Remote control method and system for intelligent constant-voltage dimming power supply

Abstract

The invention provides a remote control method and system for an intelligent constant-voltage dimming power supply, and the method comprises the steps: obtaining historical working data, environment data and power supply attribute data of a plurality of dimming power supplies through a server, and generating a plurality of control strategy models according to the historical working data, the environment data and the power supply attribute data; sending the model to a remote control terminal; the remote control terminal generates a control instruction according to the control strategy model and sends the instruction to the power supply controller; the power supply controller adjusts working parameters of the dimming power supply according to the instruction; monitoring the working state of the dimming power supply in real time, acquiring working monitoring data, and transmitting the working monitoring data to the remote control terminal; and the remote control terminal generates a new control instruction and sends the new control instruction to the power supply controller in combination with the working monitoring data and a preset dimming curve, and the power supply controller adjusts working parameters of the dimming power supply according to the new instruction. According to the scheme of the invention, through an intelligent control strategy and a dynamic adjustment mechanism, efficient, energy-saving and safe remote dimming control can be realized, and the user experience and the reliability of equipment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and in particular to a remote control method and system for an intelligent constant voltage dimming power supply. Background Art

[0002] Constant voltage dimming power supply is a type of power supply used for LED lighting. Its main feature is that it can provide a stable output voltage. Constant voltage dimming power supply controls the brightness of the LED by adjusting the output voltage. Users can adjust the voltage through the dimmer to achieve different brightness levels. This power supply is suitable for occasions where multiple LED lamps need to be connected, because it can ensure that each lamp can get the same voltage.

[0003] The existing remote control method of constant voltage dimming power supply is not intelligent enough and has low efficiency. Summary of the invention

[0004] Based on the above problems, the present invention proposes a remote control method and system for an intelligent constant voltage dimming power supply. Through intelligent control strategies and dynamic adjustment mechanisms, efficient, energy-saving and safe remote dimming control can be achieved, thereby improving user experience and equipment reliability.

[0005] In view of this, one aspect of the present invention proposes a remote control method for an intelligent constant voltage dimming power supply, comprising: The control server obtains historical working data, historical working environment data and power source attribute data of multiple dimming power sources; The control server generates a plurality of control strategy models of the dimming power supply according to the historical working data, the historical working environment data and the power supply attribute data; The control server sends the control strategy model to the corresponding remote control terminal; The remote control terminal generates a corresponding control instruction according to the control strategy model, and sends the control instruction to the corresponding power controller through the wireless communication module; The power supply controller adjusts the working parameters of the dimming power supply according to the control instruction; The working state of the dimming power supply is monitored in real time by a sensor module to obtain working monitoring data; Transmitting the work monitoring data to the remote control terminal via the wireless communication module; The remote control terminal generates a new control instruction according to the working monitoring data and the preset dimming curve, and combines the control strategy model, and sends the new control instruction to the power controller corresponding to the dimming power supply; The power supply controller adjusts the working parameters of the dimming power supply according to the new control instruction.

[0006] Optionally, the step of generating, by the control server, a plurality of control strategy models of the dimming power supply according to the historical working data, the historical working environment data and the power supply attribute data comprises: Cleaning the historical working data, the historical working environment data and the power source attribute data, removing abnormal values ​​and redundant data, and performing data standardization processing according to data types to obtain first historical working data, first historical working environment data and first power source attribute data; sorting and segmenting the first historical working data, the first historical working environment data, and the first power source attribute data in time series, and marking key characteristic parameters; Obtaining first historical power consumption data according to the first historical working data analysis; Analyzing the first historical working environment data to obtain a first correlation between environmental factors and power demand and extracting user usage habit characteristics; Establishing a device performance degradation model according to the first power source attribute data; Formulate a basic dimming curve according to the first historical power consumption data, the first correlation, the user's usage habit characteristics, and the law of natural light changes, design a lighting compensation plan in combination with the building space characteristics, and establish a mapping relationship between personnel activity rules and lighting needs to obtain a basic lighting strategy model; According to the equipment performance attenuation model, energy consumption data is analyzed to establish an efficiency optimization model, an optimal operating point is determined according to the first power source attribute data, and a dynamic power adjustment strategy is formulated to obtain an energy-saving optimization strategy model; Based on the first power source attribute data, a protection threshold is set, a temperature compensation adjustment model is established, and an overload protection control strategy is formulated to obtain a device protection strategy model; Analyze the coordination rules of multiple devices, establish environmental factor response models, and design multi-scenario switching strategies to obtain scenario linkage strategy models; The basic lighting strategy model, the energy-saving optimization strategy model, the equipment protection strategy model and the scene linkage strategy model are integrated to obtain the control strategy model.

[0007] Optionally, the step of formulating a basic dimming curve according to the first historical power consumption data, the first correlation, the user's usage habit characteristics, and the law of natural light changes, designing a lighting compensation plan in combination with building space characteristics, and establishing a mapping relationship between personnel activity laws and lighting needs to obtain a basic lighting strategy model includes: Determining a first benchmark lighting parameter based on the first historical power usage data; According to the first correlation and in combination with the law of natural light changes, a first illumination compensation model is established; Optimizing the dimming strategy according to the first reference lighting parameter, the first lighting compensation model and the user's usage habit characteristics to obtain a basic dimming curve; Analyze the characteristics of architectural space and establish a spatial light field model; According to the spatial light field model, calculating the lighting compensation parameters and establishing a spatial illumination distribution model; According to the spatial illumination distribution model, a compensation control strategy is formulated to obtain a lighting compensation scheme; Analyze the patterns of personnel activities and establish the first personnel activity prediction model; Analyze the lighting demand association according to the first personnel activity prediction model, and construct a first mapping model between personnel activity rules and lighting demand; A basic lighting strategy model is obtained according to the basic dimming curve, the lighting compensation solution and the first mapping model.

[0008] Optionally, the step of analyzing energy consumption data to establish an efficiency optimization model according to the device performance attenuation model, determining an optimal operating point according to the first power source attribute data, and formulating a dynamic power adjustment strategy to obtain an energy-saving optimization strategy model includes: Obtain energy consumption data of equipment under different working conditions, analyze the energy consumption data, identify the relationship between energy consumption and equipment performance, and obtain energy consumption analysis results; Establish an efficiency optimization model based on the equipment performance degradation model and energy consumption analysis results; Analyzing the voltage, frequency, and power factor of the power supply to determine an optimal operating point of the device according to the first power supply attribute data; Develop dynamic power regulation strategies and design control algorithms to adjust the power output of equipment in real time to ensure that the equipment operates near the optimal operating point; Integrate the results of the above steps to form an energy-saving optimization strategy model.

[0009] Optionally, the step of setting a protection threshold based on the first power source attribute data, establishing a temperature compensation adjustment model, and formulating an overload protection control strategy to obtain a device protection strategy model includes: Analyze the working characteristics of the device according to the first power source attribute data, determine the safe operating range of the device under different working conditions, and set a corresponding protection threshold; Establish a temperature compensation adjustment model, consider the impact of ambient temperature changes on equipment performance, and design a temperature sensor feedback mechanism to adjust the equipment's operating parameters in real time; Formulate overload protection control strategies, design control algorithms based on set protection thresholds and temperature compensation adjustment models to monitor the load conditions of the equipment and identify overload conditions in a timely manner; Implement equipment protection strategy model, integrate temperature compensation adjustment and overload protection control strategy, ensure that equipment can automatically adjust or cut off power supply in abnormal situations to prevent damage; The protection strategy model is verified and its effectiveness is evaluated through actual operating data to ensure that the equipment can be effectively protected under various operating conditions.

[0010] Optionally, the step of the control server sending the control strategy model to the corresponding remote control terminal includes: The control server obtains working attribute data of multiple remote control terminals; Determine the dimming power source to be controlled corresponding to each remote control terminal according to the working attribute data; Acquire second power source attribute data of the dimming power source to be controlled; A corresponding first control strategy model is determined from the plurality of control strategy models according to the second power source attribute data.

[0011] Optionally, the remote control terminal generates a corresponding control instruction according to the control strategy model, and sends the control instruction to the corresponding power controller through the wireless communication module, including: The remote control terminal obtains current working state data and current working environment data of the dimming power supply to be controlled; Adjusting the first control strategy model according to the current working state data and the current working environment data to obtain a second control strategy model; Generate a corresponding control instruction according to the second control strategy model, and send the control instruction to a power controller corresponding to the dimming power supply to be controlled through a wireless communication module.

[0012] Optionally, the step of the power controller adjusting the working parameters of the dimming power supply according to the control instruction includes: The power controller parses the control instruction and extracts the required first operating parameter and first adjustment method; Acquire first current working state data of the dimming power supply to be controlled; According to the first working parameter, the first adjustment method and the first current working state data, the required adjustment range and adjustment method are calculated to ensure that the dimming power supply can smoothly transition to the target state, and an adjustment instruction is generated according to the adjustment range and the adjustment method; Sending an adjustment instruction to the dimming power supply to be controlled to achieve the required working parameters; Monitor the response of the dimming power supply to be controlled in real time to ensure that it is adjusted according to the adjustment instruction.

[0013] Optionally, the remote control terminal generates a new control instruction according to the working monitoring data and the preset dimming curve in combination with the control strategy model and sends the new control instruction to the power controller corresponding to the dimming power supply, comprising: The remote control terminal analyzes the relationship between the working monitoring data and the preset dimming curve according to the working monitoring data and the preset dimming curve in combination with the control strategy model, and determines the required adjustment direction and amplitude; Generate new control instructions according to the required adjustment direction and amplitude; The new control instruction is sent to the power controller corresponding to the dimming power supply to be controlled via the wireless network.

