Energy-saving control system and method for intelligent electrical equipment

Abstract

The invention discloses an energy-saving control system and method for intelligent electrical equipment, and relates to the technical field of energy-saving control, and the system comprises a data collection module which is used for collecting the operation data, including equipment power, operation time and environment parameters, of the electrical equipment in real time, and a data analysis and processing module which is connected with the data collection module and is used for analyzing and processing the operation data. The data analysis and processing module is used for cleaning, converting and deeply analyzing collected data, the energy-saving control strategy module is used for generating an intelligent energy-saving control strategy according to a result of the data analysis and processing module, and the control execution module is connected with the energy-saving control strategy module and is used for accurately controlling electrical equipment according to the energy-saving control strategy. The data acquisition module intelligently identifies an equipment type interface and dynamically adjusts the frequency, the visual display module updates in real time and can be customized, the configuration management module facilitates template import and export and version management, the adaptability, convenience and stability of the system are improved, and energy-saving control of electrical equipment is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving control, and particularly to an energy-saving control system and method for intelligent electrical equipment. Background Art

[0002] In today's society, with the continuous progress of technology and the increasing emphasis on energy efficiency by people, the energy-saving control of intelligent electrical equipment has become a crucial research field.

[0003] Traditional electrical equipment often has problems of energy waste during operation. On the one hand, many electrical equipment lack effective energy management mechanisms and cannot be intelligently adjusted according to actual needs, resulting in a large amount of energy consumption even when it is unnecessary. For example, some lighting devices remain on for a long time in unoccupied rooms, and the air-conditioning system continues to operate when the indoor temperature is already suitable, which all cause unnecessary energy consumption. On the other hand, traditional electrical equipment has poor adaptability to environmental factors. Changes in environmental parameters such as temperature, humidity, and light intensity often cannot be sensed by the equipment in a timely manner and corresponding adjustments are not made, thus affecting the energy utilization efficiency of the equipment.

[0004] In the industrial production field, a large number of electrical equipment such as motors and frequency converters consume huge amounts of energy during operation. Due to the lack of precise control and optimization strategies, these equipment may have problems such as too high power and unreasonable operation time during operation, which not only increases the energy cost of enterprises but also causes greater pressure on the environment. At the same time, the traditional monitoring method of electrical equipment mainly relies on manual inspection and regular maintenance. This method is not only inefficient but also difficult to detect potential problems of the equipment in real time, and often repairs the equipment after a failure occurs, resulting in production interruption and energy waste.

[0005] In addition, with the continuous popularization of intelligent devices, people's requirements for the intelligence of electrical equipment are also getting higher and higher. Traditional electrical equipment cannot meet the user's needs for convenience and personalization. For example, users hope to be able to monitor the operation status of electrical equipment at any time and anywhere through mobile terminals such as mobile phones and perform remote control according to their own needs. However, existing electrical equipment often lacks such functions, bringing inconvenience to users.

[0006] To solve the above problems, researchers have been working hard to explore more intelligent and efficient energy-saving control technologies for electrical equipment. In recent years, with the continuous development of big data analysis technology, machine learning algorithms, and sensor technology, new ideas and methods have been provided for the energy-saving control of intelligent electrical equipment. By collecting the operation data of electrical equipment in real time and applying advanced data analysis and processing technologies, the operation status and energy consumption of the equipment can be understood more accurately, thereby formulating more scientific and reasonable energy-saving control strategies. At the same time, the introduction of an intelligent prediction module can predict equipment failures and energy consumption requirements in advance, providing a basis for preventive maintenance and energy-saving strategy adjustment, and further improving the energy utilization efficiency and reliability of electrical equipment. Summary of the Invention

[0007] An energy-saving control system and method for intelligent electrical equipment proposed by the present invention to solve the above deficiencies in the prior art.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] An energy-saving control system for intelligent electrical equipment, comprising:

[0010] Data acquisition module: used to collect the operation data of electrical equipment in real time, including equipment power, operation time, and environmental parameters. The environmental parameters cover temperature, humidity, and light intensity. The data acquisition module is equipped with a variety of sensors, which can be adapted to different types of electrical equipment to ensure accurate acquisition of various data. The operation data also includes the load rate of the equipment, and the calculation formula is load rate = actual power / rated power.

