Zero-carbon energy remote intelligent management system

Abstract

The invention provides a zero-carbon energy remote intelligent management system, and relates to the field of energy systems. The zero-carbon energy remote intelligent management system comprises a data acquisition module which comprises a sensor and a collector and is used for acquiring operation data of zero-carbon energy equipment; the data transmission module is used for transmitting the acquired data to a remote server by adopting a wireless communication technology; the data processing module is used for analyzing and processing the transmitted data; the intelligent control module is used for intelligently controlling the zero-carbon energy equipment according to an analysis result of the data processing module; and the user interface module provides an interface for a user to access and manage the system. Optimization control is carried out through data analysis, the system can predict equipment faults and maintenance requirements through data analysis and a prediction model, maintenance is carried out in advance, energy loss caused by the equipment faults is avoided, and the system can carry out intelligent scheduling, optimize energy distribution and improve the overall energy utilization efficiency according to the energy requirements and supply conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of energy systems, and in particular to a zero-carbon energy remote intelligent management system. Background Art

[0002] As the global climate change problem becomes increasingly serious, reducing carbon emissions and achieving sustainable development have become important goals for all countries. Zero-carbon energy, such as renewable energy such as solar energy, wind energy and hydropower, is gradually becoming an important direction for the transformation of the global energy structure due to its clean and sustainable advantages. Traditional fossil energy, such as coal, oil and natural gas, although it plays an important role in meeting global energy needs, will produce a large amount of carbon dioxide and other greenhouse gases during its use, leading to greenhouse effect and environmental pollution. However, the widespread application of zero-carbon energy still faces many challenges, especially in energy management, equipment monitoring, data processing and system optimization.

[0003] Traditional zero-carbon energy management systems still have some shortcomings and areas for improvement in actual use. Traditional systems rely heavily on manual monitoring and management, which is inefficient and difficult to achieve effective management of large-scale, multi-location energy equipment. The amount of data generated by zero-carbon energy equipment is huge, and traditional systems lack effective data processing and analysis capabilities, and cannot fully utilize the value of data for decision support. At the same time, the existing system does not support remote monitoring, and users cannot obtain equipment operating status and data in real time, resulting in inconvenient management and delayed response. In addition, in the process of data transmission and storage, there is a lack of effective encryption and protection measures, which can easily lead to data leakage. Therefore, technical personnel in this field provide a zero-carbon energy remote intelligent management system to solve the problems raised in the above background technology. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a zero-carbon energy remote intelligent management system, which solves the problems of low efficiency, insufficient data processing capability, inconvenient remote monitoring and insufficient system reliability and security of traditional systems.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A zero-carbon energy remote intelligent management system, comprising:

[0008] A data acquisition module, including sensors and collectors, for collecting operating data of zero-carbon energy equipment, including but not limited to voltage, current, power and power generation;

[0009] A data transmission module transmits the collected data to a remote server by using wireless communication technology;

[0010] The data processing module analyzes and processes the transmitted data, including data cleaning, feature extraction, and anomaly detection;

[0011] Intelligent control module, which performs intelligent control on zero-carbon energy equipment according to the analysis results of the data processing module;

[0012] The user interface module provides an interface for users to access and manage the system. Users can access the system through web pages or mobile applications to view equipment operating status, historical data, and alarm information.

[0013] Preferably, the data acquisition module comprises the following steps:

[0014] S1. Sensor selection and installation: According to the type of zero-carbon energy equipment and monitoring requirements, determine the parameters that need to be collected, including but not limited to voltage, current, power, temperature, humidity, wind speed and light intensity; select appropriate sensors according to the collection parameters, including but not limited to voltage sensors, current sensors, power sensors, temperature sensors and environmental sensors; install the selected sensors in the appropriate position of the equipment to ensure that the sensors can accurately collect the required data;

[0015] S2. Data collector configuration: select a suitable data collector to collect sensor data and perform preliminary processing; configure the collection parameters of the collector, including collection frequency, sampling rate and resolution. Collection frequency: set the collection frequency according to actual needs, set it to collect data every two minutes. For equipment that requires high-frequency monitoring, it can be set to collect data every 15 seconds. Connect the sensor to the collector to ensure that the sensor data can be accurately transmitted to the collector.

