Intelligent garden management and control method based on big data

Abstract

The invention relates to the technical field of smart city and garden automation, and discloses a smart garden management and control method based on big data. Multi-gear current calibration is respectively carried out on warm white and cold white channels of the LED module, an accurate mapping relation between the driving current and the luminous flux as well as the mixed color temperature is established, and a foundation is laid for subsequent control. In actual operation, plant photosynthetic rate and effective radiation data of multiple days are collected per minute, photosynthetic efficiency is calculated, a target illuminance and target color temperature sequence is dynamically generated, and cooperative adaptive control of illuminance and color temperature is realized. The system solves and issues a digital instruction of a corresponding channel current according to a current mode, ensures reliable execution of the instruction through a serial communication protocol, records all operation parameters, periodically triggers and re-calibrates, and effectively compensates equipment aging and environment change.

Description

Technical Field

[0001] The present invention relates to the field of smart city and garden automation technology, and specifically to a smart garden management and control method based on big data. Background Art

[0002] With the development of smart cities and modern landscape gardening, the management of the growing environment for garden plants is becoming increasingly intelligent. Traditional garden lighting control often relies on preset time periods and fixed illumination levels, often setting on / off times and lighting intensity based solely on empirical experience. For example, in many garden and greenhouse systems, lighting hours are often set according to sunrise and sunset or fixed time periods, while illumination intensity is primarily set manually or with simple fixed values. This approach often fails to adaptively adjust to the dynamic photosynthetic needs of plants during different physiological stages and meteorological conditions, easily leading to excessive or insufficient illumination, which neither maximizes plant growth nor results in energy waste.

[0003] Furthermore, existing LED garden lighting systems mostly feature single-channel or limited-step color temperature adjustment, making it impossible to achieve coordinated and continuous control of illumination and color temperature. While some systems incorporate light or temperature and humidity sensors to enable closed-loop feedback and simple adaptive adjustments, they still face significant deficiencies in multi-objective control, color temperature coordination, and long-term big data-driven operations. Especially in large-scale garden applications, relying on real-time feedback and a single sensor makes it difficult to systematically reflect the overall photosynthetic efficiency, environmental history, and complex spectral requirements of plants, resulting in a lack of intelligent lighting control strategies based on big data. With the widespread adoption of the Internet of Things and big data analytics, the garden sector is gradually incorporating historical meteorological, environmental, and physiological data for intelligent management. However, in the field of practical lighting management, closed-loop algorithms and engineering solutions based on historical photosynthetic efficiency, multi-day big data mining, and coordinated output of LED dual-channel color temperature have yet to be developed. Therefore, how to fully utilize multi-day and minute-level photosynthetic response data, combined with precise multi-channel LED control, to achieve adaptive and coordinated management of garden lighting has become a key technical challenge in improving intelligent garden management and energy conservation.

[0004] To this end, this project aims to propose a smart garden management method based on big data. It uses minute-level historical photosynthetic efficiency data as the core driver, combined with the offline physical calibration parameters of warm white and cool white dual-channel LED lamps, to achieve dynamic, precise, and coordinated control of the illumination and color temperature of garden plants. Summary of the Invention

[0005] The present invention provides a smart garden management and control method based on big data, which promotes the solution of the problems mentioned in the above background technology.

[0006] The present invention provides the following technical solution: a smart garden management and control method based on big data, comprising:

[0007] S1. In a constant temperature and humidity laboratory environment, apply preset drive currents to the warm white channel and cool white channel of the LED module. Use a calibrated flux meter and color temperature meter to measure the luminous flux and color temperature of each channel at different currents. Establish a mapping relationship between the drive current, the total output luminous flux, and the mixed light color temperature.

[0008] S2. During the preset lighting control period, collect data on plant photosynthetic rate and effective light radiation intensity at minute intervals for multiple days, and calculate the plant photosynthetic efficiency at each minute based on the big data;

[0009] S3, combining the maximum required illumination and photosynthetic efficiency of the plant, and generating a target illumination sequence per minute through predefined control rules;

[0010] S4. Generate a target color temperature sequence per minute using a predefined switching rule based on the inherent color temperature difference and photosynthetic efficiency threshold obtained by offline calibration;

[0011] S5. In dual-channel coordinated control mode, the warm white channel and cool white channel driving currents at the corresponding time are calculated based on the target illumination and target color temperature. In single-channel or channel failure mode, the single-channel driving current is directly determined according to the preset degradation scheme.

[0012] S6, converting the channel driving current value obtained by the solution into a digital driving instruction;

[0013] S7. Send digital drive instructions to the LED driver through the serial communication interface according to the preset protocol, baud rate and channel address, and receive a confirmation response. If no valid feedback is received or the verification fails, resend the instruction until the preset number of resends is reached and confirmed to be correct;

[0014] S8. Record the operating parameters and feedback results of each control cycle, generate the log on a daily basis and upload it to the database. When the system runs for a preset operating time or once every three months, start the parameter recalibration process.