[0011] Data analysis and processing module: connected to the data acquisition module, and performs data cleaning, conversion, and in-depth analysis on the collected data. This module uses big data analysis technology and machine learning algorithms to accurately extract valuable operation information and real-time monitor the abnormal fluctuations and trend changes of the data. It includes a data cleaning sub-module, a feature extraction sub-module, and an anomaly detection sub-module. The data cleaning sub-module is used to remove noise and duplicate data from the collected data; the feature extraction sub-module extracts key features from the cleaned data; the anomaly detection sub-module uses an advanced deep learning-based anomaly detection algorithm to monitor the operation status of the equipment in real time and make anomaly judgments.

[0012] Energy-saving control strategy module: generates an intelligent energy-saving control strategy according to the results of the data analysis and processing module. The strategies include adjusting equipment power, controlling equipment switch states, and optimizing equipment operation modes. This module uses the following formula to calculate the comprehensive energy-saving index EI:

[0013]

[0014] Among them, n is the number of different types of energy-saving parameters, including the power adjustment range and the operation mode switching frequency, P i is the power value related to the i-th energy-saving parameter, S i is the operation time factor related to the i-th energy-saving parameter, E i is the equipment status factor related to the i-th energy-saving parameter, u i is the weight coefficient of the i-th energy-saving parameter, g(P i , S i , E i ) is the energy-saving function of the i-th energy-saving parameter; m is the number of different environmental factors, T j is the temperature value in the j-th environmental factor, H j is the humidity value in the j-th environmental factor, L j is the light intensity value in the j-th environmental factor, υ j is the weight coefficient of the j-th environmental factor, h(T j , H j , L j ) is the influence function of the environmental factor. When EI reaches the optimal value, the system achieves the best energy-saving effect.

[0015] Control execution module: Connected to the energy-saving control strategy module, it precisely controls electrical equipment according to the energy-saving control strategy. It includes a power regulation unit, a switch control unit, and an operation mode adjustment unit. The power regulation unit is used to adjust the power of electrical equipment; the switch control unit is used to control the switch state of electrical equipment; the operation mode adjustment unit is used to adjust the operation mode of electrical equipment to achieve energy savings.

[0016] Visualization display module: Displays the operating status and analysis results of electrical equipment to users in the form of intuitive charts, graphs, and reports, facilitating users to understand the overall operating conditions of electrical equipment in real time. The visualization display module provides multi-dimensional view switching and custom display functions.

[0017] Configuration management module: Used to configure and manage various parameters of the monitoring system, including equipment information entry, monitoring threshold setting, energy-saving strategy customization, and user permission management. The configuration management module supports remote configuration and batch configuration operations. The following formula is used to evaluate the rationality index CI of the configuration:

[0018]

[0019] Among them, k is the number of types of configuration parameters, including the number of monitoring devices, the number of energy-saving threshold settings, and the number of user permission settings, P l is the actual value of the l-th configuration parameter, Q l is the ideal value or industry standard value of the l-th configuration parameter, w(P l , Ql ) is the rationality function for the l-th configuration parameter, which includes using an absolute value function to measure the deviation between the actual value and the ideal value, w(P l , Q l ) = |P l - Q l |), R l is the importance weight of the l-th configuration parameter, which is determined according to the influence degree of the configuration parameter on the system operation. The smaller the CI value, the more reasonable the configuration. When the CI exceeds a certain threshold, the system prompts the user to optimize the configuration.

[0020] Intelligent prediction module: Using historical data and machine learning models, predict the future operating status of electrical equipment, including equipment fault prediction and energy consumption demand prediction, providing a basis for preventive maintenance and energy-saving strategy adjustment. Adopt time series analysis and deep learning algorithms, combine the operation rules and historical data of the equipment for prediction, and the prediction results are presented in the form of probability and a detailed prediction report is provided. The following fault prediction probability formula EP is adopted:

[0021]

[0022] where t is the current time, t 0 is the time when the equipment last operated normally, σ is the time standard deviation calculated based on the equipment historical fault time interval data, reflecting the dispersion degree of the equipment fault time; n is the number of factors affecting the equipment fault, including equipment operating temperature, humidity, load, p i is the probability that the i-th factor causes the equipment fault, which is obtained through training with historical data and machine learning algorithms, x i is the current status value of the i-th factor (for example, the degree to which the temperature exceeds the normal range). The larger the FP value, the higher the probability that the equipment will fail in the future period.

