Solar power generation equipment management method and system based on Internet of Things

Abstract

The invention relates to the technical field of solar power generation equipment management, in particular to a solar power generation equipment management method and system based on the Internet of Things, and the method comprises the steps: adaptively adjusting the collection frequency and range of a sensor installed on solar power generation equipment; acquiring operation parameters, environment parameters and equipment state information of the solar power generation equipment; transmitting the acquired parameters and information to a data management platform by adopting a dynamic routing selection and data compression technology; performing multi-dimensional analysis on the received parameters and information by using a fusion algorithm, evaluating the operation state of the equipment and analyzing the energy output; and generating a targeted equipment maintenance instruction and an energy scheduling strategy according to an analysis result, and issuing the instruction and the strategy to an execution end for implementation. According to the invention, a complete and efficient management process is formed from front-end information acquisition optimization to rear-end intelligent decision execution in combination with the Internet of Things, and the operation efficiency and the management level of the solar power generation equipment are improved.

Description

Technical Field

[0001] This invention relates to the field of solar power equipment management technology, and specifically to a solar power equipment management method and system based on the Internet of Things. Background Technology

[0002] While IoT technology has been introduced into the management of solar power generation equipment, numerous problems still exist in actual operation. On one hand, equipment monitoring has loopholes. With numerous photovoltaic modules scattered across the board, traditional inspection methods struggle to provide comprehensive coverage, making it difficult to detect even minor faults in a timely manner. The operating status of critical equipment such as inverters and distribution boxes cannot be accurately monitored, making it difficult to detect and address anomalies promptly, leading to a decline in the efficiency of the power generation system.

[0003] On the other hand, data transmission reliability is poor. In remote photovoltaic power plants, due to weak communication infrastructure, data transmission is easily interfered with, frequently experiencing interruptions, delays, or even data loss. This makes it difficult for managers to obtain accurate operational data in a timely manner, hindering their ability to make informed decisions. At the operation and maintenance (O&M) level, the lack of effective monitoring means means that O&M personnel often only respond passively after a fault occurs, making preventative measures difficult. Furthermore, O&M plans are often formulated without a scientific basis, resulting in significant waste of human and material resources, high O&M costs, and a substantial reduction in the efficiency of stable operation of solar power equipment. Summary of the Invention

[0004] To address the aforementioned technical shortcomings, this invention provides a method and system for managing solar power generation equipment based on the Internet of Things (IoT), thereby resolving issues such as unreasonable information collection, unreliable data transmission, and low accuracy in analysis and decision-making in existing technologies.

[0005] This invention is achieved through the following technical solution: A method for managing solar power generation equipment based on the Internet of Things (IoT) is provided, the method comprising the following steps: Step S10: By adaptively adjusting the acquisition frequency and range of the sensors installed on the solar power generation equipment, the operating parameters, environmental parameters and equipment status information of the solar power generation equipment are obtained; Step S20: Using dynamic routing and data compression technology, the collected parameters and information are transmitted to the data management platform; Step S30: Use the fusion algorithm to perform multi-dimensional analysis on the received parameters and information, evaluate the equipment operating status and analyze energy output; Step S40: Generate targeted maintenance instructions and energy dispatch strategies for solar power generation equipment based on the analysis results, and send them to the execution end for implementation.