[0016] S3. Data collection and preprocessing. The collector collects data from the sensor according to the set collection parameters. The collector samples, quantizes and converts the sensor data into digital signals. The collector performs preliminary processing on the collected data, including but not limited to filtering, amplification and calibration;

[0017] S4. Data transmission: the collector transmits the pre-processed data to the data transmission module. The data transmission can be realized by wired or wireless means, and the standard communication protocol is used for data transmission to ensure the compatibility and interoperability of the system;

[0018] S5. Data storage and backup, the collector stores the collected data in a local storage unit to prevent data loss due to data transmission failure, and regularly transfers the data to a remote server for backup to ensure data security and reliability;

[0019] S6. Data security and privacy protection: Encrypt the transmitted data to prevent data from being stolen or tampered with. Use a user rights management mechanism to ensure that only authorized users can access the data. Protect user data privacy to prevent user privacy leakage.

[0020] Preferably, the data transmission module comprises the following steps:

[0021] S1. Communication interface configuration. Select the appropriate communication interface type according to the actual application scenario. The communication interface can be a wireless communication module or a wired communication module. Connect the communication interface to the data acquisition module to ensure that the collected data can be transmitted through the communication interface.

[0022] S2. Data transmission protocol selection: select a standard communication protocol for data transmission to ensure system compatibility and interoperability, and configure it according to the protocol requirements, including server address, port number and topic;

[0023] S3. Data encryption and compression: Encrypt the transmitted data to prevent data from being stolen or tampered with. The encryption algorithm uses AES-256 or RSA. The transmitted data is compressed to reduce the amount of data transmitted and improve the transmission efficiency. The compression algorithm uses GZIP or ZLIB.

[0024] S4. Data transmission, the data transmission module establishes a communication connection with the remote server to ensure the smooth flow of data transmission channels, and transmits the encrypted and compressed data to the remote server. Data transmission adopts real-time transmission, batch transmission and breakpoint transmission;

[0025] S5. Data transmission monitoring and logging: real-time monitoring of data transmission status, including transmission rate, transmission success rate and error rate, and recording of data transmission logs, including transmission time, transmission data volume and transmission results, for subsequent analysis and troubleshooting;

[0026] S6. Data reception and verification. The remote server receives the transmitted data, decrypts and decompresses it, and verifies the received data to ensure the integrity and accuracy of the data. The verification includes: checking whether the data is complete, whether it is lost or damaged, and whether the data is accurate, and whether there are any errors or anomalies.

[0027] S7. Exception handling and troubleshooting. When an abnormal situation occurs during data transmission, perform exception handling, troubleshoot and analyze the data transmission failure, find out the cause of the failure, and take corresponding solutions.

[0028] Preferably, the data processing module comprises the following steps:

[0029] S1. Data reception and storage. The data processing module receives data from the data transmission module. The data is stored in the database through the API interface, message queue or direct database connection for subsequent processing and analysis. The database uses a relational database or a NoSQL database;

[0030] S2. Data cleaning: remove noise from the collected data, remove data noise caused by sensor errors and communication interference, process missing values ​​in the data by interpolation or deletion, and process outliers in the data by statistical methods or machine learning algorithms for anomaly detection and removal;

[0031] S3. Data preprocessing: standardize the data to make them have the same dimension and scale for subsequent analysis and processing; normalize the data to map them to a specific interval for model training and prediction; extract features from the data, including but not limited to the mean, maximum, minimum, variance and covariance; select appropriate features based on their importance; remove redundant features to improve model training efficiency and prediction accuracy;

[0032] S4. Data analysis and modeling: conduct in-depth analysis of the preprocessed data, including descriptive statistical analysis, correlation analysis, and trend analysis; select appropriate models based on data characteristics and analysis results; use linear regression models, support vector machines, or random forests; train the models using training data; adjust model parameters so that the models can accurately predict the data; evaluate the models using validation data; and calculate the performance indicators of the models, including but not limited to mean square error, determination coefficient, accuracy, and recall;

[0033] S5. Prediction and decision support: Use the trained model to predict future data, including power generation prediction and equipment life prediction, and provide decision support for the intelligent control module based on the prediction results, including adjusting equipment operating parameters and optimizing energy utilization strategies;

[0034] S6. Output and visualization of results: output the data processing and analysis results to the user interface module or store them in the database for user query and analysis, and visualize the results, including generating charts and graphs, to facilitate user understanding and analysis;

[0035] S7. Anomaly detection and alarm: perform anomaly detection on the data processing results to find anomalies in the data. When an anomaly is detected, the alarm mechanism is triggered to notify relevant personnel to handle it.

