Photovoltaic equipment detection operation and maintenance system and method based on Internet of Things

Abstract

The invention provides a photovoltaic equipment detection operation and maintenance system and method based on the Internet of Things, and relates to the technical field of photovoltaic equipment detection operation and maintenance, and the system comprises a sensing performance monitoring module, a calibration control and analysis module, a fault analysis module and a sensing deployment module. The sensing performance monitoring module is used for monitoring and analyzing key performance indexes of the sensor to obtain an evaluation value and an index evaluation list of any index in the key performance indexes; and performing weighting processing on the evaluation values of all the indexes in the key performance indexes to obtain comprehensive index values. According to the invention, through cooperative work of a plurality of modules, key performance indexes of the sensor are comprehensively monitored, accurate measurement and monitoring in the production process of photovoltaic equipment and performance evaluation and maintenance in the operation process are realized, the real-time monitoring and analysis capability is helpful to timely discover and calibrate subtle changes of the performance of the sensor, and the real-time monitoring and analysis capability of the sensor is improved. Therefore, detection errors are reduced, photovoltaic power generation efficiency and equipment quality are improved, and manual intervention is reduced by adopting automatic calibration and maintenance processes.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic equipment detection, operation and maintenance, and particularly to an Internet of Things-based photovoltaic equipment detection, operation and maintenance system and method. Background Art

[0002] With the continuous growth of the global demand for clean energy, the photovoltaic industry has developed rapidly. As the core component of photovoltaic power generation, the performance and reliability of photovoltaic equipment directly affect the power generation efficiency and stability of the photovoltaic power generation system. During the production and operation of photovoltaic equipment, various parameters need to be accurately measured and monitored to ensure that the quality and performance of the equipment meet the requirements.

[0003] However, there are some deficiencies in the current photovoltaic equipment detection, operation and maintenance systems. Most of the existing systems can only simply monitor the operating status of the equipment, lacking a comprehensive evaluation and in-depth analysis of the key performance indicators of the equipment. For example, for photovoltaic panels, only the power generation power is concerned, while ignoring the impact of indicators such as the conversion efficiency, temperature coefficient, and light intensity response characteristics of the panels on the power generation performance; for inverters, only the output voltage and current are simply monitored, without considering the impact of indicators such as the efficiency, harmonic distortion, and power factor of the inverter on the power quality. This imperfect management of equipment performance makes it impossible to detect subtle changes in equipment performance in a timely manner during production and operation, and thus effective calibration or maintenance measures cannot be taken in a timely manner. When the equipment performance gradually declines, the power generation efficiency will decrease, affecting the overall performance of the photovoltaic power generation system. Therefore, it is necessary to propose an Internet of Things-based photovoltaic equipment detection, operation and maintenance system and method to solve the above problems. Summary of the Invention

[0004] The present invention provides an Internet of Things-based photovoltaic equipment detection, operation and maintenance system and method, which solves the problems raised in the above background art.

[0005] To solve the above technical problems, the Internet of Things-based photovoltaic equipment detection, operation and maintenance system provided by the present invention includes a sensing and acquisition module, a sensing performance monitoring module, a calibration control and analysis module, a fault analysis module, and a sensing deployment module; The sensing and acquisition module is used to obtain in real time relevant information of the sensors applied during the production and operation of photovoltaic equipment, including the output signals of the sensors, environmental information, and key performance indicators; among them, the output signals include electrical signals such as voltage, current, and power, as well as temperature and light intensity, the environmental information includes the temperature, humidity, air pressure, light intensity, and wind speed of the photovoltaic power station, and the key performance indicators include the conversion efficiency, temperature coefficient, light intensity response characteristics of the photovoltaic panel, and the efficiency, harmonic distortion, and power factor of the inverter; The sensing performance monitoring module is used to monitor and analyze the key performance indicators of the sensor, obtain the evaluation value of any indicator in the key performance indicators and the indicator evaluation list; perform weighted processing on the evaluation values of all indicators in the key performance indicators to obtain a comprehensive index value; set a comprehensive threshold for the performance indicators, compare the comprehensive index value with the comprehensive threshold, and if the comprehensive index value is greater than or equal to the comprehensive threshold, generate a sensing calibration signaling; wherein, the key performance indicators include signal accuracy, stability, linearity, sensitivity, response time, resolution, repeatability, and noise level; The calibration control and analysis module is used to perform calibration analysis on the sensor after receiving the sensing calibration signaling, and obtain a calibration effective value; set an effective threshold, and if the calibration effective value is greater than or equal to the effective threshold, it indicates that the calibration process is successful and the sensor performance is effectively restored, and the calibration process ends; if the calibration effective value obtained after calibrating and adjusting all indicators in the indicator evaluation list of the sensor is still less than its effective threshold, it means that the sensor cannot be restored through self-calibration; The fault analysis module analyzes the sensor that cannot be restored through self-calibration to obtain a sensing evaluation value; sets a sensing fault threshold, compares the sensing evaluation value with the sensing fault threshold, and if the sensing evaluation value is greater than the sensing fault threshold, it means that the sensor has a fault and generates a sensing maintenance signaling; if the sensing evaluation value is less than or equal to the sensing fault threshold, it means that the sensor needs further calibration, and then generates a sensing calibration signaling; The sensing deployment module is used to receive the sensing replacement signaling and perform tuning processing operations to obtain the corresponding maintenance personnel; mark the sensing maintenance signaling, the corresponding type, number, location, and indicator evaluation list of the sensor as sensing maintenance information, and send the sensing maintenance information to the intelligent terminal of the maintenance personnel. After the maintenance personnel obtain the sensing maintenance information through the intelligent terminal, they perform maintenance on the sensor.