[0023] Furthermore, the data acquisition module has the function of automatically identifying the type and interface of electrical equipment, and can dynamically adjust the acquisition frequency according to the working state of the equipment to reduce the impact on the normal operation of the equipment.

[0024] Furthermore, the visualization display module provides real-time data update and dynamic chart display functions. Users can access the monitoring data anytime and anywhere through the web interface or mobile client, and can customize personalized monitoring dashboards according to their own needs.

[0025] Furthermore, the configuration management module has the function of importing and exporting configuration templates, which is convenient for users to quickly perform configuration migration in different application scenarios or system upgrades. At the same time, it supports configuration version management, allowing users to roll back to historical configuration versions.

[0026] Furthermore, an energy-saving control method for intelligent electrical equipment includes the following steps:

[0027] Data collection steps: Collect the operating data of electrical equipment through sensor equipment to ensure that the collection process does not affect the normal business operation of the equipment.

[0028] Data processing steps: Clean, convert and analyze the collected data, remove noise and duplicate data, extract key features, and use machine learning algorithms to monitor the data in real time and identify anomalies.

[0029] Strategy generation step: Generate energy-saving control strategies based on data processing results, including power regulation, switch control, and operation mode optimization.

[0030] Control execution steps: Control electrical equipment according to energy-saving control strategies to achieve energy-saving goals.

[0031] Visual display step: The operating status and analysis results of the electrical equipment are visualized in the form of charts, graphs and reports, providing users with an intuitive monitoring interface so that users can understand the overall operating status of the electrical equipment in real time.

[0032] Configuration management steps: Configure and manage various parameters of the monitoring system, including adding, modifying and deleting device information, setting monitoring thresholds, customizing energy-saving strategies, and allocating and managing user permissions.

[0033] Intelligent prediction step: Use historical data and trained machine learning models to predict the future operating status of electrical equipment, generate preventive maintenance plans and recommendations based on the prediction results, and regularly update the prediction report.

[0034] Furthermore, before the data collection step, a device initialization step is also included for automatically identifying the type and interface information of the electrical device and establishing a connection with the device.

[0035] In the data processing step, real-time stream processing technology is used to perform real-time analysis on the collected data, and batch processing technology is combined to conduct in-depth analysis of historical data to explore the potential patterns and trends of the data.

[0036] In the strategy generation step, the energy-saving control strategy is dynamically adjusted according to the actual operation of the equipment and user needs to achieve the best energy-saving effect.

[0037] In the visualization step, users are supported to customize the visualization content and layout according to their own focus and business needs. At the same time, a data drilling function is provided, and users can obtain detailed device information and operating data by clicking on data points in the chart.

[0038] In the configuration management step, a configuration audit function is provided to record the modification operations and modification times of configuration parameters by users, so as to trace and analyze in case of problems.

[0039] Compared with the existing technologies, the beneficial effects of the present invention are as follows:

[0040] Through the data acquisition module, various operation data and environmental parameters of electrical equipment are collected in real time, ensuring a comprehensive understanding of the equipment operation status. At the same time, this module can automatically identify the equipment type and interface, and dynamically adjust the acquisition frequency according to the equipment working status, reducing the impact on the normal operation of the equipment.

[0041] Through the data analysis and processing module, using big data analysis technology and machine learning algorithms, the collected data is cleaned, transformed and deeply analyzed. Noise and duplicate data are removed, key features are extracted, and real-time monitoring and anomaly judgment are carried out, providing accurate data support for the formulation of energy-saving control strategies.

[0042] The energy-saving control strategy module generates intelligent energy-saving control strategies according to the analysis results, including adjusting the equipment power, controlling the equipment switch status and optimizing the equipment operation mode, etc. Through the comprehensive energy-saving index calculation formula, the best energy-saving effect is achieved, reducing energy consumption and operation costs.

[0043] The control execution module accurately executes the energy-saving control strategy to ensure that the electrical equipment operates in the optimal way. The visualization display module displays the equipment operation status and analysis results in the form of intuitive charts, graphs and reports, facilitating users to understand the equipment situation in real time, and at the same time supporting multi-dimensional view switching and custom display functions.