[0036] Preferably, the intelligent control module comprises the following steps:

[0037] S1. Receive control instructions. The intelligent control module receives control instructions from the data processing module. The control instructions may be structured data, including device ID, parameter name and target value;

[0038] S2. Instruction parsing and decision-making: parse the received control instructions and extract key information from the instructions, including device ID, parameter name and target value; make specific control decisions based on the parsed instruction information and current device status;

[0039] S3. Equipment status monitoring: the intelligent control module monitors the operating status of the equipment in real time, including voltage, current, power, temperature and speed, and receives feedback information from the equipment, including the actual operating parameters and fault status of the equipment;

[0040] S4. Generate control instructions: Generate specific control instructions based on decision results and equipment status monitoring information, and optimize the generated control instructions to ensure the rationality and feasibility of the instructions;

[0041] S5. Control instruction execution, sending the generated control instruction to the actuator, the actuator receives the control instruction and executes it, and controls the operating status of the device;

[0042] S6. Feedback and adjustment: receiving feedback from the actuator, verifying the execution of the control instructions, and adjusting the control strategy based on the feedback information to ensure that the equipment operation status meets expectations;

[0043] S7. Abnormal handling and fault recovery: perform abnormal detection on the equipment operation status to find equipment failure or abnormal operation. When an abnormal situation is detected, take corresponding fault handling measures, including shutdown protection and alarm notification. After the fault is eliminated, restart the equipment and restore the parameters to ensure that the equipment resumes normal operation.

[0044] (III) Beneficial effects

[0045] The present invention provides a zero-carbon energy remote intelligent management system. It has the following beneficial effects:

[0046] 1. In the present invention, the system can monitor the operating status of zero-carbon energy equipment in real time and optimize control through data analysis. Through data analysis and prediction models, the system can predict equipment failures and maintenance needs, perform maintenance in advance, and avoid energy losses caused by equipment failures. The system can perform intelligent scheduling according to energy demand and supply conditions, optimize energy distribution, and improve overall energy utilization efficiency.

[0047] 2. In the present invention, by adopting advanced data analysis technology, the collected data is deeply analyzed to extract valuable information to provide support for decision-making. By combining historical data and real-time data, the system can make accurate predictions and automatically generate control instructions based on the analysis results, thereby automating some decisions, reducing manual intervention and improving management efficiency.

[0048] 3. In the present invention, the system is accessed through a web page or a mobile application to monitor the equipment operating status and data in real time without on-site operation, thereby improving the convenience and flexibility of management. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the overall system process of the present invention;

[0050] Figure 2 It is a system flow diagram of the data acquisition module in the present invention;

[0051] Figure 3 It is a schematic diagram of the system flow of the data transmission module in the present invention;

[0052] Figure 4 It is a system flow diagram of the data processing module in the present invention;

[0053] Figure 5 It is a system flow diagram of the intelligent control module in the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Embodiment 1:

[0056] like Figure 1-5 As shown, an embodiment of the present invention provides a zero-carbon energy remote intelligent management system, including:

[0057] A data acquisition module, including sensors and collectors, for collecting operating data of zero-carbon energy equipment, including but not limited to voltage, current, power and power generation;

[0058] A data transmission module transmits the collected data to a remote server by using wireless communication technology;

[0059] The data processing module analyzes and processes the transmitted data, including data cleaning, feature extraction, and anomaly detection;

[0060] Intelligent control module, which performs intelligent control on zero-carbon energy equipment according to the analysis results of the data processing module;

[0061] The user interface module provides an interface for users to access and manage the system. Users can access the system through web pages or mobile applications to view equipment operating status, historical data, and alarm information.

[0062] The data acquisition module includes the following steps:

[0063] S1. Sensor selection and installation: According to the type of zero-carbon energy equipment and monitoring requirements, determine the parameters that need to be collected, including but not limited to voltage, current, power, temperature, humidity, wind speed and light intensity; select appropriate sensors according to the collection parameters, including but not limited to voltage sensors, current sensors, power sensors, temperature sensors and environmental sensors; install the selected sensors in the appropriate position of the equipment to ensure that the sensors can accurately collect the required data;

[0064] S2. Data collector configuration: select a suitable data collector to collect sensor data and perform preliminary processing; configure the collection parameters of the collector, including collection frequency, sampling rate and resolution. Collection frequency: set the collection frequency according to actual needs, set it to collect data every two minutes. For equipment that requires high-frequency monitoring, it can be set to collect data every 15 seconds. Connect the sensor to the collector to ensure that the sensor data can be accurately transmitted to the collector.