[0006] As a preferred implementation manner, the calibration analysis of the sensor is specifically as follows: Identify the sensing type of the sensor corresponding to the generated sensing calibration signaling and its corresponding indicator evaluation list. The sensing types include sensors for detecting the performance of photovoltaic panels and sensors for detecting the performance of inverters; Calibrate and adjust the indicators of the sensor in sequence from top to bottom according to the indicator evaluation list. After the indicator calibration and adjustment, obtain the comprehensive index value output by the adjusted sensing performance monitoring module, calculate the difference between this comprehensive index value and the previous comprehensive index value to obtain a comprehensive index change value, then calculate the difference between the comprehensive index value and its corresponding comprehensive threshold to obtain a comprehensive index difference, and perform weighted calculation on the comprehensive index change value and the comprehensive index difference to obtain a calibration effective value; Set an effective threshold, compare the calibrated valid value with its effective threshold. If the calibrated valid value is less than its effective threshold, it means that the calibration effect on the sensor is limited, then continue to perform calibration adjustment on the next indicator in the order of the indicator evaluation list; repeat the above process of obtaining the comprehensive index value, calculating the comprehensive index change value and the comprehensive index difference value, and obtaining the calibrated valid value; after obtaining a new calibrated valid value each time, sum it with the calibrated valid values obtained during the calibration process of all previous indicators, and update to obtain a new calibrated valid value.

[0007] As a preferred embodiment, monitor and analyze the key performance indicators of the sensor. The specific steps are as follows: Set the standard value of any indicator in the performance indicators corresponding to the sensor type, and calculate the standard difference corresponding to the indicator by calculating the difference between any indicator in the key performance indicators and its corresponding standard value; Set the sensing monitoring time zone, calculate the mean value and standard deviation of the standard differences corresponding to the indicators in the sensing monitoring time zone to obtain the difference mean value and the difference wave value; perform weighted processing on the standard difference corresponding to the indicator, the difference mean value, and the difference wave value to obtain the evaluation value corresponding to the indicator; perform weighted processing on the evaluation values of all indicators in the key performance indicators to obtain the comprehensive index value; sort the evaluation values of any indicator in descending order from top to bottom to obtain the indicator evaluation list.

[0008] As a preferred embodiment, analyze the sensors that cannot be restored by self-calibration. Specifically: Obtain the number of times the sensor generates the sensing calibration signaling, marked as the calibration times; obtain the generation times of the sensing calibration signaling with the calibration times greater than three, calculate the time difference between adjacent generation times to obtain the adjacent time difference; calculate the standard deviation of all the adjacent time differences to obtain the calibration adjacent difference fluctuation value; obtain the calibrated valid value output by the sensor each time it generates the sensing calibration signaling and calculate the standard deviation to obtain the calibration adjustment fluctuation difference value; perform weighted processing on the calibration times, the calibration adjacent difference fluctuation value, and the calibration adjustment fluctuation difference value to obtain the calibration influence value; Obtain the environmental information of the sensor that generates the sensing calibration signaling; set the environmental parameter standard value of any parameter in the environmental information corresponding to the sensor type, calculate the difference between the value of any parameter in the environmental information and its corresponding environmental parameter standard value to obtain the environmental parameter value corresponding to the parameter; set the environmental monitoring time zone corresponding to the sensor, calculate the mean value and standard deviation of the environmental parameter values in the environmental monitoring time zone to obtain the environmental parameter mean value and the environmental parameter fluctuation value; perform weighted calculation on the environmental parameter value, the environmental parameter mean value, and the environmental parameter fluctuation value to obtain the environmental parameter influence value; perform weighted processing on the environmental parameter influence values of all parameters in the environmental information to obtain the environmental influence value; perform weighted processing on the environmental influence value and the calibration influence value to obtain the sensing evaluation value.

[0009] As a preferred embodiment, the sensing and acquisition module further includes a data preprocessing unit; the data preprocessing unit is used to filter the sensor output signals collected, for removing high-frequency noise and low-frequency interference in the signals. Specifically: For high-frequency noise, a low-pass filter is adopted, and its cut-off frequency is set according to the frequency characteristics of the sensor signals; for low-frequency interference, a high-pass filter is adopted, and the cut-off frequency is set to be lower than the lower limit of the effective frequency range of the sensor signals.

[0010] As a preferred embodiment, a calibration processing operation is performed to obtain the corresponding maintenance personnel. The specific steps are as follows: Obtain the maintenance personnel to be maintained corresponding to the sensing maintenance signaling, and feedback an information acquisition instruction to the intelligent terminal of the maintenance personnel to be maintained to obtain the personnel information of the maintenance personnel to be maintained, including the current location, working years, number of working days in the current month, number of maintenance times, and number of successful maintenance times; process the personnel information to obtain the deployment value of the maintenance personnel to be maintained. Specifically: Calculate the distance difference between the current location of the maintenance personnel to be maintained and the location of the sensor corresponding to the sensing maintenance signaling to obtain the maintenance distance; calculate based on the number of maintenance times and the number of successful maintenance times to obtain the maintenance success rate of the corresponding type of sensor; perform weighted processing on the maintenance distance, maintenance success rate, working years, and number of working days in the current month to obtain the deployment value of the maintenance personnel to be maintained; select the maintenance personnel with the largest deployment value as the maintenance personnel.

[0011] As a preferred embodiment, the acquisition frequency of the sensor output signals of the sensing and acquisition module is dynamically adjusted according to the sensor type and the production process stage. Specifically: Identify the production process or operating state of the current photovoltaic device, set the key stage and the ordinary stage based on its influence degree on the quality of the final product or the power generation efficiency and its correlation degree with the subsequent process or system stability. When in the key stage, increase the acquisition frequency to the first preset frequency. When in the ordinary production stage or non-peak operation period, reduce the acquisition frequency to the second preset frequency, and the first preset frequency is higher than the second preset frequency.