[0006] Preferably, step S10, which involves adaptively adjusting the acquisition frequency and range of sensors installed on the solar power generation equipment to obtain the operating parameters, environmental parameters, and equipment status information of the solar power generation equipment, includes: Sensor deployment and initial parameter settings: Deploy sensors at key locations of the solar power generation equipment, including temperature sensors and light intensity sensors on and around the solar panel surface, voltage sensors and current sensors at power conversion equipment such as inverters and combiner boxes, vibration sensors and tilt sensors at equipment supports and core components to monitor equipment status, and environmental humidity sensors and wind speed sensors in the power plant area. Preset the basic acquisition frequency and acquisition range for each sensor, and set parameter thresholds and environmental parameter change rate thresholds. Real-time monitoring of parameter changes: After the sensor collects data according to the initial parameters, it transmits the data to the IoT node in real time. The node performs preliminary screening of the data and extracts the device operating parameters, environmental parameters and device status parameters. The IoT node continuously compares the real-time data with the preset threshold to determine whether the device operating parameters have reached the set threshold or whether the rate of change of environmental parameters has exceeded the set threshold. Adaptive adjustment of sampling frequency and range: When the device operating parameters reach the set threshold or the rate of change of environmental parameters exceeds the set threshold, the IoT node sends a command to the corresponding sensor to increase the sampling frequency and increase the sampling range. When the rate of change of device operating parameters or environmental parameters returns to the normal range, the IoT node sends a command to the corresponding sensor to restore the initial sampling frequency and reduce the sampling range. Data Acquisition and Verification: Sensors collect data according to the adjusted frequency and range. For example, in high-frequency mode, the voltage sensor collects the output voltage once per second, covering the three output ports of the inverter. The equipment status sensor increases the number of sampling points for vibration monitoring, covering easily worn parts such as bracket connections and solar panel frames. The IoT node verifies the validity of the collected data, removes obvious outliers, such as out-of-range data caused by sensor failure, and marks the frequency and range information during data acquisition. Data storage and uploading: The sensor temporarily stores the verified valid data in the local cache of the IoT node. The capacity supports at least 1 hour of high-frequency data storage to avoid data loss due to transmission delay. An upload trigger mechanism is set up so that in high-frequency acquisition mode, the data is packaged and uploaded to the management platform every 30 seconds. In basic mode, the data is summarized and uploaded every 5 minutes to ensure real-time transmission of critical data and efficient transmission of non-critical data.

[0007] Preferably, step S20, which employs dynamic routing and data compression technology to transmit the collected parameters and information to the data management platform, includes: Data classification: The verified data in the local cache of IoT nodes is classified according to data type and importance. Data types include operating parameters, environmental parameters and device status information. Importance refers to the fact that fault signals are marked as emergency data and regular temperature data are marked as ordinary data. Data compression: The classified data is processed using a lossless compression algorithm to extract features such as data periodicity and peak / valley values, remove duplicate and redundant parts to form a compressed data package, and add data identifiers, including sensor ID, acquisition time and data type; Data fragmentation and transmission: The compressed data packet is fragmented according to the maximum carrying unit of the transmission path, a check code is added, and then it is transmitted to the data management platform through the main path, while the fragmentation transmission status is recorded. Dynamic routing selection: The status of the main path is monitored in real time during transmission. When there is a sudden drop in signal strength, a packet loss rate exceeding the threshold, or a transmission interruption, the backup path with the second highest score is immediately activated to continue transmission, ensuring data continuity. Data integrity verification: After receiving fragmented data, the data management platform reassembles it according to the identifier and verifies the data integrity through the check code. When a missing fragment is found, a retransmission request is sent to the network node. The network node receives the integrity verification result from the platform. When the data transmission is successful, the local cache is cleared. When retransmission is required, the missing fragment is retransmitted until the data management platform confirms that the data has been received completely.

[0008] Preferably, step S30, which involves using a fusion algorithm to perform multi-dimensional analysis of the received parameters and information, assess the equipment's operating status, and analyze energy output, includes: Neural network model training: Collect historical operating data of solar power generation equipment and standardize it. Call the pre-trained neural network model, input the standardized historical data, and perform iterative calculations through multiple layers of neurons to explore the nonlinear correlation between parameters. The model outputs equipment operating status assessment value, failure risk probability and energy output prediction value. Fuzzy logic algorithm construction: To address uncertainties such as sensor errors and environmental interference, a fuzzy rule base is constructed. The output of the neural network model is used as the input of the fuzzy logic algorithm. Through fuzzification, rule matching, and defuzzification, the deviation in equipment status assessment is corrected. Multi-dimensional cross-validation: Cross-validation is performed from the time dimension (comparing with historical data from the same period), the spatial dimension (comparing with data from similar devices in the same region), and the parameter correlation dimension (verifying the consistency of voltage and temperature changes) to ensure the stability of the analysis results. When a significant deviation occurs in the validation of a certain dimension, the algorithm is triggered to re-iterate and calculate. Generate analysis results report: Integrate and correct the assessment results, output equipment operating status level, specific potential fault points, analysis of factors affecting energy output, and energy output trend curve for the next 48 hours.