[0065] S3. Data collection and preprocessing. The collector collects data from the sensor according to the set collection parameters. The collector samples, quantizes and converts the sensor data into digital signals. The collector performs preliminary processing on the collected data, including but not limited to filtering, amplification and calibration;

[0066] S4. Data transmission: the collector transmits the pre-processed data to the data transmission module. The data transmission can be realized by wired or wireless means, and the standard communication protocol is used for data transmission to ensure the compatibility and interoperability of the system;

[0067] S5. Data storage and backup, the collector stores the collected data in a local storage unit to prevent data loss due to data transmission failure, and regularly transfers the data to a remote server for backup to ensure data security and reliability;

[0068] S6. Data security and privacy protection: Encrypt the transmitted data to prevent data from being stolen or tampered with. Use a user rights management mechanism to ensure that only authorized users can access the data. Protect user data privacy to prevent user privacy leakage.

[0069] The data transfer module includes the following steps:

[0070] S1. Communication interface configuration. Select the appropriate communication interface type according to the actual application scenario. The communication interface can be a wireless communication module or a wired communication module. Connect the communication interface to the data acquisition module to ensure that the collected data can be transmitted through the communication interface.

[0071] S2. Data transmission protocol selection: select a standard communication protocol for data transmission to ensure system compatibility and interoperability, and configure it according to the protocol requirements, including server address, port number and topic;

[0072] S3. Data encryption and compression: Encrypt the transmitted data to prevent data from being stolen or tampered with. The encryption algorithm uses AES-256 or RSA. The transmitted data is compressed to reduce the amount of data transmitted and improve the transmission efficiency. The compression algorithm uses GZIP or ZLIB.

[0073] S4. Data transmission, the data transmission module establishes a communication connection with the remote server to ensure the smooth flow of data transmission channels, and transmits the encrypted and compressed data to the remote server. Data transmission adopts real-time transmission, batch transmission and breakpoint transmission;

[0074] S5. Data transmission monitoring and logging: real-time monitoring of data transmission status, including transmission rate, transmission success rate and error rate, and recording of data transmission logs, including transmission time, transmission data volume and transmission results, for subsequent analysis and troubleshooting;

[0075] S6. Data reception and verification. The remote server receives the transmitted data, decrypts and decompresses it, and verifies the received data to ensure the integrity and accuracy of the data. The verification includes: checking whether the data is complete, whether it is lost or damaged, and whether the data is accurate, and whether there are any errors or anomalies.

[0076] S7. Exception handling and troubleshooting. When an abnormal situation occurs during data transmission, perform exception handling, troubleshoot and analyze the data transmission failure, find out the cause of the failure, and take corresponding solutions.

[0077] The data processing module includes the following steps:

[0078] S1. Data reception and storage. The data processing module receives data from the data transmission module. The data is stored in the database through the API interface, message queue or direct database connection for subsequent processing and analysis. The database uses a relational database or a NoSQL database;

[0079] S2. Data cleaning: remove noise from the collected data, remove data noise caused by sensor errors and communication interference, process missing values ​​in the data by interpolation or deletion, and process outliers in the data by statistical methods or machine learning algorithms for anomaly detection and removal;

[0080] S3. Data preprocessing: standardize the data to make them have the same dimension and scale for subsequent analysis and processing; normalize the data to map them to a specific interval for model training and prediction; extract features from the data, including but not limited to the mean, maximum, minimum, variance and covariance; select appropriate features based on their importance; remove redundant features to improve model training efficiency and prediction accuracy;

[0081] S4. Data analysis and modeling: conduct in-depth analysis of the preprocessed data, including descriptive statistical analysis, correlation analysis, and trend analysis; select appropriate models based on data characteristics and analysis results; use linear regression models, support vector machines, or random forests; train the models using training data; adjust model parameters so that the models can accurately predict the data; evaluate the models using validation data; and calculate the performance indicators of the models, including but not limited to mean square error, determination coefficient, accuracy, and recall;

[0082] S5. Prediction and decision support: Use the trained model to predict future data, including power generation prediction and equipment life prediction, and provide decision support for the intelligent control module based on the prediction results, including adjusting equipment operating parameters and optimizing energy utilization strategies;

[0083] S6. Output and visualization of results: output the data processing and analysis results to the user interface module or store them in the database for user query and analysis, and visualize the results, including generating charts and graphs, to facilitate user understanding and analysis;

[0084] S7. Anomaly detection and alarm: perform anomaly detection on the data processing results to find anomalies in the data. When an anomaly is detected, the alarm mechanism is triggered to notify relevant personnel to handle it.