[0012] As a preferred embodiment, it further includes a remote monitoring and management platform; the remote monitoring and management platform includes a display module, a remote monitoring module, and a management registration module; The management registration module is used to submit registration information for registration. If the registration is successful, a platform login account is generated; the platform login account is used to log in to the remote monitoring and management platform; usage permissions are assigned to the platform login account according to the user position in the registration information; A display module for visually presenting various information during the production and operation of photovoltaic devices; the various information includes real-time display of the sensor output signals, environmental information, and key performance indicators obtained by the sensing and acquisition module, as well as the evaluation value of any one of the key performance indicators and the indicator evaluation list. A remote monitoring module for data interaction with the sensing and acquisition module and the sensing performance monitoring module, to obtain the latest status data and analysis results of the device in real time and synchronously update and display them on the display module; it has a remote control function to identify the usage permissions of the platform login account, and its remote control function performs corresponding operations according to the usage permissions of the login account.

[0013] In this application, the proposed Internet of Things-based photovoltaic device detection and operation and maintenance method of the present invention includes the following steps: S1, Data acquisition and preprocessing step: S11, According to the acquisition frequency dynamically adjusted according to the sensor type and the production process stage or the device operation state; S12, Real-time acquisition of the sensor output signals, environmental information, and key performance indicators during the production and operation of photovoltaic devices; S13, Filter the acquired sensor output signals. For high-frequency noise, use a low-pass filter with a cut-off frequency set according to the frequency characteristics of the sensor signal. For low-frequency interference, use a high-pass filter with a cut-off frequency lower than the lower limit of the effective frequency range of the sensor signal; S2, Performance monitoring and evaluation step: S21, Monitor and analyze the key performance indicators of the sensors to obtain the evaluation value of any one of the key performance indicators and the indicator evaluation list; S22, Perform weighted processing on the evaluation values of all indicators in the key performance indicators to obtain a comprehensive index value; set a comprehensive threshold for the performance indicators, compare the comprehensive index value with the comprehensive threshold. If the comprehensive index value is greater than or equal to the comprehensive threshold, generate a sensing calibration signal; S3, Calibration control and analysis step: S31, After receiving the sensing calibration signal, perform calibration analysis on the sensor to obtain a calibration effective value; S32, Set an effective threshold. If the calibration effective value is greater than or equal to the effective threshold, it indicates that the calibration process is successful and the sensor performance is effectively restored, and the calibration process ends; if after calibrating and adjusting all indicators in the indicator evaluation list of the sensor, the obtained calibration effective value is still less than its effective threshold, it means that the sensor cannot be restored through self-calibration; S4, Fault analysis and processing step: S41, Analyze the sensors that cannot be restored through self-calibration to obtain a sensing evaluation value; S42. Set the sensing failure threshold, compare the sensing evaluation value with the sensing failure threshold. If the sensing evaluation value is greater than the sensing failure threshold, it indicates that the sensor has failed, and a sensing maintenance signal is generated; if the sensing evaluation value is less than or equal to the sensing failure threshold, it indicates that the sensor needs further calibration, and then a sensing calibration signal is generated. S5. Personnel allocation and maintenance steps: S51. After receiving the sensing replacement signal, perform a calibration process operation to obtain the corresponding maintenance personnel. S52. Mark the sensing maintenance signal, the corresponding type, number, location, and index evaluation list of the sensor as sensing maintenance information, and send the sensing maintenance information to the smart terminal of the maintenance personnel. After the maintenance personnel obtain the sensing maintenance information through the smart terminal, they perform maintenance on the sensor.

[0014] Compared with the related technologies, the photovoltaic device detection and operation and maintenance system and method based on the Internet of Things provided by the present invention have the following beneficial effects: Through the collaborative work of multiple modules, the present invention comprehensively monitors the key performance indicators of sensors, realizes precise measurement and monitoring during the production process of photovoltaic devices, as well as performance evaluation and maintenance during the operation process. The ability of real-time monitoring and analysis helps to promptly detect and calibrate the subtle changes in sensor performance, thereby reducing detection errors, improving the power generation efficiency and equipment quality of photovoltaic power generation, and adopting an automated calibration and maintenance process reduces manual intervention, improves the timeliness of maintenance and production operation efficiency. At the same time, the reasonable allocation of maintenance personnel improves the work efficiency of the entire operation and maintenance team. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a principle block diagram of the photovoltaic device detection and operation and maintenance system and method based on the Internet of Things provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0017] The terms used in this disclosure are only for the purpose of describing specific embodiments, and are not intended to limit this disclosure. The singular forms of "group", "class", and "the" used in this disclosure and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0018] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to a determination".