[0009] Preferably, step S40, which involves generating targeted maintenance instructions and energy dispatch strategies for solar power generation equipment based on the analysis results and sending them to the execution terminal for implementation, includes: Analysis results classification and priority determination: The equipment operation status assessment results and energy output analysis results are classified and marked, and the processing priorities are set according to the urgency and scope of impact; Maintenance instruction generation: Generate instructions that include maintenance time windows, maintenance tools, operation procedures and responsible persons for equipment failure risks. When it is a real-time failure, generate emergency repair instructions, which include the fault location coordinates, fault code analysis and temporary emergency measures. The unique identifier of the equipment is embedded in the instructions. Energy dispatch strategy formulation and optimization: Based on energy output forecast and electricity demand data, calculate the supply and demand balance difference. When there is excess output, formulate a charging strategy for energy storage devices. When there is insufficient output, generate a grid energy supplementation plan or load adjustment strategy. Verify the feasibility of the strategy through simulation model. Multi-channel transmission and status tracking: Commands are sent through the main communication channel, while a backup channel is enabled for redundancy. After the execution terminal receives the command, it replies with an acknowledgment receipt to ensure that the execution end receives the command completely (handheld terminal of maintenance personnel, equipment controller). The receiving status of the execution end is monitored in real time. If no acknowledgment receipt is received within 10 seconds, a retransmission mechanism is automatically triggered, with a maximum of 3 retransmissions. Execution process monitoring and feedback: After the execution terminal executes the command, it uploads the operation progress in real time. The management platform displays the execution status through a visual interface. After the command is completed, the execution terminal provides feedback on the results. The platform compares the results with the expected target. When the deviation exceeds 5%, it triggers secondary analysis and strategy optimization.

[0010] Furthermore, to achieve the above objectives, the present invention also proposes an Internet of Things (IoT)-based solar power generation equipment management system, which includes: Solar power generation equipment information acquisition module: used to acquire the operating parameters, environmental parameters and equipment status information of the solar power generation equipment by adaptively adjusting the acquisition frequency and range of the sensors installed on the solar power generation equipment; Data transmission module: Used to transmit the collected parameters and information to the data management platform using dynamic routing and data compression technology; Data analysis and evaluation module: Used to perform multi-dimensional analysis of received parameters and information using fusion algorithms, evaluate equipment operating status and analyze energy output; Decision execution module: This module generates targeted maintenance instructions and energy dispatch strategies for solar power equipment based on the analysis results, and then sends them to the execution end for implementation.

[0011] Furthermore, to achieve the above objectives, the present invention also proposes an Internet of Things (IoT)-based solar power generation equipment management device, the device comprising: a memory, a processor, and programs such as an IoT-based solar power generation equipment management algorithm stored in the memory and executable on the processor, wherein the IoT-based solar power generation equipment management algorithm and other programs are steps for implementing the IoT-based solar power generation equipment management method described above.

[0012] In addition, to achieve the above objectives, the present invention also provides a computer program product, which includes programs such as an Internet of Things-based solar power generation equipment management algorithm. When the Internet of Things-based solar power generation equipment management algorithm and other programs are executed by a processor, they implement the Internet of Things-based solar power generation equipment management method described above.

[0013] The advantages and effects of this invention are: This invention proposes an IoT-based solar power generation equipment management method and system. By adaptively adjusting the sensor's acquisition frequency and range, it ensures that key information is not missed while reducing the generation of redundant data, lowering the consumption of sensor and network resources, and improving the rationality and efficiency of information acquisition. Simultaneously, dynamic routing technology ensures the optimality of data transmission paths, reducing the probability of data loss and delay. Data compression technology reduces data transmission volume, saves transmission costs, and improves the reliability and economy of data transmission. Furthermore, by combining the advantages of neural network algorithms for processing massive data and fuzzy logic algorithms for handling uncertain information through a fusion algorithm, it can more comprehensively and accurately assess equipment status, predict faults, and analyze energy output, providing a reliable basis for management decisions and making decisions more targeted and effective. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of a solar power generation equipment management method based on the Internet of Things according to the present invention.

[0016] Figure 2This is a schematic diagram of the structure of a solar power generation equipment management system based on the Internet of Things according to the present invention.