[0015] Optionally, in a constant temperature and humidity laboratory environment, applying a preset driving current to the warm white channel and the cool white channel of the LED module, respectively, and using a calibrated flux meter and color temperature meter to measure the luminous flux and color temperature of each channel at different currents, and establishing a mapping relationship between the driving current and the output total luminous flux and the mixed light color temperature, specifically including:

[0016] At constant temperature Constant humidity The laboratory is equipped with a constant current source, a calibration-grade flux meter and a color temperature meter;

[0017] Set the warm white channel drive current , cold white channel driving current ;

[0018] Only the warm white channel is powered and applied , measuring luminous flux and color temperature ;in, For Warm white channel luminous flux measured under conditions; For The color temperature of the warm white channel measured under the following conditions;

[0019] Only the cold white channel is powered and applied , measuring luminous flux and color temperature ;in, For The luminous flux of the cool white channel measured under the conditions; For The color temperature of the cool white channel measured under the following conditions:

[0020] Calculate the luminous flux efficiency of the warm white channel separately and cold white channel luminous flux efficiency :

[0021] , ;

[0022] Construct a physical mapping function: ;in, is the driving current of the warm white channel at any moment; is the cold white channel driving current at any time; It is the total luminous flux output under a given current of the dual channels; It is the output color temperature under dual-channel mixed light;

[0023] Only when Use ;

[0024] S100, calculate the inherent color temperature difference and determine the control mode:

[0025] S110, calculate the inherent color temperature difference ;

[0026] S120, determine the control mode:

[0027] like , then swap the two channel definitions: , , ;

[0028] After the exchange or in the initial state, steps S121 to S124 are performed in sequence:

[0029] S121, if and and , adopting dual-channel collaborative control;

[0030] S122, otherwise if , degenerated into a warm white single channel: , ;in, For the minute warm white channel current; For the Minute target illumination; For the Minute cold white channel current; is the minute number;

[0031] S123, otherwise if , degenerated into a cold white single channel: , ;

[0032] S124, otherwise if , so that all ;in, For the Minute warm white channel digital command; For the Minute cold white channel digital command;

[0033] And jump to step S7.

[0034] Optionally, within a preset lighting control period, the data of plant photosynthetic rate and effective light radiation intensity are collected at minute intervals for multiple days, and the plant photosynthetic efficiency of each minute is calculated based on the big data, specifically including:

[0035] make is the number of days of historical collection, and ;

[0036] The lighting control period starts at the time set by the user and end time , and must meet ,Right now and within the same day;

[0037] Calculate the total number of minutes of lighting period ;

[0038] For the first Tiandi Photosynthetic rate collected in minutes and Effective radiation ;in, It is the day sequence number; is the minute number;

[0039] Calculate the Tiandi photosynthetic efficiency in minutes :

[0040] ;

[0041] Calculate the Average photosynthetic efficiency for all days ;

[0042] Get All The maximum value in ;

[0043] like , then let , , , and jump to step S7; otherwise, continue to step S3; wherein, For the Minute target color temperature.

[0044] Optionally, the method combines the maximum required illumination and photosynthetic efficiency of the plant to generate a target illumination sequence per minute through a predefined control rule, specifically including:

[0045] Get the maximum illumination required by the plant, recorded as ;

[0046] Calculate the Minute target illumination .

[0047] Optionally, the generation of a target color temperature sequence per minute by a predefined switching rule based on the inherent color temperature difference and photosynthetic efficiency threshold obtained by offline calibration specifically includes:

[0048] Read inherent color temperature difference ;

[0049] like , then let Minute target color temperature ;

[0050] like , then let Minute target color temperature .

[0051] Optionally, in the dual-channel coordinated control mode, the warm white channel and the cold white channel driving current at the corresponding moment are solved according to the target illumination and the target color temperature; in the single-channel or channel failure mode, the single-channel driving current is directly determined according to a preset degradation scheme, specifically including:

[0052] If dual channel mode, Establish the cooperative equations, specifically:

[0053] ;

[0054] The closed-form solution is , ;

[0055] Single channel or failure mode is directly assigned according to the degradation scheme in S100;

[0056] Perform upper and lower limit truncation:

[0057] ;

[0058] ;in, and The maximum currents specified for warm-white and cool-white drivers respectively.

[0059] Optionally, converting the obtained channel driving current value into a digital driving instruction specifically includes:

[0060] Calculate the Minute warm white channel digital command ;

[0061] Calculate the Minute cold white channel digital command .

[0062] Optionally, the step of sending a digital drive instruction to the LED driver via the serial communication interface according to a preset protocol, baud rate, and channel address, and receiving a confirmation response, and if no valid feedback is received or verification fails, resending the instruction until a preset number of resends is reached and confirmation is made, specifically includes:

[0063] Communication Configuration: Protocol , baud rate , warm white channel address , cold white channel address ;

[0064] Instructions are issued every minute, including:

[0065] S701, function code Write to the register Write address ;

[0066] S702, if there is no response or verification fails, resend no more than Second-rate;

[0067] S703, similarly Write address , and verify the response.

[0068] Optionally, the operating parameters and feedback results of each control cycle are recorded, and the log is generated and uploaded to the database on a daily basis. When the system runs for a preset operating time or once every three months, the parameter recalibration process is started, specifically including:

[0069] Generate log fields, each record includes:

[0070] ;

[0071] Generate log files daily and upload them to the database;

[0072] System cumulative operation or every Months, perform the recalibration process, including:

[0073] S801, recalculation 、 、 and ;

[0074] S802, recalculate color temperature difference ;

[0075] S803, if , exchange channel definition and correct according to step S100 ;

[0076] S804, re-determine the control mode according to the S100 branch logic and update the controller parameters;

[0077] S805, compare this time with the most recent The calibration parameters of each version are archived together.

[0078] The present invention has the following beneficial effects:

[0079] 1. The laboratory electro-optical calibration method precisely measures the dual-channel output of the LED module. Multiple drive current levels are applied to the warm and cool white channels in a constant temperature and humidity environment. A high-precision flux meter and color temperature meter are used to record the luminous flux and color temperature changes at each drive current. This method then establishes a physical mapping relationship between current, total luminous flux, and mixed color temperature. Taking into account the nonlinear characteristics of the dual-channel light and color response, a reversible mapping model is formed through laboratory calibration, providing a solid physical foundation for subsequent real-time control. This method improves the accuracy and predictability of lighting output, ensuring that the system accurately outputs target illuminance and color temperature levels under different driving conditions. Compared to traditional methods that rely solely on single-channel empirical formulas or software estimation, this method combines detailed physical calibration data with the synergistic characteristics of the dual channels to fully quantify LED performance, thereby overcoming the control errors caused by insufficient calibration accuracy.