[0085] The intelligent control module includes the following steps:

[0086] S1. Receive control instructions. The intelligent control module receives control instructions from the data processing module. The control instructions may be structured data, including device ID, parameter name and target value;

[0087] S2. Instruction parsing and decision-making: parse the received control instructions and extract key information from the instructions, including device ID, parameter name and target value; make specific control decisions based on the parsed instruction information and current device status;

[0088] S3. Equipment status monitoring: the intelligent control module monitors the operating status of the equipment in real time, including voltage, current, power, temperature and speed, and receives feedback information from the equipment, including the actual operating parameters and fault status of the equipment;

[0089] S4. Generate control instructions: Generate specific control instructions based on decision results and equipment status monitoring information, and optimize the generated control instructions to ensure the rationality and feasibility of the instructions;

[0090] S5. Control instruction execution, sending the generated control instruction to the actuator, the actuator receives the control instruction and executes it, and controls the operating status of the device;

[0091] S6. Feedback and adjustment: receiving feedback from the actuator, verifying the execution of the control instructions, and adjusting the control strategy based on the feedback information to ensure that the equipment operation status meets expectations;

[0092] S7. Abnormal handling and fault recovery: perform abnormal detection on the equipment operation status to find equipment failure or abnormal operation. When an abnormal situation is detected, take corresponding fault handling measures, including shutdown protection and alarm notification. After the fault is eliminated, restart the equipment and restore the parameters to ensure that the equipment resumes normal operation.

[0093] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A zero-carbon energy remote intelligent management system, characterized by: include: A data acquisition module, including sensors and collectors, for collecting operating data of zero-carbon energy equipment, including but not limited to voltage, current, power and power generation; A data transmission module transmits the collected data to a remote server by using wireless communication technology; The data processing module analyzes and processes the transmitted data, including data cleaning, feature extraction, and anomaly detection; Intelligent control module, which performs intelligent control on zero-carbon energy equipment according to the analysis results of the data processing module; The user interface module provides an interface for users to access and manage the system. Users can access the system through web pages or mobile applications to view equipment operating status, historical data, and alarm information.

2. A zero-carbon energy remote intelligent management system according to claim 1, characterized in that: The data acquisition module comprises the following steps: S1. Sensor selection and installation: According to the type of zero-carbon energy equipment and monitoring requirements, determine the parameters that need to be collected, including but not limited to voltage, current, power, temperature, humidity, wind speed and light intensity; select appropriate sensors according to the collection parameters, including but not limited to voltage sensors, current sensors, power sensors, temperature sensors and environmental sensors; install the selected sensors in the appropriate position of the equipment to ensure that the sensors can accurately collect the required data; S2. Data collector configuration: select a suitable data collector to collect sensor data and perform preliminary processing; configure the collection parameters of the collector, including collection frequency, sampling rate and resolution. Collection frequency: set the collection frequency according to actual needs, set it to collect data every two minutes. For equipment that requires high-frequency monitoring, it can be set to collect data every 15 seconds. Connect the sensor to the collector to ensure that the sensor data can be accurately transmitted to the collector. S3. Data collection and preprocessing. The collector collects data from the sensor according to the set collection parameters. The collector samples, quantizes and converts the sensor data into digital signals. The collector performs preliminary processing on the collected data, including but not limited to filtering, amplification and calibration; S4. Data transmission: the collector transmits the pre-processed data to the data transmission module. The data transmission can be realized by wired or wireless means, and the standard communication protocol is used for data transmission to ensure the compatibility and interoperability of the system; S5. Data storage and backup, the collector stores the collected data in a local storage unit to prevent data loss due to data transmission failure, and regularly transfers the data to a remote server for backup to ensure data security and reliability; S6. Data security and privacy protection: Encrypt the transmitted data to prevent data from being stolen or tampered with. Use a user rights management mechanism to ensure that only authorized users can access the data. Protect user data privacy to prevent user privacy leakage.