[0019] Please refer to Figure 1 The photovoltaic device detection, operation and maintenance system and method based on the Internet of Things, including a sensing acquisition module, a sensing performance monitoring module, a calibration control and analysis module, a fault analysis module and a sensing deployment module; The sensing acquisition module is used to obtain in real time the relevant information of the sensors applied in the production and operation process of the photovoltaic device, including the output signal of the sensor, environmental information, and key performance indicators; among them, the output signal includes electrical signals such as voltage, current, power, and physical quantity signals such as temperature and light intensity, which are specifically set according to the type of sensors applied in the production and operation process of the photovoltaic device, the environmental information includes the temperature, humidity, air pressure, light intensity, wind speed, etc. of the photovoltaic power station, and the key performance indicators include the conversion efficiency, temperature coefficient, light intensity response characteristics of the photovoltaic panel, the efficiency, harmonic distortion, and power factor of the inverter; The sensing performance monitoring module is used to monitor and analyze the key performance indicators of the sensor to obtain the evaluation value of any one of the key performance indicators and the index evaluation list; the index evaluation list is sorted according to the size of the key performance indicator evaluation value, reflecting the degree of deviation of each performance indicator from the standard; the evaluation values of all the indicators in the key performance indicators are weighted to obtain a comprehensive index value; a comprehensive threshold of the performance indicator is set, and the comprehensive index value is compared with the comprehensive threshold. If the comprehensive index value is greater than or equal to the comprehensive threshold, it means that the overall performance of the sensor is poor, and a sensing calibration signal is generated; if the comprehensive index value is less than the comprehensive threshold, it means that the overall performance of the sensor is good; among them, the key performance indicators include signal accuracy, stability, linearity, sensitivity, response time, resolution, repeatability, and noise level; it should be noted that the methods for obtaining the signal accuracy, stability, linearity, sensitivity, response time, resolution, repeatability, and noise level in the key performance indicators of the sensor are existing mature technologies, and a brief description is given here: The standard signal comparison method is used to obtain the signal accurate value: input a preset standard signal into the sensor (the standard signal can be the electrical signal corresponding to the physical quantity generated by a high-precision signal generator. For example, for a force sensor, use the electrical signal corresponding to the standard force generated by a weight with a known precise mass) Set the short-term monitoring time zone of the sensor, and mark the total number of acquisition times i in the short-term monitoring time zone as n; Compare the output signal of the sensor with the standard signal, calculate the error value RMSE between the output signal and the standard signal, mark it as the signal accuracy, and quantify it using the root mean square error. The formula is expressed as ; where yi is the output value of the sensor at the i-th time, represents the standard signal value at the i-th time, and n is the total number of measurement times; Use the long-term monitoring method to obtain stability: make the sensor work continuously under constant input conditions; obtain the output signal at any acquisition moment in the short-term monitoring time zone, and calculate the fluctuation value of the output signal using the standard deviation formula. The formula is expressed as ; where xi represents the output value at the i-th moment in the short-term monitoring time zone, represents the average value of the output values in the short-term monitoring time zone; mark this fluctuation value as stability; Use the multi-point calibration method to obtain linearity: obtain the range of the sensor, set several (usually no less than five) input standard values within the range, input the input standard values into the sensor to obtain the corresponding output values, and then use the least squares method to fit a straight line , where y is the output value, k is the slope, and b represents the intercept; mark the input standard value and its corresponding output value as measurement points; calculate the deviation of each measurement point from the fitted straight line. The formula is expressed as ; Use the deviation of all measurement points from the fitted straight line to evaluate linearity. The formula is expressed as: ; where, represents the full-scale output of the sensor. The full-scale output is usually obtained based on the range of the sensor and the corresponding output signal range given in the product manual published by the sensor manufacturer.

[0020] The remaining indicators can be specifically obtained according to the corresponding well-known algorithms. For example, sensitivity can be obtained by the slope calculation method or the differential method, response time can be obtained by the step signal test method or the pulse signal test method, resolution can be obtained by the minimum distinguishable change method or the signal analysis and statistics method, repeatability can be obtained by the multiple measurement statistics method or the range method, and the noise level can be obtained by the spectrum analysis method or the time domain statistics method. The specific acquisition methods are all existing mature technologies and will not be elaborated here; The calibration control and analysis module is used to calibrate and analyze the sensor after receiving the sensing calibration signaling to obtain the calibration effective value; set the effective threshold. If the calibration effective value is greater than or equal to the effective threshold, it indicates that the calibration process is successful and the performance of the sensor is effectively restored, and the calibration process ends; if after calibrating and adjusting all the indicators in the indicator evaluation list of the sensor, the obtained calibration effective value is still less than its effective threshold, it means that the sensor cannot be restored through self-calibration; The fault analysis module analyzes the sensors that cannot be restored through self-calibration to obtain a sensing evaluation value; sets a sensing fault threshold, compares the sensing evaluation value with the sensing fault threshold. If the sensing evaluation value is greater than the sensing fault threshold, it indicates that the sensor has a fault and generates a sensing maintenance signaling; if the sensing evaluation value is less than or equal to the sensing fault threshold, it indicates that the sensor needs further calibration and then generates a sensing calibration signaling; The sensing deployment module is used to receive the sensing replacement signaling and perform a tuning process operation to obtain the corresponding maintenance personnel; mark the sensing maintenance signaling, the corresponding type, number, location, and index evaluation list of the sensor as sensing maintenance information, and send the sensing maintenance information to the smart terminal of the maintenance personnel. After the maintenance personnel obtain the sensing maintenance information through the smart terminal, they maintain the sensor.

[0021] In this application, the calibration analysis of the sensor is specifically as follows: Identify the sensing type of the sensor corresponding to the generated sensing calibration signaling and its corresponding index evaluation list. The sensing types include sensors for detecting the performance of photovoltaic panels (such as current sensors, voltage sensors, irradiance sensors, etc.), and sensors for detecting the performance of inverters (such as power sensors, harmonic sensors, etc.); Calibrate and adjust the indicators of the sensor in sequence from top to bottom according to the index evaluation list. The calibration method is the prior art. For example, for the photovoltaic panel current sensor, a standard current source can be used for calibration, and for the inverter power sensor, a high-precision power analyzer can be used for calibration, etc. After the index calibration and adjustment, obtain the comprehensive index value output by the adjusted sensing performance monitoring module; calculate the difference between this comprehensive index value and the previous comprehensive index value to obtain the comprehensive index change value; then calculate the difference between the comprehensive index value and its corresponding comprehensive threshold to obtain the comprehensive index difference; perform a weighted calculation on the comprehensive index change value and the comprehensive index difference to obtain the calibration effective value; Set an effective threshold, compare the calibration effective value with its effective threshold. If the calibration effective value is less than its effective threshold, it indicates that the calibration effect of the sensor is limited, and then continue to calibrate and adjust the next index in the order of the index evaluation list; repeat the above process of obtaining the comprehensive index value, calculating the comprehensive index change value and the comprehensive index difference, and obtaining the calibration effective value; each time a new calibration effective value is obtained, sum it with the calibration effective values obtained in all previous index calibration processes to update and obtain a new calibration effective value; realize the evaluation and optimization of the sensor calibration effect.