[0044] The configuration management module facilitates users to configure and manage system parameters, improving the flexibility and adaptability of the system. The intelligent prediction module uses historical data and machine learning models to predict the future operation status of the equipment, providing a basis for preventive maintenance and energy-saving strategy adjustment, and improving the reliability and stability of the equipment. In short, the present invention brings a new solution for the energy-saving control of intelligent electrical equipment, with broad application prospects and important social value. Brief Description of the Drawings

[0045] Figure 1 It is a block diagram of an energy-saving control system for an intelligent electrical equipment proposed by the present invention;

[0046] Figure 2 It is a block diagram of an energy-saving control method module for an intelligent electrical equipment proposed by the present invention. Detailed Embodiments

[0047] 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0048] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation to the present invention.

[0049] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined. In addition, the terms "mounted", "connected", "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal communication of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the accompanying drawings.

[0050] Refer to Figure 1-2 : An energy-saving control system for an intelligent electrical device, comprising:

[0051] A data acquisition module: used to collect the operation data of the electrical device in real time, including device power, operation time, and environmental parameters. The environmental parameters cover temperature, humidity, and light intensity. The data acquisition module is equipped with a variety of sensors and can be adapted to different types of electrical devices to ensure accurate acquisition of various data. The operation data also includes the load rate of the device, and the calculation formula is load rate = actual power / rated power.

[0052] Data Analysis and Processing Module: Connected to the data acquisition module, it cleans, transforms, and deeply analyzes the collected data. Using big data analysis techniques and machine learning algorithms, this module can accurately extract valuable operation information and real-time monitor abnormal fluctuations and trend changes in the data. It includes a data cleaning sub-module, a feature extraction sub-module, and an anomaly detection sub-module. The data cleaning sub-module is used to remove noise and duplicate data from the collected data; the feature extraction sub-module extracts key features from the cleaned data; the anomaly detection sub-module uses advanced deep learning-based anomaly detection algorithms to monitor the operation status of the device in real time and make anomaly judgments.

[0053] Energy-saving Control Strategy Module: Based on the results of the data analysis and processing module, it generates intelligent energy-saving control strategies. The strategies include adjusting the device power, controlling the device switch state, and optimizing the device operation mode. This module uses the following formula to calculate the comprehensive energy-saving index EI:

[0054]

[0055] where n is the number of different types of energy-saving parameters, including the power adjustment range and the operation mode switching frequency, P i is the power value related to the i-th energy-saving parameter, S i is the operation time factor related to the i-th energy-saving parameter, E i is the device status factor related to the i-th energy-saving parameter, u i is the weight coefficient of the i-th energy-saving parameter, g(P i , S i , E i ) is the energy-saving function of the i-th energy-saving parameter; m is the number of different environmental factors, T j is the temperature value in the j-th environmental factor, H j is the humidity value in the j-th environmental factor, L j is the light intensity value in the j-th environmental factor, υ j is the weight coefficient of the j-th environmental factor, h(T j , H j , L j ) is the influence function of the environmental factors. When EI reaches the optimal value, the system achieves the best energy-saving effect.

[0056] Control Execution Module: Connected to the energy-saving control strategy module, it accurately controls electrical equipment according to the energy-saving control strategies. It includes a power regulation unit, a switch control unit, and an operation mode adjustment unit. The power regulation unit is used to adjust the power of electrical equipment; the switch control unit is used to control the switch state of electrical equipment; the operation mode adjustment unit is used to adjust the operation mode of electrical equipment to achieve energy savings.

[0057] Visualization display module: Displays the operating status and analysis results of electrical equipment to users in the form of intuitive charts, graphs, and reports, facilitating users to understand the overall operating conditions of electrical equipment in real time. The visualization display module provides multi-dimensional view switching and custom display functions.

[0058] Configuration management module: Used to configure and manage various parameters of the monitoring system, including device information entry, monitoring threshold setting, energy-saving strategy customization, and user permission management. The configuration management module supports remote configuration and batch configuration operations. The rationality index CI of the configuration is evaluated using the following formula:

[0059]

[0060] where k is the number of types of configuration parameters, including the number of monitoring devices, the number of energy-saving threshold settings, and the number of user permission settings, P l is the actual value of the l-th configuration parameter, Q l is the ideal value or industry standard value of the l-th configuration parameter, w(P l , Q l ) is the rationality function of the l-th configuration parameter, including using the absolute value function to measure the deviation between the actual value and the ideal value, w(P l , Q l ) = |P l - Q l |), R l is the importance weight of the l-th configuration parameter, determined according to the impact degree of the configuration parameter on the system operation. The smaller the CI value, the more reasonable the configuration. When CI exceeds a certain threshold, the system prompts the user to optimize the configuration.