[0014] Another aspect of the present invention provides a remote control system for an intelligent constant voltage dimming power supply, which is used to perform a remote control method for an intelligent constant voltage dimming power supply, characterized in that it includes: a control server, a remote control terminal and a power supply controller; The control server is configured to: Obtain historical working data, historical working environment data and power source attribute data of multiple dimming power sources; Generate a plurality of control strategy models of the dimming power supply according to the historical working data, the historical working environment data and the power supply attribute data; Sending the control strategy model to a corresponding remote control terminal; The remote control terminal is configured to: generate corresponding control instructions according to the control strategy model, and send the control instructions to the corresponding power controller through the wireless communication module; The power controller is configured to: Adjusting the working parameters of the dimming power supply according to the control instruction; The working state of the dimming power supply is monitored in real time by a sensor module to obtain working monitoring data; Transmitting the work monitoring data to the remote control terminal via the wireless communication module; The remote control terminal is further configured to: generate a new control instruction based on the working monitoring data and the preset dimming curve, and in combination with the control strategy model, and send the new control instruction to the power controller corresponding to the dimming power supply; The power supply controller is further configured to adjust the working parameters of the dimming power supply according to the new control instruction.

[0015] By adopting the technical solution of the present invention, a remote control method for an intelligent constant voltage dimming power supply includes: a control server obtains historical working data, historical working environment data and power attribute data of multiple dimming power supplies; the control server generates multiple control strategy models of the dimming power supplies according to the historical working data, the historical working environment data and the power attribute data; the control server sends the control strategy model to a corresponding remote control terminal; the remote control terminal generates a corresponding control instruction according to the control strategy model, and sends the control instruction to a corresponding power controller through a wireless communication module; the power controller adjusts the working parameters of the dimming power supply according to the control instruction; the working state of the dimming power supply is monitored in real time through a sensor module to obtain working monitoring data; the working monitoring data is transmitted to the remote control terminal through the wireless communication module; the remote control terminal generates a new control instruction according to the working monitoring data and a preset dimming curve, and in combination with the control strategy model, and sends it to the power controller corresponding to the dimming power supply; the power controller adjusts the working parameters of the dimming power supply according to the new control instruction. The solution of the present invention, by acquiring historical working data, environmental data and power source attribute data of multiple dimming power sources, the control server can generate multiple control strategy models, these models are based on the analysis of historical data, and can adapt to different working environments and user needs more accurately; the remote control terminal generates new control instructions according to the real-time working monitoring data and the preset dimming curve in combination with the control strategy model, and this dynamic adjustment capability enables the dimming power source to respond to environmental changes in real time and optimize the lighting effect; through the application of energy-saving optimization strategy models, energy consumption can be effectively reduced; users can conveniently adjust lighting settings through the remote control terminal, which improves the convenience and comfort of operation; at the same time, the system can automatically adjust the lighting according to the user's habits and environmental changes, further enhancing the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of a remote control method for an intelligent constant voltage dimming power supply provided by an embodiment of the present invention; Figure 2 The present invention is a schematic block diagram of a remote control system for an intelligent constant voltage dimming power supply provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0019] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0020] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0021] Refer to the following Figure 1 to Figure 2 A remote control method and system for an intelligent constant voltage dimming power supply provided according to some embodiments of the present invention will be described.

[0022] like Figure 1 As shown, an embodiment of the present invention provides a remote control method for an intelligent constant voltage dimming power supply, comprising: The control server obtains historical working data, historical working environment data and power source attribute data of multiple dimming power sources; In this step, the historical work data includes work demand data, work status data, work time data, etc., the historical work environment data includes natural environment data, building data, building ancillary equipment data, character activity data (such as character feature data, character operation data, character movement data, etc.), etc., and the power attribute data includes product model, specification parameters, statistical service life, maintenance cycle, product three-dimensional image data, etc.; wherein, the collection of work demand data may include: the intelligent lighting control system records the user's brightness adjustment instructions, counts the lighting demand mode in different time periods and scenes, records the timing control, scene linkage and other automation instructions, etc. The acquisition of work status data may include: collecting the historical change data of output voltage and current, recording electrical parameters such as power factor and harmonic content, counting the operating status such as fault records and maintenance records, etc. The recording of working time data may include: counting the daily power on and off time, recording the duration of different brightness levels, calculating the accumulated operating time, etc. The collection method of historical environment data: collecting natural lighting data through light sensors, recording environmental parameters using temperature and humidity sensors, obtaining static data such as building orientation and space layout, recording the linkage status of equipment such as air conditioners and curtains, collecting data such as personnel activity density and movement trajectory, etc. Sorting out power attribute data: record product models, specifications, statistics on service life, maintenance cycle, save installation location, control partition and other information, scan product 3D image data, etc. Through this step, all dimensions of equipment operation can be covered, a complete historical database can be established, and a data basis can be provided for in-depth analysis; control strategies can be customized according to specific application scenarios to adapt to the usage habits of different users and improve the accuracy of control; failure risks can be predicted through historical data analysis, maintenance plans can be optimized, and equipment service life can be extended; output can be automatically adjusted according to environmental changes to avoid energy waste and improve system operation efficiency; data-driven decision optimization can be achieved to improve the system's adaptability and reduce the need for manual intervention.

[0023] The control server generates a plurality of control strategy models of the dimming power supply according to the historical working data, the historical working environment data and the power supply attribute data; The control server sends the control strategy model to the corresponding remote control terminal; The remote control terminal generates a corresponding control instruction according to the control strategy model, and sends the control instruction to the corresponding power controller through the wireless communication module; The power supply controller adjusts the working parameters of the dimming power supply according to the control instruction; Monitor the working state of the dimming power supply in real time through the sensor module to obtain working monitoring data (including output voltage and current); In this step, a sensor module is integrated into the dimming power supply, and the module includes a voltage sensor and a current sensor, etc., which are used to collect the output voltage and current data of the dimming power supply in real time; the sensor module periodically collects the working status data of the dimming power supply, including the output voltage and current, and transmits the data to the control server or local controller; the control server or controller receives and stores the working monitoring data, and performs data preprocessing to ensure the accuracy and completeness of the data; the control server or controller analyzes the received working monitoring data to determine whether the working status of the dimming power supply is normal, including detecting whether there is overload, short circuit or other abnormal conditions; based on the analysis results, the control server or controller generates corresponding alarm information or control instructions, and adjusts the working status of the dimming power supply in real time to ensure its safe and stable operation; the monitoring results and control instructions are fed back to the user interface to provide real-time monitoring and management functions. In this step, through real-time monitoring of the sensor module, the working status of the dimming power supply can be obtained in time to ensure the safety and reliability of the system; abnormal conditions (such as overload or short circuit) can be detected in time to effectively prevent equipment damage and reduce maintenance costs; based on real-time monitoring data, the control server or controller can dynamically adjust the working parameters of the dimming power supply to improve the energy efficiency and performance of the system; providing real-time monitoring and feedback functions, so that users can better manage and control the lighting system and enhance user satisfaction.

[0024] Transmitting the work monitoring data to the remote control terminal via the wireless communication module; In this step, a wireless communication module is integrated in the dimming power supply, and the module supports wireless connection with the remote control terminal; the working state of the dimming power supply is monitored in real time, and working monitoring data, including output voltage and current, are obtained; the obtained working monitoring data is formatted to meet the requirements of the wireless communication protocol; the formatted working monitoring data is sent to the preset remote control terminal through the wireless communication module to ensure the stability and reliability of data transmission; the working monitoring data is received at the remote control terminal and decoded for subsequent data analysis and display; according to the received working monitoring data, the remote control terminal generates corresponding user interface information to provide real-time monitoring and control functions. In this step, the real-time transmission of working monitoring data can be realized through the wireless communication module, and the response speed of the system can be improved; the user can view the working state of the dimming power supply in real time on the remote control terminal, which enhances the management ability of the lighting system; the wireless communication protocol is adopted to ensure the security and integrity of the data during the transmission process, and reduce the risk of data loss or tampering; the application of wireless communication makes the system wiring more flexible and convenient for deployment and expansion in different scenarios.

[0025] The remote control terminal generates a new control instruction according to the working monitoring data and the preset dimming curve, and combines the control strategy model, and sends the new control instruction to the power controller corresponding to the dimming power supply; The power supply controller adjusts the working parameters of the dimming power supply according to the new control instruction.

[0026] By adopting the technical solution of this embodiment, the control server can generate multiple control strategy models by acquiring historical working data, environmental data and power attribute data of multiple dimming power supplies. These models are based on the analysis of historical data and can adapt to different working environments and user needs more accurately; the remote control terminal generates new control instructions based on real-time working monitoring data and preset dimming curves in combination with the control strategy model. This dynamic adjustment capability enables the dimming power supply to respond to environmental changes in real time and optimize the lighting effect; through the application of energy-saving optimization strategy models, energy consumption can be effectively reduced; users can conveniently adjust lighting settings through the remote control terminal, which improves the convenience and comfort of operation; at the same time, the system can automatically adjust the lighting according to user habits and environmental changes, further enhancing the user experience.