[0022] In this application, the key performance indicators of the sensor are analyzed specifically as follows: Set the standard value of any indicator in the performance indicators corresponding to the sensor type, and calculate the standard difference B1 corresponding to the indicator by calculating the difference between any indicator in the key performance indicators and its corresponding standard value; Set the sensing monitoring time zone, calculate the mean value B2 and the difference wave value B3 of the standard deviation values corresponding to the indicators in the sensing monitoring time zone; perform weighted processing on the standard deviation values, the mean difference value, and the difference wave value corresponding to the indicators to obtain the evaluation value corresponding to the indicator, and the formula is expressed as: ; where a1, a2, and a3 respectively represent the weight factors corresponding to the standard deviation value, the mean difference value, and the difference wave value; perform weighted processing on the evaluation values of all indicators in the key performance indicators to obtain the comprehensive index value G, and the formula is expressed as: ; where u represents the number of the indicator in the key performance indicators, C represents the total number of indicators in the key performance indicators, uB and uφ respectively represent the evaluation value corresponding to the indicator u and its corresponding weight influence factor; it should be noted that by calculating the standard deviation value of the key performance indicator and the standard value, the deviation of the individual indicator is quantified, the gap between each indicator and the ideal state is clarified, then the mean difference value and the difference wave value are calculated, and the evaluation value is obtained by weighting, so as to consider the change trend and stability of the indicator; then the evaluation values of all indicators are weighted to obtain the comprehensive index value, so as to comprehensively measure the overall performance of the sensor, which can not only avoid the one-sidedness of single indicator evaluation, but also provide guidance for maintenance and improvement work, accurately judge the state and problems of the sensor, and ensure its reliable and accurate operation.

[0023] In this application, analyze the sensors that cannot be restored by self-calibration. Specifically: Obtain the number of times the sensor generates the sensing calibration signaling and mark it as the calibration times; obtain the generation time of the sensing calibration signaling with the calibration times greater than three times, calculate the time difference between adjacent generation times to obtain the adjacent time difference; calculate the standard deviation of all adjacent time differences to obtain the calibration adjacent difference fluctuation value; obtain the calibration effective value output by the sensor each time it generates the sensing calibration signaling and calculate the standard deviation to obtain the calibration adjustment fluctuation difference value; perform weighted processing on the calibration times, the calibration adjacent difference fluctuation value, and the calibration adjustment fluctuation difference value to obtain the calibration influence value; the calibration influence value helps to quantify the degree of problems related to the sensor's own calibration; Obtain the environmental information of the sensor that generates the sensing calibration signaling; set the environmental parameter standard value of any parameter in the environmental information corresponding to the sensor type, calculate the difference between the value of any parameter in the environmental information and its corresponding environmental parameter standard value to obtain the ring parameter value corresponding to the parameter; set the environmental monitoring time zone corresponding to the sensor, calculate the mean value and the standard deviation of the ring parameter values in the environmental monitoring time zone to obtain the ring parameter mean value and the ring parameter fluctuation value; perform weighted calculation on the ring parameter value, the ring parameter mean value, and the ring parameter fluctuation value to obtain the ring parameter influence value; perform weighted processing on the ring parameter influence values of all parameters in the environmental information to obtain the environmental influence value; the environmental influence value is used to measure the degree of interference of environmental factors on the sensor; The environmental impact value and the calibration impact value are weighted to obtain a sensing evaluation value; the sensing evaluation value can more accurately determine whether the problem of the sensor is caused by its own calibration function failure, environmental factors, or the combined effect of both. For example, if the sensing evaluation value is high and the proportion of the calibration impact value is large, it may be mainly due to the sensor's own failure; if the proportion of the environmental impact value is large, it may be necessary to focus on improving the environmental conditions.

[0024] In this application, the sensing acquisition module further includes a data preprocessing unit; the data preprocessing unit is used to perform filtering processing on the collected sensor output signals to remove high-frequency noise and low-frequency interference in the signals. Specifically: For high-frequency noise, a low-pass filter is used, and its cut-off frequency is set according to the frequency characteristics of the sensor signal; for low-frequency interference, a high-pass filter is used, and the cut-off frequency is set to be lower than the lower limit of the effective frequency range of the sensor signal.

[0025] In this application, to perform a calibration process to obtain the corresponding maintenance personnel, the specific steps are as follows: Obtain the personnel to be maintained corresponding to the sensing maintenance signaling, and send an information acquisition instruction to the smart terminal of the personnel to be maintained to obtain the personnel information of the personnel to be maintained, including the current location, working years, number of working days in the current month, number of maintenance times, and number of successful maintenance times; process the personnel information to obtain the allocation value of the personnel to be maintained. Specifically: Calculate the distance difference between the current location of the personnel to be maintained and the location of the sensor corresponding to the sensing maintenance signaling to obtain the maintenance distance F1; Calculate the maintenance success rate F2 of the corresponding type of sensor based on the number of maintenance times and the number of successful maintenance times; mark the working years and the number of working days in the current month of the personnel to be maintained as F3 and F4 respectively; Perform weighted processing on the maintenance distance, maintenance success rate, working years, and number of working days in the current month, using the formula: , to obtain the allocation value F of the personnel to be maintained; where f1, f2, and f3 respectively represent the weight factors corresponding to the maintenance distance, maintenance success rate, and working years; through the formula, the smaller the maintenance distance, the greater the maintenance success rate, the greater the working years, and the farther the number of working days in the current month is from 30, the greater the allocation value, indicating that the probability of the personnel to be maintained to maintain the sensor is greater; select the personnel to be maintained with the largest allocation value as the maintenance personnel.