[0061] Intelligent prediction module: Utilizes historical data and machine learning models to predict the future operating status of electrical equipment, including equipment fault prediction and energy consumption demand prediction, providing a basis for preventive maintenance and energy-saving strategy adjustment. Adopts time series analysis and deep learning algorithms, combines the operating rules and historical data of the equipment for prediction, and the prediction results are presented in the form of probabilities and detailed prediction reports are provided. The following fault prediction probability formula FP is used:

[0062]

[0063] where t is the current time, t 0 is the time when the device last operated normally, σ is the time standard deviation calculated based on the historical fault time interval data of the device, reflecting the dispersion degree of the device fault time; n is the number of factors affecting the device fault, including device operating temperature, humidity, load, p i is the probability of the i-th factor causing the device fault, obtained through training with historical data and machine learning algorithms, xi is the current status value of the i-th factor (e.g., the degree to which the temperature exceeds the normal range). The larger the FP value, the higher the probability that the device will malfunction in the future.

[0064] In the present invention, the data acquisition module demonstrates a high degree of intelligence and flexibility. This module adopts advanced sensor fusion technology and intelligent recognition algorithms, and can quickly and accurately identify various types of electrical equipment and find the corresponding interfaces. For example, by analyzing the electrical signal characteristics of the equipment and combining various factors such as the external dimensions and identification information of the equipment, the type of the equipment can be accurately determined. At the same time, using adaptive interface technology, it can automatically adjust the interface parameters to ensure a stable connection with different devices.

[0065] The data acquisition module can also dynamically adjust the acquisition frequency according to the working state of the equipment. When the electrical equipment is in a high-load operating state, the intelligent monitoring unit in the module will detect the changes in parameters such as the current and voltage of the equipment in real time. Once it determines that the equipment is in a high-load state, it will automatically reduce the acquisition frequency. For example, by reducing the sampling rate of the sensor or extending the time interval of data acquisition, the impact on the normal operation of the equipment can be reduced, and the performance degradation or malfunction of the equipment caused by data acquisition can be avoided. When the equipment is in a low-load or idle state, the module will use the idle time to perform more frequent data acquisition to obtain more detailed equipment operation data. For example, increasing the sampling rate of the sensor and shortening the time interval of data acquisition to more accurately capture the subtle changes of the equipment in the low-load state.

[0066] In the present invention, the visualization display module brings a brand-new monitoring experience to users. This module adopts advanced real-time data transmission technology and dynamic chart generation algorithms, and provides real-time data update and dynamic chart display functions. Whenever and wherever users access the monitoring data through the web interface or mobile client, they can see the latest equipment operation information. For example, by establishing a stable network connection, the collected data is transmitted to the server in real time, and then the server pushes it to the user's web interface or mobile client to ensure the timeliness of the data.

[0067] Moreover, users can customize personalized monitoring dashboards according to their own needs. The visualization display module provides a rich variety of chart types and layout options, and users can choose according to their focus of attention and usage habits. For example, users can choose different types of charts such as bar charts, line charts, and pie charts to display data such as the power consumption and running time of the equipment. At the same time, users can freely adjust the size, position, and color of the charts to meet personalized aesthetic needs. In addition, users can also set data filtering conditions and warning thresholds. When the equipment operation data exceeds the warning threshold, the system will automatically issue an alarm to remind users to handle it in a timely manner.

[0068] In the present invention, the configuration management module provides users with a convenient and efficient configuration management method. This module has the functions of importing and exporting configuration templates, which play a huge role in different application scenarios or system upgrades. For example, when a user needs to use the system in a new application scenario, the configured template can be exported and saved as a file, and then the file can be imported into the new environment. The system will automatically read the parameters in the configuration template and make corresponding settings. This can greatly save configuration time and effort and improve work efficiency.

[0069] At the same time, the configuration management module also supports configuration version management. The system will automatically record the modification time and content of each configuration and generate different version numbers. When a user needs to roll back to a historical configuration version, just select the corresponding version number in the configuration management interface, and the system will automatically restore to the configuration state of that version. For example, when a user makes some adjustments to the configuration and finds that the effect is not ideal, or when the system has problems and needs to be restored to a previous stable state, the configuration version management function can come in handy. The user can choose to roll back to the nearest stable version or select a specific historical version according to the specific situation to ensure the stable operation and reliability of the system. In the present invention, an energy-saving control method for intelligent electrical equipment covers multiple key steps, providing a comprehensive and systematic solution for achieving the high-efficiency energy saving of electrical equipment.