[0017] Figure 3 This is a schematic block diagram of an electronic device for managing solar power generation equipment based on the Internet of Things (IoT) according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, in one embodiment of the present invention, a method for managing solar power generation equipment based on the Internet of Things includes the following steps: Step S10: By adaptively adjusting the acquisition frequency and range of the sensors installed on the solar power generation equipment, the operating parameters, environmental parameters and equipment status information of the solar power generation equipment are obtained.

[0020] Specifically, step S10, which involves adaptively adjusting the acquisition frequency and range of the sensors installed on the solar power generation equipment to obtain the operating parameters, environmental parameters, and equipment status information of the solar power generation equipment, includes: Sensor Deployment and Initial Parameter Setting: Deploy sensors at key locations on the solar power generation equipment, including temperature and light intensity sensors on and around the solar panel surface; voltage and current sensors at power conversion equipment such as inverters and combiner boxes; vibration and tilt sensors at equipment supports and core components to monitor equipment status; and environmental humidity and wind speed sensors in the power plant area. Preset basic acquisition frequencies for each sensor, such as once every 5 minutes for temperature and light intensity sensors, and acquisition ranges, such as covering one monitoring point in the central area for solar panel temperature acquisition. Set parameter thresholds, such as the normal operating temperature range of the solar panel being -40℃ to 85℃, and environmental parameter change rate thresholds, such as setting the light intensity threshold to a change exceeding 200W / m² within 10 minutes. Real-time monitoring of parameter changes: After the sensor collects data according to the initial parameters, it transmits the data to the IoT node in real time. The node performs preliminary screening of the data and extracts equipment operating parameters, such as output voltage and current; environmental parameters, such as temperature, humidity, and light intensity change rate; and equipment status parameters, such as vibration frequency and tilt angle. The IoT node continuously compares the real-time data with preset thresholds to determine whether the equipment operating parameters have reached the set thresholds, such as the solar panel temperature reaching 80℃, or whether the environmental parameter change rate exceeds the set threshold, such as the light intensity decreasing from 500W / m² to 200W / m² within 10 minutes, with a change rate of 60%. Adaptive adjustment of sampling frequency and range: When the device operating parameters reach the set threshold or the rate of change of environmental parameters exceeds the set threshold, the IoT node sends a command to the corresponding sensor to increase the sampling frequency, such as increasing the temperature sensor from once every 5 minutes to once every 30 seconds, and increasing the sampling range, such as adding two monitoring points in the edge area of ​​the solar panel temperature to cover the entire panel, in order to capture transient change details. When the rate of change of device operating parameters or environmental parameters returns to the normal range, such as the solar panel temperature dropping to 60℃ and the light intensity changing by less than 50W / m² within 10 minutes, the IoT node sends a command to the corresponding sensor to restore the initial sampling frequency, such as adjusting it from once every 30 seconds back to once every 5 minutes, and reducing the sampling range, such as retaining only one monitoring point in the central area of ​​the solar panel to reduce redundant data. Data Acquisition and Verification: Sensors collect data according to the adjusted frequency and range. For example, in high-frequency mode, the voltage sensor collects the output voltage once per second, covering the three output ports of the inverter. The equipment status sensor increases the number of sampling points for vibration monitoring, covering easily worn parts such as bracket connections and solar panel frames. The IoT node verifies the validity of the collected data, removes obvious outliers, such as out-of-range data caused by sensor malfunctions, and marks the frequency and range information of the data collection, such as 2023-10-01 12:00, solar panel temperature, collection frequency 30 seconds / time, covering 3 monitoring points, to ensure that the data corresponds to the acquisition strategy. Data storage and uploading: The sensor temporarily stores the verified valid data in the local cache of the IoT node. The capacity supports at least 1 hour of high-frequency data storage to avoid data loss due to transmission delay. An upload trigger mechanism is set up so that in high-frequency acquisition mode, the data is packaged and uploaded to the management platform every 30 seconds. In basic mode, the data is summarized and uploaded every 5 minutes to ensure real-time transmission of critical data and efficient transmission of non-critical data.

[0021] Step S20: Using dynamic routing and data compression technology, the collected parameters and information are transmitted to the data management platform.