[0027] In some possible implementations of the present invention, the step of generating, by the control server, a plurality of control strategy models of the dimming power supply according to the historical working data, the historical working environment data and the power supply attribute data comprises: Cleaning the historical working data, the historical working environment data and the power source attribute data, removing abnormal values ​​and redundant data, and performing data standardization processing according to data types to obtain first historical working data, first historical working environment data and first power source attribute data; In this step, the cleaning and standardization of historical working data include: abnormal value processing (including: setting the effective range threshold of output voltage and current; identifying data points beyond the normal working range; using the median or moving average to replace abnormal values; marking and recording the time and reason of abnormal data generation, etc.), redundant data processing (including identifying repeated sampling data records, merging multiple records with the same timestamp, deleting invalid or null value data, etc.), standardization processing (including normalizing voltage values ​​to the range of 0-1, linearly transforming current data, unifying the time data format, scaling power data, etc.); the cleaning and standardization of historical working environment data include: abnormal value processing (including eliminating abnormal readings of light sensors, filtering mutation values ​​in temperature and humidity data, correcting human error, etc.), and standardization processing (including removing abnormal readings of light sensors, filtering mutation values ​​in temperature and humidity data, correcting human error, etc.). The cleaning and standardization of power attribute data include: abnormal value processing (including checking parameter unit consistency, correcting unreasonable parameter values, supplementing missing attribute information, etc.), redundant data processing (including merging repeated environmental monitoring records, screening effective scene status data, removing repeated equipment status information, etc.), standardization processing (including normalization of light intensity data, linear transformation of temperature data, unification of humidity data range, standardization of personnel density data, etc.); the cleaning and standardization of power attribute data include: abnormal value processing (including checking parameter unit consistency, correcting unreasonable parameter values, supplementing missing attribute information, etc.), redundant data processing (including merging repeated equipment information, updating outdated parameter records, deleting invalid configuration data, etc.), standardization processing (including unifying power parameter units, normalizing model specification information, standardizing performance parameter range, etc.). Through this step, data reliability can be improved, data noise can be reduced, data consistency can be enhanced, data processing time can be reduced, storage space utilization can be optimized, model training speed can be accelerated, the impact of abnormal values ​​can be reduced, pattern recognition accuracy can be improved, correlation analysis effect can be enhanced, the risk of wrong decision-making can be reduced, control stability can be improved, fault diagnosis ability can be enhanced, model convergence speed can be improved, generalization ability can be enhanced, prediction accuracy can be improved, data storage space can be reduced, data management process can be simplified, and troubleshooting efficiency can be improved. This data cleaning and standardization method can not only ensure data quality, but also significantly improve the effectiveness of subsequent analysis and control.

[0028] sorting and segmenting the first historical working data, the first historical working environment data, and the first power source attribute data in time series, and marking key characteristic parameters; In this step, the time series sorting process includes: timestamp standardization (such as unifying the time format to UTC standard time, correcting the data timestamps in different time zones, supplementing the data with missing timestamps, etc.), data time sequence rearrangement (such as reorganizing data in chronological order, establishing a unified time index, processing duplicate timestamp data, etc.), etc. Data segmentation process includes: time dimension segmentation (such as dividing daily data segments according to a 24-hour cycle, dividing weekly data segments according to a 7-day cycle, dividing quarterly data segments according to seasonal changes, dividing type data segments according to working days / holidays, etc.), work feature segmentation (such as dividing work intervals according to load levels, dividing operation stages according to work modes, dividing operation cycles according to control instructions, dividing maintenance cycles based on fault records, etc.), environmental feature segmentation (such as dividing sunshine periods according to light intensity, dividing environmental intervals according to temperature changes, dividing activity cycles according to personnel density, dividing usage stages based on scene changes, etc.), etc. Key feature parameter markings include: working data feature markings (such as marking peak power points, marking energy efficiency optimum points, marking fault occurrence points, marking maintenance time points, marking dimming response characteristics, etc.), environmental data feature markings (such as marking illumination mutation points, marking temperature critical points, marking peak traffic density, marking scene switching nodes, marking equipment linkage moments, etc.), power attribute feature markings (such as marking performance inflection points, marking protection thresholds, marking efficiency curve features, marking life cycle nodes, marking parameter drift trends, etc.), etc. Through this step, data retrieval efficiency can be improved, storage structure can be optimized, data update and maintenance can be facilitated, periodic laws can be discovered, anomaly detection accuracy can be improved, trend prediction capabilities can be enhanced, data processing speed can be accelerated, computing resource consumption can be reduced, feature extraction efficiency can be improved, strategy matching can be improved, real-time response capabilities can be enhanced, control parameter adjustment can be optimized, scene recognition accuracy can be improved, predictive control effects can be enhanced, resource allocation efficiency can be optimized, fault warning accuracy can be improved, maintenance plan formulation can be optimized, and equipment service life can be extended. This method of data processing and feature marking based on time series can effectively extract key information from data and provide a reliable foundation for subsequent model training and control strategy optimization.

[0029] Obtaining first historical power consumption data according to the first historical working data analysis; In this step, the analysis of the first historical work data includes time dimension analysis, load characteristic analysis, usage behavior analysis, etc. Among them, the time dimension analysis includes: daily cycle law analysis (such as statistical power consumption curve within 24 hours, identifying peak and valley periods, extracting daytime lighting demand change characteristics, establishing time period power consumption probability model, etc.), periodic law analysis (such as analyzing weekday / weekend power consumption differences, identifying weekly cycle power consumption patterns, extracting power consumption characteristics in special periods, establishing periodic load prediction models, etc.), seasonal law analysis (such as analyzing power consumption characteristics in different seasons, identifying seasonal load changes, extracting light cycle impact characteristics, establishing seasonal compensation models, etc.); load characteristic analysis includes: power change law (including analyzing power fluctuation range, identifying typical power levels, extracting power change rate, establishing power prediction models, etc.), energy efficiency characteristic analysis. Analysis (including calculating the efficiency under different loads, identifying the optimal working point, extracting the loss change law, establishing an energy efficiency optimization model, etc.), dimming characteristic analysis (including analyzing the dimming depth distribution, identifying the dimming response characteristics, extracting the dimming stability index, establishing the dimming control model, etc.); usage behavior analysis includes: operation mode analysis (including statistical dimming operation frequency, identifying common brightness levels, extracting manual intervention characteristics, establishing user habit models, etc.), scene demand analysis (including analyzing the illumination requirements of different scenes, identifying the scene switching rules, extracting scene related characteristics, establishing scene preset models, etc.), abnormal behavior analysis (including identifying abnormal power consumption patterns, analyzing sudden load changes, extracting fault-related characteristics, establishing abnormal detection models, etc.). Through this step, the accuracy of power demand prediction can be improved, the load prediction model can be optimized, the scene prediction capability can be enhanced, unnecessary energy consumption can be reduced, the operation working point can be optimized, the overall efficiency of the system can be improved, the dimming control accuracy can be improved, the scene switching strategy can be optimized, the adaptive adjustment capability can be enhanced, it is more in line with usage habits, the frequency of manual intervention can be reduced, the lighting comfort can be improved, potential problems can be warned in advance, the maintenance plan can be optimized, the failure rate can be reduced, the operating energy consumption can be reduced, the equipment service life can be extended, and the maintenance cost can be reduced. This multi-dimensional electricity consumption pattern analysis method can fully grasp the system operation characteristics and provide strong support for realizing intelligent control.

[0030] Analyzing the first historical working environment data to obtain a first correlation between environmental factors and power demand and extracting user usage habit characteristics; In this step, analyzing according to the first historical working environment data includes: (1) Correlation analysis of environmental factors, including: light intensity correlation analysis (including calculation of the correlation coefficient between natural light and lighting demand, establishment of a light compensation curve model, analysis of the lighting effects of different orientations, extraction of the effects of shading facilities, etc.), temperature environment correlation analysis (including analysis of the relationship between temperature and equipment efficiency, establishment of a temperature compensation model, extraction of seasonal temperature differences, identification of temperature critical point characteristics, etc.), spatial environment correlation analysis (including analysis of the effects of building layout, extraction of spatial zoning characteristics, establishment of a multi-region linkage model, identification of spatial shading effects, etc.); (2) User activity correlation analysis, including: crowd density correlation (including analyzing the relationship between personnel density and lighting demand, establishing a crowd prediction model, extracting activity pattern characteristics, identifying the impact of special events, etc.), equipment linkage correlation (including analyzing the association with the air-conditioning system, extracting the relationship with curtain control, establishing an equipment coordination model, identifying equipment interference characteristics, etc.); (3) User usage habit feature extraction, including: operation behavior analysis (including statistical dimming operation time distribution, extraction of common brightness preferences, analysis of adjustment frequency characteristics, establishment of user preference models, etc.), scene preference analysis (identification of common scene combinations, extraction of scene switching rules, analysis of special demand characteristics, establishment of scene configuration models, etc.), abnormal behavior identification (detection of atypical operation modes, extraction of temporary demand characteristics, analysis of complaint feedback records, establishment of abnormal response models, etc.).

[0031] Through this step, we can improve environmental adaptability, optimize lighting compensation effects, enhance scene switching accuracy, reduce invalid lighting time, optimize energy utilization efficiency, reduce operating energy consumption, be more in line with usage habits, improve lighting comfort, reduce the need for manual intervention, improve environmental adaptability, enhance anti-interference performance, reduce the risk of misoperation, achieve intelligent scene linkage, optimize adaptive adjustment, improve predictive control effects, optimize resource allocation, improve fault diagnosis capabilities, and simplify operation and maintenance management processes. This analysis method based on environmental factors and user habits can achieve more accurate intelligent control and greatly improve system performance and user satisfaction.

[0032] Establishing a device performance degradation model according to the first power source attribute data; In this step, establishing the equipment performance attenuation model according to the first power source attribute data includes performing core performance parameter analysis, life characteristic modeling, attenuation model construction, etc. according to the first power source attribute data. The specific method is: (1) Analysis of core performance parameters, including: electrical performance parameter tracking (including recording output voltage deviation changes, monitoring output current fluctuation trends, analyzing power factor attenuation laws, tracking harmonic content changes, etc.), dimming performance parameter analysis (including evaluating dimming depth changes, monitoring dimming response time, analyzing dimming accuracy deviations, recording dimming stability, etc.), thermal performance parameter analysis (including monitoring operating temperature changes, evaluating heat dissipation efficiency declines, analyzing temperature rise characteristics changes, recording over-temperature protection frequency, etc.); (2) Life characteristic modeling, including: cumulative operating time analysis (including statistics of actual working hours, calculation of the proportion of time under different loads, evaluation of overload operating time, recording of extreme working conditions, etc.), working environment impact analysis (including evaluation of temperature impact coefficient, analysis of humidity impact factors, calculation of environmental stress accumulation, establishment of environmental compensation model, etc.), usage intensity analysis (including statistics of switching frequency, evaluation of the impact of dimming times, analysis of load change impact, calculation of stress accumulation effect, etc.); (3) Construction of attenuation models, including: key component attenuation modeling (including establishing capacitor aging models, analyzing power device attenuation, evaluating optocoupler characteristic changes, predicting protection circuit performance, etc.), comprehensive performance attenuation model (including establishing efficiency attenuation curves, building reliability prediction models, analyzing failure probability distribution, predicting maintenance cycles, etc.), and compensation strategy formulation (including automatic compensation of design parameters, formulating performance balance strategies, establishing protection threshold adjustments, optimizing maintenance plans, etc.).