[0026] It should be noted that by using the allocation value to select the maintenance personnel, the rational allocation of human resources is realized. This method comprehensively considers multiple factors instead of being determined by a single factor, which improves the work efficiency of the entire maintenance team and avoids problems such as idleness of some personnel and untimely maintenance of some sensors caused by unreasonable allocation; In this application, the acquisition frequency of the sensor output signal of the sensing acquisition module is dynamically adjusted according to the sensor type and the production process stage. Specifically: Identify the production process or operating state of the current photovoltaic device, and set the critical stage and the normal stage according to its impact on the quality of the final product or the power generation efficiency and its relevance to the subsequent process or system stability. When in the critical stage (such as the coating process in the production of solar cells, the peak power generation period of a photovoltaic power station), the acquisition frequency is increased to the first preset frequency. When in the normal production stage or the non-peak operation period, the acquisition frequency is reduced to the second preset frequency, and the first preset frequency is higher than the second preset frequency.

[0027] In this application, the present invention further includes a remote monitoring and management platform; the remote monitoring and management platform includes a display module, a remote monitoring module, and a management registration module; The management registration module is used to submit registration information for registration. If the registration is successful, a platform login account is generated; the platform login account is used to log in to the remote monitoring and management platform; usage permissions are assigned to the platform login account according to the user position in the registration information. It should be noted that the registration information includes the user name, password, email address, contact phone number, affiliated unit, and user position (such as operation and maintenance personnel, management personnel, data analysis personnel, etc.). By collecting the registration information to generate a unique login account, the effective management of users is realized. Precise permission allocation is based on the user position to ensure that different role users (such as operation and maintenance personnel, management personnel, data analysis personnel) can only perform operations within their responsibilities on the platform, ensuring system security and data confidentiality, and at the same time providing an identity credential for subsequent interactions between users and the platform; The display module is used to display various information in the production and operation process of the photovoltaic device in a visual interface; the various information includes the sensor output signal, environmental information, and key performance indicators obtained by the sensing acquisition module in real-time display, as well as the evaluation value of any one of the key performance indicators and the indicator evaluation list; among them, the display of the key performance indicator evaluation value and the evaluation list can assist users in quickly judging the device performance status, providing an important basis for operation and maintenance decision-making, helping operation and maintenance personnel to timely discover potential problems of the device, so as to take corresponding measures; The remote monitoring module is used to interact with the sensing acquisition module and the sensing performance monitoring module, obtain the latest status data and analysis results of the device in real-time, and synchronously update and display them on the display module; it has a remote control function, identifies the usage permissions of the platform login account, and its remote control function performs corresponding operations according to the usage permissions of the login account. For example, operation and maintenance personnel can perform basic device control, and management personnel can perform system-level operations, which not only improves the operation and maintenance efficiency but also ensures the security and standardization of operations, realizing the remote effective management and precise control of photovoltaic devices.

[0028] The photovoltaic device detection and operation and maintenance method based on the Internet of Things proposed by the present invention further includes the following steps: S1. Data acquisition and preprocessing step: S11. According to the acquisition frequency dynamically adjusted according to the sensor type, production process stage or equipment operation status; S12. Real-time obtain the output signals of sensors, environmental information and key performance indicators during the production and operation of photovoltaic devices; S13. Filter the acquired sensor output signals. For high-frequency noise, use a low-pass filter with a cut-off frequency set according to the frequency characteristics of the sensor signal. For low-frequency interference, use a high-pass filter with a cut-off frequency lower than the lower limit of the effective frequency range of the sensor signal; S2. Performance monitoring and evaluation step: S21. Monitor and analyze the key performance indicators of the sensors to obtain the evaluation value of any indicator in the key performance indicators and the indicator evaluation list; S22. Perform weighted processing on the evaluation values of all indicators in the key performance indicators to obtain a comprehensive index value; set a comprehensive threshold for the performance indicators, compare the comprehensive index value with the comprehensive threshold. If the comprehensive index value is greater than or equal to the comprehensive threshold, generate a sensor calibration signaling; S3. Calibration control and analysis step: S31. After receiving the sensor calibration signaling, perform calibration analysis on the sensors to obtain calibration effective values; S32. Set an effective threshold. If the calibration effective value is greater than or equal to the effective threshold, it indicates that the calibration process is successful and the sensor performance is effectively restored, and the calibration process ends; if the calibration effective value obtained after calibrating and adjusting all indicators in the indicator evaluation list of the sensor is still less than its effective threshold, it means that the sensor cannot be restored through self-calibration; S4. Fault analysis and processing step: S41. Analyze the sensors that cannot be restored through self-calibration to obtain sensor evaluation values; S42. Set a sensor fault threshold, compare the sensor evaluation value with the sensor fault threshold. If the sensor evaluation value is greater than the sensor fault threshold, it means that the sensor has a fault and generate a sensor maintenance signaling; if the sensor evaluation value is less than or equal to the sensor fault threshold, it means that the sensor needs further calibration and then generate a sensor calibration signaling; S5. Personnel allocation and maintenance step: S51. After receiving the sensor replacement signaling, perform calibration processing operations to obtain the corresponding maintenance personnel; S52, mark the sensing maintenance signaling, the corresponding type, number, location, and index evaluation list of the sensor as sensing maintenance information, and send the sensing maintenance information to the smart terminal of the maintenance personnel. After the maintenance personnel obtain the sensing maintenance information through the smart terminal, they perform maintenance on the sensor.

[0029] Those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include known common knowledge or conventional technical means in the technical field not disclosed in this disclosure. The specification and examples are only regarded as exemplary, and the true scope and spirit of the present invention are pointed out by the following claims.