[0022] Specifically, step S20, which employs dynamic routing and data compression technology to transmit the collected parameters and information to the data management platform, includes the following steps: Data classification: The verified data in the local cache of IoT nodes is classified according to data type and importance. Data types include operating parameters, environmental parameters and device status information. Importance refers to the fact that fault signals are marked as emergency data and regular temperature data are marked as ordinary data. Data compression: The classified data is processed using a lossless compression algorithm to extract features such as data periodicity and peak / valley values, remove duplicate and redundant parts to form a compressed data package, and add data identifiers, including sensor ID, acquisition time and data type; Data fragmentation and transmission: The compressed data packet is fragmented according to the maximum carrying unit of the transmission path, a check code is added, and then it is transmitted to the data management platform through the main path, while the fragmentation transmission status is recorded. Dynamic routing selection: The status of the main path is monitored in real time during transmission. When there is a sudden drop in signal strength, a packet loss rate exceeding the threshold, or a transmission interruption, the backup path with the second highest score is immediately activated to continue transmission, ensuring data continuity. Data integrity verification: After receiving fragmented data, the data management platform reassembles it according to the identifier and verifies the data integrity through the check code. When a missing fragment is found, a retransmission request is sent to the network node. The network node receives the integrity verification result from the platform. When the data transmission is successful, the local cache is cleared. When retransmission is required, the missing fragment is retransmitted until the data management platform confirms that the data has been received completely.

[0023] Step S30: Use the fusion algorithm to perform multi-dimensional analysis on the received parameters and information, evaluate the equipment operating status, and analyze energy output.

[0024] Specifically, step S30, which involves using a fusion algorithm to perform multi-dimensional analysis of the received parameters and information, assess the equipment's operating status, and analyze energy output, includes: Neural network model training: Collect historical operating data of solar power generation equipment and standardize it. Call pre-trained neural network models, such as LSTM and CNN, and input the standardized historical data. Through iterative calculation of multi-layer neurons, explore the nonlinear correlation between parameters, such as the dynamic relationship between light intensity and solar panel power generation efficiency. The model outputs equipment operating status assessment values, such as health index 0-100 points, fault risk probability, including inverter fault probability, and energy output prediction values, such as power generation in the next 24 hours. Fuzzy logic algorithm construction: To address uncertainties such as sensor errors and environmental interference, a fuzzy rule base is constructed. For example, when light intensity drops sharply and humidity rises sharply, the risk of equipment load fluctuation is high. The output of the neural network model is used as the input of the fuzzy logic algorithm. Through fuzzification, rule matching, and defuzzification, the bias in equipment status assessment is corrected, such as correcting the health index from 85 points to 82 points. The fault risk boundary is optimized, such as clarifying the probability threshold corresponding to high risk and adjusting it from 70% to 65%. Multi-dimensional cross-validation: Cross-validation is performed from the time dimension (comparing with historical data from the same period), the spatial dimension (comparing with data from similar equipment in the same region), and the parameter correlation dimension (verifying the consistency of voltage and temperature changes) to ensure the stability of the analysis results. When a significant deviation occurs in the validation of a certain dimension, such as when the real-time evaluation value deviates from the historical trend by more than 15%, the algorithm is triggered to re-iterate and calculate. Generate analysis results report: Integrate the revised assessment results, output the equipment operation status level, such as normal, warning and abnormal, specific potential fault points, such as poor contact of the third string of the solar panel, analysis of factors affecting energy output, such as the 0.3% decrease in power generation efficiency for every 1°C increase in temperature, and the energy output trend curve for the next 48 hours.

[0025] Step S40: Generate targeted maintenance instructions and energy dispatch strategies for solar power generation equipment based on the analysis results, and send them to the execution end for implementation.