[0070] First is the data collection step. In this step, the operating data of electrical equipment is collected through advanced sensor devices. These sensors, like sensitive "antennae", can accurately capture various operating state information of the equipment, including but not limited to equipment power, operating time, environmental parameters, etc. During the collection process, the system is carefully designed to ensure that it will not cause any interference to the normal business operation of the equipment. This is like quietly observing and recording a person's working state without disturbing a person who is concentrating on work.

[0071] Next is the data processing step. This step performs a series of delicate operations on the collected data. First, data cleaning is carried out to remove noise and duplicate data, just like polishing precious gems to remove impurities and make them more pure. Then data conversion is performed to convert the original data into a form that is more conducive to analysis and processing. At the same time, by extracting key features, the most valuable information is screened out from a large amount of data. And machine learning algorithms are used to monitor and judge anomalies in the data in real time, like a vigilant guardian, constantly paying attention to changes in the equipment operating state and immediately issuing an alarm once an anomaly is found.

[0072] The strategy generation step is one of the cores of the entire energy-saving control method. Based on the results of data processing, the system intelligently generates energy-saving control strategies. These strategies cover multiple aspects, including power regulation, dynamically adjusting the power output of devices according to the actual needs and operating status of the devices to avoid unnecessary energy waste; switch control, automatically controlling the on / off state of devices at appropriate times to achieve reasonable startup and shutdown of the devices; operation mode optimization, selecting the most energy-saving and efficient operation mode by analyzing the performance characteristics and usage scenarios of the devices.

[0073] The control execution step puts the generated energy-saving control strategies into practice. Strictly in accordance with the requirements of the strategies, precise control is carried out on electrical devices to ensure that the devices operate in an energy-saving manner, thereby achieving the energy-saving goal. This is like an executor who precisely executes instructions, transforming the strategies into actual actions.

[0074] The visualization display step provides an intuitive and convenient monitoring interface for users. By presenting the operating status and analysis results of electrical devices in the form of charts, graphs, and reports, users can clearly understand the overall operating conditions of the devices at a glance. These visualization forms are like clear maps, enabling users to easily grasp the operating dynamics of the devices and facilitating real-time monitoring and decision-making by users.

[0075] The configuration management step gives users the power to perform personalized settings on the monitoring system. Users can configure and manage various parameters of the monitoring system, including adding, modifying, and deleting device information, setting monitoring thresholds, customizing energy-saving strategies, and allocating and managing user permissions, etc. This is like providing users with a set of personalized tools, allowing users to flexibly adjust and optimize the system according to their own needs and actual situations.

[0076] Finally, there is the intelligent prediction step. Using historical data and trained machine learning models, the system predicts the future operating status of electrical devices. Based on the prediction results, preventive maintenance plans and suggestions are generated to detect potential problems of the devices in advance and take corresponding preventive measures. At the same time, the prediction reports are updated regularly to ensure the accuracy and timeliness of the predictions. This is like a visionary prophet, providing strong guarantees for the stable operation and energy-saving effect of the devices.

[0077] In the present invention, the device initialization step before the data acquisition step plays a crucial role. The device initialization step can automatically identify the type and interface information of electrical devices. This process is like a smart "detective" that, through advanced identification technologies, quickly and accurately distinguishes different types of electrical devices and their corresponding interface types. After determining the device type and interface information, the system will quickly establish a connection with the device, laying a solid foundation for subsequent data acquisition and processing. This ability of automatic identification and connection greatly improves the convenience and efficiency of the system, reduces the need for manual intervention, and enables the entire system to operate more smoothly.

[0078] In the data processing step, an innovative approach that combines real-time stream processing technology and batch processing technology is adopted. The real-time stream processing technology is like a sensitive "sentry" that can perform real-time analysis on the collected data to ensure that abnormal situations and change trends in the data are detected in the first place. It can quickly respond to changes in the operating state of the device, providing a basis for timely adjustment of the energy-saving control strategy. At the same time, the batch processing technology is like a "miner" who digs deep into historical data. By mining a large amount of historical data, it finds hidden potential rules and trends. The combination of these two technologies not only ensures the timely processing of current data but also draws experience and wisdom from historical data, providing more comprehensive and accurate support for the energy-saving control of the system.