[0030] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. Photovoltaic equipment detection and operation and maintenance system based on the Internet of Things, characterized by: It includes sensor acquisition module, sensor performance monitoring module, calibration control and analysis module, fault analysis module and sensor deployment module; The sensor acquisition module is used to obtain relevant information of sensors used in the production and operation of photovoltaic equipment in real time, including sensor output signals, environmental information, and key performance indicators; among which, the output signals include electrical signals such as voltage, current, power, as well as temperature and light intensity; the environmental information includes the temperature, humidity, air pressure, light intensity, and wind speed of the photovoltaic power station; the key performance indicators include the conversion efficiency, temperature coefficient, light intensity response characteristics of the photovoltaic panels, the efficiency of the inverter, harmonic distortion, and power factor; The sensor performance monitoring module is used to monitor and analyze the key performance indicators of the sensor, obtain the evaluation value of any indicator in the key performance indicators and the indicator evaluation list; weight the evaluation values ​​of all indicators in the key performance indicators to obtain the comprehensive index value; set the comprehensive threshold of the performance indicator, compare the comprehensive index value with the comprehensive threshold, and generate a sensor calibration signal if the comprehensive index value is greater than or equal to the comprehensive threshold; the key performance indicators include signal accuracy, stability, linearity, sensitivity, response time, resolution, repeatability, and noise level; The calibration control and analysis module is used to perform calibration analysis on the sensor after receiving the sensor calibration signal to obtain the calibration effective value; set the effective threshold value. If the calibration effective value is greater than or equal to the effective threshold value, it indicates that the calibration process is successful and the sensor performance is effectively restored, and the calibration process ends; if the calibration effective value obtained after calibration adjustment of all indicators in the indicator evaluation list of the sensor is still less than its effective threshold value, it indicates that the sensor cannot be restored by self-calibration; The fault analysis module analyzes the sensors that cannot be restored by self-calibration to obtain the sensor evaluation value; sets the sensor fault threshold, compares the sensor evaluation value with the sensor fault threshold, and if the sensor evaluation value is greater than the sensor fault threshold, it indicates that the sensor is faulty and generates a sensor maintenance signal; if the sensor evaluation value is less than or equal to the sensor fault threshold, it indicates that the sensor needs further calibration and generates a sensor calibration signal; The sensor deployment module is used to receive the sensor replacement signaling and perform adjustment processing operations to obtain the corresponding maintenance personnel; mark the sensor maintenance signaling and the type, number, location, and index evaluation list corresponding to the sensor as sensor maintenance information, and send the sensor maintenance information to the maintenance personnel's smart terminal. After the maintenance personnel obtains the sensor maintenance information through the smart terminal, they maintain the sensor.

2. The photovoltaic equipment detection and operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: Perform a calibration analysis on the sensor, specifically: Identify the sensor type of the sensor corresponding to the sensor calibration signaling and the corresponding indicator evaluation list, where the sensor type includes a sensor for detecting the performance of a photovoltaic panel and a sensor for detecting the performance of an inverter; According to the indicator evaluation list, the indicators of the sensor are calibrated and adjusted from top to bottom. After the indicator calibration and adjustment, the comprehensive index value output by the adjusted sensor performance monitoring module is obtained, and the difference between the current comprehensive index value and the previous comprehensive index value is calculated to obtain the comprehensive index change value, and then the comprehensive index value and the corresponding comprehensive threshold value are calculated to obtain the comprehensive index difference value, and the comprehensive index change value and the comprehensive index difference are weighted to obtain the calibration effective value; Set the effective threshold, compare the calibration effective value with its effective threshold, if the calibration effective value is less than its effective threshold, it means that the calibration effect on the sensor is limited, then continue to calibrate and adjust the next indicator in the order of the indicator evaluation list; repeat the above process of obtaining the comprehensive index value, calculating the comprehensive index change value and the comprehensive index difference, and obtaining the calibration effective value; Each time a new calibration effective value is obtained, it is summed with the calibration effective values ​​obtained in all previous indicator calibration processes to update the new calibration effective value.

3. The photovoltaic equipment detection and operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: Monitor and analyze the key performance indicators of the sensor. The specific steps are as follows: Set a standard value of any indicator among the performance indicators corresponding to the sensor type, and perform difference calculation between any indicator among the key performance indicators and its corresponding standard value to obtain a standard deviation value corresponding to the indicator; Set the sensor monitoring time zone, calculate the mean and standard deviation of the standard deviation values ​​corresponding to the indicators in the sensor monitoring time zone to obtain the difference mean and difference wave value; perform weighted processing on the standard deviation values, difference mean and difference wave values ​​corresponding to the indicators to obtain the evaluation values ​​corresponding to the indicators; perform weighted processing on the evaluation values ​​of all indicators in the key performance indicators to obtain the comprehensive index value; sort the evaluation values ​​of any indicator from top to bottom in order of size to obtain the indicator evaluation list.

4. The photovoltaic equipment detection and operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: Analyze sensors that cannot be recovered by self-calibration, specifically: The number of times the sensor generates a sensor calibration signal is obtained and marked as the calibration number; the generation time of the sensor calibration signal when the calibration number is greater than three times is obtained, and the time difference between adjacent generation times is calculated to obtain adjacent time differences; Calculate the standard deviation of all adjacent time differences to obtain the calibration adjacent time difference fluctuation value; Obtain the calibration effective value output by the sensor each time it generates a sensor calibration signal and calculate the standard deviation to obtain the calibration fluctuation difference; The calibration impact value is obtained by weighting the calibration times, the calibration adjacent error fluctuation value, and the calibration adjustment fluctuation difference value; Obtain environmental information of the sensor that generates the sensor calibration signal; set the environmental parameter standard value of any parameter in the environmental information corresponding to the sensor type, perform difference calculation on the numerical value of any parameter in the environmental information and its corresponding environmental parameter standard value, and obtain the environmental parameter value corresponding to the parameter; set the environmental monitoring time zone corresponding to the sensor, perform mean and standard deviation calculation on the environmental parameter value in the environmental monitoring time zone to obtain the environmental parameter mean value and environmental parameter fluctuation value; perform weighted calculation on the environmental parameter value, environmental parameter mean value and environmental parameter fluctuation value to obtain the environmental parameter impact value; perform weighted processing on the environmental parameter impact values ​​of all parameters in the environmental information to obtain the environmental impact value; perform weighted processing on the environmental impact value and the calibration impact value to obtain the sensor evaluation value.