[0080] 2. The big data photosynthetic efficiency evaluation algorithm synchronously collects multi-day plant photosynthetic rate and radiation intensity data at a granularity of minutes within the preset lighting control period. Using big data technology, the multi-day data is normalized and outliers are filtered out before calculating the photosynthetic efficiency for each minute and counting key average and peak parameters. Statistical analysis of multi-day historical data is introduced, comprehensively considering the impact of weather changes and extreme events to ensure the stability and representativeness of the efficiency evaluation. The interference of single-day fluctuations on the control target is reduced, making the generated illuminance and color temperature sequences more robust and reliable. Compared with existing methods that rely on real-time single-day monitoring or empirical models, this method solves the problem of traditional real-time feedback being sensitive to occasional anomalies through dynamic adjustment of the sliding window, thereby improving the practical performance of the system in complex outdoor environments.

[0081] 3. The dynamic illumination generation method combines the plant's maximum growth requirements with the calculated photosynthetic efficiency, converting the plant's physiological needs into a target illumination value per minute through predefined control rules. By using plant photosynthetic efficiency to drive lighting output, the system considers both light source performance and the plant's actual ability to absorb light. This approach optimizes energy consumption while ensuring healthy plant growth. Through intelligent adjustment, it avoids over- or under-illumination, reduces overall energy consumption, and promotes growth quality. Compared to traditional lighting control based on fixed timing or empirical values, this method achieves a transition from static regulation to real-time self-adaptation, addressing the growth inhibition and resource waste caused by traditional solutions' inability to respond to changes in plant physiological state.

[0082] 4. The adaptive color temperature generation algorithm is based on the inherent color temperature difference obtained by offline calibration and the real-time photosynthetic efficiency threshold, and generates a target color temperature sequence every minute through predefined switching or gradient rules. By integrating physical calibration with plant physiological feedback, the color temperature is dynamically switched according to photosynthetic efficiency, so that the light source can meet the photosynthesis needs of plants while taking into account the visual comfort and artistic effect of the night landscape. It improves the photosynthetic efficiency of plants and optimizes the night view of gardens, avoiding the lack of adaptability caused by a single fixed color temperature. Compared with the existing methods of fixed color temperature or dynamic adjustment based only on aesthetics, this method takes into account the dual needs of plant growth and landscape effects, and realizes the coordinated optimization control of illumination and color temperature.

[0083] 5. The drive current allocation and degradation processing method, designed for dual-channel coordinated control scenarios, solves the inverse relationship of the electro-optical mapping model to calculate the drive currents for the warm and cool white channels in real time to meet the target illumination and color temperature requirements. In the event of a single channel failure or degradation, the remaining channel currents are directly determined based on a preset degradation strategy, and input values ​​are truncated when necessary to prevent them from exceeding the hardware rating. This method also incorporates intra-cycle channel status monitoring, identifying anomalies through safety thresholds and automatically triggering degradation switching. Parameters and logs during the degradation process are simultaneously recorded, providing data support for subsequent maintenance and performance analysis. While simultaneously addressing dual output targets and fail-safe degradation, the method ensures real-time, safety, and stability through closed-form analysis and limiting. The system also features fault-adaptive capabilities, maintaining a minimum photosynthetic environment without manual intervention in the event of channel anomalies. This effectively addresses the issue of system downtime caused by hardware failures or uncontrolled single-channel failures. The degradation mode can be adjusted according to different plant growth stages, further enhancing the system's robustness and applicability.

[0084] 6. The digital command generation algorithm losslessly quantizes the analog drive current value into digital commands recognizable by the LED driver. This conversion is performed using a pre-defined lookup table or quantization mapping. The mapping parameters are adjusted in conjunction with real-time feedback to compensate for quantization deviations caused by temperature and voltage fluctuations. This algorithm balances quantization accuracy and execution efficiency, ensuring that digital commands accurately reflect the analog target and avoiding rounding errors associated with traditional floating-point to integer conversion. The conversion process is highly sensitive to subtle differences in illuminance and color temperature, enabling the downstream driver module to accurately execute control commands. Compared to existing simple linear scaling or coarse-grained quantization methods, this method addresses control errors and system oscillations caused by quantization inconsistencies, improving the accuracy and stability of the lighting system.

[0085] 7. Reliable Communication and Feedback Confirmation: This method uses a standardized serial interface and communication protocol to send digital commands to the driver and monitor responses in real time. If no response or verification errors are detected, an automatic retransmission mechanism is activated until successful confirmation is obtained, ensuring that each command is effectively executed. The integrated handshake confirmation and retry process enables the control system to adapt to complex communication environments such as occasional fieldbus packet loss or electromagnetic interference. This reduces the risk of lighting control failures due to communication failures and improves system robustness and maintainability. Compared to traditional communication mechanisms that use one-time transmission and lack feedback verification, this method provides high reliability while ensuring real-time performance.