3. A zero-carbon energy remote intelligent management system according to claim 1, characterized in that: The data transmission module comprises the following steps: S1. Communication interface configuration. Select the appropriate communication interface type according to the actual application scenario. The communication interface can be a wireless communication module or a wired communication module. Connect the communication interface to the data acquisition module to ensure that the collected data can be transmitted through the communication interface. S2. Data transmission protocol selection: select a standard communication protocol for data transmission to ensure system compatibility and interoperability, and configure it according to the protocol requirements, including server address, port number and topic; S3. Data encryption and compression: Encrypt the transmitted data to prevent data from being stolen or tampered with. The encryption algorithm uses AES-256 or RSA. The transmitted data is compressed to reduce the amount of data transmitted and improve the transmission efficiency. The compression algorithm uses GZIP or ZLIB. S4. Data transmission, the data transmission module establishes a communication connection with the remote server to ensure the smooth flow of data transmission channels, and transmits the encrypted and compressed data to the remote server. Data transmission adopts real-time transmission, batch transmission and breakpoint transmission; S5. Data transmission monitoring and logging: real-time monitoring of data transmission status, including transmission rate, transmission success rate and error rate, and recording of data transmission logs, including transmission time, transmission data volume and transmission results, for subsequent analysis and troubleshooting; S6. Data reception and verification. The remote server receives the transmitted data, decrypts and decompresses it, and verifies the received data to ensure the integrity and accuracy of the data. The verification includes: checking whether the data is complete, whether it is lost or damaged, and whether the data is accurate, and whether there are any errors or anomalies. S7. Exception handling and troubleshooting. When an abnormal situation occurs during data transmission, perform exception handling, troubleshoot and analyze the data transmission failure, find out the cause of the failure, and take corresponding solutions.

4. The zero-carbon energy remote intelligent management system according to claim 1 is characterized by: The data processing module comprises the following steps: S1. Data reception and storage. The data processing module receives data from the data transmission module. The data is stored in the database through the API interface, message queue or direct database connection for subsequent processing and analysis. The database uses a relational database or a NoSQL database; S2. Data cleaning: remove noise from the collected data, remove data noise caused by sensor errors and communication interference, process missing values ​​in the data by interpolation or deletion, and process outliers in the data by statistical methods or machine learning algorithms for anomaly detection and removal; S3. Data preprocessing: standardize the data to make them have the same dimension and scale for subsequent analysis and processing; normalize the data to map them to a specific interval for model training and prediction; extract features from the data, including but not limited to the mean, maximum, minimum, variance and covariance; select appropriate features based on their importance; remove redundant features to improve model training efficiency and prediction accuracy; S4. Data analysis and modeling: conduct in-depth analysis of the preprocessed data, including descriptive statistical analysis, correlation analysis, and trend analysis; select appropriate models based on data characteristics and analysis results; use linear regression models, support vector machines, or random forests; train the models using training data; adjust model parameters so that the models can accurately predict the data; evaluate the models using validation data; and calculate the performance indicators of the models, including but not limited to mean square error, determination coefficient, accuracy, and recall; S5. Prediction and decision support: Use the trained model to predict future data, including power generation prediction and equipment life prediction, and provide decision support for the intelligent control module based on the prediction results, including adjusting equipment operating parameters and optimizing energy utilization strategies; S6. Output and visualization of results: output the data processing and analysis results to the user interface module or store them in the database for user query and analysis, and visualize the results, including generating charts and graphs, to facilitate user understanding and analysis; S7. Anomaly detection and alarm: perform anomaly detection on the data processing results to find anomalies in the data. When an anomaly is detected, the alarm mechanism is triggered to notify relevant personnel to handle it.

5. The zero-carbon energy remote intelligent management system according to claim 1 is characterized by: The intelligent control module comprises the following steps: S1. Receive control instructions. The intelligent control module receives control instructions from the data processing module. The control instructions may be structured data, including device ID, parameter name and target value; S2. Instruction parsing and decision-making: parse the received control instructions and extract key information from the instructions, including device ID, parameter name and target value; make specific control decisions based on the parsed instruction information and current device status; S3. Equipment status monitoring: the intelligent control module monitors the operating status of the equipment in real time, including voltage, current, power, temperature and speed, and receives feedback information from the equipment, including the actual operating parameters and fault status of the equipment; S4. Generate control instructions: Generate specific control instructions based on decision results and equipment status monitoring information, and optimize the generated control instructions to ensure the rationality and feasibility of the instructions; S5. Control instruction execution, sending the generated control instruction to the actuator, the actuator receives the control instruction and executes it, and controls the operating status of the device; S6. Feedback and adjustment: receiving feedback from the actuator, verifying the execution of the control instructions, and adjusting the control strategy based on the feedback information to ensure that the equipment operation status meets expectations; S7. Abnormal handling and fault recovery: perform abnormal detection on the equipment operation status to find equipment failure or abnormal operation. When an abnormal situation is detected, take corresponding fault handling measures, including shutdown protection and alarm notification. After the fault is eliminated, restart the equipment and restore the parameters to ensure that the equipment resumes normal operation.

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