[0033] Through this step, potential faults can be predicted in advance, the risk of sudden faults can be reduced, the service life of the equipment can be extended, stable output characteristics can be maintained, high working efficiency can be maintained, dimming quality can be ensured, predictive maintenance can be achieved, maintenance schedule can be optimized, maintenance costs can be reduced, control parameters can be dynamically adjusted, protection strategies can be optimized, system adaptability can be improved, efficiency degradation can be delayed, working point selection can be optimized, energy loss can be reduced, replacement frequency can be reduced, and operation and maintenance costs can be reduced. This performance degradation modeling method based on multi-dimensional parameters can effectively predict and compensate for changes in equipment performance and improve system reliability and economy.

[0034] Formulate a basic dimming curve according to the first historical power consumption data, the first correlation, the user's usage habit characteristics, and the law of natural light changes, design a lighting compensation plan in combination with the building space characteristics, and establish a mapping relationship between personnel activity rules and lighting needs to obtain a basic lighting strategy model; According to the equipment performance attenuation model, energy consumption data is analyzed to establish an efficiency optimization model, an optimal operating point is determined according to the first power source attribute data, and a dynamic power adjustment strategy is formulated to obtain an energy-saving optimization strategy model; Based on the first power source attribute data, a protection threshold is set, a temperature compensation adjustment model is established, and an overload protection control strategy is formulated to obtain a device protection strategy model; Analyze the coordination rules of multiple devices, establish environmental factor response models, and design multi-scenario switching strategies to obtain scenario linkage strategy models; In this step, the working data and environmental factor data of multiple devices (such as other dimming power supplies, other power supplies, other electrical equipment, etc.) are collected, including the operating status of the equipment, ambient temperature, humidity, light and other information; the collected data is preprocessed to ensure the integrity and accuracy of the data and remove noise and outliers; data analysis algorithms are applied to analyze the coordination rules among multiple devices and identify the mutual influence and dependency between devices; an environmental factor response model is established, and machine learning or statistical analysis methods are used to evaluate the impact of environmental factors on device performance and collaborative work; a multi-scenario switching strategy is designed, and corresponding scene switching rules are formulated according to different environmental conditions and device states; the analysis results are integrated to generate a scene linkage strategy model to clarify the collaborative work strategy of each device under specific environmental conditions; the scene linkage strategy model is applied to the actual system for verification and optimization to ensure its effectiveness and reliability. In this step, by analyzing the coordination rules of multiple devices, we can optimize the collaboration between devices and improve the working efficiency of the overall system; establish an environmental factor response model so that the system can automatically adjust the working status of the equipment according to environmental changes and improve adaptability; design a multi-scenario switching strategy to achieve intelligent equipment control, reduce manual intervention, and improve user experience; by optimizing the collaborative work of equipment, we can effectively reduce energy consumption and improve energy utilization efficiency.

[0035] The basic lighting strategy model, the energy-saving optimization strategy model, the equipment protection strategy model and the scene linkage strategy model are integrated to obtain the control strategy model.

[0036] In this step, the basic lighting strategy model is mainly responsible for providing basic lighting needs, the energy-saving optimization strategy model focuses on reducing energy consumption, the equipment protection strategy model ensures the safe operation of the equipment, and the scene linkage strategy model handles the lighting needs in different scenes; collect data related to each model, including ambient light intensity, equipment status, user needs, etc. These data will be used for model input and adjustment; select appropriate fusion methods, such as weighted average, decision tree, neural network, etc. According to the importance and priority of different models, set the corresponding weights; design a fusion algorithm to integrate the outputs of each strategy model (machine learning algorithms can be used to optimize the fusion process to ensure that the final control strategy model can comprehensively consider the needs of all aspects); verify the fused control strategy model and evaluate its effect using actual operation data. Make necessary adjustments and optimizations based on feedback to improve the accuracy and reliability of the model; apply the fused control strategy model to the actual system, monitor its operation effect in real time, and make dynamic adjustments based on environmental changes and user feedback. Through this step, each strategy model can be effectively fused into a comprehensive control strategy model, thereby realizing a more intelligent and efficient lighting control system.

[0037] Through the solution of this embodiment, accurate lighting adjustment can be achieved, the system response speed can be improved, and control shock can be reduced; the system has self-learning ability, dynamic optimization of control strategy, and stronger adaptability; ineffective lighting can be avoided, energy utilization can be optimized, and operating costs can be reduced; equipment overload can be effectively prevented, service life can be extended, and failure rate can be reduced; lighting effects are more natural, operation is more convenient, and satisfaction is improved; it is easy to add new control strategies, support multi-scenario applications, and facilitate system upgrades. This control strategy model generation method based on multi-dimensional data analysis can not only achieve accurate intelligent control, but also continuously optimize system performance.

[0038] In some possible implementations of the present invention, the step of formulating a basic dimming curve according to the first historical power consumption data, the first correlation, the user's usage habit characteristics, and the law of natural light changes, designing a lighting compensation plan in combination with building space characteristics, and establishing a mapping relationship between personnel activity laws and lighting needs to obtain a basic lighting strategy model includes: Based on the first historical electricity consumption data, determine the first benchmark lighting parameters (including analyzing the characteristics of the 24-hour electricity consumption curve, extracting the variation law of the typical day lighting load, establishing a benchmark lighting power curve, determining the lighting threshold during the key period, etc.); In this step, the first historical electricity consumption data of the target area covers at least 24 hours of electricity consumption records to obtain the characteristics of the electricity consumption curve; the first historical electricity consumption data is analyzed to extract the peak and valley periods of electricity consumption within 24 hours and identify the electricity consumption pattern and characteristics; the lighting load change law of a typical day is analyzed to determine the lighting demand in different time periods, including the electricity consumption characteristics in the morning, daytime and night; a benchmark lighting power curve is established, and the lighting power demand curve of different time periods is drawn based on the analysis results to reflect the actual lighting load; the lighting threshold of the key time period is determined, and the upper and lower limits of the lighting power in a specific time period are set to ensure the efficient operation of the lighting system; the above-obtained data is used as the first benchmark lighting parameter. In this step, by analyzing the historical electricity consumption law, more reasonable lighting parameters can be formulated to improve the management efficiency of the lighting system; the benchmark lighting power curve based on the actual electricity demand helps to reduce unnecessary energy consumption and achieve energy saving; through reasonable lighting threshold setting, a suitable lighting environment can be provided in different time periods to improve the comfort and satisfaction of users; combined with historical electricity consumption data, the intelligent adjustment of the lighting system can be realized to adapt to changes in different scenarios and needs.

[0039] According to the first correlation and in combination with the law of natural light changes, a first light compensation model is established (including analyzing the changes in sunshine duration throughout the year, establishing a natural light intensity prediction model, calculating the light supplement demand at different time periods, generating a dynamic light supplement curve, etc.); In this step, the data of sunshine duration changes throughout the year are collected and analyzed to determine the sunshine duration characteristics of different seasons and months; a natural light intensity prediction model is established to use meteorological data (such as cloud cover, humidity, temperature, etc.) and historical sunshine data to predict the natural light intensity in a specific time period; the fill light demand in different time periods is calculated, and the amount of fill light required in different time periods is determined based on the natural light intensity prediction results and indoor lighting standards, combined with the first correlation between environmental factors and electricity demand; a dynamic fill light curve is generated to associate the fill light demand with the change in natural light intensity to form a dynamic fill light strategy to facilitate real-time adjustment of the lighting system; the first light compensation model is applied to the lighting control system to realize intelligent light adjustment to ensure the comfort and energy efficiency of the indoor light environment. In this step, by establishing a light compensation model, the indoor lighting can be dynamically adjusted according to the change in natural light to improve the energy efficiency of the lighting system; the fill light demand is accurately calculated to reduce unnecessary power consumption and achieve energy saving goals; according to the change in natural light intensity, appropriate indoor lighting is provided to improve the comfort and satisfaction of users; combined with the law of natural light changes, the lighting system is intelligently adjusted to meet the needs of different time periods and environmental conditions.

[0040] According to the first reference lighting parameter, the first lighting compensation model and the user's usage habit characteristics, the dimming strategy is optimized (such as extracting user brightness preference data, analyzing the time rule of dimming operation, establishing a user habit response model, generating a personalized adjustment curve, etc.), and a basic dimming curve is obtained; In this step, the user brightness preference data and the user's lighting usage records in different time periods are extracted from the user's usage habit characteristics, and the user's preferences for different brightness levels are analyzed; the dimming operation time pattern is analyzed, the time points when the user performs dimming operations in a day are recorded, and the user's dimming habits and high-frequency operation periods are identified; a user habit response model is established, and a model is constructed using the extracted user brightness preference data and dimming operation time pattern to predict the user's dimming needs under specific environmental conditions; a personalized adjustment curve is generated, and the user habit response model is combined with the first reference lighting parameter and the first illumination compensation model to form a personalized dimming strategy; the basic dimming curve is applied to the lighting control system to realize intelligent adjustment of the lighting brightness to meet the user's personalized needs. In this step, by analyzing the user's brightness preference and dimming habits, a personalized lighting solution that better meets the user's needs can be provided to improve user satisfaction; optimizing the dimming strategy can effectively reduce unnecessary energy consumption and improve the energy efficiency of the lighting system; dynamically adjust the lighting brightness according to the user's usage habits and environmental changes to ensure the user's comfortable experience in different scenarios; combine user habits and environmental factors to realize intelligent management of the lighting system and improve the automation level of the system.