[0086] 8. The operation log recording and periodic recalibration algorithm collects and saves key parameters such as illumination, color temperature, drive current, and communication feedback in each control cycle, and uploads them to the database in the form of log files on a daily basis. When the system's cumulative operating time or calendar month reaches the preset conditions, the recalibration process is automatically triggered, and the electro-optical mapping model and control logic are recalibrated in combination with the latest operating data, and the multiple calibration results are archived and managed. The online operating data and offline calibration process are organically integrated to achieve adaptive compensation for factors such as equipment aging and environmental drift. The benefits are reflected in the continuous maintenance of control accuracy and system stability without the need for manual periodic maintenance. Compared with solutions that require manual intervention or fixed-period manual calibration, this method realizes the transition from passive maintenance to active adaptation, effectively solving the problems of performance degradation and accuracy loss during long-term operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0088] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0089] Example, see Figure 1 , a smart garden management and control method based on big data, including:

[0090] S1. In a constant temperature and humidity laboratory environment, apply preset drive currents to the warm white channel and cool white channel of the LED module. Use a calibrated flux meter and color temperature meter to measure the luminous flux and color temperature of each channel at different currents. Establish a mapping relationship between the drive current, the total output luminous flux, and the mixed light color temperature.

[0091] S2. During the preset lighting control period, collect data on plant photosynthetic rate and effective light radiation intensity at minute intervals for multiple days, and calculate the plant photosynthetic efficiency at each minute based on the big data;

[0092] S3, combining the maximum required illumination and photosynthetic efficiency of the plant, and generating a target illumination sequence per minute through predefined control rules;

[0093] S4. Generate a target color temperature sequence per minute using a predefined switching rule based on the inherent color temperature difference and photosynthetic efficiency threshold obtained by offline calibration;

[0094] S5. In dual-channel coordinated control mode, the warm white channel and cool white channel driving currents at the corresponding time are calculated based on the target illumination and target color temperature. In single-channel or channel failure mode, the single-channel driving current is directly determined according to the preset degradation scheme.

[0095] S6, converting the channel driving current value obtained by the solution into a digital driving instruction;

[0096] S7. Send digital drive instructions to the LED driver through the serial communication interface according to the preset protocol, baud rate and channel address, and receive a confirmation response. If no valid feedback is received or the verification fails, resend the instruction until the preset number of resends is reached and confirmed to be correct;

[0097] S8. Record the operating parameters and feedback results of each control cycle, generate the log on a daily basis and upload it to the database. When the system runs for a preset operating time or once every three months, start the parameter recalibration process.

[0098] Through an eight-step complete process, closed-loop management of the garden lighting system is achieved from physical calibration to online adaptive control and then to periodic calibration. Specifically, the light-emitting diode module is first calibrated with multiple current levels in a laboratory environment to establish a mapping relationship between current, luminous flux and mixed color temperature, providing a reliable physical basis for subsequent real-time control; then, plant photosynthetic rate and effective radiation intensity data are collected for multiple days during the actual lighting period at a granularity of minutes to calculate photosynthetic efficiency, solving the problem of unstable control caused by sudden meteorological or equipment jitter in single-day observations; then, the target illuminance is dynamically generated through preset rules based on the maximum physiological light demand and efficiency data of the plant; then, the target color temperature is switched based on the offline calibrated color temperature difference and efficiency threshold, realizing the coordinated adaptation of illuminance and color temperature; in the control execution stage, the driving current is solved according to the dual-channel or degradation mode, and the analog current is losslessly quantized into digital instructions; instructions are issued and feedback is confirmed in real time through a reliable serial communication protocol, eliminating the risk of communication packet loss or verification failure; finally, the operating parameters are continuously recorded and the recalibration process is triggered on schedule, solving the control deviation caused by equipment aging and environmental drift. This method combines physical calibration, big data analysis and intelligent feedback, effectively overcoming problems such as traditional static or empirical adjustments being unable to respond to the dynamic needs of plants, fault recovery relying on manual intervention and prone to errors, and improving the system's accuracy, reliability and sustainable operation capabilities.

[0099] In a constant temperature and humidity laboratory environment, a preset driving current is applied to the warm white channel and the cool white channel of the LED module, and a calibrated flux meter and a color temperature meter are used to measure the luminous flux and color temperature of each channel at different currents, thereby establishing a mapping relationship between the driving current and the output total luminous flux and the mixed light color temperature. Specifically, the mapping relationship includes:

[0100] At constant temperature Constant humidity The laboratory is equipped with a constant current source, a calibration-grade flux meter and a color temperature meter; it provides a high-precision measurement environment to ensure that the subsequent electro-optical characteristic calibration is accurate;

[0101] Set the warm white channel drive current , cold white channel driving current ; Determine the operating current of the two channels under standard conditions respectively, providing a calibration benchmark for performance comparison and mapping formula;

[0102] Only the warm white channel is powered and applied , measuring luminous flux and color temperature ;in, For Warm white channel luminous flux measured under conditions; For The color temperature of the warm white channel is measured under the following conditions; the output performance of the warm white channel under the calibration current is obtained, and its electro-optical and electro-chromaticity responses are quantified;

[0103] Only the cold white channel is powered and applied , measuring luminous flux and color temperature ;in, For The luminous flux of the cool white channel measured under the conditions; For The color temperature of the cold white channel is measured under the conditions; the output performance of the cold white channel under the calibration current is obtained to establish a comparison basis for dual-channel mapping;

[0104] Calculate the luminous flux efficiency of the warm white channel separately and cold white channel luminous flux efficiency :

[0105] , Quantify the light output per mA current and provide linear mapping parameters for subsequent illumination control;

[0106] Construct a physical mapping function: ;in, is the driving current of the warm white channel at any moment; is the cold white channel driving current at any time; It is the total luminous flux output under a given current of the dual channels; Output color temperature for dual-channel mixed light; establishes a precise mapping between physical output and input for any channel current combination, supporting subsequent algorithms to infer the required current to achieve the desired illumination and color temperature targets; Map any current combination to total luminous flux; Mapping luminous flux weights to mixed color temperatures supports simultaneous control of color temperature and illumination;

[0107] Only when Use ;

[0108] S100, calculate the inherent color temperature difference and determine the control mode:

[0109] S110, calculate the inherent color temperature difference ; Clarify the physical differences between cool white and warm white, providing a physical basis for subsequent color temperature interpolation;

[0110] S120, determine the control mode:

[0111] like , then swap the two channel definitions: , , ; Ensure that all subsequent interpolation and allocation are in positive order, reducing subsequent operation branches and complexity;

[0112] After the exchange or in the initial state, steps S121 to S124 are performed in sequence:

[0113] S121, if and and , adopts dual-channel collaborative control; two channels can be adjusted simultaneously to achieve target illumination and color temperature, meeting wider lighting needs;

[0114] S122, otherwise if , degenerated into a warm white single channel: , ;in, For the minute warm white channel current; For the Minute target illumination; For the Minute cold white channel current; is the minute number;

[0115] S123, otherwise if , degenerated into a cold white single channel: , ;

[0116] If one channel fails or has no color temperature distinction, the illumination is controlled only through the remaining channels;

[0117] S124, otherwise if , so that all ;in, For the Minute warm white channel digital command; For the Minute cold white channel digital instruction; if the two channel parameters are invalid, avoid malfunction and skip the control;

[0118] And jump to step S7.

[0119] Through refined laboratory electro-optical calibration, a high-precision quantification of the drive current and output performance of a dual-channel light-emitting module was achieved. Under a constant temperature and humidity environment, a high-precision constant current source, a calibration-grade luminous flux meter, and a color temperature meter were used to apply multiple preset current levels to the warm and cool channels, respectively. Luminous flux and color temperature were recorded in real time, and the luminous flux output efficiency at each current level was calculated. By establishing a mapping relationship between the dual-channel drive current and the mixed light characteristics, this method overcomes the mapping distortion caused by traditional reliance on empirical formulas or crude single-channel calibration, improving the accuracy and stability of subsequent control command backpropagation. This method also provides a physical basis for real-time current allocation by comparatively analyzing the performance of the two channels, avoiding control deviations and uneven light color distribution caused by channel differences. Compared to the coarse calibration of the entire light source or superficial single-channel measurements commonly used in existing technologies, this method, through systematic, multi-dimensional data acquisition and modeling, improves the accuracy of electro-optical response to a fine-grained level, providing a solid foundation for intelligent lighting control and providing traceable calibration data for subsequent device performance verification and maintenance.

[0120] The method collects plant photosynthetic rate and light effective radiation intensity data for multiple days at minute intervals within a preset lighting control period, and calculates the plant photosynthetic efficiency for each minute based on the big data, specifically including:

[0121] make is the number of days of historical collection, and ; Historical days It is used to count and average the number of days of photosynthetic efficiency data collected over multiple days. It is directly used in the formula , that is, the photosynthetic efficiency per minute is the average of multiple days' data. By averaging over multiple days, the impact of occasional anomalies (such as extreme weather and equipment failure) on the data can be reduced, ensuring that the target illumination curve is more representative and robust. Too small (e.g. only 1-2 days): The average value is easily affected by single-day fluctuations, special climate and other occasional events, resulting in the target illumination not being smooth or representative, and the control effect is prone to change and instability. Moderate (e.g. 7-14 days): can effectively reflect periodic changes in weather and management, is representative, and is suitable for short-term dynamic adjustments. Too large (e.g. greater than 30 days): will greatly smooth all details. If the environment, vegetation type or season changes significantly, it may mask the new changes, causing the target illumination to "lag" and reduce its adaptability to recent growth conditions. Value selection basis: If the garden environment changes slowly and there is less human intervention, A larger value can be used to enhance stability; if the weather fluctuates greatly or there are many human influences, it is recommended Take a moderate value to maintain real-time performance while suppressing extreme anomalies; if extreme weather or special growth stages occur, it is recommended to use a sliding window to dynamically adjust . Value suggestion: Generally it is recommended to take to , which can better balance the impact of seasons, weather and daily fluctuations; for scenarios that require higher real-time response (such as exhibition period, sensitive species), it can be temporarily shortened to sky.

[0122] The lighting control period starts at the time set by the user and end time , and must meet ,Right now and within the same day;

[0123] Calculate the total number of minutes of lighting period ; Determine the subsequent control step length and the corresponding sequence number range to ensure that all sequence calculations are consistent with the actual duration;

[0124] For the first Tiandi Photosynthetic rate collected in minutes and Effective radiation ;in, It is the day sequence number; Minute number; obtain plant physiological response and driving light source input to provide basic raw data for efficiency evaluation;

[0125] Calculate the Tiandi photosynthetic efficiency in minutes :

[0126] Normalize all efficiencies to make the data comparable and avoid extreme outliers from interfering with subsequent models;

[0127] Calculate the Average photosynthetic efficiency for all days Eliminate the impact of the environment and occasional anomalies to make the control objectives representative in the long term;

[0128] Get All The maximum value in ; Used for subsequent illumination normalization mapping to ensure that the target illumination does not exceed the upper limit of the equipment capability;

[0129] like , then let , , , and jump to step S7; ensure that the system is safe and does not trigger the lighting action by mistake when there is no data or extreme abnormality; otherwise, continue to step S3; wherein, For the Minute target color temperature.

[0130] Through multi-day data collection and big data statistical analysis, the stability and representativeness of plant photosynthetic efficiency assessment have been improved. Plant photosynthetic rate and effective radiation intensity data are collected synchronously on a minute-by-minute basis during a fixed lighting period. After normalization and outlier removal, the raw data are averaged and the minute-by-minute photosynthetic efficiency value is calculated. Peak efficiency values ​​are also statistically analyzed for potential assessment. Multi-day averaging and peak analysis address the vulnerability of single- or single-day monitoring to interference from extreme weather or equipment failure, resulting in smoother efficiency assessment results and longer-term representativeness. A sliding window strategy, when necessary, also allows for rapid response to new environmental changes. This method, which seamlessly integrates physiological feedback with statistical processing, provides an accurate basis for adaptively adjusting target illuminance and color temperature, avoiding operational errors and energy waste caused by short-term anomalies. Compared to existing technologies that rely solely on real-time monitoring or empirical model estimation, this method offers improved robustness to sudden fluctuations, providing solid support for the precise management of intelligent lighting systems in complex outdoor environments.