[0026] Specifically, step S40, which involves generating targeted maintenance instructions and energy dispatch strategies for solar power generation equipment based on the analysis results and then sending them to the execution end for implementation, includes the following steps: Analysis results classification and priority judgment: The equipment operation status assessment results, such as health index and failure risk level, and the energy output analysis results, such as power generation forecast and supply-demand difference, are classified and marked, and the processing priority is set according to the urgency and scope of impact. For example, failure risk > 80% is classified as Level 1 emergency, and energy supply-demand difference > 20% is classified as Level 2 emergency, such as single component failure and regional power plant efficiency decline. Maintenance instruction generation: Based on equipment failure risks, maintenance instructions are generated, including maintenance time windows, maintenance tools such as infrared detectors, operation procedures such as component surface cleaning and sealing checks, and instructions for responsible persons. When it is a real-time failure, such as inverter overload, an emergency repair instruction is generated, which includes fault location coordinates, fault code analysis, and temporary emergency measures, such as disconnecting the load. The instruction embeds the unique equipment identifier, such as component ID and inverter number, to ensure accurate location. Energy dispatch strategy formulation and optimization: Based on energy output forecasts and electricity demand data, calculate the supply-demand balance difference. When there is excess output, formulate energy storage device charging strategies, such as prioritizing charging battery packs with a capacity >80% and setting a charging cutoff threshold. When there is insufficient output, generate grid replenishment schemes, such as specifying replenishment periods and power limits, or load adjustment strategies, such as reducing the power supply priority of non-critical equipment. Verify the feasibility of the strategies through simulation models, such as simulating the impact of dispatch schemes on grid stability under extreme weather conditions, and optimize adjustment parameters. Multi-channel transmission and status tracking: Commands are sent through the main communication channel, such as NB-IoT, while a backup channel, such as LoRa, is activated for redundancy. After the execution terminal receives the command, it replies with an acknowledgment receipt to ensure that the execution end receives the complete command (maintenance personnel handheld terminal, device controller). The receiving status of the execution end is monitored in real time. If no acknowledgment receipt is received within 10 seconds, a retransmission mechanism is automatically triggered, with a maximum of 3 retransmissions. Execution process monitoring and feedback: After the execution terminal executes the command, it uploads the operation progress in real time, such as maintenance personnel arriving on site and energy storage charging to 60%. The management platform displays the execution status through a visual interface. After the command is executed, the execution terminal provides feedback on the results, such as fault repair confirmation and actual power generation after the scheduling strategy is executed. The platform compares the results with the expected target. When the deviation exceeds 5%, it triggers secondary analysis and strategy optimization.

[0027] In addition, such as Figure 2 As shown, in one embodiment of the present invention, an Internet of Things (IoT)-based solar power generation equipment management system is proposed, which includes: Solar power generation equipment information acquisition module: used to acquire the operating parameters, environmental parameters and equipment status information of the solar power generation equipment by adaptively adjusting the acquisition frequency and range of the sensors installed on the solar power generation equipment; Data transmission module: Used to transmit the collected parameters and information to the data management platform using dynamic routing and data compression technology; Data analysis and evaluation module: Used to perform multi-dimensional analysis of received parameters and information using fusion algorithms, evaluate equipment operating status and analyze energy output; Decision execution module: This module generates targeted maintenance instructions and energy dispatch strategies for solar power equipment based on the analysis results, and then sends them to the execution end for implementation.

[0028] This application provides an IoT-based solar power equipment management system, employing an IoT-based solar power equipment management method as described in the above embodiments, which can solve the technical problem of low efficiency in traditional solar power equipment management. Compared with the prior art, the beneficial effects of the IoT-based solar power equipment management system provided in this application are the same as those of the IoT-based solar power equipment management method provided in the above embodiments, and other technical features in the IoT-based solar power equipment management system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0029] This application provides an IoT-based solar power generation equipment management device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to execute the IoT-based solar power generation equipment management method in the above embodiment 1.

[0030] like Figure 3 As shown in the illustration, in one embodiment of the present invention, a structural schematic diagram of an IoT-based solar power generation equipment management device suitable for implementing the embodiments of this application is presented. An IoT-based solar power generation equipment management device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The IoT-based solar power generation equipment management device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0031] Figure 3The illustrated IoT-based solar power generation equipment management device may include a processing system 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage system 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the IoT-based solar power generation equipment management device. The processing system 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input systems 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output systems 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage systems 1003 including, for example, magnetic tapes, hard drives, etc.; and communication systems 1009. Communication system 1009 allows an IoT-based solar power generation equipment management device to communicate wirelessly or wiredly with other devices to exchange data. Although an IoT-based solar power generation equipment management device with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0032] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication system, or installed from storage system 1003, or installed from ROM 1002. When the computer program is executed by processing system 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0033] This application provides an IoT-based solar power equipment management device, employing an IoT-based solar power equipment management method as described in the above embodiments, which can solve the technical problem of low efficiency in traditional solar power equipment management. Compared with the prior art, the beneficial effects of the IoT-based solar power equipment management device provided in this application are the same as those of the IoT-based solar power equipment management method provided in the above embodiments, and other technical features of this IoT-based solar power equipment management device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0034] The various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0035] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for managing solar power generation equipment based on the Internet of Things.