[0041] Analyze the characteristics of architectural space (such as measuring spatial geometric dimensions, evaluating surface reflection characteristics, analyzing the layout of daylighting windows, etc.) and establish a spatial light field model; In this step, the geometric dimensions of the building space, including the length, width, and height of the room, are measured to obtain the basic shape and volume information of the space; the reflective characteristics of the building surface are evaluated, and the reflectivity data of different surface materials (such as walls, floors, and ceilings) are obtained using optical measuring instruments; the layout of the skylights is analyzed, and the number, size, location, and orientation of the windows are recorded to evaluate the entry of natural light; the previous data are integrated to establish a three-dimensional model of the building space to reflect the geometric characteristics and optical properties of the space; based on the three-dimensional model, a spatial light field model is constructed to simulate the propagation and distribution of light in space, taking into account optical phenomena such as reflection, refraction, and scattering; the accuracy of the spatial light field model is verified, and the model parameters are adjusted to improve the reliability of the model by comparing it with the actual lighting measurement results. In this step, by establishing a spatial light field model, the light distribution inside the building can be accurately analyzed to provide a scientific basis for lighting design. By evaluating the surface reflection characteristics and the layout of the skylights, optimization suggestions can be provided for the building design to improve the utilization rate of natural lighting. Through a reasonable light field model, the demand for artificial lighting can be reduced, energy consumption can be reduced, and the energy efficiency of the building can be improved. The lighting conditions can be optimized to improve the comfort and functionality of the indoor environment and enhance the user experience.

[0042] According to the spatial light field model, calculate lighting compensation parameters (such as evaluating the natural light attenuation coefficient, calculating the impact of spatial occlusion, analyzing the contribution of reflected light, etc.), and establish a spatial illumination distribution model; In this step, based on the established spatial light field model, the natural light attenuation coefficient is evaluated, and the attenuation characteristics of natural light in space are analyzed, taking into account factors such as the height of the light source, the orientation of the window, and the external environmental conditions; the spatial occlusion effect is calculated, the effect of obstructions in the space (such as furniture, walls, etc.) on natural light illumination is identified and quantified, and the specific degree of influence of obstructions on light distribution is determined; the contribution of reflected light is analyzed, the reflectivity of different surfaces in the space (such as walls, floors, and ceilings) is measured and calculated, and the contribution of reflected light to indoor illumination is evaluated; the previous calculation results are integrated to generate lighting compensation parameters to ensure that lighting needs in different time periods are met; based on the above lighting compensation parameters, a spatial illumination distribution model is established to simulate the illumination distribution under different light sources and natural light conditions; the accuracy of the spatial illumination distribution model is verified through actual measurement data, and the model parameters are adjusted to improve its reliability and applicability. In this step, by calculating the lighting compensation parameters, the indoor lighting can be accurately adjusted to adapt to changes in natural light and ensure the stability of the indoor light environment; establishing a spatial illumination distribution model helps to optimize the lighting design and improve the uniformity and comfort of lighting; through a reasonable lighting compensation strategy, unnecessary artificial lighting can be reduced, energy consumption can be reduced, and energy-saving goals can be achieved; indoor lighting conditions can be improved to enhance the user's visual comfort and overall experience.

[0043] According to the spatial illumination distribution model, a compensation control strategy is formulated (such as dividing lighting control zones, designing zone linkage rules, establishing a compensation control algorithm, optimizing transition response characteristics, etc.) to obtain a lighting compensation plan; In this step, according to the results of the spatial illumination distribution model, the indoor space is divided into multiple lighting control zones to facilitate independent control of the lighting needs of different areas; the zone linkage rules are designed to determine the linkage relationship between the lighting control zones to ensure that the lighting adjustment in one area can properly affect the lighting status of the adjacent areas to achieve the coordination of the overall lighting effect; a compensation control algorithm is established, and based on the spatial illumination distribution model and the zone linkage rules, a control algorithm is developed to calculate the compensation lighting intensity required for each zone and realize dynamic adjustment; the transition response characteristics are optimized to ensure that the system can smoothly transition during the lighting adjustment process, avoid abrupt brightness changes, improve the user's visual comfort, and obtain a compensation control strategy. In this step, by dividing the lighting control zones, the lighting can be flexibly adjusted according to the actual needs of different areas to improve the adaptability of the system; the zone linkage rules and compensation control algorithms are designed to achieve a more uniform and comfortable lighting effect and reduce the phenomenon of uneven lighting; the transition response characteristics are optimized to ensure a smooth transition during the lighting adjustment process and improve the user's visual comfort and satisfaction; through precise compensation control strategies, unnecessary lighting energy consumption is reduced to achieve energy saving goals.

[0044] Analyze the patterns of personnel activities (including statistical changes in crowd density, identification of activity time characteristics, analysis of movement trajectory patterns, etc.) and establish the first personnel activity prediction model; In this step, data on the number of people in a specific area is collected through sensors or monitoring equipment, and the trend of changes in crowd density in different time periods is analyzed; activity time characteristics are identified, and data mining technology is used to analyze the activity frequency and duration of people in a specific time period, and peak activity periods and trough activity periods are extracted; movement trajectory patterns are analyzed, and positioning technology (such as GPS or Wi-Fi positioning) is used to collect movement trajectory data of people, and common movement paths and stop points are identified; the data in the previous steps are integrated to build a personnel activity feature database to record the activity patterns of people in different time periods and different areas; based on the activity feature database, a first personnel activity prediction model is established, and a machine learning algorithm (such as regression analysis, decision tree or neural network) is used to model personnel activities and predict future personnel activity trends; the prediction model is verified and optimized, and the model parameters are adjusted by comparing with actual personnel activity data to improve the accuracy and reliability of the prediction. In this step, through the comprehensive analysis of crowd density, activity time characteristics and movement trajectory, the prediction accuracy of personnel activities can be significantly improved; the prediction model can provide data support for managers, help optimize personnel configuration and resource allocation, and improve the operational efficiency of the venue; through real-time monitoring and prediction of personnel activities, abnormal situations can be identified in time and the safety management capabilities of the venue can be enhanced; based on the prediction results, environmental settings (such as lighting, temperature, etc.) can be adjusted to improve the overall user experience.

[0045] According to the first personnel activity prediction model, analyze the lighting demand association (including calculating the activity-lighting correlation, establishing a demand response model, designing a dynamic adjustment strategy, optimizing scene switching rules, etc.), and construct a first mapping model between personnel activity rules and lighting demand (including establishing a multi-dimensional mapping matrix, designing a model training algorithm, optimizing a parameter adaptation mechanism, verifying model reliability, etc.); In this step, the activity-lighting correlation is calculated, and the influence of different activity types on lighting demand is quantified by analyzing the relationship between personnel activity data and lighting usage data; a demand response model is established, and a model is constructed based on the activity-lighting correlation to predict changes in lighting demand under different personnel activity conditions; a dynamic adjustment strategy is designed, and a dynamic adjustment strategy for the lighting system is formulated according to the output of the demand response model to adapt to real-time changes in personnel activities; scene switching rules are optimized, and switching conditions and rules for different lighting scenes are defined to achieve intelligent management of the lighting system; a first mapping model between personnel activity rules and lighting demand is constructed, and the results of the previous output are integrated to form a comprehensive model; a multi-dimensional mapping matrix is ​​established to multi-dimensionally associate personnel activity characteristics with lighting demand characteristics to form a data structure to support subsequent analysis; a model training algorithm is designed, and machine learning technology is used to train the mapping model to improve the model's predictive ability and adaptability; the parameter adaptive mechanism is optimized to ensure that the model can automatically adjust parameters under different environments and conditions to maintain efficient prediction performance; the model reliability is verified, and the accuracy and stability of the model are evaluated by comparing with actual lighting demand data, and the model is adjusted when necessary. In this step, by analyzing the relationship between personnel activities and lighting needs, the intelligent adjustment of the lighting system can be realized and the system's automatic management capabilities can be improved; the optimization of dynamic adjustment strategies and scene switching rules will help to reasonably allocate lighting resources under different activity situations and reduce energy consumption.

[0046] A basic lighting strategy model is obtained according to the basic dimming curve, the lighting compensation solution and the first mapping model.

[0047] In this step, the parameters of the basic dimming curve, lighting compensation scheme and the first mapping model are integrated to form a comprehensive basic lighting strategy model; this step needs to ensure that the parameters between the various models can work together effectively; based on the integrated model, the control algorithm is designed and optimized to achieve intelligent control of the lighting system. The algorithm should be able to dynamically adjust the lighting output according to real-time data to ensure that user needs are met; the effectiveness of the basic lighting strategy model is tested through practical applications, feedback data is collected and analyzed, and the model is adjusted and optimized when necessary to improve its accuracy and adaptability. Through this step, an efficient basic lighting strategy model can be realized, which can dynamically adjust the lighting according to environmental changes and user needs, and improve the intelligence level of the lighting system and user experience.

[0048] Through the solution of this embodiment, it is possible to improve illumination uniformity, reduce response delay, enhance dimming stability, reduce overall energy consumption, reduce ineffective lighting, reduce peak power, optimize user experience, reduce failure rate and false trigger rate, improve system stability, reduce maintenance costs, shorten fault handling time, improve operation and maintenance efficiency, improve scene recognition accuracy, improve predictive control accuracy, and enhance adaptability. This basic lighting strategy model construction method based on multidimensional data analysis achieves precise lighting control through systematic steps, significantly improving system performance and user experience.

