists, a bracket impact monitoring early warning signal is generated.

5. The photovoltaic power generation component safety monitoring system based on Internet of Things data transmission according to claim 4 is characterized in that: If there is no alarm area, the support impact warning coefficient is obtained by numerically calculating the total sway risk inspection value, the sway risk numerical condition value and the sway risk inspection amplitude value. If the support impact warning coefficient exceeds the preset support impact warning coefficient threshold, a support impact monitoring warning signal is generated; If the bracket impact warning coefficient does not exceed the preset bracket impact warning coefficient threshold, a bracket impact monitoring safety signal is generated.

6. The photovoltaic power generation component safety monitoring system based on Internet of Things data transmission according to claim 1 is characterized in that: The specific analysis process of the photovoltaic panel surface monitoring module includes: The surface image of the photovoltaic panel is obtained and grayscale processed. If the corrosion coefficient or the surface gray coefficient exceeds the corresponding preset threshold, a photovoltaic panel surface risk warning signal is generated.

7. The photovoltaic power generation component safety monitoring system based on Internet of Things data transmission according to claim 6 is characterized in that: If the corrosion coefficient and the surface ash coefficient do not exceed the corresponding preset thresholds, the photovoltaic panel surface risk coefficient is obtained by numerically calculating the corrosion coefficient, the surface ash coefficient and the corrosion amplitude value. If the photovoltaic panel surface risk coefficient exceeds the preset photovoltaic panel surface risk coefficient threshold, a photovoltaic panel surface risk warning signal is generated; If the photovoltaic panel surface risk coefficient does not exceed the preset photovoltaic panel surface risk coefficient threshold, a photovoltaic panel surface risk safety signal is generated.

8. The photovoltaic power generation component safety monitoring system based on Internet of Things data transmission according to claim 1 is characterized in that: The visual monitoring center communicates with the management status analysis module, which is used to set the detection period, analyze the management status of the photovoltaic power generation components during the detection period, and strengthen the subsequent on-site supervision of the visual monitoring center when the background management abnormality signal is generated.

9. The photovoltaic power generation component safety monitoring system based on Internet of Things data transmission according to claim 8 is characterized in that: The specific analysis process of the management status analysis module is as follows: The monitoring center abnormal value is obtained by numerically calculating the total unmanned time value and the excessively long unmanned detection value. The corresponding preset monitoring center abnormal threshold is allocated based on the warning value and the warning value. If the monitoring center abnormal value exceeds the corresponding preset monitoring center abnormal threshold, a background management abnormal signal is generated; if the monitoring center abnormal value does not exceed the corresponding preset monitoring center abnormal threshold, a background management qualified signal is generated.

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