[0036] The computer program product provided in this application can solve the technical problem of low management efficiency of traditional solar power generation equipment. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the IoT-based solar power generation equipment management method provided in the above embodiments, and will not be repeated here.

[0037] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for managing solar power generation equipment based on the Internet of Things, characterized in that, The method includes the following steps: Step S10: By adaptively adjusting the acquisition frequency and range of the sensors installed on the solar power generation equipment, the operating parameters, environmental parameters and equipment status information of the solar power generation equipment are obtained; Step S20: Using dynamic routing and data compression technology, the collected parameters and information are transmitted to the data management platform; Step S30: Use the fusion algorithm to perform multi-dimensional analysis on the received parameters and information, evaluate the equipment operating status and analyze energy output; Step S40: Generate targeted maintenance instructions and energy dispatch strategies for solar power generation equipment based on the analysis results, and send them to the execution end for implementation.

2. The method for managing solar power generation equipment based on the Internet of Things according to claim 1, characterized in that, The step S10, which involves adaptively adjusting the acquisition frequency and range of the sensors installed on the solar power generation equipment to obtain the operating parameters, environmental parameters, and equipment status information of the solar power generation equipment, includes: Sensor deployment and initial parameter settings: Deploy sensors at key locations of the solar power generation equipment, including temperature sensors and light intensity sensors on and around the solar panel surface, voltage sensors and current sensors at the inverter and combiner box, vibration sensors and tilt sensors at the equipment bracket and core components, and environmental humidity sensors and wind speed sensors in the power plant area. Preset the basic acquisition frequency and acquisition range for each sensor, and set parameter thresholds and environmental parameter change rate thresholds. Real-time monitoring of parameter changes: After the sensor collects data according to the initial parameters, it transmits the data to the IoT node in real time. The node performs preliminary screening of the data and extracts the device operating parameters, environmental parameters and device status parameters. The IoT node continuously compares the real-time data with the preset threshold to determine whether the device operating parameters have reached the set threshold or whether the rate of change of environmental parameters has exceeded the set threshold. Adaptive adjustment of sampling frequency and range: When the device operating parameters reach the set threshold or the rate of change of environmental parameters exceeds the set threshold, the IoT node sends a command to the corresponding sensor to increase the sampling frequency and increase the sampling range. When the rate of change of device operating parameters or environmental parameters returns to the normal range, the IoT node sends a command to the corresponding sensor to restore the initial sampling frequency and reduce the sampling range. Data acquisition and verification: The sensor collects data according to the adjusted frequency and range, and the IoT node verifies the validity of the collected data and removes outliers; Data storage and upload: The sensor temporarily stores the verified valid data in the local cache of the IoT node and sets up an upload trigger mechanism.

3. The method for managing solar power generation equipment based on the Internet of Things according to claim 1, characterized in that, The step S20, which employs dynamic routing and data compression technology to transmit the collected parameters and information to the data management platform, includes the following steps: Data classification: Classify the verified data in the local cache of IoT nodes according to data type and importance; Data compression: The classified data is processed using a lossless compression algorithm to extract the periodicity and peak-valley characteristics of the data, remove duplicate and redundant parts to form a compressed data package, and add data identifiers, including sensor ID, acquisition time and data type; Data fragmentation and transmission: The compressed data packet is fragmented according to the maximum carrying unit of the transmission path, a check code is added, and then it is transmitted to the data management platform through the main path, while the fragmentation transmission status is recorded. Dynamic routing selection: The status of the main path is monitored in real time during transmission. When the signal strength drops sharply, the packet loss rate exceeds the threshold, or the transmission is interrupted, the backup path is immediately activated to continue the transmission. Data integrity verification: After receiving fragmented data, the data management platform reassembles it according to the identifier and verifies the data integrity through the check code. When a missing fragment is found, a retransmission request is sent to the network node. The network node receives the integrity verification result from the platform. When the data transmission is successful, the local cache is cleared. When retransmission is performed, the missing fragment is retransmitted until the data management platform confirms that the data has been received completely.