[0049] In some possible implementations of the present invention, the steps of analyzing energy consumption data to establish an efficiency optimization model according to the device performance attenuation model, determining an optimal operating point according to the first power source attribute data, and formulating a dynamic power adjustment strategy to obtain an energy-saving optimization strategy model include: Obtain energy consumption data of equipment under different working conditions, analyze the energy consumption data, identify the relationship between energy consumption and equipment performance, and obtain energy consumption analysis results; In this step, the energy consumption data of the equipment (such as power supply, other power-consuming equipment, etc.) under different working conditions are obtained, and the power consumption of the equipment under various operating modes is recorded using energy consumption monitoring equipment or sensors; an equipment performance indicator system is established, and key indicators related to equipment performance, such as output power, work efficiency, failure rate, etc., are defined for subsequent analysis; energy consumption data is correlated with equipment performance data, and statistical analysis methods (such as regression analysis, correlation analysis, etc.) are used to identify the relationship between energy consumption and equipment performance; a visual report on the relationship between energy consumption and equipment performance is generated, and charts or models are used to display the energy consumption characteristics under different working conditions and their impact on equipment performance; based on the analysis results, optimization suggestions are put forward to improve the energy efficiency and performance of the equipment, and a feedback mechanism is formed to continuously monitor and optimize equipment operation. In this step, by identifying the relationship between energy consumption and equipment performance, working conditions with excessive energy consumption can be effectively discovered, so that measures can be taken to optimize equipment operation and reduce energy consumption; by analyzing energy consumption data, key factors affecting equipment performance can be identified, and targeted improvements can be made to improve the overall performance of the equipment.

[0050] Based on the equipment performance degradation model and energy consumption analysis results, establish an efficiency optimization model (using statistical analysis or machine learning methods to identify potential measures to improve equipment efficiency); In this step, according to the equipment performance attenuation model, analyze the changing trend of equipment performance over time; perform energy consumption analysis, obtain energy consumption data of the equipment under different working conditions, and identify the relationship between energy consumption and equipment performance; use statistical analysis methods or machine learning algorithms (such as regression analysis, decision trees, random forests, etc.) to comprehensively analyze the equipment performance attenuation model and energy consumption analysis results, and identify the key factors affecting equipment efficiency; establish an efficiency optimization model, and based on the analysis results, propose potential measures to improve equipment efficiency, including but not limited to adjusting operating parameters, optimizing maintenance strategies, and improving equipment design; verify the efficiency optimization model, test the effectiveness of optimization measures through practical applications, collect feedback data and analyze them to continuously improve the model. In this step, by establishing an efficiency optimization model, effective optimization measures can be identified and implemented, thereby significantly improving the working efficiency of the equipment; the implementation of optimization measures will directly lead to a reduction in energy consumption, thereby reducing operating costs and improving economic benefits.

[0051] Analyzing the voltage, frequency, and power factor of the power supply to determine an optimal operating point of the device according to the first power supply attribute data; In this step, the first power attribute data includes key parameters such as power voltage, frequency and power factor; according to the power attribute data, analyze the power voltage characteristics, evaluate the impact of voltage fluctuations on equipment performance, and determine the working efficiency of the equipment at different voltage levels; analyze the power frequency characteristics, determine the impact of frequency changes on equipment operation, and identify the optimal working state of the equipment under different frequency conditions; evaluate the power factor, calculate the energy efficiency of the equipment under different power factors, and determine the degree of influence of the power factor on equipment performance; combine the above analysis results, establish a relationship model between equipment performance and power attributes, and identify the optimal working point of the equipment. In this step, by determining the optimal working point, the operating efficiency of the equipment can be significantly improved and energy consumption can be reduced; analyzing the power attribute data helps to better utilize power resources and ensure that the equipment operates under optimal conditions.

[0052] Develop dynamic power regulation strategies and design control algorithms to adjust the power output of equipment in real time to ensure that the equipment operates near the optimal operating point; In this step, analyze the working characteristics and performance requirements of the equipment, determine the power output requirements of the equipment under different working conditions; collect real-time operation data, including the current power output of the equipment, load changes, environmental conditions and other information, to facilitate subsequent dynamic adjustments; establish a power regulation model, use historical data and real-time data to analyze the power response characteristics of the equipment under different conditions; design a control algorithm, and use adaptive control or fuzzy control and other methods to ensure that the algorithm can respond to changes in the power demand of the equipment in real time; implement a dynamic power regulation strategy, set the upper and lower limits of the power output, and adjust the power output of the equipment according to real-time data to ensure that the equipment always operates near the optimal working point. In this step, by dynamically adjusting the power output, the equipment is ensured to operate near the optimal working point, thereby significantly improving energy efficiency and reducing energy consumption; real-time adjustment of power output can effectively cope with load fluctuations and improve the operating stability and reliability of the equipment.

[0053] Integrate the results of the above steps to form an energy-saving optimization strategy model.

[0054] In this step, the data and analysis results obtained in the previous steps are integrated, including equipment performance data, energy consumption analysis results, dynamic power adjustment strategies, etc., to ensure data integrity and consistency; an energy-saving optimization strategy model framework is established to define the model's input variables (such as equipment operating status, environmental conditions, load changes, etc.) and output variables (such as optimized power output, energy efficiency indicators, etc.); statistical analysis or machine learning algorithms are applied to model the integrated data, identify key factors affecting equipment energy efficiency, and generate corresponding optimization strategies; specific operation and management strategies are formulated, including equipment operating parameter adjustments, maintenance plans, load management, etc., to guide the optimal operation of the equipment under different working conditions; the energy-saving optimization strategy model is verified, and the effectiveness of the model is evaluated through actual operation data to ensure that it can continuously improve energy efficiency in actual applications; a feedback mechanism is established to continuously monitor the equipment's operating status and energy efficiency performance, and dynamically adjust the optimization strategy to adapt to changing working environments and needs. In this step, by integrating the results of each step, the energy-saving optimization strategy model formed can effectively guide the operation and management of the equipment, ensure that the equipment operates in the best state, and thus achieve continuous improvement in energy efficiency; by dynamically adjusting the optimization strategy, the model can adapt to different working environments and load changes, and improve the flexibility and responsiveness of the system.

[0055] The solution of this embodiment can significantly improve the energy efficiency of the equipment and reduce energy consumption by establishing an efficiency optimization model and a dynamic power adjustment strategy; optimize the working state of the equipment and reduce performance degradation caused by overload or improper use, thereby extending the service life of the equipment; through the implementation of energy-saving optimization strategies, it can effectively reduce the energy costs of the enterprise and improve economic benefits; the dynamic adjustment strategy enables the equipment to flexibly adjust the operating state according to actual needs, thereby improving the adaptability of the system.

[0056] In some possible implementations of the present invention, the step of setting a protection threshold based on the first power source attribute data, establishing a temperature compensation adjustment model, and formulating an overload protection control strategy to obtain a device protection strategy model includes: Analyze the working characteristics of the device according to the first power source attribute data, determine the safe operating range of the device under different working conditions, and set a corresponding protection threshold; In this step, the first power attribute data includes key parameters such as power voltage, frequency, and power factor; analyze the working characteristics of the equipment, and based on the power attribute data, evaluate the performance of the equipment under different working conditions, including load capacity, efficiency, and stability; determine the safe operating range of the equipment under different working conditions, consider the rated parameters and actual operating conditions of the equipment, and identify the maximum and minimum safe operating limits of the equipment; set the corresponding protection threshold according to the analysis results to ensure that the equipment does not exceed the safety limit during operation to prevent overload, overheating, or other potential failures. In this step, by analyzing the power attribute data and setting the protection threshold, it is possible to effectively prevent damage to the equipment under abnormal conditions and ensure the safe operation of the equipment.

[0057] Establish a temperature compensation adjustment model, consider the impact of ambient temperature changes on equipment performance, and design a temperature sensor feedback mechanism to adjust the equipment's operating parameters in real time; In this step, collect ambient temperature data and equipment performance data; analyze the impact of ambient temperature changes on equipment performance, identify performance change trends of equipment at different ambient temperatures, including efficiency, response time, and stability; establish a temperature compensation adjustment model, use mathematical models or algorithms (such as linear regression, neural networks, etc.) to describe the relationship between ambient temperature and equipment performance, and determine compensation parameters; design a temperature sensor feedback mechanism, select a suitable temperature sensor and integrate it into the equipment to monitor the ambient temperature and equipment temperature in real time; develop a control algorithm, based on the data fed back by the temperature sensor, adjust the equipment's operating parameters (such as power, operating speed, etc.) in real time to ensure that the equipment maintains optimal performance under different environmental conditions. In this step, by adjusting the operating parameters in real time, ensure that the equipment maintains optimal performance at different ambient temperatures and improve overall work efficiency; the application of the temperature compensation adjustment model can effectively reduce performance fluctuations caused by ambient temperature changes and improve the stability and reliability of the equipment.

[0058] Formulate overload protection control strategies, design control algorithms based on set protection thresholds and temperature compensation adjustment models to monitor the load conditions of the equipment and identify overload conditions in a timely manner; In this step, a control algorithm is designed, and a monitoring strategy is formulated based on the set protection threshold and temperature compensation adjustment model to obtain the load condition data of the equipment in real time; load monitoring is implemented, and the current, voltage, power and other parameters of the equipment are monitored in real time using sensors and data acquisition systems to ensure the accuracy and timeliness of the data; real-time monitoring data is analyzed through control algorithms to timely identify overload conditions and determine whether the equipment exceeds the set protection threshold; when an overload condition is identified, protective measures are automatically implemented, including but not limited to reducing equipment power, sounding an alarm or cutting off power to prevent equipment damage. In this step, by formulating an overload protection control strategy, damage to the equipment caused by overload can be effectively prevented, ensuring the safe operation of the equipment; real-time monitoring and timely identification of overload conditions help keep the equipment in optimal working condition and reduce the probability of failure.

[0059] Implement equipment protection strategy model, integrate temperature compensation adjustment and overload protection control strategy, ensure that equipment can automatically adjust or cut off power supply in abnormal situations to prevent damage; In this step, the temperature compensation adjustment model and the overload protection control strategy are integrated to form a comprehensive equipment protection strategy framework; according to the working characteristics and environmental conditions of the equipment, the temperature compensation parameters and overload protection thresholds are set to ensure safe operation under different working conditions; when the equipment temperature or load exceeds the set threshold, protection measures are automatically executed, including adjusting the equipment's working parameters or cutting off the power supply. In this step, by integrating the temperature compensation adjustment and overload protection control strategy, it is possible to effectively prevent damage to the equipment under abnormal conditions and ensure the safe operation of the equipment.