[0131] The method combines the maximum required illumination and photosynthetic efficiency of the plant to generate a target illumination sequence per minute through predefined control rules, specifically including:

[0132] Get the maximum illumination required by the plant, recorded as ;Limit the maximum value of illumination output and protect The device is not overloaded;

[0133] Calculate the Minute target illumination ; Adaptively convert the actual photosynthetic efficiency of plants into achievable lighting output of the equipment, realizing intelligent linkage with the environment.

[0134] By combining the photosynthetic efficiency of plants with the maximum physiological light requirement, the target illuminance is dynamically generated in a regularized interpolation manner, achieving precise adaptive control of lighting intensity. Under the premise of ensuring that the minimum light requirement of the plant is met, the illuminance target is adjusted within a predefined range according to the real-time efficiency level, and dual constraints are imposed on the target value and the upper limit of the equipment, avoiding the adverse effects of excessive or insufficient lighting on plant growth and equipment life. At the same time, the dynamic generation process has good smoothness and scalability, and can adapt to the specific needs of different garden areas and plant varieties. By adjusting the illuminance output in real time, this method solves the problem that traditional lighting timing or experience-based setting methods cannot respond to changes in the physiological state of plants, achieving the dual goals of energy saving and increasing production and extending the service life of equipment. Unlike the static or coarse-grained control in the existing technology, it provides intelligent lighting adjustment based on physiological feedback, providing an innovative solution for accurately meeting plant growth needs and improving energy utilization efficiency.

[0135] The method generates a target color temperature sequence per minute based on the inherent color temperature difference and photosynthetic efficiency threshold obtained by offline calibration through predefined switching rules, specifically including:

[0136] Read inherent color temperature difference ;

[0137] like , then let Minute target color temperature ;

[0138] like , then let Minute target color temperature ;

[0139] Achieve dynamic color temperature gradient, seamlessly combine physical calibration with adaptive illumination, and maximize the adaptability of plants and landscapes; if there is no color temperature distinction, it remains constant.

[0140] By coupling physical calibration with physiological feedback, adaptive dynamic switching of the target color temperature is achieved. In the offline stage, the inherent color temperature difference between the warm light and cold light channels is obtained, and according to the photosynthetic efficiency threshold calculated in real time, a cooler color temperature is selected when the efficiency is above the threshold, and a warmer color temperature is selected when the efficiency is below the threshold, so as to take into account both plant photosynthesis and the visual effect of the night landscape. Through smooth switching rules, this method effectively avoids the impact of sudden changes in color temperature on the spectral response of plants, while improving the comfort and aesthetics of the garden night view. It solves the conflict between growth efficiency and viewing effect caused by traditional fixed color temperature or adjustment based solely on aesthetics, so that color temperature adjustment can meet both ecological needs and landscape needs. Compared with the limitations of existing solutions in which color temperature adjustment lacks biofeedback or is single-target-oriented, this method achieves dual optimization of photosynthetic performance and color comfort, providing a more precise and flexible color temperature control strategy.

[0141] In the dual-channel coordinated control mode, the warm white channel and the cold white channel driving current at the corresponding moment are solved according to the target illumination and the target color temperature. In the single-channel or channel failure mode, the single-channel driving current is directly determined according to the preset degradation scheme, specifically including:

[0142] If dual channel mode, Establish the cooperative equations, specifically:

[0143] ; Establish a linear equation system between the target and physical parameters to achieve precise dual-target control of color temperature and illumination;

[0144] The closed-form solution is , Analyze and obtain the optimal current distribution to achieve the target output under the current physical parameters, ensuring the real-time and efficient control algorithm;

[0145] Single channels or failure modes are directly assigned values ​​according to the degradation scheme in S100; this maintains algorithm robustness to prevent system unavailability in the event of partial hardware failure;

[0146] Perform upper and lower limit truncation:

[0147] ;

[0148] ;in, and The maximum current specified for warm white and cold white drivers respectively; prevent the control current from exceeding the hardware safety range, protect and drivers.

[0149] The current distribution algorithm, which combines real-time analytical solution with safety limiting, ensures the stable operation of the system in both dual-channel collaboration and channel degradation modes. In the dual-channel mode, the pre-calibrated electro-optical mapping relationship is used to quickly calculate the optimal current ratio that meets the dual goals of illumination and color temperature; when a channel failure is detected, the preset degradation scheme is automatically executed, the remaining channel current is assigned and the upper and lower limits are cut off to prevent exceeding the hardware safety range. This design solves the contradiction between the inability to maintain physiological lighting requirements and hardware protection after a single channel failure, so that the system can still provide minimum available lighting in the state of local failure. Unlike the static backup or manual intervention switching methods commonly used in existing technologies, it realizes fault-adaptive closed-loop control, taking into account dual-target output, real-time and safety, and effectively improving the robustness and maintenance convenience of the garden lighting system.

[0150] The converting the obtained channel driving current value into a digital driving instruction specifically includes:

[0151] Calculate the Minute warm white channel digital command ;

[0152] Calculate the Minute cold white channel digital command ;

[0153] The analog quantity is losslessly quantified into digital instructions that can be recognized by the driver module, ensuring that the lighting output is precisely controllable at every moment.