[0079] In the strategy generation step, the system fully considers the actual operating conditions of the device and user requirements. According to the actual operating conditions such as the real-time power, operating time, and environmental parameters of the device, as well as the user's requirements for energy-saving effects, device stability, etc., the energy-saving control strategy is dynamically adjusted. This is like a flexible "commander" who can formulate the most suitable combat plan according to different situations. By continuously adjusting and optimizing the energy-saving control strategy, the system can achieve the best energy-saving effect, meeting the user's needs while reducing energy consumption and operating costs.

[0080] In the visualization display step, the system demonstrates a high degree of personalization and interactivity. It supports users to customize the visualization display content and layout according to their focus of attention and business needs. Users can, like a designer, choose which device information to display, what chart form to use, and how to layout the display interface according to their preferences and actual needs. At the same time, the data drilling function provided by the system provides a convenient way for users to deeply understand device information. Users can click on the data points in the chart, just like opening mysterious doors, to obtain detailed device information and operating data. This personalized visualization display and data drilling function enable users to more intuitively and deeply understand the operating state of electrical devices, providing strong support for decision-making.

[0081] In the configuration management step, the provided configuration audit function reflects the rigor and traceability of the system. The configuration audit function will record in detail the modification operations and modification times of the configuration parameters by users. Just like a faithful "recorder", it records every modification one by one. When problems occur, users can trace and analyze through these records, quickly find out the root cause of the problems, and provide strong clues for problem-solving. This traceability not only improves the stability and reliability of the system, but also provides guarantees for users' operations, making users more at ease when performing configuration management.

[0082] As described above, it is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, with the same replacement or change, should be covered within the protection scope of the present invention.

Claims

1. An energy-saving control system for intelligent electrical equipment, characterized in that: include: Data acquisition module: used to collect real-time operating data of electrical equipment, including equipment power, operating time, and environmental parameters. Environmental parameters include temperature, humidity, and light intensity; Data analysis and processing module: connected with the data acquisition module, cleans, converts and deeply analyzes the collected data, uses big data analysis technology and machine learning algorithms to extract valuable operation information, and monitors abnormal fluctuations and trend changes of data in real time. It includes data cleaning submodule, feature extraction submodule and anomaly detection submodule. The data cleaning submodule is used to remove noise and duplicate data in the collected data; the feature extraction submodule extracts key features from the cleaned data; The anomaly detection submodule uses an anomaly detection algorithm based on deep learning to monitor the operating status of the equipment in real time and judge anomalies; Energy-saving control strategy module: Generates intelligent energy-saving control strategies based on the results of the data analysis and processing module. The strategies include adjusting equipment power, controlling equipment switch status, and optimizing equipment operation mode. The comprehensive energy-saving index EI is calculated using the following formula: Where n is the number of different types of energy-saving parameters, including power adjustment amplitude, operation mode switching frequency, P i is the power value related to the i-th energy-saving parameter, S i is the operating time factor related to the i-th energy-saving parameter, E i is the device status factor related to the i-th energy-saving parameter, u i is the weight coefficient of the i-th energy-saving parameter, g(P i , S i , E i ) is the energy-saving function of the i-th energy-saving parameter; m is the number of different environmental factors, T j is the temperature value of the jth environmental factor, H j is the humidity value of the jth environmental factor, L j is the light intensity value in the jth environmental factor, v j is the weight coefficient of the jth environmental factor, h(T j , H j , L j ) is the influence function of environmental factors. When EI reaches the optimal value, the system achieves the best energy-saving effect; Control execution module: connected to the energy-saving control strategy module, and accurately controls the electrical equipment according to the energy-saving control strategy, including a power regulation unit, a switch control unit and an operation mode adjustment unit. The power regulation unit is used to regulate the power of the electrical equipment; the switch control unit is used to control the switch state of the electrical equipment; the operation mode adjustment unit is used to adjust the operation mode of the electrical equipment to achieve energy saving; Visual display module: displays the operating status and analysis results of electrical equipment to users in the form of intuitive charts, graphs and reports. The visual display module provides multi-dimensional view switching and customized display functions. Configuration management module: used to configure and manage various parameters of the monitoring system, including device information entry, monitoring threshold setting, energy-saving strategy customization and user authority management. The configuration management module supports remote configuration and batch configuration operations, and uses the following formula to evaluate the configuration rationality index CI: Where k is the number of configuration parameters, including the number of monitoring devices, the number of energy-saving threshold settings, and the number of user permission settings. l is the actual value of the lth configuration parameter, Q l is the ideal value or industry standard value of the lth configuration parameter, ω(P l , Q l ) is the rationality function of the lth configuration parameter, including the use of absolute value function to measure the deviation between the actual value and the ideal value, ω(P l , Q l )=|P l -Q l |), R l is the importance weight of the lth configuration parameter; Intelligent prediction module: Use historical data and machine learning models to predict the future operating status of electrical equipment, including equipment failure prediction and energy consumption demand prediction. Use time series analysis and deep learning algorithms to combine the equipment's operating rules and historical data for prediction. The prediction results are presented in the form of probability and a detailed prediction report is provided. The following fault prediction probability formula FP is used: Where t is the current time, t0 is the time when the equipment last operated normally, σ is the time standard deviation calculated based on the historical failure time interval data of the equipment, which reflects the discrete degree of equipment failure time; n is the number of factors that affect equipment failure, including equipment operating temperature, humidity, load, and p i is the probability that the ith factor causes equipment failure, x i is the current state value of the i-th factor.