4. The method for managing solar power generation equipment based on the Internet of Things according to claim 1, characterized in that, The step S30, which uses a fusion algorithm to perform multi-dimensional analysis of the received parameters and information, evaluates the equipment's operating status, and analyzes energy output, includes: Neural network model training: Collect historical operating data of solar power generation equipment and standardize it, call the pre-trained neural network model, input the standardized historical data, and through iterative calculation of neurons, explore the nonlinear correlation between parameters. The model outputs equipment operating status assessment value, failure risk probability and energy output prediction value. Fuzzy logic algorithm construction: A fuzzy rule base is constructed to address sensor errors and environmental interference. The output of the neural network model is used as the input of the fuzzy logic algorithm. Through fuzzification, rule matching, and defuzzification, the deviation in equipment status assessment is corrected. Multi-dimensional cross-validation: Cross-validation is performed from the time dimension, spatial dimension, and parameter correlation dimension. When a deviation occurs in the validation of a certain dimension, the algorithm is triggered to re-iterate and calculate. Generate analysis results report: Integrate and correct the assessment results, output equipment operating status level, specific potential fault points, analysis of factors affecting energy output, and energy output trend curve for the next 48 hours.

5. The method for managing solar power generation equipment based on the Internet of Things according to claim 1, characterized in that, The step S40, which involves generating targeted maintenance instructions and energy dispatch strategies for solar power equipment based on the analysis results and then sending them to the execution end for implementation, includes: Analysis results classification and priority determination: The equipment operation status assessment results and energy output analysis results are classified and marked, and the processing priorities are set according to the urgency and scope of impact; Maintenance instruction generation: Generate instructions that include maintenance time windows, maintenance tools, operation procedures and responsible persons for equipment failure risks. When it is a real-time failure, generate emergency repair instructions, which include fault location coordinates, fault code analysis and temporary emergency measures. The unique identifier of the equipment is embedded in the instructions. Energy dispatch strategy formulation and optimization: Based on energy output forecast and electricity demand data, calculate the supply and demand balance difference. When there is excess output, formulate a charging strategy for energy storage devices. When there is insufficient output, generate a grid energy supplementation plan or load adjustment strategy. Verify the feasibility of the strategy through simulation model. Multi-channel transmission and status tracking: Commands are sent through the main communication channel, while a backup channel is enabled for redundancy. After the execution terminal receives the command, it replies with an acknowledgment receipt to ensure that the execution terminal receives the complete command and to monitor the reception status of the execution terminal in real time. Execution process monitoring and feedback: After the execution terminal executes the command, it uploads the operation progress in real time. The management platform displays the execution status through a visual interface. After the command is completed, the execution terminal provides feedback on the results. The platform compares the results with the expected target. When the deviation exceeds 5%, it triggers secondary analysis and strategy optimization.

6. A solar power generation equipment management system based on the Internet of Things, characterized in that, The IoT-based solar power generation equipment management system includes: Solar power generation equipment information acquisition module: used to acquire the operating parameters, environmental parameters and equipment status information of the solar power generation equipment by adaptively adjusting the acquisition frequency and range of the sensors installed on the solar power generation equipment; Data transmission module: Used to transmit the collected parameters and information to the data management platform using dynamic routing and data compression technology; Data analysis and evaluation module: Used to perform multi-dimensional analysis of received parameters and information using fusion algorithms, evaluate equipment operating status and analyze energy output; Decision execution module: This module generates targeted maintenance instructions and energy dispatch strategies for solar power equipment based on the analysis results, and then sends them to the execution end for implementation.

7. A solar power generation equipment management device based on the Internet of Things, characterized in that, The IoT-based solar power generation equipment management device includes: The device includes a memory, a processor, and an Internet of Things (IoT)-based solar power generation device management program stored in the memory and executable on the processor. When executed by the processor, the IoT-based solar power generation device management program implements an IoT-based solar power generation device management method as described in any one of claims 1 to 5.

8. A computer program product, characterized in that, The computer program product includes an Internet of Things (IoT)-based solar power equipment management program, which, when executed by a processor, implements an IoT-based solar power equipment management method as described in any one of claims 1 to 5.

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