[0154] Through the digital instruction conversion method of lossless quantization and online correction, the continuous analog current is accurately converted into discrete digital instructions that can be recognized by the driver, while eliminating the rounding error in the quantization process. Based on the preset lookup table, the driving current of each channel is finely quantized, and the mapping parameters are corrected online in combination with the real-time monitoring of temperature and voltage changes, realizing dynamic compensation of quantization errors. This design solves the problems of inaccurate instruction execution and illumination jitter caused by traditional linear scaling or coarse-grained mapping, ensuring that the lower-level driver module can execute control commands with high fidelity. Unlike the existing technology that relies heavily on offline calibration and fixed mapping strategies, this method combines adaptive correction with lossless quantization, which not only improves the control accuracy but also enhances the stability of the system in a changing environment.

[0155] The method of sending a digital drive instruction to the LED driver through the serial communication interface according to the preset protocol, baud rate and channel address and receiving a confirmation response, and if no valid feedback is received or the verification fails, resending the instruction until the preset number of resends is reached and confirmed to be correct, specifically includes:

[0156] Communication Configuration: Protocol , baud rate , warm white channel address , cold white channel address ; Standardized fieldbus selection and address allocation to ensure orderly and reliable command transmission;

[0157] Instructions are issued every minute, including:

[0158] S701, function code Write to the register Write address ;

[0159] S702, if there is no response or verification fails, resend no more than Second-rate;

[0160] S703, similarly Write address , and verify the response;

[0161] Ensure that instructions arrive in real time and reliably, avoiding lighting out of control due to occasional communication errors.

[0162] The communication strategy that combines feedback confirmation with multiple rounds of retransmission improves the reliability and real-time performance of command issuance. After sending digital drive instructions through the serial bus according to a unified protocol and baud rate within the control cycle of each minute, this step monitors the response of the light-emitting module in real time; when no confirmation is received or a data verification error is found, the retransmission mechanism within the preset number of times is automatically started, and an alarm or safety degradation is triggered in the event of continuous failure. This mechanism solves the risk of missing instructions or abnormal execution caused by communication packet loss, electromagnetic interference and line jitter that are common in outdoor environments, ensuring that each control instruction can be reliably executed and promptly fed back. Compared with the single transmission method in the existing technology that lacks two-way verification or relies solely on hardware retries, it enhances the robustness of transmission while ensuring real-time performance, providing important support for the stable operation of smart garden lighting under complex field conditions.

[0163] The operating parameters and feedback results of each control cycle are recorded, and the log is generated and uploaded to the database on a daily basis. The system runs for a preset operating time or once every three months, and the parameter recalibration process is started, specifically including:

[0164] Generate log fields, each record includes:

[0165] ; Ensure that input and output are fully traceable at every moment to facilitate subsequent optimization and fault analysis;

[0166] Generate log files daily and upload them to the database;

[0167] System cumulative operation or every Months, perform the recalibration process, including:

[0168] S801, recalculation 、 、 and ;

[0169] S802, recalculate color temperature difference ;

[0170] S803, if , exchange channel definition and correct according to step S100 ;

[0171] S804, re-determine the control mode according to the S100 branch logic and update the controller parameters;

[0172] S805, compare this time with the most recent The calibration parameters of each version are archived together;

[0173] compensate Aging and environmental drift, maintaining control accuracy and system stability.

[0174] Through a closed-loop maintenance solution with continuous operation data recording and automatic recalibration, effective compensation for equipment aging and environmental drift is achieved. This step records key parameters such as illumination target, color temperature target, drive current and communication feedback in each control cycle, and uploads the log to the remote database on a daily basis to establish a complete operation archive; when the cumulative operation time or the preset cycle node is reached, the system automatically triggers the recalibration process, re-measures the electro-optical response and color temperature difference, updates the mapping relationship and control logic, and archives the calibration results together with the historical data. Through this continuous monitoring and dynamic correction mechanism, the problem of low efficiency and easy error generation of traditional manual periodic calibration is solved, and the accuracy and stability of the system in the long-term operation process are improved. Compared with the existing technology that relies more on fixed-interval manual maintenance or no systematic recalibration, a fully automatic adaptive maintenance closed loop is realized, which reduces operation and maintenance costs and provides a solid guarantee for the continuous and efficient operation of smart garden lighting.

[0175] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0176] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A smart garden management and control method based on big data, characterized in that: include: S1. In a constant temperature and humidity laboratory environment, apply preset drive currents to the warm white channel and cool white channel of the LED module. Use a calibrated flux meter and color temperature meter to measure the luminous flux and color temperature of each channel at different currents. Establish a mapping relationship between the drive current, the total output luminous flux, and the mixed light color temperature. S2. During the preset lighting control period, collect data on plant photosynthetic rate and effective light radiation intensity at minute intervals for multiple days, and calculate the plant photosynthetic efficiency at each minute based on the big data; S3, combining the maximum required illumination and photosynthetic efficiency of the plant, and generating a target illumination sequence per minute through predefined control rules; S4. Generate a target color temperature sequence per minute using a predefined switching rule based on the inherent color temperature difference and photosynthetic efficiency threshold obtained by offline calibration; S5. In dual-channel coordinated control mode, the warm white channel and cool white channel driving currents at the corresponding time are calculated based on the target illumination and target color temperature. In single-channel or channel failure mode, the single-channel driving current is directly determined according to the preset degradation scheme. S6, converting the channel driving current value obtained by the solution into a digital driving instruction; S7. Send digital drive instructions to the LED driver through the serial communication interface according to the preset protocol, baud rate and channel address, and receive a confirmation response. If no valid feedback is received or the verification fails, resend the instruction until the preset number of resends is reached and confirmed to be correct; S8. Record the operating parameters and feedback results of each control cycle, generate the log on a daily basis and upload it to the database. When the system runs for a preset operating time or once every three months, start the parameter recalibration process.