[0060] The protection strategy model is verified and its effectiveness is evaluated through actual operating data to ensure that the equipment can be effectively protected under various operating conditions.

[0061] The solution of this embodiment can effectively prevent damage to the equipment due to overload or abnormal temperature by setting protection thresholds and implementing overload protection control strategies; the application of the temperature compensation adjustment model ensures the stable operation of the equipment under different environmental conditions and reduces performance fluctuations caused by temperature changes.

[0062] In some possible implementations of the present invention, the step of the control server sending the control strategy model to the corresponding remote control terminal includes: The control server obtains working attribute data of multiple remote control terminals; In this step, ensure that a stable network connection is established between the control server and multiple remote control terminals; the control server first needs to identify and obtain the information of all connected remote control terminals (this can be done by sending a request to each terminal, asking it to return the current working attribute data); on each remote control terminal, deploy a data acquisition module, which is responsible for collecting the working attribute data of the terminal, such as CPU usage, memory usage, network status, etc.; the control server sends a module call instruction to each remote terminal, asking it to send the working attribute data back to the server. After receiving the instruction, the terminal calls the corresponding module and packages the data; the collected working attribute data is sent back to the control server through the network. JSON or XML format can be used for data transmission to facilitate parsing and processing; after receiving the data, the control server parses and processes it. The working attribute data is stored in the database for subsequent analysis and monitoring; the control server can monitor the working status of each remote control terminal in real time, and send feedback or adjustment instructions to the terminal as needed to optimize its performance. Through this step, the control server can effectively obtain and manage the working attribute data of multiple remote control terminals, thereby realizing centralized monitoring and management of the equipment.

[0063] Determine the dimming power source to be controlled corresponding to each remote control terminal according to the working attribute data; In this step, the working attribute data is analyzed to identify the specific needs of each remote control terminal (for example, which terminals need to increase the light source and which terminals need to reduce the light source, etc.); based on the analysis results, a mapping relationship between the remote control terminal and the dimming power supply is established, and the working attribute data of each terminal will correspond to a specific dimming power supply (for example, if the ambient light intensity of a terminal is lower than the set threshold, the terminal will correspond to a dimming power supply to increase the light); formulate a control strategy to clarify how each remote control terminal should adjust its corresponding dimming power supply under different working conditions. This may include setting specific parameters for dimming, such as brightness, color temperature, etc.; the control server sends the formulated control strategy and instructions to each remote control terminal, instructing them how to adjust their corresponding dimming power supply. Through this step, the dimming power supply to be controlled corresponding to each remote control terminal can be effectively determined based on the working attribute data, thereby realizing intelligent lighting control.

[0064] Acquire second power source attribute data of the dimming power source to be controlled; A corresponding first control strategy model is determined from the plurality of control strategy models according to the second power source attribute data.

[0065] In this embodiment, the second power attribute data of the dimming power supply to be controlled is obtained through a sensor or a data interface (these data may include voltage, frequency, power factor, load status, etc.); the collected second power attribute data is transmitted to the control server to ensure the accuracy and real-time nature of the data; the acquired second power attribute data is cleaned to remove abnormal values ​​and noise to ensure the reliability of subsequent analysis; the data is converted into a format suitable for analysis, such as JSON or XML, for subsequent processing; key features, such as voltage fluctuation range, power factor, etc., are extracted from the second power attribute data; the extracted features are matched with a model in a control strategy model library containing multiple control strategy models (each control strategy model is targeted at different working conditions and requirements (for example, corresponding control strategies can be established according to different ambient light intensities, load conditions, etc.); parameters are set for each control strategy model, including input conditions, output control signals, etc.), to determine the first control strategy model that best suits the current second power attribute data; the control instruction of the determined first control strategy model is issued to the dimming power supply to be controlled to implement corresponding dimming control.

[0066] The solution of this embodiment can effectively obtain the second power source attribute data of the dimming power source to be controlled, and determine the corresponding first control strategy model according to these data, thereby realizing intelligent dimming control.

[0067] In some possible implementations of the present invention, the remote control terminal generates a corresponding control instruction according to the control strategy model, and sends the control instruction to the corresponding power controller through the wireless communication module, including: The remote control terminal obtains current working state data and current working environment data of the dimming power supply to be controlled; In this step, the remote control terminal obtains the current working status data of the dimming power supply to be controlled in real time through sensors or data interfaces. These data may include the voltage, power output, load conditions, etc. of the power supply. At the same time, the terminal also needs to obtain the current environmental data, such as ambient light intensity, temperature, humidity, etc. These data are crucial to the formulation of dimming control strategies.

[0068] Adjusting the first control strategy model according to the current working state data and the current working environment data to obtain a second control strategy model; In this step, the collected current working state data and current working environment data are transmitted to the control server or local processing unit to ensure the accuracy and real-time nature of the data; the acquired data is cleaned and formatted to remove abnormal values ​​and ensure the reliability of the data. The data can be processed using a standardized or normalized method for subsequent analysis; according to the current working state data and environmental data, the existing first control strategy model is analyzed to identify the parameters or strategies that need to be adjusted; based on the analysis results, the parameters of the first control strategy model are adjusted to form a new second control strategy model (which may involve resetting control parameters such as dimming intensity and response time); the adjusted second control strategy model is applied to the dimming power supply to be controlled, and its working state is adjusted in real time to adapt to the current working environment and state; during the implementation process, the working state and environmental changes of the dimming power supply are continuously monitored, and the second control strategy model is further adjusted when necessary to ensure the best dimming effect. Through this step, the remote control terminal can effectively obtain the current working state and environmental data of the dimming power supply to be controlled, and adjust the control strategy model according to these data, thereby realizing intelligent dimming control.

[0069] Generate a corresponding control instruction according to the second control strategy model, and send the control instruction to a power controller corresponding to the dimming power supply to be controlled through a wireless communication module.

[0070] In some possible implementations of the present invention, the step of the power controller adjusting the operating parameters of the dimming power supply according to the control instruction includes: The power controller parses the control instruction and extracts the required first operating parameter and first adjustment method; In this step, the control instruction is input by a remote control terminal or a user, and includes target operating parameters of the dimming power supply, such as brightness, color temperature, etc.

[0071] Acquire the first current working state data of the dimming power supply to be controlled (including current brightness, power output and load condition, etc., so as to facilitate parameter adjustment); According to the first working parameter, the first adjustment method and the first current working state data, the required adjustment range and adjustment method are calculated to ensure that the dimming power supply can smoothly transition to the target state, and an adjustment instruction is generated according to the adjustment range and the adjustment method; Sending adjustment instructions (specifically including adjusting voltage, current or PWM signal, etc.) to the dimming power supply to be controlled to achieve the required working parameters; Monitor the response of the dimming power supply to be controlled in real time to ensure that it is adjusted according to the adjustment instruction.

[0072] The solution of this embodiment can achieve higher dimming accuracy and meet users' needs for lighting effects by accurately parsing control instructions and adjusting working parameters in real time; the real-time monitoring and feedback mechanism ensures that the dimming power supply remains stable during the adjustment process, reducing flickering or instability caused by parameter changes.

[0073] In some possible implementations of the present invention, the remote control terminal generates a new control instruction based on the working monitoring data and the preset dimming curve and in combination with the control strategy model and sends the new control instruction to the power controller corresponding to the dimming power supply, including: The remote control terminal analyzes the relationship between the working monitoring data and the preset dimming curve according to the working monitoring data and the preset dimming curve in combination with the control strategy model, and determines the required adjustment direction and amplitude; In this step, the working monitoring data includes information such as the current working state of the dimming power supply, load conditions, and ambient light intensity. The dimming curve defines the ideal brightness and color temperature change mode under different environmental conditions.

[0074] Generate a new control instruction according to the required adjustment direction and amplitude (the control instruction includes the target brightness, color temperature and other related parameters of the dimming power supply); The new control instruction is sent to the power controller corresponding to the dimming power supply to be controlled via the wireless network.

[0075] The solution of this embodiment, by combining work monitoring data and preset dimming curves, can achieve more intelligent and flexible dimming control to adapt to different environmental changes; generating control instructions based on real-time data helps to reduce energy consumption and improve energy efficiency while meeting lighting needs; users can easily adjust lighting effects through a remote control terminal, which improves the convenience and comfort of operation.

[0076] See also Figure 2 , Another embodiment of the present invention provides a remote control system for an intelligent constant voltage dimming power supply, which is used to perform a remote control method for an intelligent constant voltage dimming power supply, including: a control server, a remote control terminal and a power supply controller; The control server is configured to: Obtain historical working data, historical working environment data and power source attribute data of multiple dimming power sources; Generate a plurality of control strategy models of the dimming power supply according to the historical working data, the historical working environment data and the power supply attribute data; Sending the control strategy model to a corresponding remote control terminal; The remote control terminal is configured to: generate corresponding control instructions according to the control strategy model, and send the control instructions to the corresponding power controller through the wireless communication module; The power controller is configured to: Adjusting the working parameters of the dimming power supply according to the control instruction; The working state of the dimming power supply is monitored in real time by a sensor module to obtain working monitoring data; Transmitting the work monitoring data to the remote control terminal via the wireless communication module; The remote control terminal is further configured to: generate a new control instruction based on the working monitoring data and the preset dimming curve, and in combination with the control strategy model, and send the new control instruction to the power controller corresponding to the dimming power supply; The power supply controller is further configured to adjust the working parameters of the dimming power supply according to the new control instruction.

[0077] It should be known that Figure 2 The block diagram of the remote control system for the intelligent constant voltage dimming power supply is for illustration only, and the number of modules shown does not limit the protection scope of the present invention. The remote control system for the intelligent constant voltage dimming power supply provided in this embodiment can be used to execute the corresponding embodiments of the remote control method for the intelligent constant voltage dimming power supply. For the specific implementation process, please refer to the description of the embodiments of each method, which will not be repeated here.