5. The photovoltaic equipment detection and operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: The sensor acquisition module also includes a data preprocessing unit; the data preprocessing unit is used to filter the collected sensor output signal to remove high-frequency noise and low-frequency interference in the signal, specifically: For high-frequency noise, a low-pass filter is used, and its cut-off frequency is set according to the frequency characteristics of the sensor signal; for low-frequency interference, a high-pass filter is used, and the cut-off frequency is set to be lower than the lower limit of the effective frequency range of the sensor signal.

6. The photovoltaic equipment detection and operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: Perform the calibration operation to obtain the corresponding maintenance personnel. The specific steps are as follows: Obtain the personnel to be maintained corresponding to the sensor maintenance signaling, and feedback the information acquisition instruction to the intelligent terminal of the personnel to be maintained to obtain the personnel information of the personnel to be maintained, including the current position, working years, working days in the month, maintenance times, and maintenance success times; process the personnel information to obtain the deployment value of the personnel to be maintained, specifically: Calculate the distance difference between the current position of the maintenance personnel and the position of the sensor corresponding to the sensor maintenance signaling to obtain the maintenance interval; calculate the maintenance success rate of the corresponding sensor type based on the number of maintenance times and the number of maintenance successes; weight the maintenance interval, maintenance success rate, years of work, and number of working days in the month to obtain the deployment value of the maintenance personnel; select the maintenance personnel with the largest deployment value as the maintenance personnel.

7. The photovoltaic equipment detection and operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: The sensor output signal acquisition frequency of the sensor acquisition module is dynamically adjusted according to the sensor type and the production process stage, specifically: Identify the current production process or operating status of the photovoltaic equipment, set the key stage and the normal stage according to its impact on the final product quality or power generation efficiency and the correlation with the subsequent process or system stability; when in the key stage, increase the collection frequency to the first preset frequency; when in the normal production stage or non-peak operation period, reduce the collection frequency to the second preset frequency; the first preset frequency is higher than the second preset frequency.

8. The photovoltaic equipment detection and operation and maintenance system based on the Internet of Things according to claim 1 is characterized in that: It also includes a remote monitoring and management platform; the remote monitoring and management platform includes a display module, a remote monitoring module, and a management registration module; The management registration module is used to submit registration information for registration. If the registration is successful, a platform login account is generated; the platform login account is used to log in to the remote monitoring and management platform; Assign usage permissions to the platform login account based on the user position in the registration information; A display module is used to display various information of the production and operation process of the photovoltaic equipment in a visual interface; the various information includes real-time display of sensor output signals obtained by the sensor acquisition module, environmental information, and key performance indicators, as well as the evaluation value of any indicator in the key performance indicators and the indicator evaluation list; The remote monitoring module is used to interact with the sensor acquisition module and the sensor performance monitoring module to obtain the latest status data and analysis results of the equipment in real time, and update and display them synchronously on the display module; it has a remote control function and identifies the usage permissions of the platform login account. Its remote control function performs corresponding operations based on the usage permissions of the login account.

9. A photovoltaic equipment detection and operation method based on the Internet of Things, using the photovoltaic equipment detection and operation system based on the Internet of Things as described in any one of claims 1 to 8, characterized in that: The following steps are involved: S1, data collection and preprocessing steps: S11, according to the acquisition frequency dynamically adjusted according to the sensor type and the production process stage or the equipment operation status; S12, real-time acquisition of sensor output signals, environmental information, and key performance indicators during the production and operation of photovoltaic equipment; S13, filtering the collected sensor output signal, using a low-pass filter with a cutoff frequency set according to the frequency characteristics of the sensor signal for high-frequency noise, and using a high-pass filter with a cutoff frequency lower than the lower limit of the effective frequency range of the sensor signal for low-frequency interference; S2, performance monitoring and evaluation steps: S21, monitoring and analyzing the key performance indicators of the sensor, and obtaining an evaluation value of any indicator in the key performance indicators and an indicator evaluation list; S22, weighting the evaluation values ​​of all indicators in the key performance indicators to obtain a comprehensive index value; setting a comprehensive threshold of the performance indicators, comparing the comprehensive index value with the comprehensive threshold, and generating a sensor calibration signal if the comprehensive index value is greater than or equal to the comprehensive threshold; S3, calibration control and analysis steps: S31, after receiving the sensor calibration signaling, perform calibration analysis on the sensor to obtain a calibration effective value; S32, setting an effective threshold. If the calibration effective value is greater than or equal to the effective threshold, it indicates that the calibration process is successful and the sensor performance is effectively restored, and the calibration process ends. If the calibration effective value obtained after calibrating and adjusting all indicators in the indicator evaluation list of the sensor is still less than its effective threshold, it indicates that the sensor cannot be restored by self-calibration. S4, Fault analysis and processing steps: S41, analyzing the sensor that cannot be restored by self-calibration to obtain a sensor evaluation value; S42, setting a sensor fault threshold, comparing the sensor evaluation value with the sensor fault threshold, if the sensor evaluation value is greater than the sensor fault threshold, it indicates that the sensor is faulty, and a sensor maintenance signaling is generated; if the sensor evaluation value is less than or equal to the sensor fault threshold, it indicates that the sensor needs further calibration, and a sensor calibration signaling is generated; S5, personnel deployment and maintenance steps: S51, after receiving the sensor replacement signaling, an adjustment processing operation is performed to obtain the corresponding maintenance personnel; S52, mark the sensor maintenance signaling and the type, number, location, and indicator evaluation list corresponding to the sensor as sensor maintenance information, and send the sensor maintenance information to the maintenance personnel's smart terminal. After the maintenance personnel obtains the sensor maintenance information through the smart terminal, they maintain the sensor.

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