2. A smart garden management and control method based on big data according to claim 1, characterized in that: In a constant temperature and humidity laboratory environment, a preset driving current is applied to the warm white channel and the cool white channel of the LED module, and a calibrated flux meter and a color temperature meter are used to measure the luminous flux and color temperature of each channel at different currents, thereby establishing a mapping relationship between the driving current and the output total luminous flux and the mixed light color temperature. Specifically, the mapping relationship includes: At constant temperature Constant humidity The laboratory is equipped with a constant current source, a calibration-grade flux meter and a color temperature meter; Set the warm white channel drive current , cold white channel driving current ; Only the warm white channel is powered and applied , measuring luminous flux and color temperature ;in, For The luminous flux of the warm white channel measured under the following conditions: For The color temperature of the warm white channel measured under the following conditions; Only the cold white channel is powered and applied , measuring luminous flux and color temperature ;in, For The luminous flux of the cool white channel measured under the conditions; For The color temperature of the cool white channel measured under the following conditions: Calculate the luminous flux efficiency of the warm white channel separately and cold white channel luminous flux efficiency : ， ； Construct a physical mapping function: ;in, is the driving current of the warm white channel at any moment; is the cold white channel driving current at any time; It is the total luminous flux output under a given current of the dual channels; It is the output color temperature under dual-channel mixed light; Only when Use ; S100, calculate the inherent color temperature difference and determine the control mode: S110, calculate the inherent color temperature difference ; S120, determine the control mode: like , then swap the two channel definitions: , , ; After the exchange or in the initial state, steps S121 to S124 are performed in sequence: S121, if and and , adopting dual-channel collaborative control; S122, otherwise if , degenerated into a warm white single channel: , ;in, For the minute warm white channel current; For the Minute target illumination; For the Minute cold white channel current; is the minute number; S123, otherwise if , degenerated into a cold white single channel: , ; S124, otherwise if , so that all ;in, For the Minute warm white channel digital command; For the Minute cold white channel digital command; And jump to step S7.

3. A smart garden management and control method based on big data according to claim 2, characterized in that: The method collects plant photosynthetic rate and light effective radiation intensity data for multiple days at minute intervals within a preset lighting control period, and calculates the plant photosynthetic efficiency for each minute based on the big data, specifically including: make is the number of days of historical collection, and ; The lighting control period starts at the time set by the user and end time , and must meet ,Right now and within the same day; Calculate the total number of minutes of lighting period ; For the first Tiandi Photosynthetic rate collected in minutes and Effective radiation ;in, It is the day sequence number; is the minute number; Calculate the Tiandi photosynthetic efficiency in minutes : ； Calculate the Average photosynthetic efficiency for all days ; Get All The maximum value in ; like , then let , , , and jump to step S7; otherwise, continue to step S3; wherein, For the Minute target color temperature.

4. The method for intelligent garden management based on big data according to claim 3, characterized in that: The method combines the maximum required illumination and photosynthetic efficiency of the plant to generate a target illumination sequence per minute through predefined control rules, specifically including: Get the maximum illumination required by the plant, recorded as ; Calculate the Minute target illumination .

5. The method for intelligent garden management and control based on big data according to claim 4, characterized in that: The method generates a target color temperature sequence per minute based on the inherent color temperature difference and photosynthetic efficiency threshold obtained by offline calibration through a predefined switching rule, specifically including: Read inherent color temperature difference ; like , then let Minute target color temperature ; like , then let Minute target color temperature .

6. A smart garden management and control method based on big data according to claim 5, characterized in that: In the dual-channel coordinated control mode, the warm white channel and the cold white channel driving current at the corresponding moment are solved according to the target illumination and the target color temperature. In the single-channel or channel failure mode, the single-channel driving current is directly determined according to the preset degradation scheme, specifically including: If dual channel mode, Establish the cooperative equations, specifically: ； The closed-form solution is , ; Single channel or failure mode is directly assigned according to the degradation scheme in S100; Perform upper and lower limit truncation: ； ;in, and The maximum currents specified for warm-white and cool-white drivers respectively.

7. The method for intelligent garden management based on big data according to claim 6, characterized in that: The converting the obtained channel driving current value into a digital driving instruction specifically includes: Calculate the Minute warm white channel digital command ; Calculate the Minute cold white channel digital command .

8. The method for intelligent garden management based on big data according to claim 7, characterized in that: The method of sending a digital drive instruction to the LED driver through the serial communication interface according to the preset protocol, baud rate and channel address and receiving a confirmation response, and if no valid feedback is received or the verification fails, resending the instruction until the preset number of resends is reached and confirmed to be correct, specifically includes: Communication Configuration: Protocol , baud rate , warm white channel address , cold white channel address ; Instructions are issued every minute, including: S701, function code Write to the register Write address ; S702, if there is no response or verification fails, resend no more than Second-rate; S703, similarly Write address , and verify the response.

9. The method for intelligent garden management and control based on big data according to claim 8, characterized in that: The operating parameters and feedback results of each control cycle are recorded, and the log is generated and uploaded to the database on a daily basis. The system runs for a preset operating time or once every three months, and the parameter recalibration process is started, specifically including: Generate log fields, each record includes: ； Generate log files daily and upload them to the database; System cumulative operation or every Months, perform the recalibration process, including: S801, recalculation 、 、 and ; S802, recalculate color temperature difference ; S803, if , exchange channel definition and correct according to step S100 ; S804, re-determine the control mode according to the S100 branch logic and update the controller parameters; S805, compare this time with the most recent The calibration parameters of each version are archived together.

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