2. The energy-saving control system of intelligent electrical equipment according to claim 1, characterized in that: The data acquisition module has the function of automatically identifying the type and interface of electrical equipment, and dynamically adjusts the acquisition frequency according to the working status of the equipment to reduce the impact on the normal operation of the equipment.

3. The energy-saving control system of intelligent electrical equipment according to claim 1, characterized in that: The visualization module provides real-time data updates and dynamic chart display functions. Users can access monitoring data anytime and anywhere through the web interface or mobile client, and customize personalized monitoring dashboards according to their needs.

4. The energy-saving control system of intelligent electrical equipment according to claim 1, characterized in that: The configuration management module has the function of importing and exporting configuration templates, allowing users to quickly migrate configurations in different application scenarios or system upgrades. It also supports configuration version management, allowing users to roll back to historical configuration versions.

5. An energy-saving control method for intelligent electrical equipment, using the energy-saving control system for intelligent electrical equipment according to claims 1-4, characterized in that: The following steps are involved: Data collection steps: collect the operating data of electrical equipment through sensor equipment to ensure that the collection process does not affect the normal business operation of the equipment; Data processing steps: clean, convert and analyze the collected data, remove noise and duplicate data, extract key features, and use machine learning algorithms to monitor the data in real time and judge anomalies; Strategy generation step: Generate energy-saving control strategies based on data processing results, including power regulation, switch control, and operation mode optimization; Control execution steps: Control electrical equipment according to energy-saving control strategies to achieve energy-saving goals; Visual display step: Visually display the operating status and analysis results of electrical equipment in the form of charts, graphs and reports, providing users with an intuitive monitoring interface so that users can understand the overall operating status of electrical equipment in real time; Configuration management steps: configure and manage various parameters of the monitoring system, including adding, modifying and deleting device information, setting monitoring thresholds, customizing energy-saving strategies, and allocating and managing user permissions; Intelligent prediction step: Use historical data and trained machine learning models to predict the future operating status of electrical equipment, generate preventive maintenance plans and recommendations based on the prediction results, and regularly update the prediction report.

6. The energy-saving control method of intelligent electrical equipment according to claim 5, characterized in that: Before the data collection step, a device initialization step is also included, which is used to automatically identify the type and interface information of the electrical device and establish a connection with the device; In the data processing step, real-time stream processing technology is used to analyze the collected data in real time, and batch processing technology is combined to conduct in-depth analysis of historical data to explore the potential patterns and trends of the data; In the strategy generation step, the energy-saving control strategy is dynamically adjusted according to the actual operation of the equipment and user needs to achieve the best energy-saving effect; In the visualization step, users are supported to customize the visualization content and layout according to their own focus and business needs, and data drilling function is provided. Users can obtain detailed equipment information and operation data by clicking on data points in the chart. In the configuration management step, a configuration audit function is provided to record the user's modification operations and modification time of configuration parameters, which is used for tracing and analysis when problems occur.

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