[0078] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0079] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0080] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0081] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0083] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0084] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0085] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0086] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions without departing from the spirit and scope of the present invention, and can make various changes and modifications, including the combination of the above-mentioned different functions and implementation steps, including software and hardware implementation methods, all of which are within the scope of protection of the present invention.

Claims

1. A remote control method for an intelligent constant voltage dimming power supply, characterized in that: include: The control server obtains historical working data, historical working environment data and power source attribute data of multiple dimming power sources; The control server generates a plurality of control strategy models of the dimming power supply according to the historical working data, the historical working environment data and the power supply attribute data; The control server sends the control strategy model to the corresponding remote control terminal; The remote control terminal generates a corresponding control instruction according to the control strategy model, and sends the control instruction to the corresponding power controller through the wireless communication module; The power supply controller adjusts the working parameters of the dimming power supply according to the control instruction; The working state of the dimming power supply is monitored in real time by a sensor module to obtain working monitoring data; Transmitting the work monitoring data to the remote control terminal via the wireless communication module; The remote control terminal generates a new control instruction according to the working monitoring data and the preset dimming curve, and combines the control strategy model, and sends the new control instruction to the power controller corresponding to the dimming power supply; The power supply controller adjusts the working parameters of the dimming power supply according to the new control instruction.

2. The remote control method for an intelligent constant voltage dimming power supply according to claim 1, characterized in that: The step of generating, by the control server, a plurality of control strategy models of the dimming power supply according to the historical working data, the historical working environment data and the power supply attribute data comprises: Cleaning the historical working data, the historical working environment data and the power source attribute data, removing abnormal values ​​and redundant data, and performing data standardization processing according to data types to obtain first historical working data, first historical working environment data and first power source attribute data; sorting and segmenting the first historical working data, the first historical working environment data, and the first power source attribute data in time series, and marking key characteristic parameters; Obtaining first historical power consumption data according to the first historical working data analysis; Analyzing the first historical working environment data to obtain a first correlation between environmental factors and power demand and extracting user usage habit characteristics; Establishing a device performance degradation model according to the first power source attribute data; Formulate a basic dimming curve according to the first historical power consumption data, the first correlation, the user's usage habit characteristics, and the law of natural light changes, design a lighting compensation plan in combination with the building space characteristics, and establish a mapping relationship between personnel activity rules and lighting needs to obtain a basic lighting strategy model; According to the equipment performance attenuation model, energy consumption data is analyzed to establish an efficiency optimization model, an optimal operating point is determined according to the first power source attribute data, and a dynamic power adjustment strategy is formulated to obtain an energy-saving optimization strategy model; Based on the first power source attribute data, a protection threshold is set, a temperature compensation adjustment model is established, and an overload protection control strategy is formulated to obtain a device protection strategy model; Analyze the coordination rules of multiple devices, establish environmental factor response models, and design multi-scenario switching strategies to obtain scenario linkage strategy models; The basic lighting strategy model, the energy-saving optimization strategy model, the equipment protection strategy model and the scene linkage strategy model are integrated to obtain the control strategy model.

3. The remote control method for an intelligent constant voltage dimming power supply according to claim 2, characterized in that: The step of formulating a basic dimming curve according to the first historical power consumption data, the first correlation, the user's usage habit characteristics and the law of natural light changes, designing a lighting compensation plan in combination with the building space characteristics, and establishing a mapping relationship between personnel activity rules and lighting needs to obtain a basic lighting strategy model includes: Determining a first benchmark lighting parameter based on the first historical power usage data; According to the first correlation and in combination with the law of natural light changes, a first illumination compensation model is established; Optimizing the dimming strategy according to the first reference lighting parameter, the first lighting compensation model and the user's usage habit characteristics to obtain a basic dimming curve; Analyze the characteristics of architectural space and establish a spatial light field model; According to the spatial light field model, calculating the lighting compensation parameters and establishing a spatial illumination distribution model; According to the spatial illumination distribution model, a compensation control strategy is formulated to obtain a lighting compensation scheme; Analyze the patterns of personnel activities and establish the first personnel activity prediction model; Analyze the lighting demand association according to the first personnel activity prediction model, and construct a first mapping model between personnel activity rules and lighting demand; A basic lighting strategy model is obtained according to the basic dimming curve, the lighting compensation solution and the first mapping model.

4. The remote control method for an intelligent constant voltage dimming power supply according to claim 3, characterized in that: The step of analyzing energy consumption data to establish an efficiency optimization model according to the equipment performance attenuation model, determining the optimal operating point according to the first power source attribute data, and formulating a dynamic power adjustment strategy to obtain an energy-saving optimization strategy model includes: Obtain energy consumption data of equipment under different working conditions, analyze the energy consumption data, identify the relationship between energy consumption and equipment performance, and obtain energy consumption analysis results; Establish an efficiency optimization model based on the equipment performance degradation model and energy consumption analysis results; Analyzing the voltage, frequency, and power factor of the power supply to determine an optimal operating point of the device according to the first power supply attribute data; Develop dynamic power regulation strategies and design control algorithms to adjust the power output of equipment in real time to ensure that the equipment operates near the optimal operating point; Integrate the results of the above steps to form an energy-saving optimization strategy model.

5. The remote control method for an intelligent constant voltage dimming power supply according to claim 4, characterized in that: The step of setting a protection threshold based on the first power source attribute data, establishing a temperature compensation adjustment model, and formulating an overload protection control strategy to obtain a device protection strategy model includes: Analyze the working characteristics of the device according to the first power source attribute data, determine the safe operating range of the device under different working conditions, and set a corresponding protection threshold; Establish a temperature compensation adjustment model, consider the impact of ambient temperature changes on equipment performance, and design a temperature sensor feedback mechanism to adjust the equipment's operating parameters in real time; Formulate overload protection control strategies, design control algorithms based on set protection thresholds and temperature compensation adjustment models to monitor the load conditions of the equipment and identify overload conditions in a timely manner; Implement equipment protection strategy model, integrate temperature compensation adjustment and overload protection control strategy, ensure that equipment can automatically adjust or cut off power supply in abnormal situations to prevent damage; The protection strategy model is verified and its effectiveness is evaluated through actual operating data to ensure that the equipment can be effectively protected under various operating conditions.

6. The remote control method for an intelligent constant voltage dimming power supply according to claim 5, characterized in that: The step of the control server sending the control strategy model to the corresponding remote control terminal includes: The control server obtains working attribute data of multiple remote control terminals; Determine the dimming power source to be controlled corresponding to each remote control terminal according to the working attribute data; Acquire second power source attribute data of the dimming power source to be controlled; A corresponding first control strategy model is determined from the plurality of control strategy models according to the second power source attribute data.

7. The remote control method for an intelligent constant voltage dimming power supply according to claim 6, characterized in that: The remote control terminal generates a corresponding control instruction according to the control strategy model, and sends the control instruction to the corresponding power controller through the wireless communication module, including: The remote control terminal obtains current working state data and current working environment data of the dimming power supply to be controlled; Adjusting the first control strategy model according to the current working state data and the current working environment data to obtain a second control strategy model; Generate a corresponding control instruction according to the second control strategy model, and send the control instruction to a power controller corresponding to the dimming power supply to be controlled through a wireless communication module.

8. The remote control method for an intelligent constant voltage dimming power supply according to claim 7, characterized in that: The step of the power controller adjusting the working parameters of the dimming power supply according to the control instruction comprises: The power controller parses the control instruction and extracts the required first operating parameter and first adjustment method; Acquire first current working state data of the dimming power supply to be controlled; According to the first working parameter, the first adjustment method and the first current working state data, the required adjustment range and adjustment method are calculated to ensure that the dimming power supply can smoothly transition to the target state, and an adjustment instruction is generated according to the adjustment range and the adjustment method; Sending an adjustment instruction to the dimming power supply to be controlled to achieve the required working parameters; Monitor the response of the dimming power supply to be controlled in real time to ensure that it is adjusted according to the adjustment instruction.

9. The remote control method for an intelligent constant voltage dimming power supply according to claim 8, characterized in that: The step of the remote control terminal generating a new control instruction according to the working monitoring data and the preset dimming curve and combining the control strategy model and sending the new control instruction to the power controller corresponding to the dimming power supply includes: The remote control terminal analyzes the relationship between the working monitoring data and the preset dimming curve according to the working monitoring data and the preset dimming curve in combination with the control strategy model, and determines the required adjustment direction and amplitude; Generate new control instructions according to the required adjustment direction and amplitude; The new control instruction is sent to the power controller corresponding to the dimming power supply to be controlled via the wireless network.

10. A remote control system for an intelligent constant voltage dimming power supply, used to execute the remote control method for an intelligent constant voltage dimming power supply as claimed in any one of claims 1 to 9, characterized in that: include: Control servers, remote control terminals and power controllers; The control server is configured to: Obtain historical working data, historical working environment data and power source attribute data of multiple dimming power sources; Generate a plurality of control strategy models of the dimming power supply according to the historical working data, the historical working environment data and the power supply attribute data; Sending the control strategy model to a corresponding remote control terminal; The remote control terminal is configured to: generate corresponding control instructions according to the control strategy model, and send the control instructions to the corresponding power controller through the wireless communication module; The power controller is configured to: Adjusting the working parameters of the dimming power supply according to the control instruction; The working state of the dimming power supply is monitored in real time by a sensor module to obtain working monitoring data; Transmitting the work monitoring data to the remote control terminal via the wireless communication module; The remote control terminal is further configured to: generate a new control instruction based on the working monitoring data and the preset dimming curve, and in combination with the control strategy model, and send the new control instruction to the power controller corresponding to the dimming power supply; The power supply controller is further configured to adjust the working parameters of the dimming power supply according to the new control instruction.

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